From e6160f081cf3e33b29de83899f7c633c081a85ee Mon Sep 17 00:00:00 2001
From: vecna <vecna@globaleaks.org>
Date: Thu, 8 Oct 2026 11:10:11 +0200
Subject: [PATCH 1/9] added gif small animation and links

---
 images/gif/bouncing-animation-green.gif | Bin 0 -> 4725608 bytes
 tutorials/brand-voice.md                |  22 +++++++++++++---------
 2 files changed, 13 insertions(+), 9 deletions(-)
 create mode 100644 images/gif/bouncing-animation-green.gif

diff --git a/images/gif/bouncing-animation-green.gif b/images/gif/bouncing-animation-green.gif
new file mode 100644
index 0000000000000000000000000000000000000000..250905c121a67e21510cc9668d15a8f30faf7703
GIT binary patch
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diff --git a/tutorials/brand-voice.md b/tutorials/brand-voice.md
index 031dab1..11e547e 100644
--- a/tutorials/brand-voice.md
+++ b/tutorials/brand-voice.md
@@ -17,15 +17,19 @@ Ghostmaxxing is not an invisibility product. It is a public-facing research lab,
 
 ## Theory of change
 
-Ghostmaxxing is designed to work as a research loop, not as a collection of unrelated pages. The loop below is the target operating model. Public copy must distinguish capabilities that are already available from integrations that are still being completed.
-
-1. **Local experiments produce Ghostyles and test records.** People use the browser lab to make and test visual interventions under stated conditions.
-2. **The Fediverse becomes the federated data backend.** Ghostyles, public test records, limitations, references and project updates are represented and exchanged through ActivityPub rather than being confined to one website or application.
-3. **The Fediverse distributes the work.** Different communities, applications and instances can follow, question, reproduce and retest the research.
-4. **The lab reads from the same network.** Relevant updates arriving through the Fediverse become new inputs for the lab after validation and moderation. The network is both an output and an input of the research process.
-5. **Public visibility creates routes to private reporting.** The distributed work can reach people who know how face recognition systems are built, bought, operated or experienced.
-6. **Anonymous reports update the investigation.** Evidence from deployments, contracts, operators, affected people and technical insiders informs the archive, the threat model and the next round of tests.
-7. **The lab tests again.** New evidence changes the research questions and may produce new Ghostyles, revised limits or a clear finding that an earlier result no longer transfers.
+[Ghostmaxxing](https://ghostmaxxing.vecna.eu) wants to play there, with a feedback loop, it’s [free software](https://github.com/vecna/ghostmaxxing) and oped data, designed this way:
+
+1. **Experiment locally.** Use the [browser lab](https://ghostmaxxing.vecna.eu/lab.html) to test makeup and other visual interventions against face recognition. Experiments remain in the browser unless you choose to upload them. Has been tried to find recurring “patterns”, but they mostly work when you talk to a Visual Model and you ask to mod a picture for you. [A catalogue of Ghostmaxxing visual interventions](https://ghostmaxxing.vecna.eu/images/various/ghostmaxxing-style-catalog-4x4.png)
+2. **Document what works!** (With the participant’s consent), a success can be saved/shared/and re-produced a **Ghostyles.** (which is a javascript capable of casting an overlay in realtime over a face - [check the example plugin](https://github.com/vecna/ghostmaxxing/blob/main/ghostyles/00-template.js)).
+3. Successful camouflage (via images, before-after, or videos) can be submitted to the [community gallery](https://ghostmaxxing.vecna.eu/gallery.html). Every submission is reviewed by a human moderator before publication. `This part of the project is still in beta.` [The Ghostmaxxing community gallery](https://cdn.hackernoon.com/images/4JE28BStmmgAtRRuN3Uh5uGnN1q2-zoa2yhd.jpeg)
+4. **Distribute the results.** Approved experiments, Ghostyles, images, videos, and project updates are published through separate [Fediverse accounts](https://ghostmaxxing.vecna.eu/fediverse.html). Readers can follow only the streams relevant to them. This ActivityPub integration, built with Fedify.
+5. **Bring results back into workshops.** The network is both an output and an input. Promising techniques can be reproduced and evaluated in [workshops](https://ghostmaxxing.vecna.eu/workshops.html), where participants test them under shared conditions. Facilitators can coordinate through the [Signal group](https://ghostmaxxing.vecna.eu/workshops.html#lets-connect). [Participants testing face paint during a Ghostmaxxing workshop](https://ghostmaxxing.vecna.eu/images/workshops/ws1-painting-1.jpg)
+6. **Collect evidence from the field.** The [reporting channel](https://ghostmaxxing.vecna.eu/report.html) accepts information from affected people, operators, developers, technical insiders, and people involved in procurement. These reports help map deployments, contracts, failures, and risks. The anonymous submission service is operated separately by [NINA](https://nina.watch).
+7. **Start the next round.** New techniques and findings are published through the [website](https://ghostmaxxing.vecna.eu/) and the Fediverse. Responses, reports, and reproduced results inform the next experiments. The loop then begins again.
+
+## #Status of the project
+
+**It's a working beta version**. The face [recognition technology uses the 2D Face API library and 3D MediaPipe face detection](https://ghostmaxxing.vecna.eu/docs). Contributions to the repository are welcome. Although the Fediverse integration and whistleblowing channels are established, they are not very well tested. Currently, the tool has been used to support workshops on the public stage. When the tool display a green color, it’s a go! your face fingerprint isn’t recognized anymore.
 
 In compact form:
 

From f035f603f5f019e300d0f821e16f4a298c4de3ae Mon Sep 17 00:00:00 2001
From: vecna <vecna@globaleaks.org>
Date: Wed, 26 Aug 2026 08:25:56 +0200
Subject: [PATCH 2/9] not fully working realtime analysis concept

---
 lab-js/realtime/realtime-capture.js  |  526 ++++++++++++++
 lab-js/realtime/realtime-gallery.js  |  500 +++++++++++++
 lab-js/realtime/realtime-graph.js    |  703 ++++++++++++++++++
 lab-js/realtime/realtime-settings.js |  238 ++++++
 lab-js/realtime/realtime.js          |  929 ++++++++++++++++++++++++
 realtime-fr-method.md                | 1005 ++++++++++++++++++++++++++
 realtime.html                        |  166 +++++
 styles/realtime.css                  |  688 ++++++++++++++++++
 tests/e2e/realtime.spec.js           |  210 ++++++
 9 files changed, 4965 insertions(+)
 create mode 100644 lab-js/realtime/realtime-capture.js
 create mode 100644 lab-js/realtime/realtime-gallery.js
 create mode 100644 lab-js/realtime/realtime-graph.js
 create mode 100644 lab-js/realtime/realtime-settings.js
 create mode 100644 lab-js/realtime/realtime.js
 create mode 100644 realtime-fr-method.md
 create mode 100644 realtime.html
 create mode 100644 styles/realtime.css
 create mode 100644 tests/e2e/realtime.spec.js

diff --git a/lab-js/realtime/realtime-capture.js b/lab-js/realtime/realtime-capture.js
new file mode 100644
index 0000000..2ce4c15
--- /dev/null
+++ b/lab-js/realtime/realtime-capture.js
@@ -0,0 +1,526 @@
+/**
+ * @module realtime-capture
+ * @description
+ * Everything that decides whether a frame is good enough to believe.
+ *
+ * The baseline is captured once and every number for the rest of the session
+ * is measured against it, so it is worth being slow and picky about. The
+ * prototype took the first frame that returned a descriptor — no confidence
+ * check, no framing check, no pose check — which meant a blurred half-turned
+ * frame could silently become the reference for an hour of work.
+ *
+ * The same gate is reused for spike captures, for the same reason: a single
+ * frame at 0.50 is as likely to be motion blur as a result, and "the makeup is
+ * working" is not a claim you want resting on a photo of someone moving.
+ */
+
+/** Landmark indices in face-api's 68-point model. */
+const LM_LEFT_EYE_OUTER = 36;
+const LM_RIGHT_EYE_OUTER = 45;
+const LM_NOSE_TIP = 30;
+
+const DEFAULT_GATE = {
+  minScore: 0.9,
+  minBoxWidthRatio: 0.25,
+  edgeMarginRatio: 0.02,
+  maxRollDegrees: 8,
+  maxYawOffset: 0.12,
+  minLuma: 0.18,
+  maxLuma: 0.86,
+  maxClippedRatio: 0.02,
+};
+
+/**
+ * Euclidean distance between two descriptors.
+ *
+ * Kept local rather than reaching for the global `faceapi` so this module can
+ * be imported and unit tested without the vendor bundle present.
+ *
+ * @param {Float32Array|number[]} a First descriptor.
+ * @param {Float32Array|number[]} b Second descriptor.
+ * @returns {number} Euclidean distance.
+ */
+export function euclidean(a, b) {
+  let sum = 0;
+  for (let i = 0; i < a.length; i += 1) {
+    const d = a[i] - b[i];
+    sum += d * d;
+  }
+  return Math.sqrt(sum);
+}
+
+/**
+ * Picks the medoid of a descriptor set and reports its spread.
+ *
+ * The medoid — the member with the smallest summed distance to the others — is
+ * used rather than the mean because it stays a real point in the embedding.
+ * Averaging near-unit vectors that are not perfectly aligned yields something
+ * slightly shorter than its inputs, which bakes a small systematic offset into
+ * every distance measured for the rest of the session.
+ *
+ * The returned spread is the session's NOISE FLOOR: how far the descriptor
+ * moves when nothing about the face has changed. It is the only empirical
+ * reference line this tool draws.
+ *
+ * @param {Array<Float32Array|number[]>} descriptors Collected descriptors.
+ * @returns {{descriptor: number[], spread: number, index: number}} Medoid result.
+ */
+export function medoid(descriptors) {
+  if (!descriptors.length) throw new Error('medoid() needs at least one descriptor');
+  if (descriptors.length === 1) {
+    return { descriptor: Array.from(descriptors[0]), spread: 0, index: 0 };
+  }
+
+  const n = descriptors.length;
+  const matrix = Array.from({ length: n }, () => new Float64Array(n));
+
+  for (let i = 0; i < n; i += 1) {
+    for (let j = i + 1; j < n; j += 1) {
+      const d = euclidean(descriptors[i], descriptors[j]);
+      matrix[i][j] = d;
+      matrix[j][i] = d;
+    }
+  }
+
+  let bestIndex = 0;
+  let bestSum = Infinity;
+  for (let i = 0; i < n; i += 1) {
+    let sum = 0;
+    for (let j = 0; j < n; j += 1) sum += matrix[i][j];
+    if (sum < bestSum) {
+      bestSum = sum;
+      bestIndex = i;
+    }
+  }
+
+  const spread = bestSum / (n - 1);
+  return { descriptor: Array.from(descriptors[bestIndex]), spread, index: bestIndex };
+}
+
+/**
+ * Creates a reusable scratch canvas for crop analysis.
+ *
+ * @param {number} size Square edge length in pixels.
+ * @returns {{canvas: HTMLCanvasElement, ctx: CanvasRenderingContext2D}} Scratch surface.
+ */
+function createScratch(size) {
+  const canvas = document.createElement('canvas');
+  canvas.width = size;
+  canvas.height = size;
+  return { canvas, ctx: canvas.getContext('2d', { willReadFrequently: true }) };
+}
+
+/**
+ * Measures relative sharpness and exposure of a face crop.
+ *
+ * Sharpness is gradient energy and is used only for RANKING candidates against
+ * each other, never against a fixed floor — an absolute threshold would be one
+ * more magic number that varies with every camera and lighting setup.
+ *
+ * @param {HTMLVideoElement} video Source video element.
+ * @param {{x: number, y: number, width: number, height: number}} box Face box in video pixels.
+ * @param {{canvas: HTMLCanvasElement, ctx: CanvasRenderingContext2D}} scratch Scratch surface.
+ * @returns {{sharpness: number, luma: number, clipped: number}} Crop statistics.
+ */
+function analyseCrop(video, box, scratch) {
+  const size = scratch.canvas.width;
+  scratch.ctx.drawImage(
+    video,
+    Math.max(0, box.x),
+    Math.max(0, box.y),
+    Math.max(1, box.width),
+    Math.max(1, box.height),
+    0,
+    0,
+    size,
+    size,
+  );
+
+  const { data } = scratch.ctx.getImageData(0, 0, size, size);
+  const gray = new Float32Array(size * size);
+  let lumaSum = 0;
+  let clipped = 0;
+
+  for (let i = 0, p = 0; i < data.length; i += 4, p += 1) {
+    const value = (data[i] * 0.299 + data[i + 1] * 0.587 + data[i + 2] * 0.114) / 255;
+    gray[p] = value;
+    lumaSum += value;
+    if (value > 0.98) clipped += 1;
+  }
+
+  let energy = 0;
+  for (let y = 1; y < size - 1; y += 1) {
+    for (let x = 1; x < size - 1; x += 1) {
+      const idx = y * size + x;
+      const gx = gray[idx + 1] - gray[idx - 1];
+      const gy = gray[idx + size] - gray[idx - size];
+      energy += gx * gx + gy * gy;
+    }
+  }
+
+  const pixels = size * size;
+  return {
+    sharpness: energy / pixels,
+    luma: lumaSum / pixels,
+    clipped: clipped / pixels,
+  };
+}
+
+/**
+ * Creates the frame quality gate.
+ *
+ * @param {HTMLVideoElement} video Source video element.
+ * @param {object} [overrides] Partial gate threshold overrides.
+ * @returns {{evaluate: Function}} Quality gate.
+ */
+export function createQualityGate(video, overrides = {}) {
+  const limits = { ...DEFAULT_GATE, ...overrides };
+  const scratch = createScratch(96);
+
+  return {
+    /**
+     * Judges one detection against every framing and quality rule.
+     *
+     * @param {*} detection face-api detection with landmarks and descriptor.
+     * @returns {{ok: boolean, reason: string, sharpness: number}} Verdict.
+     */
+    evaluate(detection) {
+      const reject = (reason) => ({ ok: false, reason, sharpness: 0 });
+
+      const box = detection?.detection?.box;
+      const score = detection?.detection?.score ?? 0;
+      const points = detection?.landmarks?.positions;
+      if (!box || !points) return reject('No usable detection.');
+
+      const videoW = video.videoWidth || 1;
+      const videoH = video.videoHeight || 1;
+
+      if (score < limits.minScore) return reject('Low confidence — improve the lighting.');
+      if (box.width < videoW * limits.minBoxWidthRatio) return reject('Move closer to the camera.');
+
+      const marginX = videoW * limits.edgeMarginRatio;
+      const marginY = videoH * limits.edgeMarginRatio;
+      const clipsFrame =
+        box.x < marginX ||
+        box.y < marginY ||
+        box.x + box.width > videoW - marginX ||
+        box.y + box.height > videoH - marginY;
+      if (clipsFrame) return reject('Centre your face in the frame.');
+
+      const leftEye = points[LM_LEFT_EYE_OUTER];
+      const rightEye = points[LM_RIGHT_EYE_OUTER];
+      const nose = points[LM_NOSE_TIP];
+      if (!leftEye || !rightEye || !nose) return reject('Landmarks incomplete.');
+
+      const rollDegrees = Math.abs(
+        (Math.atan2(rightEye.y - leftEye.y, rightEye.x - leftEye.x) * 180) / Math.PI,
+      );
+      if (rollDegrees > limits.maxRollDegrees) return reject('Level your head.');
+
+      const interocular = Math.hypot(rightEye.x - leftEye.x, rightEye.y - leftEye.y) || 1;
+      const eyeMidX = (leftEye.x + rightEye.x) / 2;
+      const yawOffset = Math.abs(nose.x - eyeMidX) / interocular;
+      if (yawOffset > limits.maxYawOffset) return reject('Look straight at the camera.');
+
+      const crop = analyseCrop(video, box, scratch);
+      if (crop.luma < limits.minLuma) return reject('Too dark — add some light.');
+      if (crop.luma > limits.maxLuma || crop.clipped > limits.maxClippedRatio) {
+        return reject('Overexposed — reduce the light.');
+      }
+
+      return { ok: true, reason: '', sharpness: crop.sharpness };
+    },
+  };
+}
+
+/**
+ * Creates the baseline collector.
+ *
+ * Progress advances on ACCEPTED samples only, never on elapsed time. A stalled
+ * ring means frames are being rejected, and the reason is reported alongside —
+ * so the control diagnoses itself instead of silently timing out.
+ *
+ * @param {object} options Collector options.
+ * @param {number} options.targetSamples Accepted samples required.
+ * @param {number} options.minDurationMs Floor on capture duration.
+ * @param {number} options.timeoutMs Abandon threshold.
+ * @param {number} options.maxSpread Largest acceptable noise floor.
+ * @returns {object} Baseline collector.
+ */
+export function createBaselineCollector(options) {
+  const { targetSamples, minDurationMs, timeoutMs, maxSpread } = options;
+
+  let descriptors = [];
+  let startedAt = 0;
+  let active = false;
+
+  return {
+    /**
+     * Begins a fresh collection run.
+     *
+     * @returns {void}
+     */
+    start() {
+      descriptors = [];
+      startedAt = Date.now();
+      active = true;
+    },
+
+    /**
+     * Ends the current run without producing a baseline.
+     *
+     * @returns {void}
+     */
+    cancel() {
+      active = false;
+      descriptors = [];
+    },
+
+    /**
+     * Reports whether a run is in progress.
+     *
+     * @returns {boolean} True while collecting.
+     */
+    isActive() {
+      return active;
+    },
+
+    /**
+     * Offers one gated frame to the collector.
+     *
+     * @param {{ok: boolean, reason: string}} verdict Quality gate verdict.
+     * @param {Float32Array|number[]} descriptor Descriptor for the frame.
+     * @returns {{status: string, accepted: number, target: number, reason?: string, baseline?: object}} Progress.
+     */
+    offer(verdict, descriptor) {
+      if (!active) return { status: 'idle', accepted: 0, target: targetSamples };
+
+      const elapsed = Date.now() - startedAt;
+
+      if (!verdict.ok) {
+        if (elapsed > timeoutMs) {
+          active = false;
+          return {
+            status: 'failed',
+            accepted: descriptors.length,
+            target: targetSamples,
+            reason: verdict.reason,
+          };
+        }
+        return {
+          status: 'blocked',
+          accepted: descriptors.length,
+          target: targetSamples,
+          reason: verdict.reason,
+        };
+      }
+
+      if (descriptors.length < targetSamples) descriptors.push(Array.from(descriptor));
+
+      const enoughSamples = descriptors.length >= targetSamples;
+      const enoughTime = elapsed >= minDurationMs;
+
+      if (!enoughSamples || !enoughTime) {
+        return { status: 'collecting', accepted: descriptors.length, target: targetSamples };
+      }
+
+      const result = medoid(descriptors);
+      active = false;
+
+      // A spread this wide means the subject moved or the lighting flickered
+      // during collection. Accepting it would set a noise floor so high that
+      // real results disappear underneath it.
+      if (result.spread > maxSpread) {
+        return {
+          status: 'failed',
+          accepted: descriptors.length,
+          target: targetSamples,
+          reason: 'Too much movement during capture.',
+        };
+      }
+
+      return {
+        status: 'done',
+        accepted: descriptors.length,
+        target: targetSamples,
+        baseline: {
+          descriptor: result.descriptor,
+          noiseFloor: result.spread,
+          capturedAt: Date.now(),
+          samples: descriptors.length,
+        },
+      };
+    },
+  };
+}
+
+/**
+ * Creates the sustained-spike detector.
+ *
+ * Capture triggers on a reading that HOLDS, not on an instantaneous value, and
+ * the frame written to disk is the sharpest one observed across the sustain
+ * window rather than whichever frame happened to trip the trigger.
+ *
+ * Two conditions fire a capture:
+ *   personal best — a new session maximum by a real margin, which is what you
+ *                   iterate against between makeup passes
+ *   crossing      — the first time the run clears the cited match threshold
+ *
+ * @param {object} options Detector options.
+ * @param {number} options.sustainMs Time a reading must hold.
+ * @param {number} options.bestMargin Margin over the session best.
+ * @param {number} options.threshold Cited match threshold.
+ * @param {number} options.cooldownMs Minimum spacing between captures.
+ * @returns {object} Spike detector.
+ */
+export function createSpikeDetector(options) {
+  const { sustainMs, bestMargin, threshold, cooldownMs } = options;
+
+  let sessionBest = 0;
+  let crossedThreshold = false;
+  let runStartedAt = 0;
+  let runValueFloor = 0;
+  let lastCaptureAt = 0;
+
+  return {
+    /**
+     * Clears session state after a new baseline is set.
+     *
+     * @returns {void}
+     */
+    reset() {
+      sessionBest = 0;
+      crossedThreshold = false;
+      runStartedAt = 0;
+      runValueFloor = 0;
+      lastCaptureAt = 0;
+    },
+
+    /**
+     * Marks a capture as taken so the cooldown applies.
+     *
+     * @param {number} value Distance at capture time.
+     * @returns {void}
+     */
+    noteCapture(value) {
+      lastCaptureAt = Date.now();
+      sessionBest = Math.max(sessionBest, value);
+      if (value >= threshold) crossedThreshold = true;
+      runStartedAt = 0;
+    },
+
+    /**
+     * Feeds one live distance reading to the detector.
+     *
+     * @param {number} value Live distance for this frame.
+     * @returns {{capture: boolean, kind: string, candidate: boolean}} Verdict.
+     */
+    update(value) {
+      const now = Date.now();
+      const beatsBest = value >= sessionBest + bestMargin;
+      const crosses = !crossedThreshold && value >= threshold;
+      const interesting = beatsBest || crosses;
+
+      if (!interesting) {
+        runStartedAt = 0;
+        return { capture: false, kind: '', candidate: false };
+      }
+
+      if (!runStartedAt) {
+        runStartedAt = now;
+        runValueFloor = value;
+        return { capture: false, kind: '', candidate: true };
+      }
+
+      // A dip below where the run started means the reading did not hold.
+      if (value < runValueFloor - bestMargin) {
+        runStartedAt = 0;
+        return { capture: false, kind: '', candidate: false };
+      }
+
+      if (now - runStartedAt < sustainMs) {
+        return { capture: false, kind: '', candidate: true };
+      }
+
+      if (now - lastCaptureAt < cooldownMs) {
+        return { capture: false, kind: '', candidate: true };
+      }
+
+      return { capture: true, kind: crosses ? 'crossing' : 'best', candidate: true };
+    },
+  };
+}
+
+/**
+ * Creates a rotating buffer of full video frames.
+ *
+ * Snapshots are only taken while a spike candidate is live, so the buffer costs
+ * nothing during ordinary tracking.
+ *
+ * @param {HTMLVideoElement} video Source video element.
+ * @param {number} slots Number of frames retained.
+ * @param {number} maxWidth Longest edge written to a snapshot.
+ * @returns {object} Frame buffer.
+ */
+export function createFrameBuffer(video, slots, maxWidth) {
+  const buffer = [];
+  let cursor = 0;
+
+  return {
+    /**
+     * Empties the buffer.
+     *
+     * @returns {void}
+     */
+    clear() {
+      buffer.length = 0;
+      cursor = 0;
+    },
+
+    /**
+     * Records the current video frame with its measured sharpness.
+     *
+     * @param {number} sharpness Relative sharpness of this frame.
+     * @param {number} distance Distance reading at this frame.
+     * @returns {void}
+     */
+    push(sharpness, distance) {
+      const videoW = video.videoWidth || 0;
+      const videoH = video.videoHeight || 0;
+      if (!videoW || !videoH) return;
+
+      const scale = Math.min(1, maxWidth / videoW);
+      const width = Math.round(videoW * scale);
+      const height = Math.round(videoH * scale);
+
+      let slot = buffer[cursor];
+      if (!slot) {
+        slot = { canvas: document.createElement('canvas'), sharpness: 0, distance: 0, t: 0 };
+        buffer[cursor] = slot;
+      }
+      if (slot.canvas.width !== width || slot.canvas.height !== height) {
+        slot.canvas.width = width;
+        slot.canvas.height = height;
+      }
+
+      slot.canvas.getContext('2d').drawImage(video, 0, 0, width, height);
+      slot.sharpness = sharpness;
+      slot.distance = distance;
+      slot.t = Date.now();
+      cursor = (cursor + 1) % slots;
+    },
+
+    /**
+     * Returns the sharpest retained frame.
+     *
+     * @returns {{canvas: HTMLCanvasElement, sharpness: number, distance: number, t: number}|null} Best frame.
+     */
+    best() {
+      let winner = null;
+      for (const slot of buffer) {
+        if (slot && (!winner || slot.sharpness > winner.sharpness)) winner = slot;
+      }
+      return winner;
+    },
+  };
+}
diff --git a/lab-js/realtime/realtime-gallery.js b/lab-js/realtime/realtime-gallery.js
new file mode 100644
index 0000000..202b828
--- /dev/null
+++ b/lab-js/realtime/realtime-gallery.js
@@ -0,0 +1,500 @@
+/**
+ * @module realtime-gallery
+ * @description
+ * Stores, renders and exports spike captures.
+ *
+ * STORAGE. IndexedDB, not localStorage. localStorage caps around 5MB per
+ * origin, is shared with the baseline, the settings and the existing thumbnail
+ * store, and inflates binary by a third on the way through base64 — a 640x480
+ * JPEG lands near 70KB encoded, so the quota starts evicting things you care
+ * about after roughly fifty frames. IndexedDB takes Blobs natively.
+ *
+ * PRIVACY. This module is the point where the tool starts holding photographs
+ * of a face rather than a 128-d template. Everything stays on the device, the
+ * count is always visible in the settings bar, and "Delete all" really empties
+ * the store. Deleting the baseline deliberately does NOT clear captures: the
+ * gallery is expected to outlive a re-baseline, and that is stated in the UI
+ * rather than left to be inferred.
+ *
+ * EXPORT. Store-only ZIP written here in ~70 lines. JPEGs do not compress, so
+ * DEFLATE would buy nothing, and pulling in JSZip would put a third-party
+ * script on a page whose whole point is that it makes no third-party requests.
+ */
+
+const DB_NAME = 'gstmxx-realtime';
+const DB_VERSION = 1;
+const STORE = 'captures';
+
+/**
+ * Opens (and upgrades) the capture database.
+ *
+ * @returns {Promise<IDBDatabase>} Open database handle.
+ */
+function openDb() {
+  return new Promise((resolve, reject) => {
+    const request = indexedDB.open(DB_NAME, DB_VERSION);
+    request.onupgradeneeded = () => {
+      const db = request.result;
+      if (!db.objectStoreNames.contains(STORE)) {
+        const store = db.createObjectStore(STORE, { keyPath: 'id' });
+        store.createIndex('t', 't');
+      }
+    };
+    request.onsuccess = () => resolve(request.result);
+    request.onerror = () => reject(request.error);
+  });
+}
+
+/**
+ * Runs one transaction against the capture store.
+ *
+ * @param {IDBDatabase} db Open database.
+ * @param {IDBTransactionMode} mode Transaction mode.
+ * @param {(store: IDBObjectStore) => IDBRequest} work Callback issuing the request.
+ * @returns {Promise<*>} Request result.
+ */
+function run(db, mode, work) {
+  return new Promise((resolve, reject) => {
+    const tx = db.transaction(STORE, mode);
+    const request = work(tx.objectStore(STORE));
+    request.onsuccess = () => resolve(request.result);
+    request.onerror = () => reject(request.error);
+  });
+}
+
+/**
+ * Formats a timestamp for watermarks and filenames.
+ *
+ * @param {number} timestamp Epoch milliseconds.
+ * @param {boolean} [fileSafe] Whether to avoid characters illegal in filenames.
+ * @returns {string} Formatted stamp.
+ */
+function stamp(timestamp, fileSafe = false) {
+  const d = new Date(timestamp);
+  const pad = (n) => String(n).padStart(2, '0');
+  const date = `${d.getFullYear()}-${pad(d.getMonth() + 1)}-${pad(d.getDate())}`;
+  const time = `${pad(d.getHours())}${fileSafe ? '' : ':'}${pad(d.getMinutes())}${fileSafe ? '' : ':'}${pad(d.getSeconds())}`;
+  return fileSafe ? `${date}_${time}` : `${date} ${time}`;
+}
+
+/**
+ * Burns provenance into the frame.
+ *
+ * The measurement travels with the image on purpose. An exported frame carrying
+ * only a date is a selfie; one carrying the distance, the measured noise floor
+ * and the threshold it was judged against is a record.
+ *
+ * @param {HTMLCanvasElement} source Frame to watermark.
+ * @param {object} meta Measurement metadata.
+ * @param {number} meta.t Capture timestamp.
+ * @param {number} meta.distance Distance at capture.
+ * @param {number} meta.noiseFloor Measured session noise floor.
+ * @param {number} meta.threshold Cited match threshold.
+ * @returns {HTMLCanvasElement} Watermarked canvas.
+ */
+export function watermark(source, meta) {
+  const barHeight = Math.max(26, Math.round(source.height * 0.062));
+  const canvas = document.createElement('canvas');
+  canvas.width = source.width;
+  canvas.height = source.height + barHeight;
+
+  const ctx = canvas.getContext('2d');
+  ctx.drawImage(source, 0, 0);
+
+  ctx.fillStyle = '#0c0906';
+  ctx.fillRect(0, source.height, canvas.width, barHeight);
+
+  const fontSize = Math.max(10, Math.round(barHeight * 0.42));
+  ctx.font = `${fontSize}px ui-monospace, SFMono-Regular, Menlo, monospace`;
+  ctx.textBaseline = 'middle';
+  ctx.textAlign = 'left';
+
+  const y = source.height + barHeight / 2;
+  ctx.fillStyle = '#ffe7a8';
+  ctx.fillText('ghostmaxxing', 12, y);
+
+  const detail = [
+    stamp(meta.t),
+    `d=${meta.distance.toFixed(3)}`,
+    `noise ±${meta.noiseFloor.toFixed(3)}`,
+    `dlib t=${meta.threshold.toFixed(2)}`,
+  ].join('  ·  ');
+
+  ctx.fillStyle = '#b9a892';
+  ctx.textAlign = 'right';
+  ctx.fillText(detail, canvas.width - 12, y);
+
+  return canvas;
+}
+
+/**
+ * Encodes a canvas to a Blob.
+ *
+ * @param {HTMLCanvasElement} canvas Canvas to encode.
+ * @param {string} type MIME type.
+ * @param {number} [quality] Encoder quality for lossy types.
+ * @returns {Promise<Blob>} Encoded blob.
+ */
+function toBlob(canvas, type, quality) {
+  return new Promise((resolve, reject) => {
+    canvas.toBlob(
+      (blob) => (blob ? resolve(blob) : reject(new Error('Encoding failed'))),
+      type,
+      quality,
+    );
+  });
+}
+
+/* --- Store-only ZIP -------------------------------------------------------- */
+
+const CRC_TABLE = (() => {
+  const table = new Uint32Array(256);
+  for (let n = 0; n < 256; n += 1) {
+    let c = n;
+    for (let k = 0; k < 8; k += 1) c = c & 1 ? 0xedb88320 ^ (c >>> 1) : c >>> 1;
+    table[n] = c >>> 0;
+  }
+  return table;
+})();
+
+/**
+ * Computes a CRC-32 checksum.
+ *
+ * @param {Uint8Array} bytes Input bytes.
+ * @returns {number} Unsigned checksum.
+ */
+function crc32(bytes) {
+  let c = 0xffffffff;
+  for (let i = 0; i < bytes.length; i += 1) {
+    c = CRC_TABLE[(c ^ bytes[i]) & 0xff] ^ (c >>> 8);
+  }
+  return (c ^ 0xffffffff) >>> 0;
+}
+
+/**
+ * Encodes a date as a DOS time/date pair.
+ *
+ * @param {Date} date Source date.
+ * @returns {{time: number, date: number}} DOS fields.
+ */
+function dosDate(date) {
+  return {
+    time: (date.getHours() << 11) | (date.getMinutes() << 5) | (date.getSeconds() >> 1),
+    date: ((date.getFullYear() - 1980) << 9) | ((date.getMonth() + 1) << 5) | date.getDate(),
+  };
+}
+
+/**
+ * Builds an uncompressed ZIP archive.
+ *
+ * @param {Array<{name: string, bytes: Uint8Array, date: Date}>} files Archive members.
+ * @returns {Blob} ZIP blob.
+ */
+export function buildZip(files) {
+  const encoder = new TextEncoder();
+  const locals = [];
+  const centrals = [];
+  let offset = 0;
+
+  for (const file of files) {
+    const nameBytes = encoder.encode(file.name);
+    const checksum = crc32(file.bytes);
+    const { time, date } = dosDate(file.date);
+
+    const local = new Uint8Array(30 + nameBytes.length);
+    const lv = new DataView(local.buffer);
+    lv.setUint32(0, 0x04034b50, true);
+    lv.setUint16(4, 20, true);
+    lv.setUint16(6, 0, true);
+    lv.setUint16(8, 0, true);
+    lv.setUint16(10, time, true);
+    lv.setUint16(12, date, true);
+    lv.setUint32(14, checksum, true);
+    lv.setUint32(18, file.bytes.length, true);
+    lv.setUint32(22, file.bytes.length, true);
+    lv.setUint16(26, nameBytes.length, true);
+    lv.setUint16(28, 0, true);
+    local.set(nameBytes, 30);
+
+    const central = new Uint8Array(46 + nameBytes.length);
+    const cv = new DataView(central.buffer);
+    cv.setUint32(0, 0x02014b50, true);
+    cv.setUint16(4, 20, true);
+    cv.setUint16(6, 20, true);
+    cv.setUint16(8, 0, true);
+    cv.setUint16(10, 0, true);
+    cv.setUint16(12, time, true);
+    cv.setUint16(14, date, true);
+    cv.setUint32(16, checksum, true);
+    cv.setUint32(20, file.bytes.length, true);
+    cv.setUint32(24, file.bytes.length, true);
+    cv.setUint16(28, nameBytes.length, true);
+    cv.setUint32(42, offset, true);
+    central.set(nameBytes, 46);
+
+    locals.push(local, file.bytes);
+    centrals.push(central);
+    offset += local.length + file.bytes.length;
+  }
+
+  const centralSize = centrals.reduce((sum, part) => sum + part.length, 0);
+  const end = new Uint8Array(22);
+  const ev = new DataView(end.buffer);
+  ev.setUint32(0, 0x06054b50, true);
+  ev.setUint16(8, files.length, true);
+  ev.setUint16(10, files.length, true);
+  ev.setUint32(12, centralSize, true);
+  ev.setUint32(16, offset, true);
+
+  return new Blob([...locals, ...centrals, end], { type: 'application/zip' });
+}
+
+/**
+ * Triggers a browser download for a blob.
+ *
+ * @param {Blob} blob Payload.
+ * @param {string} filename Suggested filename.
+ * @returns {void}
+ */
+function download(blob, filename) {
+  const url = URL.createObjectURL(blob);
+  const link = document.createElement('a');
+  link.href = url;
+  link.download = filename;
+  document.body.appendChild(link);
+  link.click();
+  link.remove();
+  window.setTimeout(() => URL.revokeObjectURL(url), 2000);
+}
+
+/**
+ * Creates the gallery controller.
+ *
+ * @param {object} options Wiring options.
+ * @param {HTMLElement} options.viewerEl Viewer overlay root.
+ * @param {HTMLImageElement} options.imageEl Viewer image element.
+ * @param {HTMLElement} options.metaEl Viewer metadata line.
+ * @param {HTMLElement} options.countEl Settings-bar count element.
+ * @param {(count: number) => void} options.onCountChange Count change callback.
+ * @param {(captureId: string) => void} options.onDelete Deletion callback.
+ * @param {(message: string) => void} options.onStatus Status message callback.
+ * @returns {object} Gallery controller.
+ */
+export function createGallery(options) {
+  const { viewerEl, imageEl, metaEl, countEl, onCountChange, onDelete, onStatus } = options;
+
+  let db = null;
+  let count = 0;
+  let openId = null;
+  let openUrl = null;
+
+  /**
+   * Publishes the current capture count.
+   *
+   * @returns {void}
+   */
+  function publishCount() {
+    if (countEl) countEl.textContent = String(count);
+    if (typeof onCountChange === 'function') onCountChange(count);
+  }
+
+  /**
+   * Opens the database and reads the current count.
+   *
+   * @returns {Promise<void>}
+   */
+  async function init() {
+    db = await openDb();
+    count = await run(db, 'readonly', (store) => store.count());
+    publishCount();
+  }
+
+  /**
+   * Watermarks, encodes and stores one captured frame.
+   *
+   * @param {HTMLCanvasElement} frame Raw captured frame.
+   * @param {object} meta Measurement metadata.
+   * @returns {Promise<string>} Stored capture id.
+   */
+  async function save(frame, meta) {
+    if (!db) throw new Error('Gallery not ready');
+    const marked = watermark(frame, meta);
+    const blob = await toBlob(marked, 'image/jpeg', 0.85);
+    const id = `cap_${meta.t}_${Math.random().toString(36).slice(2, 7)}`;
+
+    await run(db, 'readwrite', (store) =>
+      store.put({
+        id,
+        t: meta.t,
+        distance: meta.distance,
+        noiseFloor: meta.noiseFloor,
+        threshold: meta.threshold,
+        kind: meta.kind || 'manual',
+        blob,
+      }),
+    );
+
+    count += 1;
+    publishCount();
+    return id;
+  }
+
+  /**
+   * Displays one stored capture over the video stage.
+   *
+   * @param {string} id Capture id.
+   * @returns {Promise<void>}
+   */
+  async function open(id) {
+    if (!db) return;
+    const record = await run(db, 'readonly', (store) => store.get(id));
+    if (!record) return;
+
+    if (openUrl) URL.revokeObjectURL(openUrl);
+    openUrl = URL.createObjectURL(record.blob);
+    openId = id;
+
+    imageEl.src = openUrl;
+    metaEl.textContent = `${stamp(record.t)}  ·  d=${record.distance.toFixed(3)}  ·  noise ±${record.noiseFloor.toFixed(3)}  ·  ${record.kind}`;
+    viewerEl.hidden = false;
+  }
+
+  /**
+   * Closes the viewer overlay and releases its object URL.
+   *
+   * @returns {void}
+   */
+  function close() {
+    viewerEl.hidden = true;
+    imageEl.removeAttribute('src');
+    if (openUrl) {
+      URL.revokeObjectURL(openUrl);
+      openUrl = null;
+    }
+    openId = null;
+  }
+
+  /**
+   * Copies the open capture to the clipboard.
+   *
+   * The Clipboard API only reliably accepts PNG, so the JPEG is re-encoded on
+   * the way out. Falls back to a download where the API is unavailable.
+   *
+   * @returns {Promise<void>}
+   */
+  async function copyOpen() {
+    if (!db || !openId) return;
+    const record = await run(db, 'readonly', (store) => store.get(openId));
+    if (!record) return;
+
+    try {
+      const bitmap = await createImageBitmap(record.blob);
+      const canvas = document.createElement('canvas');
+      canvas.width = bitmap.width;
+      canvas.height = bitmap.height;
+      canvas.getContext('2d').drawImage(bitmap, 0, 0);
+      const png = await toBlob(canvas, 'image/png');
+      await navigator.clipboard.write([new ClipboardItem({ 'image/png': png })]);
+      onStatus('Capture copied to the clipboard.');
+    } catch {
+      download(record.blob, `ghostmaxxing_${stamp(record.t, true)}.jpg`);
+      onStatus('Clipboard unavailable — downloaded instead.');
+    }
+  }
+
+  /**
+   * Downloads the open capture.
+   *
+   * @returns {Promise<void>}
+   */
+  async function downloadOpen() {
+    if (!db || !openId) return;
+    const record = await run(db, 'readonly', (store) => store.get(openId));
+    if (!record) return;
+    download(record.blob, `ghostmaxxing_${stamp(record.t, true)}.jpg`);
+  }
+
+  /**
+   * Deletes the open capture.
+   *
+   * @returns {Promise<void>}
+   */
+  async function deleteOpen() {
+    if (!db || !openId) return;
+    const id = openId;
+    await run(db, 'readwrite', (store) => store.delete(id));
+    count = Math.max(0, count - 1);
+    publishCount();
+    close();
+    if (typeof onDelete === 'function') onDelete(id);
+    onStatus('Capture deleted.');
+  }
+
+  /**
+   * Exports every stored capture as one uncompressed archive.
+   *
+   * @returns {Promise<void>}
+   */
+  async function downloadAll() {
+    if (!db || !count) return;
+    onStatus('Building archive…');
+    const records = await run(db, 'readonly', (store) => store.getAll());
+    records.sort((a, b) => a.t - b.t);
+
+    const files = [];
+    for (const record of records) {
+      const bytes = new Uint8Array(await record.blob.arrayBuffer());
+      files.push({
+        name: `ghostmaxxing_${stamp(record.t, true)}_d${record.distance.toFixed(3)}.jpg`,
+        bytes,
+        date: new Date(record.t),
+      });
+    }
+
+    download(buildZip(files), `ghostmaxxing_captures_${stamp(Date.now(), true)}.zip`);
+    onStatus(`Exported ${files.length} capture${files.length === 1 ? '' : 's'}.`);
+  }
+
+  /**
+   * Empties the capture store.
+   *
+   * @returns {Promise<void>}
+   */
+  async function deleteAll() {
+    if (!db) return;
+    await run(db, 'readwrite', (store) => store.clear());
+    count = 0;
+    publishCount();
+    close();
+    if (typeof onDelete === 'function') onDelete(null);
+    onStatus('All captures deleted from this device.');
+  }
+
+  return {
+    init,
+    save,
+    open,
+    close,
+    copyOpen,
+    downloadOpen,
+    deleteOpen,
+    downloadAll,
+    deleteAll,
+    /**
+     * Reports how many captures are stored.
+     *
+     * @returns {number} Capture count.
+     */
+    size() {
+      return count;
+    },
+    /**
+     * Reports whether the viewer overlay is showing.
+     *
+     * @returns {boolean} True while open.
+     */
+    isOpen() {
+      return !viewerEl.hidden;
+    },
+  };
+}
diff --git a/lab-js/realtime/realtime-graph.js b/lab-js/realtime/realtime-graph.js
new file mode 100644
index 0000000..9780814
--- /dev/null
+++ b/lab-js/realtime/realtime-graph.js
@@ -0,0 +1,703 @@
+/**
+ * @module realtime-graph
+ * @description
+ * The left column: a scrollable distance plot with a FIXED time scale.
+ *
+ * Two things here are load-bearing and easy to undo by accident.
+ *
+ * 1. FIXED SCALE. The prototype stretched whatever history it had across the
+ *    full canvas height, so ten points and two hundred points both filled the
+ *    panel and a trace's steepness meant nothing. Every sample now occupies
+ *    the same number of pixels. The visible window is 60 seconds, always.
+ *
+ * 2. COLLAPSED GAPS. An absence is drawn as a fixed-height break labelled with
+ *    its real duration, not as a proportional stretch of empty axis. The
+ *    working session is: measure, walk to the mirror, apply makeup for eight
+ *    minutes, come back. Drawn to scale that is ~5,700px of nothing wedged
+ *    between the two measurements you are actually trying to compare. The
+ *    elapsed time is not lost, it just isn't spatialised.
+ *
+ * Rendering is virtualised: the canvas is sticky and sized to the scroll
+ * viewport, a spacer supplies the scroll height, and only the visible slice is
+ * drawn. Memory is constant, so session length is unbounded. Growing one tall
+ * canvas instead would hit the ~32k px dimension cap after ~45 minutes and
+ * hold ~30MB on the way there.
+ */
+
+const GAP_BLOCK_PX = 30;
+const AXIS_STRIP_PX = 17;
+const PAD_LEFT = 6;
+const GUTTER_RIGHT = 24;
+const MARKER_RADIUS = 7;
+const FOLLOW_SLACK_PX = 10;
+
+/**
+ * Formats a duration in milliseconds as a compact human string.
+ *
+ * @param {number} ms Duration in milliseconds.
+ * @returns {string} Compact duration such as "8m 12s" or "4s".
+ */
+export function formatDuration(ms) {
+  const totalSeconds = Math.max(0, Math.round(ms / 1000));
+  const hours = Math.floor(totalSeconds / 3600);
+  const minutes = Math.floor((totalSeconds % 3600) / 60);
+  const seconds = totalSeconds % 60;
+  if (hours) return `${hours}h ${minutes}m`;
+  if (minutes) return `${minutes}m ${seconds}s`;
+  return `${seconds}s`;
+}
+
+/**
+ * Formats a timestamp as a zero-padded wall clock label.
+ *
+ * @param {number} timestamp Epoch milliseconds.
+ * @returns {string} Clock label such as "14:32:07".
+ */
+function formatClock(timestamp) {
+  const date = new Date(timestamp);
+  const hh = String(date.getHours()).padStart(2, '0');
+  const mm = String(date.getMinutes()).padStart(2, '0');
+  const ss = String(date.getSeconds()).padStart(2, '0');
+  return `${hh}:${mm}:${ss}`;
+}
+
+/**
+ * Reads a resolved CSS custom property from an element with a fallback.
+ *
+ * @param {Element} el Element whose computed style is read.
+ * @param {string} name Custom property name including leading dashes.
+ * @param {string} fallback Value used when the property resolves empty.
+ * @returns {string} Resolved colour string.
+ */
+function token(el, name, fallback) {
+  const value = getComputedStyle(el).getPropertyValue(name).trim();
+  return value || fallback;
+}
+
+/**
+ * Creates the virtualised distance graph controller.
+ *
+ * @param {object} options Wiring and tuning options.
+ * @param {HTMLElement} options.scrollEl Scroll container element.
+ * @param {HTMLElement} options.spacerEl Spacer element supplying scroll height.
+ * @param {HTMLCanvasElement} options.canvasEl Sticky canvas element.
+ * @param {HTMLElement} options.followEl "Jump to now" button.
+ * @param {number} options.windowSeconds Seconds visible in one viewport.
+ * @param {number} options.samplesPerSecond Sample cadence used for layout.
+ * @param {number} options.maxDistance Right edge of the distance axis.
+ * @param {number} options.threshold Cited match threshold line.
+ * @param {(captureId: string) => void} options.onMarkerClick Marker click handler.
+ * @returns {object} Graph controller.
+ */
+export function createGraph(options) {
+  const {
+    scrollEl,
+    spacerEl,
+    canvasEl,
+    followEl,
+    windowSeconds,
+    samplesPerSecond,
+    maxDistance,
+    threshold,
+    onMarkerClick,
+  } = options;
+
+  const ctx = canvasEl.getContext('2d');
+
+  /** @type {Array<{kind: string, t: number, value?: number, endT?: number, seq?: number, captureId?: string}>} */
+  let entries = [];
+  /** @type {number[]} Virtual top offset of each entry, in CSS pixels. */
+  let tops = [];
+  let contentHeight = 0;
+  let sampleSeq = 0;
+  let openGapIndex = -1;
+
+  let noiseFloor = null;
+  let follow = true;
+  let programmaticScroll = false;
+
+  let viewportH = 0;
+  let viewportW = 0;
+  let pxPerSample = 4;
+  let labelEvery = samplesPerSecond * 10;
+
+  /**
+   * Height in CSS pixels contributed by one timeline entry.
+   *
+   * @param {object} entry Timeline entry.
+   * @returns {number} Entry height.
+   */
+  function entryHeight(entry) {
+    return entry.kind === 'gap' ? GAP_BLOCK_PX : pxPerSample;
+  }
+
+  /**
+   * Rebuilds cumulative offsets. O(n), called only on resize.
+   *
+   * @returns {void}
+   */
+  function relayout() {
+    tops = new Array(entries.length);
+    let acc = 0;
+    for (let i = 0; i < entries.length; i += 1) {
+      tops[i] = acc;
+      acc += entryHeight(entries[i]);
+    }
+    contentHeight = acc;
+    syncSpacer();
+  }
+
+  /**
+   * Applies the current virtual height to the scroll spacer.
+   *
+   * @returns {void}
+   */
+  function syncSpacer() {
+    spacerEl.style.height = `${Math.round(Math.max(contentHeight, viewportH))}px`;
+  }
+
+  /**
+   * Scrolls to the live edge without tripping the follow-disengage handler.
+   *
+   * @returns {void}
+   */
+  function scrollToNow() {
+    programmaticScroll = true;
+    scrollEl.scrollTop = scrollEl.scrollHeight;
+    window.requestAnimationFrame(() => {
+      programmaticScroll = false;
+    });
+  }
+
+  /**
+   * Shows or hides the "Jump to now" affordance.
+   *
+   * @returns {void}
+   */
+  function syncFollowButton() {
+    followEl.hidden = follow;
+  }
+
+  /**
+   * Binary search for the last entry whose top is at or above a virtual y.
+   *
+   * @param {number} y Virtual y offset in CSS pixels.
+   * @returns {number} Index of the entry containing y.
+   */
+  function indexAt(y) {
+    let lo = 0;
+    let hi = tops.length - 1;
+    let best = 0;
+    while (lo <= hi) {
+      const mid = (lo + hi) >> 1;
+      if (tops[mid] <= y) {
+        best = mid;
+        lo = mid + 1;
+      } else {
+        hi = mid - 1;
+      }
+    }
+    return best;
+  }
+
+  /**
+   * Maps a distance value to a canvas x coordinate, clamped to the axis.
+   *
+   * @param {number} value Distance value.
+   * @returns {number} Canvas x in CSS pixels.
+   */
+  function xFor(value) {
+    const plotWidth = viewportW - PAD_LEFT - GUTTER_RIGHT;
+    const normalized = Math.max(0, Math.min(1, value / maxDistance));
+    return PAD_LEFT + normalized * plotWidth;
+  }
+
+  /**
+   * Repaints the visible slice of the timeline.
+   *
+   * @returns {void}
+   */
+  function draw() {
+    if (!ctx || viewportW <= 0 || viewportH <= 0) return;
+
+    const colors = {
+      plotBg: token(canvasEl, '--rt-plot-bg', '#2c2318'),
+      trace: token(canvasEl, '--rt-trace', '#ffe7a8'),
+      thresholdLine: token(canvasEl, '--rt-threshold', '#f3c747'),
+      noise: token(canvasEl, '--rt-noise', '#7fe3b0'),
+      grid: token(canvasEl, '--rt-grid', 'rgba(255,231,168,0.08)'),
+      gap: token(canvasEl, '--rt-gap', '#8a7b6a'),
+      faint: token(canvasEl, '--rt-gap', '#8a7b6a'),
+    };
+
+    const scrollTop = scrollEl.scrollTop;
+    const plotWidth = viewportW - PAD_LEFT - GUTTER_RIGHT;
+
+    ctx.clearRect(0, 0, viewportW, viewportH);
+    ctx.fillStyle = colors.plotBg;
+    ctx.fillRect(0, 0, viewportW, viewportH);
+
+    // Axis ticks. These carry no claim; they exist so a value can be read off
+    // the trace. Kept at hairline weight so they never compete with the two
+    // reference lines below, which do carry claims.
+    ctx.strokeStyle = colors.grid;
+    ctx.lineWidth = 1;
+    for (const value of [0.25, 0.5, 0.75, 1.0, 1.25]) {
+      if (value > maxDistance) continue;
+      const x = Math.round(xFor(value)) + 0.5;
+      ctx.beginPath();
+      ctx.moveTo(x, 0);
+      ctx.lineTo(x, viewportH);
+      ctx.stroke();
+    }
+
+    if (noiseFloor !== null) {
+      const x = Math.round(xFor(noiseFloor)) + 0.5;
+      ctx.strokeStyle = colors.noise;
+      ctx.lineWidth = 1.5;
+      ctx.setLineDash([3, 3]);
+      ctx.beginPath();
+      ctx.moveTo(x, 0);
+      ctx.lineTo(x, viewportH);
+      ctx.stroke();
+      ctx.setLineDash([]);
+    }
+
+    const thresholdX = Math.round(xFor(threshold)) + 0.5;
+    ctx.strokeStyle = colors.thresholdLine;
+    ctx.lineWidth = 2;
+    ctx.beginPath();
+    ctx.moveTo(thresholdX, 0);
+    ctx.lineTo(thresholdX, viewportH);
+    ctx.stroke();
+
+    if (entries.length) {
+      const firstIndex = indexAt(Math.max(0, scrollTop - GAP_BLOCK_PX));
+      const limit = scrollTop + viewportH;
+
+      ctx.lineWidth = 2;
+      ctx.strokeStyle = colors.trace;
+      ctx.lineJoin = 'round';
+
+      let penDown = false;
+      ctx.beginPath();
+
+      for (let i = firstIndex; i < entries.length; i += 1) {
+        const top = tops[i];
+        if (top > limit) break;
+        const entry = entries[i];
+        const y = top - scrollTop;
+
+        if (entry.kind === 'gap') {
+          if (penDown) {
+            ctx.stroke();
+            ctx.beginPath();
+            penDown = false;
+          }
+          drawGap(entry, y, colors, plotWidth);
+          continue;
+        }
+
+        const x = xFor(entry.value);
+        const yMid = y + pxPerSample / 2;
+        if (!penDown) {
+          ctx.moveTo(x, yMid);
+          penDown = true;
+        } else {
+          ctx.lineTo(x, yMid);
+        }
+
+        if (entry.value > maxDistance) {
+          ctx.stroke();
+          drawOverflow(yMid, colors.trace);
+          ctx.beginPath();
+          ctx.moveTo(x, yMid);
+        }
+      }
+
+      if (penDown) ctx.stroke();
+
+      // Second pass so markers and labels sit above the trace.
+      for (let i = firstIndex; i < entries.length; i += 1) {
+        const top = tops[i];
+        if (top > limit) break;
+        const entry = entries[i];
+        if (entry.kind !== 'sample') continue;
+        const yMid = top - scrollTop + pxPerSample / 2;
+
+        if (entry.seq % labelEvery === 0) {
+          ctx.fillStyle = colors.faint;
+          ctx.font = '9px ui-monospace, SFMono-Regular, Menlo, monospace';
+          ctx.textAlign = 'left';
+          ctx.textBaseline = 'middle';
+          ctx.fillText(formatClock(entry.t), PAD_LEFT + 2, yMid);
+        }
+
+        if (entry.captureId) drawMarker(yMid, colors);
+      }
+    }
+
+    drawAxisStrip(colors);
+  }
+
+  /**
+   * Draws a collapsed absence block with its true duration.
+   *
+   * @param {object} entry Gap entry.
+   * @param {number} y Canvas y of the block top.
+   * @param {object} colors Resolved palette.
+   * @param {number} plotWidth Usable plot width in CSS pixels.
+   * @returns {void}
+   */
+  function drawGap(entry, y, colors, plotWidth) {
+    const endT = entry.endT ?? Date.now();
+    ctx.save();
+    ctx.beginPath();
+    ctx.rect(PAD_LEFT, y, plotWidth, GAP_BLOCK_PX);
+    ctx.clip();
+
+    ctx.strokeStyle = colors.gap;
+    ctx.globalAlpha = 0.32;
+    ctx.lineWidth = 1;
+    for (let hx = -GAP_BLOCK_PX; hx < plotWidth + GAP_BLOCK_PX; hx += 7) {
+      ctx.beginPath();
+      ctx.moveTo(PAD_LEFT + hx, y + GAP_BLOCK_PX);
+      ctx.lineTo(PAD_LEFT + hx + GAP_BLOCK_PX, y);
+      ctx.stroke();
+    }
+    ctx.restore();
+
+    ctx.strokeStyle = colors.gap;
+    ctx.lineWidth = 1;
+    ctx.beginPath();
+    ctx.moveTo(PAD_LEFT, Math.round(y) + 0.5);
+    ctx.lineTo(PAD_LEFT + plotWidth, Math.round(y) + 0.5);
+    ctx.moveTo(PAD_LEFT, Math.round(y + GAP_BLOCK_PX) + 0.5);
+    ctx.lineTo(PAD_LEFT + plotWidth, Math.round(y + GAP_BLOCK_PX) + 0.5);
+    ctx.stroke();
+
+    const label = `no face · ${formatDuration(endT - entry.t)}`;
+    ctx.font = '9px ui-monospace, SFMono-Regular, Menlo, monospace';
+    ctx.textAlign = 'center';
+    ctx.textBaseline = 'middle';
+    const metrics = ctx.measureText(label);
+    const cx = PAD_LEFT + plotWidth / 2;
+    const cy = y + GAP_BLOCK_PX / 2;
+
+    ctx.fillStyle = token(canvasEl, '--rt-plot-bg', '#2c2318');
+    ctx.fillRect(cx - metrics.width / 2 - 5, cy - 7, metrics.width + 10, 14);
+    ctx.fillStyle = colors.gap;
+    ctx.fillText(label, cx, cy);
+  }
+
+  /**
+   * Draws the off-axis indicator for a sample beyond the plotted ceiling.
+   *
+   * @param {number} y Canvas y of the sample.
+   * @param {string} color Trace colour.
+   * @returns {void}
+   */
+  function drawOverflow(y, color) {
+    const x = viewportW - GUTTER_RIGHT;
+    ctx.fillStyle = color;
+    ctx.beginPath();
+    ctx.moveTo(x, y - 3.5);
+    ctx.lineTo(x + 5, y);
+    ctx.lineTo(x, y + 3.5);
+    ctx.closePath();
+    ctx.fill();
+  }
+
+  /**
+   * Draws a saved-capture marker in the right gutter with a connector.
+   *
+   * @param {number} y Canvas y of the marked sample.
+   * @param {object} colors Resolved palette.
+   * @returns {void}
+   */
+  function drawMarker(y, colors) {
+    const cx = viewportW - GUTTER_RIGHT / 2;
+
+    ctx.strokeStyle = colors.grid;
+    ctx.lineWidth = 1;
+    ctx.beginPath();
+    ctx.moveTo(viewportW - GUTTER_RIGHT, y);
+    ctx.lineTo(cx - MARKER_RADIUS, y);
+    ctx.stroke();
+
+    ctx.fillStyle = token(canvasEl, '--rt-plot-bg', '#2c2318');
+    ctx.strokeStyle = colors.trace;
+    ctx.lineWidth = 1.25;
+    ctx.beginPath();
+    ctx.arc(cx, y, MARKER_RADIUS, 0, Math.PI * 2);
+    ctx.fill();
+    ctx.stroke();
+
+    ctx.fillStyle = colors.trace;
+    ctx.fillRect(cx - 3.5, y - 2, 7, 4.5);
+    ctx.fillStyle = token(canvasEl, '--rt-plot-bg', '#2c2318');
+    ctx.beginPath();
+    ctx.arc(cx, y + 0.25, 1.4, 0, Math.PI * 2);
+    ctx.fill();
+  }
+
+  /**
+   * Draws the pinned scale strip. The canvas is sticky, so anything painted at
+   * fixed canvas coordinates behaves as a fixed overlay while content scrolls.
+   *
+   * @param {object} colors Resolved palette.
+   * @returns {void}
+   */
+  function drawAxisStrip(colors) {
+    ctx.fillStyle = token(canvasEl, '--rt-surface-solid', 'rgba(12,9,6,0.86)');
+    ctx.fillRect(0, 0, viewportW, AXIS_STRIP_PX);
+    ctx.strokeStyle = colors.grid;
+    ctx.lineWidth = 1;
+    ctx.beginPath();
+    ctx.moveTo(0, AXIS_STRIP_PX + 0.5);
+    ctx.lineTo(viewportW, AXIS_STRIP_PX + 0.5);
+    ctx.stroke();
+
+    ctx.font = '9px ui-monospace, SFMono-Regular, Menlo, monospace';
+    ctx.textBaseline = 'middle';
+    ctx.fillStyle = colors.faint;
+
+    const ticks = [0, 0.5, 1.0];
+    for (const value of ticks) {
+      if (value > maxDistance) continue;
+      const x = xFor(value);
+      ctx.textAlign = value === 0 ? 'left' : 'center';
+      ctx.fillText(value.toFixed(1), x, AXIS_STRIP_PX / 2);
+    }
+
+    ctx.textAlign = 'center';
+    ctx.fillStyle = colors.thresholdLine;
+    ctx.fillText('0.6', xFor(threshold), AXIS_STRIP_PX / 2);
+  }
+
+  /**
+   * Recomputes canvas backing store and layout for the current viewport.
+   *
+   * @returns {void}
+   */
+  function resize() {
+    const rect = scrollEl.getBoundingClientRect();
+    const nextW = Math.max(Math.floor(rect.width), 1);
+    const nextH = Math.max(Math.floor(rect.height), 1);
+    const dpr = Math.max(window.devicePixelRatio || 1, 1);
+
+    viewportW = nextW;
+    viewportH = nextH;
+    pxPerSample = viewportH / (windowSeconds * samplesPerSecond);
+    labelEvery = samplesPerSecond * 10;
+
+    canvasEl.style.height = `${nextH}px`;
+    canvasEl.width = Math.floor(nextW * dpr);
+    canvasEl.height = Math.floor(nextH * dpr);
+    ctx.setTransform(dpr, 0, 0, dpr, 0, 0);
+
+    relayout();
+    if (follow) scrollToNow();
+    draw();
+  }
+
+  /**
+   * Appends one measured sample at the live edge.
+   *
+   * @param {number} t Epoch milliseconds of the sample.
+   * @param {number} value Averaged distance for the tick.
+   * @returns {number} Index of the appended entry.
+   */
+  function pushSample(t, value) {
+    openGapIndex = -1;
+    const entry = { kind: 'sample', t, value, seq: sampleSeq };
+    sampleSeq += 1;
+    tops.push(contentHeight);
+    entries.push(entry);
+    contentHeight += pxPerSample;
+    syncSpacer();
+    if (follow) scrollToNow();
+    draw();
+    return entries.length - 1;
+  }
+
+  /**
+   * Opens an absence block, or extends the one already open.
+   *
+   * @param {number} t Epoch milliseconds when the face was last seen.
+   * @returns {void}
+   */
+  function beginGap(t) {
+    if (openGapIndex !== -1) {
+      draw();
+      return;
+    }
+    const entry = { kind: 'gap', t, endT: null };
+    tops.push(contentHeight);
+    entries.push(entry);
+    contentHeight += GAP_BLOCK_PX;
+    openGapIndex = entries.length - 1;
+    syncSpacer();
+    if (follow) scrollToNow();
+    draw();
+  }
+
+  /**
+   * Closes the open absence block.
+   *
+   * @param {number} t Epoch milliseconds when the face returned.
+   * @returns {void}
+   */
+  function endGap(t) {
+    if (openGapIndex === -1) return;
+    entries[openGapIndex].endT = t;
+    openGapIndex = -1;
+    draw();
+  }
+
+  /**
+   * Attaches a saved capture to the most recent sample.
+   *
+   * @param {string} captureId Identifier of the stored capture.
+   * @returns {void}
+   */
+  function markLatestCapture(captureId) {
+    for (let i = entries.length - 1; i >= 0; i -= 1) {
+      if (entries[i].kind === 'sample') {
+        entries[i].captureId = captureId;
+        draw();
+        return;
+      }
+    }
+  }
+
+  /**
+   * Removes a capture marker after the capture is deleted.
+   *
+   * @param {string} captureId Identifier of the removed capture.
+   * @returns {void}
+   */
+  function clearCapture(captureId) {
+    for (const entry of entries) {
+      if (entry.captureId === captureId) delete entry.captureId;
+    }
+    draw();
+  }
+
+  /**
+   * Sets the measured noise floor drawn as the lower reference line.
+   *
+   * @param {number|null} value Measured noise floor, or null to hide it.
+   * @returns {void}
+   */
+  function setNoiseFloor(value) {
+    noiseFloor = value;
+    draw();
+  }
+
+  /**
+   * Clears the timeline and returns to the live edge.
+   *
+   * @returns {void}
+   */
+  function reset() {
+    entries = [];
+    tops = [];
+    contentHeight = 0;
+    sampleSeq = 0;
+    openGapIndex = -1;
+    follow = true;
+    syncFollowButton();
+    syncSpacer();
+    scrollToNow();
+    draw();
+  }
+
+  /**
+   * Resolves a pointer event to a capture marker, if one was hit.
+   *
+   * @param {PointerEvent|MouseEvent} event Pointer event on the canvas.
+   * @returns {string|null} Capture id, or null when nothing was hit.
+   */
+  function markerAt(event) {
+    const rect = canvasEl.getBoundingClientRect();
+    const x = event.clientX - rect.left;
+    if (x < viewportW - GUTTER_RIGHT - MARKER_RADIUS) return null;
+
+    const virtualY = scrollEl.scrollTop + (event.clientY - rect.top);
+    const index = indexAt(virtualY);
+
+    for (let i = Math.max(0, index - 3); i <= Math.min(entries.length - 1, index + 3); i += 1) {
+      const entry = entries[i];
+      if (entry.kind !== 'sample' || !entry.captureId) continue;
+      const centre = tops[i] + pxPerSample / 2;
+      if (Math.abs(centre - virtualY) <= MARKER_RADIUS + 3) return entry.captureId;
+    }
+    return null;
+  }
+
+  scrollEl.addEventListener('scroll', () => {
+    if (programmaticScroll) {
+      draw();
+      return;
+    }
+    const distanceFromBottom =
+      scrollEl.scrollHeight - scrollEl.scrollTop - scrollEl.clientHeight;
+    follow = distanceFromBottom <= FOLLOW_SLACK_PX;
+    syncFollowButton();
+    draw();
+  });
+
+  canvasEl.addEventListener('click', (event) => {
+    const captureId = markerAt(event);
+    if (captureId && typeof onMarkerClick === 'function') onMarkerClick(captureId);
+  });
+
+  canvasEl.addEventListener('pointermove', (event) => {
+    canvasEl.classList.toggle('is-clickable', Boolean(markerAt(event)));
+  });
+
+  followEl.addEventListener('click', () => {
+    follow = true;
+    syncFollowButton();
+    scrollToNow();
+    draw();
+  });
+
+  const observer = new ResizeObserver(() => resize());
+  observer.observe(scrollEl);
+
+  return {
+    resize,
+    draw,
+    reset,
+    pushSample,
+    beginGap,
+    endGap,
+    markLatestCapture,
+    clearCapture,
+    setNoiseFloor,
+    /**
+     * Reports timeline size for diagnostics and tests.
+     *
+     * @returns {{samples: number, gaps: number, following: boolean}} Snapshot.
+     */
+    stats() {
+      let samples = 0;
+      let gaps = 0;
+      for (const entry of entries) {
+        if (entry.kind === 'gap') gaps += 1;
+        else samples += 1;
+      }
+      return { samples, gaps, following: follow };
+    },
+    /**
+     * Disconnects observers held by the graph.
+     *
+     * @returns {void}
+     */
+    destroy() {
+      observer.disconnect();
+    },
+  };
+}
diff --git a/lab-js/realtime/realtime-settings.js b/lab-js/realtime/realtime-settings.js
new file mode 100644
index 0000000..712a75d
--- /dev/null
+++ b/lab-js/realtime/realtime-settings.js
@@ -0,0 +1,238 @@
+/**
+ * @module realtime-settings
+ * @description
+ * The bottom bar over the video stage, plus the persistence behind it.
+ *
+ * The match threshold is deliberately NOT adjustable here. 0.60 is the one
+ * number on this page with a citable provenance — dlib's default, measured on
+ * LFW — and a moved threshold means nothing at all. The information lives in
+ * the measured noise floor and in how far the trace climbs above it, not in
+ * where an operator decided to drag a line.
+ *
+ * The brightness control is display only. face-api reads raw frames from the
+ * video element and CSS filters apply at composite, so turning this dial cannot
+ * change a descriptor. That is stated in the control's tooltip so nobody later
+ * reads an exposure change as a lighting-robustness result.
+ */
+
+const SETTINGS_KEY = 'gstmxx.realtime.settings';
+const BASELINE_KEY = 'gstmxx.realtime.baseline';
+const BASELINE_FORMAT = 1;
+
+const DEFAULT_SETTINGS = {
+  brightness: 0.85,
+  equalizer: true,
+};
+
+/**
+ * Reads persisted view settings.
+ *
+ * @returns {{brightness: number, equalizer: boolean}} Stored or default settings.
+ */
+export function loadSettings() {
+  try {
+    const raw = window.localStorage.getItem(SETTINGS_KEY);
+    if (!raw) return { ...DEFAULT_SETTINGS };
+    const parsed = JSON.parse(raw);
+    return {
+      brightness: Number.isFinite(parsed.brightness) ? parsed.brightness : DEFAULT_SETTINGS.brightness,
+      equalizer: typeof parsed.equalizer === 'boolean' ? parsed.equalizer : DEFAULT_SETTINGS.equalizer,
+    };
+  } catch {
+    return { ...DEFAULT_SETTINGS };
+  }
+}
+
+/**
+ * Persists view settings.
+ *
+ * @param {object} settings Settings to store.
+ * @returns {void}
+ */
+export function saveSettings(settings) {
+  try {
+    window.localStorage.setItem(SETTINGS_KEY, JSON.stringify(settings));
+  } catch {
+    /* Storage disabled or full; settings simply do not persist. */
+  }
+}
+
+/**
+ * Reads a persisted baseline.
+ *
+ * The baseline is a biometric template. It never leaves the device, and
+ * "Delete baseline" removes it outright rather than flipping a flag.
+ *
+ * @returns {{descriptor: number[], noiseFloor: number, capturedAt: number}|null} Stored baseline.
+ */
+export function loadBaseline() {
+  try {
+    const raw = window.localStorage.getItem(BASELINE_KEY);
+    if (!raw) return null;
+    const parsed = JSON.parse(raw);
+    if (parsed.format !== BASELINE_FORMAT) return null;
+    if (!Array.isArray(parsed.descriptor) || parsed.descriptor.length !== 128) return null;
+    if (!Number.isFinite(parsed.noiseFloor)) return null;
+    return parsed;
+  } catch {
+    return null;
+  }
+}
+
+/**
+ * Persists a baseline.
+ *
+ * @param {object} baseline Baseline record to store.
+ * @returns {void}
+ */
+export function saveBaseline(baseline) {
+  try {
+    window.localStorage.setItem(
+      BASELINE_KEY,
+      JSON.stringify({ ...baseline, format: BASELINE_FORMAT }),
+    );
+  } catch {
+    /* Storage disabled; the baseline holds for this session only. */
+  }
+}
+
+/**
+ * Removes the persisted baseline.
+ *
+ * @returns {void}
+ */
+export function clearBaseline() {
+  try {
+    window.localStorage.removeItem(BASELINE_KEY);
+  } catch {
+    /* Nothing to clear. */
+  }
+}
+
+/**
+ * Wraps a destructive button in a two-step confirmation.
+ *
+ * Deleting a baseline mid-session costs the whole session, and "Delete all"
+ * costs every capture on the device. Neither should be one stray click.
+ *
+ * @param {HTMLButtonElement} button Button to guard.
+ * @param {string} idleLabel Label in the resting state.
+ * @param {string} confirmLabel Label while awaiting confirmation.
+ * @param {() => void} onConfirm Action to run on the second click.
+ * @param {number} [timeoutMs] Time before the button reverts.
+ * @returns {{reset: () => void}} Guard handle.
+ */
+export function guardDestructive(button, idleLabel, confirmLabel, onConfirm, timeoutMs = 4000) {
+  let armed = false;
+  let timer = null;
+
+  /**
+   * Returns the button to its resting label.
+   *
+   * @returns {void}
+   */
+  function reset() {
+    armed = false;
+    button.textContent = idleLabel;
+    button.classList.remove('is-confirming');
+    if (timer) window.clearTimeout(timer);
+    timer = null;
+  }
+
+  button.addEventListener('click', () => {
+    if (!armed) {
+      armed = true;
+      button.textContent = confirmLabel;
+      button.classList.add('is-confirming');
+      timer = window.setTimeout(reset, timeoutMs);
+      return;
+    }
+    reset();
+    onConfirm();
+  });
+
+  return { reset };
+}
+
+/**
+ * Creates the settings bar controller.
+ *
+ * @param {object} options Wiring options.
+ * @param {HTMLElement} options.rootEl Element carrying the --rt-* custom properties.
+ * @param {HTMLInputElement} options.brightnessEl Brightness range input.
+ * @param {HTMLElement} options.brightnessValueEl Brightness readout.
+ * @param {HTMLButtonElement} options.equalizerEl Equalizer switch.
+ * @param {HTMLElement} options.layoutEl Grid root toggled by the equalizer switch.
+ * @param {() => void} options.onLayoutChange Called after a layout-affecting change.
+ * @returns {object} Settings controller.
+ */
+export function createSettings(options) {
+  const {
+    rootEl,
+    brightnessEl,
+    brightnessValueEl,
+    equalizerEl,
+    layoutEl,
+    onLayoutChange,
+  } = options;
+
+  const settings = loadSettings();
+
+  /**
+   * Applies the brightness value to the stage and the readout.
+   *
+   * @returns {void}
+   */
+  function applyBrightness() {
+    rootEl.style.setProperty('--rt-brightness', String(settings.brightness));
+    brightnessEl.value = String(settings.brightness);
+    brightnessValueEl.textContent = settings.brightness.toFixed(2);
+  }
+
+  /**
+   * Applies equalizer visibility to the grid.
+   *
+   * Hiding the right column resizes the left one without firing a window
+   * resize event, which is why the graph observes its own container rather
+   * than listening on window alone.
+   *
+   * @returns {void}
+   */
+  function applyEqualizer() {
+    layoutEl.classList.toggle('hide-equalizer', !settings.equalizer);
+    equalizerEl.setAttribute('aria-checked', String(settings.equalizer));
+  }
+
+  brightnessEl.addEventListener('input', () => {
+    settings.brightness = Number(brightnessEl.value);
+    applyBrightness();
+    saveSettings(settings);
+  });
+
+  equalizerEl.addEventListener('click', () => {
+    settings.equalizer = !settings.equalizer;
+    applyEqualizer();
+    saveSettings(settings);
+    if (typeof onLayoutChange === 'function') onLayoutChange();
+  });
+
+  equalizerEl.addEventListener('keydown', (event) => {
+    if (event.key !== ' ' && event.key !== 'Enter') return;
+    event.preventDefault();
+    equalizerEl.click();
+  });
+
+  applyBrightness();
+  applyEqualizer();
+
+  return {
+    /**
+     * Returns the live settings object.
+     *
+     * @returns {{brightness: number, equalizer: boolean}} Current settings.
+     */
+    values() {
+      return settings;
+    },
+  };
+}
diff --git a/lab-js/realtime/realtime.js b/lab-js/realtime/realtime.js
new file mode 100644
index 0000000..1bc634b
--- /dev/null
+++ b/lab-js/realtime/realtime.js
@@ -0,0 +1,929 @@
+/**
+ * @module realtime
+ * @description
+ * Entry point for the latent space visualizer. Owns the detection loop, the
+ * descriptor equalizer, the presence state machine and the page lifecycle;
+ * everything else lives in the sibling realtime-* modules.
+ *
+ * The page measures one thing: how far a face has moved from a reference
+ * captured before the makeup session. Two lines on the plot carry claims and
+ * they are different kinds of claim — the noise floor is measured here, on this
+ * camera, from this face; 0.60 is dlib's LFW default and is a property of a
+ * benchmark, not of anyone's face. Nothing is drawn between them, because
+ * nothing between them has been measured.
+ */
+
+import { MODEL_URLS, DETECTOR_OPTIONS } from '../config.js';
+import { createGraph, formatDuration } from './realtime-graph.js';
+import {
+  createQualityGate,
+  createBaselineCollector,
+  createSpikeDetector,
+  createFrameBuffer,
+  euclidean,
+} from './realtime-capture.js';
+import { createGallery } from './realtime-gallery.js';
+import {
+  createSettings,
+  guardDestructive,
+  loadBaseline,
+  saveBaseline,
+  clearBaseline,
+} from './realtime-settings.js';
+
+const APP_STATES = {
+  NO_BASELINE: 'NO BASELINE',
+  CAPTURING: 'CAPTURING',
+  TRACKING: 'TRACKING',
+};
+
+const DESCRIPTOR_SIZE = 128;
+const CELLS_PER_ROW = 10;
+const MAX_DELTA = 0.15;
+
+/** dlib's documented default, measured on LFW. Cited, not tuned, not movable. */
+const MATCH_THRESHOLD = 0.6;
+
+/** Impostor pairs live below this; the ceiling exists so success has headroom. */
+const GRAPH_MAX_DISTANCE = 1.25;
+const GRAPH_WINDOW_SECONDS = 60;
+const SAMPLES_PER_SECOND = 3;
+
+/** Long enough that a blink or a head turn does not punch a hole in the trace. */
+const NO_FACE_DEBOUNCE_MS = 1500;
+
+const BASELINE_TARGET_SAMPLES = 12;
+const BASELINE_MIN_MS = 3000;
+const BASELINE_TIMEOUT_MS = 15000;
+const BASELINE_MAX_SPREAD = 0.2;
+
+const SPIKE_SUSTAIN_MS = 1500;
+const SPIKE_BEST_MARGIN = 0.05;
+const SPIKE_COOLDOWN_MS = 8000;
+const FRAME_BUFFER_SLOTS = 5;
+const CAPTURE_MAX_WIDTH = 960;
+
+/** Grace period before a hidden tab loses the camera entirely. */
+const HIDDEN_STOP_DELAY_MS = 45000;
+
+const RING_RADIUS = 54;
+const RING_CIRCUMFERENCE = 2 * Math.PI * RING_RADIUS;
+
+const runtime = {
+  appState: APP_STATES.NO_BASELINE,
+  baseline: null,
+  equalizerRows: [],
+  equalizerLit: new Int16Array(DESCRIPTOR_SIZE).fill(-1),
+  distanceBuffer: [],
+  frameRequestId: null,
+  flushTimerId: null,
+  hiddenStopTimerId: null,
+  detectorBusy: false,
+  initialized: false,
+  running: false,
+  streamStopped: false,
+  faceMissing: false,
+  lastFaceAt: 0,
+  stream: null,
+};
+
+const els = {
+  layer: document.getElementById('ui-layer'),
+  video: document.getElementById('webcam'),
+  centerView: document.getElementById('center-view'),
+  embeddingBar: document.getElementById('embedding-bar'),
+  graphScroll: document.getElementById('graph-scroll'),
+  graphSpacer: document.getElementById('graph-spacer'),
+  graphCanvas: document.getElementById('distance-graph'),
+  graphFollow: document.getElementById('graph-follow'),
+  legendNoise: document.getElementById('legend-noise'),
+  calibrationUi: document.getElementById('calibration-ui'),
+  captureRing: document.getElementById('capture-ring'),
+  captureRingArc: document.getElementById('capture-ring-arc'),
+  captureRingCount: document.getElementById('capture-ring-count'),
+  captureHint: document.getElementById('capture-hint'),
+  btnBaselineStart: document.getElementById('btn-baseline-start'),
+  btnBaseline: document.getElementById('btn-baseline'),
+  btnShutter: document.getElementById('btn-shutter'),
+  btnDownloadAll: document.getElementById('btn-download-all'),
+  btnDeleteAll: document.getElementById('btn-delete-all'),
+  galleryCount: document.getElementById('gallery-count'),
+  brightness: document.getElementById('brightness-input'),
+  brightnessValue: document.getElementById('brightness-value'),
+  toggleEqualizer: document.getElementById('toggle-equalizer'),
+  statusPill: document.getElementById('status-pill'),
+  distanceValue: document.getElementById('distance-value'),
+  trackingState: document.getElementById('tracking-state'),
+  viewer: document.getElementById('photo-viewer'),
+  viewerImage: document.getElementById('viewer-image'),
+  viewerMeta: document.getElementById('viewer-meta'),
+  viewerCopy: document.getElementById('viewer-copy'),
+  viewerDownload: document.getElementById('viewer-download'),
+  viewerDelete: document.getElementById('viewer-delete'),
+  viewerClose: document.getElementById('viewer-close'),
+};
+
+let graph = null;
+let gallery = null;
+let qualityGate = null;
+let baselineCollector = null;
+let spikeDetector = null;
+let frameBuffer = null;
+
+/**
+ * Writes the floating status message over the video stage.
+ *
+ * @param {string} message Message to display.
+ * @param {boolean} [warn] Whether the message needs emphasis.
+ * @returns {void}
+ */
+function setStatus(message, warn = false) {
+  if (!els.statusPill) return;
+  els.statusPill.textContent = message;
+  els.statusPill.classList.toggle('is-warn', warn);
+}
+
+/**
+ * Writes the right-panel state label.
+ *
+ * @param {string} state Current app state.
+ * @returns {void}
+ */
+function setTrackingState(state) {
+  if (els.trackingState) els.trackingState.textContent = state;
+}
+
+/**
+ * Writes the numeric distance readout.
+ *
+ * @param {number} value Distance to display.
+ * @returns {void}
+ */
+function setDistanceValue(value) {
+  if (!els.distanceValue) return;
+  els.distanceValue.textContent = Number.isFinite(value) ? value.toFixed(3) : '0.000';
+}
+
+/**
+ * Builds the descriptor equalizer.
+ *
+ * Level is baked into each cell at build time so the runtime loop only toggles
+ * .is-on for cells that actually changed. The prototype rewrote all 1,280
+ * className strings on every detection frame.
+ *
+ * @returns {void}
+ */
+function initEqualizer() {
+  if (!els.embeddingBar) return;
+  els.embeddingBar.textContent = '';
+  runtime.equalizerRows = [];
+  runtime.equalizerLit = new Int16Array(DESCRIPTOR_SIZE).fill(0);
+
+  for (let rowIndex = 0; rowIndex < DESCRIPTOR_SIZE; rowIndex += 1) {
+    const row = document.createElement('div');
+    row.className = 'descriptor-row';
+
+    for (let cellIndex = 0; cellIndex < CELLS_PER_ROW; cellIndex += 1) {
+      const cell = document.createElement('div');
+      let level = 'lvl-high';
+      if (cellIndex < 3) level = 'lvl-low';
+      else if (cellIndex < 7) level = 'lvl-med';
+      cell.className = `cell ${level}`;
+      row.appendChild(cell);
+    }
+
+    els.embeddingBar.appendChild(row);
+    runtime.equalizerRows.push(row);
+  }
+}
+
+/**
+ * Updates the equalizer from per-index baseline deltas.
+ *
+ * @param {number[]} baseline Baseline descriptor.
+ * @param {Float32Array|number[]} current Live descriptor.
+ * @returns {void}
+ */
+function updateEqualizer(baseline, current) {
+  if (!runtime.equalizerRows.length) return;
+
+  for (let i = 0; i < DESCRIPTOR_SIZE; i += 1) {
+    const delta = Math.abs(baseline[i] - current[i]);
+    const scaled = Math.floor((delta / MAX_DELTA) * CELLS_PER_ROW);
+    const lit = Math.max(0, Math.min(CELLS_PER_ROW, scaled));
+    const previous = runtime.equalizerLit[i];
+    if (lit === previous) continue;
+
+    const cells = runtime.equalizerRows[i].children;
+    if (lit > previous) {
+      for (let c = previous; c < lit; c += 1) cells[c].classList.add('is-on');
+    } else {
+      for (let c = lit; c < previous; c += 1) cells[c].classList.remove('is-on');
+    }
+    runtime.equalizerLit[i] = lit;
+  }
+}
+
+/**
+ * Returns the equalizer to its unlit state.
+ *
+ * @returns {void}
+ */
+function resetEqualizer() {
+  for (let i = 0; i < runtime.equalizerRows.length; i += 1) {
+    const cells = runtime.equalizerRows[i].children;
+    for (const cell of cells) cell.classList.remove('is-on');
+    runtime.equalizerLit[i] = 0;
+  }
+}
+
+/**
+ * Updates the baseline capture ring.
+ *
+ * @param {number} accepted Accepted samples so far.
+ * @param {number} target Samples required.
+ * @returns {void}
+ */
+function setRingProgress(accepted, target) {
+  if (!els.captureRingArc) return;
+  const ratio = Math.max(0, Math.min(1, accepted / target));
+  els.captureRingArc.style.strokeDasharray = String(RING_CIRCUMFERENCE);
+  els.captureRingArc.style.strokeDashoffset = String(RING_CIRCUMFERENCE * (1 - ratio));
+  if (els.captureRingCount) els.captureRingCount.textContent = String(accepted);
+  if (els.captureRing) els.captureRing.setAttribute('aria-valuenow', String(accepted));
+}
+
+/**
+ * Shows or hides the baseline capture overlay.
+ *
+ * @param {boolean} visible Whether the overlay should show.
+ * @returns {void}
+ */
+function setCalibrationVisible(visible) {
+  if (els.calibrationUi) els.calibrationUi.hidden = !visible;
+}
+
+/**
+ * Reflects baseline presence across the controls that depend on it.
+ *
+ * @returns {void}
+ */
+function syncBaselineControls() {
+  const hasBaseline = Boolean(runtime.baseline);
+  if (els.btnBaseline) els.btnBaseline.hidden = !hasBaseline;
+  if (els.btnShutter) els.btnShutter.disabled = !hasBaseline;
+  setCalibrationVisible(!hasBaseline);
+  if (els.btnBaselineStart) els.btnBaselineStart.hidden = false;
+
+  if (els.legendNoise) {
+    els.legendNoise.textContent = hasBaseline ? runtime.baseline.noiseFloor.toFixed(3) : '—';
+  }
+  if (graph) graph.setNoiseFloor(hasBaseline ? runtime.baseline.noiseFloor : null);
+}
+
+/**
+ * Begins a baseline capture run.
+ *
+ * @returns {void}
+ */
+function startBaselineCapture() {
+  runtime.appState = APP_STATES.CAPTURING;
+  runtime.baseline = null;
+  runtime.distanceBuffer = [];
+  clearBaseline();
+
+  setTrackingState(APP_STATES.CAPTURING);
+  setDistanceValue(0);
+  resetEqualizer();
+  graph.reset();
+  graph.setNoiseFloor(null);
+  spikeDetector.reset();
+  frameBuffer.clear();
+
+  setCalibrationVisible(true);
+  if (els.captureRing) els.captureRing.classList.add('is-active');
+  if (els.btnBaselineStart) els.btnBaselineStart.hidden = true;
+  if (els.btnBaseline) els.btnBaseline.hidden = true;
+  if (els.btnShutter) els.btnShutter.disabled = true;
+  if (els.legendNoise) els.legendNoise.textContent = '—';
+
+  setRingProgress(0, BASELINE_TARGET_SAMPLES);
+  setHint('Hold still and look at the camera.', true);
+  setStatus('Capturing baseline…');
+  baselineCollector.start();
+}
+
+/**
+ * Writes the capture overlay hint line.
+ *
+ * @param {string} text Hint text.
+ * @param {boolean} [blocking] Whether the hint is an instruction to act on.
+ * @returns {void}
+ */
+function setHint(text, blocking = false) {
+  if (!els.captureHint) return;
+  els.captureHint.textContent = text;
+  els.captureHint.classList.toggle('is-blocking', blocking);
+}
+
+/**
+ * Adopts a captured or restored baseline and enters tracking.
+ *
+ * @param {object} baseline Baseline record.
+ * @param {boolean} [restored] Whether the baseline came from storage.
+ * @returns {void}
+ */
+function adoptBaseline(baseline, restored = false) {
+  runtime.baseline = baseline;
+  runtime.appState = APP_STATES.TRACKING;
+  runtime.distanceBuffer = [];
+  runtime.faceMissing = false;
+
+  setTrackingState(APP_STATES.TRACKING);
+  setDistanceValue(0);
+  resetEqualizer();
+  spikeDetector.reset();
+  frameBuffer.clear();
+
+  if (els.captureRing) els.captureRing.classList.remove('is-active');
+  syncBaselineControls();
+
+  if (restored) {
+    const age = formatDuration(Date.now() - baseline.capturedAt);
+    setStatus(`Baseline restored, captured ${age} ago. Noise floor ${baseline.noiseFloor.toFixed(3)}.`);
+  } else {
+    setStatus(`Baseline set. Noise floor ${baseline.noiseFloor.toFixed(3)} — anything below that is not a result.`);
+  }
+}
+
+/**
+ * Discards the current baseline and returns to the capture prompt.
+ *
+ * @returns {void}
+ */
+function deleteBaseline() {
+  runtime.baseline = null;
+  runtime.appState = APP_STATES.NO_BASELINE;
+  clearBaseline();
+  baselineCollector.cancel();
+
+  setTrackingState(APP_STATES.NO_BASELINE);
+  setDistanceValue(0);
+  resetEqualizer();
+  graph.reset();
+  spikeDetector.reset();
+  frameBuffer.clear();
+
+  if (els.captureRing) els.captureRing.classList.remove('is-active');
+  syncBaselineControls();
+  setHint('Capture a reference of your bare face before you start.');
+  setStatus('Baseline deleted. Saved captures are kept.');
+}
+
+/**
+ * Feeds one detection into the baseline collector.
+ *
+ * @param {*} detection face-api detection result.
+ * @returns {void}
+ */
+function processBaselineFrame(detection) {
+  const verdict = qualityGate.evaluate(detection);
+  const progress = baselineCollector.offer(verdict, detection.descriptor);
+
+  setRingProgress(progress.accepted, progress.target);
+
+  if (progress.status === 'blocked') {
+    setHint(progress.reason, true);
+    return;
+  }
+  if (progress.status === 'collecting') {
+    setHint('Hold still and look at the camera.', true);
+    return;
+  }
+  if (progress.status === 'failed') {
+    if (els.captureRing) els.captureRing.classList.remove('is-active');
+    if (els.btnBaselineStart) els.btnBaselineStart.hidden = false;
+    runtime.appState = APP_STATES.NO_BASELINE;
+    setTrackingState(APP_STATES.NO_BASELINE);
+    setHint(`Could not get a clean read. ${progress.reason}`, true);
+    setStatus('Baseline capture abandoned.', true);
+    return;
+  }
+  if (progress.status === 'done') {
+    saveBaseline(progress.baseline);
+    adoptBaseline(progress.baseline, false);
+  }
+}
+
+/**
+ * Measures one tracking frame and evaluates it for a spike capture.
+ *
+ * @param {*} detection face-api detection result.
+ * @returns {Promise<void>}
+ */
+async function processTrackingFrame(detection) {
+  const distance = euclidean(runtime.baseline.descriptor, detection.descriptor);
+  runtime.distanceBuffer.push(distance);
+  setDistanceValue(distance);
+  updateEqualizer(runtime.baseline.descriptor, detection.descriptor);
+
+  if (distance > MATCH_THRESHOLD) {
+    setStatus(`Past the 0.60 match threshold — reads as a different person.`);
+  } else if (distance <= runtime.baseline.noiseFloor) {
+    setStatus('Inside the noise floor — nothing has changed yet.');
+  } else {
+    setStatus(`Above the noise floor, below 0.60 — still reads as a match.`);
+  }
+
+  const verdict = spikeDetector.update(distance);
+  if (!verdict.candidate) return;
+
+  // The gate's crop analysis only runs while a spike is live, so ordinary
+  // tracking never pays for the getImageData.
+  const quality = qualityGate.evaluate(detection);
+  if (quality.ok) frameBuffer.push(quality.sharpness, distance);
+
+  if (!verdict.capture) return;
+
+  const best = frameBuffer.best();
+  if (!best) return;
+
+  await storeCapture(best.canvas, best.distance, verdict.kind);
+  spikeDetector.noteCapture(best.distance);
+  frameBuffer.clear();
+}
+
+/**
+ * Watermarks and stores one frame, then marks it on the timeline.
+ *
+ * @param {HTMLCanvasElement} frame Frame to store.
+ * @param {number} distance Distance at capture time.
+ * @param {string} kind Capture trigger.
+ * @returns {Promise<void>}
+ */
+async function storeCapture(frame, distance, kind) {
+  try {
+    const id = await gallery.save(frame, {
+      t: Date.now(),
+      distance,
+      noiseFloor: runtime.baseline ? runtime.baseline.noiseFloor : 0,
+      threshold: MATCH_THRESHOLD,
+      kind,
+    });
+    graph.markLatestCapture(id);
+    setStatus(`Capture saved at ${distance.toFixed(3)}.`);
+  } catch (err) {
+    console.error('capture save failed:', err);
+    setStatus('Could not save the capture.', true);
+  }
+}
+
+/**
+ * Captures the current frame on demand.
+ *
+ * @returns {Promise<void>}
+ */
+async function captureNow() {
+  if (!runtime.baseline || !els.video || els.video.readyState < 2) return;
+
+  const videoW = els.video.videoWidth;
+  const scale = Math.min(1, CAPTURE_MAX_WIDTH / videoW);
+  const canvas = document.createElement('canvas');
+  canvas.width = Math.round(videoW * scale);
+  canvas.height = Math.round(els.video.videoHeight * scale);
+  canvas.getContext('2d').drawImage(els.video, 0, 0, canvas.width, canvas.height);
+
+  const distance = Number(els.distanceValue.textContent) || 0;
+  await storeCapture(canvas, distance, 'manual');
+}
+
+/**
+ * Handles a frame in which no face was found.
+ *
+ * After the debounce the timeline stops advancing entirely and the absence is
+ * drawn as one collapsed block. Nothing is sampled until a face returns, so
+ * stepping away to apply makeup costs no scroll and produces no readings.
+ *
+ * @returns {void}
+ */
+function processNoFace() {
+  const now = Date.now();
+  if (!runtime.lastFaceAt) runtime.lastFaceAt = now;
+  if (now - runtime.lastFaceAt < NO_FACE_DEBOUNCE_MS) return;
+
+  if (!runtime.faceMissing) {
+    runtime.faceMissing = true;
+    runtime.distanceBuffer = [];
+    resetEqualizer();
+    setDistanceValue(0);
+    setStatus('No face — measurement paused.');
+  }
+
+  if (runtime.appState === APP_STATES.TRACKING) {
+    graph.beginGap(runtime.lastFaceAt);
+  }
+
+  if (runtime.appState === APP_STATES.CAPTURING) {
+    setHint('Step into frame.', true);
+  }
+}
+
+/**
+ * Handles a face reappearing after an absence.
+ *
+ * @returns {void}
+ */
+function processFaceReturned() {
+  const now = Date.now();
+  if (runtime.faceMissing) {
+    runtime.faceMissing = false;
+    graph.endGap(now);
+    setStatus('Face back — reading resuming.');
+  }
+  runtime.lastFaceAt = now;
+}
+
+/**
+ * Runs one detection pass and schedules the next frame.
+ *
+ * @returns {Promise<void>}
+ */
+async function onFrame() {
+  if (!runtime.running) return;
+  runtime.frameRequestId = window.requestAnimationFrame(onFrame);
+  if (runtime.detectorBusy) return;
+  if (!els.video || els.video.readyState < 2) return;
+
+  runtime.detectorBusy = true;
+  try {
+    const detection = await faceapi
+      .detectSingleFace(els.video, DETECTOR_OPTIONS)
+      .withFaceLandmarks()
+      .withFaceDescriptor();
+
+    if (!detection || !detection.descriptor) {
+      processNoFace();
+      return;
+    }
+
+    processFaceReturned();
+
+    if (runtime.appState === APP_STATES.CAPTURING) {
+      processBaselineFrame(detection);
+    } else if (runtime.appState === APP_STATES.TRACKING && runtime.baseline) {
+      await processTrackingFrame(detection);
+    }
+  } catch (err) {
+    console.error('realtime detection loop error:', err);
+    setStatus('Detection error. Check the console.', true);
+  } finally {
+    runtime.detectorBusy = false;
+  }
+}
+
+/**
+ * Averages buffered readings into one timeline sample.
+ *
+ * @returns {void}
+ */
+function flushDistanceBuffer() {
+  if (runtime.appState !== APP_STATES.TRACKING) return;
+  if (runtime.faceMissing) {
+    graph.draw();
+    return;
+  }
+  if (!runtime.distanceBuffer.length) return;
+
+  const sum = runtime.distanceBuffer.reduce((acc, value) => acc + value, 0);
+  const average = sum / runtime.distanceBuffer.length;
+  runtime.distanceBuffer = [];
+  graph.pushSample(Date.now(), average);
+}
+
+/**
+ * Starts the detection and sampling loops.
+ *
+ * @returns {void}
+ */
+function startLoops() {
+  if (runtime.running) return;
+  runtime.running = true;
+  if (runtime.flushTimerId) window.clearInterval(runtime.flushTimerId);
+  runtime.flushTimerId = window.setInterval(flushDistanceBuffer, 1000 / SAMPLES_PER_SECOND);
+  onFrame();
+}
+
+/**
+ * Stops the detection and sampling loops without touching the camera.
+ *
+ * @returns {void}
+ */
+function stopLoops() {
+  runtime.running = false;
+  if (runtime.frameRequestId) window.cancelAnimationFrame(runtime.frameRequestId);
+  runtime.frameRequestId = null;
+  if (runtime.flushTimerId) window.clearInterval(runtime.flushTimerId);
+  runtime.flushTimerId = null;
+}
+
+/**
+ * Releases the camera.
+ *
+ * Only stopping the tracks turns the hardware indicator off. Pausing the video
+ * element leaves the light on, which on this project is exactly the wrong
+ * impression to give.
+ *
+ * @returns {void}
+ */
+function stopStream() {
+  if (!runtime.stream) return;
+  for (const track of runtime.stream.getTracks()) track.stop();
+  runtime.stream = null;
+  runtime.streamStopped = true;
+  if (els.video) els.video.srcObject = null;
+}
+
+/**
+ * Requests the camera and binds it to the video element.
+ *
+ * @returns {Promise<void>}
+ */
+async function initWebcam() {
+  if (!els.video) throw new Error('Missing #webcam element');
+
+  runtime.stream = await navigator.mediaDevices.getUserMedia({
+    video: { facingMode: 'user',
+             width: { ideal: 1080 },
+             height: { ideal: 1920 }
+    },
+    audio: false,
+  });
+  runtime.streamStopped = false;
+  els.video.srcObject = runtime.stream;
+  await els.video.play();
+}
+
+/**
+ * Loads the vendored face-api models.
+ *
+ * Every shard lives under /lab-js/vendor, so the page makes no third-party
+ * request and runs with the network unplugged. MODEL_URLS.tiny, .landmarks and
+ * .recognition all resolve to that one directory.
+ *
+ * @returns {Promise<void>}
+ */
+async function loadModels() {
+  const vendorRoot = MODEL_URLS.recognition;
+  await Promise.all([
+    faceapi.nets.tinyFaceDetector.loadFromUri(vendorRoot),
+    faceapi.nets.faceLandmark68Net.loadFromUri(vendorRoot),
+    faceapi.nets.faceRecognitionNet.loadFromUri(vendorRoot),
+  ]);
+  setStatus('Models loaded. Preparing camera…');
+}
+
+/**
+ * Suspends work while the tab is hidden.
+ *
+ * Window blur is deliberately not the trigger: it fires when another window is
+ * clicked while this tab is still on screen, which would kill the camera while
+ * the person is looking straight at it.
+ *
+ * @returns {void}
+ */
+function handleHidden() {
+  stopLoops();
+  if (els.video) els.video.pause();
+  if (runtime.hiddenStopTimerId) window.clearTimeout(runtime.hiddenStopTimerId);
+  runtime.hiddenStopTimerId = window.setTimeout(stopStream, HIDDEN_STOP_DELAY_MS);
+}
+
+/**
+ * Resumes work when the tab becomes visible again.
+ *
+ * @returns {Promise<void>}
+ */
+async function handleVisible() {
+  if (runtime.hiddenStopTimerId) {
+    window.clearTimeout(runtime.hiddenStopTimerId);
+    runtime.hiddenStopTimerId = null;
+  }
+
+  try {
+    if (runtime.streamStopped) {
+      setStatus('Reconnecting the camera…');
+      await initWebcam();
+    } else if (els.video) {
+      await els.video.play();
+    }
+    if (runtime.appState === APP_STATES.TRACKING) runtime.lastFaceAt = Date.now();
+    startLoops();
+    setStatus('Camera live.');
+  } catch (err) {
+    console.error('failed to resume camera:', err);
+    setStatus('Could not reconnect the camera.', true);
+  }
+}
+
+/**
+ * Fully releases every resource held by the page.
+ *
+ * @returns {void}
+ */
+function teardown() {
+  stopLoops();
+  stopStream();
+  if (runtime.hiddenStopTimerId) window.clearTimeout(runtime.hiddenStopTimerId);
+  runtime.hiddenStopTimerId = null;
+  if (graph) graph.destroy();
+}
+
+/**
+ * Binds interaction and lifecycle listeners.
+ *
+ * @returns {void}
+ */
+function bindListeners() {
+  els.btnBaselineStart.addEventListener('click', startBaselineCapture);
+
+  guardDestructive(els.btnBaseline, 'Delete baseline', 'Delete — sure?', deleteBaseline);
+  guardDestructive(els.btnDeleteAll, 'Delete all', 'Delete all — sure?', () => gallery.deleteAll());
+
+  els.btnShutter.addEventListener('click', captureNow);
+  els.btnDownloadAll.addEventListener('click', () => gallery.downloadAll());
+
+  els.viewerClose.addEventListener('click', () => gallery.close());
+  els.viewerCopy.addEventListener('click', () => gallery.copyOpen());
+  els.viewerDownload.addEventListener('click', () => gallery.downloadOpen());
+  els.viewerDelete.addEventListener('click', () => gallery.deleteOpen());
+
+  document.addEventListener('keydown', (event) => {
+    if (event.key === 'Escape' && gallery.isOpen()) gallery.close();
+  });
+
+  document.addEventListener('visibilitychange', () => {
+    if (document.hidden) handleHidden();
+    else handleVisible();
+  });
+
+  window.addEventListener('pagehide', teardown);
+}
+
+/**
+ * Runtime bridge for diagnostics and Playwright checks.
+ *
+ * @type {object}
+ */
+window.gstmxxRealtime = {
+  /**
+   * Returns a serialisable snapshot of runtime state.
+   *
+   * @returns {object} State snapshot.
+   */
+  getState() {
+    const stats = graph ? graph.stats() : { samples: 0, gaps: 0, following: true };
+    return {
+      appState: runtime.appState,
+      hasBaseline: Boolean(runtime.baseline),
+      noiseFloor: runtime.baseline ? runtime.baseline.noiseFloor : null,
+      threshold: MATCH_THRESHOLD,
+      graphMaxDistance: GRAPH_MAX_DISTANCE,
+      windowSeconds: GRAPH_WINDOW_SECONDS,
+      faceMissing: runtime.faceMissing,
+      distanceBufferLength: runtime.distanceBuffer.length,
+      sampleCount: stats.samples,
+      gapCount: stats.gaps,
+      following: stats.following,
+      captureCount: gallery ? gallery.size() : 0,
+      running: runtime.running,
+      streamStopped: runtime.streamStopped,
+    };
+  },
+  /**
+   * Starts a baseline capture run without pressing UI controls.
+   *
+   * @returns {void}
+   */
+  triggerCalibration() {
+    startBaselineCapture();
+  },
+  /**
+   * Releases camera and loops.
+   *
+   * @returns {void}
+   */
+  teardown,
+  /**
+   * Restarts camera and loops after a teardown.
+   *
+   * @returns {Promise<void>}
+   */
+  resume: handleVisible,
+};
+
+/**
+ * Initialises the visualizer and starts its loops.
+ *
+ * @returns {Promise<void>}
+ */
+export async function initRealtime() {
+  if (runtime.initialized) return;
+  runtime.initialized = true;
+
+  setStatus('Loading models…');
+  setTrackingState(APP_STATES.NO_BASELINE);
+  initEqualizer();
+
+  graph = createGraph({
+    scrollEl: els.graphScroll,
+    spacerEl: els.graphSpacer,
+    canvasEl: els.graphCanvas,
+    followEl: els.graphFollow,
+    windowSeconds: GRAPH_WINDOW_SECONDS,
+    samplesPerSecond: SAMPLES_PER_SECOND,
+    maxDistance: GRAPH_MAX_DISTANCE,
+    threshold: MATCH_THRESHOLD,
+    onMarkerClick: (captureId) => gallery.open(captureId),
+  });
+
+  gallery = createGallery({
+    viewerEl: els.viewer,
+    imageEl: els.viewerImage,
+    metaEl: els.viewerMeta,
+    countEl: els.galleryCount,
+    onCountChange: (count) => {
+      els.btnDownloadAll.disabled = count === 0;
+      els.btnDeleteAll.disabled = count === 0;
+    },
+    onDelete: (captureId) => {
+      if (captureId) graph.clearCapture(captureId);
+      else graph.reset();
+    },
+    onStatus: (message) => setStatus(message),
+  });
+
+  createSettings({
+    rootEl: els.layer,
+    brightnessEl: els.brightness,
+    brightnessValueEl: els.brightnessValue,
+    equalizerEl: els.toggleEqualizer,
+    layoutEl: els.layer,
+    onLayoutChange: () => graph.resize(),
+  });
+
+  qualityGate = createQualityGate(els.video);
+  baselineCollector = createBaselineCollector({
+    targetSamples: BASELINE_TARGET_SAMPLES,
+    minDurationMs: BASELINE_MIN_MS,
+    timeoutMs: BASELINE_TIMEOUT_MS,
+    maxSpread: BASELINE_MAX_SPREAD,
+  });
+  spikeDetector = createSpikeDetector({
+    sustainMs: SPIKE_SUSTAIN_MS,
+    bestMargin: SPIKE_BEST_MARGIN,
+    threshold: MATCH_THRESHOLD,
+    cooldownMs: SPIKE_COOLDOWN_MS,
+  });
+  frameBuffer = createFrameBuffer(els.video, FRAME_BUFFER_SLOTS, CAPTURE_MAX_WIDTH);
+
+  bindListeners();
+  graph.resize();
+
+  try {
+    await gallery.init();
+  } catch (err) {
+    console.error('capture store unavailable:', err);
+    setStatus('Capture storage unavailable — measurement still works.', true);
+  }
+
+  await loadModels();
+  await initWebcam();
+
+  // A stored baseline skips straight to tracking. The trace itself is not
+  // restored: replaying old history and appending new samples would draw one
+  // continuous line across a discontinuity that could be days wide.
+  const stored = loadBaseline();
+  if (stored) adoptBaseline(stored, true);
+  else {
+    syncBaselineControls();
+    setStatus('Camera ready. Capture a baseline to start measuring.');
+  }
+
+  startLoops();
+}
+
+/**
+ * Bootstraps the page and reports startup failures in the status pill.
+ *
+ * @returns {Promise<void>}
+ */
+async function boot() {
+  try {
+    await initRealtime();
+  } catch (err) {
+    console.error('Failed to initialize realtime visualizer:', err);
+    setStatus(`Initialization failed: ${err.message || String(err)}`, true);
+  }
+}
+
+boot();
diff --git a/realtime-fr-method.md b/realtime-fr-method.md
new file mode 100644
index 0000000..202bf1c
--- /dev/null
+++ b/realtime-fr-method.md
@@ -0,0 +1,1005 @@
+# Ghostmaxxing — Realtime Visualizer: Methodology, Sources and Limits
+
+**What this document is.** A full accounting of what the realtime latent-space
+visualizer measures, how it measures it, where every number came from, and what
+the surrounding research does and does not support. It is written so that
+someone who has never opened the code can judge whether a reading from this tool
+means anything.
+
+**Scope.** Covers `realtime.html`, `realtime.css`, and the `realtime-*.js`
+modules. The 2D/3D effect pipelines in `lab.html` use different models
+(MediaPipe FaceLandmarker, MobileNetV3 ImageEmbedder) and are out of scope here.
+
+**Status of claims.** Every threshold in this document is tagged with its
+provenance:
+
+| Tag | Meaning |
+|---|---|
+| **CITED** | Traceable to a published source, linked inline |
+| **MEASURED** | Computed from the operator's own session, on their own hardware |
+| **CHOSEN** | A design decision with stated reasoning but no external authority |
+| **UNTUNED** | Chosen, and known to need empirical calibration |
+
+If a number is not tagged CITED, it is not evidence of anything about face
+recognition in general. It is a setting.
+
+---
+
+## Table of contents
+
+1. [The honest summary](#1-the-honest-summary)
+2. [The pipeline](#2-the-pipeline)
+3. [What a 128-dimensional face descriptor is](#3-what-a-128-dimensional-face-descriptor-is)
+4. [Where 0.6 comes from](#4-where-06-comes-from)
+5. [What LFW does and does not tell you](#5-what-lfw-does-and-does-not-tell-you)
+6. [Demographic differentials](#6-demographic-differentials)
+7. [The metrics vocabulary](#7-the-metrics-vocabulary)
+8. [The table that is not in this tool, and why](#8-the-table-that-is-not-in-this-tool-and-why)
+9. [Algorithms implemented here](#9-algorithms-implemented-here)
+10. [Every threshold, and where it came from](#10-every-threshold-and-where-it-came-from)
+11. [Adversarial makeup: the research](#11-adversarial-makeup-the-research)
+12. [Transferability — the central caveat](#12-transferability--the-central-caveat)
+13. [What this tool can and cannot tell you](#13-what-this-tool-can-and-cannot-tell-you)
+14. [Privacy and data handling](#14-privacy-and-data-handling)
+15. [Regulatory context](#15-regulatory-context)
+16. [Vendored assets and provenance](#16-vendored-assets-and-provenance)
+17. [Reading list](#17-reading-list)
+18. [Glossary](#18-glossary)
+
+---
+
+## 1. The honest summary
+
+This tool measures **one number**: the Euclidean distance between a 128-dimensional
+descriptor of your face captured before a makeup session, and a descriptor of
+your face right now, both produced by dlib's face recognition ResNet as shipped
+in face-api.js.
+
+That number tells you how far one specific, seven-year-old, publicly-available
+model has moved you in its own embedding space. It does **not** tell you:
+
+- whether you would be recognised by any deployed surveillance system
+- whether the makeup would work on a different model
+- whether the makeup would work under different lighting or from a different angle
+- whether your face is "safe"
+
+What it is genuinely good for is **iteration**. If you are developing a makeup
+technique, you need a fast local signal to compare variant A against variant B.
+This gives you that, cheaply, offline, on your own device, without sending a
+face anywhere. Treat it as a rehearsal instrument, not a verdict.
+
+The rest of this document explains why that framing is the correct one.
+
+---
+
+## 2. The pipeline
+
+Three separate models run in sequence on each frame. They are frequently
+conflated in public discussion, and the distinction matters because
+countermeasures that defeat one do not necessarily touch the others.
+
+### 2.1 Detection — TinyFaceDetector
+
+Finds face bounding boxes. A compact CNN shipped with
+[face-api.js](https://github.com/vladmandic/face-api), based on Tiny YOLO v2.
+Configured in `config.js`:
+
+```js
+export const DETECTOR_OPTIONS = new faceapi.TinyFaceDetectorOptions({
+   inputSize: 416,      // square input dimension
+   scoreThreshold: 0.5  // minimum reported confidence
+});
+```
+
+**This is the stage that classical anti-surveillance makeup attacked.** See
+[§11.1](#111-cv-dazzle-2010-and-its-own-obsolescence).
+
+### 2.2 Alignment — 68-point facial landmarks
+
+`faceLandmark68Net` locates 68 points: jaw outline (0–16), eyebrows (17–26),
+nose (27–35), eyes (36–47), mouth (48–67). The recognition network needs a
+canonically-aligned crop, so landmarks are used to rotate and scale the face
+before embedding.
+
+This tool also reads landmarks directly for pose estimation — see
+[§9.2](#92-the-quality-gate).
+
+### 2.3 Recognition — 128-d descriptor
+
+`faceRecognitionNet`, a port of
+[dlib's `dlib_face_recognition_resnet_model_v1`](https://github.com/davisking/dlib-models).
+Takes a 150×150×3 aligned face crop, outputs 128 floating-point numbers.
+
+Architecture and training, from
+[Davis King's announcement post](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html):
+
+- A ResNet with **29 convolutional layers** — a reduced
+  [ResNet-34](https://arxiv.org/abs/1512.03385) (He, Zhang, Ren & Sun, 2015)
+  with layers removed and filters per layer halved
+- Trained from scratch on **~3 million face images** across **7,485 identities**
+- Sources: [FaceScrub](http://vintage.winklerbros.net/facescrub.html),
+  the [VGG Face dataset](https://www.robots.ox.ac.uk/~vgg/data/vgg_face/),
+  and images scraped from the web by the author
+- LFW identities were deliberately excluded so the LFW evaluation stayed valid
+- The model file is public domain
+
+**Age note.** This model was published in February 2017 and has not been
+retrained. It is a reasonable stand-in for "an open-source face recogniser," and
+a poor stand-in for the state of the art. See
+[§12](#12-transferability--the-central-caveat).
+
+---
+
+## 3. What a 128-dimensional face descriptor is
+
+### 3.1 Deep metric learning
+
+Classical face recognition extracted hand-designed features
+([Eigenfaces](https://www.face-rec.org/algorithms/PCA/jcn.pdf), Turk & Pentland
+1991; Fisherfaces; LBP histograms). Modern systems instead learn a **mapping**
+from face image to a vector space where geometric distance corresponds to
+identity difference.
+
+The canonical statement of this idea is
+[**FaceNet**](https://arxiv.org/abs/1503.03832) (Schroff, Kalenichenko & Philbin,
+CVPR 2015), which trained with a *triplet loss*: pull an anchor face toward
+another image of the same person, push it away from a different person, by at
+least a margin. Everything downstream — including dlib's model — is a variation
+on this.
+
+The practical consequence: **recognition becomes arithmetic.** Once faces are
+vectors, identifying someone is a nearest-neighbour lookup. There is no
+"template" of your face in a human-legible sense; there is a point in R¹²⁸.
+
+### 3.2 Distance metrics
+
+For descriptors *a* and *b*, this tool uses **Euclidean (L2) distance**:
+
+```
+d(a, b) = √( Σᵢ (aᵢ − bᵢ)² )
+```
+
+Implemented in `realtime-capture.js` as `euclidean()`. Deliberately not
+`faceapi.euclideanDistance` — keeping it local means the module can be unit
+tested without loading the vendor bundle.
+
+**Do not substitute cosine similarity.** Davis King has answered this directly
+[in the comments of the announcement post](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html):
+a user reported high cosine similarity between images of different people, and
+the answer was that the model is calibrated for Euclidean distance. Cosine
+similarity discards vector magnitude; for this model, that magnitude carries
+information.
+
+Other systems differ.
+[ArcFace](https://arxiv.org/abs/1801.07698) (Deng et al., CVPR 2019) optimises
+angular margin and *is* naturally read with cosine similarity. **Thresholds and
+metrics are not portable between models.** A distance of 0.6 means something in
+dlib's space and nothing at all in ArcFace's.
+
+### 3.3 Vector norm — a subtlety that affects the baseline
+
+dlib's descriptors are not explicitly L2-normalised to unit length, though in
+practice their norms cluster near 1. This matters for one reason: **averaging
+several descriptors produces a vector shorter than its inputs**, because
+imperfectly-aligned vectors partially cancel. A mean-of-N baseline therefore
+sits slightly inside the manifold, and every subsequent distance measured
+against it carries a small systematic offset.
+
+This tool avoids the problem entirely by using the **medoid** rather than the
+mean — see [§9.3](#93-medoid-baseline).
+
+---
+
+## 4. Where 0.6 comes from
+
+This is the single most-cited and least-understood number in open-source face
+recognition. It has two independent justifications, and most write-ups mention
+only the second.
+
+### 4.1 It is baked into the training objective
+
+From [Davis King's post](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html):
+the network was trained with a structured metric loss that attempts to project
+each identity into a **non-overlapping ball of radius 0.6**. It is a pairwise
+hinge loss over all pairs in a mini-batch, with hard-negative mining.
+
+This is the important part. **0.6 is not a threshold discovered by tuning after
+training — it is the geometry the network was explicitly optimised to produce.**
+The model was told to make same-identity clusters fit inside spheres of that
+radius. The threshold is the training target, read back out.
+
+That makes 0.6 far more principled than a tuned hyperparameter, *and* far more
+model-specific. It is a fact about this network's loss function. It transfers to
+no other model.
+
+### 4.2 It is the operating point of the reported benchmark
+
+From [dlib's `face_recognition.py` example](https://github.com/davisking/dlib/blob/master/python_examples/face_recognition.py):
+at a distance threshold of 0.6, the model reaches **99.38% accuracy on LFW**.
+The docs state the plain-language rule — descriptors closer than 0.6 are
+generally the same person, further apart generally are not.
+
+`face-api.js` inherits this as the default `distanceThreshold` in its
+[`FaceMatcher`](https://github.com/vladmandic/face-api).
+
+### 4.3 What "99.38%" actually measures
+
+It is a **verification** figure on a specific protocol: given a pair of images,
+correctly say same/different, on LFW's balanced 6,000-pair test. It is not:
+
+- an identification (1:N search) figure — those degrade with gallery size
+- a figure for any demographic subgroup
+- a figure for non-frontal, low-resolution, or surveillance-quality imagery
+- a figure that has been re-validated on any recent dataset
+
+Accuracy on 1:1 verification and accuracy on 1:N identification against a
+12-million-person gallery are different problems. See
+[§7](#7-the-metrics-vocabulary).
+
+---
+
+## 5. What LFW does and does not tell you
+
+[Labeled Faces in the Wild](https://vis-www.cs.umass.edu/lfw/) (Huang, Ramesh,
+Berg & Learned-Miller, UMass Amherst TR 07-49, 2007) is ~13,000 images of ~5,700
+individuals, scraped from news photographs. It became the field's default
+yardstick.
+
+### 5.1 It is demographically skewed
+
+The widely-cited estimate is that LFW is approximately **83.5% white** and
+**77.5% male**. This figure originates from Han & Jain's
+[demographic analysis of unconstrained face images](https://www.researchgate.net/publication/263697688_Age_Gender_and_Race_Estimation_from_Unconstrained_Face_Images)
+and is reproduced across the fairness literature — for example in
+[Gender Classification and Bias Mitigation in Facial Images](https://arxiv.org/abs/2007.06141)
+and the
+[Harvard JOLT digest on racial bias in facial recognition](https://jolt.law.harvard.edu/digest/why-racial-bias-is-prevalent-in-facial-recognition-technology).
+The [Algorithmic Fairness Datasets survey](https://arxiv.org/abs/2202.01711)
+(Fabris et al.) documents LFW's skew toward white, male and under-60 subjects.
+
+Because the images are news photographs of public figures, this skew reflects
+whose faces circulate in Anglophone news media — which is a fact about media,
+now baked into a benchmark.
+
+### 5.2 The model's author says so himself
+
+In the comments of the dlib announcement post, Davis King notes that LFW is
+heavily biased toward white adult American public figures, that the training
+data carries a similar bias, and that this creates an obvious problem for people
+outside that distribution — who would **likely require a different threshold**.
+
+There is empirical support for this. A study of the model on Asian faces
+([Neural Network Facial Authentication for Public Electric Vehicle Charging Station](https://arxiv.org/abs/2106.10432))
+reports accuracy dropping from the 99.38% LFW figure to **98.18%**, citing a
+GitHub issue raised against dlib.
+
+### 5.3 It is saturated
+
+A benchmark where everything scores 99%+ has stopped discriminating between
+methods. The field moved on to IJB-A/B/C, MegaFace, and NIST's ongoing FRVT
+precisely because LFW no longer separates good systems from great ones.
+
+**Consequence for this tool.** The 0.60 line is labelled in the UI as dlib's LFW
+default, explicitly as a benchmark's number rather than a property of your face.
+If you are not a white adult American public figure photographed by press
+photographers, its calibration is less well established for you — and there is no
+correction factor to apply, because nobody has published one.
+
+---
+
+## 6. Demographic differentials
+
+This is the best-evidenced finding in the whole field and the most relevant to
+anyone building anti-surveillance tooling.
+
+### 6.1 NIST FRVT Part 3
+
+[**NISTIR 8280: Face Recognition Vendor Test Part 3 — Demographic Effects**](https://doi.org/10.6028/NIST.IR.8280)
+(Grother, Ngan & Hanaoka, December 2019) —
+[full PDF](https://pages.nist.gov/frvt/reports/demographics/nistir_8280.pdf),
+[NIST summary](https://www.nist.gov/news-events/news/2019/12/nist-study-evaluates-effects-race-age-sex-face-recognition-software).
+
+189 algorithms from 99 developers. Key findings:
+
+- Demographic differentials exist in the **majority** of algorithms studied
+- Among US-developed algorithms, elevated false-positive rates in one-to-one
+  matching for Asian, African American and Native American groups, with the
+  American Indian demographic showing the highest false positive rates
+- Many algorithms were 10–100× more likely to misidentify a Black or East Asian
+  face than a white one
+- Most algorithms were substantially less likely to correctly identify a Black
+  woman than any other demographic group
+
+Grother's own framing in the NIST release is careful: the report quantifies the
+differentials without claiming to explain their causes.
+
+### 6.2 The follow-up: false positives are the harder problem
+
+[**NISTIR 8429: Face Recognition Vendor Test Part 8 — Summarizing Demographic Differentials**](https://pages.nist.gov/frvt/reports/demographics/nistir_8429.pdf)
+
+The finding that matters:
+
+- **False positive differentials are widespread and occur even in pristine
+  images.** Within-group false positive rates were found to vary by a factor of
+  several thousand.
+- False *negative* inequities are substantially attributable to poor photography
+  of certain groups — notably under-exposure of dark-skinned individuals — and
+  are fixable with better cameras and capture conditions.
+- The much larger false *positive* variations are not fixable that way. They must
+  be addressed in the algorithm.
+
+This asymmetry is important. "Better lighting" is a real mitigation for one class
+of error and no mitigation at all for the other.
+
+### 6.3 The academic literature
+
+- [**Gender Shades**](https://proceedings.mlr.press/v81/buolamwini18a.html)
+  (Buolamwini & Gebru, FAccT 2018) — the study that put this on the public
+  agenda. **Note carefully:** it audits gender *classification*, not face
+  verification. It is frequently miscited as a face-recognition-accuracy study.
+  Its finding — error rates far higher for darker-skinned women than
+  lighter-skinned men — is about a different task on the same faces.
+- [Characterizing the Variability in Face Recognition Accuracy Relative to Race](https://arxiv.org/abs/1904.07325)
+  (Krishnapriya et al.) — impostor distributions for African-American cohorts
+  shift toward higher similarity scores relative to Caucasian cohorts across
+  multiple matchers, raising false match rates at any fixed threshold.
+- [Analysis of Gender Inequality In Face Recognition Accuracy](https://arxiv.org/abs/2002.00065)
+  (Albiero et al.) — female impostor distributions shift toward higher
+  similarity, female genuine distributions toward lower; d′ separation is
+  consistently worse for women. The paper's subset analysis (neutral expression,
+  head pose, visible forehead, **no makeup**) is directly relevant to this
+  project.
+- [Exploring Causes of Demographic Variations In Face Recognition Accuracy](https://arxiv.org/abs/2304.07175)
+- [Impact of Blur and Resolution on Demographic Disparities in 1-to-Many Facial Identification](https://arxiv.org/abs/2309.04447)
+
+**Consequence for this tool.** A single global threshold produces systematically
+different error rates for different people. Since this tool measures *your*
+distance from *your own* baseline, it sidesteps the cross-identity false-match
+problem — but the underlying embedding still carries whatever demographic
+structure the training data gave it, and there is no way to measure that from
+one face.
+
+---
+
+## 7. The metrics vocabulary
+
+Necessary to read any of the linked literature without being misled.
+
+| Term | Meaning |
+|---|---|
+| **Verification (1:1)** | Are these two images the same person? What LFW measures. |
+| **Identification (1:N)** | Which of N enrolled people is this? Harder; error grows with N. |
+| **Genuine / mated pair** | Two images of the same person. |
+| **Impostor / non-mated pair** | Two images of different people. |
+| **FMR** (False Match Rate) | Fraction of impostor pairs accepted. A false accept. |
+| **FNMR** (False Non-Match Rate) | Fraction of genuine pairs rejected. A false reject. |
+| **FPIR / FNIR** | The 1:N identification analogues of FMR/FNMR. |
+| **ROC** | FNMR plotted against FMR as the threshold sweeps. The whole curve, not one point. |
+| **d′ (d-prime)** | Separation between genuine and impostor distributions: `|μ_G − μ_I| / √((σ²_G + σ²_I)/2)`. Assumes roughly Gaussian distributions. Higher is better. |
+
+**The threshold is a dial, not a fact.** Moving it trades FMR against FNMR. Any
+single accuracy number is one point on a curve, and quoting it without the
+operating point is meaningless. This is why the visualizer does *not* let you
+move the threshold: 0.60 is the one operating point with a documented
+provenance, and a moved line would have none.
+
+---
+
+## 8. The table that is not in this tool, and why
+
+During design, a banded interpretation table was proposed:
+
+| Distance | Proposed meaning |
+|---|---|
+| 0.00–0.25 | Same face, session noise |
+| 0.25–0.45 | Same face, natural variation |
+| 0.45–0.60 | Approaching the boundary |
+| 0.60+ | Reads as a different person |
+
+**It was rejected, and it is documented here so nobody reintroduces it.**
+
+Only the 0.60 boundary is sourceable. The 0.25 and 0.45 edges were a plausible
+synthesis, not a published finding. A search of the literature turned up no
+source: the genuine/impostor distribution work that exists
+([Krishnapriya et al.](https://arxiv.org/abs/1904.07325),
+[Albiero et al.](https://arxiv.org/abs/2002.00065)) covers other matchers and
+reports similarity scores rather than dlib Euclidean distances, so it does not
+transfer.
+
+Rendering unsourced numbers as thin labelled lines on a plot would read as
+*calibration*. Users would reasonably assume someone measured them. In a project
+whose subject is facial recognition accountability, shipping a fabricated axis is
+the wrong failure to have.
+
+**What replaced it.** The tool draws exactly two reference lines, and they carry
+different kinds of claim:
+
+| Line | Provenance | Meaning |
+|---|---|---|
+| Noise floor | **MEASURED** — this session, this camera, this face | Below it, nothing changed |
+| 0.60 | **CITED** — dlib training objective + LFW | A benchmark's number |
+
+The gridlines at 0.25 / 0.50 / 0.75 / 1.00 / 1.25 are **axis ticks only**, drawn
+at hairline weight so they cannot be mistaken for claims. They let you read a
+value off the trace. They assert nothing.
+
+The space between the noise floor and 0.60 is deliberately left uncalibrated,
+and the UI says so.
+
+---
+
+## 9. Algorithms implemented here
+
+### 9.1 Sampling and smoothing
+
+Detection runs on `requestAnimationFrame` with a busy-flag guard, so the
+detector is never re-entered while a pass is in flight. Readings accumulate in a
+buffer; a separate 3 Hz timer averages the buffer into one timeline sample.
+
+Rationale: raw per-frame distance is noisy, and the graph's job is trend
+legibility, not maximum temporal resolution. The numeric readout is unsmoothed
+and updates per frame, so instantaneous values remain visible.
+
+### 9.2 The quality gate
+
+`realtime-capture.js → createQualityGate()`. Every frame considered for a
+baseline or a saved capture must clear all of:
+
+**Confidence.** Detector score ≥ 0.90, well above the 0.5 reporting floor in
+`DETECTOR_OPTIONS`. A frame that barely registered as a face produces a
+correspondingly unreliable descriptor.
+
+**Scale.** Face box width ≥ 25% of video width. The recognition network resamples
+to 150×150; a small box means feeding it upscaled mush.
+
+**Framing.** Box fully inside the frame with ≥ 2% margin on every edge. A clipped
+face yields a garbage descriptor that looks perfectly valid downstream — one of
+the more insidious failure modes, because nothing errors.
+
+**Roll**, from landmarks 36 and 45 (outer eye corners):
+
+```
+roll = |atan2(y₄₅ − y₃₆, x₄₅ − x₃₆)|   →   gate at 8°
+```
+
+**Yaw proxy**, from the nose tip (30) against the eye-corner midpoint,
+normalised by interocular distance:
+
+```
+yaw ≈ |x₃₀ − (x₃₆ + x₄₅)/2| / ‖p₄₅ − p₃₆‖   →   gate at 0.12
+```
+
+This is a cheap monocular approximation, not a calibrated pose estimate. For
+real head-pose work, see
+[OpenFace](https://github.com/TadasBaltrusaitis/OpenFace) (Baltrušaitis et al.).
+
+**Sharpness.** Gradient energy over a 96×96 greyscale crop:
+
+```
+E = (1/N) Σ (∂I/∂x)² + (∂I/∂y)²
+```
+
+A discrete approximation of the Tenengrad focus measure, related to the standard
+variance-of-Laplacian blur detector. **Used only for ranking candidates against
+each other, never against a fixed floor** — an absolute sharpness threshold would
+vary with every camera, lens and lighting setup, and would be one more invented
+constant.
+
+**Exposure.** Mean crop luminance in [0.18, 0.86] (Rec. 601 weighting), and fewer
+than 2% of pixels above 0.98. Rejects both under- and over-exposure.
+
+The exposure gate connects directly to
+[NISTIR 8429's finding](https://pages.nist.gov/frvt/reports/demographics/nistir_8429.pdf)
+that false-negative inequities are substantially caused by under-exposure of
+dark-skinned subjects. Refusing to build a baseline from an under-exposed frame
+is a small, concrete mitigation.
+
+### 9.3 Medoid baseline
+
+Given N accepted descriptors, compute the pairwise distance matrix and select the
+member minimising summed distance to all others:
+
+```
+medoid = argmin_i  Σ_j d(xᵢ, xⱼ)
+spread = (1/(N−1)) Σ_j d(x_medoid, xⱼ)
+```
+
+**Why medoid rather than mean:**
+
+1. It stays a **real point** in the embedding, not a synthetic average.
+2. It sidesteps the norm-shrinkage problem in [§3.3](#33-vector-norm--a-subtlety-that-affects-the-baseline)
+   — no systematic offset gets baked into the reference.
+3. It is naturally robust to one marginal frame slipping the gate. The mean is not.
+
+Cost is O(N²) in distance computations. At N = 12 that is 66 comparisons of
+128-element vectors, computed once. Irrelevant.
+
+### 9.4 The noise floor
+
+`spread` above **is** the noise floor: how far the descriptor moves when nothing
+about the face has changed. It is the tool's only empirical reference line, and
+the most useful number it produces.
+
+Without it you cannot distinguish "the makeup did something" from "I moved my
+head." With it, a reading of 0.09 against a measured floor of 0.06 is visibly
+nothing, and a reading of 0.55 against the same floor is visibly something.
+
+If spread exceeds 0.20 (**UNTUNED**) the batch is rejected outright — the subject
+moved or the lighting flickered, and accepting it would set a floor so high that
+real results vanish beneath it.
+
+### 9.5 Sustained spike detection
+
+Capture fires on a reading that **holds**, not an instantaneous value. Two
+triggers:
+
+- **Personal best** — a new session maximum by ≥ 0.05, held for ≥ 1.5s
+- **Threshold crossing** — the first time the run clears 0.60, held for ≥ 1.5s
+
+Plus a manual shutter, because no heuristic catches everything.
+
+The frame written to disk is the **sharpest** observed across the sustain window,
+not whichever frame tripped the trigger. Rationale: a single frame at 0.50 is as
+likely to be motion blur as a result, and "the makeup is working" should not rest
+on a photograph of someone moving. A run that dips back below its starting value
+aborts.
+
+### 9.6 What the equalizer shows
+
+The right panel shows **per-dimension absolute delta** — `|baseline[i] − current[i]|`
+for each of the 128 components, scaled against `MAX_DELTA = 0.15`.
+
+**This is a debugging aid, not a measurement.** Individual dimensions of a
+learned embedding have no assigned semantic meaning. Dimension 47 is not "nose
+width." The panel is useful for seeing *whether* change is concentrated or
+diffuse across the embedding, and for nothing else. Work such as
+[Feature Representation in Deep Metric Embeddings](https://arxiv.org/abs/2102.03176)
+(which uses this exact dlib model) examines what structure embeddings do carry;
+per-dimension interpretation is not part of it.
+
+---
+
+## 10. Every threshold, and where it came from
+
+### Cited
+
+| Constant | Value | Source |
+|---|---|---|
+| `MATCH_THRESHOLD` | 0.60 | dlib's [structured metric loss target radius](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html) and the [documented LFW operating point](https://github.com/davisking/dlib/blob/master/python_examples/face_recognition.py) |
+
+That is the complete list. One number.
+
+### Measured
+
+| Quantity | Source |
+|---|---|
+| Noise floor | Medoid spread over 12 gated frames, this session |
+
+### Chosen
+
+| Constant | Value | Reasoning |
+|---|---|---|
+| `GRAPH_MAX_DISTANCE` | 1.25 | Headroom above 0.60. Effective adversarial makeup exceeds 1.0; a ceiling at 1.0 would saturate exactly when the experiment starts working. |
+| `GRAPH_WINDOW_SECONDS` | 60 | Long enough for a makeup pass, short enough to read. |
+| `SAMPLES_PER_SECOND` | 3 | Smoothing vs. responsiveness. |
+| `NO_FACE_DEBOUNCE_MS` | 1500 | Longer than a blink or head turn. |
+| `GAP_BLOCK_PX` | 30 | Fixed height for a collapsed absence. |
+| `BASELINE_TARGET_SAMPLES` | 12 | Enough for a stable medoid; ~4s at typical detector rates. |
+| `BASELINE_MIN_MS` | 3000 | Prevents 12 frames of a single instant. |
+| `BASELINE_TIMEOUT_MS` | 15000 | Fails loudly rather than hanging. |
+| `SPIKE_SUSTAIN_MS` | 1500 | ~4–5 samples at 3 Hz. |
+| `SPIKE_BEST_MARGIN` | 0.05 | Avoids capturing noise as a personal best. |
+| `SPIKE_COOLDOWN_MS` | 8000 | Keeps the gallery to keepers. |
+| `minScore` | 0.90 | Detector floor is 0.5; the baseline deserves better. |
+| `minBoxWidthRatio` | 0.25 | Recognition net input is 150×150. |
+| `maxRollDegrees` | 8 | Tight enough to matter, loose enough to achieve. |
+| `maxYawOffset` | 0.12 | As above; proxy metric, see §9.2. |
+| `MAX_DELTA` | 0.15 | Equalizer scaling only. Purely visual. |
+
+### Untuned — needs empirical calibration
+
+| Constant | Value | Why it needs work |
+|---|---|---|
+| `BASELINE_MAX_SPREAD` | 0.20 | Should be derived from observed spread distributions across cameras and subjects. Currently a guess. |
+| `minLuma` / `maxLuma` | 0.18 / 0.86 | Should be validated across skin tones specifically, given §6.2. A gate tuned on light skin would systematically reject dark-skinned subjects and is exactly the failure NIST documents. **This is the highest-priority calibration task in the codebase.** |
+
+### Inherited from `config.js`
+
+| Constant | Value |
+|---|---|
+| `inputSize` | 416 |
+| `scoreThreshold` | 0.5 |
+
+---
+
+## 11. Adversarial makeup: the research
+
+### 11.1 CV Dazzle (2010) — and its own obsolescence
+
+[**CV Dazzle**](https://adam.harvey.studio/cvdazzle), Adam Harvey. Named after
+[dazzle camouflage](https://en.wikipedia.org/wiki/Dazzle_camouflage) — WWI ship
+patterns that confused rangefinders rather than concealing the ship. Uses makeup,
+hair and styling to break the light/dark relationships a face detector expects.
+The first documented camouflage technique to successfully attack a computer
+vision algorithm.
+
+**Read Harvey's own current statement on the project before using any of it.**
+The original 2010–2013 looks targeted the
+[Viola-Jones](https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-cvpr-01.pdf)
+Haar cascade detector. Harvey's site states plainly that Viola-Jones was
+deprecated in security use around 2013–2016, and that **the original patterns are
+no longer active looks.** He developed new looks in 2020 aimed at CNN-based
+recognition.
+
+This is the most intellectually honest thing in the field, and the lesson
+generalises: **anti-surveillance techniques have expiry dates.** A look that
+worked against one detector generation says nothing about the next.
+
+Note also the *category* shift. CV Dazzle originally attacked **detection** —
+being found at all. This tool measures **recognition** — being identified once
+found. Defeating a detector and moving an embedding are different problems with
+different countermeasures. If the detector loses your face entirely, this tool
+records a gap, not a low distance.
+
+Further reading:
+[Harvey's DIS Magazine writeup](https://dismagazine.com/dystopia/evolved-lifestyles/8115/anti-surveillance-how-to-hide-from-machines/),
+[2011 Artblog interview](https://www.theartblog.org/2020/01/adam-harvey-and-the-anti-face/),
+[Wikipedia overview](https://en.wikipedia.org/wiki/Computer_vision_dazzle).
+
+### 11.2 Accessorize to a Crime (2016)
+
+[**Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition**](https://dl.acm.org/doi/10.1145/2976749.2978392)
+— Sharif, Bhagavatula, Bauer & Reiter, ACM CCS 2016.
+[PDF](https://www.cs.cmu.edu/~sbhagava/papers/face-rec-ccs16.pdf) ·
+[author page](https://mahmoods01.github.io/publication/ccs16-adv-ml/)
+
+The paper that established physically-realisable adversarial attacks on face
+recognition. Printed eyeglass frames that let the wearer evade recognition **or
+impersonate a specific different person**. Primarily white-box, with black-box
+and detection-evasion demonstrations.
+
+The impersonation result is the striking one: the attack is not just "make me
+unrecognisable" but "make me register as this specific other individual."
+
+Extended in [A General Framework for Adversarial Examples with Objectives](https://dl.acm.org/doi/10.1145/3317611)
+(ACM TOPS, 2019).
+
+### 11.3 The physical attack lineage
+
+- [**AdvHat**](https://arxiv.org/abs/1908.08705) (Komkov & Petiushko, ICPR 2020)
+  — printed sticker on a hat, attacking ArcFace.
+- [**Adversarial Mask**](https://arxiv.org/abs/2111.10759) — universal patterned
+  face mask.
+- [**Towards Effective Adversarial Textured 3D Meshes**](https://arxiv.org/abs/2303.15818)
+  — 3D-aware physical attacks.
+- [**Imperceptible Physical Attack via LED Illumination Modulation**](https://arxiv.org/abs/2307.13294)
+  — attacks the *illumination* rather than the face. Evaluated against dlib,
+  FaceNet and ArcFace, and useful for seeing how the three compare.
+
+### 11.4 Makeup-specific attacks — the directly relevant work
+
+- [**Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition**](https://arxiv.org/abs/2105.03162)
+  (Yin et al., IJCAI 2021) —
+  [proceedings](https://www.ijcai.org/proceedings/2021/173) ·
+  [code](https://github.com/TencentYoutuResearch/Adv-Makeup).
+  Synthesises adversarial eye shadow over the orbital region. Explicitly targets
+  **imperceptibility** (looks like makeup, not like an attack) and
+  **transferability** (works black-box), using meta-learning across multiple
+  surrogate models. Includes a physical-world case study against two commercial
+  platforms. **The closest published analogue to what this project is doing.**
+- [**AMT-GAN: Protecting Facial Privacy via Style-robust Makeup Transfer**](https://arxiv.org/abs/2203.03121)
+  (Hu et al., CVPR 2022) — adversarial identity masks through makeup transfer.
+- [Generating Adversarial Examples by Makeup Attacks on Face Recognition](https://ieeexplore.ieee.org/document/8803269)
+  (Zhu, Lu & Chiang, ICIP 2019) — earlier work in the same direction.
+- [Adv-Attribute: Inconspicuous and Transferable Adversarial Attack](https://arxiv.org/abs/2210.06871)
+  — attribute-space rather than pixel-space perturbation.
+
+**Note the methodological gap.** These papers optimise perturbations with
+gradient access to a model. Physical makeup applied by hand is a much coarser
+instrument, and none of these results should be read as predicting what a human
+with brushes can achieve. That gap is precisely the space this tool is meant to
+explore empirically.
+
+### 11.5 Digital cloaking — a different threat model
+
+- [**Fawkes: Protecting Privacy against Unauthorized Deep Learning Models**](https://www.usenix.org/conference/usenixsecurity20/presentation/shan)
+  (Shan et al., USENIX Security 2020) —
+  [PDF](https://people.cs.uchicago.edu/~ravenben/publications/pdf/fawkes-usenix20.pdf) ·
+  [code](https://github.com/Shawn-Shan/fawkes) ·
+  [project site](https://sandlab.cs.uchicago.edu/fawkes/).
+  Perturbs images *before upload* so that a scraper training on them builds a
+  model that misidentifies the real you. Reported 95%+ protection, 80%+ even when
+  clean images leak.
+- [**LowKey**](https://arxiv.org/abs/2101.07922) (Cherepanova et al., ICLR 2021)
+  — similar goal, ensemble-based for better black-box transfer, with a perceptual
+  metric in the loss.
+
+**Critically, this is poisoning, not evasion.** Fawkes protects a *training set*.
+It does nothing for you walking past a camera. Independent work has also
+questioned its practical robustness — see
+[Oriole](https://arxiv.org/abs/2102.11502), which reports the perturbations
+becoming plainly visible on high-resolution images.
+
+### 11.6 The survey to read first
+
+[**SoK: Anti-Facial Recognition Technology**](https://arxiv.org/abs/2112.04558)
+— Wenger, Shan, Zheng & Zhao. Systematises the whole space: evasion vs.
+poisoning, digital vs. physical, detection vs. recognition. If you read one
+paper from this list, read this one.
+
+Also useful:
+[Privacy-Enhancing Face Biometrics: A Comprehensive Survey](https://ieeexplore.ieee.org/document/9481149)
+(Meden et al., IEEE TIFS 2021).
+
+---
+
+## 12. Transferability — the central caveat
+
+**A low score in this tool does not mean you are unrecognisable.**
+
+The entire adversarial ML literature converges on this: attacks optimised against
+one model **transfer poorly** to others. Transferability is treated as the core
+open challenge in
+[Adv-Makeup](https://arxiv.org/abs/2105.03162), which needed a dedicated
+meta-learning strategy across multiple surrogate models just to get black-box
+success — and that with full gradient access during optimisation.
+
+The model in this tool is:
+
+- **From 2017**, never retrained
+- **Trained with a different loss** (structured metric loss) than modern systems
+  (ArcFace's angular margin, AdaFace's quality-adaptive margin)
+- **Read with a different metric** (Euclidean, not cosine)
+- **Small** — 29 conv layers, halved filters
+- **Not** what any commercial system uses
+
+Deployed systems — Clearview, Idemia, NEC, Azure Face, Amazon Rekognition — use
+proprietary models, typically larger, trained on far more data, and often
+ensembled. NIST's FRVT programme
+([ongoing results](https://pages.nist.gov/frvt/html/frvt11.html)) evaluates
+hundreds of them; the open-source models in this class sit well below the
+commercial frontier. NISTIR 8280 itself notes that some open-source algorithms
+included in prior comparative work are inaccurate enough not to represent
+commercial deployment.
+
+So: **moving 0.9 in dlib's space is evidence that you moved something in dlib's
+space.** It is weak evidence about ArcFace, and near-zero evidence about a
+production surveillance stack.
+
+### What would strengthen a claim
+
+If the goal is a defensible finding rather than a local signal, the standard
+approach is an **ensemble** of independent recognisers, reporting the distance in
+each. A technique that moves all of them is meaningfully more likely to
+generalise than one that moves a single 2017 model. Candidates:
+
+- [ArcFace / InsightFace](https://github.com/deepinsight/insightface)
+- [FaceNet (facenet-pytorch)](https://github.com/timesler/facenet-pytorch)
+- [AdaFace](https://github.com/mk-minchul/AdaFace)
+- [DeepFace](https://github.com/serengilx/deepface) as a wrapper over several
+
+This is a substantial architectural change — different preprocessing, different
+metrics, different thresholds per model — and it is the natural next step for the
+project, not something the current page does.
+
+---
+
+## 13. What this tool can and cannot tell you
+
+### It can
+
+- Tell you whether a makeup pass moved dlib's embedding **at all**, above your
+  own measured noise floor
+- Let you **compare two makeup variants** on the same face, same session, same
+  camera — the comparison it is actually built for
+- Show **which direction** a change pushes the descriptor, and whether the change
+  is diffuse or concentrated
+- Give you a **timestamped, watermarked record** with the measurement burned in,
+  so a result is reproducible and citable rather than anecdotal
+- Do all of this **offline, on-device**, with no face leaving the machine
+
+### It cannot
+
+- Tell you whether any deployed system would recognise you
+- Tell you whether the technique transfers to another model
+- Account for camera, angle, distance or lighting conditions other than the ones
+  you are sitting in
+- Tell you anything about **detection** evasion — if the detector loses you, the
+  tool records a gap, not a success
+- Establish that a technique works for anyone other than you
+- Correct for demographic differentials in the underlying embedding
+
+### Methodological hygiene, if you want the results to mean something
+
+1. **Re-baseline whenever lighting or camera changes.** A baseline captured under
+   different light is a different experiment.
+2. **Report the noise floor with every result.** A distance without its noise
+   floor is uninterpretable.
+3. **Compare within a session.** Cross-session comparison confounds makeup with
+   lighting, camera position and time of day.
+4. **Vary pose deliberately and record the envelope.** If a result only holds at
+   one exact angle, it is not a result.
+5. **Never report a peak.** Report a sustained value with its duration.
+
+---
+
+## 14. Privacy and data handling
+
+The tool stores two categories of data, both local, both deletable.
+
+| Data | Store | Deletion |
+|---|---|---|
+| Baseline descriptor + noise floor + timestamp | `localStorage` (`gstmxx.realtime.baseline`) | "Delete baseline" — removes the key |
+| Spike captures (watermarked JPEGs) | IndexedDB (`gstmxx-realtime` / `captures`) | "Delete all", or per-capture in the viewer |
+| View settings | `localStorage` (`gstmxx.realtime.settings`) | Browser storage clear |
+
+**Why IndexedDB for captures.** `localStorage` caps near 5MB per origin, is
+shared with everything else the site stores, and inflates binary by ~33% through
+base64. Photographs belong in a store that takes Blobs natively.
+
+**A 128-d descriptor is biometric data.** Under
+[GDPR Article 9](https://gdpr-info.eu/art-9-gdpr/), biometric data processed for
+unique identification is a special category. Nothing here is transmitted, so no
+processor relationship arises — but the classification is why deletion is a real
+removal and not a flag.
+
+**Deleting the baseline does not clear the gallery.** Deliberate: the gallery is
+expected to outlive a re-baseline. Stated in the UI rather than left to be
+inferred.
+
+No third-party requests. All models are vendored under `/lab-js/vendor`; fonts
+are self-hosted. The page functions with the network unplugged.
+
+---
+
+## 15. Regulatory context
+
+Relevant because it explains what this tooling is a response to.
+
+### EU AI Act
+
+[Regulation (EU) 2024/1689](https://eur-lex.europa.eu/eli/reg/2024/1689/oj) —
+[Article 5, Prohibited AI Practices](https://artificialintelligenceact.eu/article/5/).
+Prohibitions applicable from **2 February 2025**, with the penalty regime from
+2 August 2025. Penalties for prohibited practices reach €35M or 7% of global
+annual turnover.
+
+Provisions bearing on facial recognition:
+
+- **Art. 5(1)(e)** — bans creating or expanding facial recognition databases
+  through **untargeted scraping** of internet images or CCTV footage. "Untargeted"
+  is the operative word. See the Future of Privacy Forum's analysis:
+  [Red Lines under the EU AI Act](https://fpf.org/blog/red-lines-under-the-eu-ai-act-understanding-the-ban-of-the-untargeted-scraping-of-facial-images-and-facial-recognition-databases/).
+- **Art. 5(1)(g)** — bans biometric categorisation inferring race, political
+  opinions, trade union membership, religious belief, sex life or sexual
+  orientation.
+- **Art. 5(1)(h)** — bans **real-time** remote biometric identification in
+  publicly accessible spaces for law enforcement, with narrow exceptions
+  requiring prior judicial or independent administrative authorisation.
+
+**The gap worth knowing.** The ban targets *real-time* RBI. **Post-remote**
+biometric identification — running recognition over footage after the fact — is
+classified high-risk rather than prohibited. A demonstration can be filmed and
+every face run against a database days later, subject to authorisation and
+documentation requirements. Restrictive, but not a ban.
+
+*These provisions are recent and interpretation is still developing; verify
+against the current consolidated text and any Commission guidelines before
+relying on this summary.*
+
+### GDPR
+
+[Article 9](https://gdpr-info.eu/art-9-gdpr/) — biometric data for unique
+identification is a special category requiring explicit consent or another
+Art. 9 condition. Between 2022 and 2024 several EU data protection authorities
+fined Clearview AI over facial recognition practices; those decisions are the
+enforcement backdrop to Art. 5(1)(e).
+
+### Standards
+
+- [ISO/IEC 19795](https://www.iso.org/standard/73515.html) — biometric performance
+  testing and reporting. Part 10 addresses quantifying performance variation
+  across demographic groups.
+- [NIST FRVT](https://pages.nist.gov/frvt/html/frvt11.html) — the ongoing
+  independent evaluation programme.
+
+---
+
+## 16. Vendored assets and provenance
+
+Full inventory in `lab-js/vendor/README.md`; refresh via
+`lab-js/vendor/fetch-sources.sh`.
+
+| Asset | Upstream | Version |
+|---|---|---|
+| `face-api.js` | [@vladmandic/face-api](https://github.com/vladmandic/face-api) | floating — no pinned version in the source URL |
+| Model shards | [justadudewhohacks/face-api.js-models](https://github.com/justadudewhohacks/face-api.js-models) | `@master` |
+| Original model weights | [davisking/dlib-models](https://github.com/davisking/dlib-models) | `dlib_face_recognition_resnet_model_v1` |
+
+**Two provenance weaknesses worth fixing.** The face-api bundle URL carries no
+version, and the model shards track `@master`. Both mean a re-run of
+`fetch-sources.sh` could silently change the model — and therefore every
+measurement — with no record of what changed. For research output, pin both to
+commit SHAs and record the SHA alongside results.
+
+---
+
+## 17. Reading list
+
+### Start here
+
+1. [SoK: Anti-Facial Recognition Technology](https://arxiv.org/abs/2112.04558) — the map of the territory
+2. [High Quality Face Recognition with Deep Metric Learning](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html) — the model in this tool, from its author
+3. [NISTIR 8280: FRVT Part 3 — Demographic Effects](https://doi.org/10.6028/NIST.IR.8280) — the evidence base for bias
+4. [CV Dazzle](https://adam.harvey.studio/cvdazzle) — including its own obsolescence notice
+
+### Foundations
+
+- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://arxiv.org/abs/1503.03832) — Schroff et al., CVPR 2015
+- [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) — He et al., 2015
+- [ArcFace: Additive Angular Margin Loss](https://arxiv.org/abs/1801.07698) — Deng et al., CVPR 2019
+- [Rapid Object Detection using a Boosted Cascade of Simple Features](https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-cvpr-01.pdf) — Viola & Jones, 2001
+- [Deep Face Recognition: A Survey](https://arxiv.org/abs/1804.06655) — Wang & Deng
+
+### Benchmarks and their critique
+
+- [Labeled Faces in the Wild](https://vis-www.cs.umass.edu/lfw/) — the original
+- [Algorithmic Fairness Datasets: the Story so Far](https://arxiv.org/abs/2202.01711) — Fabris et al.
+- [FairFace](https://github.com/joojs/fairface) — a demographically balanced alternative
+- [Racial Faces in the Wild](https://github.com/whdeng/RFW) — Wang et al.
+
+### Bias and fairness
+
+- [Gender Shades](https://proceedings.mlr.press/v81/buolamwini18a.html) — Buolamwini & Gebru (gender *classification*, not verification)
+- [NISTIR 8429: FRVT Part 8](https://pages.nist.gov/frvt/reports/demographics/nistir_8429.pdf)
+- [Characterizing the Variability in Face Recognition Accuracy Relative to Race](https://arxiv.org/abs/1904.07325)
+- [Analysis of Gender Inequality In Face Recognition Accuracy](https://arxiv.org/abs/2002.00065)
+- [Exploring Causes of Demographic Variations In Face Recognition Accuracy](https://arxiv.org/abs/2304.07175)
+
+### Attacks
+
+- [Accessorize to a Crime](https://www.cs.cmu.edu/~sbhagava/papers/face-rec-ccs16.pdf) — Sharif et al., CCS 2016
+- [Adv-Makeup](https://arxiv.org/abs/2105.03162) — Yin et al., IJCAI 2021
+- [AMT-GAN](https://arxiv.org/abs/2203.03121) — Hu et al., CVPR 2022
+- [AdvHat](https://arxiv.org/abs/1908.08705) — Komkov & Petiushko
+- [Fawkes](https://www.usenix.org/conference/usenixsecurity20/presentation/shan) — Shan et al., USENIX Sec 2020
+- [LowKey](https://arxiv.org/abs/2101.07922) — Cherepanova et al., ICLR 2021
+- [Intriguing Properties of Neural Networks](https://arxiv.org/abs/1312.6199) — Szegedy et al., where adversarial examples begin
+
+### Policy
+
+- [EU AI Act, Article 5](https://artificialintelligenceact.eu/article/5/)
+- [FPF: Red Lines under the EU AI Act](https://fpf.org/blog/red-lines-under-the-eu-ai-act-understanding-the-ban-of-the-untargeted-scraping-of-facial-images-and-facial-recognition-databases/)
+- [GDPR Article 9](https://gdpr-info.eu/art-9-gdpr/)
+
+---
+
+## 18. Glossary
+
+**Alignment** — Warping a detected face to a canonical pose before embedding.
+
+**Descriptor / embedding / template** — The vector representing a face. Here, 128 floats.
+
+**Detection vs. recognition** — Finding that a face exists vs. determining whose it is. Different models, different attacks.
+
+**Euclidean distance** — Straight-line distance in vector space. The metric this model is calibrated for.
+
+**Genuine / impostor distribution** — Distance distributions for same-person and different-person pairs. Their overlap determines error rates.
+
+**Medoid** — The member of a set with smallest summed distance to the others. Unlike the mean, always a real member.
+
+**Noise floor** — Here: descriptor movement when nothing about the face changed. Measured per session.
+
+**Threshold** — The distance below which two descriptors are declared a match. A dial trading false accepts against false rejects, not a fact.
+
+**Transferability** — Whether an attack developed against one model works against another. Generally poor. The reason this tool's readings are local signal rather than evidence.
+
+**White-box / black-box** — Whether the attacker has access to model internals and gradients. This tool is white-box against dlib and tells you nothing about black-box performance.
+
+---
+
+*Sources current as of August 2026. The EU AI Act sections describe recent and
+evolving provisions — verify against the consolidated text before relying on
+them. If you re-run `fetch-sources.sh`, record the resulting commit SHAs: the
+upstream model references are unpinned, and an unnoticed model change invalidates
+every prior measurement.*
diff --git a/realtime.html b/realtime.html
new file mode 100644
index 0000000..b09ddf9
--- /dev/null
+++ b/realtime.html
@@ -0,0 +1,166 @@
+<!doctype html>
+<html lang="en">
+
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1, viewport-fit=cover" />
+  <title>Ghostmaxxing | Latent Space Visualizer for Face Descriptor Drift</title>
+  <meta
+    name="description"
+    content="Real-time visual debugger for face descriptor drift, equalizer deltas, and biometric distance thresholds."
+  />
+  <meta property="og:title" content="Ghostmaxxing | Latent Space Visualizer for Face Descriptor Drift" />
+  <meta property="og:description" content="Watch live face descriptor drift, equalizer deltas, and distance thresholds in a real-time visual debugging view." />
+  <meta property="og:type" content="website" />
+  <meta property="og:image" content="/images/social-card.svg" />
+  <meta name="twitter:card" content="summary_large_image" />
+  <meta name="twitter:title" content="Ghostmaxxing | Latent Space Visualizer for Face Descriptor Drift" />
+  <meta name="twitter:description" content="Watch live face descriptor drift, equalizer deltas, and distance thresholds in a real-time visual debugging view." />
+  <meta name="twitter:image" content="/images/social-card.svg" />
+  <link rel="icon" type="image/svg+xml" href="/images/logo/mark-onlight.svg">
+
+  <link rel="stylesheet" href="/styles/styles.css" />
+  <link rel="stylesheet" href="/styles/lab.css" />
+  <link rel="stylesheet" href="/styles/realtime.css" />
+</head>
+
+<body>
+  <main id="ui-layer" aria-label="Latent space visualizer">
+
+    <!-- .selector is a real component in lab.css and supplies the pill this
+         link never had. The prototype used .seg--home, which is defined
+         nowhere, leaving a transparent glyph on dark video. -->
+    <div class="selector realtime-home">
+      <a class="seg" href="/" aria-label="Home">
+        <svg aria-hidden="true" viewBox="0 0 24 24">
+          <path d="M3 10.5 12 3l9 7.5" />
+          <path d="M5 9.5V21h14V9.5" />
+          <path d="M9.5 21v-6h5v6" />
+        </svg>
+      </a>
+    </div>
+
+    <section id="graph-container" aria-label="Descriptor distance over time">
+      <header class="panel-header">
+        <p class="panel-title">Distance</p>
+        <p class="panel-value"><span id="distance-value">0.000</span></p>
+      </header>
+
+      <div class="panel-body">
+        <div id="graph-scroll" class="graph-scroll" tabindex="0" aria-label="Distance history, scroll up for earlier measurements">
+          <div id="graph-spacer" class="graph-spacer">
+            <canvas id="distance-graph" aria-label="Distance history plot"></canvas>
+          </div>
+        </div>
+        <button id="graph-follow" class="graph-follow" type="button" hidden>Jump to now</button>
+      </div>
+
+      <!-- Only two lines on this plot make a claim, and they are different
+           kinds of claim. Said plainly so neither is mistaken for the other. -->
+      <div class="panel-note" id="graph-legend">
+        <div class="legend-row">
+          <span class="legend-swatch legend-swatch--noise" aria-hidden="true"></span>
+          <span>Noise floor <span class="legend-value" id="legend-noise">—</span></span>
+        </div>
+        <div class="legend-row">
+          <span class="legend-swatch legend-swatch--threshold" aria-hidden="true"></span>
+          <span>Match threshold <span class="legend-value">0.60</span></span>
+        </div>
+        <span class="legend-note" id="legend-explainer">
+          The noise floor is measured from your own baseline capture: below it,
+          nothing changed. 0.60 is dlib's default on the LFW benchmark — a
+          benchmark's number, not a property of your face. The space between
+          them is not calibrated.
+        </span>
+      </div>
+    </section>
+
+    <section id="center-view" aria-label="Webcam, baseline capture and controls">
+      <video id="webcam" autoplay muted playsinline></video>
+      <div class="video-scrim"></div>
+      <div id="status-pill" aria-live="polite">Loading models…</div>
+
+      <div id="calibration-ui">
+        <div id="capture-ring" class="capture-ring" role="progressbar" aria-valuemin="0" aria-valuemax="12" aria-valuenow="0" aria-label="Baseline samples collected">
+          <svg viewBox="0 0 116 116" aria-hidden="true">
+            <circle class="capture-ring__track" cx="58" cy="58" r="54" />
+            <circle class="capture-ring__arc" id="capture-ring-arc" cx="58" cy="58" r="54" />
+          </svg>
+          <span class="capture-ring__count" id="capture-ring-count">0</span>
+        </div>
+        <p class="capture-hint" id="capture-hint">Capture a reference of your bare face before you start.</p>
+        <button id="btn-baseline-start" class="stage-btn" type="button">Set baseline</button>
+      </div>
+
+      <div id="photo-viewer" class="viewer-overlay" role="dialog" aria-modal="true" aria-label="Saved capture" hidden>
+        <div class="viewer-stage">
+          <img id="viewer-image" alt="Saved capture" />
+        </div>
+        <div class="viewer-actions">
+          <span class="viewer-meta" id="viewer-meta"></span>
+          <button id="viewer-copy" class="sb-btn" type="button">Copy</button>
+          <button id="viewer-download" class="sb-btn" type="button">Download</button>
+          <button id="viewer-delete" class="sb-btn sb-btn--warn" type="button">Delete</button>
+          <button id="viewer-close" class="sb-btn" type="button">Close</button>
+        </div>
+      </div>
+
+      <div id="settings-bar" class="settingsbar" aria-label="View and capture controls">
+        <div class="sb-group">
+          <label class="sb-label" for="brightness-input">View</label>
+          <input
+            id="brightness-input"
+            type="range"
+            min="0.4"
+            max="1.4"
+            step="0.05"
+            value="0.85"
+            title="Display brightness only. This does not reach the detector and cannot change a measurement."
+          />
+          <span class="sb-value" id="brightness-value">0.85</span>
+        </div>
+
+        <div class="sb-group">
+          <span class="sb-label" id="equalizer-label">Equalizer</span>
+          <button
+            id="toggle-equalizer"
+            class="switch"
+            type="button"
+            role="switch"
+            aria-checked="true"
+            aria-labelledby="equalizer-label"
+          ></button>
+        </div>
+
+        <div class="sb-spacer"></div>
+
+        <div class="sb-group">
+          <button id="btn-shutter" class="sb-btn" type="button" disabled>Capture now</button>
+          <span class="sb-count" id="gallery-count" aria-label="Saved captures">0</span>
+          <button id="btn-download-all" class="sb-btn" type="button" disabled>Download all</button>
+          <button id="btn-delete-all" class="sb-btn sb-btn--warn" type="button" disabled>Delete all</button>
+        </div>
+
+        <div class="sb-group">
+          <button id="btn-baseline" class="sb-btn sb-btn--warn" type="button" hidden>Delete baseline</button>
+        </div>
+      </div>
+    </section>
+
+    <section id="embedding-panel" aria-label="128-dimensional descriptor equalizer">
+      <header class="panel-header">
+        <p class="panel-title">Descriptor Delta</p>
+        <p class="panel-value"><span id="tracking-state">NO BASELINE</span></p>
+      </header>
+      <div class="panel-body">
+        <div id="embedding-bar" aria-label="128 descriptor rows"></div>
+      </div>
+    </section>
+
+  </main>
+
+  <script src="/lab-js/vendor/face-api.js"></script>
+  <script type="module" src="/lab-js/realtime/realtime.js"></script>
+</body>
+
+</html>
diff --git a/styles/realtime.css b/styles/realtime.css
new file mode 100644
index 0000000..e46698b
--- /dev/null
+++ b/styles/realtime.css
@@ -0,0 +1,688 @@
+/* ============================================================================
+   Ghostmaxxing — realtime.css
+   Page layer for realtime.html. Placement and page-local components only.
+
+   PALETTE CONTRACT
+   ----------------
+   This page draws from the LAB palette (lab.css: --ink, --panel, --fg, --dev,
+   --line ...), not the poster --gm-* system. tokens.css names lab.css as the
+   one deliberate exception and that still holds.
+
+   The prototype picked colours by appearance. Every colour used on this page
+   now goes through the --rt-* alias block below, so the MEANING is declared
+   once and the hex can move without hunting through draw calls.
+
+   Deliberate changes from the prototype:
+     --net was doing duty as a "threshold" colour. --net means "online zone"
+     in lab.css and this page has no online zone. Released.
+
+     .cell.on-high used --orange (#fd5503), the retired v1 poster orange.
+     Now --gm-bg (#f26a1b), the palette that won. This is the only place the
+     poster system reaches into the lab, and it is intentional: the equalizer's
+     hottest state is the one thing on this page that should agree with the
+     rest of the site.
+
+     .seg--home was never defined anywhere. The home link is now wrapped in
+     .selector, a real component in lab.css, which gives it the pill it was
+     missing. It was previously a transparent --muted glyph on dark video.
+
+   Layer: PAGE. Loaded after styles.css and lab.css. Defines no --gm-* value.
+   ========================================================================= */
+
+
+#ui-layer,
+#ui-layer * {
+  box-sizing: border-box;
+}
+
+
+/* --- Semantic aliases ------------------------------------------------------
+   Left column is a measuring instrument. Only two of its lines make a claim:
+
+     --rt-noise      measured, this session, from your baseline capture
+     --rt-threshold  cited, dlib/LFW default — external, benchmark-specific
+
+   Everything else is scaffolding and is deliberately quiet so those two read
+   as the only assertions on the plot. --rt-grid ticks carry no claim at all;
+   they exist so a value can be read off the trace. */
+
+#ui-layer {
+  --rt-trace: var(--cream);
+  --rt-threshold: var(--yellow);
+  --rt-noise: var(--dev);
+  --rt-grid: var(--line-soft);
+  --rt-gap: var(--faint);
+  --rt-plot-bg: var(--panel-2);
+
+  --rt-delta-low: var(--faint);
+  --rt-delta-med: var(--yellow);
+  --rt-delta-high: var(--gm-bg);
+
+  --rt-surface: rgba(12, 9, 6, 0.52);
+  --rt-surface-solid: rgba(12, 9, 6, 0.86);
+
+  /* Display exposure. Does NOT reach the detector — face-api reads raw video
+     frames and CSS filters apply at composite. Changing this cannot change a
+     descriptor, which is the point: it is a viewing control, not a variable. */
+  --rt-brightness: 0.85;
+
+  --rt-bar-h: 44px;
+  --rt-gutter: 24px;
+}
+
+
+/* --- Shell ---------------------------------------------------------------- */
+
+#ui-layer {
+  width: 100%;
+  height: 100dvh;
+  display: grid;
+  grid-template-columns: 244px minmax(0, 1fr) 244px;
+  gap: 14px;
+  padding: 14px;
+}
+
+#ui-layer > * {
+  min-width: 0;
+  min-height: 0;
+}
+
+#ui-layer.hide-equalizer {
+  grid-template-columns: 244px minmax(0, 1fr);
+}
+
+#ui-layer.hide-equalizer #embedding-panel {
+  display: none;
+}
+
+.realtime-home {
+  position: fixed;
+  top: 12px;
+  left: 12px;
+  z-index: var(--z-chrome);
+}
+
+
+/* --- Panels --------------------------------------------------------------- */
+
+#graph-container,
+#embedding-panel {
+  display: flex;
+  flex-direction: column;
+  min-height: 0;
+  border: 1px solid var(--line-soft);
+  border-radius: 12px;
+  background: var(--rt-surface);
+  backdrop-filter: blur(8px);
+  overflow: hidden;
+}
+
+.panel-header {
+  flex: none;
+  border-bottom: 1px solid var(--line-soft);
+  padding: 11px 12px;
+}
+
+/* flex:1 + min-height:0 replaces the prototype's calc(100% - 92px) and
+   calc(100% - 66px), which hardcoded two different header heights and had
+   already drifted apart from each other. */
+.panel-body {
+  position: relative;
+  flex: 1;
+  min-height: 0;
+}
+
+.panel-title,
+.panel-value,
+.panel-note {
+  margin: 0;
+}
+
+.panel-title {
+  font: 700 10px var(--mono);
+  text-transform: uppercase;
+  letter-spacing: 0.12em;
+  color: var(--faint);
+}
+
+.panel-value {
+  margin-top: 7px;
+  font: 400 22px/1 var(--serif);
+  color: var(--yellow);
+  font-variant-numeric: tabular-nums;
+}
+
+.panel-note {
+  flex: none;
+  padding: 9px 12px;
+  border-top: 1px solid var(--line-soft);
+  color: var(--muted);
+  font: 10.5px/1.5 var(--mono);
+}
+
+.legend-row {
+  display: flex;
+  align-items: baseline;
+  gap: 7px;
+}
+
+.legend-row + .legend-row {
+  margin-top: 4px;
+}
+
+.legend-swatch {
+  flex: none;
+  width: 14px;
+  height: 2px;
+  border-radius: 1px;
+  transform: translateY(-3px);
+}
+
+.legend-swatch--noise {
+  background: var(--rt-noise);
+}
+
+.legend-swatch--threshold {
+  background: var(--rt-threshold);
+}
+
+.legend-value {
+  color: var(--fg);
+  font-variant-numeric: tabular-nums;
+}
+
+.legend-note {
+  display: block;
+  margin-top: 6px;
+  color: var(--faint);
+  font-size: 10px;
+  line-height: 1.45;
+}
+
+
+/* --- Graph: virtualised scroll ---------------------------------------------
+   The canvas is sticky inside a tall spacer, so it always covers the scroll
+   viewport while the spacer supplies the scrollable height. Memory stays
+   constant regardless of session length, and we sidestep the ~32k px canvas
+   dimension cap that a naively-grown canvas would hit after ~45 minutes. */
+
+.graph-scroll {
+  position: absolute;
+  inset: 0;
+  overflow-y: auto;
+  overflow-x: hidden;
+  overscroll-behavior: contain;
+  scrollbar-width: thin;
+  scrollbar-color: var(--line) transparent;
+}
+
+.graph-scroll::-webkit-scrollbar {
+  width: 6px;
+}
+
+.graph-scroll::-webkit-scrollbar-thumb {
+  background: var(--line);
+  border-radius: 3px;
+}
+
+.graph-spacer {
+  position: relative;
+  width: 100%;
+}
+
+#distance-graph {
+  position: sticky;
+  top: 0;
+  display: block;
+  width: 100%;
+}
+
+#distance-graph.is-clickable {
+  cursor: pointer;
+}
+
+.graph-follow {
+  position: absolute;
+  left: 50%;
+  bottom: 10px;
+  transform: translateX(-50%);
+  display: inline-flex;
+  align-items: center;
+  gap: 5px;
+  min-height: 30px;
+  padding: 0 12px;
+  border-radius: 999px;
+  border: 1px solid var(--line);
+  background: var(--rt-surface-solid);
+  color: var(--fg);
+  font: 600 11px var(--sans);
+  cursor: pointer;
+  backdrop-filter: blur(8px);
+}
+
+.graph-follow[hidden] {
+  display: none;
+}
+
+.graph-follow:focus-visible {
+  outline: 2px solid var(--yellow);
+  outline-offset: 2px;
+}
+
+
+/* --- Centre stage --------------------------------------------------------- */
+
+#center-view {
+  position: relative;
+  border: 1px solid var(--line);
+  border-radius: 14px;
+  overflow: hidden;
+  background: var(--ink-deep);
+}
+
+#webcam {
+  width: 100%;
+  height: 100%;
+  object-fit: cover;
+  transform: scaleX(-1);
+  filter: brightness(var(--rt-brightness));
+}
+
+/* Scrim recedes as exposure rises, so turning the view up actually turns it
+   up. Negative opacity clamps to 0 in every engine. */
+.video-scrim {
+  position: absolute;
+  inset: 0;
+  pointer-events: none;
+  opacity: calc(1.3 - var(--rt-brightness));
+  background:
+    linear-gradient(
+      to bottom,
+      rgba(12, 9, 6, 0.45) 0%,
+      rgba(12, 9, 6, 0.05) 40%,
+      rgba(12, 9, 6, 0.58) 100%
+    ),
+    repeating-linear-gradient(
+      to right,
+      rgba(255, 231, 168, 0.04) 0,
+      rgba(255, 231, 168, 0.04) 1px,
+      transparent 1px,
+      transparent 12px
+    );
+}
+
+#status-pill {
+  position: absolute;
+  top: 12px;
+  left: 12px;
+  max-width: calc(100% - 24px);
+  padding: 7px 12px;
+  border: 1px solid var(--line);
+  border-radius: 999px;
+  background: rgba(12, 9, 6, 0.6);
+  color: var(--muted);
+  font: 11px var(--mono);
+  letter-spacing: 0.04em;
+  backdrop-filter: blur(8px);
+}
+
+#status-pill.is-warn {
+  color: var(--cream);
+  border-color: rgba(255, 231, 168, 0.28);
+}
+
+
+/* --- Baseline capture ----------------------------------------------------- */
+
+#calibration-ui {
+  position: absolute;
+  inset: 0 0 var(--rt-bar-h) 0;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  gap: 16px;
+  padding: 16px;
+}
+
+#calibration-ui[hidden] {
+  display: none;
+}
+
+.capture-ring {
+  position: relative;
+  width: 116px;
+  height: 116px;
+  display: none;
+  place-items: center;
+}
+
+.capture-ring.is-active {
+  display: grid;
+}
+
+.capture-ring svg {
+  position: absolute;
+  inset: 0;
+  width: 100%;
+  height: 100%;
+  transform: rotate(-90deg);
+}
+
+.capture-ring__track {
+  fill: rgba(12, 9, 6, 0.62);
+  stroke: var(--line);
+  stroke-width: 3;
+}
+
+/* Advances on ACCEPTED samples, never on elapsed time. If the arc stalls, the
+   frames are being rejected — the control diagnoses itself. */
+.capture-ring__arc {
+  fill: none;
+  stroke: var(--cream);
+  stroke-width: 3;
+  stroke-linecap: round;
+  transition: stroke-dashoffset 0.18s linear;
+}
+
+.capture-ring__count {
+  position: relative;
+  color: var(--cream);
+  font: 400 30px/1 var(--serif);
+  font-variant-numeric: tabular-nums;
+}
+
+.capture-hint {
+  max-width: 22rem;
+  margin: 0;
+  text-align: center;
+  color: var(--muted);
+  font: 12px/1.5 var(--mono);
+  text-wrap: balance;
+}
+
+.capture-hint.is-blocking {
+  color: var(--cream);
+}
+
+.stage-btn {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  gap: 0.4rem;
+  min-width: 9rem;
+  min-height: 44px;
+  padding: 0.72rem 1.2rem;
+  border: 1px solid var(--line);
+  border-radius: 999px;
+  background: rgba(20, 16, 12, 0.5);
+  color: var(--fg);
+  font: 600 13px var(--sans);
+  cursor: pointer;
+  backdrop-filter: blur(8px);
+  transition: transform 0.12s ease, background 0.2s ease, border-color 0.2s ease;
+}
+
+.stage-btn:hover {
+  border-color: rgba(255, 231, 168, 0.24);
+  background: rgba(33, 26, 18, 0.86);
+}
+
+.stage-btn:active {
+  transform: scale(0.97);
+}
+
+.stage-btn:focus-visible {
+  outline: 2px solid var(--yellow);
+  outline-offset: 3px;
+}
+
+.stage-btn[hidden] {
+  display: none;
+}
+
+
+/* --- Settings bar --------------------------------------------------------- */
+
+.settingsbar {
+  position: absolute;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  z-index: 6;
+  display: flex;
+  align-items: center;
+  gap: 14px;
+  min-height: var(--rt-bar-h);
+  padding: 0 12px;
+  background: linear-gradient(to top, rgba(12, 9, 6, 0.92), rgba(12, 9, 6, 0.58));
+  border-top: 1px solid var(--line-soft);
+  backdrop-filter: blur(10px);
+}
+
+.sb-group {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+}
+
+.sb-spacer {
+  flex: 1;
+}
+
+.sb-label {
+  color: var(--faint);
+  font: 700 9px var(--mono);
+  letter-spacing: 0.14em;
+  text-transform: uppercase;
+  white-space: nowrap;
+}
+
+.sb-value {
+  min-width: 2.4rem;
+  color: var(--fg);
+  font: 10.5px var(--mono);
+  font-variant-numeric: tabular-nums;
+  text-align: right;
+}
+
+.settingsbar input[type="range"] {
+  width: 104px;
+  accent-color: var(--gm-bg);
+}
+
+.sb-btn {
+  display: inline-flex;
+  align-items: center;
+  gap: 6px;
+  min-height: 30px;
+  padding: 0 11px;
+  border: 1px solid var(--line);
+  border-radius: 999px;
+  background: transparent;
+  color: var(--fg);
+  font: 600 11.5px var(--sans);
+  white-space: nowrap;
+  cursor: pointer;
+}
+
+.sb-btn:hover:not(:disabled) {
+  border-color: rgba(255, 231, 168, 0.26);
+  background: rgba(33, 26, 18, 0.7);
+}
+
+.sb-btn:disabled {
+  opacity: 0.38;
+  cursor: default;
+}
+
+.sb-btn:focus-visible,
+.settingsbar .switch:focus-visible,
+.settingsbar input[type="range"]:focus-visible {
+  outline: 2px solid var(--yellow);
+  outline-offset: 2px;
+}
+
+.sb-btn--warn {
+  border-color: rgba(242, 106, 27, 0.45);
+  color: #ffb98b;
+}
+
+.sb-btn.is-confirming {
+  border-color: var(--gm-bg);
+  background: rgba(242, 106, 27, 0.14);
+  color: #ffd0b0;
+}
+
+.sb-count {
+  min-width: 1.5rem;
+  color: var(--muted);
+  font: 10.5px var(--mono);
+  font-variant-numeric: tabular-nums;
+  text-align: center;
+}
+
+
+/* --- Capture viewer ------------------------------------------------------- */
+
+.viewer-overlay {
+  position: absolute;
+  inset: 0;
+  z-index: 8;
+  display: flex;
+  flex-direction: column;
+  background: rgba(6, 4, 3, 0.96);
+}
+
+.viewer-overlay[hidden] {
+  display: none;
+}
+
+.viewer-stage {
+  flex: 1;
+  min-height: 0;
+  display: grid;
+  place-items: center;
+  padding: 16px;
+}
+
+.viewer-stage img {
+  max-width: 100%;
+  max-height: 100%;
+  border: 1px solid var(--line);
+  border-radius: 8px;
+}
+
+.viewer-actions {
+  flex: none;
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  padding: 10px 12px;
+  border-top: 1px solid var(--line-soft);
+}
+
+.viewer-meta {
+  flex: 1;
+  min-width: 0;
+  color: var(--muted);
+  font: 10.5px var(--mono);
+  font-variant-numeric: tabular-nums;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+
+/* --- Descriptor equalizer --------------------------------------------------
+   Level is fixed per cell at build time and only .is-on toggles at runtime, so
+   a steady face writes nothing to the DOM. The prototype rewrote all 1,280
+   className strings on every detection frame. */
+
+#embedding-bar {
+  position: absolute;
+  inset: 0;
+  display: flex;
+  flex-direction: column;
+  gap: 1px;
+  padding: 8px;
+  overflow: hidden;
+}
+
+.descriptor-row {
+  display: grid;
+  grid-template-columns: repeat(10, 1fr);
+  gap: 1px;
+  flex: 1;
+  min-height: 2px;
+}
+
+.cell {
+  background: var(--panel-2);
+}
+
+.cell.lvl-low.is-on {
+  background: var(--rt-delta-low);
+}
+
+.cell.lvl-med.is-on {
+  background: var(--rt-delta-med);
+}
+
+.cell.lvl-high.is-on {
+  background: var(--rt-delta-high);
+}
+
+
+/* --- Narrow ---------------------------------------------------------------- */
+
+@media (max-width: 1050px) {
+  #ui-layer {
+    grid-template-columns: minmax(0, 1fr);
+    grid-template-rows: minmax(0, 1fr) 170px 190px;
+    padding: 10px;
+    gap: 10px;
+  }
+
+  #ui-layer.hide-equalizer {
+    grid-template-columns: minmax(0, 1fr);
+    grid-template-rows: minmax(0, 1fr) 170px;
+  }
+
+  #center-view {
+    order: -1;
+  }
+
+  #ui-layer {
+    --rt-bar-h: 52px;
+  }
+
+  .settingsbar {
+    flex-wrap: wrap;
+    gap: 8px 12px;
+    padding: 7px 10px;
+  }
+
+  .sb-spacer {
+    display: none;
+  }
+
+  .capture-ring {
+    width: 96px;
+    height: 96px;
+  }
+}
+
+@media (pointer: coarse) {
+  .sb-btn {
+    min-height: 36px;
+    padding: 0 13px;
+  }
+}
+
+@media (prefers-reduced-motion: reduce) {
+  .capture-ring__arc,
+  .stage-btn {
+    transition: none;
+  }
+}
diff --git a/tests/e2e/realtime.spec.js b/tests/e2e/realtime.spec.js
new file mode 100644
index 0000000..3959922
--- /dev/null
+++ b/tests/e2e/realtime.spec.js
@@ -0,0 +1,210 @@
+import { test, expect } from '@playwright/test';
+
+/**
+ * These run without a camera. getUserMedia rejects in headless Chromium unless
+ * --use-fake-device-for-media-stream is set, so anything past initWebcam() is
+ * out of scope here: the page is asserted up to the point where it needs a
+ * camera, plus the pure functions that do not.
+ */
+
+const MODULE_PATHS = [
+  '/lab-js/realtime.js',
+  '/lab-js/realtime-graph.js',
+  '/lab-js/realtime-capture.js',
+  '/lab-js/realtime-gallery.js',
+  '/lab-js/realtime-settings.js',
+];
+
+test.describe('Realtime page', () => {
+  test('loads html, css and every module without 404 responses', async ({ page }) => {
+    const targetPaths = new Set(['/realtime.html', '/styles/realtime.css', ...MODULE_PATHS]);
+    const seen = new Set();
+    const failing = [];
+
+    page.on('response', (response) => {
+      const url = new URL(response.url());
+      if (!targetPaths.has(url.pathname)) return;
+      seen.add(url.pathname);
+      if (response.status() === 404) {
+        failing.push({ path: url.pathname, status: response.status() });
+      }
+    });
+
+    await page.goto('/realtime.html');
+
+    await expect.poll(() => Array.from(seen).sort()).toEqual(Array.from(targetPaths).sort());
+    expect(failing).toEqual([]);
+
+    await expect(page.locator('#ui-layer')).toBeVisible();
+    await expect(page.locator('#embedding-bar .descriptor-row')).toHaveCount(128);
+  });
+
+  test('exposes the runtime bridge with its documented shape', async ({ page }) => {
+    await page.goto('/realtime.html');
+
+    await expect
+      .poll(() => page.evaluate(() => Boolean(window.gstmxxRealtime)))
+      .toBeTruthy();
+
+    const shape = await page.evaluate(() => ({
+      getState: typeof window.gstmxxRealtime.getState,
+      triggerCalibration: typeof window.gstmxxRealtime.triggerCalibration,
+      teardown: typeof window.gstmxxRealtime.teardown,
+      resume: typeof window.gstmxxRealtime.resume,
+    }));
+
+    expect(shape).toEqual({
+      getState: 'function',
+      triggerCalibration: 'function',
+      teardown: 'function',
+      resume: 'function',
+    });
+  });
+
+  test('reports the cited threshold and plotted ceiling, not tunable values', async ({ page }) => {
+    await page.goto('/realtime.html');
+    await expect.poll(() => page.evaluate(() => Boolean(window.gstmxxRealtime))).toBeTruthy();
+
+    const state = await page.evaluate(() => window.gstmxxRealtime.getState());
+    expect(state.threshold).toBe(0.6);
+    expect(state.graphMaxDistance).toBe(1.25);
+    expect(state.windowSeconds).toBe(60);
+    expect(state.hasBaseline).toBe(false);
+    expect(state.noiseFloor).toBeNull();
+
+    // No control may move the threshold: it is the one number here with a
+    // citable provenance, and a moved threshold means nothing.
+    await expect(page.locator('#settings-bar input[type="range"]')).toHaveCount(1);
+    await expect(page.locator('#settings-bar #brightness-input')).toBeVisible();
+  });
+
+  test('legend states the two reference lines and distinguishes their provenance', async ({ page }) => {
+    await page.goto('/realtime.html');
+
+    const legend = page.locator('#graph-legend');
+    await expect(legend).toContainText('Noise floor');
+    await expect(legend).toContainText('Match threshold');
+    await expect(legend).toContainText('0.60');
+    await expect(legend).toContainText('LFW');
+    await expect(page.locator('#legend-noise')).toHaveText('—');
+  });
+
+  test('equalizer toggle collapses the right column and persists', async ({ page }) => {
+    await page.goto('/realtime.html');
+
+    const toggle = page.locator('#toggle-equalizer');
+    const layer = page.locator('#ui-layer');
+
+    await expect(toggle).toHaveAttribute('aria-checked', 'true');
+    await expect(page.locator('#embedding-panel')).toBeVisible();
+
+    await toggle.click();
+    await expect(toggle).toHaveAttribute('aria-checked', 'false');
+    await expect(layer).toHaveClass(/hide-equalizer/);
+    await expect(page.locator('#embedding-panel')).toBeHidden();
+
+    // Rows stay in the DOM while hidden, so the descriptor grid is never
+    // rebuilt on toggle.
+    await expect(page.locator('#embedding-bar .descriptor-row')).toHaveCount(128);
+
+    await page.reload();
+    await expect(page.locator('#ui-layer')).toHaveClass(/hide-equalizer/);
+  });
+
+  test('brightness control moves only the display variable', async ({ page }) => {
+    await page.goto('/realtime.html');
+
+    const slider = page.locator('#brightness-input');
+    await slider.fill('1.2');
+    await slider.dispatchEvent('input');
+
+    await expect(page.locator('#brightness-value')).toHaveText('1.20');
+
+    const applied = await page.evaluate(() =>
+      document.getElementById('ui-layer').style.getPropertyValue('--rt-brightness'),
+    );
+    expect(applied).toBe('1.2');
+  });
+
+  test('destructive controls require a second click', async ({ page }) => {
+    await page.goto('/realtime.html');
+
+    const deleteAll = page.locator('#btn-delete-all');
+    await expect(deleteAll).toBeDisabled();
+    await expect(deleteAll).toHaveText('Delete all');
+
+    // Baseline controls stay hidden until a baseline exists.
+    await expect(page.locator('#btn-baseline')).toBeHidden();
+    await expect(page.locator('#btn-shutter')).toBeDisabled();
+    await expect(page.locator('#calibration-ui')).toBeVisible();
+    await expect(page.locator('#btn-baseline-start')).toBeVisible();
+  });
+
+  test('graph canvas is sticky inside a scrollable spacer', async ({ page }) => {
+    await page.goto('/realtime.html');
+
+    const position = await page.evaluate(
+      () => getComputedStyle(document.getElementById('distance-graph')).position,
+    );
+    expect(position).toBe('sticky');
+
+    const overflow = await page.evaluate(
+      () => getComputedStyle(document.getElementById('graph-scroll')).overflowY,
+    );
+    expect(overflow).toBe('auto');
+
+    await expect(page.locator('#graph-follow')).toBeHidden();
+  });
+
+  test('medoid picks a real member and reports its spread', async ({ page }) => {
+    await page.goto('/realtime.html');
+
+    const result = await page.evaluate(async () => {
+      const { medoid, euclidean } = await import('/lab-js/realtime-capture.js');
+      const tight = [
+        [0, 0, 0],
+        [0.01, 0, 0],
+        [0, 0.01, 0],
+        [0.4, 0.4, 0.4],
+      ];
+      const picked = medoid(tight);
+      return {
+        descriptor: picked.descriptor,
+        spread: picked.spread,
+        isMember: tight.some((row) => euclidean(row, picked.descriptor) === 0),
+      };
+    });
+
+    expect(result.isMember).toBe(true);
+    expect(result.spread).toBeGreaterThan(0);
+    // The outlier must not be chosen.
+    expect(result.descriptor).not.toEqual([0.4, 0.4, 0.4]);
+  });
+
+  test('zip export produces a readable archive header', async ({ page }) => {
+    await page.goto('/realtime.html');
+
+    const header = await page.evaluate(async () => {
+      const { buildZip } = await import('/lab-js/realtime-gallery.js');
+      const blob = buildZip([
+        { name: 'a.jpg', bytes: new Uint8Array([1, 2, 3]), date: new Date(2026, 0, 1) },
+      ]);
+      const bytes = new Uint8Array(await blob.arrayBuffer());
+      return { signature: Array.from(bytes.slice(0, 4)), type: blob.type };
+    });
+
+    expect(header.signature).toEqual([0x50, 0x4b, 0x03, 0x04]);
+    expect(header.type).toBe('application/zip');
+  });
+
+  test('duration formatting collapses long absences readably', async ({ page }) => {
+    await page.goto('/realtime.html');
+
+    const labels = await page.evaluate(async () => {
+      const { formatDuration } = await import('/lab-js/realtime-graph.js');
+      return [formatDuration(4000), formatDuration(492000), formatDuration(7500000)];
+    });
+
+    expect(labels).toEqual(['4s', '8m 12s', '2h 5m']);
+  });
+});

From 66f5cddf8357e2f55220b4e9ef7f32c0d6c5e92a Mon Sep 17 00:00:00 2001
From: vecna <vecna@globaleaks.org>
Date: Sun, 11 Oct 2026 09:27:16 +0200
Subject: [PATCH 3/9] new large video-loader redesign

---
 .gitignore                                |   11 +-
 lab-js/FOLDER-DESCRIPTION.md              |    2 +-
 lab-js/loader.js                          | 1056 +++++++++++----------
 lab-js/video-loader/FOLDER-DESCRIPTION.md |   22 +
 lab-js/video-loader/analysis.js           |  253 +++++
 lab-js/video-loader/controller.js         |  871 +++++++++++++++++
 lab-js/video-loader/session.js            |  411 ++++++++
 loader.html                               |  178 ++--
 package.json                              |    1 +
 scripts-dev/FOLDER-DESCRIPTION.md         |    2 +-
 scripts-dev/README.md                     |   37 +-
 scripts-dev/build-face-fixtures.cjs       |  254 -----
 scripts-dev/capture-doc-screenshots.cjs   |   20 +-
 scripts-dev/video-loader.cjs              |  532 +++++++++++
 styles/loader.css                         |  459 +++++----
 tests/e2e/loader.spec.js                  |  310 +++---
 tests/fixtures/FOLDER-DESCRIPTION.md      |    5 +
 tests/unit/loader.test.js                 |  383 +++-----
 tests/unit/video-loader-analysis.test.js  |   73 ++
 tests/unit/video-loader-session.test.js   |  151 +++
 tutorials/tutorials.json                  |    3 +
 tutorials/video-loader.md                 |  519 ++++++++++
 22 files changed, 4037 insertions(+), 1516 deletions(-)
 create mode 100644 lab-js/video-loader/FOLDER-DESCRIPTION.md
 create mode 100644 lab-js/video-loader/analysis.js
 create mode 100644 lab-js/video-loader/controller.js
 create mode 100644 lab-js/video-loader/session.js
 delete mode 100755 scripts-dev/build-face-fixtures.cjs
 create mode 100644 scripts-dev/video-loader.cjs
 create mode 100644 tests/unit/video-loader-analysis.test.js
 create mode 100644 tests/unit/video-loader-session.test.js
 create mode 100644 tutorials/video-loader.md

diff --git a/.gitignore b/.gitignore
index 8d3437b..31657c6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -23,8 +23,15 @@ EXTRACTED-text-*.md
 # Generated technical reference
 /docs/jsdoc/
 
-# Fake-webcam fixtures and measurements built by scripts-dev/build-face-fixtures.cjs
-/tests/fixtures/synthetic-faces/y4m/
+# Local long-form research videos. Small committed fixtures remain available to CI.
+/tests/fixtures/screencast-with-overlay.mp4
+/tests/fixtures/*[[]hpAi9PM0uAM[]].webm
+/tests/fixtures/*[[]SJWOErYvxVI[]].webm
+/tests/fixtures/*[[]PD-Cc5XpY5w[]].webm
+/tests/fixtures/*[[]suny-SfWufU[]].webm
+/tests/fixtures/*[[]HilDlfMxQKQ[]].mkv
+
+# Local measurements produced while calibrating model thresholds.
 /tests/fixtures/synthetic-faces/lab-measurements.json
 
 # scripts-dev/lab-capture.cjs render writes candidate pictures here. They are
diff --git a/lab-js/FOLDER-DESCRIPTION.md b/lab-js/FOLDER-DESCRIPTION.md
index 86dcc26..5dbaecc 100644
--- a/lab-js/FOLDER-DESCRIPTION.md
+++ b/lab-js/FOLDER-DESCRIPTION.md
@@ -10,6 +10,6 @@ Face analysis is split across several modules. `engine.js` owns the primary face
 
 Ghostyle execution is handled by `ghostyles-manager.js` for 2D plugin loading and by `plugins3d-loader.js` plus `ghostyle3d-uv-renderer.js` for UV/3D rendering. `utils.js` supplies geometry, canvas, logging, and public helper functions exposed to plugin authors through `window.gstmxx`. `Ghostmaxxing.d.ts` documents that public API for editor support.
 
-Standalone page scripts include `loader.js` for repeatable MP4/video fixture analysis, `mobile-ui.js` for mobile drawer/fullscreen controls, `lab-ui.js` for shared lab-page UI wiring, `export-makeup.js` for PNG clipboard/share export, and `upload-consent.js` for the local clip consent and receipt flow.
+Standalone page scripts include `loader.js` for the seekable local-video report UI, with its analysis core in `video-loader/`; `mobile-ui.js` for mobile drawer/fullscreen controls; `lab-ui.js` for shared lab-page UI wiring; `export-makeup.js` for PNG clipboard/share export; and `upload-consent.js` for the local clip consent and receipt flow.
 
 `lab-js/i18n.js` is intentionally excluded from code2prompt output. It is the runtime localization catalog containing the English baseline plus Italian and Portuguese translations. The project has localization support via `initI18n()`, `applyI18n()`, and `setupLocaleSelect()`, with the selected locale stored in `localStorage` under `ghostmaxxing-locale`; the message catalog itself is omitted from prompt uploads by policy.
diff --git a/lab-js/loader.js b/lab-js/loader.js
index 346ca77..c7e2532 100644
--- a/lab-js/loader.js
+++ b/lab-js/loader.js
@@ -1,630 +1,646 @@
-import { state } from './state.js';
-import { loadDb, loadDb3d, renderDbStats } from './db.js';
-import { detectFaceInCam, saveFace, seekFaceInDb } from './engine.js';
-import { cosineSimilarity, getFaceEmbedding, loadMobileNet, saveFace3d } from './engine-3d.js';
-import { MODEL_URLS, DETECTOR_OPTIONS } from './config.js';
-import { els, setStatus, clearOverlay } from './dom.js';
-import { resizeCanvas } from './camera.js';
-import { distance, setLog } from './utils.js';
-import { applyI18n, initI18n, setupLocaleSelect, t } from './i18n.js';
-import { captureThumbnail, saveThumbnail } from './face-thumbnails.js';
-
-const fileInput = document.getElementById('videoFile');
-const seekInput = document.getElementById('videoSeek');
-const currentTimeLabel = document.getElementById('currentTimeLabel');
-const durationLabel = document.getElementById('durationLabel');
-const recordFaceBtn = document.getElementById('recordFaceBtn');
-const seekFaceBtn = document.getElementById('seekFaceBtn');
-const loaderLog = document.getElementById('loaderLog');
-
-let objectUrl = null;
-let entryCounter = 0;
-let modelsReady = false;
-let actionInFlight = false;
-let currentStatus = { kind: 'init', key: 'loader_loading_models_status' };
-
 /**
- * Runtime bridge used by diagnostic overlays, tests, and loader-adjacent tools.
- *
- * @type {object}
+ * @module loader
+ * @description
+ * User-interface adapter for the seekable Video Loader. This page analyses
+ * existing local videos only; live camera assistance belongs to realtime.html.
+ * The model pipeline and the serialisable session live in dedicated modules so
+ * Playwright can drive exactly the same behaviour as a person using the page.
  */
-window.gstmxx = {
-   /**
-    * Writes a message into the shared loader log.
-    *
-    * @param {string} message Message to display.
-    * @param {string} sourcePlugin Source label for the log entry.
-    * @returns {void}
-    */
-   log: (message, sourcePlugin) => setLog(message, sourcePlugin),
-   events: state.gstmxxEvents,
-   /**
-    * Returns a defensive copy of the 2D face archive.
-    *
-    * @returns {object} Cloned 2D face database state.
-    */
-   getDb: () => structuredClone(state.db),
-   /**
-    * Returns a defensive copy of the 3D face archive.
-    *
-    * @returns {object} Cloned 3D face database state.
-    */
-   getDb3d: () => structuredClone(state.db3d),
-   /**
-    * Returns the active 2D effect tracked by shared state.
-    *
-    * @returns {*} Active effect metadata, or null when no effect is active.
-    */
-   getActiveEffect: () => state.activeEffect,
-   /**
-    * Returns the last known 2D detection result.
-    *
-    * @returns {*} Last face-api detection result, or null when unavailable.
-    */
-   getLastResult: () => state.lastKnownEffectResult,
-   /**
-    * Returns the active 2D match threshold.
-    *
-    * @returns {number} 2D descriptor distance threshold.
-    */
-   getMatchThreshold: () => state.MATCH_THRESHOLD,
-   /**
-    * Returns the active 3D match threshold.
-    *
-    * @returns {number} 3D embedding similarity threshold.
-    */
-   getMatchThreshold3d: () => state.MATCH_THRESHOLD_3D,
-   /**
-    * Reports that the standalone loader has no active 3D Ghostyle effect.
-    *
-    * @returns {null} Always null in the MP4 loader.
-    */
-   getActiveEffect3d: () => null,
-   detectorOptions: DETECTOR_OPTIONS,
-   get lastLandmarks3d() { return state.lastLandmarks3d; },
-   set lastLandmarks3d(v) { state.lastLandmarks3d = v; },
+
+import { applyI18n, initI18n, setupLocaleSelect } from './i18n.js';
+import { createVideoAnalysisController } from './video-loader/controller.js';
+import {
+   applyEditorialCommand,
+   redoEditorialCommand,
+   undoEditorialCommand,
+} from './video-loader/session.js';
+
+const elements = {
+   file: document.getElementById('videoFile'),
+   video: document.getElementById('video'),
+   placeholder: document.getElementById('placeholder'),
+   seek: document.getElementById('videoSeek'),
+   currentTime: document.getElementById('currentTimeLabel'),
+   duration: document.getElementById('durationLabel'),
+   statusDot: document.getElementById('statusDot'),
+   statusText: document.getElementById('statusText'),
+   start: document.getElementById('startAnalysisBtn'),
+   cancel: document.getElementById('cancelAnalysisBtn'),
+   sampleInterval: document.getElementById('sampleInterval'),
+   baselineTime: document.getElementById('baselineTime'),
+   useCurrentTime: document.getElementById('useCurrentTimeBtn'),
+   progress: document.getElementById('analysisProgress'),
+   progressLabel: document.getElementById('analysisProgressLabel'),
+   baselineSummary: document.getElementById('baselineSummary'),
+   timeline: document.getElementById('analysisTimeline'),
+   eventList: document.getElementById('eventList'),
+   emptyReport: document.getElementById('emptyReport'),
+   reportSummary: document.getElementById('reportSummary'),
+   downloadReport: document.getElementById('downloadReportBtn'),
+   downloadLog: document.getElementById('downloadLogBtn'),
+   undo: document.getElementById('undoEditBtn'),
+   redo: document.getElementById('redoEditBtn'),
 };
 
-/**
- * Updates the loader status label from a translation key and remembers the
- * active status so it can be refreshed after a locale change.
- *
- * @param {string} kind Status class hint passed to the shared status widget.
- * @param {string} key Translation key for the status text.
- * @returns {void}
- */
-function setLoaderStatus(kind, key) {
-   currentStatus = { kind, key };
-   setStatus(kind, t(key));
-}
+const controller = createVideoAnalysisController(elements.video);
+let pendingSourceMetadata = {};
+let sourceLoaded = false;
+let analysisRunning = false;
 
 /**
- * Formats a floating-point video timestamp as `mm:ss.mmm`.
+ * Formats a media timestamp as `mm:ss.mmm` or `hh:mm:ss.mmm`.
  *
- * @param {number} seconds Video time in seconds.
- * @returns {string} Zero-padded display time.
+ * @param {number} seconds Media timestamp in seconds.
+ * @returns {string} Human-readable timestamp.
  */
-function formatVideoTime(seconds) {
+function formatMediaTime(seconds) {
    if (!Number.isFinite(seconds)) return '00:00.000';
-   const minutes = Math.floor(seconds / 60);
-   const rest = seconds - minutes * 60;
-   return `${String(minutes).padStart(2, '0')}:${rest.toFixed(3).padStart(6, '0')}`;
+   const hours = Math.floor(seconds / 3600);
+   const minutes = Math.floor((seconds % 3600) / 60);
+   const rest = seconds % 60;
+   const prefix = hours ? `${String(hours).padStart(2, '0')}:` : '';
+   return `${prefix}${String(minutes).padStart(2, '0')}:${rest.toFixed(3).padStart(6, '0')}`;
 }
 
 /**
- * Reads the current video playhead, duration, percentage, and display label.
+ * Updates the visible application status.
  *
- * @returns {{current: number, duration: number, percent: number, label: string}} Current timeline position.
+ * @param {'init'|'ready'|'working'|'error'} kind Status kind.
+ * @param {string} message Human-readable status.
+ * @returns {void}
  */
-function currentPosition() {
-   const current = els.video.currentTime || 0;
-   const duration = Number.isFinite(els.video.duration) ? els.video.duration : 0;
-   const percent = duration > 0 ? (current / duration) * 100 : 0;
-   return {
-      current,
-      duration,
-      percent,
-      label: `${formatVideoTime(current)} / ${formatVideoTime(duration)} (${percent.toFixed(2)}%)`,
-   };
+function setStatus(kind, message) {
+   elements.statusDot.className = 'status-dot';
+   if (kind === 'ready') elements.statusDot.classList.add('live');
+   if (kind === 'working') elements.statusDot.classList.add('working');
+   if (kind === 'error') elements.statusDot.classList.add('error');
+   elements.statusText.textContent = message;
 }
 
 /**
- * Enables or disables face actions based on model, video, and lock state.
+ * Dispatches a stable browser event consumed by Playwright and optional tools.
  *
- * @param {boolean} enabled Whether the caller wants actions to be available.
+ * @param {string} type Event suffix.
+ * @param {object} [detail={}] Serializable detail.
  * @returns {void}
  */
-function setActionButtonsEnabled(enabled) {
-   const ready = enabled && modelsReady && els.video.readyState >= 2 && !actionInFlight;
-   recordFaceBtn.disabled = !ready;
-   seekFaceBtn.disabled = !ready;
+function dispatchLoaderEvent(type, detail = {}) {
+   window.dispatchEvent(new CustomEvent(`gstmxx-video-loader:${type}`, { detail }));
 }
 
 /**
- * Synchronizes timeline labels and the seek input with the video element.
+ * Starts a browser download without coupling the report and technical log.
  *
+ * @param {string} filename Suggested filename.
+ * @param {string} contents Text contents.
+ * @param {string} mimeType MIME type.
  * @returns {void}
  */
-function updateTimelineFromVideo() {
-   const position = currentPosition();
-   currentTimeLabel.textContent = formatVideoTime(position.current);
-   durationLabel.textContent = formatVideoTime(position.duration);
-   if (!seekInput.matches(':active')) seekInput.value = String(position.current);
+function downloadText(filename, contents, mimeType) {
+   const url = URL.createObjectURL(new Blob([contents], { type: mimeType }));
+   const anchor = document.createElement('a');
+   anchor.href = url;
+   anchor.download = filename;
+   anchor.click();
+   window.setTimeout(() => URL.revokeObjectURL(url), 0);
 }
 
 /**
- * Resizes all diagnostic overlay canvases to match the active video surface.
+ * Downloads a selected evidence frame from its data URL.
  *
+ * @param {string} filename Suggested filename.
+ * @param {string} dataUrl JPEG data URL.
  * @returns {void}
  */
-function syncCanvasSize() {
-   resizeCanvas();
-   for (const canvas of [document.getElementById('mesh3dOverlay'), document.getElementById('bboxOverlay')]) {
-      if (!canvas) continue;
-      canvas.width = els.overlay.width;
-      canvas.height = els.overlay.height;
-   }
+function downloadFrame(filename, dataUrl) {
+   const anchor = document.createElement('a');
+   anchor.href = dataUrl;
+   anchor.download = filename;
+   anchor.click();
 }
 
 /**
- * Captures the current video frame plus diagnostic overlays into a canvas.
+ * Synchronises video labels and the manual seek control.
  *
- * @returns {HTMLCanvasElement} Snapshot canvas used in activity-log entries.
+ * @returns {void}
  */
-function snapshotCanvas() {
-   const canvas = document.createElement('canvas');
-   const width = els.video.videoWidth || els.overlay.width || 1280;
-   const height = els.video.videoHeight || els.overlay.height || 720;
-   canvas.width = width;
-   canvas.height = height;
-   const ctx = canvas.getContext('2d');
-   ctx.fillStyle = '#050505';
-   ctx.fillRect(0, 0, width, height);
-   try {
-      ctx.drawImage(els.video, 0, 0, width, height);
-      if (els.overlay.width && els.overlay.height) ctx.drawImage(els.overlay, 0, 0, width, height);
-      const bbox = document.getElementById('bboxOverlay');
-      if (bbox?.width && bbox?.height) ctx.drawImage(bbox, 0, 0, width, height);
-   } catch (err) {
-      ctx.fillStyle = '#ffe7a8';
-      ctx.font = '700 24px system-ui, sans-serif';
-      ctx.fillText(err.message || String(err), 24, 48);
-   }
-   return canvas;
+function syncVideoTimeline() {
+   const duration = Number.isFinite(elements.video.duration) ? elements.video.duration : 0;
+   elements.currentTime.textContent = formatMediaTime(elements.video.currentTime);
+   elements.duration.textContent = formatMediaTime(duration);
+   elements.seek.max = String(duration);
+   if (!elements.seek.matches(':active')) elements.seek.value = String(elements.video.currentTime || 0);
 }
 
 /**
- * Captures and persists the same saved-face preview used by lab.html's history
- * drawer. The loader already has the successful face-api result, so reuse its
- * box instead of running a second detection pass.
+ * Draws three separate evidence lanes: MediaPipe presence, ImageEmbedder drift,
+ * and sparse face-api distances. Values from different models are never
+ * merged into one scale.
  *
- * @param {number} id Saved face id shared by the 2D and 3D stores.
- * @param {*} result face-api detection result returned by saveFace().
- * @returns {Promise<boolean>} Whether a thumbnail was saved.
+ * @param {object|null} session Current analysis session.
+ * @returns {void}
  */
-async function saveFacePreview(id, result) {
-   const box = result?.detection?.box;
-   if (!box) return false;
+function drawTimeline(session) {
+   const canvas = elements.timeline;
+   const context = canvas.getContext('2d');
+   if (!context || typeof context.setTransform !== 'function') return;
+   const cssWidth = Math.max(320, canvas.clientWidth || 960);
+   const cssHeight = 210;
+   const ratio = window.devicePixelRatio || 1;
+   canvas.width = Math.round(cssWidth * ratio);
+   canvas.height = Math.round(cssHeight * ratio);
+   context.setTransform(ratio, 0, 0, ratio, 0, 0);
+   context.clearRect(0, 0, cssWidth, cssHeight);
+   context.fillStyle = '#17130f';
+   context.fillRect(0, 0, cssWidth, cssHeight);
+
+   const left = 112;
+   const right = 14;
+   const plotWidth = cssWidth - left - right;
+   const duration = session?.source?.duration || 1;
+   const x = (mediaTime) => left + (mediaTime / duration) * plotWidth;
+   const lanes = {
+      presence: { top: 18, height: 34, label: 'MediaPipe face' },
+      drift: { top: 74, height: 50, label: 'Visual drift' },
+      face: { top: 146, height: 48, label: 'face-api L2' },
+   };
 
-   try {
-      const dataUrl = await captureThumbnail(els.video, box);
-      if (!dataUrl) return false;
-      saveThumbnail(id, dataUrl);
-      return true;
-   } catch (err) {
-      setLog(t('thumbnail_capture_failed_log', { message: err.message || String(err) }), 'thumbnails');
-      return false;
+   context.font = '700 11px ui-monospace, monospace';
+   context.textBaseline = 'middle';
+   for (const lane of Object.values(lanes)) {
+      context.fillStyle = '#b8aa96';
+      context.fillText(lane.label, 10, lane.top + lane.height / 2);
+      context.strokeStyle = 'rgba(255, 231, 168, 0.12)';
+      context.strokeRect(left, lane.top, plotWidth, lane.height);
+   }
+   if (!session?.samples?.length) return;
+   const timelineSamples = session.samples
+      .filter((sample) => sample.phase !== 'baseline')
+      .sort((leftSample, rightSample) => leftSample.mediaTime - rightSample.mediaTime);
+   if (!timelineSamples.length) return;
+
+   const threshold = session.baseline?.visualTriggerThreshold || 0.1;
+   const driftMax = Math.max(0.1, threshold * 3, ...timelineSamples.map((sample) => sample.visualDrift || 0));
+   context.lineWidth = 2;
+   context.strokeStyle = '#7fe3b0';
+   context.beginPath();
+   timelineSamples.forEach((sample, index) => {
+      const px = x(sample.mediaTime);
+      const py = lanes.presence.top + (sample.facePresent ? 7 : lanes.presence.height - 7);
+      if (index === 0) context.moveTo(px, py);
+      else context.lineTo(px, py);
+   });
+   context.stroke();
+
+   context.strokeStyle = '#f3c747';
+   context.beginPath();
+   let started = false;
+   for (const sample of timelineSamples) {
+      if (sample.visualDrift === null) {
+         started = false;
+         continue;
+      }
+      const px = x(sample.mediaTime);
+      const py = lanes.drift.top + lanes.drift.height - (sample.visualDrift / driftMax) * lanes.drift.height;
+      if (!started) context.moveTo(px, py);
+      else context.lineTo(px, py);
+      started = true;
+   }
+   context.stroke();
+
+   if (session.baseline) {
+      const thresholdY = lanes.drift.top + lanes.drift.height
+         - (session.baseline.visualTriggerThreshold / driftMax) * lanes.drift.height;
+      context.strokeStyle = 'rgba(243, 199, 71, 0.65)';
+      context.setLineDash([4, 4]);
+      context.beginPath();
+      context.moveTo(left, thresholdY);
+      context.lineTo(left + plotWidth, thresholdY);
+      context.stroke();
+      context.setLineDash([]);
    }
-}
 
-/**
- * Appends a compact metadata chip to an activity-log container.
- *
- * @param {HTMLElement} parent Element that receives the chip.
- * @param {string} label Chip label.
- * @param {*} value Chip value rendered as text.
- * @returns {void}
- */
-function appendChip(parent, label, value) {
-   const chip = document.createElement('span');
-   chip.className = 'loader-chip';
-   chip.textContent = `${label}: ${value}`;
-   parent.appendChild(chip);
+   const faceApiEntries = session.journal.filter((entry) => entry.type === 'face-api-result');
+   context.fillStyle = '#ff8e72';
+   for (const entry of faceApiEntries) {
+      const value = entry.faceDistance === null ? 0.8 : Math.min(1, entry.faceDistance);
+      const px = x(entry.mediaTime);
+      const py = lanes.face.top + lanes.face.height - value * lanes.face.height;
+      context.beginPath();
+      context.arc(px, py, 3.5, 0, Math.PI * 2);
+      context.fill();
+   }
+   const faceThresholdY = lanes.face.top + lanes.face.height - 0.6 * lanes.face.height;
+   context.strokeStyle = 'rgba(255, 142, 114, 0.7)';
+   context.setLineDash([4, 4]);
+   context.beginPath();
+   context.moveTo(left, faceThresholdY);
+   context.lineTo(left + plotWidth, faceThresholdY);
+   context.stroke();
+   context.setLineDash([]);
 }
 
 /**
- * Prepends a diagnostic activity entry with snapshot, summary chips, and JSON details.
+ * Builds one editable evidence card.
  *
- * @param {string} kind Human-readable action name.
- * @param {HTMLCanvasElement} snapshot Canvas snapshot for the entry.
- * @param {{position: object, metrics: Object.<string, *>}} summary Position and headline metrics.
- * @param {*} details Structured details rendered in the entry body.
- * @returns {void}
+ * @param {object} event Session evidence event.
+ * @param {object} session Current session.
+ * @returns {HTMLElement} Event card.
  */
-function appendEntry(kind, snapshot, summary, details) {
-   entryCounter += 1;
-   const entry = document.createElement('article');
-   entry.className = 'loader-entry';
-
-   entry.appendChild(snapshot);
+function createEventCard(event, session) {
+   const card = document.createElement('article');
+   card.className = `loader-event loader-event--${event.status}`;
+   card.dataset.eventId = event.id;
+
+   if (event.selectedFrame) {
+      const image = document.createElement('img');
+      image.src = event.selectedFrame.dataUrl;
+      image.alt = `Selected video frame at ${formatMediaTime(event.selectedFrame.mediaTime)}`;
+      card.appendChild(image);
+   }
 
    const body = document.createElement('div');
-   body.className = 'loader-entry__body';
-
+   body.className = 'loader-event__body';
    const title = document.createElement('h3');
-   title.textContent = `#${entryCounter} ${kind}`;
+   const eventLabel = event.classification === 'identity-distance'
+      ? 'Identity-distance transition'
+      : event.classification === 'detector-divergence'
+         ? 'Detector divergence — review'
+         : event.status === 'confirmed' ? 'Mixed transition — review' : 'Candidate';
+   title.textContent = `${eventLabel} · ${formatMediaTime(event.anchorMediaTime)}`;
    body.appendChild(title);
 
-   const meta = document.createElement('div');
-   meta.className = 'loader-entry__meta';
-   appendChip(meta, t('loader_chip_pressed'), new Date().toLocaleString());
-   appendChip(meta, t('loader_chip_video'), summary.position.label);
-   body.appendChild(meta);
-
-   const metrics = document.createElement('div');
-   metrics.className = 'loader-entry__metrics';
-   for (const [label, value] of Object.entries(summary.metrics)) appendChip(metrics, label, value);
+   const best = event.observations.at(-1);
+   const metrics = document.createElement('p');
+   metrics.className = 'loader-event__metrics';
+   metrics.textContent = [
+      best?.faceDistance === null ? 'face-api: no face' : `face-api: ${best?.faceDistance?.toFixed(4) ?? 'n/a'}`,
+      best?.visualDrift === null ? 'visual drift: n/a' : `visual drift: ${best.visualDrift.toFixed(4)}`,
+   ].join(' · ');
    body.appendChild(metrics);
 
-   const pre = document.createElement('pre');
-   pre.textContent = JSON.stringify(details, null, 2);
-   body.appendChild(pre);
-
-   entry.appendChild(body);
-   loaderLog.prepend(entry);
+   const reasons = document.createElement('p');
+   reasons.className = 'loader-event__reasons';
+   reasons.textContent = `Trigger: ${(best?.reasons || []).join(', ') || 'verification'}`;
+   body.appendChild(reasons);
+
+   const caption = document.createElement('textarea');
+   caption.value = event.caption;
+   caption.placeholder = 'Explain why this before/after moment matters…';
+   caption.setAttribute('aria-label', 'Event caption');
+   caption.addEventListener('change', () => {
+      applyEditorialCommand(session, { type: 'caption', eventId: event.id, value: caption.value });
+      renderSession(session);
+   });
+   body.appendChild(caption);
+
+   const actions = document.createElement('div');
+   actions.className = 'loader-event__actions';
+   appendStoryOrderButtons(actions, event.id, session);
+   const promote = document.createElement('button');
+   promote.type = 'button';
+   promote.textContent = event.promoted ? 'Selected for story' : 'Select for story';
+   promote.disabled = event.promoted;
+   promote.addEventListener('click', () => {
+      applyEditorialCommand(session, { type: 'promote', eventId: event.id });
+      renderSession(session);
+   });
+   actions.appendChild(promote);
+
+   const reject = document.createElement('button');
+   reject.type = 'button';
+   reject.textContent = 'Reject';
+   reject.addEventListener('click', () => {
+      applyEditorialCommand(session, { type: 'reject', eventId: event.id });
+      renderSession(session);
+   });
+   actions.appendChild(reject);
+
+   if (event.selectedFrame) {
+      const save = document.createElement('button');
+      save.type = 'button';
+      save.textContent = 'Download frame';
+      save.addEventListener('click', () => downloadFrame(
+         `ghostmaxxing-${formatMediaTime(event.selectedFrame.mediaTime).replaceAll(':', '-')}.jpg`,
+         event.selectedFrame.dataUrl,
+      ));
+      actions.appendChild(save);
+   }
+   body.appendChild(actions);
+   card.appendChild(body);
+   return card;
 }
 
 /**
- * Extracts stable 2D and 3D face-detection details for logging.
+ * Returns report items in human-controlled story order. Source timestamps are
+ * never changed by an editorial move.
  *
- * @param {*} result face-api detection result.
- * @returns {object} Serializable face summary.
+ * @param {object} session Current session.
+ * @returns {Array<{id: string, kind: 'baseline'|'event', value: object}>} Ordered items.
  */
-function summarizeFace(result) {
-   const landmarks = result?.landmarks;
-   const box = result?.detection?.box;
-   return {
-      detectionScore: result?.detection?.score ?? null,
-      descriptorLength: result?.descriptor?.length ?? 0,
-      landmarks2d: landmarks?.positions?.length ?? landmarks?.getPositions?.()?.length ?? 0,
-      age: Number.isFinite(result?.age) ? Math.round(result.age) : null,
-      gender: result?.gender || null,
-      genderProbability: Number.isFinite(result?.genderProbability) ? result.genderProbability : null,
-      box: box ? {
-         x: Math.round(box.x),
-         y: Math.round(box.y),
-         width: Math.round(box.width),
-         height: Math.round(box.height),
-      } : null,
-      landmarks3d: Array.isArray(state.lastLandmarks3d) ? state.lastLandmarks3d.length : 0,
-   };
+function orderedStoryItems(session) {
+   if (!session) return [];
+   const events = new Map(session.events
+      .filter((event) => event.status === 'confirmed')
+      .map((event) => [event.id, event]));
+   const ordered = [];
+   for (const id of session.editorial.storyOrder) {
+      if (id === 'baseline' && session.baseline) {
+         ordered.push({ id, kind: 'baseline', value: session.baseline });
+      } else if (events.has(id)) {
+         ordered.push({ id, kind: 'event', value: events.get(id) });
+         events.delete(id);
+      }
+   }
+   for (const event of events.values()) ordered.push({ id: event.id, kind: 'event', value: event });
+   return ordered;
 }
 
 /**
- * Computes sorted descriptor distances between a live 2D result and saved faces.
+ * Adds reversible story-order controls to one evidence card.
  *
- * @param {*} result face-api detection result containing a descriptor.
- * @returns {Array<object>} Distances sorted from nearest to farthest.
+ * @param {HTMLElement} actions Action container.
+ * @param {string} itemId Event identifier or the reserved baseline identifier.
+ * @param {object} session Current session.
+ * @returns {void}
  */
-function get2dDistances(result) {
-   const descriptor = result?.descriptor;
-   return (state.db?.faces || []).map((face) => {
-      const faceDistance = distance(descriptor, face.descriptor);
-      return {
-         id: face.id,
-         distance: faceDistance,
-         threshold: state.MATCH_THRESHOLD,
-         match: faceDistance <= state.MATCH_THRESHOLD,
-      };
-   }).sort((a, b) => a.distance - b.distance);
+function appendStoryOrderButtons(actions, itemId, session) {
+   const order = orderedStoryItems(session).map((item) => item.id);
+   const index = order.indexOf(itemId);
+   const earlier = document.createElement('button');
+   earlier.type = 'button';
+   earlier.textContent = 'Move earlier';
+   earlier.disabled = index <= 0;
+   earlier.addEventListener('click', () => {
+      applyEditorialCommand(session, { type: 'move-earlier', eventId: itemId });
+      renderSession(session);
+   });
+   actions.appendChild(earlier);
+
+   const later = document.createElement('button');
+   later.type = 'button';
+   later.textContent = 'Move later';
+   later.disabled = index < 0 || index >= order.length - 1;
+   later.addEventListener('click', () => {
+      applyEditorialCommand(session, { type: 'move-later', eventId: itemId });
+      renderSession(session);
+   });
+   actions.appendChild(later);
 }
 
 /**
- * Computes sorted cosine-similarity matches for a live 3D embedding.
+ * Builds the computational reference as a reorderable story item. Its source
+ * timestamp and role in the distance calculation remain unchanged.
  *
- * @param {Float32Array|number[]|null} embedding Live MediaPipe embedding.
- * @returns {Array<object>} Matches sorted from highest to lowest similarity.
+ * @param {object} baseline Session baseline.
+ * @param {object} session Current session.
+ * @returns {HTMLElement} Reference card.
  */
-function get3dDistances(embedding) {
-   if (!embedding) return [];
-   return (state.db3d?.faces || []).map((face) => {
-      const similarity = cosineSimilarity(embedding, face.descriptor3d);
-      return {
-         id: face.id,
-         similarity,
-         distance: 1 - similarity,
-         threshold: state.MATCH_THRESHOLD_3D,
-         match: similarity >= state.MATCH_THRESHOLD_3D,
-      };
-   }).sort((a, b) => b.similarity - a.similarity);
-}
+function createBaselineCard(baseline, session) {
+   const card = document.createElement('article');
+   card.className = 'loader-event loader-event--reference';
+   card.dataset.eventId = 'baseline';
+
+   if (baseline.frame) {
+      const image = document.createElement('img');
+      image.src = baseline.frame.dataUrl;
+      image.alt = `Computational reference frame at ${formatMediaTime(baseline.mediaTime)}`;
+      card.appendChild(image);
+   }
 
-/**
- * Collapses 2D and 3D detection states into the shared overall match state.
- *
- * @param {?object} faceapiSection 2D match section.
- * @param {?object} mediapipeSection 3D match section.
- * @returns {string} Overall state used by the readout event bus.
- */
-function buildOverall(faceapiSection, mediapipeSection) {
-   const f = faceapiSection?.detectionState;
-   const m = mediapipeSection?.detectionState;
-   if (!f || !m) return 'unknown';
-   return f === m ? f : 'partial-elusion';
+   const body = document.createElement('div');
+   body.className = 'loader-event__body';
+   const title = document.createElement('h3');
+   title.textContent = `Computational reference · ${formatMediaTime(baseline.mediaTime)}`;
+   body.appendChild(title);
+
+   const metrics = document.createElement('p');
+   metrics.className = 'loader-event__metrics';
+   metrics.textContent = `visual trigger ${baseline.visualTriggerThreshold.toFixed(4)} · face noise ${baseline.faceSpread.toFixed(4)}`;
+   body.appendChild(metrics);
+
+   const explanation = document.createElement('p');
+   explanation.className = 'loader-event__reasons';
+   explanation.textContent = 'This frame anchors the distances. Move its story position without changing its source timestamp or computational role.';
+   body.appendChild(explanation);
+
+   const actions = document.createElement('div');
+   actions.className = 'loader-event__actions';
+   appendStoryOrderButtons(actions, 'baseline', session);
+   const save = document.createElement('button');
+   save.type = 'button';
+   save.textContent = 'Download frame';
+   save.addEventListener('click', () => downloadFrame(
+      `ghostmaxxing-reference-${formatMediaTime(baseline.mediaTime).replaceAll(':', '-')}.jpg`,
+      baseline.frame.dataUrl,
+   ));
+   actions.appendChild(save);
+   body.appendChild(actions);
+   card.appendChild(body);
+   return card;
 }
 
 /**
- * Runs a face action while preventing concurrent Record/Seek operations.
+ * Re-renders report summary, timeline, and evidence cards from session state.
  *
- * @param {Function} fn Action callback to execute under the lock.
- * @returns {Promise<void>}
+ * @param {object|null} session Current analysis session.
+ * @returns {void}
  */
-async function withActionLock(fn) {
-   if (actionInFlight) return;
-   actionInFlight = true;
-   setActionButtonsEnabled(false);
-   try {
-      await fn();
-   } catch (err) {
-      setLoaderStatus('error', 'loader_status_action_failed');
-      appendEntry(t('loader_action_error'), snapshotCanvas(), {
-         position: currentPosition(),
-         metrics: { error: err.message || String(err) },
-      }, { error: err.stack || err.message || String(err) });
-   } finally {
-      actionInFlight = false;
-      setActionButtonsEnabled(true);
+function renderSession(session) {
+   drawTimeline(session);
+   elements.eventList.replaceChildren();
+   const storyItems = orderedStoryItems(session);
+   elements.emptyReport.hidden = storyItems.length > 0;
+   for (const item of storyItems) {
+      elements.eventList.appendChild(item.kind === 'baseline'
+         ? createBaselineCard(item.value, session)
+         : createEventCard(item.value, session));
    }
-}
 
-/**
- * Saves the face visible at the current video frame into the 2D and 3D archives.
- *
- * @returns {Promise<void>}
- */
-async function recordFace() {
-   await withActionLock(async () => {
-      syncCanvasSize();
-      const position = currentPosition();
-      const saved2d = await saveFace();
-      if (!saved2d) {
-         appendEntry(t('loader_action_record_face'), snapshotCanvas(), {
-            position,
-            metrics: { result: t('loader_no_face_result') },
-         }, { message: t('loader_no_2d_face_message') });
-         return;
-      }
+   if (session?.baseline) {
+      elements.baselineSummary.textContent = [
+         `Reference ${formatMediaTime(session.baseline.mediaTime)}`,
+         `visual trigger ${session.baseline.visualTriggerThreshold.toFixed(4)}`,
+         `face noise ${session.baseline.faceSpread.toFixed(4)}`,
+      ].join(' · ');
+   } else {
+      elements.baselineSummary.textContent = 'Distance reference not acquired yet.';
+   }
 
-      const previewSaved = await saveFacePreview(saved2d.id, saved2d.result);
-      const saved3d = await saveFace3d(saved2d.id);
-      const embedding = saved3d ? (state.db3d.faces.find((face) => face.id === saved2d.id)?.descriptor3d || null) : null;
-      const features = summarizeFace(saved2d.result);
-
-      const faceapiSection = {
-         detectionState: 'matched',
-         distance: 0,
-         matchedId: saved2d.id,
-         liveMinDist: 0,
-         liveMinId: saved2d.id,
-      };
-      const mediapipeSection = saved3d ? {
-         detectionState: 'matched',
-         similarity: saved3d.liveInfo3d?.liveMaxSim ?? 1,
-         matchedId: saved2d.id,
-         liveMaxSim: saved3d.liveInfo3d?.liveMaxSim ?? 1,
-         liveMaxId: saved2d.id,
-      } : null;
-
-      state.gstmxxEvents.dispatchEvent(new CustomEvent('detection', {
-         detail: { result: saved2d.result, activeEffect: null }
-      }));
-      state.gstmxxEvents.dispatchEvent(new CustomEvent('matchStateChanged', {
-         detail: {
-            source: 'save',
-            ghostylePresent: false,
-            faceapi: faceapiSection,
-            mediapipe: mediapipeSection,
-            overall: buildOverall(faceapiSection, mediapipeSection),
-         }
-      }));
-
-      appendEntry(t('loader_action_record_face'), snapshotCanvas(), {
-         position,
-         metrics: {
-            id: saved2d.id,
-            '2D score': features.detectionScore?.toFixed(4) ?? 'n/a',
-            '2D landmarks': features.landmarks2d,
-            '3D vector': embedding ? embedding.length : 'unavailable',
-            preview: previewSaved ? 'saved' : 'unavailable',
-         },
-      }, {
-         action: 'record face',
-         savedId: saved2d.id,
-         previewSaved,
-         video: position,
-         faceapi: features,
-         mediapipe: {
-            saved: !!saved3d,
-            embeddingLength: embedding ? embedding.length : 0,
-            selfSimilarity: saved3d?.liveInfo3d?.liveMaxSim ?? null,
-         },
-      });
-   });
+   if (session) {
+      elements.reportSummary.textContent = [
+         `${session.summary.sampledFrames} samples`,
+         `${session.summary.faceApiRuns} face-api checks`,
+         `${session.summary.confirmedEvents} confirmed events`,
+      ].join(' · ');
+      elements.downloadReport.disabled = session.status !== 'complete';
+      elements.downloadLog.disabled = session.samples.length === 0;
+      elements.undo.disabled = session.editorial.undo.length === 0;
+      elements.redo.disabled = session.editorial.redo.length === 0;
+   }
 }
 
 /**
- * Searches the saved 2D and 3D face archives for the face in the current frame.
+ * Updates controls after model, file, and analysis state changes.
  *
- * @returns {Promise<void>}
+ * @returns {void}
  */
-async function seekFace() {
-   await withActionLock(async () => {
-      syncCanvasSize();
-      const position = currentPosition();
-      const result = await detectFaceInCam(true);
-      if (!result) {
-         appendEntry(t('loader_action_seek_face'), snapshotCanvas(), {
-            position,
-            metrics: { result: t('loader_no_face_result') },
-         }, { message: t('loader_no_2d_face_message') });
-         return;
-      }
-
-      const liveInfo = seekFaceInDb(result);
-      const embedding = await getFaceEmbedding(els.video).catch(() => null);
-      const distances2d = get2dDistances(result);
-      const distances3d = get3dDistances(embedding);
-      const best3d = distances3d[0] || null;
-
-      const faceapiSection = {
-         detectionState: liveInfo.liveMinDist != null && liveInfo.liveMinDist <= state.MATCH_THRESHOLD ? 'matched' : 'eluded',
-         distance: liveInfo.liveMinDist,
-         matchedId: liveInfo.liveMinDist != null && liveInfo.liveMinDist <= state.MATCH_THRESHOLD ? liveInfo.liveMinId : null,
-         liveMinDist: liveInfo.liveMinDist,
-         liveMinId: liveInfo.liveMinId,
-      };
-      const mediapipeSection = best3d ? {
-         detectionState: best3d.similarity >= state.MATCH_THRESHOLD_3D ? 'matched' : 'eluded',
-         similarity: best3d.similarity,
-         matchedId: best3d.similarity >= state.MATCH_THRESHOLD_3D ? best3d.id : null,
-         liveMaxSim: best3d.similarity,
-         liveMaxId: best3d.id,
-      } : null;
-
-      state.gstmxxEvents.dispatchEvent(new CustomEvent('detection', {
-         detail: { result, activeEffect: null }
-      }));
-      state.gstmxxEvents.dispatchEvent(new CustomEvent('matchStateChanged', {
-         detail: {
-            source: 'find',
-            ghostylePresent: false,
-            faceapi: faceapiSection,
-            mediapipe: mediapipeSection,
-            overall: buildOverall(faceapiSection, mediapipeSection),
-         }
-      }));
-
-      appendEntry(t('loader_action_seek_face'), snapshotCanvas(), {
-         position,
-         metrics: {
-            '2D closest': distances2d[0] ? `ID ${distances2d[0].id} / ${distances2d[0].distance.toFixed(4)}` : 'empty DB',
-            '3D closest': best3d ? `ID ${best3d.id} / ${best3d.similarity.toFixed(4)}` : 'empty DB',
-            'faces compared': Math.max(distances2d.length, distances3d.length),
-         },
-      }, {
-         action: 'seek face',
-         video: position,
-         features: summarizeFace(result),
-         faceapi: {
-            threshold: state.MATCH_THRESHOLD,
-            nearest: liveInfo,
-            distances: distances2d,
-         },
-         mediapipe: {
-            threshold: state.MATCH_THRESHOLD_3D,
-            embeddingLength: embedding ? embedding.length : 0,
-            distances: distances3d,
-         },
-      });
-   });
+function syncControls() {
+   elements.start.disabled = !sourceLoaded || !controller.isModelsReady() || analysisRunning;
+   elements.cancel.disabled = !analysisRunning;
+   elements.file.disabled = analysisRunning;
+   elements.sampleInterval.disabled = analysisRunning;
+   elements.baselineTime.disabled = analysisRunning;
+   elements.useCurrentTime.disabled = !sourceLoaded || analysisRunning;
 }
 
 /**
- * Loads face-api model weights and the MediaPipe 3D embedder required by the loader.
+ * Loads the file currently selected in the input.
  *
- * @returns {Promise<void>}
+ * @returns {Promise<object|null>} Loaded session, or null when no file exists.
  */
-async function loadModels() {
-   setLoaderStatus('init', 'loader_status_loading_faceapi');
-   await Promise.all([
-      faceapi.nets.tinyFaceDetector.loadFromUri(MODEL_URLS.tiny),
-      faceapi.nets.faceLandmark68Net.loadFromUri(MODEL_URLS.landmarks),
-      faceapi.nets.faceRecognitionNet.loadFromUri(MODEL_URLS.recognition),
-      faceapi.nets.ageGenderNet.loadFromUri(MODEL_URLS.ageGender),
-   ]);
-
-   setLoaderStatus('init', 'loader_status_loading_3d_embedder');
-   await loadMobileNet();
+async function loadSelectedFile() {
+   const file = elements.file.files?.[0];
+   if (!file) return null;
+   setStatus('working', 'loading video');
+   const session = await controller.loadFile(file, pendingSourceMetadata);
+   pendingSourceMetadata = {};
+   sourceLoaded = true;
+   elements.placeholder.hidden = true;
+   elements.seek.disabled = false;
+   syncVideoTimeline();
+   renderSession(session);
+   setStatus('ready', 'ready to analyse');
+   syncControls();
+   dispatchLoaderEvent('source-loaded', { source: session.source });
+   return session;
 }
 
 /**
- * Wires file input, video timeline, seek, playback, and resize event handlers.
+ * Runs analysis using the currently selected sampling interval.
  *
- * @returns {void}
+ * @param {object} [options] Optional API-provided range and cadence overrides.
+ * @returns {Promise<object>} Completed session.
  */
-function bindVideo() {
-   fileInput.addEventListener('change', () => {
-      const file = fileInput.files?.[0];
-      if (!file) return;
-      if (objectUrl) URL.revokeObjectURL(objectUrl);
-      objectUrl = URL.createObjectURL(file);
-      clearOverlay();
-      els.video.srcObject = null;
-      els.video.src = objectUrl;
-      els.video.load();
-      els.placeholder.style.display = 'none';
-      setLog(t('loader_log_loaded_local_video', { name: file.name }), 'loader');
-   });
-
-   els.video.addEventListener('loadedmetadata', () => {
-      seekInput.max = String(Number.isFinite(els.video.duration) ? els.video.duration : 0);
-      seekInput.disabled = false;
-      syncCanvasSize();
-      updateTimelineFromVideo();
-      setActionButtonsEnabled(true);
-   });
-   els.video.addEventListener('loadeddata', () => {
-      syncCanvasSize();
-      setActionButtonsEnabled(true);
-   });
-   els.video.addEventListener('timeupdate', updateTimelineFromVideo);
-   els.video.addEventListener('seeked', () => {
-      syncCanvasSize();
-      updateTimelineFromVideo();
-   });
-   els.video.addEventListener('play', () => setActionButtonsEnabled(true));
-   els.video.addEventListener('pause', () => setActionButtonsEnabled(true));
+async function startAnalysis(options = {}) {
+   analysisRunning = true;
+   syncControls();
+   setStatus('working', 'analysing video');
+   elements.progress.value = 0;
+   elements.progressLabel.textContent = '0%';
+   try {
+      const session = await controller.analyze({
+         sampleIntervalSeconds: Number(options.sampleIntervalSeconds ?? elements.sampleInterval.value),
+         baselineTime: Number(options.baselineTime ?? elements.baselineTime.value),
+         start: options.start,
+         end: options.end,
+      });
+      renderSession(session);
+      setStatus(session.status === 'complete' ? 'ready' : 'init', session.status);
+      return session;
+   } catch (error) {
+      setStatus('error', error.message || String(error));
+      dispatchLoaderEvent('error', { message: error.message || String(error) });
+      throw error;
+   } finally {
+      analysisRunning = false;
+      syncControls();
+   }
+}
 
-   seekInput.addEventListener('input', () => {
-      els.video.currentTime = Number(seekInput.value) || 0;
-      updateTimelineFromVideo();
+controller.subscribe((update) => {
+   if (update.type === 'models-ready') {
+      setStatus(sourceLoaded ? 'ready' : 'init', sourceLoaded ? 'ready to analyse' : 'select a local video');
+      syncControls();
+      dispatchLoaderEvent('models-ready');
+      return;
+   }
+   if (update.type === 'sample') {
+      const percentage = Math.max(0, Math.min(100, Math.round(update.progress * 100)));
+      elements.progress.value = percentage;
+      elements.progressLabel.textContent = `${percentage}%`;
+      renderSession(update.session);
+   }
+   if (update.type === 'baseline-ready' || update.type === 'event-confirmed') renderSession(update.session);
+   dispatchLoaderEvent(update.type, {
+      progress: update.progress,
+      eventId: update.event?.id,
    });
+});
+
+elements.file.addEventListener('change', () => {
+   loadSelectedFile().catch((error) => setStatus('error', error.message || String(error)));
+});
+elements.video.addEventListener('timeupdate', syncVideoTimeline);
+elements.video.addEventListener('seeked', syncVideoTimeline);
+elements.video.addEventListener('loadedmetadata', syncVideoTimeline);
+elements.seek.addEventListener('input', () => {
+   elements.video.currentTime = Number(elements.seek.value) || 0;
+   syncVideoTimeline();
+});
+elements.useCurrentTime.addEventListener('click', () => {
+   elements.baselineTime.value = elements.video.currentTime.toFixed(3);
+});
+elements.start.addEventListener('click', () => startAnalysis().catch(() => {}));
+elements.cancel.addEventListener('click', () => controller.cancel());
+elements.undo.addEventListener('click', () => {
+   const session = controller.getSession();
+   if (session && undoEditorialCommand(session)) renderSession(session);
+});
+elements.redo.addEventListener('click', () => {
+   const session = controller.getSession();
+   if (session && redoEditorialCommand(session)) renderSession(session);
+});
+elements.downloadReport.addEventListener('click', () => {
+   const bundle = controller.exportBundle();
+   downloadText('ghostmaxxing-video-report.json', JSON.stringify(bundle.report, null, 2), 'application/json');
+});
+elements.downloadLog.addEventListener('click', () => {
+   const bundle = controller.exportBundle();
+   downloadText('ghostmaxxing-analysis.ndjson', bundle.technicalLog, 'application/x-ndjson');
+});
 
-   window.addEventListener('resize', syncCanvasSize);
-}
+/**
+ * Stable automation API shared by the UI and the Playwright CLI.
+ *
+ * @type {object}
+ */
+window.gstmxxVideoLoader = {
+   /** @returns {boolean} Whether vendored models are ready. */
+   isModelsReady: () => controller.isModelsReady(),
+   /** @returns {boolean} Whether a source has loaded metadata. */
+   isSourceLoaded: () => sourceLoaded,
+   /** @returns {boolean} Whether an analysis is in progress. */
+   isAnalysisRunning: () => analysisRunning,
+   /** @returns {object|null} Current mutable session for diagnostic automation. */
+   getSession: () => controller.getSession(),
+   /** @returns {object} Report, technical log, and selected frame artifacts. */
+   exportBundle: () => controller.exportBundle(),
+   /** @param {object} metadata Source provenance supplied before setInputFiles(). */
+   setSourceMetadata(metadata) { pendingSourceMetadata = { ...metadata }; },
+   /** @param {object} options Analysis range and cadence. */
+   analyze: (options = {}) => startAnalysis(options),
+   /** @returns {void} Requests cancellation at the next sample boundary. */
+   cancel: () => controller.cancel(),
+};
 
 /**
- * Initializes localization, local databases, video bindings, models, and actions.
+ * Initialises localisation and model loading without requesting a camera.
  *
- * @returns {Promise<void>}
+ * @returns {Promise<void>} Resolves after model loading succeeds or fails.
  */
 async function init() {
    initI18n();
-   setupLocaleSelect(document.getElementById('localeSelect'), () => {
-      applyI18n();
-      setLoaderStatus(currentStatus.kind, currentStatus.key);
-   });
+   setupLocaleSelect(document.getElementById('localeSelect'), applyI18n);
    applyI18n();
-   state.db = loadDb();
-   state.db3d = loadDb3d();
-   state.isMirrored = false;
-   renderDbStats();
-   bindVideo();
-
-   recordFaceBtn.addEventListener('click', recordFace);
-   seekFaceBtn.addEventListener('click', seekFace);
-
+   renderSession(null);
+   syncControls();
+   setStatus('init', 'loading models');
    try {
-      await loadModels();
-      modelsReady = true;
-      setLoaderStatus('live', 'loader_status_ready');
-      setLog(t('loader_ready_log'), 'loader');
-      state.gstmxxEvents.dispatchEvent(new CustomEvent('ready', { detail: {} }));
-      window.dispatchEvent(new CustomEvent('gstmxxReady'));
-      setActionButtonsEnabled(true);
-   } catch (err) {
-      setLoaderStatus('error', 'loader_status_model_load_failed');
-      setLog(t('loader_model_error_log', { message: err.message || err }), 'loader');
+      await controller.loadModels();
+   } catch (error) {
+      setStatus('error', `model loading failed: ${error.message || String(error)}`);
+      dispatchLoaderEvent('error', { message: error.message || String(error) });
    }
 }
 
diff --git a/lab-js/video-loader/FOLDER-DESCRIPTION.md b/lab-js/video-loader/FOLDER-DESCRIPTION.md
new file mode 100644
index 0000000..3849dfb
--- /dev/null
+++ b/lab-js/video-loader/FOLDER-DESCRIPTION.md
@@ -0,0 +1,22 @@
+# lab-js/video-loader
+
+This folder contains the seekable-video analysis core used by `loader.html`.
+It is intentionally independent from the live-camera state owned by
+`realtime.html` and the main lab.
+
+`controller.js` owns local video loading, deterministic seeking, MediaPipe
+FaceLandmarker and ImageEmbedder sampling, sparse face-api verification,
+baseline acquisition, and native-resolution evidence capture. It retains no
+pixel queue: only selected evidence frames survive, while neighbouring frames
+can be recovered later by seeking the original source.
+
+`analysis.js` contains pure numeric helpers and the trigger state machine. Its
+default face-api identity distance is `0.6`; ImageEmbedder drift is maintained
+as a separate generic visual signal and never presented as identity distance.
+
+`session.js` owns the versioned analysis session, append-only observations,
+review classifications, report projection, separately downloadable NDJSON
+technical log, and reversible editorial actions.
+
+The detailed product boundary, signal semantics, calibration decisions, CLI
+contract, and test policy live in `../../tutorials/video-loader.md`.
diff --git a/lab-js/video-loader/analysis.js b/lab-js/video-loader/analysis.js
new file mode 100644
index 0000000..e593019
--- /dev/null
+++ b/lab-js/video-loader/analysis.js
@@ -0,0 +1,253 @@
+/**
+ * @module video-loader/analysis
+ * @description
+ * Pure numerical helpers and trigger policy for the seekable-video analyser.
+ * The module deliberately has no DOM or model dependencies so thresholds,
+ * baseline selection, and event decisions can be tested without loading the
+ * large vendored runtimes.
+ */
+
+/** Default face-api descriptor distance used by the vendored FaceMatcher. */
+export const DEFAULT_FACE_DISTANCE_THRESHOLD = 0.6;
+
+/**
+ * Computes Euclidean distance between equal-length vectors.
+ *
+ * @param {ArrayLike<number>} left First vector.
+ * @param {ArrayLike<number>} right Second vector.
+ * @returns {number} L2 distance.
+ * @throws {Error} When vector lengths differ.
+ */
+export function euclideanDistance(left, right) {
+   if (left.length !== right.length) throw new Error('Vectors must have the same length.');
+   let sum = 0;
+   for (let index = 0; index < left.length; index += 1) {
+      const delta = left[index] - right[index];
+      sum += delta * delta;
+   }
+   return Math.sqrt(sum);
+}
+
+/**
+ * Computes cosine similarity between equal-length vectors.
+ *
+ * @param {ArrayLike<number>} left First vector.
+ * @param {ArrayLike<number>} right Second vector.
+ * @returns {number} Similarity in the range -1..1, or zero for a zero vector.
+ * @throws {Error} When vector lengths differ.
+ */
+export function cosineSimilarity(left, right) {
+   if (left.length !== right.length) throw new Error('Vectors must have the same length.');
+   let dot = 0;
+   let leftNorm = 0;
+   let rightNorm = 0;
+   for (let index = 0; index < left.length; index += 1) {
+      dot += left[index] * right[index];
+      leftNorm += left[index] * left[index];
+      rightNorm += right[index] * right[index];
+   }
+   const denominator = Math.sqrt(leftNorm) * Math.sqrt(rightNorm);
+   return denominator === 0 ? 0 : dot / denominator;
+}
+
+/**
+ * Returns the median without mutating the caller's collection.
+ *
+ * @param {number[]} values Numeric values.
+ * @returns {number} Median, or zero for an empty collection.
+ */
+export function median(values) {
+   if (!values.length) return 0;
+   const sorted = [...values].sort((left, right) => left - right);
+   const middle = Math.floor(sorted.length / 2);
+   return sorted.length % 2 ? sorted[middle] : (sorted[middle - 1] + sorted[middle]) / 2;
+}
+
+/**
+ * Computes median absolute deviation, a robust estimate of baseline noise.
+ *
+ * @param {number[]} values Numeric values.
+ * @returns {number} Median absolute deviation.
+ */
+export function medianAbsoluteDeviation(values) {
+   const centre = median(values);
+   return median(values.map((value) => Math.abs(value - centre)));
+}
+
+/**
+ * Picks the real vector with the smallest total distance to its peers.
+ *
+ * @param {Array<ArrayLike<number>>} vectors Candidate vectors.
+ * @param {function(ArrayLike<number>, ArrayLike<number>): number} metric Distance metric.
+ * @returns {{vector: number[], index: number, distances: number[]}} Medoid and distances to it.
+ * @throws {Error} When no vector is provided.
+ */
+export function vectorMedoid(vectors, metric) {
+   if (!vectors.length) throw new Error('vectorMedoid() requires at least one vector.');
+   let bestIndex = 0;
+   let bestTotal = Infinity;
+   for (let left = 0; left < vectors.length; left += 1) {
+      let total = 0;
+      for (let right = 0; right < vectors.length; right += 1) {
+         if (left !== right) total += metric(vectors[left], vectors[right]);
+      }
+      if (total < bestTotal) {
+         bestTotal = total;
+         bestIndex = left;
+      }
+   }
+   const vector = Array.from(vectors[bestIndex]);
+   return {
+      vector,
+      index: bestIndex,
+      distances: vectors.map((candidate) => metric(candidate, vector)),
+   };
+}
+
+/**
+ * Builds a robust ImageEmbedder baseline and its adaptive drift threshold.
+ *
+ * ImageEmbedder is a generic visual model, not a biometric matcher. The
+ * threshold therefore comes from the current video's stable baseline rather
+ * than a universal identity claim.
+ *
+ * @param {Array<ArrayLike<number>>} embeddings Stable aligned-face embeddings.
+ * @param {object} [options] Threshold tuning.
+ * @param {number} [options.minimumDelta] Minimum drift above baseline noise.
+ * @param {number} [options.madMultiplier] Robust-noise multiplier.
+ * @returns {{embedding: number[], medoidIndex: number, medianDrift: number, mad: number, triggerThreshold: number, drifts: number[]}} Baseline model.
+ */
+export function buildEmbeddingBaseline(embeddings, options = {}) {
+   const minimumDelta = options.minimumDelta ?? 0.025;
+   const madMultiplier = options.madMultiplier ?? 6;
+   const result = vectorMedoid(embeddings, (left, right) => 1 - cosineSimilarity(left, right));
+   const medianDrift = median(result.distances);
+   const mad = medianAbsoluteDeviation(result.distances);
+   return {
+      embedding: result.vector,
+      medoidIndex: result.index,
+      medianDrift,
+      mad,
+      triggerThreshold: medianDrift + Math.max(minimumDelta, mad * madMultiplier),
+      drifts: result.distances,
+   };
+}
+
+/**
+ * Builds the face-api descriptor baseline as a medoid and reports its spread.
+ *
+ * @param {Array<ArrayLike<number>>} descriptors Stable face-api descriptors.
+ * @returns {{descriptor: number[], medoidIndex: number, spread: number}} Baseline descriptor.
+ */
+export function buildFaceBaseline(descriptors) {
+   const result = vectorMedoid(descriptors, euclideanDistance);
+   return {
+      descriptor: result.vector,
+      medoidIndex: result.index,
+      spread: median(result.distances),
+   };
+}
+
+/**
+ * Computes ImageEmbedder visual drift from a baseline embedding.
+ *
+ * @param {ArrayLike<number>} embedding Current aligned-face embedding.
+ * @param {ArrayLike<number>} baseline Baseline aligned-face embedding.
+ * @returns {number} Cosine distance where zero means identical direction.
+ */
+export function visualDrift(embedding, baseline) {
+   return 1 - cosineSimilarity(embedding, baseline);
+}
+
+/**
+ * Creates the stateful policy that decides when expensive face-api work is
+ * justified. The policy retains numeric samples only; it never retains frames.
+ *
+ * @param {object} [options] Trigger policy overrides.
+ * @param {number} [options.heartbeatSeconds=4] Maximum gap between identity checks.
+ * @param {number} [options.minimumFaceApiGapSeconds=0.75] Cooldown between expensive checks.
+ * @param {number} [options.jumpDelta=0.035] Single-sample visual-drift jump.
+ * @param {number} [options.requiredVotes=2] Above-threshold samples required.
+ * @param {number} [options.voteWindow=3] Recent samples considered for votes.
+ * @returns {{evaluate: Function, markFaceApi: Function, reset: Function}} Trigger detector.
+ */
+export function createTriggerDetector(options = {}) {
+   const heartbeatSeconds = options.heartbeatSeconds ?? 4;
+   const minimumFaceApiGapSeconds = options.minimumFaceApiGapSeconds ?? 0.75;
+   const jumpDelta = options.jumpDelta ?? 0.035;
+   const requiredVotes = options.requiredVotes ?? 2;
+   const voteWindow = options.voteWindow ?? 3;
+
+   let previousPresence = null;
+   let previousDrift = null;
+   let lastFaceApiAt = -Infinity;
+   /** @type {boolean[]} */
+   let recentVotes = [];
+
+   return {
+      /**
+       * Evaluates one lightweight sample.
+       *
+       * @param {{mediaTime: number, facePresent: boolean, comparable: boolean, visualDrift: number|null, visualThreshold: number|null}} sample Current sample.
+       * @returns {{shouldVerify: boolean, reasons: string[], observations: string[]}} Trigger decision.
+       */
+      evaluate(sample) {
+         const reasons = [];
+         const observations = [];
+
+         if (previousPresence !== null && sample.facePresent !== previousPresence) {
+            observations.push(sample.facePresent ? 'mediapipe-face-returned' : 'mediapipe-face-lost');
+            if (sample.facePresent) reasons.push('mediapipe-face-returned');
+         }
+
+         if (sample.comparable && sample.visualDrift !== null && sample.visualThreshold !== null) {
+            const aboveThreshold = sample.visualDrift > sample.visualThreshold;
+            recentVotes.push(aboveThreshold);
+            recentVotes = recentVotes.slice(-voteWindow);
+            const votes = recentVotes.filter(Boolean).length;
+            if (recentVotes.length >= requiredVotes && votes >= requiredVotes) reasons.push('visual-drift-sustained');
+
+            if (previousDrift !== null && sample.visualDrift - previousDrift > jumpDelta) {
+               reasons.push('visual-drift-jump');
+            }
+            previousDrift = sample.visualDrift;
+         } else {
+            recentVotes = [];
+         }
+
+         if (sample.facePresent && sample.mediaTime - lastFaceApiAt >= heartbeatSeconds) {
+            reasons.push('identity-heartbeat');
+         }
+
+         previousPresence = sample.facePresent;
+         const outsideCooldown = sample.mediaTime - lastFaceApiAt >= minimumFaceApiGapSeconds;
+         return {
+            shouldVerify: sample.facePresent && reasons.length > 0 && outsideCooldown,
+            reasons: [...new Set(reasons)],
+            observations,
+         };
+      },
+
+      /**
+       * Records completion of a face-api pass.
+       *
+       * @param {number} mediaTime Video timestamp in seconds.
+       * @returns {void}
+       */
+      markFaceApi(mediaTime) {
+         lastFaceApiAt = mediaTime;
+      },
+
+      /**
+       * Clears temporal state before analysing another source.
+       *
+       * @returns {void}
+       */
+      reset() {
+         previousPresence = null;
+         previousDrift = null;
+         lastFaceApiAt = -Infinity;
+         recentVotes = [];
+      },
+   };
+}
diff --git a/lab-js/video-loader/controller.js b/lab-js/video-loader/controller.js
new file mode 100644
index 0000000..c4a328e
--- /dev/null
+++ b/lab-js/video-loader/controller.js
@@ -0,0 +1,871 @@
+/**
+ * @module video-loader/controller
+ * @description
+ * Seekable local-video analysis controller. MediaPipe FaceLandmarker supplies
+ * frequent presence and geometry, MediaPipe ImageEmbedder supplies an aligned
+ * visual-change signal, and the heavier face-api recognition path runs only
+ * for baseline capture, trigger verification, and a bounded heartbeat.
+ */
+
+import {
+   MEDIAPIPE_FACE_LANDMARKER_URL,
+   MEDIAPIPE_IMAGE_EMBEDDER_URL,
+   MEDIAPIPE_TASKS_VISION_URL,
+   MEDIAPIPE_WASM_URL,
+   MODEL_URLS,
+} from '../config.js';
+import {
+   DEFAULT_FACE_DISTANCE_THRESHOLD,
+   buildEmbeddingBaseline,
+   buildFaceBaseline,
+   createTriggerDetector,
+   euclideanDistance,
+   visualDrift,
+} from './analysis.js';
+import {
+   appendJournal,
+   appendSample,
+   buildReport,
+   createAnalysisSession,
+   recordEvidenceObservation,
+   selectEventFrame,
+   serializeTechnicalLog,
+} from './session.js';
+
+/**
+ * @typedef {object} VideoLoaderConfig
+ * @property {number} sampleIntervalSeconds Media-time interval between lightweight samples.
+ * @property {number} baselineEmbeddingSamples Stable ImageEmbedder samples required.
+ * @property {number} baselineFaceSamples Stable face-api descriptors required.
+ * @property {number} faceDistanceThreshold Face-api L2 distance threshold.
+ * @property {number} heartbeatSeconds Maximum media-time gap between face-api checks.
+ * @property {number} minimumFaceApiGapSeconds Minimum media-time gap between face-api checks.
+ * @property {number} minimumVisualDelta Minimum ImageEmbedder drift above baseline noise.
+ * @property {number} visualMadMultiplier Baseline MAD multiplier for adaptive visual drift.
+ * @property {number} visualJumpDelta One-sample visual drift jump threshold.
+ * @property {number} maximumBaselineAppearanceStep Largest accepted low-resolution crop change.
+ * @property {number} maximumBaselineRollStep Largest accepted roll change in degrees.
+ * @property {number} maximumBaselineYawStep Largest accepted approximate yaw change.
+ * @property {number} maximumBaselineScaleStep Largest accepted face-width ratio change.
+ * @property {number} maximumBaselineFaceSpread Largest accepted face-api baseline spread.
+ * @property {number} alignedCropSize ImageEmbedder crop edge in pixels.
+ * @property {number} evidenceRefinementRadiusSeconds Neighbourhood around a confirmed event.
+ * @property {number} evidenceRefinementStepSeconds MediaPipe-only refinement cadence.
+ * @property {number} evidenceRefinementFaceApiCandidates Maximum refined candidates verified by face-api.
+ */
+
+/** @type {VideoLoaderConfig} */
+export const DEFAULT_VIDEO_LOADER_CONFIG = Object.freeze({
+   sampleIntervalSeconds: 0.5,
+   baselineEmbeddingSamples: 8,
+   baselineFaceSamples: 3,
+   faceDistanceThreshold: DEFAULT_FACE_DISTANCE_THRESHOLD,
+   heartbeatSeconds: 4,
+   minimumFaceApiGapSeconds: 0.75,
+   minimumVisualDelta: 0.025,
+   visualMadMultiplier: 6,
+   visualJumpDelta: 0.035,
+   maximumBaselineAppearanceStep: 0.14,
+   maximumBaselineRollStep: 10,
+   maximumBaselineYawStep: 0.15,
+   maximumBaselineScaleStep: 0.15,
+   maximumBaselineFaceSpread: 0.3,
+   alignedCropSize: 224,
+   evidenceRefinementRadiusSeconds: 1,
+   evidenceRefinementStepSeconds: 0.2,
+   evidenceRefinementFaceApiCandidates: 3,
+});
+
+/** MediaPipe landmark indices used only to normalise the face crop. */
+const FACE_MESH = Object.freeze({
+   rightEyeOuter: 33,
+   leftEyeOuter: 263,
+   noseTip: 1,
+});
+
+/**
+ * Waits for a one-shot media event with a timeout that prevents a corrupt
+ * source from hanging the analysis forever.
+ *
+ * @param {HTMLMediaElement} media Media element.
+ * @param {string} eventName Event name.
+ * @param {number} [timeoutMs=15000] Timeout in milliseconds.
+ * @returns {Promise<void>} Resolves when the event fires.
+ */
+function waitForMediaEvent(media, eventName, timeoutMs = 15000) {
+   return new Promise((resolve, reject) => {
+      const timer = window.setTimeout(() => {
+         cleanup();
+         reject(new Error(`Timed out waiting for media event: ${eventName}`));
+      }, timeoutMs);
+      const onEvent = () => {
+         cleanup();
+         resolve();
+      };
+      const onError = () => {
+         cleanup();
+         reject(media.error || new Error(`Media error while waiting for ${eventName}.`));
+      };
+      const cleanup = () => {
+         window.clearTimeout(timer);
+         media.removeEventListener(eventName, onEvent);
+         media.removeEventListener('error', onError);
+      };
+      media.addEventListener(eventName, onEvent, { once: true });
+      media.addEventListener('error', onError, { once: true });
+   });
+}
+
+/**
+ * Seeks to a decoded video frame.
+ *
+ * @param {HTMLVideoElement} video Seekable video element.
+ * @param {number} mediaTime Target media timestamp in seconds.
+ * @returns {Promise<void>} Resolves after Chromium reports the seek complete.
+ */
+async function seekVideo(video, mediaTime) {
+   const target = Math.max(0, Math.min(mediaTime, Number.isFinite(video.duration) ? video.duration : mediaTime));
+   if (video.readyState >= 2 && Math.abs(video.currentTime - target) < 0.001) return;
+   const pending = waitForMediaEvent(video, 'seeked');
+   video.currentTime = target;
+   await pending;
+}
+
+/**
+ * Extracts a numeric float embedding from a MediaPipe ImageEmbedder result.
+ *
+ * @param {object} result MediaPipe result.
+ * @returns {number[]|null} Serializable embedding.
+ */
+function unpackEmbedding(result) {
+   const vector = result?.embeddings?.[0]?.floatEmbedding;
+   return vector ? Array.from(vector) : null;
+}
+
+/**
+ * Draws an eye-levelled face crop using MediaPipe's normalised landmarks.
+ * The crop removes most background and normalises roll and scale before the
+ * generic ImageEmbedder sees any pixels.
+ *
+ * @param {HTMLVideoElement} video Source video.
+ * @param {Array<{x: number, y: number}>} landmarks FaceLandmarker mesh.
+ * @param {HTMLCanvasElement} canvas Reusable square crop surface.
+ * @returns {{comparable: boolean, rollDegrees: number, yawOffset: number, faceWidthRatio: number}} Geometry metrics.
+ */
+function drawAlignedFaceCrop(video, landmarks, canvas) {
+   const width = video.videoWidth || 1;
+   const height = video.videoHeight || 1;
+   const points = landmarks.map((point) => ({ x: point.x * width, y: point.y * height }));
+   const xs = points.map((point) => point.x);
+   const ys = points.map((point) => point.y);
+   const minX = Math.min(...xs);
+   const maxX = Math.max(...xs);
+   const minY = Math.min(...ys);
+   const maxY = Math.max(...ys);
+   const faceWidth = Math.max(1, maxX - minX);
+   const faceHeight = Math.max(1, maxY - minY);
+
+   const eyeA = points[FACE_MESH.rightEyeOuter];
+   const eyeB = points[FACE_MESH.leftEyeOuter];
+   const nose = points[FACE_MESH.noseTip];
+   if (!eyeA || !eyeB || !nose) {
+      return { comparable: false, rollDegrees: 0, yawOffset: Infinity, faceWidthRatio: 0 };
+   }
+   const leftEye = eyeA.x <= eyeB.x ? eyeA : eyeB;
+   const rightEye = eyeA.x <= eyeB.x ? eyeB : eyeA;
+   const eyeMid = {
+      x: (leftEye.x + rightEye.x) / 2,
+      y: (leftEye.y + rightEye.y) / 2,
+   };
+   const interocular = Math.max(1, Math.hypot(rightEye.x - leftEye.x, rightEye.y - leftEye.y));
+   const roll = Math.atan2(rightEye.y - leftEye.y, rightEye.x - leftEye.x);
+   const rollDegrees = Math.abs((roll * 180) / Math.PI);
+   const yawOffset = Math.abs(nose.x - eyeMid.x) / interocular;
+   const faceWidthRatio = faceWidth / width;
+
+   const centreX = (minX + maxX) / 2;
+   const centreY = (minY + maxY) / 2;
+   const sourceEdge = Math.max(faceWidth, faceHeight) * 1.35;
+   const scale = canvas.width / sourceEdge;
+   const context = canvas.getContext('2d', { willReadFrequently: true });
+   context.save();
+   context.setTransform(1, 0, 0, 1, 0, 0);
+   context.fillStyle = '#000';
+   context.fillRect(0, 0, canvas.width, canvas.height);
+   context.translate(canvas.width / 2, canvas.height / 2);
+   context.rotate(-roll);
+   context.scale(scale, scale);
+   context.translate(-centreX, -centreY);
+   context.drawImage(video, 0, 0);
+   context.restore();
+
+   return {
+      comparable: faceWidthRatio >= 0.12 && rollDegrees <= 25 && yawOffset <= 0.42,
+      rollDegrees,
+      yawOffset,
+      faceWidthRatio,
+   };
+}
+
+/**
+ * Measures crop exposure and gradient energy. Sharpness is a ranking signal,
+ * not a universal accept/reject threshold.
+ *
+ * @param {HTMLCanvasElement} canvas Aligned crop.
+ * @returns {{sharpness: number, luma: number, clippedRatio: number, exposed: boolean, signature: number[]}} Quality metrics and a low-resolution RGB signature.
+ */
+function measureCropQuality(canvas) {
+   const context = canvas.getContext('2d', { willReadFrequently: true });
+   const { data, width, height } = context.getImageData(0, 0, canvas.width, canvas.height);
+   const gray = new Float32Array(width * height);
+   let lumaTotal = 0;
+   let clipped = 0;
+   const signatureGrid = 8;
+   const signatureChannels = new Float64Array(signatureGrid * signatureGrid * 3);
+   const signatureCounts = new Uint16Array(signatureGrid * signatureGrid);
+   for (let offset = 0, pixel = 0; offset < data.length; offset += 4, pixel += 1) {
+      const value = (data[offset] * 0.299 + data[offset + 1] * 0.587 + data[offset + 2] * 0.114) / 255;
+      gray[pixel] = value;
+      lumaTotal += value;
+      if (value < 0.02 || value > 0.98) clipped += 1;
+      const pixelX = pixel % width;
+      const pixelY = Math.floor(pixel / width);
+      const cellX = Math.min(signatureGrid - 1, Math.floor((pixelX / width) * signatureGrid));
+      const cellY = Math.min(signatureGrid - 1, Math.floor((pixelY / height) * signatureGrid));
+      const cell = cellY * signatureGrid + cellX;
+      const channel = cell * 3;
+      signatureChannels[channel] += data[offset] / 255;
+      signatureChannels[channel + 1] += data[offset + 1] / 255;
+      signatureChannels[channel + 2] += data[offset + 2] / 255;
+      signatureCounts[cell] += 1;
+   }
+   let gradientEnergy = 0;
+   for (let y = 1; y < height - 1; y += 1) {
+      for (let x = 1; x < width - 1; x += 1) {
+         const index = y * width + x;
+         const horizontal = gray[index + 1] - gray[index - 1];
+         const vertical = gray[index + width] - gray[index - width];
+         gradientEnergy += horizontal * horizontal + vertical * vertical;
+      }
+   }
+   const pixels = width * height;
+   const luma = lumaTotal / pixels;
+   const clippedRatio = clipped / pixels;
+   const signature = Array.from(signatureChannels, (value, index) => {
+      const count = signatureCounts[Math.floor(index / 3)] || 1;
+      return value / count;
+   });
+   return {
+      sharpness: gradientEnergy / pixels,
+      luma,
+      clippedRatio,
+      exposed: luma >= 0.12 && luma <= 0.92 && clippedRatio <= 0.18,
+      signature,
+   };
+}
+
+/**
+ * Computes mean absolute difference between equal-length appearance signatures.
+ *
+ * @param {number[]} left First RGB grid.
+ * @param {number[]} right Second RGB grid.
+ * @returns {number} Mean absolute channel difference in the range 0..1.
+ */
+function appearanceDistance(left, right) {
+   if (!left || !right || left.length !== right.length) return Infinity;
+   let total = 0;
+   for (let index = 0; index < left.length; index += 1) total += Math.abs(left[index] - right[index]);
+   return total / left.length;
+}
+
+/**
+ * Captures the decoded source frame at native video resolution.
+ *
+ * @param {HTMLVideoElement} video Source video.
+ * @param {number} mediaTime Current video timestamp.
+ * @param {number} sharpness Crop sharpness associated with the frame.
+ * @param {string} reason Selection reason.
+ * @returns {{mediaTime: number, dataUrl: string, width: number, height: number, sharpness: number, reason: string}} Frame artifact.
+ */
+function captureSourceFrame(video, mediaTime, sharpness, reason) {
+   const canvas = document.createElement('canvas');
+   canvas.width = video.videoWidth;
+   canvas.height = video.videoHeight;
+   canvas.getContext('2d').drawImage(video, 0, 0);
+   return {
+      mediaTime,
+      dataUrl: canvas.toDataURL('image/jpeg', 0.92),
+      width: canvas.width,
+      height: canvas.height,
+      sharpness,
+      reason,
+   };
+}
+
+/**
+ * Creates the seekable-video analysis controller.
+ *
+ * @param {HTMLVideoElement} video Local video element.
+ * @param {Partial<VideoLoaderConfig>} [overrides] Configuration overrides.
+ * @returns {object} Controller API.
+ */
+export function createVideoAnalysisController(video, overrides = {}) {
+   /** @type {VideoLoaderConfig} */
+   const config = { ...DEFAULT_VIDEO_LOADER_CONFIG, ...overrides };
+   const cropCanvas = document.createElement('canvas');
+   cropCanvas.width = config.alignedCropSize;
+   cropCanvas.height = config.alignedCropSize;
+   const triggerDetector = createTriggerDetector({
+      heartbeatSeconds: config.heartbeatSeconds,
+      minimumFaceApiGapSeconds: config.minimumFaceApiGapSeconds,
+      jumpDelta: config.visualJumpDelta,
+   });
+
+   let faceLandmarker = null;
+   let imageEmbedder = null;
+   let modelsReady = false;
+   let session = null;
+   let sourceFile = null;
+   let objectUrl = null;
+   let cancelRequested = false;
+   /** @type {Array<Function>} */
+   const listeners = [];
+
+   /**
+    * Notifies the UI and Playwright bridge after state changes.
+    *
+    * @param {string} type Update type.
+    * @param {object} [detail={}] Update detail.
+    * @returns {void}
+    */
+   function notify(type, detail = {}) {
+      const update = { type, session, ...detail };
+      for (const listener of listeners) listener(update);
+   }
+
+   /**
+    * Loads vendored face-api and MediaPipe models.
+    *
+    * @returns {Promise<void>} Resolves after every model is ready.
+    */
+   async function loadModels() {
+      if (modelsReady) return;
+      await Promise.all([
+         faceapi.nets.tinyFaceDetector.loadFromUri(MODEL_URLS.tiny),
+         faceapi.nets.faceLandmark68Net.loadFromUri(MODEL_URLS.landmarks),
+         faceapi.nets.faceRecognitionNet.loadFromUri(MODEL_URLS.recognition),
+      ]);
+      const { FilesetResolver, FaceLandmarker, ImageEmbedder } = await import(MEDIAPIPE_TASKS_VISION_URL);
+      const vision = await FilesetResolver.forVisionTasks(MEDIAPIPE_WASM_URL);
+      [faceLandmarker, imageEmbedder] = await Promise.all([
+         FaceLandmarker.createFromOptions(vision, {
+            baseOptions: { modelAssetPath: MEDIAPIPE_FACE_LANDMARKER_URL, delegate: 'CPU' },
+            runningMode: 'IMAGE',
+            numFaces: 1,
+            outputFaceBlendshapes: false,
+         }),
+         ImageEmbedder.createFromOptions(vision, {
+            baseOptions: { modelAssetPath: MEDIAPIPE_IMAGE_EMBEDDER_URL, delegate: 'CPU' },
+            runningMode: 'IMAGE',
+            l2Normalize: true,
+         }),
+      ]);
+      modelsReady = true;
+      notify('models-ready');
+   }
+
+   /**
+    * Loads a local file into the video element and creates a fresh session.
+    *
+    * @param {File} file User-selected local video.
+    * @param {object} [metadata] Optional Node-computed provenance.
+    * @param {string|null} [metadata.fingerprint] Bounded source fingerprint.
+    * @returns {Promise<object>} Fresh analysis session.
+    */
+   async function loadFile(file, metadata = {}) {
+      cancelRequested = true;
+      if (objectUrl) URL.revokeObjectURL(objectUrl);
+      sourceFile = file;
+      objectUrl = URL.createObjectURL(file);
+      video.pause();
+      video.srcObject = null;
+      const metadataReady = waitForMediaEvent(video, 'loadedmetadata');
+      video.src = objectUrl;
+      video.load();
+      await metadataReady;
+      session = createAnalysisSession({
+         name: file.name,
+         size: file.size,
+         type: file.type || 'application/octet-stream',
+         lastModified: file.lastModified || null,
+         duration: video.duration,
+         width: video.videoWidth,
+         height: video.videoHeight,
+         fingerprint: metadata.fingerprint || null,
+      }, config);
+      session.models = {
+         faceApi: '@vladmandic/face-api vendored bundle',
+         faceMatcherDefaultThreshold: DEFAULT_FACE_DISTANCE_THRESHOLD,
+         mediaPipeTasksVision: '0.10.35',
+         mediaPipeDelegate: 'CPU',
+         faceLandmarker: 'float16/1',
+         imageEmbedder: 'mobilenet_v3_small float32/1',
+      };
+      appendJournal(session, 'source-loaded', { source: session.source });
+      triggerDetector.reset();
+      cancelRequested = false;
+      notify('source-loaded');
+      return session;
+   }
+
+   /**
+    * Runs face-api recognition against the current decoded frame.
+    *
+    * @returns {Promise<object|null>} Detection with landmarks and descriptor.
+    */
+   async function runFaceApi() {
+      session.summary.faceApiRuns += 1;
+      const options = new faceapi.TinyFaceDetectorOptions({ inputSize: 416, scoreThreshold: 0.5 });
+      return faceapi.detectSingleFace(video, options).withFaceLandmarks().withFaceDescriptor();
+   }
+
+   /**
+    * Finds a clear frame near a confirmed event without retaining a frame
+    * queue. MediaPipe ranks timestamps first; face-api then verifies only the
+    * sharpest bounded subset before the native source frame is captured.
+    *
+    * @param {object} event Confirmed evidence event.
+    * @param {number} analysisStart First allowed media timestamp.
+    * @param {number} analysisEnd Last allowed media timestamp.
+    * @returns {Promise<object>} Selected frame payload.
+    */
+   async function refineEvidenceFrame(event, analysisStart, analysisEnd) {
+      const radius = config.evidenceRefinementRadiusSeconds;
+      const rangeStart = Math.max(analysisStart, event.startMediaTime - radius);
+      const rangeEnd = Math.min(analysisEnd, event.endMediaTime + radius);
+      const candidates = [];
+
+      for (let candidateTime = rangeStart; candidateTime <= rangeEnd + 0.0001; candidateTime += config.evidenceRefinementStepSeconds) {
+         const mediaTime = Math.min(candidateTime, rangeEnd);
+         await seekVideo(video, mediaTime);
+         const result = faceLandmarker.detect(video);
+         const landmarks = result?.faceLandmarks?.[0];
+         if (!landmarks) continue;
+         const geometry = drawAlignedFaceCrop(video, landmarks, cropCanvas);
+         const measured = measureCropQuality(cropCanvas);
+         const quality = {
+            sharpness: measured.sharpness,
+            luma: measured.luma,
+            clippedRatio: measured.clippedRatio,
+            exposed: measured.exposed,
+         };
+         if (!geometry.comparable || !quality.exposed) continue;
+         candidates.push({ mediaTime, quality, geometry });
+      }
+
+      const ranked = candidates
+         .sort((left, right) => right.quality.sharpness - left.quality.sharpness)
+         .slice(0, config.evidenceRefinementFaceApiCandidates);
+      let selected = null;
+      let selectionSource = 'refined';
+      for (const candidate of ranked) {
+         await seekVideo(video, candidate.mediaTime);
+         const detection = await runFaceApi();
+         const faceDistance = detection?.descriptor
+            ? euclideanDistance(detection.descriptor, session.baseline.descriptor)
+            : null;
+         const detectorMissing = !detection;
+         const identityDistance = Number.isFinite(faceDistance)
+            && faceDistance >= config.faceDistanceThreshold;
+         const retainsEvidence = event.classification === 'identity-distance'
+            ? identityDistance
+            : event.classification === 'detector-divergence'
+               ? detectorMissing
+               : detectorMissing || identityDistance;
+         appendJournal(session, 'evidence-refinement-check', {
+            eventId: event.id,
+            mediaTime: candidate.mediaTime,
+            sharpness: candidate.quality.sharpness,
+            faceApiDetected: !!detection,
+            faceDistance,
+            retainsEvidence,
+         });
+         if (retainsEvidence) {
+            selected = {
+               ...candidate,
+               faceApiDetected: !!detection,
+               faceDistance,
+            };
+            break;
+         }
+      }
+
+      if (!selected) {
+         const fallback = event.observations.reduce((best, item) => (
+            (item.quality?.sharpness || 0) > (best.quality?.sharpness || 0) ? item : best
+         ));
+         selected = {
+            mediaTime: fallback.mediaTime,
+            quality: fallback.quality || { sharpness: 0 },
+            faceApiDetected: fallback.faceApiDetected,
+            faceDistance: fallback.faceDistance,
+         };
+         selectionSource = 'original-observation';
+      }
+
+      await seekVideo(video, selected.mediaTime);
+      const reason = selected.faceApiDetected
+         ? `${selectionSource}-face-distance-above-threshold`
+         : `${selectionSource}-mediapipe-present-face-api-missing`;
+      const frame = captureSourceFrame(
+         video,
+         selected.mediaTime,
+         selected.quality?.sharpness || 0,
+         reason,
+      );
+      selectEventFrame(event, frame);
+      appendJournal(session, 'evidence-frame-selected', {
+         eventId: event.id,
+         mediaTime: frame.mediaTime,
+         sharpness: frame.sharpness,
+         reason,
+         refinementCandidates: candidates.length,
+         faceApiCandidates: ranked.length,
+      });
+      return frame;
+   }
+
+   /**
+    * Finalises the baseline after enough stable samples have been accepted.
+    *
+    * @param {Array<object>} embeddingSamples Accepted embedding samples.
+    * @param {Array<object>} faceSamples Accepted face-api samples.
+    * @param {object} representativeFrame Frame nearest the embedding medoid.
+    * @returns {object} Stored session baseline.
+    */
+   function finaliseBaseline(embeddingSamples, faceSamples, representativeFrame) {
+      const embeddingBaseline = buildEmbeddingBaseline(
+         embeddingSamples.map((sample) => sample.embedding),
+         {
+            minimumDelta: config.minimumVisualDelta,
+            madMultiplier: config.visualMadMultiplier,
+         },
+      );
+      const faceBaseline = buildFaceBaseline(faceSamples.map((sample) => sample.descriptor));
+      session.baseline = {
+         mediaTime: representativeFrame.mediaTime,
+         embedding: embeddingBaseline.embedding,
+         descriptor: faceBaseline.descriptor,
+         faceSpread: faceBaseline.spread,
+         embeddingMedianDrift: embeddingBaseline.medianDrift,
+         embeddingMad: embeddingBaseline.mad,
+         visualTriggerThreshold: embeddingBaseline.triggerThreshold,
+         frame: representativeFrame,
+      };
+      appendJournal(session, 'baseline-ready', {
+         mediaTime: session.baseline.mediaTime,
+         faceSpread: session.baseline.faceSpread,
+         embeddingMedianDrift: session.baseline.embeddingMedianDrift,
+         embeddingMad: session.baseline.embeddingMad,
+         visualTriggerThreshold: session.baseline.visualTriggerThreshold,
+      });
+      return session.baseline;
+   }
+
+   /**
+    * Runs the complete coarse scan over a selected media-time interval.
+    *
+    * @param {object} [options] Scan range and baseline search start.
+    * @param {number} [options.start] First source timestamp.
+    * @param {number} [options.end] Last source timestamp.
+    * @param {number} [options.baselineTime] Baseline search start.
+    * @param {number} [options.sampleIntervalSeconds] Coarse scan cadence.
+    * @returns {Promise<object>} Completed or cancelled session.
+    */
+   async function analyze(options = {}) {
+      if (!sourceFile || !session) throw new Error('Load a local video before starting analysis.');
+      if (!modelsReady) await loadModels();
+      const start = Math.max(0, Number(options.start ?? 0));
+      const end = Math.min(video.duration, Number(options.end ?? video.duration));
+      const baselineTime = Math.max(start, Math.min(end, Number(options.baselineTime ?? start)));
+      const step = Math.max(0.1, Number(options.sampleIntervalSeconds ?? config.sampleIntervalSeconds));
+      if (!(end > start)) throw new Error('Analysis end time must be greater than start time.');
+
+      cancelRequested = false;
+      video.pause();
+      session.status = 'analysing';
+      session.config.sampleIntervalSeconds = step;
+      session.config.analysisRange = { start, end };
+      session.config.baselineSearchStart = baselineTime;
+      session.config.baselineSampleIntervalSeconds = Math.min(step, 0.25);
+      appendJournal(session, 'analysis-started', {
+         start,
+         end,
+         baselineTime,
+         sampleIntervalSeconds: step,
+      });
+      notify('analysis-started', { start, end });
+
+      /** @type {Array<{mediaTime: number, embedding: number[], quality: object, signature: number[], geometry: object}>} */
+      const embeddingSamples = [];
+      /** @type {Array<{mediaTime: number, descriptor: number[]}>} */
+      const faceSamples = [];
+
+      /**
+       * Drops an unstable baseline run while retaining the reason in the
+       * technical journal. No frame pixels are retained by the candidate.
+       *
+       * @param {string} reason Stable reset reason.
+       * @param {number} atMediaTime Timestamp that broke continuity.
+       * @param {object} [detail={}] Numeric diagnostics.
+       * @returns {void}
+       */
+      function resetBaselineCandidate(reason, atMediaTime, detail = {}) {
+         if (embeddingSamples.length || faceSamples.length) {
+            appendJournal(session, 'baseline-candidate-reset', {
+               reason,
+               mediaTime: atMediaTime,
+               acceptedEmbeddings: embeddingSamples.length,
+               acceptedDescriptors: faceSamples.length,
+               ...detail,
+            });
+         }
+         embeddingSamples.length = 0;
+         faceSamples.length = 0;
+      }
+
+      let phase = 'baseline';
+      let mediaTime = baselineTime;
+      while (mediaTime <= end + 0.0001) {
+         if (cancelRequested) break;
+         const currentTime = Math.min(mediaTime, end);
+         await seekVideo(video, currentTime);
+         const mediaPipeResult = faceLandmarker.detect(video);
+         const landmarks = mediaPipeResult?.faceLandmarks?.[0] || null;
+         let geometry = null;
+         let quality = null;
+         let appearanceSignature = null;
+         let embedding = null;
+         let comparable = false;
+
+         if (landmarks) {
+            geometry = drawAlignedFaceCrop(video, landmarks, cropCanvas);
+            const measuredQuality = measureCropQuality(cropCanvas);
+            const { signature, ...serialisableQuality } = measuredQuality;
+            quality = serialisableQuality;
+            appearanceSignature = signature;
+            comparable = geometry.comparable && quality.exposed;
+            if (comparable) embedding = unpackEmbedding(imageEmbedder.embed(cropCanvas));
+         }
+
+         if (!session.baseline && comparable && embedding) {
+            const previous = embeddingSamples.at(-1);
+            const continuityDrift = previous ? visualDrift(embedding, previous.embedding) : 0;
+            const appearanceStep = previous ? appearanceDistance(appearanceSignature, previous.signature) : 0;
+            const rollStep = previous ? Math.abs(geometry.rollDegrees - previous.geometry.rollDegrees) : 0;
+            const yawStep = previous ? Math.abs(geometry.yawOffset - previous.geometry.yawOffset) : 0;
+            const scaleStep = previous ? Math.abs(geometry.faceWidthRatio - previous.geometry.faceWidthRatio) : 0;
+            const unstable = appearanceStep > config.maximumBaselineAppearanceStep
+               || rollStep > config.maximumBaselineRollStep
+               || yawStep > config.maximumBaselineYawStep
+               || scaleStep > config.maximumBaselineScaleStep;
+            if (unstable) {
+               resetBaselineCandidate('appearance-or-pose-discontinuity', currentTime, {
+                  appearanceStep,
+                  continuityDrift,
+                  rollStep,
+                  yawStep,
+                  scaleStep,
+               });
+            }
+            embeddingSamples.push({
+               mediaTime: currentTime,
+               embedding,
+               quality,
+               signature: appearanceSignature,
+               geometry,
+            });
+            if (embeddingSamples.length >= config.baselineEmbeddingSamples) {
+               const verificationSamples = [...embeddingSamples]
+                  .sort((left, right) => right.quality.sharpness - left.quality.sharpness)
+                  .slice(0, config.baselineFaceSamples)
+                  .sort((left, right) => left.mediaTime - right.mediaTime);
+               for (const candidate of verificationSamples) {
+                  await seekVideo(video, candidate.mediaTime);
+                  const detection = await runFaceApi();
+                  if (detection?.descriptor) {
+                     faceSamples.push({
+                        mediaTime: candidate.mediaTime,
+                        descriptor: Array.from(detection.descriptor),
+                     });
+                  }
+               }
+
+               if (faceSamples.length >= config.baselineFaceSamples) {
+                  const provisionalFaceBaseline = buildFaceBaseline(faceSamples.map((sample) => sample.descriptor));
+                  if (provisionalFaceBaseline.spread <= config.maximumBaselineFaceSpread) {
+                     const provisionalEmbeddingBaseline = buildEmbeddingBaseline(
+                        embeddingSamples.map((sample) => sample.embedding),
+                        {
+                           minimumDelta: config.minimumVisualDelta,
+                           madMultiplier: config.visualMadMultiplier,
+                        },
+                     );
+                     const representative = embeddingSamples[provisionalEmbeddingBaseline.medoidIndex];
+                     await seekVideo(video, representative.mediaTime);
+                     const representativeFrame = captureSourceFrame(
+                        video,
+                        representative.mediaTime,
+                        representative.quality.sharpness,
+                        'baseline-embedding-medoid',
+                     );
+                     finaliseBaseline(embeddingSamples, faceSamples, representativeFrame);
+                     triggerDetector.markFaceApi(currentTime);
+                     notify('baseline-ready');
+                  } else {
+                     resetBaselineCandidate('face-descriptor-spread', currentTime, {
+                        faceSpread: provisionalFaceBaseline.spread,
+                     });
+                  }
+               } else {
+                  resetBaselineCandidate('face-api-baseline-incomplete', currentTime);
+               }
+            }
+         } else if (!session.baseline && embeddingSamples.length) {
+            resetBaselineCandidate('non-comparable-frame', currentTime);
+         }
+
+         const drift = session.baseline && embedding
+            ? visualDrift(embedding, session.baseline.embedding)
+            : null;
+         const sample = appendSample(session, {
+            phase,
+            mediaTime: currentTime,
+            facePresent: !!landmarks,
+            comparable,
+            visualDrift: drift,
+            visualThreshold: session.baseline?.visualTriggerThreshold ?? null,
+            geometry,
+            quality,
+         });
+
+         if (session.baseline && phase === 'scan') {
+            const decision = triggerDetector.evaluate(sample);
+            for (const observation of decision.observations) {
+               appendJournal(session, observation, { mediaTime: currentTime });
+            }
+            if (decision.shouldVerify) {
+               const detection = await runFaceApi();
+               triggerDetector.markFaceApi(currentTime);
+               const faceDistance = detection?.descriptor
+                  ? euclideanDistance(detection.descriptor, session.baseline.descriptor)
+                  : null;
+               const divergent = !detection || faceDistance >= config.faceDistanceThreshold;
+               const observation = {
+                  mediaTime: currentTime,
+                  outcome: divergent ? 'divergent' : 'matched',
+                  faceApiDetected: !!detection,
+                  faceDistance,
+                  faceThreshold: config.faceDistanceThreshold,
+                  visualDrift: drift,
+                  reasons: decision.reasons,
+                  quality,
+               };
+               appendJournal(session, 'face-api-result', observation);
+
+               if (divergent) {
+                  const confirmedBeforeObservation = session.summary.confirmedEvents;
+                  const event = recordEvidenceObservation(session, observation);
+                  const newlyConfirmed = session.summary.confirmedEvents > confirmedBeforeObservation;
+                  if (newlyConfirmed) {
+                     await refineEvidenceFrame(event, start, end);
+                     notify('event-confirmed', { event });
+                  }
+               }
+            }
+         }
+
+         notify('sample', {
+            sample,
+            phase,
+            progress: phase === 'baseline' ? 0 : (currentTime - start) / (end - start),
+         });
+         if (session.baseline && phase === 'baseline') {
+            phase = 'scan';
+            mediaTime = start;
+            triggerDetector.reset();
+            appendJournal(session, 'analysis-scan-started', { start, end });
+            notify('analysis-scan-started', { start, end });
+            await new Promise((resolve) => window.setTimeout(resolve, 0));
+            continue;
+         }
+         if (currentTime >= end) break;
+         const nextTime = currentTime + (phase === 'scan' ? step : session.config.baselineSampleIntervalSeconds);
+         mediaTime = nextTime > end ? end : nextTime;
+         await new Promise((resolve) => window.setTimeout(resolve, 0));
+      }
+
+      if (!session.baseline && !cancelRequested) {
+         session.status = 'failed';
+         appendJournal(session, 'analysis-failed', { reason: 'baseline-not-found' });
+         notify('analysis-failed', { reason: 'baseline-not-found' });
+         throw new Error('No stable face baseline was found in the selected interval.');
+      }
+
+      session.status = cancelRequested ? 'cancelled' : 'complete';
+      session.updatedAt = new Date().toISOString();
+      appendJournal(session, cancelRequested ? 'analysis-cancelled' : 'analysis-complete', {
+         summary: session.summary,
+      });
+      notify(session.status === 'complete' ? 'analysis-complete' : 'analysis-cancelled');
+      return session;
+   }
+
+   /**
+    * Requests cancellation at the next sample boundary.
+    *
+    * @returns {void}
+    */
+   function cancel() {
+      cancelRequested = true;
+   }
+
+   /**
+    * Returns report, technical log, and separately addressable image artifacts.
+    *
+    * @returns {{report: object, technicalLog: string, frames: Array<object>}} Export bundle.
+    */
+   function exportBundle() {
+      if (!session) throw new Error('No analysis session is available.');
+      const frames = [];
+      if (session.baseline?.frame) frames.push({ id: 'baseline', ...session.baseline.frame });
+      for (const event of session.events) {
+         if (event.status === 'confirmed' && event.selectedFrame) {
+            frames.push({ id: event.id, ...event.selectedFrame });
+         }
+      }
+      return {
+         report: buildReport(session),
+         technicalLog: serializeTechnicalLog(session),
+         frames,
+      };
+   }
+
+   return {
+      config,
+      loadModels,
+      loadFile,
+      analyze,
+      cancel,
+      exportBundle,
+      getSession: () => session,
+      isModelsReady: () => modelsReady,
+      subscribe(listener) {
+         listeners.push(listener);
+         return () => {
+            const index = listeners.indexOf(listener);
+            if (index >= 0) listeners.splice(index, 1);
+         };
+      },
+   };
+}
diff --git a/lab-js/video-loader/session.js b/lab-js/video-loader/session.js
new file mode 100644
index 0000000..900d17a
--- /dev/null
+++ b/lab-js/video-loader/session.js
@@ -0,0 +1,411 @@
+/**
+ * @module video-loader/session
+ * @description
+ * Versioned analysis session, technical journal, and reversible editorial
+ * commands for the Video Loader. The technical journal is intentionally
+ * independent from the report so it can later drive clip extraction or other
+ * post-processing without rendering the editorial output again.
+ */
+
+/** Current machine-readable session and log schema version. */
+export const VIDEO_ANALYSIS_SCHEMA_VERSION = 2;
+
+/**
+ * Creates a stable random identifier with a deterministic fallback for older
+ * browsers and constrained test environments.
+ *
+ * @param {string} prefix Identifier prefix.
+ * @returns {string} Unique identifier.
+ */
+function createId(prefix) {
+   const random = globalThis.crypto?.randomUUID?.() || `${Date.now()}-${Math.random().toString(16).slice(2)}`;
+   return `${prefix}-${random}`;
+}
+
+/**
+ * Creates an empty video-analysis session.
+ *
+ * @param {object} source Source metadata.
+ * @param {string} source.name Source filename.
+ * @param {number} source.size Source byte size.
+ * @param {string} source.type Source media type.
+ * @param {number} [source.lastModified] Source modification time.
+ * @param {number} [source.duration] Decoded duration.
+ * @param {number} [source.width] Decoded width.
+ * @param {number} [source.height] Decoded height.
+ * @param {string|null} [source.fingerprint] Bounded source fingerprint.
+ * @param {object} config Effective analyser configuration.
+ * @returns {object} Mutable session owned by one analyser controller.
+ */
+export function createAnalysisSession(source, config) {
+   const createdAt = new Date().toISOString();
+   return {
+      schemaVersion: VIDEO_ANALYSIS_SCHEMA_VERSION,
+      sessionId: createId('session'),
+      createdAt,
+      updatedAt: createdAt,
+      source: { ...source },
+      config: structuredClone(config),
+      models: {},
+      baseline: null,
+      samples: [],
+      events: [],
+      journal: [],
+      editorial: {
+         storyOrder: ['baseline'],
+         undo: [],
+         redo: [],
+      },
+      status: 'idle',
+      summary: {
+         sampledFrames: 0,
+         mediaPipeFaces: 0,
+         faceApiRuns: 0,
+         confirmedEvents: 0,
+      },
+   };
+}
+
+/**
+ * Appends one structured technical-journal entry.
+ *
+ * @param {object} session Analysis session.
+ * @param {string} type Stable entry type.
+ * @param {object} [payload={}] Serializable entry payload.
+ * @returns {object} Appended journal entry.
+ */
+export function appendJournal(session, type, payload = {}) {
+   const entry = {
+      sequence: session.journal.length + 1,
+      recordedAt: new Date().toISOString(),
+      type,
+      ...structuredClone(payload),
+   };
+   session.journal.push(entry);
+   session.updatedAt = entry.recordedAt;
+   return entry;
+}
+
+/**
+ * Adds one lightweight sample without image pixels or model vectors.
+ *
+ * @param {object} session Analysis session.
+ * @param {object} sample Serializable sample metrics.
+ * @returns {object} Stored sample.
+ */
+export function appendSample(session, sample) {
+   const stored = {
+      sequence: session.samples.length + 1,
+      ...structuredClone(sample),
+   };
+   session.samples.push(stored);
+   session.summary.sampledFrames += 1;
+   if (stored.facePresent) session.summary.mediaPipeFaces += 1;
+   return stored;
+}
+
+/**
+ * Adds an evidence event or merges a nearby verification into the latest open
+ * event. Images may be attached to the session for report rendering, but they
+ * are excluded from the separately downloadable technical log.
+ *
+ * @param {object} session Analysis session.
+ * @param {object} observation Face-api observation and trigger context.
+ * @param {number} [mergeWindowSeconds=3] Maximum distance from the previous observation.
+ * @returns {object} Created or updated event.
+ */
+export function recordEvidenceObservation(session, observation, mergeWindowSeconds = 3) {
+   const latest = session.events.at(-1);
+   const mayMerge = latest
+      && latest.status !== 'rejected'
+      && observation.mediaTime - latest.endMediaTime <= mergeWindowSeconds;
+   const event = mayMerge ? latest : {
+      id: createId('event'),
+      startMediaTime: observation.mediaTime,
+      endMediaTime: observation.mediaTime,
+      anchorMediaTime: observation.mediaTime,
+      status: 'candidate',
+      classification: 'unclassified',
+      reviewRequired: true,
+      promoted: false,
+      caption: '',
+      observations: [],
+      selectedFrame: null,
+      recommendedClip: {
+         start: Math.max(0, observation.mediaTime - 2),
+         end: observation.mediaTime + 3,
+      },
+   };
+
+   if (!mayMerge) {
+      session.events.push(event);
+   }
+   event.observations.push(structuredClone(observation));
+   event.endMediaTime = observation.mediaTime;
+   event.recommendedClip.end = observation.mediaTime + 3;
+
+   const divergent = event.observations.filter((item) => item.outcome === 'divergent');
+   if (divergent.length >= 2 && event.status !== 'confirmed') {
+      event.status = 'confirmed';
+      event.anchorMediaTime = divergent.reduce((best, item) => (
+         (item.quality?.sharpness || 0) > (best.quality?.sharpness || 0) ? item : best
+      )).mediaTime;
+      session.editorial.storyOrder.push(event.id);
+      session.editorial.storyOrder.sort((leftId, rightId) => {
+         const leftTime = leftId === 'baseline'
+            ? session.baseline?.mediaTime ?? Number.POSITIVE_INFINITY
+            : session.events.find((candidate) => candidate.id === leftId)?.anchorMediaTime ?? Number.POSITIVE_INFINITY;
+         const rightTime = rightId === 'baseline'
+            ? session.baseline?.mediaTime ?? Number.POSITIVE_INFINITY
+            : session.events.find((candidate) => candidate.id === rightId)?.anchorMediaTime ?? Number.POSITIVE_INFINITY;
+         return leftTime - rightTime;
+      });
+      session.summary.confirmedEvents += 1;
+   }
+   const distanceDivergences = divergent.filter((item) => (
+      Number.isFinite(item.faceDistance)
+      && item.faceDistance >= (item.faceThreshold ?? session.config.faceDistanceThreshold)
+   ));
+   const missingDetections = divergent.filter((item) => item.faceApiDetected === false);
+   if (distanceDivergences.length >= 2) {
+      event.classification = 'identity-distance';
+      event.reviewRequired = false;
+   } else if (missingDetections.length >= 2) {
+      event.classification = 'detector-divergence';
+      event.reviewRequired = true;
+   } else if (divergent.length >= 2) {
+      event.classification = 'mixed-divergence';
+      event.reviewRequired = true;
+   }
+   return event;
+}
+
+/**
+ * Attaches or replaces the selected high-resolution frame for an event.
+ *
+ * @param {object} event Evidence event.
+ * @param {{mediaTime: number, dataUrl: string, width: number, height: number, sharpness: number, reason: string}} frame Frame payload.
+ * @returns {boolean} Whether the frame replaced the prior selection.
+ */
+export function selectEventFrame(event, frame) {
+   if (event.selectedFrame && event.selectedFrame.sharpness > frame.sharpness) return false;
+   event.selectedFrame = { ...frame };
+   return true;
+}
+
+/**
+ * Applies one reversible editorial command.
+ *
+ * @param {object} session Analysis session.
+ * @param {object} command Editorial command.
+ * @param {'promote'|'reject'|'caption'|'move-earlier'|'move-later'} command.type Command type.
+ * @param {string} command.eventId Event identifier or the reserved `baseline` story item.
+ * @param {string} [command.value] Caption value.
+ * @returns {boolean} Whether the command changed an event.
+ */
+export function applyEditorialCommand(session, command) {
+   const event = session.events.find((candidate) => candidate.id === command.eventId);
+   const isMovement = command.type === 'move-earlier' || command.type === 'move-later';
+   if (!event && !(isMovement && command.eventId === 'baseline')) return false;
+   const before = {
+      event: event ? {
+         status: event.status,
+         promoted: event.promoted,
+         caption: event.caption,
+      } : null,
+      storyOrder: [...session.editorial.storyOrder],
+   };
+   if (command.type === 'promote') event.promoted = true;
+   if (command.type === 'reject') {
+      event.status = 'rejected';
+      session.editorial.storyOrder = session.editorial.storyOrder.filter((id) => id !== event.id);
+   }
+   if (command.type === 'caption') event.caption = command.value || '';
+   if (isMovement) {
+      const index = session.editorial.storyOrder.indexOf(command.eventId);
+      const offset = command.type === 'move-earlier' ? -1 : 1;
+      const target = index + offset;
+      if (index < 0 || target < 0 || target >= session.editorial.storyOrder.length) return false;
+      [session.editorial.storyOrder[index], session.editorial.storyOrder[target]] = [
+         session.editorial.storyOrder[target],
+         session.editorial.storyOrder[index],
+      ];
+   }
+   const after = {
+      event: event ? {
+         status: event.status,
+         promoted: event.promoted,
+         caption: event.caption,
+      } : null,
+      storyOrder: [...session.editorial.storyOrder],
+   };
+   session.editorial.undo.push({ command: structuredClone(command), before, after });
+   session.editorial.redo = [];
+   appendJournal(session, 'editorial-command', { command, before, after });
+   return true;
+}
+
+/**
+ * Restores the state before the latest editorial command.
+ *
+ * @param {object} session Analysis session.
+ * @returns {boolean} Whether a command was undone.
+ */
+export function undoEditorialCommand(session) {
+   const change = session.editorial.undo.pop();
+   if (!change) return false;
+   const event = session.events.find((candidate) => candidate.id === change.command.eventId);
+   if (change.before.event && !event) return false;
+   if (event && change.before.event) Object.assign(event, change.before.event);
+   session.editorial.storyOrder = [...change.before.storyOrder];
+   session.editorial.redo.push(change);
+   appendJournal(session, 'editorial-undo', { command: change.command });
+   return true;
+}
+
+/**
+ * Reapplies the latest undone editorial command.
+ *
+ * @param {object} session Analysis session.
+ * @returns {boolean} Whether a command was redone.
+ */
+export function redoEditorialCommand(session) {
+   const change = session.editorial.redo.pop();
+   if (!change) return false;
+   const event = session.events.find((candidate) => candidate.id === change.command.eventId);
+   if (change.after.event && !event) return false;
+   if (event && change.after.event) Object.assign(event, change.after.event);
+   session.editorial.storyOrder = [...change.after.storyOrder];
+   session.editorial.undo.push(change);
+   appendJournal(session, 'editorial-redo', { command: change.command });
+   return true;
+}
+
+/**
+ * Produces the user-facing report without technical journal internals or image
+ * byte duplication. Selected frame data URLs remain available to the caller as
+ * separate frame artifacts.
+ *
+ * @param {object} session Analysis session.
+ * @returns {object} Serializable report model.
+ */
+export function buildReport(session) {
+   const storyIndex = new Map(session.editorial.storyOrder.map((id, index) => [id, index]));
+   const events = session.events
+      .filter((event) => event.status === 'confirmed' && event.status !== 'rejected')
+      .map((event) => ({
+         id: event.id,
+         startMediaTime: event.startMediaTime,
+         endMediaTime: event.endMediaTime,
+         anchorMediaTime: event.anchorMediaTime,
+         classification: event.classification,
+         reviewRequired: event.reviewRequired,
+         promoted: event.promoted,
+         caption: event.caption,
+         recommendedClip: event.recommendedClip,
+         selectedFrame: event.selectedFrame ? {
+            mediaTime: event.selectedFrame.mediaTime,
+            width: event.selectedFrame.width,
+            height: event.selectedFrame.height,
+            sharpness: event.selectedFrame.sharpness,
+            reason: event.selectedFrame.reason,
+         } : null,
+         evidence: event.observations.map((observation) => ({
+            mediaTime: observation.mediaTime,
+            outcome: observation.outcome,
+            faceDistance: observation.faceDistance,
+            visualDrift: observation.visualDrift,
+            reasons: observation.reasons,
+         })),
+      }))
+      .sort((left, right) => (
+         (storyIndex.get(left.id) ?? Number.POSITIVE_INFINITY)
+         - (storyIndex.get(right.id) ?? Number.POSITIVE_INFINITY)
+      ));
+   const includedIds = new Set(events.map((event) => event.id));
+   const storyOrder = session.editorial.storyOrder.filter((id) => id === 'baseline' || includedIds.has(id));
+
+   return {
+      schemaVersion: session.schemaVersion,
+      sessionId: session.sessionId,
+      createdAt: session.createdAt,
+      updatedAt: session.updatedAt,
+      source: structuredClone(session.source),
+      config: structuredClone(session.config),
+      models: structuredClone(session.models),
+      storyOrder,
+      baseline: session.baseline ? {
+         mediaTime: session.baseline.mediaTime,
+         faceSpread: session.baseline.faceSpread,
+         embeddingMedianDrift: session.baseline.embeddingMedianDrift,
+         embeddingMad: session.baseline.embeddingMad,
+         visualTriggerThreshold: session.baseline.visualTriggerThreshold,
+         frame: session.baseline.frame ? {
+            mediaTime: session.baseline.frame.mediaTime,
+            width: session.baseline.frame.width,
+            height: session.baseline.frame.height,
+         } : null,
+      } : null,
+      summary: structuredClone(session.summary),
+      events,
+   };
+}
+
+/**
+ * Serialises the technical log as newline-delimited JSON. The first record is
+ * a self-contained header; later records can be streamed or processed without
+ * loading report images into memory.
+ *
+ * @param {object} session Analysis session.
+ * @returns {string} NDJSON document ending with a newline.
+ */
+export function serializeTechnicalLog(session) {
+   const header = {
+      type: 'analysis-header',
+      schemaVersion: session.schemaVersion,
+      sessionId: session.sessionId,
+      createdAt: session.createdAt,
+      source: session.source,
+      config: session.config,
+      models: session.models,
+      baseline: session.baseline ? {
+         mediaTime: session.baseline.mediaTime,
+         faceSpread: session.baseline.faceSpread,
+         embeddingMedianDrift: session.baseline.embeddingMedianDrift,
+         embeddingMad: session.baseline.embeddingMad,
+         visualTriggerThreshold: session.baseline.visualTriggerThreshold,
+      } : null,
+   };
+   const samples = session.samples.map((sample) => ({ type: 'sample', ...sample }));
+   const events = session.events.map((event) => ({
+      type: 'evidence-event',
+      id: event.id,
+      startMediaTime: event.startMediaTime,
+      endMediaTime: event.endMediaTime,
+      anchorMediaTime: event.anchorMediaTime,
+      status: event.status,
+      classification: event.classification,
+      reviewRequired: event.reviewRequired,
+      promoted: event.promoted,
+      caption: event.caption,
+      recommendedClip: event.recommendedClip,
+      selectedFrame: event.selectedFrame ? {
+         mediaTime: event.selectedFrame.mediaTime,
+         width: event.selectedFrame.width,
+         height: event.selectedFrame.height,
+         sharpness: event.selectedFrame.sharpness,
+         reason: event.selectedFrame.reason,
+      } : null,
+      observations: event.observations,
+   }));
+   const footer = {
+      type: 'analysis-footer',
+      status: session.status,
+      updatedAt: session.updatedAt,
+      summary: session.summary,
+      storyOrder: session.editorial.storyOrder,
+   };
+   return [header, ...samples, ...session.journal, ...events, footer]
+      .map((entry) => JSON.stringify(entry))
+      .join('\n') + '\n';
+}
diff --git a/loader.html b/loader.html
index a54f171..86d527e 100644
--- a/loader.html
+++ b/loader.html
@@ -4,53 +4,17 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1" />
-  <title data-i18n="loader_page_title">Makeup Video Loader &amp; Face Engine Debugger — Ghostmaxxing</title>
-  <meta name="description" content="The Video Loader runs local MP4 files through the 2D face-matching pipeline and an experimental visual-embedding path, allowing repeatable frame-by-frame comparison without a live camera.  " data-i18n-content="loader_meta_description" />
-  <meta property="og:title" content="Makeup Video Loader &amp; Face Engine Debugger — Ghostmaxxing" />
-  <meta property="og:description" content="The Video Loader runs local MP4 files through the 2D face-matching pipeline and an experimental visual-embedding path, allowing repeatable frame-by-frame comparison without a live camera.  " />
+  <title data-i18n="loader_page_title">Video Loader — Ghostmaxxing</title>
+  <meta name="description" content="Analyse an existing local makeup video, review face-model transitions, and export reproducible evidence without uploading the source." />
+  <meta property="og:title" content="Makeup Video Analyser — Ghostmaxxing" />
+  <meta property="og:description" content="Analyse local makeup videos into timelines, before-and-after evidence, and reusable technical logs." />
   <meta property="og:type" content="website" />
   <meta property="og:url" content="https://ghostmaxxing.vecna.eu/loader.html" />
   <meta property="og:image" content="https://ghostmaxxing.vecna.eu/images/social/ghostmaxxing-generic.jpg" />
-  <meta property="og:image:type" content="image/jpeg" />
-  <meta property="og:image:width" content="1200" />
-  <meta property="og:image:height" content="630" />
-  <meta property="og:image:alt" content="Ghostmaxxing: test face-recognition camouflage in your browser." />
   <meta name="twitter:card" content="summary_large_image" />
-  <meta name="twitter:title" content="Makeup Video Loader &amp; Face Engine Debugger — Ghostmaxxing" />
-  <meta name="twitter:description" content="Load local MP4 makeup videos into Ghostmaxxing's 2D and 3D face engines for recording, comparison, and debugging." />
-  <meta name="twitter:image" content="https://ghostmaxxing.vecna.eu/images/social/ghostmaxxing-generic.jpg" />
-  <meta name="twitter:image:alt" content="Ghostmaxxing: test face-recognition camouflage in your browser." />
   <link rel="canonical" href="https://ghostmaxxing.vecna.eu/loader.html" />
   <meta name="theme-color" content="#14100c" />
   <link rel="icon" type="image/svg+xml" href="/images/logo/mark-color.svg" />
-  <link rel="icon" type="image/png" sizes="32x32" href="/images/logo/favicon-32.png" />
-  <script type="application/ld+json">
-  {
-    "@context": "https://schema.org",
-    "@type": "WebApplication",
-    "@id": "https://ghostmaxxing.vecna.eu/loader.html#application",
-    "name": "Ghostmaxxing Video Loader",
-    "url": "https://ghostmaxxing.vecna.eu/loader.html",
-    "description": "Load local MP4 files into the Ghostmaxxing face-matching pipeline for repeatable frame-by-frame comparison without a live camera.",
-    "applicationCategory": "EducationalApplication",
-    "operatingSystem": "Web",
-    "browserRequirements": "A modern browser with WebGL and WebAssembly support",
-    "featureList": [
-      "Load a local video file",
-      "Compare selected frames using the local face-matching pipeline",
-      "Record repeatable before-and-after experiments"
-    ],
-    "author": {
-      "@type": "Person",
-      "@id": "https://me.vecna.eu/#vecna",
-      "name": "Claudio Agosti",
-      "alternateName": "vecna",
-      "url": "https://me.vecna.eu/",
-      "sameAs": ["https://github.com/vecna", "https://retro.pizza/@vecna"]
-    }
-  }
-  </script>
-
   <link rel="stylesheet" href="/styles/styles.css" />
   <link rel="stylesheet" href="/styles/pages.css" />
   <link rel="stylesheet" href="/styles/loader.css" />
@@ -64,89 +28,117 @@
           <img class="wordmark__mark" src="/images/logo/mark-ondark.svg" alt="" aria-hidden="true" />
           <span class="wordmark__title">Ghostmaxxing</span>
         </span>
-        <span class="wordmark__subtitle" data-i18n="loader_subtitle">Internal video loader</span>
+        <span class="wordmark__subtitle">Seekable video analysis</span>
       </a>
       <div class="loader-header__tools">
-        <span class="locale-control" aria-hidden="false">
+        <label class="locale-control">
           <span class="locale-control__flag" aria-hidden="true"></span>
           <select id="localeSelect" class="locale-select" aria-label="Language" data-i18n-aria-label="language_label"></select>
-        </span>
+        </label>
         <div class="loader-status" aria-live="polite">
           <span id="statusDot" class="status-dot"></span>
-          <span id="statusText" data-i18n="loader_loading_models_status">loading models</span>
+          <span id="statusText">loading models</span>
         </div>
       </div>
     </header>
 
-    <h1 id="loader-title" class="loader-title" data-i18n="loader_title">Makeup Video Test Loader</h1>
+    <section class="loader-intro">
+      <div>
+        <p class="loader-kicker">Existing video → evidence</p>
+        <h1 id="loader-title" class="loader-title">Makeup video analyser</h1>
+      </div>
+      <p class="loader-intro__copy">
+        Select a local video. It stays on this device while MediaPipe maps frequent visual changes and face-api verifies selected moments.
+      </p>
+    </section>
 
-    <section class="loader-band loader-band--stage" aria-label="Load and play video" data-i18n-aria-label="loader_stage_label">
-      <div id="viewer" class="loader-viewer" data-view="camera">
-        <div id="placeholder" class="loader-placeholder" data-i18n="loader_placeholder">Select a local MP4 video to begin.</div>
+    <section class="loader-stage" aria-label="Local video source">
+      <div id="viewer" class="loader-viewer">
+        <div id="placeholder" class="loader-placeholder">Select a local MP4, WebM, or compatible video to begin.</div>
         <video id="video" controls playsinline preload="metadata"></video>
-        <img id="previewImage" alt="diagnostic preview" data-i18n-alt="diagnostic_preview_alt" />
-        <canvas id="overlay"></canvas>
-        <canvas id="mesh3dOverlay"></canvas>
-        <canvas id="bboxOverlay"></canvas>
       </div>
-      <div class="loader-controls">
+
+      <div class="loader-source-controls">
         <label class="file-control">
-          <span data-i18n="loader_load_mp4">Load MP4</span>
-          <input id="videoFile" type="file" accept="video/mp4,video/*" />
+          <span>Select local video</span>
+          <input id="videoFile" type="file" accept="video/*,.mkv" />
         </label>
-        <div class="timeline" aria-label="Video progress" data-i18n-aria-label="loader_timeline_label">
+        <div class="loader-seek" aria-label="Video position">
           <span id="currentTimeLabel">00:00.000</span>
           <input id="videoSeek" type="range" min="0" max="0" step="0.001" value="0" disabled />
           <span id="durationLabel">00:00.000</span>
         </div>
       </div>
-    </section>
 
-    <section class="loader-band loader-band--actions" aria-label="Face actions" data-i18n-aria-label="loader_actions_label">
-      <button id="recordFaceBtn" class="loader-action" type="button" disabled data-i18n="loader_record_face_button">Record face</button>
-      <button id="seekFaceBtn" class="loader-action" type="button" disabled data-i18n="loader_seek_face_button">Seek face</button>
-      <div class="loader-readout">
-        <span data-i18n="loader_faces_in_memory">Faces in memory</span>
-        <strong id="dbCount">0</strong>
-        <span data-i18n="next_id_metric">Next ID</span>
-        <strong id="nextId">0</strong>
-        <span data-i18n="loader_2d_threshold">2D threshold</span>
-        <strong id="thresholdLabel">0.58</strong>
-        <span id="dbCountBadge" hidden>0</span>
+      <div class="loader-analysis-controls" aria-label="Analysis controls">
+        <label>
+          <span>Sample every</span>
+          <select id="sampleInterval">
+            <option value="0.25">0.25 seconds</option>
+            <option value="0.5" selected>0.5 seconds</option>
+            <option value="1">1 second</option>
+            <option value="2">2 seconds</option>
+          </select>
+        </label>
+        <label>
+          <span>Find distance reference after</span>
+          <input id="baselineTime" type="number" min="0" step="0.1" value="0" inputmode="decimal" />
+          <span>seconds</span>
+        </label>
+        <button id="useCurrentTimeBtn" type="button" disabled>Use current video time</button>
+        <button id="startAnalysisBtn" class="loader-primary" type="button" disabled>Analyse video</button>
+        <button id="cancelAnalysisBtn" type="button" disabled>Stop after this sample</button>
+        <div class="loader-progress">
+          <progress id="analysisProgress" max="100" value="0"></progress>
+          <span id="analysisProgressLabel">0%</span>
+        </div>
       </div>
     </section>
 
-    <section class="loader-band loader-band--log" aria-label="Button activity log" data-i18n-aria-label="loader_log_label">
-      <div class="loader-log-head">
-        <h2 data-i18n="loader_activity_log_title">Activity log</h2>
-        <p data-i18n="loader_activity_log_description">Each entry is captured at the exact video timestamp where the button was pressed.</p>
+    <section class="loader-report" aria-labelledby="report-title">
+      <header class="loader-report__header">
+        <div>
+          <p class="loader-kicker">Analysis report</p>
+          <h2 id="report-title">Evidence and story order</h2>
+          <p id="reportSummary" class="loader-report__summary">No video analysed yet.</p>
+        </div>
+        <div class="loader-report__actions">
+          <button id="undoEditBtn" type="button" disabled>Undo</button>
+          <button id="redoEditBtn" type="button" disabled>Redo</button>
+          <button id="downloadReportBtn" type="button" disabled>Download report</button>
+          <button id="downloadLogBtn" type="button" disabled>Download technical log</button>
+        </div>
+      </header>
+
+      <div class="loader-baseline">
+        <strong>Distance reference</strong>
+        <span id="baselineSummary">Distance reference not acquired yet.</span>
       </div>
-      <div id="loaderLog" class="loader-log" aria-live="polite"></div>
-      <div id="logBox" class="u-hidden" aria-hidden="true"></div>
+
+      <p class="loader-story-note">
+        Source timestamps stay factual. Use “Move earlier” and “Move later” to choose the narrative order independently—for example, when a finished-look teaser appears before the tutorial itself.
+      </p>
+
+      <figure class="loader-timeline-figure">
+        <canvas id="analysisTimeline" aria-label="Analysis timeline with separate MediaPipe, ImageEmbedder, and face-api lanes"></canvas>
+        <figcaption>
+          MediaPipe presence and visual drift are frequent signals. face-api distance is sampled only at verification points; its dashed line is the 0.6 threshold.
+        </figcaption>
+      </figure>
+
+      <div id="emptyReport" class="loader-empty-report">
+        Confirmed transitions will appear here with a high-resolution source frame and editable story text.
+      </div>
+      <div id="eventList" class="loader-event-list"></div>
     </section>
 
-    <p>
-      Test the synthetic before-and-after faces in <code>tests/fixtures/</code>
-      with the <a href="/docs/jsdoc/tutorials/lab-screenshots/">Playwright script</a>.
-    </p>
+    <aside class="loader-privacy">
+      <strong>Local by design.</strong>
+      The source video is not uploaded. The editorial report and the versioned technical log are separate downloads.
+    </aside>
   </main>
 
-  <select id="fpsSelect" class="u-hidden" aria-hidden="true" tabindex="-1">
-    <option value="120" selected>120 ms</option>
-  </select>
-  <button id="saveBtn" class="u-hidden" aria-hidden="true" tabindex="-1"></button>
-  <button id="scanBtn" class="u-hidden" aria-hidden="true" tabindex="-1"></button>
-  <button id="copyMakeupBtn" class="u-hidden" aria-hidden="true" tabindex="-1"></button>
-  <button id="analyzeBtn" class="u-hidden" aria-hidden="true" tabindex="-1"></button>
-  <button id="toggleSettingsBtn" class="u-hidden" aria-hidden="true" tabindex="-1"></button>
-  <button id="overlayModeBtn" class="u-hidden" aria-hidden="true" tabindex="-1"></button>
-  <button id="clearDbBtn" class="u-hidden" aria-hidden="true" tabindex="-1"></button>
-  <button id="recordBtn" class="u-hidden" aria-hidden="true" tabindex="-1"></button>
-  <div id="ghostylesContainer" class="u-hidden" aria-hidden="true"></div>
-
   <script src="/lab-js/vendor/face-api.js"></script>
-  <script type="module" src="/lab-js/bbox-overlay.js"></script>
-  <script type="module" src="/lab-js/mediapipe-loop.js"></script>
   <script type="module" src="/lab-js/loader.js"></script>
 </body>
 
diff --git a/package.json b/package.json
index 6a7bd46..08a3e13 100644
--- a/package.json
+++ b/package.json
@@ -41,6 +41,7 @@
     "htmls": "npm run docs:functional && npm run docs:rebuild && npm run test:coverage && npm run update:coverage-badge && npm run update:projects && npm run update:references && npm run update:genealogy && npm run codemap",
     "htmls:install": "npm run docs:functional && npm run docs:rebuild && npm run test:coverage && npm run update:coverage-badge && npm run update:projects && npm run update:references && npm run update:genealogy && npm run codemap && npm run check:seo && npm run web-client-setup",
     "docs:screenshots": "node scripts-dev/capture-doc-screenshots.cjs",
+    "analyze:video": "node scripts-dev/video-loader.cjs analyze",
     "capture:render": "node scripts-dev/lab-capture.cjs render",
     "capture:measure": "node scripts-dev/lab-capture.cjs measure",
     "capture:shots": "node scripts-dev/lab-capture.cjs shots",
diff --git a/scripts-dev/FOLDER-DESCRIPTION.md b/scripts-dev/FOLDER-DESCRIPTION.md
index 447be75..47a69b1 100644
--- a/scripts-dev/FOLDER-DESCRIPTION.md
+++ b/scripts-dev/FOLDER-DESCRIPTION.md
@@ -8,7 +8,7 @@ Related-project generation lives beside its data in `projects/`, mirroring the e
 
 ## Script roles
 
-- `build-face-fixtures.cjs` — uses ffmpeg to turn synthetic clean/painted JPEG pairs into large, git-ignored Y4M fake-camera clips under `tests/fixtures/synthetic-faces/y4m/`.
+- `video-loader.cjs` — drives the seekable local-video analyser through Playwright, writes report HTML/JSON, selected JPEG evidence, and a separate versioned NDJSON technical log. Its `extract-clips` command can later reprocess that log with the original source and ffmpeg. It does not create fake-camera or Y4M inputs.
 
 - `lab-capture.cjs` — drives the Lab in a headless browser with a picture or video in place of the webcam (converted to Y4M with ffmpeg). `render` draws the Lab's layers and a Ghostyle on one picture, `measure` reports the recognition distance between a baseline and a dazzled picture, `shots` captures the workshop screenshots; `--debug` dumps page state. Results do not establish protection against external systems.
 
diff --git a/scripts-dev/README.md b/scripts-dev/README.md
index 85decef..3dbd697 100644
--- a/scripts-dev/README.md
+++ b/scripts-dev/README.md
@@ -22,6 +22,7 @@ Run the commands below from the `ghostmaxxing` repository root. Most scripts res
 | Draw the lab's layers on a picture | `node scripts-dev/lab-capture.cjs render --baseline <file> --layers landmarks --output <file>` | One picture with the lab's overlays (needs `ffmpeg`) |
 | Measure the distance between two pictures | `node scripts-dev/lab-capture.cjs measure --baseline <file> --dazzled <file>` | Readings and verdict on stdout; `--output-visual-log <file>` for a composite picture |
 | Capture the workshop screenshots | `node scripts-dev/lab-capture.cjs shots --baseline <file> --dazzled <file>` | Three JPEGs in `images/workshops/` |
+| Analyse a seekable makeup video | `npm run analyze:video -- <video> --output <directory>` | Report JSON/HTML, selected JPEGs, and a separate NDJSON technical log |
 | Generate a code map and viewer | `npm run codemap` | `codemap/codemap.json`, `codemap/codemap.html` |
 | Export UI translations | `npm run i18n:extract` | POT and pipe-delimited CSV under `translations/` |
 | Extract public copy | `node scripts-dev/extract-text-only.js` | `EXTRACTED-text-YYYY-MM-DD.md` |
@@ -364,6 +365,37 @@ Choose checks for the changed behaviour. `check` does not rebuild docs, verify t
 
 When adding or changing a script, record its exact command, inputs, outputs, overwrite behaviour and failure conditions here. Update the task index and the short folder description. Keep executable behaviour separate from intended future behaviour, and document scripts without npm aliases as well as those exposed through `package.json`.
 
+## Analyse an existing makeup video
+
+Source: `scripts-dev/video-loader.cjs`. This Playwright script controls the
+same file input and public API as `loader.html`; it does not emulate a webcam or
+convert the source to Y4M.
+
+```sh
+npm run analyze:video -- video.webm \
+  --output /tmp/video-analysis \
+  --baseline-time 18 \
+  --sample-interval 0.5
+
+node scripts-dev/video-loader.cjs replay-log \
+  /tmp/video-analysis/analysis.ndjson \
+  --output /tmp/video-analysis/replay
+
+node scripts-dev/video-loader.cjs extract-clips \
+  /tmp/video-analysis/analysis.ndjson \
+  video.webm \
+  --output /tmp/video-analysis/clips
+```
+
+`analyze` writes `report.json`, portable `report.html`, selected JPEG evidence
+under `frames/`, and `analysis.ndjson`. Report images and technical telemetry
+remain separate artifacts. `replay-log` needs no video or model inference.
+`extract-clips` verifies the source byte size and sampled fingerprint recorded
+in the log, then uses ffmpeg to re-encode logged event windows as H.264/AAC MP4
+files. The source is read in place and never overwritten. Detailed signal semantics, baseline
+selection, story ordering, failure preservation, and calibration policy are in
+`tutorials/video-loader.md`.
+
 ## Draw the lab's layers on a picture and measure the distance
 
 Source: `scripts-dev/lab-capture.cjs`. Run from the repository root.
@@ -564,9 +596,8 @@ script that produced it.
 `figureN-painted.jpeg` pairs: the same generated face bare and with
 adversarial makeup. Nobody is depicted, so they can appear in documentation
 and be re-rendered at any time. Any of them is a valid `--baseline` or
-`--dazzled`. `scripts-dev/build-face-fixtures.cjs` still builds concatenated
-`.y4m` clips from them for anyone who wants a single clean-then-painted feed;
-`lab-capture.cjs` no longer needs those, since it converts pictures itself.
+`--dazzled`. `lab-capture.cjs` converts these inputs for its own specialised
+fake-camera workflow. The separate seekable Video Loader does not use Y4M.
 
 To measure the whole set, loop in the shell:
 
diff --git a/scripts-dev/build-face-fixtures.cjs b/scripts-dev/build-face-fixtures.cjs
deleted file mode 100755
index dce7794..0000000
--- a/scripts-dev/build-face-fixtures.cjs
+++ /dev/null
@@ -1,254 +0,0 @@
-#!/usr/bin/env node
-/**
- * Build fake-webcam fixtures from the synthetic face pairs.
- *
- * Chromium can replace the webcam with a raw Y4M file. This script turns each
- * `figureN-clean.jpeg` / `figureN-painted.jpeg` pair in
- * `tests/fixtures/synthetic-faces/` into three Y4M clips:
- *
- *   figureN-clean.y4m     the bare face, long enough to save an identity
- *   figureN-painted.y4m   the same face wearing the makeup
- *   figureN-pair.y4m      clean then painted, concatenated, for one session
- *
- * The pair file gives a single clean-then-painted feed for anyone driving the
- * lab by hand: save the identity while the clean segment is on screen, read
- * the distance as the painted segment arrives. `lab-capture.cjs` no longer
- * needs these clips: it converts pictures itself and takes the two files
- * directly (`measure --baseline clean.jpeg --dazzled painted.jpeg`).
- *
- * Requires ffmpeg on PATH. Output is large (roughly 7 MB per second at
- * 640x480) and is git-ignored.
- *
- * Usage:
- *   node scripts-dev/build-face-fixtures.cjs --figure 9,10
- *   node scripts-dev/build-face-fixtures.cjs --figure all --force
- *   node scripts-dev/build-face-fixtures.cjs --figure 9 --size 320x240
- */
-
-const fs = require('node:fs');
-const path = require('node:path');
-const { spawnSync } = require('node:child_process');
-
-const ROOT = path.resolve(__dirname, '..');
-const DEFAULT_SOURCE = path.join(ROOT, 'tests', 'fixtures', 'synthetic-faces');
-const DEFAULT_OUTPUT = path.join(DEFAULT_SOURCE, 'y4m');
-const VARIANTS = ['clean', 'painted'];
-
-function usage() {
-   return `
-Build Y4M fake-webcam fixtures from the synthetic face pairs.
-
-Options:
-  --figure <list>       Figure numbers, comma-separated, or all (required)
-  --source <path>       Where the JPEG pairs live
-                        (default: tests/fixtures/synthetic-faces)
-  --output <path>       Where the Y4M files are written
-                        (default: tests/fixtures/synthetic-faces/y4m)
-  --size <WxH>          Frame size (default: 640x480)
-  --fps <n>             Frames per second (default: 15)
-  --clean-seconds <n>   Length of the clean segment (default: 6)
-  --painted-seconds <n> Length of the painted segment (default: 10)
-  --face-height <n>     Face height inside the frame, in pixels (default: 300)
-  --background <hex>    Padding colour (default: 0x9a938c)
-  --keep-singles        Keep figureN-clean.y4m and figureN-painted.y4m
-                        (default: kept; use --pair-only to delete them)
-  --pair-only           Delete the single-variant clips after concatenating
-  --force               Rebuild files that already exist
-  --help                Show this help
-
-Examples:
-  node scripts-dev/build-face-fixtures.cjs --figure 9,10
-  node scripts-dev/build-face-fixtures.cjs --figure all --force
-`;
-}
-
-function requireValue(argv, index, option) {
-   const value = argv[index + 1];
-   if (!value || value.startsWith('--')) throw new Error(`${option} requires a value.`);
-   return value;
-}
-
-function parseArgs(argv) {
-   const options = {
-      figures: null,
-      source: DEFAULT_SOURCE,
-      output: DEFAULT_OUTPUT,
-      size: '640x480',
-      fps: 15,
-      cleanSeconds: 6,
-      paintedSeconds: 10,
-      faceHeight: 300,
-      background: '0x9a938c',
-      pairOnly: false,
-      force: false,
-   };
-
-   for (let index = 0; index < argv.length; index += 1) {
-      const arg = argv[index];
-      if (arg === '--help') {
-         process.stdout.write(usage());
-         process.exit(0);
-      }
-      if (arg === '--force') { options.force = true; continue; }
-      if (arg === '--pair-only') { options.pairOnly = true; continue; }
-      if (arg === '--keep-singles') { options.pairOnly = false; continue; }
-      if (arg === '--figure') { options.figures = requireValue(argv, index, arg); index += 1; continue; }
-      if (arg === '--source') { options.source = path.resolve(ROOT, requireValue(argv, index, arg)); index += 1; continue; }
-      if (arg === '--output') { options.output = path.resolve(ROOT, requireValue(argv, index, arg)); index += 1; continue; }
-      if (arg === '--size') { options.size = requireValue(argv, index, arg); index += 1; continue; }
-      if (arg === '--background') { options.background = requireValue(argv, index, arg); index += 1; continue; }
-      if (arg === '--fps' || arg === '--clean-seconds' || arg === '--painted-seconds' || arg === '--face-height') {
-         const value = Number(requireValue(argv, index, arg));
-         if (!Number.isFinite(value) || value <= 0) throw new Error(`${arg} must be a positive number.`);
-         if (arg === '--fps') options.fps = value;
-         if (arg === '--clean-seconds') options.cleanSeconds = value;
-         if (arg === '--painted-seconds') options.paintedSeconds = value;
-         if (arg === '--face-height') options.faceHeight = value;
-         index += 1;
-         continue;
-      }
-      throw new Error(`Unknown option: ${arg}`);
-   }
-
-   if (!options.figures) throw new Error('--figure is required. Pass numbers such as 9,10 or the word all.');
-   if (!/^\d+x\d+$/.test(options.size)) throw new Error(`--size must look like 640x480, received ${options.size}`);
-   return options;
-}
-
-function discoverFigures(sourceDir) {
-   if (!fs.existsSync(sourceDir)) throw new Error(`Source folder not found: ${sourceDir}`);
-   const numbers = new Set();
-   for (const entry of fs.readdirSync(sourceDir)) {
-      const match = /^figure(\d+)-clean\.(jpe?g|png)$/i.exec(entry);
-      if (match) numbers.add(Number(match[1]));
-   }
-   return [...numbers].sort((a, b) => a - b);
-}
-
-function resolveFigures(options) {
-   if (options.figures === 'all') {
-      const found = discoverFigures(options.source);
-      if (!found.length) throw new Error(`No figureN-clean.jpeg files in ${options.source}`);
-      return found;
-   }
-   const list = options.figures.split(',').map((item) => item.trim()).filter(Boolean);
-   const numbers = list.map((item) => {
-      const value = Number(item);
-      if (!Number.isInteger(value) || value <= 0) throw new Error(`Invalid figure number: ${item}`);
-      return value;
-   });
-   return [...new Set(numbers)].sort((a, b) => a - b);
-}
-
-function findSource(sourceDir, figure, variant) {
-   for (const extension of ['jpeg', 'jpg', 'png']) {
-      const candidate = path.join(sourceDir, `figure${figure}-${variant}.${extension}`);
-      if (fs.existsSync(candidate)) return candidate;
-   }
-   return null;
-}
-
-function run(command, args) {
-   const result = spawnSync(command, args, { encoding: 'utf8' });
-   if (result.error) throw new Error(`${command} could not be started: ${result.error.message}`);
-   if (result.status !== 0) {
-      const tail = String(result.stderr || '').trim().split('\n').slice(-6).join('\n');
-      throw new Error(`${command} exited with ${result.status}\n${tail}`);
-   }
-   return result;
-}
-
-function assertFfmpeg() {
-   const probe = spawnSync('ffmpeg', ['-version'], { encoding: 'utf8' });
-   if (probe.error || probe.status !== 0) {
-      throw new Error('ffmpeg is required and was not found on PATH. Install it, then run this script again.');
-   }
-}
-
-function buildSegment(source, target, seconds, options) {
-   const [width, height] = options.size.split('x').map(Number);
-   const filter = [
-      `scale=-1:${options.faceHeight}`,
-      `pad=${width}:${height}:(ow-iw)/2:(oh-ih)/2:color=${options.background}`,
-      'format=yuv420p',
-   ].join(',');
-   run('ffmpeg', [
-      '-y', '-loglevel', 'error',
-      '-loop', '1', '-i', source,
-      '-t', String(seconds),
-      '-r', String(options.fps),
-      '-vf', filter,
-      '-pix_fmt', 'yuv420p',
-      target,
-   ]);
-}
-
-function concatSegments(parts, target, outputDir) {
-   const listFile = path.join(outputDir, `.concat-${path.basename(target, '.y4m')}.txt`);
-   fs.writeFileSync(listFile, parts.map((part) => `file '${path.basename(part)}'`).join('\n') + '\n');
-   try {
-      run('ffmpeg', ['-y', '-loglevel', 'error', '-f', 'concat', '-safe', '0', '-i', listFile, '-c', 'copy', target]);
-   } finally {
-      fs.rmSync(listFile, { force: true });
-   }
-}
-
-function megabytes(file) {
-   return (fs.statSync(file).size / (1024 * 1024)).toFixed(1);
-}
-
-function main() {
-   const options = parseArgs(process.argv.slice(2));
-   assertFfmpeg();
-   const figures = resolveFigures(options);
-   fs.mkdirSync(options.output, { recursive: true });
-
-   const built = [];
-   const skipped = [];
-   for (const figure of figures) {
-      const sources = {};
-      for (const variant of VARIANTS) {
-         const file = findSource(options.source, figure, variant);
-         if (!file) {
-            skipped.push(`figure${figure}: no ${variant} image in ${path.relative(ROOT, options.source)}`);
-            sources.missing = true;
-            break;
-         }
-         sources[variant] = file;
-      }
-      if (sources.missing) continue;
-
-      const pair = path.join(options.output, `figure${figure}-pair.y4m`);
-      if (fs.existsSync(pair) && !options.force) {
-         skipped.push(`figure${figure}: already built, pass --force to rebuild`);
-         continue;
-      }
-
-      const singles = [];
-      for (const variant of VARIANTS) {
-         const target = path.join(options.output, `figure${figure}-${variant}.y4m`);
-         const seconds = variant === 'clean' ? options.cleanSeconds : options.paintedSeconds;
-         process.stdout.write(`figure${figure} ${variant}: encoding ${seconds}s ${options.size} @ ${options.fps}fps\n`);
-         buildSegment(sources[variant], target, seconds, options);
-         singles.push(target);
-      }
-      concatSegments(singles, pair, options.output);
-      process.stdout.write(`figure${figure} pair   : ${megabytes(pair)} MB\n`);
-      if (options.pairOnly) for (const file of singles) fs.rmSync(file, { force: true });
-      built.push(figure);
-   }
-
-   process.stdout.write('\n');
-   if (built.length) {
-      process.stdout.write(`Built ${built.length} fixture set(s) in ${path.relative(ROOT, options.output)}: ${built.map((n) => `figure${n}`).join(', ')}\n`);
-   }
-   for (const line of skipped) process.stdout.write(`Skipped ${line}\n`);
-   if (!built.length && !skipped.length) process.stdout.write('Nothing to do.\n');
-}
-
-try {
-   main();
-} catch (error) {
-   process.stderr.write(`${String(error?.message || error)}\n`);
-   process.exit(1);
-}
diff --git a/scripts-dev/capture-doc-screenshots.cjs b/scripts-dev/capture-doc-screenshots.cjs
index 976d0e9..b60f937 100644
--- a/scripts-dev/capture-doc-screenshots.cjs
+++ b/scripts-dev/capture-doc-screenshots.cjs
@@ -444,13 +444,13 @@ async function captureLab(context, run) {
 async function captureLoader(context, run) {
    const page = await preparePage(context, run.baseUrl, 'loader.html', run.locale);
    try {
-      await page.waitForFunction(() => document.getElementById('statusDot')?.classList.contains('live'), undefined, {
+      await page.waitForFunction(() => window.gstmxxVideoLoader?.isModelsReady(), undefined, {
          timeout: 90000,
       });
       await run.capture(page, 'loader-01-empty', {
          page: 'loader.html',
          purpose: 'The Loader before selecting a local video.',
-         locator: '.loader-band--stage',
+         locator: '.loader-stage',
       });
 
       await page.setInputFiles('#videoFile', LOADER_VIDEO);
@@ -469,15 +469,21 @@ async function captureLoader(context, run) {
       await run.capture(page, 'loader-02-video-loaded', {
          page: 'loader.html',
          purpose: `The committed MP4 loaded at ${capturedTime.toFixed(3)} seconds with active controls.`,
-         locator: '.loader-band--stage',
+         locator: '.loader-stage',
       });
 
-      await page.locator('#recordFaceBtn').click();
-      await page.locator('#loaderLog .loader-entry').first().waitFor({ state: 'visible' });
+      await page.evaluate(async () => {
+         const duration = document.getElementById('video').duration;
+         await window.gstmxxVideoLoader.analyze({
+            start: 0,
+            end: Math.min(duration, 12),
+            sampleIntervalSeconds: 1,
+         });
+      });
       await run.capture(page, 'loader-03-record-result', {
          page: 'loader.html',
-         purpose: 'A frame-level Record face result with timestamped diagnostic metadata.',
-         locator: '.loader-band--log',
+         purpose: 'The model-separated timeline and report after a bounded local-video analysis.',
+         locator: '.loader-report',
       });
    } finally {
       await page.close();
diff --git a/scripts-dev/video-loader.cjs b/scripts-dev/video-loader.cjs
new file mode 100644
index 0000000..8fc9177
--- /dev/null
+++ b/scripts-dev/video-loader.cjs
@@ -0,0 +1,532 @@
+#!/usr/bin/env node
+/**
+ * @file Seekable-video analysis CLI for Ghostmaxxing.
+ * @description
+ * Playwright drives loader.html through its public browser API. The CLI never
+ * converts a source into a fake camera format and never creates a frame queue.
+ * It writes editorial report artifacts separately from a versioned NDJSON
+ * technical log that can later be replayed to extract short source clips.
+ *
+ * Usage:
+ *   node scripts-dev/video-loader.cjs analyze video.webm --output analysis/video
+ *   node scripts-dev/video-loader.cjs replay-log analysis.ndjson --output replay
+ *   node scripts-dev/video-loader.cjs extract-clips analysis.ndjson video.webm
+ */
+
+const crypto = require('node:crypto');
+const fs = require('node:fs');
+const path = require('node:path');
+const childProcess = require('node:child_process');
+const httpServer = require('http-server');
+const { chromium } = require('@playwright/test');
+
+const ROOT = path.resolve(__dirname, '..');
+const FINGERPRINT_CHUNK_BYTES = 1024 * 1024;
+
+/**
+ * Prints command help.
+ *
+ * @returns {void}
+ */
+function usage() {
+   process.stdout.write(`
+Ghostmaxxing seekable-video analyser
+
+Commands:
+  analyze <video>             Analyse a local seekable video through loader.html
+  replay-log <log>            Rebuild a text-only report from a technical log
+  extract-clips <log> <video> Extract event windows from the original source
+
+Analyze options:
+  --output <directory>        Output directory (default: video-analysis/<source>)
+  --sample-interval <seconds> Lightweight sample interval (default: 0.5)
+  --start <seconds>           First media timestamp (default: 0)
+  --end <seconds>             Last media timestamp (default: source duration)
+  --baseline-time <seconds>   Begin baseline search here, then scan full range
+  --base-url <url>            Use an existing local server
+  --headed                    Show Chromium while analysing
+  --clips                     Extract clips after analysis
+  --clip-before <seconds>     Clip pre-roll (default: 2)
+  --clip-after <seconds>      Clip post-roll (default: 3)
+
+Replay options:
+  --output <directory>        Output directory
+
+Clip options:
+  --output <directory>        Clip directory (default: <log directory>/clips)
+  --clip-before <seconds>     Override logged pre-roll
+  --clip-after <seconds>      Override logged post-roll
+
+The source remains in place. Reports, selected JPEG frames, and analysis.ndjson
+are written as separate artifacts.
+`);
+}
+
+/**
+ * Returns a required option value.
+ *
+ * @param {string[]} argv Argument vector.
+ * @param {number} index Current option index.
+ * @param {string} option Option name.
+ * @returns {string} Following argument.
+ */
+function requireValue(argv, index, option) {
+   const value = argv[index + 1];
+   if (!value || value.startsWith('--')) throw new Error(`${option} requires a value.`);
+   return value;
+}
+
+/**
+ * Parses one finite non-negative numeric option.
+ *
+ * @param {string} value Raw value.
+ * @param {string} option Option name.
+ * @returns {number} Parsed number.
+ */
+function parseNumber(value, option) {
+   const parsed = Number(value);
+   if (!Number.isFinite(parsed) || parsed < 0) throw new Error(`${option} must be a non-negative number.`);
+   return parsed;
+}
+
+/**
+ * Produces a filesystem-safe source stem.
+ *
+ * @param {string} filename Source filename.
+ * @returns {string} Safe stem.
+ */
+function safeStem(filename) {
+   const stem = path.basename(filename, path.extname(filename));
+   return stem.normalize('NFKD')
+      .replace(/[^a-zA-Z0-9._-]+/g, '-')
+      .replace(/^-+|-+$/g, '')
+      .slice(0, 80) || 'video';
+}
+
+/**
+ * Parses CLI options without introducing another command-line dependency.
+ *
+ * @param {string[]} argv User arguments excluding node and script path.
+ * @returns {object} Parsed command options.
+ */
+function parseArgs(argv) {
+   if (!argv.length || argv.includes('--help') || argv.includes('-h')) {
+      usage();
+      process.exit(0);
+   }
+   const command = argv[0];
+   if (!['analyze', 'replay-log', 'extract-clips'].includes(command)) {
+      throw new Error(`Unknown command: ${command}`);
+   }
+   const positional = [];
+   const options = {
+      command,
+      output: null,
+      sampleIntervalSeconds: 0.5,
+      start: 0,
+      end: null,
+      baselineTime: null,
+      baseUrl: null,
+      headed: false,
+      clips: false,
+      clipBefore: null,
+      clipAfter: null,
+   };
+
+   for (let index = 1; index < argv.length; index += 1) {
+      const argument = argv[index];
+      if (!argument.startsWith('--')) {
+         positional.push(argument);
+         continue;
+      }
+      if (argument === '--headed') {
+         options.headed = true;
+         continue;
+      }
+      if (argument === '--clips') {
+         options.clips = true;
+         continue;
+      }
+      const value = requireValue(argv, index, argument);
+      index += 1;
+      if (argument === '--output') options.output = path.resolve(value);
+      else if (argument === '--sample-interval') options.sampleIntervalSeconds = parseNumber(value, argument);
+      else if (argument === '--start') options.start = parseNumber(value, argument);
+      else if (argument === '--end') options.end = parseNumber(value, argument);
+      else if (argument === '--baseline-time') options.baselineTime = parseNumber(value, argument);
+      else if (argument === '--base-url') options.baseUrl = value.replace(/\/$/, '');
+      else if (argument === '--clip-before') options.clipBefore = parseNumber(value, argument);
+      else if (argument === '--clip-after') options.clipAfter = parseNumber(value, argument);
+      else throw new Error(`Unknown option: ${argument}`);
+   }
+   options.positional = positional;
+   return options;
+}
+
+/**
+ * Validates one readable regular file.
+ *
+ * @param {string} input User path.
+ * @param {string} label Diagnostic label.
+ * @returns {string} Absolute path.
+ */
+function resolveFile(input, label) {
+   if (!input) throw new Error(`${label} is required.`);
+   const absolute = path.resolve(input);
+   const stat = fs.statSync(absolute, { throwIfNoEntry: false });
+   if (!stat?.isFile()) throw new Error(`${label} is not a readable file: ${absolute}`);
+   return absolute;
+}
+
+/**
+ * Computes a bounded provenance fingerprint for very large videos. The digest
+ * includes file size plus the first and last MiB; it identifies the expected
+ * source without spending minutes hashing multi-gigabyte recordings.
+ *
+ * @param {string} filename Absolute source path.
+ * @returns {Promise<string>} Algorithm-labelled digest.
+ */
+async function sampledFingerprint(filename) {
+   const handle = await fs.promises.open(filename, 'r');
+   try {
+      const stat = await handle.stat();
+      const firstLength = Math.min(FINGERPRINT_CHUNK_BYTES, stat.size);
+      const lastLength = Math.min(FINGERPRINT_CHUNK_BYTES, Math.max(0, stat.size - firstLength));
+      const first = Buffer.alloc(firstLength);
+      const last = Buffer.alloc(lastLength);
+      if (firstLength) await handle.read(first, 0, firstLength, 0);
+      if (lastLength) await handle.read(last, 0, lastLength, stat.size - lastLength);
+      const digest = crypto.createHash('sha256')
+         .update(String(stat.size))
+         .update('\0')
+         .update(first)
+         .update(last)
+         .digest('hex');
+      return `sampled-sha256:${digest}`;
+   } finally {
+      await handle.close();
+   }
+}
+
+/**
+ * Starts a no-cache local server rooted at the repository.
+ *
+ * @returns {Promise<{baseUrl: string, close: Function}>} Server handle.
+ */
+async function startServer() {
+   const server = httpServer.createServer({ root: ROOT, cache: -1, cors: false });
+   await new Promise((resolve, reject) => {
+      server.server.once('error', reject);
+      server.server.listen(0, '127.0.0.1', resolve);
+   });
+   const address = server.server.address();
+   return {
+      baseUrl: `http://127.0.0.1:${address.port}`,
+      close: () => new Promise((resolve) => server.server.close(resolve)),
+   };
+}
+
+/**
+ * Decodes a browser-exported data URL.
+ *
+ * @param {string} dataUrl Data URL.
+ * @returns {Buffer} Decoded bytes.
+ */
+function decodeDataUrl(dataUrl) {
+   const separator = dataUrl.indexOf(',');
+   if (separator < 0) throw new Error('Invalid frame data URL.');
+   return Buffer.from(dataUrl.slice(separator + 1), 'base64');
+}
+
+/**
+ * Escapes untrusted report text for standalone HTML output.
+ *
+ * @param {*} value Value to render.
+ * @returns {string} HTML-safe text.
+ */
+function escapeHtml(value) {
+   return String(value ?? '')
+      .replaceAll('&', '&amp;')
+      .replaceAll('<', '&lt;')
+      .replaceAll('>', '&gt;')
+      .replaceAll('"', '&quot;')
+      .replaceAll("'", '&#39;');
+}
+
+/**
+ * Formats seconds for report headings and clip filenames.
+ *
+ * @param {number} seconds Media timestamp.
+ * @returns {string} Compact timestamp.
+ */
+function formatTime(seconds) {
+   const hours = Math.floor(seconds / 3600);
+   const minutes = Math.floor((seconds % 3600) / 60);
+   const rest = seconds % 60;
+   return `${hours ? `${String(hours).padStart(2, '0')}:` : ''}${String(minutes).padStart(2, '0')}:${rest.toFixed(3).padStart(6, '0')}`;
+}
+
+/**
+ * Renders a portable report that references separately written JPEG evidence.
+ *
+ * @param {object} report Browser report model.
+ * @param {Map<string, string>} frameFiles Artifact ID to relative filename.
+ * @returns {string} Standalone HTML.
+ */
+function renderReportHtml(report, frameFiles) {
+   const baseline = frameFiles.get('baseline');
+   const eventMarkup = new Map(report.events.map((event) => {
+      const image = frameFiles.get(event.id);
+      const evidence = event.evidence.at(-1) || {};
+      const eventLabel = event.promoted
+         ? 'Selected story moment'
+         : event.classification === 'identity-distance'
+            ? 'Identity-distance transition'
+            : 'Detector divergence — review';
+      return [event.id, `<article class="event">
+        ${image ? `<img src="${escapeHtml(image)}" alt="Selected frame at ${escapeHtml(formatTime(event.anchorMediaTime))}">` : ''}
+        <div>
+          <p class="kicker">${eventLabel}</p>
+          <h2>${escapeHtml(formatTime(event.anchorMediaTime))}</h2>
+          <p>${escapeHtml(event.caption || 'A repeatable model divergence was detected at this point in the source video.')}</p>
+          <dl>
+            <dt>face-api distance</dt><dd>${evidence.faceDistance == null ? 'no face returned' : Number(evidence.faceDistance).toFixed(4)}</dd>
+            <dt>visual drift</dt><dd>${evidence.visualDrift == null ? 'n/a' : Number(evidence.visualDrift).toFixed(4)}</dd>
+            <dt>clip window</dt><dd>${escapeHtml(formatTime(event.recommendedClip.start))}–${escapeHtml(formatTime(event.recommendedClip.end))}</dd>
+          </dl>
+        </div>
+      </article>`];
+   }));
+   const baselineMarkup = baseline ? `<article class="event reference">
+      <img src="${escapeHtml(baseline)}" alt="Computational reference frame at ${escapeHtml(formatTime(report.baseline.mediaTime))}">
+      <div><p class="kicker">Computational distance reference</p>
+      <h2>${escapeHtml(formatTime(report.baseline.mediaTime))}</h2>
+      <p>This source frame anchors the reported distances. Its position here is editorial and does not change its timestamp or computational role.</p></div>
+   </article>` : '';
+   const storyMarkup = (report.storyOrder || ['baseline', ...report.events.map((event) => event.id)])
+      .map((id) => id === 'baseline' ? baselineMarkup : eventMarkup.get(id) || '')
+      .filter(Boolean)
+      .join('\n');
+
+   return `<!doctype html>
+<html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1">
+<title>Ghostmaxxing video analysis — ${escapeHtml(report.source.name)}</title>
+<style>
+body{margin:0;background:#f26a1b;color:#0a0a08;font:17px/1.5 system-ui,sans-serif}main{width:min(1100px,calc(100% - 2rem));margin:auto;padding:3rem 0}h1,h2{font-family:Georgia,serif;line-height:1}.meta,.event{border:2px solid #0a0a08;background:#fff1cf}.meta{padding:1rem}.event{display:grid;grid-template-columns:minmax(260px,1fr) 1fr;margin:1.5rem 0}.event.reference{border-color:#0b4a3c}.event img{width:100%;height:100%;object-fit:contain;background:#050505}.event>div{padding:1.25rem}.kicker,dt{font-size:.75rem;font-weight:800;text-transform:uppercase;letter-spacing:.08em;color:#0b4a3c}dd{margin:0 0 .6rem}@media(max-width:700px){.event{grid-template-columns:1fr}}
+</style></head><body><main>
+<p class="kicker">Ghostmaxxing · seekable video report</p>
+<h1>${escapeHtml(report.source.name)}</h1>
+<div class="meta">${report.summary.sampledFrames} samples · ${report.summary.faceApiRuns} face-api checks · ${report.summary.confirmedEvents} confirmed events · threshold ${escapeHtml(report.config.faceDistanceThreshold)}</div>
+<p>Story order is editorial. Every card retains its original source timestamp.</p>
+${storyMarkup || '<p>No event passed the confirmation policy.</p>'}
+</main></body></html>`;
+}
+
+/**
+ * Writes report JSON, portable HTML, selected JPEGs, and the separate log.
+ *
+ * @param {string} outputDirectory Destination directory.
+ * @param {{report: object, technicalLog: string, frames: object[]}} bundle Browser export bundle.
+ * @returns {Promise<{logFile: string, reportFile: string}>} Key output paths.
+ */
+async function writeBundle(outputDirectory, bundle) {
+   const framesDirectory = path.join(outputDirectory, 'frames');
+   await fs.promises.mkdir(framesDirectory, { recursive: true });
+   const frameFiles = new Map();
+   for (const frame of bundle.frames) {
+      const filename = `${frame.id === 'baseline' ? 'baseline' : safeStem(frame.id)}-${frame.mediaTime.toFixed(3)}.jpg`;
+      const absolute = path.join(framesDirectory, filename);
+      await fs.promises.writeFile(absolute, decodeDataUrl(frame.dataUrl));
+      frameFiles.set(frame.id, `frames/${filename}`);
+   }
+   const logFile = path.join(outputDirectory, 'analysis.ndjson');
+   const reportFile = path.join(outputDirectory, 'report.json');
+   await Promise.all([
+      fs.promises.writeFile(logFile, bundle.technicalLog),
+      fs.promises.writeFile(reportFile, `${JSON.stringify(bundle.report, null, 2)}\n`),
+      fs.promises.writeFile(path.join(outputDirectory, 'report.html'), renderReportHtml(bundle.report, frameFiles)),
+   ]);
+   return { logFile, reportFile };
+}
+
+/**
+ * Reads an NDJSON technical log and returns parsed records.
+ *
+ * @param {string} logFile Absolute log path.
+ * @returns {object[]} Records.
+ */
+function readTechnicalLog(logFile) {
+   return fs.readFileSync(logFile, 'utf8')
+      .split(/\r?\n/)
+      .filter(Boolean)
+      .map((line, index) => {
+         try {
+            return JSON.parse(line);
+         } catch (error) {
+            throw new Error(`Invalid JSON on ${logFile}:${index + 1}: ${error.message}`);
+         }
+      });
+}
+
+/**
+ * Extracts event clips from the original media using logged media timestamps.
+ *
+ * @param {string} logFile Technical log.
+ * @param {string} sourceVideo Original source.
+ * @param {object} options Clip options.
+ * @returns {Promise<string[]>} Written clip paths.
+ */
+async function extractClips(logFile, sourceVideo, options) {
+   const records = readTechnicalLog(logFile);
+   const header = records.find((record) => record.type === 'analysis-header');
+   const events = records.filter((record) => record.type === 'evidence-event' && record.status === 'confirmed');
+   if (!header) throw new Error('Technical log has no analysis-header record.');
+   const sourceStat = fs.statSync(sourceVideo);
+   if (header.source?.size != null && header.source.size !== sourceStat.size) {
+      throw new Error('Source size does not match the video recorded in the technical log.');
+   }
+   if (header.source?.fingerprint) {
+      const fingerprint = await sampledFingerprint(sourceVideo);
+      if (fingerprint !== header.source.fingerprint) {
+         throw new Error('Source fingerprint does not match the video recorded in the technical log.');
+      }
+   }
+   const outputDirectory = options.output || path.join(path.dirname(logFile), 'clips');
+   fs.mkdirSync(outputDirectory, { recursive: true });
+   const written = [];
+   events.forEach((event, index) => {
+      const start = options.clipBefore == null
+         ? event.recommendedClip.start
+         : Math.max(0, event.anchorMediaTime - options.clipBefore);
+      const end = options.clipAfter == null
+         ? event.recommendedClip.end
+         : event.anchorMediaTime + options.clipAfter;
+      const duration = Math.max(0.1, end - start);
+      const filename = path.join(outputDirectory, `event-${String(index + 1).padStart(3, '0')}-${event.anchorMediaTime.toFixed(3)}.mp4`);
+      const result = childProcess.spawnSync('ffmpeg', [
+         '-hide_banner', '-loglevel', 'error', '-y',
+         '-ss', String(start), '-i', sourceVideo, '-t', String(duration),
+         '-map', '0:v:0', '-map', '0:a?',
+         '-c:v', 'libx264', '-preset', 'veryfast', '-crf', '20',
+         '-c:a', 'aac', '-movflags', '+faststart', filename,
+      ], { stdio: 'inherit' });
+      if (result.error) throw result.error;
+      if (result.status !== 0) throw new Error(`ffmpeg failed while writing ${filename}`);
+      written.push(filename);
+   });
+   return written;
+}
+
+/**
+ * Analyses a video through the real loader page.
+ *
+ * @param {object} options Parsed options.
+ * @returns {Promise<void>}
+ */
+async function analyze(options) {
+   const source = resolveFile(options.positional[0], 'Source video');
+   const output = options.output || path.join(ROOT, 'video-analysis', safeStem(source));
+   await fs.promises.mkdir(output, { recursive: true });
+   const fingerprint = await sampledFingerprint(source);
+   const localServer = options.baseUrl ? null : await startServer();
+   const baseUrl = options.baseUrl || localServer.baseUrl;
+   const browser = await chromium.launch({ headless: !options.headed });
+
+   try {
+      const page = await browser.newPage({ viewport: { width: 1440, height: 1100 } });
+      page.on('console', (message) => {
+         if (message.type() === 'error') process.stderr.write(`[browser] ${message.text()}\n`);
+      });
+      process.stdout.write(`Opening ${baseUrl}/loader.html\n`);
+      await page.goto(`${baseUrl}/loader.html`, { waitUntil: 'domcontentloaded' });
+      await page.waitForFunction(() => window.gstmxxVideoLoader?.isModelsReady(), null, { timeout: 120000 });
+      await page.evaluate((value) => window.gstmxxVideoLoader.setSourceMetadata({ fingerprint: value }), fingerprint);
+      await page.setInputFiles('#videoFile', source);
+      await page.waitForFunction(() => window.gstmxxVideoLoader?.isSourceLoaded(), null, { timeout: 30000 });
+      process.stdout.write(`Analysing ${source}\n`);
+      let analysisError = null;
+      try {
+         await page.evaluate(async (analysisOptions) => {
+            await window.gstmxxVideoLoader.analyze(analysisOptions);
+         }, {
+            start: options.start,
+            end: options.end,
+            baselineTime: options.baselineTime,
+            sampleIntervalSeconds: options.sampleIntervalSeconds,
+         });
+      } catch (error) {
+         analysisError = error;
+      }
+      const bundle = await page.evaluate(() => window.gstmxxVideoLoader.exportBundle());
+      const outputs = await writeBundle(output, bundle);
+      process.stdout.write(`Report: ${outputs.reportFile}\nTechnical log: ${outputs.logFile}\n`);
+      if (analysisError) {
+         throw new Error(`Browser analysis failed; diagnostic artifacts were preserved in ${output}: ${analysisError.message}`);
+      }
+      if (options.clips) {
+         const clips = await extractClips(outputs.logFile, source, {
+            output: path.join(output, 'clips'),
+            clipBefore: options.clipBefore ?? 2,
+            clipAfter: options.clipAfter ?? 3,
+         });
+         process.stdout.write(`Clips: ${clips.length}\n`);
+      }
+   } finally {
+      await browser.close();
+      if (localServer) await localServer.close();
+   }
+}
+
+/**
+ * Rebuilds a text-only report from the independent technical log.
+ *
+ * @param {object} options Parsed options.
+ * @returns {Promise<void>}
+ */
+async function replayLog(options) {
+   const logFile = resolveFile(options.positional[0], 'Technical log');
+   const records = readTechnicalLog(logFile);
+   const header = records.find((record) => record.type === 'analysis-header');
+   const footer = records.find((record) => record.type === 'analysis-footer');
+   const events = records.filter((record) => record.type === 'evidence-event');
+   if (!header || !footer) throw new Error('Technical log is missing its header or footer.');
+   const output = options.output || path.join(path.dirname(logFile), 'replayed');
+   await fs.promises.mkdir(output, { recursive: true });
+   const report = {
+      schemaVersion: header.schemaVersion,
+      sessionId: header.sessionId,
+      createdAt: header.createdAt,
+      source: header.source,
+      config: header.config,
+      models: header.models,
+      baseline: header.baseline,
+      summary: footer.summary,
+      storyOrder: footer.storyOrder || [],
+      events,
+      replayedFrom: logFile,
+   };
+   await fs.promises.writeFile(path.join(output, 'report.replayed.json'), `${JSON.stringify(report, null, 2)}\n`);
+   process.stdout.write(`Replayed report: ${path.join(output, 'report.replayed.json')}\n`);
+}
+
+/**
+ * Runs the selected command and reports actionable errors.
+ *
+ * @returns {Promise<void>}
+ */
+async function main() {
+   const options = parseArgs(process.argv.slice(2));
+   if (options.command === 'analyze') await analyze(options);
+   else if (options.command === 'replay-log') await replayLog(options);
+   else {
+      const logFile = resolveFile(options.positional[0], 'Technical log');
+      const source = resolveFile(options.positional[1], 'Source video');
+      const clips = await extractClips(logFile, source, options);
+      process.stdout.write(`Extracted ${clips.length} clip(s).\n`);
+   }
+}
+
+main().catch((error) => {
+   process.stderr.write(`video-loader: ${error.stack || error.message || String(error)}\n`);
+   process.exitCode = 1;
+});
diff --git a/styles/loader.css b/styles/loader.css
index 4b19adc..b50d7f8 100644
--- a/styles/loader.css
+++ b/styles/loader.css
@@ -1,33 +1,42 @@
+/* Video Loader layout. Realtime camera guidance intentionally uses its own stylesheet. */
 .loader-page {
   min-height: 100svh;
   overflow: auto;
+  background:
+    radial-gradient(circle at 90% 5%, rgba(243, 199, 71, 0.16), transparent 28rem),
+    var(--gm-bg);
 }
 
 .loader-shell {
-  width: min(100%, 1440px);
-  min-height: 100svh;
+  width: min(100%, 1480px);
   margin: 0 auto;
-  padding: 1.25rem;
+  padding: clamp(1rem, 2vw, 2rem);
   display: grid;
-  grid-template-rows: auto auto minmax(0, 1.1fr) auto minmax(13rem, 0.8fr);
-  gap: 0.9rem;
+  gap: 1.25rem;
 }
 
-.loader-header {
+.loader-header,
+.loader-intro,
+.loader-source-controls,
+.loader-analysis-controls,
+.loader-report__header,
+.loader-report__actions,
+.loader-event__actions {
   display: flex;
   align-items: center;
+}
+
+.loader-header {
   justify-content: space-between;
   gap: 1.5rem;
-  padding-bottom: 0.85rem;
+  padding-bottom: 0.9rem;
   border-bottom: 1px solid var(--gm-border);
 }
 
 .loader-header__tools {
-  display: inline-flex;
+  display: flex;
   align-items: center;
-  justify-content: flex-end;
   gap: 0.75rem;
-  flex-wrap: wrap;
 }
 
 .loader-status {
@@ -35,11 +44,11 @@
   align-items: center;
   gap: 0.55rem;
   min-height: 2.6rem;
-  padding: 0.75rem 1rem;
+  padding: 0.7rem 0.9rem;
   border: 1.5px solid var(--gm-border-strong);
-  background: rgba(255, 231, 168, 0.18);
+  background: rgba(255, 231, 168, 0.15);
   color: var(--gm-ink);
-  font: 700 0.82rem var(--sans);
+  font: 800 0.78rem var(--sans);
   text-transform: uppercase;
 }
 
@@ -54,108 +63,123 @@
   background: var(--gm-green);
 }
 
+.status-dot.working {
+  background: var(--gm-yellow, #f3c747);
+  animation: loader-pulse 1s ease-in-out infinite alternate;
+}
+
 .status-dot.error {
-  background: #9a1616;
+  background: #b4212b;
 }
 
-.loader-title {
+@keyframes loader-pulse {
+  to { opacity: 0.35; }
+}
+
+.loader-intro {
+  justify-content: space-between;
+  gap: 2rem;
+}
+
+.loader-title,
+.loader-report h2 {
   margin: 0;
-  font-family: var(--serif);
-  font-size: clamp(2.2rem, 4vw, 4rem);
-  line-height: 0.95;
-  letter-spacing: 0;
   color: var(--gm-ink);
+  font-family: var(--serif);
+  line-height: 0.98;
 }
 
-.loader-band {
-  min-width: 0;
-  border: 1.5px solid var(--gm-border-strong);
-  background: rgba(255, 231, 168, 0.2);
-  box-shadow: var(--gm-shadow-block);
+.loader-title {
+  max-width: 13ch;
+  font-size: clamp(2.5rem, 5vw, 5.5rem);
 }
 
-.loader-band--stage {
-  display: grid;
-  grid-template-rows: minmax(0, 1fr) auto;
-  min-height: 24rem;
+.loader-kicker {
+  margin: 0 0 0.45rem;
+  color: var(--gm-green);
+  font: 900 0.76rem var(--sans);
+  letter-spacing: 0.13em;
+  text-transform: uppercase;
+}
+
+.loader-intro__copy {
+  max-width: 40rem;
+  margin: 0;
+  color: var(--gm-text);
+  font: 600 clamp(1rem, 1.4vw, 1.25rem)/1.5 var(--sans);
+}
+
+.loader-stage,
+.loader-report {
+  border: 1.5px solid var(--gm-border-strong);
+  background: rgba(255, 231, 168, 0.16);
+  box-shadow: var(--gm-shadow-block);
 }
 
 .loader-viewer {
   position: relative;
-  min-height: 0;
+  min-height: min(62vw, 48rem);
+  max-height: 72svh;
   overflow: hidden;
   background: #050505;
 }
 
-#video,
-#previewImage,
-#overlay,
-#mesh3dOverlay,
-#bboxOverlay {
+#video {
   position: absolute;
   inset: 0;
   width: 100%;
   height: 100%;
   object-fit: contain;
-}
-
-#video {
-  z-index: 1;
   background: #050505;
 }
 
-#previewImage {
-  display: none;
-}
-
-#overlay {
-  z-index: 2;
-  pointer-events: none;
-}
-
-#mesh3dOverlay {
-  z-index: 3;
-  pointer-events: none;
-}
-
-#bboxOverlay {
-  z-index: 4;
-  pointer-events: none;
-}
-
 .loader-placeholder {
   position: absolute;
   inset: 0;
-  z-index: 5;
+  z-index: 2;
   display: grid;
   place-items: center;
-  padding: 1.5rem;
+  padding: 2rem;
   color: var(--gm-cream);
-  background: linear-gradient(135deg, rgba(5, 5, 5, 0.92), rgba(6, 63, 50, 0.72));
-  font: 700 1rem var(--sans);
+  background:
+    linear-gradient(135deg, rgba(5, 5, 5, 0.94), rgba(6, 63, 50, 0.76)),
+    repeating-linear-gradient(45deg, transparent 0 18px, rgba(255, 255, 255, 0.03) 18px 19px);
+  font: 750 clamp(1rem, 2vw, 1.35rem) var(--sans);
   text-align: center;
 }
 
-.loader-controls,
-.loader-band--actions {
-  display: flex;
-  align-items: center;
+.loader-placeholder[hidden] {
+  display: none;
+}
+
+.loader-source-controls,
+.loader-analysis-controls {
   gap: 0.85rem;
-  padding: 0.8rem;
+  padding: 0.9rem;
+  border-top: 1px solid var(--gm-border);
+}
+
+.loader-analysis-controls {
+  flex-wrap: wrap;
 }
 
 .file-control,
-.loader-action {
-  display: inline-flex;
-  align-items: center;
-  justify-content: center;
-  min-height: 2.9rem;
-  padding: 0.75rem 1.15rem;
+.loader-analysis-controls button,
+.loader-report button {
+  min-height: 2.8rem;
+  padding: 0.7rem 1rem;
   border: 1.5px solid var(--gm-border-strong);
   background: var(--gm-cream);
   color: var(--gm-ink);
-  font: 800 0.95rem var(--sans);
+  font: 800 0.86rem var(--sans);
   cursor: pointer;
+}
+
+.file-control {
+  position: relative;
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
   white-space: nowrap;
 }
 
@@ -167,32 +191,38 @@
   pointer-events: none;
 }
 
-.loader-action {
-  background: var(--gm-green);
-  color: var(--gm-cream);
+.loader-primary {
+  background: var(--gm-green) !important;
+  color: var(--gm-cream) !important;
 }
 
-.loader-action:disabled {
-  opacity: 0.45;
+button:disabled,
+.file-control:has(input:disabled) {
+  opacity: 0.42;
   cursor: not-allowed;
 }
 
 .file-control:focus-within,
-.loader-action:focus-visible,
-#videoSeek:focus-visible {
+button:focus-visible,
+select:focus-visible,
+textarea:focus-visible,
+input[type="range"]:focus-visible {
   outline: var(--gm-focus-ring);
-  outline-offset: 0.15rem;
+  outline-offset: 0.16rem;
 }
 
-.timeline {
+.loader-seek {
   flex: 1;
-  min-width: 16rem;
   display: grid;
-  grid-template-columns: 6.5rem minmax(0, 1fr) 6.5rem;
+  grid-template-columns: 7rem minmax(8rem, 1fr) 7rem;
   align-items: center;
   gap: 0.75rem;
-  font: 700 0.82rem ui-monospace, SFMono-Regular, Menlo, Consolas, monospace;
   color: var(--gm-ink);
+  font: 750 0.78rem ui-monospace, SFMono-Regular, Menlo, monospace;
+}
+
+.loader-seek span:last-child {
+  text-align: right;
 }
 
 #videoSeek {
@@ -200,159 +230,228 @@
   accent-color: var(--gm-green);
 }
 
-.loader-band--actions {
-  justify-content: space-between;
-  flex-wrap: wrap;
-}
-
-.loader-readout {
-  margin-left: auto;
+.loader-analysis-controls label {
   display: flex;
   align-items: center;
-  gap: 0.55rem;
-  flex-wrap: wrap;
+  gap: 0.6rem;
   color: var(--gm-text);
-  font: 700 0.8rem var(--sans);
+  font: 750 0.82rem var(--sans);
 }
 
-.loader-readout strong {
-  min-width: 2.5rem;
-  padding: 0.35rem 0.45rem;
-  background: rgba(255, 231, 168, 0.45);
-  border: 1px solid var(--gm-border);
+.loader-analysis-controls select {
+  min-height: 2.6rem;
+  border: 1.5px solid var(--gm-border-strong);
+  background: var(--gm-cream);
   color: var(--gm-ink);
-  text-align: center;
+  font: 700 0.82rem var(--sans);
 }
 
-.loader-band--log {
-  min-height: 0;
-  display: grid;
-  grid-template-rows: auto minmax(0, 1fr);
+.loader-analysis-controls input[type="number"] {
+  width: 6rem;
+  min-height: 2.6rem;
+  padding: 0.45rem 0.55rem;
+  border: 1.5px solid var(--gm-border-strong);
+  background: var(--gm-cream);
+  color: var(--gm-ink);
+  font: 700 0.82rem var(--sans);
 }
 
-.loader-log-head {
+.loader-progress {
+  flex: 1;
+  min-width: 10rem;
   display: flex;
-  align-items: baseline;
+  align-items: center;
+  gap: 0.65rem;
+  color: var(--gm-ink);
+  font: 800 0.78rem ui-monospace, SFMono-Regular, Menlo, monospace;
+}
+
+.loader-progress progress {
+  width: 100%;
+  height: 0.8rem;
+  accent-color: var(--gm-green);
+}
+
+.loader-report {
+  padding: clamp(1rem, 2vw, 1.5rem);
+  display: grid;
+  gap: 1rem;
+}
+
+.loader-report__header {
   justify-content: space-between;
   gap: 1rem;
-  padding: 0.8rem 0.95rem;
-  border-bottom: 1px solid var(--gm-border);
 }
 
-.loader-log-head h2 {
+.loader-report h2 {
+  font-size: clamp(1.8rem, 3vw, 3.25rem);
+}
+
+.loader-report__summary {
+  margin: 0.45rem 0 0;
+  color: var(--gm-text);
+  font: 650 0.9rem var(--sans);
+}
+
+.loader-report__actions,
+.loader-event__actions {
+  flex-wrap: wrap;
+  justify-content: flex-end;
+  gap: 0.55rem;
+}
+
+.loader-baseline {
+  display: flex;
+  gap: 0.75rem;
+  padding: 0.8rem 1rem;
+  border-left: 0.35rem solid var(--gm-green);
+  background: rgba(6, 63, 50, 0.09);
+  color: var(--gm-text);
+  font: 650 0.85rem var(--sans);
+}
+
+.loader-story-note {
   margin: 0;
-  font-family: var(--serif);
-  font-size: 1.55rem;
-  color: var(--gm-ink);
+  padding: 0.85rem 1rem;
+  border: 1px dashed var(--gm-border-strong);
+  color: var(--gm-text);
+  font: 650 0.84rem/1.5 var(--sans);
 }
 
-.loader-log-head p {
+.loader-timeline-figure {
   margin: 0;
+  border: 1.5px solid var(--gm-border-strong);
+  background: #17130f;
+}
+
+#analysisTimeline {
+  display: block;
+  width: 100%;
+  height: 210px;
+}
+
+.loader-timeline-figure figcaption {
+  padding: 0.7rem 0.9rem;
+  border-top: 1px solid rgba(255, 231, 168, 0.2);
+  color: #d5c6b2;
+  font: 600 0.76rem/1.45 var(--sans);
+}
+
+.loader-empty-report {
+  padding: 2rem;
+  border: 1px dashed var(--gm-border-strong);
   color: var(--gm-muted);
-  font-size: 0.9rem;
+  font: 650 0.95rem/1.5 var(--sans);
+  text-align: center;
 }
 
-.loader-log {
-  overflow: auto;
-  padding: 0.85rem;
+.loader-event-list {
   display: grid;
-  gap: 0.8rem;
-  align-content: start;
+  gap: 1rem;
 }
 
-.loader-entry {
+.loader-event {
   display: grid;
-  grid-template-columns: minmax(13rem, 18rem) minmax(0, 1fr);
-  gap: 0.85rem;
-  padding: 0.8rem;
-  border: 1px solid var(--gm-border);
-  background: rgba(255, 231, 168, 0.24);
+  grid-template-columns: minmax(14rem, 32rem) 1fr;
+  border: 1.5px solid var(--gm-border-strong);
+  background: var(--gm-cream);
+  overflow: hidden;
+}
+
+.loader-event--reference {
+  border-color: var(--gm-green);
+  box-shadow: inset 0.4rem 0 0 var(--gm-green);
 }
 
-.loader-entry canvas {
+.loader-event img {
   width: 100%;
-  aspect-ratio: 16 / 9;
-  background: #050505;
-  border: 1px solid var(--gm-border-strong);
+  height: 100%;
+  min-height: 18rem;
   object-fit: contain;
+  background: #050505;
 }
 
-.loader-entry__body {
-  min-width: 0;
+.loader-event__body {
+  padding: 1rem;
+  display: grid;
+  align-content: start;
+  gap: 0.75rem;
 }
 
-.loader-entry__body h3 {
-  margin: 0 0 0.35rem;
+.loader-event h3,
+.loader-event p {
+  margin: 0;
+}
+
+.loader-event h3 {
   color: var(--gm-ink);
-  font-size: 1rem;
+  font: 800 1.1rem var(--sans);
 }
 
-.loader-entry__meta,
-.loader-entry__metrics {
-  display: flex;
-  gap: 0.45rem;
-  flex-wrap: wrap;
-  margin: 0.35rem 0;
+.loader-event__metrics {
+  color: var(--gm-green);
+  font: 800 0.8rem ui-monospace, SFMono-Regular, Menlo, monospace;
 }
 
-.loader-chip {
-  display: inline-flex;
-  align-items: center;
-  min-height: 1.7rem;
-  padding: 0.25rem 0.45rem;
-  background: rgba(255, 231, 168, 0.5);
-  border: 1px solid var(--gm-border);
-  color: var(--gm-ink);
-  font: 700 0.76rem ui-monospace, SFMono-Regular, Menlo, Consolas, monospace;
+.loader-event__reasons {
+  color: var(--gm-text);
+  font: 650 0.8rem/1.45 var(--sans);
 }
 
-.loader-entry pre {
-  max-height: 11rem;
-  overflow: auto;
-  margin: 0.55rem 0 0;
-  padding: 0.65rem;
-  background: rgba(5, 5, 5, 0.86);
-  color: var(--gm-cream);
-  font: 0.74rem/1.45 ui-monospace, SFMono-Regular, Menlo, Consolas, monospace;
-  white-space: pre-wrap;
+.loader-event textarea {
+  width: 100%;
+  min-height: 6rem;
+  resize: vertical;
+  padding: 0.7rem;
+  border: 1.5px solid var(--gm-border-strong);
+  background: #fffaf0;
+  color: var(--gm-ink);
+  font: 600 0.9rem/1.45 var(--sans);
 }
 
-.u-hidden {
-  display: none !important;
+.loader-event__actions {
+  justify-content: flex-start;
 }
 
-@media (max-width: 780px) {
-  .loader-shell {
-    grid-template-rows: auto auto auto auto minmax(14rem, 1fr);
-    padding: 0.8rem;
-  }
+.loader-privacy {
+  padding: 1rem 1.15rem;
+  border: 1px solid var(--gm-border);
+  color: var(--gm-text);
+  background: rgba(255, 231, 168, 0.12);
+  font: 620 0.85rem/1.5 var(--sans);
+}
 
+@media (max-width: 820px) {
   .loader-header,
-  .loader-controls,
-  .loader-band--actions,
-  .loader-log-head {
+  .loader-intro,
+  .loader-source-controls,
+  .loader-analysis-controls,
+  .loader-report__header {
     align-items: stretch;
     flex-direction: column;
   }
 
-  .loader-band--stage {
-    min-height: 20rem;
+  .loader-header__tools {
+    justify-content: space-between;
   }
 
-  .timeline {
-    min-width: 0;
-    grid-template-columns: 1fr;
+  .loader-viewer {
+    min-height: 62vw;
   }
 
-  .loader-header__tools {
-    justify-content: flex-start;
+  .loader-seek {
+    grid-template-columns: 5.7rem minmax(5rem, 1fr) 5.7rem;
   }
 
-  .loader-readout {
-    margin-left: 0;
+  .loader-event {
+    grid-template-columns: 1fr;
   }
 
-  .loader-entry {
-    grid-template-columns: 1fr;
+  .loader-event img {
+    min-height: 13rem;
+  }
+
+  .loader-report__actions {
+    justify-content: flex-start;
   }
 }
diff --git a/tests/e2e/loader.spec.js b/tests/e2e/loader.spec.js
index 4589350..a82f057 100644
--- a/tests/e2e/loader.spec.js
+++ b/tests/e2e/loader.spec.js
@@ -1,208 +1,154 @@
 import { test, expect } from '@playwright/test';
-
-test.describe('MP4 loader tool', () => {
-  test('boots the internal loader interface with mocked model runtimes', async ({ page }) => {
-    await page.addInitScript(() => {
-      localStorage.removeItem('local-face-lab-db-v1');
-      localStorage.removeItem('local-face-lab-db-3d-v1');
-      localStorage.removeItem('local-face-lab-thumbnails-v1');
-      localStorage.removeItem('ghostati-overlay-mode-v1');
-      localStorage.removeItem('ghostmaxxing-locale');
-    });
-
-    await page.route(/(cdn\.jsdelivr\.net\/npm\/@vladmandic\/face-api\/dist\/face-api\.js|\/lab-js\/vendor\/face-api\.js)$/, (route) => {
-      route.fulfill({
-        contentType: 'application/javascript',
-        body: `
+import path from 'node:path';
+
+/**
+ * Installs deterministic model bundles so browser tests exercise the complete
+ * Video Loader orchestration without spending CI time on real inference.
+ *
+ * @param {import('@playwright/test').Page} page Playwright page.
+ * @returns {Promise<void>}
+ */
+async function mockModels(page) {
+  await page.route('/lab-js/vendor/face-api.js', route => {
+    route.fulfill({
+      contentType: 'application/javascript',
+      body: `
+        (() => {
+          let recognitionRuns = 0;
+          const descriptor = () => {
+            recognitionRuns += 1;
+            const vector = new Float32Array(128);
+            if (recognitionRuns > 3) vector[0] = 0.75;
+            return vector;
+          };
           window.faceapi = {
-            TinyFaceDetectorOptions: class TinyFaceDetectorOptions { constructor(opts) { this.opts = opts; } },
+            TinyFaceDetectorOptions: class TinyFaceDetectorOptions { constructor(options) { this.options = options; } },
             nets: {
               tinyFaceDetector: { loadFromUri: async () => {} },
               faceLandmark68Net: { loadFromUri: async () => {} },
               faceRecognitionNet: { loadFromUri: async () => {} },
-              ageGenderNet: { loadFromUri: async () => {} },
             },
-            detectSingleFace: () => null,
-            resizeResults: (result) => result,
+            detectSingleFace: () => ({
+              withFaceLandmarks: () => ({
+                withFaceDescriptor: async () => ({
+                  detection: { score: 0.98, box: { x: 180, y: 80, width: 280, height: 280 } },
+                  landmarks: { positions: Array.from({ length: 68 }, () => ({ x: 300, y: 220 })) },
+                  descriptor: descriptor(),
+                }),
+              }),
+            }),
           };
-        `,
-      });
+        })();
+      `,
     });
+  });
 
-    await page.route(/(cdn\.jsdelivr\.net\/npm\/@mediapipe\/tasks-vision@0\.10\.35|\/lab-js\/vendor\/tasks-vision@0\.10\.35\.js)$/, (route) => {
-      route.fulfill({
-        contentType: 'application/javascript',
-        body: `
-          export class FilesetResolver {
-            static async forVisionTasks() { return {}; }
-          }
-          export class ImageEmbedder {
-            static async createFromOptions() {
-              return { embedForVideo: () => ({ embeddings: [{ floatEmbedding: new Float32Array([1, 0, 0]) }] }) };
-            }
+  await page.route('/lab-js/vendor/tasks-vision@0.10.35.js', route => {
+    route.fulfill({
+      contentType: 'application/javascript',
+      body: `
+        let embedRuns = 0;
+        const landmarks = Array.from({ length: 478 }, (_, index) => {
+          const angle = (index / 478) * Math.PI * 2;
+          return { x: 0.5 + Math.cos(angle) * 0.2, y: 0.5 + Math.sin(angle) * 0.28, z: 0 };
+        });
+        landmarks[33] = { x: 0.41, y: 0.42, z: 0 };
+        landmarks[263] = { x: 0.59, y: 0.42, z: 0 };
+        landmarks[1] = { x: 0.5, y: 0.52, z: 0 };
+        export class FilesetResolver { static async forVisionTasks() { return {}; } }
+        export class FaceLandmarker {
+          static async createFromOptions() {
+            return { detect: () => ({ faceLandmarks: [landmarks] }) };
           }
-          export class FaceLandmarker {
-            static async createFromOptions() {
-              return { detectForVideo: () => ({ faceLandmarks: [] }) };
-            }
+        }
+        export class ImageEmbedder {
+          static async createFromOptions() {
+            return {
+              embed: () => {
+                embedRuns += 1;
+                return { embeddings: [{ floatEmbedding: new Float32Array(embedRuns <= 8 ? [1, 0, 0] : [0, 1, 0]) }] };
+              },
+            };
           }
-        `,
-      });
+        }
+      `,
     });
+  });
+}
 
-    await page.goto('/loader.html');
+test.describe('seekable Video Loader', () => {
+  test.beforeEach(async ({ page }) => {
+    await mockModels(page);
+  });
 
-    await expect(page.getByRole('heading', { name: 'Makeup Video Test Loader' })).toBeVisible();
-    await expect(page.getByLabel('Load and play video')).toBeVisible();
-    await expect(page.getByLabel('Face actions')).toBeVisible();
-    await expect(page.getByLabel('Button activity log')).toBeVisible();
-    await expect(page.getByText('Select a local MP4 video to begin.')).toBeVisible();
-    await expect(page.locator('#statusText')).toHaveText('ready', { timeout: 5000 });
-    await page.locator('#localeSelect').selectOption('it');
-    await expect(page.getByRole('heading', { name: 'Loader di test video makeup' })).toBeVisible();
-    await expect(page.getByText('Seleziona un video MP4 locale per iniziare.')).toBeVisible();
-    await expect(page.locator('#statusText')).toHaveText('pronto');
-    await expect(page.locator('#recordFaceBtn')).toBeDisabled();
-    await expect(page.locator('#seekFaceBtn')).toBeDisabled();
+  test('boots as an existing-video analyser without camera controls', async ({ page }) => {
+    await page.goto('/loader.html');
+    await expect(page.getByRole('heading', { name: 'Makeup video analyser' })).toBeVisible();
+    await expect(page.getByLabel('Local video source')).toBeVisible();
+    await expect(page.getByLabel('Analysis controls')).toBeVisible();
+    await expect(page.getByRole('heading', { name: 'Evidence and story order' })).toBeVisible();
+    await expect(page.getByText('Select a local MP4, WebM, or compatible video to begin.')).toBeVisible();
+    await expect(page.locator('#statusText')).toHaveText('select a local video', { timeout: 5000 });
+    await expect(page.locator('#startAnalysisBtn')).toBeDisabled();
+    await expect(page.locator('text=webcam')).toHaveCount(0);
+    await expect.poll(() => page.evaluate(() => window.gstmxxVideoLoader?.isModelsReady())).toBe(true);
   });
 
-  test('loads a mock video and enables/executes face actions', async ({ page }) => {
+  test('loads through setInputFiles and produces separate report and technical log artifacts', async ({ page }) => {
     await page.addInitScript(() => {
-      localStorage.removeItem('local-face-lab-db-v1');
-      localStorage.removeItem('local-face-lab-db-3d-v1');
-      localStorage.removeItem('local-face-lab-thumbnails-v1');
-      localStorage.removeItem('ghostati-overlay-mode-v1');
-      localStorage.removeItem('ghostmaxxing-locale');
-      const originalToDataURL = HTMLCanvasElement.prototype.toDataURL;
-      HTMLCanvasElement.prototype.toDataURL = function(type, quality) {
-        if (type === 'image/jpeg' && quality === 0.8) return 'data:image/jpeg;base64,loader-preview';
-        return originalToDataURL.call(this, type, quality);
+      CanvasRenderingContext2D.prototype.drawImage = function drawImage() {};
+      CanvasRenderingContext2D.prototype.getImageData = function getImageData(x, y, width, height) {
+        const data = new Uint8ClampedArray(width * height * 4);
+        for (let offset = 0; offset < data.length; offset += 4) {
+          data[offset] = 128;
+          data[offset + 1] = 128;
+          data[offset + 2] = 128;
+          data[offset + 3] = 255;
+        }
+        return { data, width, height };
       };
-      CanvasRenderingContext2D.prototype.drawImage = function() {};
     });
-
-    await page.route(/(cdn\.jsdelivr\.net\/npm\/@vladmandic\/face-api\/dist\/face-api\.js|\/lab-js\/vendor\/face-api\.js)$/, (route) => {
-      route.fulfill({
-        contentType: 'application/javascript',
-        body: `
-          window.faceapi = {
-            TinyFaceDetectorOptions: class TinyFaceDetectorOptions { constructor(opts) { this.opts = opts; } },
-            nets: {
-              tinyFaceDetector: { loadFromUri: async () => {} },
-              faceLandmark68Net: { loadFromUri: async () => {} },
-              faceRecognitionNet: { loadFromUri: async () => {} },
-              ageGenderNet: { loadFromUri: async () => {} },
-            },
-            detectSingleFace: () => {
-              const landmarks = {
-                positions: Array.from({ length: 68 }, (_, i) => ({ x: 100 + i, y: 120 + i })),
-                getLeftEye: () => [{ x: 230, y: 220 }, { x: 250, y: 220 }],
-                getRightEye: () => [{ x: 320, y: 220 }, { x: 340, y: 220 }],
-                getNose: () => [{ x: 280, y: 240 }, { x: 282, y: 252 }, { x: 284, y: 264 }, { x: 286, y: 276 }],
-                getJawOutline: () => [{ x: 220, y: 300 }, { x: 280, y: 335 }, { x: 350, y: 300 }],
-                getMouth: () => [{ x: 260, y: 310 }, { x: 275, y: 318 }, { x: 290, y: 320 }, { x: 305, y: 318 }, { x: 320, y: 310 }, { x: 290, y: 326 }, { x: 275, y: 318 }],
-              };
-              const result = {
-                detection: { score: 0.93, box: { x: 210, y: 160, width: 180, height: 220 } },
-                landmarks,
-                age: 31,
-                gender: 'female',
-                genderProbability: 0.82,
-                descriptor: Array.from({ length: 128 }, (_, i) => i / 128),
-              };
-              return {
-                withFaceLandmarks: () => ({
-                  withAgeAndGender: () => ({
-                    withFaceDescriptor: async () => result,
-                  }),
-                }),
-              };
-            },
-            resizeResults: (result) => result,
-          };
-        `,
-      });
-    });
-
-    await page.route(/(cdn\.jsdelivr\.net\/npm\/@mediapipe\/tasks-vision@0\.10\.35|\/lab-js\/vendor\/tasks-vision@0\.10\.35\.js)$/, (route) => {
-      route.fulfill({
-        contentType: 'application/javascript',
-        body: `
-          export class FilesetResolver {
-            static async forVisionTasks() { return {}; }
-          }
-          export class ImageEmbedder {
-            static async createFromOptions() {
-              return { embedForVideo: () => ({ embeddings: [{ floatEmbedding: new Float32Array([1, 0, 0]) }] }) };
-            }
-          }
-          export class FaceLandmarker {
-            static async createFromOptions() {
-              return { detectForVideo: () => ({ faceLandmarks: [] }) };
-            }
-          }
-        `,
-      });
-    });
-
     await page.goto('/loader.html');
-
-    // Wait for the models to be ready
-    await expect(page.locator('#statusText')).toHaveText('ready', { timeout: 5000 });
-
-    // Upload a mock MP4 file
-    await page.setInputFiles('#videoFile', {
-      name: 'mock-video.mp4',
-      mimeType: 'video/mp4',
-      buffer: Buffer.from('fake mp4 data'),
-    });
-
-    // Mock HTMLVideoElement properties and dispatch loaded metadata/data events
-    await page.evaluate(() => {
-      const video = document.getElementById('video');
-      Object.defineProperties(video, {
-        duration: { value: 12.5, configurable: true },
-        currentTime: { value: 3.2, configurable: true },
-        readyState: { value: 4, configurable: true },
-        videoWidth: { value: 1280, configurable: true },
-        videoHeight: { value: 720, configurable: true },
-      });
-      video.dispatchEvent(new Event('loadedmetadata'));
-      video.dispatchEvent(new Event('loadeddata'));
-    });
-
-    // Verify timeline UI is updated and seek bar is active
-    await expect(page.locator('#durationLabel')).toHaveText('00:12.500');
-    await expect(page.locator('#currentTimeLabel')).toHaveText('00:03.200');
-
-    // Buttons should now be enabled
-    await expect(page.locator('#recordFaceBtn')).toBeEnabled();
-    await expect(page.locator('#seekFaceBtn')).toBeEnabled();
-
-    // Click Record face
-    await page.locator('#recordFaceBtn').click();
-    await expect(page.locator('#loaderLog .loader-entry')).toHaveCount(1);
-    await expect(page.locator('#loaderLog')).toContainText('#1 record face');
-    await expect(page.locator('#loaderLog')).toContainText('preview: saved');
-    await expect.poll(async () => page.evaluate(() => {
-      const store = JSON.parse(localStorage.getItem('local-face-lab-thumbnails-v1') || '{"entries":[]}');
-      const first = store.entries[0] || {};
+    await expect.poll(() => page.evaluate(() => window.gstmxxVideoLoader?.isModelsReady())).toBe(true);
+    await page.evaluate(() => window.gstmxxVideoLoader.setSourceMetadata({ fingerprint: 'sampled-sha256:e2e' }));
+    await page.setInputFiles('#videoFile', path.join(process.cwd(), 'tests/fixtures/my-moving-face.mp4'));
+
+    await expect.poll(() => page.evaluate(() => window.gstmxxVideoLoader?.isSourceLoaded())).toBe(true);
+    await expect(page.locator('#startAnalysisBtn')).toBeEnabled();
+    await page.evaluate(() => window.gstmxxVideoLoader.analyze({ end: 6, sampleIntervalSeconds: 0.5 }));
+
+    await expect(page.locator('#analysisProgressLabel')).toHaveText('100%');
+    await expect(page.locator('#reportSummary')).toContainText('confirmed event');
+    await expect(page.locator('.loader-event--confirmed')).toHaveCount(1);
+    await expect(page.locator('#downloadReportBtn')).toBeEnabled();
+    await expect(page.locator('#downloadLogBtn')).toBeEnabled();
+
+    const exported = await page.evaluate(() => {
+      const bundle = window.gstmxxVideoLoader.exportBundle();
       return {
-        count: store.entries.length,
-        id: first.id,
-        dataUrl: first.dataUrl,
-        hasSavedAt: typeof first.savedAt === 'string' && first.savedAt.length > 0,
+        reportEvents: bundle.report.events.length,
+        reportHasImageBytes: JSON.stringify(bundle.report).includes('base64'),
+        logHasImageBytes: bundle.technicalLog.includes('base64'),
+        logTypes: bundle.technicalLog.trim().split('\n').map(line => JSON.parse(line).type),
+        frames: bundle.frames.map(frame => frame.id),
+        fingerprint: bundle.report.source.fingerprint,
+        eventFrameReason: bundle.report.events[0]?.selectedFrame?.reason,
+        storyOrder: bundle.report.storyOrder,
       };
-    })).toEqual({
-      count: 1,
-      id: 0,
-      dataUrl: 'data:image/jpeg;base64,loader-preview',
-      hasSavedAt: true,
     });
-
-    // Click Seek face
-    await page.locator('#seekFaceBtn').click();
-    await expect(page.locator('#loaderLog .loader-entry')).toHaveCount(2);
-    await expect(page.locator('#loaderLog')).toContainText('#2 seek face');
+    expect(exported).toMatchObject({
+      reportEvents: 1,
+      reportHasImageBytes: false,
+      logHasImageBytes: false,
+      fingerprint: 'sampled-sha256:e2e',
+    });
+    expect(exported.logTypes).toContain('analysis-header');
+    expect(exported.logTypes).toContain('evidence-refinement-check');
+    expect(exported.logTypes).toContain('evidence-frame-selected');
+    expect(exported.logTypes).toContain('analysis-footer');
+    expect(exported.frames).toContain('baseline');
+    expect(exported.frames).toHaveLength(2);
+    expect(exported.eventFrameReason).toMatch(/^refined-/);
+    expect(exported.storyOrder).toContain('baseline');
   });
 });
diff --git a/tests/fixtures/FOLDER-DESCRIPTION.md b/tests/fixtures/FOLDER-DESCRIPTION.md
index e43c01f..c492541 100644
--- a/tests/fixtures/FOLDER-DESCRIPTION.md
+++ b/tests/fixtures/FOLDER-DESCRIPTION.md
@@ -26,3 +26,8 @@ JPEG/PNG files in place; `--dry-run` previews the selection. The marker uses the
 project font and colour tokens and skips existing visible badges. Marking
 changes test inputs and re-encodes JPEGs; restore unmarked files from Git before
 changing badge sizes or comparing against an earlier unmarked baseline.
+
+Large makeup tutorials and event recordings may be placed directly in this
+folder for local Video Loader calibration. They are git-ignored by source ID or
+filename, are never copied or modified by the analyser, and are not required by
+CI. See `tutorials/video-loader.md` for the manual validation order.
diff --git a/tests/unit/loader.test.js b/tests/unit/loader.test.js
index a662797..3cdcd45 100644
--- a/tests/unit/loader.test.js
+++ b/tests/unit/loader.test.js
@@ -1,302 +1,139 @@
 import { readFileSync } from 'node:fs';
-import { describe, expect, it, vi } from 'vitest';
-
-const deps = vi.hoisted(() => ({
-  loadDb: vi.fn(), loadDb3d: vi.fn(), renderDbStats: vi.fn(),
-  detectFaceInCam: vi.fn(), saveFace: vi.fn(), seekFaceInDb: vi.fn(),
-  cosineSimilarity: vi.fn(), getFaceEmbedding: vi.fn(), loadMobileNet: vi.fn(), saveFace3d: vi.fn(),
-  resizeCanvas: vi.fn(), distance: vi.fn(), setLog: vi.fn(), captureThumbnail: vi.fn(), saveThumbnail: vi.fn(),
-  initI18n: vi.fn(), setupLocaleSelect: vi.fn(), applyI18n: vi.fn(), setStatus: vi.fn(),
+import { beforeEach, describe, expect, it, vi } from 'vitest';
+
+const runtime = vi.hoisted(() => ({
+  modelsReady: false,
+  session: null,
+  listener: null,
+  loadModels: vi.fn(),
+  loadFile: vi.fn(),
+  analyze: vi.fn(),
+  cancel: vi.fn(),
+  exportBundle: vi.fn(),
 }));
 
-vi.mock('../../lab-js/db.js', () => ({ loadDb: deps.loadDb, loadDb3d: deps.loadDb3d, renderDbStats: deps.renderDbStats }));
-vi.mock('../../lab-js/engine.js', () => ({
-  detectFaceInCam: deps.detectFaceInCam, saveFace: deps.saveFace, seekFaceInDb: deps.seekFaceInDb,
-}));
-vi.mock('../../lab-js/engine-3d.js', () => ({
-  cosineSimilarity: deps.cosineSimilarity, getFaceEmbedding: deps.getFaceEmbedding,
-  loadMobileNet: deps.loadMobileNet, saveFace3d: deps.saveFace3d,
-}));
-vi.mock('../../lab-js/camera.js', () => ({ resizeCanvas: deps.resizeCanvas }));
-vi.mock('../../lab-js/utils.js', () => ({ distance: deps.distance, setLog: deps.setLog }));
-vi.mock('../../lab-js/face-thumbnails.js', () => ({ captureThumbnail: deps.captureThumbnail, saveThumbnail: deps.saveThumbnail }));
-vi.mock('../../lab-js/config.js', () => ({
-  MODEL_URLS: { tiny: '/tiny', landmarks: '/landmarks', recognition: '/recognition', ageGender: '/age' },
-  DETECTOR_OPTIONS: { inputSize: 128 },
-}));
 vi.mock('../../lab-js/i18n.js', () => ({
-  initI18n: deps.initI18n, setupLocaleSelect: deps.setupLocaleSelect, applyI18n: deps.applyI18n, t: key => key,
+  initI18n: vi.fn(),
+  setupLocaleSelect: vi.fn(),
+  applyI18n: vi.fn(),
 }));
-vi.mock('../../lab-js/dom.js', async importOriginal => {
-  const actual = await importOriginal();
-  return { ...actual, setStatus: deps.setStatus };
-});
 
-import { state } from '../../lab-js/state.js';
+vi.mock('../../lab-js/video-loader/controller.js', () => ({
+  createVideoAnalysisController: () => ({
+    loadModels: runtime.loadModels,
+    loadFile: runtime.loadFile,
+    analyze: runtime.analyze,
+    cancel: runtime.cancel,
+    exportBundle: runtime.exportBundle,
+    getSession: () => runtime.session,
+    isModelsReady: () => runtime.modelsReady,
+    subscribe: listener => { runtime.listener = listener; return () => {}; },
+  }),
+}));
 
-async function flushPromises() {
-  for (let i = 0; i < 16; i++) await Promise.resolve();
-}
+vi.mock('../../lab-js/video-loader/session.js', () => ({
+  applyEditorialCommand: vi.fn(),
+  undoEditorialCommand: vi.fn(),
+  redoEditorialCommand: vi.fn(),
+}));
 
 function loaderMarkup() {
   const source = readFileSync(`${process.cwd()}/loader.html`, 'utf8');
   return new DOMParser().parseFromString(source, 'text/html').body.innerHTML;
 }
 
-describe('loader', () => {
-  it('loads a video, records and seeks faces, and captures success/error details', async () => {
+function makeSession(status = 'idle') {
+  return {
+    source: { name: 'tutorial.webm', duration: 20 },
+    status,
+    baseline: null,
+    samples: [],
+    events: [],
+    journal: [],
+    summary: { sampledFrames: 0, faceApiRuns: 0, confirmedEvents: 0 },
+    editorial: { storyOrder: ['baseline'], undo: [], redo: [] },
+  };
+}
+
+async function flushPromises() {
+  for (let index = 0; index < 12; index += 1) await Promise.resolve();
+}
+
+describe('loader UI adapter', () => {
+  beforeEach(() => {
+    vi.resetModules();
+    vi.clearAllMocks();
     document.body.innerHTML = loaderMarkup();
+    runtime.modelsReady = false;
+    runtime.session = null;
+    runtime.listener = null;
+    runtime.loadModels.mockImplementation(async () => {
+      runtime.modelsReady = true;
+      runtime.listener?.({ type: 'models-ready', session: runtime.session });
+    });
+    runtime.loadFile.mockImplementation(async file => {
+      runtime.session = makeSession();
+      runtime.session.source.name = file.name;
+      return runtime.session;
+    });
+    runtime.analyze.mockImplementation(async () => {
+      runtime.session.status = 'complete';
+      runtime.session.samples.push({ mediaTime: 1, facePresent: true, visualDrift: null });
+      runtime.session.summary.sampledFrames = 1;
+      runtime.listener?.({ type: 'sample', session: runtime.session, progress: 1 });
+      return runtime.session;
+    });
+    runtime.exportBundle.mockReturnValue({ report: {}, technicalLog: '{}\n', frames: [] });
+
     const video = document.getElementById('video');
+    video.load = vi.fn();
+    video.pause = vi.fn();
     Object.defineProperties(video, {
-      readyState: { configurable: true, value: 0 },
+      duration: { configurable: true, value: 20 },
       currentTime: { configurable: true, writable: true, value: 0 },
-      duration: { configurable: true, writable: true, value: 0 },
-      videoWidth: { configurable: true, value: 640 },
-      videoHeight: { configurable: true, value: 360 },
     });
-    video.load = vi.fn();
-    state.db = { nextId: 1, faces: [] };
-    state.db3d = { faces: [] };
-    state.gstmxxEvents = new EventTarget();
-    deps.loadDb.mockReturnValue(state.db);
-    deps.loadDb3d.mockReturnValue(state.db3d);
-    deps.loadMobileNet.mockResolvedValue();
-    deps.detectFaceInCam.mockResolvedValue(null);
-    deps.saveFace.mockResolvedValue(null);
-    deps.saveFace3d.mockResolvedValue(null);
-    deps.seekFaceInDb.mockReturnValue({ liveMinDist: null, liveMinId: null });
-    deps.getFaceEmbedding.mockResolvedValue(null);
-    deps.cosineSimilarity.mockImplementation((_, saved) => saved[0]);
-    deps.distance.mockImplementation((live, saved) => Math.abs(live[0] - saved[0]));
-    deps.captureThumbnail.mockResolvedValue(null);
-    let localeCallback;
-    deps.setupLocaleSelect.mockImplementation((select, callback) => { localeCallback = callback; });
-    const events = [];
-    state.gstmxxEvents.addEventListener('matchStateChanged', event => events.push(event.detail));
-    const createObjectURL = vi.fn(() => 'blob:fixture');
-    const revokeObjectURL = vi.fn();
-    Object.defineProperty(URL, 'createObjectURL', { configurable: true, value: createObjectURL });
-    Object.defineProperty(URL, 'revokeObjectURL', { configurable: true, value: revokeObjectURL });
-
-    await import('../../lab-js/loader.js?loader-success');
-    await flushPromises();
-    expect(deps.setStatus).toHaveBeenCalledWith('live', 'loader_status_ready');
-    expect(document.getElementById('recordFaceBtn').disabled).toBe(true);
-    const api = window.gstmxx;
-    expect(api.getDb()).toEqual(state.db);
-    expect(api.getDb3d()).toEqual(state.db3d);
-    expect(api.getActiveEffect3d()).toBeNull();
-    api.log('loader log', 'test');
-    state.activeEffect = 'style';
-    state.lastKnownEffectResult = { detection: { score: 1 } };
-    state.MATCH_THRESHOLD = 0.51;
-    state.MATCH_THRESHOLD_3D = 0.77;
-    expect(api.getActiveEffect()).toBe('style');
-    expect(api.getLastResult()).toBe(state.lastKnownEffectResult);
-    expect(api.getMatchThreshold()).toBe(0.51);
-    expect(api.getMatchThreshold3d()).toBe(0.77);
-    api.lastLandmarks3d = [{ x: 0.5 }];
-    expect(api.lastLandmarks3d).toHaveLength(1);
-    expect(deps.setLog).toHaveBeenCalledWith('loader log', 'test');
-
-    const fileInput = document.getElementById('videoFile');
-    Object.defineProperty(fileInput, 'files', { configurable: true, value: [] });
-    fileInput.dispatchEvent(new Event('change'));
-    Object.defineProperty(fileInput, 'files', { configurable: true, value: [new File(['video'], 'one.mp4')] });
-    video.srcObject = { stream: true };
-    fileInput.dispatchEvent(new Event('change'));
-    expect(createObjectURL).toHaveBeenCalledOnce();
-    expect(video.srcObject).toBeNull();
-    expect(video.load).toHaveBeenCalledOnce();
-    fileInput.dispatchEvent(new Event('change'));
-    expect(revokeObjectURL).toHaveBeenCalledWith('blob:fixture');
-
-    Object.defineProperty(video, 'currentTime', { configurable: true, writable: true, value: 0 });
-    Object.defineProperty(video, 'duration', { configurable: true, writable: true, value: Infinity });
-    video.dispatchEvent(new Event('timeupdate'));
-    video.dispatchEvent(new Event('loadedmetadata'));
-    expect(document.getElementById('videoSeek').max).toBe('0');
-    document.getElementById('mesh3dOverlay').remove();
-
-    Object.defineProperty(video, 'readyState', { configurable: true, value: 2 });
-    Object.defineProperty(video, 'currentTime', { configurable: true, writable: true, value: 12.25 });
-    Object.defineProperty(video, 'duration', { configurable: true, writable: true, value: 90 });
-    video.dispatchEvent(new Event('loadedmetadata'));
-    expect(document.getElementById('videoSeek').max).toBe('90');
-    expect(document.getElementById('currentTimeLabel').textContent).toBe('00:12.250');
-    video.dispatchEvent(new Event('loadeddata'));
-    video.dispatchEvent(new Event('timeupdate'));
-    video.dispatchEvent(new Event('seeked'));
-    video.dispatchEvent(new Event('play'));
-    video.dispatchEvent(new Event('pause'));
-    window.dispatchEvent(new Event('resize'));
-    const seek = document.getElementById('videoSeek');
-    seek.value = '25.5';
-    seek.dispatchEvent(new Event('input'));
-    expect(video.currentTime).toBe(25.5);
-    seek.matches = () => true;
-    seek.dispatchEvent(new Event('input'));
-    seek.matches = () => false;
-    seek.value = '0';
-    seek.dispatchEvent(new Event('input'));
-    Object.defineProperty(video, 'currentTime', { configurable: true, writable: true, value: Infinity });
-    video.dispatchEvent(new Event('timeupdate'));
-    expect(document.getElementById('currentTimeLabel').textContent).toBe('00:00.000');
-
-    const record = document.getElementById('recordFaceBtn');
-    const seekFace = document.getElementById('seekFaceBtn');
-    Object.defineProperty(video, 'videoWidth', { configurable: true, value: 0 });
-    Object.defineProperty(video, 'videoHeight', { configurable: true, value: 0 });
-    document.getElementById('overlay').width = 0;
-    document.getElementById('overlay').height = 0;
-    document.getElementById('bboxOverlay').width = 0;
-    document.getElementById('bboxOverlay').height = 0;
-    record.click();
-    await flushPromises();
-    expect(document.querySelector('.loader-entry__metrics').textContent).toContain('loader_no_face_result');
-
-    const savedResult = {
-      detection: { score: 0.94, box: { x: 12.4, y: 20.2, width: 90.1, height: 110.8 } },
-      descriptor: [0.2, 0.4], landmarks: { positions: [{ x: 1 }, { x: 2 }] },
-      age: 31.6, gender: 'female', genderProbability: 0.8,
-    };
-    document.getElementById('overlay').width = 200;
-    document.getElementById('overlay').height = 100;
-    state.lastLandmarks3d = null;
-    deps.saveFace.mockResolvedValueOnce({ id: 2, result: savedResult });
-    deps.captureThumbnail.mockResolvedValueOnce(null);
-    record.click();
-    await flushPromises();
-    expect(events.at(-1)).toMatchObject({ source: 'save', overall: 'unknown', faceapi: { matchedId: 2 }, mediapipe: null });
-
-    state.db3d.faces = [{ id: 3, descriptor3d: [0.93, 0.1] }];
-    deps.saveFace.mockResolvedValueOnce({ id: 3, result: savedResult });
-    deps.captureThumbnail.mockResolvedValueOnce('data:image/png;base64,preview');
-    deps.saveFace3d.mockResolvedValueOnce({ liveInfo3d: { liveMaxSim: 0.91 } });
-    record.click();
-    await flushPromises();
-    expect(deps.saveThumbnail).toHaveBeenCalledWith(3, 'data:image/png;base64,preview');
-    expect(events.at(-1).overall).toBe('matched');
-
-    state.lastLandmarks3d = [{ x: 0.5 }];
-    deps.saveFace.mockResolvedValueOnce({ id: 5, result: {} });
-    deps.saveFace3d.mockResolvedValueOnce(null);
-    record.click();
-    await flushPromises();
-    expect(events.at(-1).faceapi.matchedId).toBe(5);
-
-    deps.saveFace.mockResolvedValueOnce({ id: 6, result: {} });
-    deps.saveFace3d.mockResolvedValueOnce(null);
-    record.click();
-    await flushPromises();
-    expect(events.at(-1).faceapi.matchedId).toBe(6);
-
-    deps.captureThumbnail.mockRejectedValueOnce({});
-    deps.saveFace.mockResolvedValueOnce({ id: 4, result: savedResult });
-    deps.saveFace3d.mockResolvedValueOnce({});
-    record.click();
-    await flushPromises();
-    expect(deps.setLog).toHaveBeenCalledWith('thumbnail_capture_failed_log', 'thumbnails');
-
-    deps.saveFace.mockRejectedValueOnce(new Error('record failed'));
-    record.click();
-    await flushPromises();
-    expect(deps.setStatus).toHaveBeenCalledWith('error', 'loader_status_action_failed');
-
-    let resolvePendingSave;
-    deps.saveFace.mockReturnValueOnce(new Promise(resolve => { resolvePendingSave = resolve; }));
-    const savesBeforeLock = deps.saveFace.mock.calls.length;
-    record.click();
-    await flushPromises();
-    record.dispatchEvent(new Event('click'));
-    await flushPromises();
-    expect(deps.saveFace).toHaveBeenCalledTimes(savesBeforeLock + 1);
-    resolvePendingSave(null);
-    await flushPromises();
-
-    deps.saveFace.mockRejectedValueOnce({});
-    record.click();
-    await flushPromises();
-
-    deps.detectFaceInCam.mockResolvedValueOnce(null);
-    seekFace.click();
-    await flushPromises();
-    expect(document.querySelectorAll('.loader-entry')).not.toHaveLength(0);
-
-    const liveResult = {
-      detection: { score: 0.8, box: { x: 1, y: 2, width: 3, height: 4 } },
-      descriptor: [0.1], landmarks: { getPositions: () => [{}, {}, {}] },
-      age: NaN, gender: '', genderProbability: Infinity,
-    };
-    state.db.faces = [
-      { id: 8, descriptor: [0.8] }, { id: 7, descriptor: [0.2] },
-    ];
-    state.db3d.faces = [
-      { id: 9, descriptor3d: [0.95] }, { id: 10, descriptor3d: [0.72] },
-    ];
-    deps.detectFaceInCam.mockResolvedValueOnce(liveResult);
-    deps.seekFaceInDb.mockReturnValueOnce({ liveMinDist: 0.2, liveMinId: 7 });
-    deps.getFaceEmbedding.mockResolvedValueOnce([1]);
-    seekFace.click();
-    await flushPromises();
-    expect(events.at(-1)).toMatchObject({ source: 'find', overall: 'matched' });
+  });
 
-    deps.detectFaceInCam.mockResolvedValueOnce(liveResult);
-    deps.seekFaceInDb.mockReturnValueOnce({ liveMinDist: 0.9, liveMinId: 8 });
-    deps.getFaceEmbedding.mockRejectedValueOnce(new Error('embed unavailable'));
-    seekFace.click();
+  it('exposes one Playwright API for the same local-file workflow as the UI', async () => {
+    await import('../../lab-js/loader.js?new-loader-success');
     await flushPromises();
-    expect(events.at(-1).mediapipe).toBeNull();
-    expect(events.at(-1).faceapi.detectionState).toBe('eluded');
+    expect(window.gstmxxVideoLoader.isModelsReady()).toBe(true);
+    expect(document.getElementById('statusText').textContent).toBe('select a local video');
 
-    state.db = null;
-    state.db3d = null;
-    deps.detectFaceInCam.mockResolvedValueOnce(liveResult);
-    deps.seekFaceInDb.mockReturnValueOnce({ liveMinDist: 0.1, liveMinId: 12 });
-    deps.getFaceEmbedding.mockResolvedValueOnce([1]);
-    seekFace.click();
+    window.gstmxxVideoLoader.setSourceMetadata({ fingerprint: 'sampled-sha256:test' });
+    const input = document.getElementById('videoFile');
+    Object.defineProperty(input, 'files', {
+      configurable: true,
+      value: [new File(['video'], 'tutorial.webm', { type: 'video/webm' })],
+    });
+    input.dispatchEvent(new Event('change'));
     await flushPromises();
-    expect(events.at(-1).mediapipe).toBeNull();
+    expect(runtime.loadFile).toHaveBeenCalledWith(expect.objectContaining({ name: 'tutorial.webm' }), {
+      fingerprint: 'sampled-sha256:test',
+    });
+    expect(window.gstmxxVideoLoader.isSourceLoaded()).toBe(true);
+    expect(document.getElementById('startAnalysisBtn').disabled).toBe(false);
 
-    state.db = { faces: [{ id: 9, descriptor: [0.1] }] };
-    state.db3d = { faces: [{ id: 9, descriptor3d: [0.4] }] };
-    deps.detectFaceInCam.mockResolvedValueOnce(liveResult);
-    deps.seekFaceInDb.mockReturnValueOnce({ liveMinDist: 0.1, liveMinId: 9 });
-    deps.getFaceEmbedding.mockResolvedValueOnce([1]);
-    seekFace.click();
-    await flushPromises();
-    expect(events.at(-1).overall).toBe('partial-elusion');
+    document.getElementById('video').currentTime = 12.5;
+    document.getElementById('useCurrentTimeBtn').click();
+    expect(document.getElementById('baselineTime').value).toBe('12.500');
 
-    const originalGetContext = HTMLCanvasElement.prototype.getContext;
-    HTMLCanvasElement.prototype.getContext = vi.fn(() => ({
-      fillRect: vi.fn(), drawImage: vi.fn(() => { throw {}; }),
-      fillText: vi.fn(),
-    }));
-    deps.saveFace.mockResolvedValueOnce(null);
-    record.click();
-    await flushPromises();
-    expect(HTMLCanvasElement.prototype.getContext).toHaveBeenCalled();
-    HTMLCanvasElement.prototype.getContext = originalGetContext;
+    await window.gstmxxVideoLoader.analyze({ sampleIntervalSeconds: 1 });
+    expect(runtime.analyze).toHaveBeenCalledWith(expect.objectContaining({ sampleIntervalSeconds: 1 }));
+    expect(document.getElementById('analysisProgressLabel').textContent).toBe('100%');
+    expect(document.getElementById('downloadReportBtn').disabled).toBe(false);
 
-    localeCallback();
-    expect(deps.applyI18n).toHaveBeenCalled();
+    window.gstmxxVideoLoader.cancel();
+    expect(runtime.cancel).toHaveBeenCalled();
+    expect(window.gstmxxVideoLoader.exportBundle()).toEqual({ report: {}, technicalLog: '{}\n', frames: [] });
   });
 
-  it('reports model initialization failures without enabling actions', async () => {
-    vi.resetModules();
-    document.body.innerHTML = loaderMarkup();
-    const { state: isolatedState } = await import('../../lab-js/state.js');
-    isolatedState.db = { nextId: 0, faces: [] };
-    isolatedState.db3d = { faces: [] };
-    isolatedState.gstmxxEvents = new EventTarget();
-    window.faceapi.nets.tinyFaceDetector.loadFromUri.mockRejectedValueOnce({});
-    deps.loadDb.mockReturnValue(isolatedState.db);
-    deps.loadDb3d.mockReturnValue(isolatedState.db3d);
-
-    await import('../../lab-js/loader.js?loader-failure');
+  it('reports model-loading failures and keeps analysis unavailable', async () => {
+    runtime.loadModels.mockRejectedValueOnce(new Error('model unavailable'));
+    await import('../../lab-js/loader.js?new-loader-failure');
     await flushPromises();
-    expect(deps.setStatus).toHaveBeenCalledWith('error', 'loader_status_model_load_failed');
-    expect(deps.setLog).toHaveBeenCalledWith('loader_model_error_log', 'loader');
-    expect(document.getElementById('recordFaceBtn').disabled).toBe(true);
+    expect(document.getElementById('statusText').textContent).toContain('model loading failed');
+    expect(document.getElementById('statusDot').classList.contains('error')).toBe(true);
+    expect(document.getElementById('startAnalysisBtn').disabled).toBe(true);
   });
-});
\ No newline at end of file
+});
diff --git a/tests/unit/video-loader-analysis.test.js b/tests/unit/video-loader-analysis.test.js
new file mode 100644
index 0000000..3d14711
--- /dev/null
+++ b/tests/unit/video-loader-analysis.test.js
@@ -0,0 +1,73 @@
+import { describe, expect, it } from 'vitest';
+import {
+  buildEmbeddingBaseline,
+  buildFaceBaseline,
+  cosineSimilarity,
+  createTriggerDetector,
+  euclideanDistance,
+  median,
+  medianAbsoluteDeviation,
+  visualDrift,
+} from '../../lab-js/video-loader/analysis.js';
+
+describe('video-loader analysis policy', () => {
+  it('computes vector metrics and robust statistics', () => {
+    expect(euclideanDistance([0, 0], [3, 4])).toBe(5);
+    expect(cosineSimilarity([1, 0], [1, 0])).toBeCloseTo(1);
+    expect(cosineSimilarity([1, 0], [0, 1])).toBeCloseTo(0);
+    expect(visualDrift([1, 0], [1, 0])).toBeCloseTo(0);
+    expect(median([9, 1, 5, 3])).toBe(4);
+    expect(medianAbsoluteDeviation([1, 2, 2, 3, 99])).toBe(1);
+    expect(() => euclideanDistance([1], [1, 2])).toThrow(/same length/);
+  });
+
+  it('keeps a real embedding and descriptor as each baseline medoid', () => {
+    const embeddings = [
+      [1, 0, 0],
+      [0.999, 0.04, 0],
+      [0.998, -0.05, 0],
+    ];
+    const embeddingBaseline = buildEmbeddingBaseline(embeddings, {
+      minimumDelta: 0.02,
+      madMultiplier: 4,
+    });
+    expect(embeddings).toContainEqual(embeddingBaseline.embedding);
+    expect(embeddingBaseline.triggerThreshold).toBeGreaterThanOrEqual(0.02);
+
+    const descriptors = [[0, 0], [0.01, 0], [0.04, 0]];
+    const faceBaseline = buildFaceBaseline(descriptors);
+    expect(descriptors).toContainEqual(faceBaseline.descriptor);
+    expect(faceBaseline.spread).toBeGreaterThanOrEqual(0);
+  });
+
+  it('triggers on sustained drift, face return, jump, and heartbeat without queueing frames', () => {
+    const detector = createTriggerDetector({
+      heartbeatSeconds: 4,
+      minimumFaceApiGapSeconds: 0.5,
+      jumpDelta: 0.04,
+      requiredVotes: 2,
+      voteWindow: 3,
+    });
+
+    const first = detector.evaluate({
+      mediaTime: 0,
+      facePresent: true,
+      comparable: true,
+      visualDrift: 0.01,
+      visualThreshold: 0.05,
+    });
+    expect(first.reasons).toContain('identity-heartbeat');
+    detector.markFaceApi(0);
+
+    detector.evaluate({ mediaTime: 1, facePresent: false, comparable: false, visualDrift: null, visualThreshold: 0.05 });
+    const returned = detector.evaluate({ mediaTime: 2, facePresent: true, comparable: true, visualDrift: 0.06, visualThreshold: 0.05 });
+    expect(returned.reasons).toContain('mediapipe-face-returned');
+    expect(returned.observations).toContain('mediapipe-face-returned');
+    detector.markFaceApi(2);
+
+    const sustained = detector.evaluate({ mediaTime: 3, facePresent: true, comparable: true, visualDrift: 0.11, visualThreshold: 0.05 });
+    expect(sustained.shouldVerify).toBe(true);
+    expect(sustained.reasons).toContain('visual-drift-sustained');
+    expect(sustained.reasons).toContain('visual-drift-jump');
+  });
+});
diff --git a/tests/unit/video-loader-session.test.js b/tests/unit/video-loader-session.test.js
new file mode 100644
index 0000000..f759cdb
--- /dev/null
+++ b/tests/unit/video-loader-session.test.js
@@ -0,0 +1,151 @@
+import { describe, expect, it } from 'vitest';
+import {
+  appendJournal,
+  appendSample,
+  applyEditorialCommand,
+  buildReport,
+  createAnalysisSession,
+  recordEvidenceObservation,
+  redoEditorialCommand,
+  selectEventFrame,
+  serializeTechnicalLog,
+  undoEditorialCommand,
+} from '../../lab-js/video-loader/session.js';
+
+describe('video-loader session', () => {
+  function session() {
+    return createAnalysisSession({ name: 'tutorial.webm', size: 42, type: 'video/webm', duration: 10 }, {
+      faceDistanceThreshold: 0.6,
+    });
+  }
+
+  it('keeps technical samples separate from report image bytes', () => {
+    const current = session();
+    current.models = { faceApi: 'vendor' };
+    current.baseline = {
+      mediaTime: 1,
+      faceSpread: 0.02,
+      embeddingMedianDrift: 0.01,
+      embeddingMad: 0.002,
+      visualTriggerThreshold: 0.04,
+      frame: { mediaTime: 1, width: 1920, height: 1080, dataUrl: 'data:image/jpeg;base64,baseline' },
+    };
+    appendSample(current, { mediaTime: 1, facePresent: true, visualDrift: 0.01 });
+    appendJournal(current, 'face-api-result', { mediaTime: 1, faceDistance: 0.2 });
+
+    const first = recordEvidenceObservation(current, {
+      mediaTime: 5,
+      outcome: 'divergent',
+      faceDistance: 0.72,
+      visualDrift: 0.1,
+      reasons: ['visual-drift-sustained'],
+      quality: { sharpness: 0.1 },
+    });
+    const confirmed = recordEvidenceObservation(current, {
+      mediaTime: 5.5,
+      outcome: 'divergent',
+      faceDistance: 0.75,
+      visualDrift: 0.12,
+      reasons: ['visual-drift-sustained'],
+      quality: { sharpness: 0.2 },
+    });
+    expect(confirmed).toBe(first);
+    expect(confirmed.status).toBe('confirmed');
+    expect(confirmed.classification).toBe('identity-distance');
+    expect(confirmed.reviewRequired).toBe(false);
+    selectEventFrame(confirmed, {
+      mediaTime: 5.5,
+      dataUrl: 'data:image/jpeg;base64,event',
+      width: 1920,
+      height: 1080,
+      sharpness: 0.2,
+      reason: 'best',
+    });
+
+    current.status = 'complete';
+    const report = buildReport(current);
+    const log = serializeTechnicalLog(current);
+    expect(report.events).toHaveLength(1);
+    expect(JSON.stringify(report)).not.toContain('base64,event');
+    expect(log).not.toContain('base64');
+    expect(log.trim().split('\n').map(line => JSON.parse(line)).at(-1)).toMatchObject({
+      type: 'analysis-footer',
+      status: 'complete',
+    });
+  });
+
+  it('marks repeated face-api misses as reviewable detector divergence', () => {
+    const current = session();
+    recordEvidenceObservation(current, {
+      mediaTime: 2,
+      outcome: 'divergent',
+      faceApiDetected: false,
+      faceDistance: null,
+      faceThreshold: 0.6,
+      reasons: ['face-returned'],
+    });
+    const event = recordEvidenceObservation(current, {
+      mediaTime: 3,
+      outcome: 'divergent',
+      faceApiDetected: false,
+      faceDistance: null,
+      faceThreshold: 0.6,
+      reasons: ['visual-drift-sustained'],
+    });
+    expect(event.status).toBe('confirmed');
+    expect(event.classification).toBe('detector-divergence');
+    expect(event.reviewRequired).toBe(true);
+  });
+
+  it('supports reversible editorial choices', () => {
+    const current = session();
+    const event = recordEvidenceObservation(current, {
+      mediaTime: 2,
+      outcome: 'divergent',
+      faceDistance: 0.7,
+      visualDrift: 0.08,
+      reasons: [],
+    });
+    expect(applyEditorialCommand(current, { type: 'promote', eventId: event.id })).toBe(true);
+    expect(event.promoted).toBe(true);
+    expect(undoEditorialCommand(current)).toBe(true);
+    expect(event.promoted).toBe(false);
+    expect(redoEditorialCommand(current)).toBe(true);
+    expect(event.promoted).toBe(true);
+  });
+
+  it('keeps source time fixed while a person reorders the story', () => {
+    const current = session();
+    current.baseline = {
+      mediaTime: 5,
+      faceSpread: 0.02,
+      embeddingMedianDrift: 0.01,
+      embeddingMad: 0.002,
+      visualTriggerThreshold: 0.04,
+      frame: null,
+    };
+    recordEvidenceObservation(current, {
+      mediaTime: 1,
+      outcome: 'divergent',
+      faceApiDetected: false,
+      faceDistance: null,
+      faceThreshold: 0.6,
+      reasons: [],
+    });
+    const event = recordEvidenceObservation(current, {
+      mediaTime: 2,
+      outcome: 'divergent',
+      faceApiDetected: false,
+      faceDistance: null,
+      faceThreshold: 0.6,
+      reasons: [],
+    });
+    expect(current.editorial.storyOrder).toEqual([event.id, 'baseline']);
+    expect(applyEditorialCommand(current, { type: 'move-earlier', eventId: 'baseline' })).toBe(true);
+    expect(current.editorial.storyOrder).toEqual(['baseline', event.id]);
+    expect(event.anchorMediaTime).toBe(1);
+    expect(buildReport(current).storyOrder).toEqual(['baseline', event.id]);
+    expect(undoEditorialCommand(current)).toBe(true);
+    expect(current.editorial.storyOrder).toEqual([event.id, 'baseline']);
+  });
+});
diff --git a/tutorials/tutorials.json b/tutorials/tutorials.json
index 66af44c..32519ae 100644
--- a/tutorials/tutorials.json
+++ b/tutorials/tutorials.json
@@ -29,6 +29,9 @@
   "lab-screenshots": {
      "title": "(internal) Lab screenshots and distance measurements"
   },
+  "video-loader": {
+     "title": "(internal) Seekable Video Loader architecture"
+  },
   "aigen-image-labeler": {
      "title": "(internal) AI Gen image labeler"
   },
diff --git a/tutorials/video-loader.md b/tutorials/video-loader.md
new file mode 100644
index 0000000..000ec7c
--- /dev/null
+++ b/tutorials/video-loader.md
@@ -0,0 +1,519 @@
+# Seekable Video Loader architecture
+
+## Purpose and product boundary
+
+The Video Loader analyses an existing, seekable local video. It is not a live
+camera application and it must never request `getUserMedia()`. Its output is an
+evidence-oriented account of how face-model signals changed over media time:
+
+- a visual timeline below the video;
+- a baseline frame and selected high-resolution evidence frames;
+- an editorial report intended for a person to read and revise;
+- a separate, versioned technical log intended for software to replay;
+- optional short clips derived later from the original source and logged media
+  timestamps.
+
+`realtime.html` has a different purpose: it gives immediate feedback to a
+person applying makeup in front of a webcam. Realtime latency, gaps, and camera
+state are different from deterministic processing of a seekable file. The two
+applications may share pure mathematics or model adapters, but they must not
+share UI state, scheduling state, or assumptions about whether old frames can
+be recovered.
+
+The future Ghostyle Studio is a third application. It consumes one or more
+selected frames to reconstruct, edit, and export a Ghostyle. It does not run
+the long-video scan.
+
+## Files and responsibilities
+
+| File | Responsibility |
+|---|---|
+| `loader.html` | Accessible video, analysis controls, timeline, and report shell. |
+| `styles/loader.css` | Video Loader layout only. No Realtime selectors. |
+| `lab-js/loader.js` | UI rendering, downloads, editing, and public Playwright API. |
+| `lab-js/video-loader/controller.js` | Model lifecycle, seeking, sampling, baseline acquisition, and verification. |
+| `lab-js/video-loader/analysis.js` | Pure metrics, robust baselines, and trigger policy. |
+| `lab-js/video-loader/session.js` | Versioned session, report projection, technical journal, undo/redo. |
+| `scripts-dev/video-loader.cjs` | Specialized Playwright CLI and optional clip post-processing. |
+
+Every JavaScript and CommonJS function added for this application uses English
+JSDoc. Inline comments explain reasons and constraints rather than restating
+syntax. This tutorial records architectural decisions that would be too broad
+for local source comments.
+
+## Why the source remains a normal video
+
+Chromium can decode MP4 and WebM files assigned to an ordinary
+`HTMLVideoElement`. Playwright can populate the page's real file input with
+`page.setInputFiles()`. There is therefore no reason to convert a long video to
+Y4M or present it as a fake webcam.
+
+The source is represented by a Blob URL in the browser. The Blob URL is local
+to the page and is revoked when another file is selected. The analysis session
+stores only source metadata and a bounded provenance fingerprint; it does not
+copy the source into the output directory.
+
+The CLI fingerprint is `sampled-sha256`: SHA-256 over the file size, first MiB,
+and last MiB. This is intentionally bounded for multi-gigabyte recordings. It
+is a provenance guard, not a cryptographic claim over every source byte.
+
+## Browser automation contract
+
+The UI and Playwright use the same file input and the same browser API. The
+stable entry point is `window.gstmxxVideoLoader`:
+
+```js
+window.gstmxxVideoLoader.isModelsReady();
+window.gstmxxVideoLoader.isSourceLoaded();
+window.gstmxxVideoLoader.isAnalysisRunning();
+window.gstmxxVideoLoader.setSourceMetadata({ fingerprint });
+await window.gstmxxVideoLoader.analyze({
+  start: 0,
+  end: 120,
+  baselineTime: 18,
+  sampleIntervalSeconds: 0.5,
+});
+window.gstmxxVideoLoader.cancel();
+window.gstmxxVideoLoader.getSession();
+window.gstmxxVideoLoader.exportBundle();
+```
+
+The page also dispatches `gstmxx-video-loader:*` events for model readiness,
+source readiness, samples, baseline readiness, confirmed events, completion,
+and errors. Automation should prefer this contract over button text or screen
+coordinates.
+
+## Model provenance
+
+The repository vendors the following browser assets:
+
+- `@vladmandic/face-api` as a classic global `faceapi` bundle;
+- MediaPipe Tasks Vision `0.10.35`;
+- FaceLandmarker `float16/1` with 478 landmarks;
+- ImageEmbedder MobileNetV3 Small `float32/1`.
+
+Both MediaPipe tasks explicitly use the CPU delegate. The sampled offline
+workflow does not need continuous GPU throughput, and the CPU path remains
+reliable in headless Playwright, software-rendered documentation captures, and
+machines without a usable WebGL delegate. The chosen delegate is recorded with
+the session model provenance.
+
+The face-api bundle currently comes from a floating package URL in the vendor
+refresh documentation. A future vendor refresh should pin the package version
+or record a digest. Reports record model labels so results are not silently
+presented as timeless or implementation-independent.
+
+The vendored FaceMatcher default is `0.6` and accepts a match when the L2
+distance is strictly below that threshold. The Video Loader therefore uses
+`0.6` as its default identity-distance threshold. The threshold and the
+baseline spread are recorded in each report and technical log.
+
+## Three distinct model signals
+
+### FaceLandmarker: frequent presence and geometry
+
+FaceLandmarker is the light, frequent sensor. For every sampled media timestamp
+it answers whether a mesh is present and supplies geometry for crop alignment.
+It is not used as an identity matcher.
+
+The Video Loader uses MediaPipe in `IMAGE` mode rather than `VIDEO` mode. A
+seekable analysis may revisit timestamps during refinement. `VIDEO` mode
+requires monotonically increasing timestamps, while `IMAGE` mode describes the
+actual contract: independent decoded frames selected from a file.
+
+### ImageEmbedder: aligned visual drift
+
+ImageEmbedder is a generic image-similarity model, not a facial-recognition
+model. It can react to makeup, texture, expression, light, occlusion, and crop
+errors. Its output must be treated as a trigger signal, never proof of identity
+or protection.
+
+Before embedding, the 478-point mesh is used to:
+
+1. find the face extent;
+2. find the outer eye landmarks;
+3. remove roll;
+4. normalise face scale;
+5. exclude most background;
+6. draw a 224 × 224 aligned crop.
+
+The small crop canvas is produced only at the configured sample cadence. The
+application does not copy every decoded video frame to a canvas.
+
+For embedding `e_t` and baseline embedding `e_0`, the displayed signal is:
+
+```text
+similarity(t) = cosine(e_t, e_0)
+visualDrift(t) = 1 - similarity(t)
+```
+
+Smaller drift means more similar visual features. This numeric scale is not
+the face-api L2 scale and is displayed in a separate timeline lane.
+
+### face-api: sparse identity verification
+
+face-api performs detection, 68-point alignment, and a 128-dimensional face
+descriptor. It runs only for:
+
+- baseline acquisition;
+- a MediaPipe face returning after an absence;
+- sustained ImageEmbedder drift;
+- a sudden ImageEmbedder drift jump;
+- a bounded heartbeat that prevents gradual changes from escaping checks.
+
+Only one face-api pass is awaited at a time. The controller stores no pending
+bitmap queue. A cooldown prevents several trigger reasons on adjacent samples
+from producing an uncontrolled burst of work.
+
+An `async` function does not automatically make TensorFlow computation run on
+another CPU thread. The current implementation serialises expensive work and
+yields between samples; this is sufficient for an offline, paused, seekable
+video. A Web Worker should only be introduced after measuring actual UI stalls
+and verifying the vendored backend in a worker environment.
+
+## Baseline acquisition
+
+The first decoded frame is not automatically trusted. The scan advances until
+it collects enough comparable aligned face crops and face-api descriptors.
+
+Makeup tutorials often open with a finished-look teaser. The UI and CLI expose
+`baselineTime`, the timestamp from which baseline search begins. After a stable
+baseline is acquired, the controller rewinds and scans the complete requested
+range, so a teaser before the bare-face baseline can still appear as evidence.
+In the UI, a person can scrub the normal video player to the start of the
+bare-face section and press **Use current video time** instead of transcribing a
+timestamp. The CLI equivalent is `--baseline-time`.
+
+A sample is comparable when:
+
+- FaceLandmarker returns one mesh;
+- the face occupies a useful portion of the frame;
+- roll and approximate yaw remain within broad bounds;
+- exposure is neither nearly black nor heavily clipped;
+- ImageEmbedder returns a float vector.
+
+The default baseline requires eight ImageEmbedder vectors and three face-api
+descriptors. Baseline candidates are sampled at no more than 0.25-second
+intervals even when the coarse scan uses a wider interval; this prevents a
+two-second long-video cadence from stretching one baseline across many scene
+changes. A candidate run is reset when an 8 × 8 RGB crop signature changes too
+abruptly, when pose or scale jumps, when a non-comparable frame breaks the run,
+or when the eventual face descriptors have excessive spread. Generic
+ImageEmbedder distance is deliberately not the stability gate: calibration on
+real tutorial footage showed that it can vary strongly between neighbouring
+frames that still depict the same face. face-api
+does not run for every failed candidate: it verifies the three sharpest frames
+only after eight lightweight samples have formed a continuous run. Both
+baselines are medoids: real observed vectors with the
+smallest total distance to their peers. A medoid is preferred to a mean because
+it remains a genuine model output and is robust to one unusual sample.
+
+For baseline visual drifts `d_i`, the adaptive trigger threshold is:
+
+```text
+median(d_i) + max(minimumDelta, MAD(d_i) × madMultiplier)
+```
+
+`MAD` is median absolute deviation. The defaults are hypotheses that must be
+validated against real makeup videos; they are configuration, not scientific
+constants.
+
+The frame attached to the baseline is captured at the ImageEmbedder medoid's
+timestamp, rather than simply choosing the sharpest candidate. This keeps the
+visible baseline and its numeric visual representative aligned, and avoids
+systematically preferring unusually high-contrast hand or product edges. It is
+retained at native source resolution. All other baseline pixels are discarded.
+
+## Sampling and memory policy
+
+The scan seeks through media time at a configurable interval. The default is
+0.5 seconds, with UI choices from 0.25 to 2 seconds. This is a coarse analysis,
+not a frame-perfect decoder.
+
+For every sample, the session stores only:
+
+- media timestamp;
+- face presence;
+- comparison eligibility;
+- visual drift and its active threshold;
+- compact geometry metrics;
+- compact quality metrics.
+
+It does not store raw landmarks, model embeddings, source pixels, canvases,
+`ImageBitmap` objects, or a ring buffer. Because a local source is seekable,
+the video itself is the recoverable frame store.
+
+Native-resolution JPEG pixels are retained only for:
+
+- the selected baseline;
+- confirmed divergence events.
+
+If a later pass needs neighbouring frames, it should seek to neighbouring
+timestamps rather than cache the original scan.
+
+## Trigger state and event confirmation
+
+A lightweight sample becomes eligible for face-api verification when at least
+one trigger reason exists:
+
+- `identity-heartbeat`;
+- `mediapipe-face-returned`;
+- `visual-drift-sustained`;
+- `visual-drift-jump`.
+
+Sustained drift currently means at least two above-threshold samples in a
+three-sample window. A lost MediaPipe face is logged immediately but does not
+invoke face-api when no crop exists. The return transition does invoke it.
+
+A face-api observation is divergent when either:
+
+- MediaPipe has a face but face-api returns no detection; or
+- face-api returns a descriptor with L2 distance greater than or equal to 0.6.
+
+One divergent result creates a candidate. Two divergent observations within a
+three-second media window confirm the event. This confirmation rule rejects
+isolated decode glitches without requiring a pixel queue.
+
+Confirmation does not by itself claim that makeup defeated recognition. Every
+confirmed event receives one of these classifications:
+
+- `identity-distance`: at least two descriptors meet or exceed the configured
+  L2 threshold. This is the strongest current identity-change signal;
+- `detector-divergence`: at least two frames contain a MediaPipe mesh while
+  face-api returns no face. This requires human review because a hand,
+  applicator, pose, or partial occlusion can cause it;
+- `mixed-divergence`: two divergent checks exist, but they do not form either
+  homogeneous case above. This also requires review.
+
+The report says “detector divergence — review” rather than calling that event a
+successful transformation. A person may still promote it after inspecting the
+frame or an extracted source clip.
+
+Confirmation starts a bounded evidence-refinement pass. FaceLandmarker revisits
+the event interval plus one second on either side at 0.2-second steps and keeps
+only timestamps and quality metrics. It does not retain those frame pixels.
+The three sharpest comparable timestamps are then checked by face-api, in
+order, until one preserves the event's classification: a distance crossing for
+`identity-distance`, or a missing face-api detection while MediaPipe still has
+a mesh for `detector-divergence`. The controller then re-seeks exactly that
+timestamp and captures one native-resolution JPEG. If no refined timestamp
+preserves the signal, it falls back to the sharpest original divergent
+observation rather than substituting a prettier but contradictory image.
+
+Candidate events remain in the technical log even when they never reach the
+report. Refinement checks and the final selection reason are journal records,
+so a later tool can reconstruct why that image was chosen.
+
+## Timeline semantics
+
+The report timeline has three independent lanes:
+
+1. **MediaPipe face** — a binary presence trace.
+2. **Visual drift** — frequent `1 - cosine` samples and an adaptive dashed threshold.
+3. **face-api L2** — sparse verification points and the fixed 0.6 dashed threshold.
+
+Missing values create gaps; they are never converted into zeros. ImageEmbedder
+and face-api values are not overlaid or normalised into one synthetic score.
+
+## Session, report, and technical log
+
+The mutable browser session owns all observations and editorial decisions.
+The report and technical log are projections with different audiences.
+
+### Editorial report
+
+The report contains:
+
+- source and model provenance;
+- configuration;
+- baseline metrics;
+- confirmed, non-rejected events;
+- captions and promotion state;
+- compact face-api and visual-drift evidence;
+- event classification and whether human review is required;
+- references to separately written image files.
+
+It deliberately excludes raw embeddings, raw image data URLs, and diagnostic
+errors unrelated to the story.
+
+### Technical log
+
+`analysis.ndjson` is newline-delimited JSON with a versioned header, sample
+records, journal records, evidence-event records, and a footer. It excludes
+image bytes but keeps exact media timestamps and recommended clip windows.
+
+The technical log is downloaded separately from the report. This separation
+allows a user to share the report without accidentally sharing detailed model
+telemetry, while retaining a machine-readable research artifact locally.
+
+The CLI writes both artifacts even when analysis fails after the source has
+loaded. A failed run is precisely when the reset reasons, last successful
+sample, and model diagnostics are most valuable.
+
+The log can be replayed even when the UI layout changes. Its source fingerprint
+lets tooling warn if a different video is supplied later.
+
+## Reversible editorial choices
+
+Automatic observations are append-only. User choices are reversible commands:
+
+- promote an event into the story;
+- reject an event;
+- edit its caption.
+- move an evidence frame earlier or later in the story.
+
+Each command records before/after state. Undo and redo change editorial state;
+they do not delete automatic observations or rewrite the technical history.
+
+### Source chronology is not story chronology
+
+Makeup videos often open with the finished look, cut back to a bare face, and
+then show the transformation. Media timestamps therefore cannot define
+“before” and “after”. The Video Loader preserves two independent orders:
+
+- source time is immutable evidence and remains attached to every frame,
+  observation, log record, and clip window;
+- story order is a human-editable sequence used by the browser report and the
+  portable HTML export.
+
+The computational baseline is included as a reorderable story card, but moving
+it never changes the descriptor used for distance calculations. Confirmed
+events enter the story in source-time order by default; candidates remain only
+in the technical log until a second divergent observation confirms them. The
+person can then move the teaser after the reference, or construct another
+sequence, without falsifying where either frame came from.
+
+## CLI workflow
+
+Analyse a video:
+
+```sh
+node scripts-dev/video-loader.cjs analyze \
+  "tests/fixtures/10 Minute Makeup Tutorial For Beginners [PD-Cc5XpY5w].webm" \
+  --output /tmp/beginner-analysis \
+  --baseline-time 18 \
+  --sample-interval 0.5
+```
+
+Limit an exploratory pass:
+
+```sh
+node scripts-dev/video-loader.cjs analyze tests/fixtures/my-moving-face.mp4 \
+  --output /tmp/loader-smoke \
+  --end 20 \
+  --sample-interval 1
+```
+
+Generate clips during the same invocation:
+
+```sh
+node scripts-dev/video-loader.cjs analyze video.webm \
+  --output /tmp/video-report \
+  --clips \
+  --clip-before 2 \
+  --clip-after 3
+```
+
+Or reprocess a log later:
+
+```sh
+node scripts-dev/video-loader.cjs extract-clips \
+  /tmp/video-report/analysis.ndjson \
+  video.webm \
+  --output /tmp/video-report/clips
+```
+
+`ffmpeg` is used only by `extract-clips`. The browser analysis path itself has
+no ffmpeg or Y4M dependency.
+
+## Clip extraction
+
+Every evidence event records:
+
+```json
+{
+  "anchorMediaTime": 83.417,
+  "recommendedClip": {
+    "start": 81.417,
+    "end": 86.417
+  }
+}
+```
+
+The post-processor opens the original source, seeks by media time, and writes a
+short H.264/AAC MP4. Re-encoding is intentional: WebM, MKV, and unusual source
+codecs should produce a broadly usable report clip. The operation is optional
+and does not alter report validity. Before extraction, the source byte size and
+bounded sampled fingerprint must match the provenance stored in the log.
+
+The clip extractor has been exercised by replaying real tutorial logs:
+seven-second windows were generated from confirmed event records without
+rerunning MediaPipe, ImageEmbedder, or face-api.
+
+## Real-video test corpus
+
+Large research videos under `tests/fixtures/` are local-only and git-ignored.
+They include makeup tutorials and event recordings. Automated CI must continue
+to use the small committed fixture; the large corpus is for manual calibration,
+performance tests, and qualitative review.
+
+Recommended progression:
+
+1. `my-moving-face.mp4` for a short smoke test;
+2. the 39 MiB romantic-vampire recording for a realistic makeup pass;
+3. longer tutorials to test baseline search and gradual transformations;
+4. the 1.8 GiB screencast only after bounded tests pass.
+
+Tests must never overwrite, transcode, move, or copy these sources.
+
+The 39 MiB MKV decoded successfully in Chromium during a bounded 30-second
+pass. That run also demonstrated why the reference cannot be inferred from
+chronology alone: the default search selected the stable finished-look title
+card at six seconds. Later detector divergences included a product held in
+front of the face and application in progress, so they were correctly marked
+for review. A person should scrub past the intro and set the reference-search
+time before a substantive analysis.
+
+## Known limitations
+
+- The coarse seek interval can miss a very brief transition. A later refinement
+  pass already revisits confirmed-event neighbourhoods at a smaller interval,
+  but it cannot recover an event that the coarse trigger never observed.
+- Browser codec support determines whether a given MKV can be decoded.
+- Generic ImageEmbedder drift has not yet been calibrated as a reliable makeup
+  signal; it remains a trigger hypothesis.
+- Lighting and expression can still affect the aligned crop.
+- `baselineTime` is a search start, not semantic knowledge of “before”. In the
+  tested beginner tutorial, `18` seconds begins while cream is already visible;
+  the medoid correctly represents that window but cannot infer that an earlier
+  bare-face moment is editorially preferable. The UI therefore exposes the
+  timestamp instead of pretending the first stable face is always the desired
+  before image.
+- face-api work is serialised but currently runs in the page's execution
+  context, not a dedicated worker.
+- The current report exports single evidence frames and portable HTML/JSON.
+  Paired cards, animated sequences, Fediverse publishing, and Ghostyle Studio
+  are later phases built on the same event/session contract.
+
+## Test strategy
+
+Pure unit tests cover:
+
+- Euclidean and cosine metrics;
+- medoid selection and robust noise;
+- trigger cooldown and sustained-vote logic;
+- event merging and confirmation;
+- technical-log exclusion of image bytes;
+- editorial undo and redo.
+
+Browser tests mock model runtimes to verify:
+
+- the page never requests a camera;
+- Playwright can use `setInputFiles()`;
+- the public automation API becomes ready;
+- source metadata, progress, report, and log stay separately addressable.
+
+Manual real-video validation records duration, sampling interval, wall-clock
+time, sample count, face-api count, confirmed events, false positives, and
+missed visually obvious transitions. Threshold changes require evidence from
+that calibration rather than adjustment by appearance alone.

From f0b856d8192bf2929ea8c8e9a813df1b4bab286e Mon Sep 17 00:00:00 2001
From: vecna <vecna@globaleaks.org>
Date: Mon, 12 Oct 2026 10:59:09 +0200
Subject: [PATCH 4/9] added vision_bundle_mjs.js.map

---
 lab-js/vendor/FOLDER-DESCRIPTION.md    |    4 +-
 lab-js/vendor/fetch-sources.sh         |    6 +-
 lab-js/vendor/scripts-vendor-README.md |    9 +-
 lab-js/vendor/tasks-vision@0.10.35.js  | 9218 +-----------------------
 lab-js/vendor/vision_bundle_mjs.js.map |    1 +
 5 files changed, 17 insertions(+), 9221 deletions(-)
 create mode 100644 lab-js/vendor/vision_bundle_mjs.js.map

diff --git a/lab-js/vendor/FOLDER-DESCRIPTION.md b/lab-js/vendor/FOLDER-DESCRIPTION.md
index 4b02d30..4ecce84 100644
--- a/lab-js/vendor/FOLDER-DESCRIPTION.md
+++ b/lab-js/vendor/FOLDER-DESCRIPTION.md
@@ -4,6 +4,6 @@ This folder contains third-party browser runtime assets mirrored into the reposi
 
 The files come from face-api.js, MediaPipe Tasks Vision, face-api model weights, MediaPipe model files, and the local refresh helper `fetch-sources.sh`. They are used by the detection, recognition, landmarking, and MediaPipe/3D paths at runtime.
 
-`face-api.js` is loaded as a classic script before the ES modules that call the global `faceapi` object. The model shards and manifests provide TinyFaceDetector, 68-point landmarks, recognition descriptors, age/gender, and expression models. `tasks-vision@0.10.35.js`, `face_landmarker.task`, `mobilenet_v3_small.tflite`, and the nested `wasm/` runtime support the MediaPipe face-landmark and ImageEmbedder paths.
+`face-api.js` is loaded as a classic script before the ES modules that call the global `faceapi` object. The model shards and manifests provide TinyFaceDetector, 68-point landmarks, recognition descriptors, age/gender, and expression models. `tasks-vision@0.10.35.js`, its `vision_bundle_mjs.js.map` source map, `face_landmarker.task`, `mobilenet_v3_small.tflite`, and the nested `wasm/` runtime support the MediaPipe face-landmark and ImageEmbedder paths.
 
-These files are excluded from code2prompt because they are vendored libraries, model weights, WASM support files, and generated manifests. Their exact source URLs and refresh rules are documented in `lab-js/vendor/README.md` in the repository, but only this summary and the nested folder description are uploaded to the chatbot.
+These files are excluded from code2prompt because they are vendored libraries, source maps, model weights, WASM support files, and generated manifests. Their exact source URLs and refresh rules are documented in `lab-js/vendor/scripts-vendor-README.md` in the repository, but only this summary and the nested folder description are uploaded to the chatbot.
diff --git a/lab-js/vendor/fetch-sources.sh b/lab-js/vendor/fetch-sources.sh
index bde6341..511e571 100755
--- a/lab-js/vendor/fetch-sources.sh
+++ b/lab-js/vendor/fetch-sources.sh
@@ -27,7 +27,11 @@ fetch "https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/f
 fetch "https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/face_expression/face_expression_model-shard1" "face_expression_model-shard1"
 fetch "https://cdn.jsdelivr.net/gh/justadudewhohacks/face-api.js-models@master/age_gender_model/age_gender_model-shard1" "age_gender_model-shard1"
 
-# MediaPipe Tasks Vision @ 0.10.35 runtime files.
+# MediaPipe Tasks Vision @ 0.10.35 bundle, source map, and runtime files.
+# Keep the bundle byte-for-byte aligned with the upstream file: the source map
+# describes the minified one-line bundle and is invalid after pretty-printing.
+fetch "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35/vision_bundle.mjs" "tasks-vision@0.10.35.js"
+fetch "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35/vision_bundle.mjs.map" "vision_bundle_mjs.js.map"
 fetch "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35/wasm/vision_wasm_internal.js" "wasm/vision_wasm_internal.js"
 fetch "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35/wasm/vision_wasm_internal.wasm" "wasm/vision_wasm_internal.wasm"
 
diff --git a/lab-js/vendor/scripts-vendor-README.md b/lab-js/vendor/scripts-vendor-README.md
index 01d8a5c..99ffce5 100644
--- a/lab-js/vendor/scripts-vendor-README.md
+++ b/lab-js/vendor/scripts-vendor-README.md
@@ -7,7 +7,6 @@ Use `lab-js/vendor/fetch-sources.sh` to refresh the assets it manages.
 | Local file | Source URL | Upstream version/reference | Notes |
 |---|---|---|---|
 | `face-api.js` | `https://cdn.jsdelivr.net/npm/@vladmandic/face-api/dist/face-api.js` | floating (no `@x.y.z` in URL) | Main `faceapi` browser bundle used by the Lab pages. |
-| `tasks-vision@0.10.35.js` | `https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35` | `0.10.35` | ESM bundle imported by 3D/embedder paths. |
 
 ## URLs synced by `fetch-sources.sh`
 
@@ -32,9 +31,17 @@ Source reference for this block is currently `justadudewhohacks/face-api.js-mode
 
 | Local file | Source URL |
 |---|---|
+| `tasks-vision@0.10.35.js` | `https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35/vision_bundle.mjs` |
+| `vision_bundle_mjs.js.map` | `https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35/vision_bundle.mjs.map` |
 | `wasm/vision_wasm_internal.js` | `https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35/wasm/vision_wasm_internal.js` |
 | `wasm/vision_wasm_internal.wasm` | `https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.35/wasm/vision_wasm_internal.wasm` |
 
+The source map is stored as `vision_bundle_mjs.js.map` because that is the
+relative filename declared by the upstream bundle. Keep
+`tasks-vision@0.10.35.js` byte-for-byte aligned with `vision_bundle.mjs`:
+pretty-printing the generated JavaScript changes line and column positions and
+invalidates the upstream mappings.
+
 ### MediaPipe model files
 
 | Local file | Source URL | Version segment |
diff --git a/lab-js/vendor/tasks-vision@0.10.35.js b/lab-js/vendor/tasks-vision@0.10.35.js
index d704ee3..f2aa30d 100644
--- a/lab-js/vendor/tasks-vision@0.10.35.js
+++ b/lab-js/vendor/tasks-vision@0.10.35.js
@@ -1,9218 +1,2 @@
-/**
- * Skipped minification because the original files appears to be already minified.
- * Original file: /npm/@mediapipe/tasks-vision@0.10.35/vision_bundle.mjs
- *
- * Do NOT use SRI with dynamically generated files! More information: https://www.jsdelivr.com/using-sri-with-dynamic-files
- */
-var t = "undefined" != typeof self ? self : {};
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-        break t;
-      }
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-}
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-    } catch {
-      Yo = !1;
-    }
-  return Yo;
-}
-async function Jo(t, e, n) {
-  return {
-    wasmLoaderPath: `${e}/${t}_${(n = `wasm${n ? "_module" : ""}${(await $o(n)) ? "" : "_nosimd"}_internal`)}.js`,
-    wasmBinaryPath: `${e}/${t}_${n}.wasm`,
-  };
-}
-var Zo = class {};
-function Qo() {
-  var t = navigator;
-  return (
-    "undefined" != typeof OffscreenCanvas &&
-    (!(function (t = navigator) {
-      return (t = t.userAgent).includes("Safari") && !t.includes("Chrome");
-    })(t) ||
-      !!(
-        (t = t.userAgent.match(/Version\/([\d]+).*Safari/)) &&
-        t.length >= 1 &&
-        Number(t[1]) >= 17
-      ))
-  );
-}
-async function ta(t) {
-  if ("function" != typeof importScripts) {
-    const e = document.createElement("script");
-    return (
-      (e.src = t.toString()),
-      (e.crossOrigin = "anonymous"),
-      new Promise((t, n) => {
-        (e.addEventListener(
-          "load",
-          () => {
-            t();
-          },
-          !1,
-        ),
-          e.addEventListener(
-            "error",
-            (t) => {
-              n(t);
-            },
-            !1,
-          ),
-          document.body.appendChild(e));
-      })
-    );
-  }
-  try {
-    importScripts(t.toString());
-  } catch (e) {
-    if (!(e instanceof TypeError)) throw e;
-    {
-      const e = self.import;
-      e ? await e(t.toString()) : await import(t.toString());
-    }
-  }
-}
-function ea(t) {
-  return void 0 !== t.videoWidth
-    ? [t.videoWidth, t.videoHeight]
-    : void 0 !== t.naturalWidth
-      ? [t.naturalWidth, t.naturalHeight]
-      : void 0 !== t.displayWidth
-        ? [t.displayWidth, t.displayHeight]
-        : [t.width, t.height];
-}
-function na(t, e, n) {
-  (t.m ||
-    console.error(
-      "No wasm multistream support detected: ensure dependency inclusion of :gl_graph_runner_internal_multi_input target",
-    ),
-    n((e = t.i.stringToNewUTF8(e))),
-    t.i._free(e));
-}
-function ra(t, e, n) {
-  if (!t.i.canvas) throw Error("No OpenGL canvas configured.");
-  if (
-    (n ? t.i._bindTextureToStream(n) : t.i._bindTextureToCanvas(),
-    !(n = t.i.canvas.getContext("webgl2") || t.i.canvas.getContext("webgl")))
-  )
-    throw Error(
-      "Failed to obtain WebGL context from the provided canvas. `getContext()` should only be invoked with `webgl` or `webgl2`.",
-    );
-  (t.i.gpuOriginForWebTexturesIsBottomLeft &&
-    n.pixelStorei(n.UNPACK_FLIP_Y_WEBGL, !0),
-    n.texImage2D(n.TEXTURE_2D, 0, n.RGBA, n.RGBA, n.UNSIGNED_BYTE, e),
-    t.i.gpuOriginForWebTexturesIsBottomLeft &&
-      n.pixelStorei(n.UNPACK_FLIP_Y_WEBGL, !1));
-  const [r, i] = ea(e);
-  return (
-    !t.l ||
-      (r === t.i.canvas.width && i === t.i.canvas.height) ||
-      ((t.i.canvas.width = r), (t.i.canvas.height = i)),
-    [r, i]
-  );
-}
-function ia(t, e, n) {
-  t.m ||
-    console.error(
-      "No wasm multistream support detected: ensure dependency inclusion of :gl_graph_runner_internal_multi_input target",
-    );
-  const r = new Uint32Array(e.length);
-  for (let n = 0; n < e.length; n++) r[n] = t.i.stringToNewUTF8(e[n]);
-  ((e = t.i._malloc(4 * r.length)), t.i.HEAPU32.set(r, e >> 2), n(e));
-  for (const e of r) t.i._free(e);
-  t.i._free(e);
-}
-function sa(t, e, n) {
-  ((t.i.simpleListeners = t.i.simpleListeners || {}),
-    (t.i.simpleListeners[e] = n));
-}
-function oa(t, e, n) {
-  let r = [];
-  ((t.i.simpleListeners = t.i.simpleListeners || {}),
-    (t.i.simpleListeners[e] = (t, e, i) => {
-      e ? (n(r, i), (r = [])) : r.push(t);
-    }));
-}
-((Zo.forVisionTasks = function (t, e = !1) {
-  return Jo("vision", t ?? Hi``, e);
-}),
-  (Zo.forTextTasks = function (t, e = !1) {
-    return Jo("text", t ?? Hi``, e);
-  }),
-  (Zo.forGenAiTasks = function (t, e = !1) {
-    return Jo("genai", t ?? Hi``, e);
-  }),
-  (Zo.forAudioTasks = function (t, e = !1) {
-    return Jo("audio", t ?? Hi``, e);
-  }),
-  (Zo.isSimdSupported = function (t = !1) {
-    return $o(t);
-  }));
-async function aa(t, e, n, r) {
-  return (
-    (t = await (async (t, e, n, r, i) => {
-      if ((e && (await ta(e)), !self.ModuleFactory))
-        throw Error("ModuleFactory not set.");
-      if (n && (await ta(n), !self.ModuleFactory))
-        throw Error("ModuleFactory not set.");
-      return (
-        self.Module &&
-          i &&
-          (((e = self.Module).locateFile = i.locateFile),
-          i.mainScriptUrlOrBlob &&
-            (e.mainScriptUrlOrBlob = i.mainScriptUrlOrBlob)),
-        (i = await self.ModuleFactory(self.Module || i)),
-        (self.ModuleFactory = self.Module = void 0),
-        new t(i, r)
-      );
-    })(t, n.wasmLoaderPath, n.assetLoaderPath, e, {
-      locateFile: (t) =>
-        t.endsWith(".wasm")
-          ? n.wasmBinaryPath.toString()
-          : n.assetBinaryPath && t.endsWith(".data")
-            ? n.assetBinaryPath.toString()
-            : t,
-    })),
-    await t.o(r),
-    t
-  );
-}
-function ca(t, e) {
-  const n = yn(t.baseOptions, Vs, 1) || new Vs();
-  ("string" == typeof e
-    ? (Ze(n, 2, ue(e)), Ze(n, 1))
-    : e instanceof Uint8Array && (Ze(n, 1, ht(e, !1)), Ze(n, 2)),
-    wn(t.baseOptions, 0, 1, n));
-}
-function ha(t) {
-  try {
-    const e = t.H.length;
-    if (1 === e) throw Error(t.H[0].message);
-    if (e > 1)
-      throw Error(
-        "Encountered multiple errors: " + t.H.map((t) => t.message).join(", "),
-      );
-  } finally {
-    t.H = [];
-  }
-}
-function ua(t, e) {
-  t.C = Math.max(t.C, e);
-}
-function la(t, e) {
-  ((t.B = new is()),
-    Rn(t.B, 2, "PassThroughCalculator"),
-    ns(t.B, "free_memory"),
-    rs(t.B, "free_memory_unused_out"),
-    hs(e, "free_memory"),
-    cs(e, t.B));
-}
-function fa(t, e) {
-  (ns(t.B, e), rs(t.B, e + "_unused_out"));
-}
-function da(t) {
-  t.g.addBoolToStream(!0, "free_memory", t.C);
-}
-var pa = class {
-  constructor(t) {
-    ((this.g = t),
-      (this.H = []),
-      (this.C = 0),
-      this.g.setAutoRenderToScreen(!1));
-  }
-  l(t, e = !0) {
-    if (e) {
-      const e = t.baseOptions || {};
-      if (t.baseOptions?.modelAssetBuffer && t.baseOptions?.modelAssetPath)
-        throw Error(
-          "Cannot set both baseOptions.modelAssetPath and baseOptions.modelAssetBuffer",
-        );
-      if (
-        !(
-          yn(this.baseOptions, Vs, 1)?.g() ||
-          yn(this.baseOptions, Vs, 1)?.l() ||
-          t.baseOptions?.modelAssetBuffer ||
-          t.baseOptions?.modelAssetPath
-        )
-      )
-        throw Error(
-          "Either baseOptions.modelAssetPath or baseOptions.modelAssetBuffer must be set",
-        );
-      if (
-        ((function (t, e) {
-          let n = yn(t.baseOptions, Gs, 3);
-          if (!n) {
-            var r = (n = new Gs()),
-              i = new $i();
-            Tn(r, 4, js, i);
-          }
-          ("delegate" in e &&
-            ("GPU" === e.delegate
-              ? ((e = n), (r = new zi()), Tn(e, 2, js, r))
-              : ((e = n), (r = new $i()), Tn(e, 4, js, r))),
-            wn(t.baseOptions, 0, 3, n));
-        })(this, e),
-        e.modelAssetPath)
-      )
-        return fetch(e.modelAssetPath.toString())
-          .then((t) => {
-            if (t.ok) return t.arrayBuffer();
-            throw Error(
-              `Failed to fetch model: ${e.modelAssetPath} (${t.status})`,
-            );
-          })
-          .then((t) => {
-            try {
-              this.g.i.FS_unlink("/model.dat");
-            } catch {}
-            (this.g.i.FS_createDataFile(
-              "/",
-              "model.dat",
-              new Uint8Array(t),
-              !0,
-              !1,
-              !1,
-            ),
-              ca(this, "/model.dat"),
-              this.m(),
-              this.L());
-          });
-      if (e.modelAssetBuffer instanceof Uint8Array)
-        ca(this, e.modelAssetBuffer);
-      else if (e.modelAssetBuffer)
-        return (async function (t) {
-          const e = [];
-          for (var n = 0; ; ) {
-            const { done: r, value: i } = await t.read();
-            if (r) break;
-            (e.push(i), (n += i.length));
-          }
-          if (0 === e.length) return new Uint8Array(0);
-          if (1 === e.length) return e[0];
-          ((t = new Uint8Array(n)), (n = 0));
-          for (const r of e) (t.set(r, n), (n += r.length));
-          return t;
-        })(e.modelAssetBuffer).then((t) => {
-          (ca(this, t), this.m(), this.L());
-        });
-    }
-    return (this.m(), this.L(), Promise.resolve());
-  }
-  L() {}
-  ca() {
-    let t;
-    if (
-      (this.g.ca((e) => {
-        t = ds(e);
-      }),
-      !t)
-    )
-      throw Error("Failed to retrieve CalculatorGraphConfig");
-    return t;
-  }
-  setGraph(t, e) {
-    (this.g.attachErrorListener((t, e) => {
-      this.H.push(Error(e));
-    }),
-      this.g.Ja(),
-      this.g.setGraph(t, e),
-      (this.B = void 0),
-      ha(this));
-  }
-  finishProcessing() {
-    (this.g.finishProcessing(), ha(this));
-  }
-  close() {
-    ((this.B = void 0), this.g.closeGraph());
-  }
-};
-function ga(t, e) {
-  if (!t) throw Error(`Unable to obtain required WebGL resource: ${e}`);
-  return t;
-}
-pa.prototype.close = pa.prototype.close;
-class ma {
-  constructor(t, e, n, r) {
-    ((this.g = t), (this.h = e), (this.m = n), (this.l = r));
-  }
-  bind() {
-    this.g.bindVertexArray(this.h);
-  }
-  close() {
-    (this.g.deleteVertexArray(this.h),
-      this.g.deleteBuffer(this.m),
-      this.g.deleteBuffer(this.l));
-  }
-}
-function ya(t, e, n) {
-  const r = t.g;
-  if (
-    ((n = ga(r.createShader(n), "Failed to create WebGL shader")),
-    r.shaderSource(n, e),
-    r.compileShader(n),
-    !r.getShaderParameter(n, r.COMPILE_STATUS))
-  )
-    throw Error(`Could not compile WebGL shader: ${r.getShaderInfoLog(n)}`);
-  return (r.attachShader(t.h, n), n);
-}
-function _a(t, e) {
-  const n = t.g,
-    r = ga(n.createVertexArray(), "Failed to create vertex array");
-  n.bindVertexArray(r);
-  const i = ga(n.createBuffer(), "Failed to create buffer");
-  (n.bindBuffer(n.ARRAY_BUFFER, i),
-    n.enableVertexAttribArray(t.O),
-    n.vertexAttribPointer(t.O, 2, n.FLOAT, !1, 0, 0),
-    n.bufferData(
-      n.ARRAY_BUFFER,
-      new Float32Array([-1, -1, -1, 1, 1, 1, 1, -1]),
-      n.STATIC_DRAW,
-    ));
-  const s = ga(n.createBuffer(), "Failed to create buffer");
-  return (
-    n.bindBuffer(n.ARRAY_BUFFER, s),
-    n.enableVertexAttribArray(t.L),
-    n.vertexAttribPointer(t.L, 2, n.FLOAT, !1, 0, 0),
-    n.bufferData(
-      n.ARRAY_BUFFER,
-      new Float32Array(e ? [0, 1, 0, 0, 1, 0, 1, 1] : [0, 0, 0, 1, 1, 1, 1, 0]),
-      n.STATIC_DRAW,
-    ),
-    n.bindBuffer(n.ARRAY_BUFFER, null),
-    n.bindVertexArray(null),
-    new ma(n, r, i, s)
-  );
-}
-function va(t, e) {
-  if (t.g) {
-    if (e !== t.g) throw Error("Cannot change GL context once initialized");
-  } else t.g = e;
-}
-function Ea(t, e, n, r) {
-  return (
-    va(t, e),
-    t.h || (t.m(), t.D()),
-    n
-      ? (t.u || (t.u = _a(t, !0)), (n = t.u))
-      : (t.A || (t.A = _a(t, !1)), (n = t.A)),
-    e.useProgram(t.h),
-    n.bind(),
-    t.l(),
-    (t = r()),
-    n.g.bindVertexArray(null),
-    t
-  );
-}
-function wa(t, e, n) {
-  return (
-    va(t, e),
-    (t = ga(e.createTexture(), "Failed to create texture")),
-    e.bindTexture(e.TEXTURE_2D, t),
-    e.texParameteri(e.TEXTURE_2D, e.TEXTURE_WRAP_S, e.CLAMP_TO_EDGE),
-    e.texParameteri(e.TEXTURE_2D, e.TEXTURE_WRAP_T, e.CLAMP_TO_EDGE),
-    e.texParameteri(e.TEXTURE_2D, e.TEXTURE_MIN_FILTER, n ?? e.LINEAR),
-    e.texParameteri(e.TEXTURE_2D, e.TEXTURE_MAG_FILTER, n ?? e.LINEAR),
-    e.bindTexture(e.TEXTURE_2D, null),
-    t
-  );
-}
-function Ta(t, e, n) {
-  (va(t, e),
-    t.B || (t.B = ga(e.createFramebuffer(), "Failed to create framebuffe.")),
-    e.bindFramebuffer(e.FRAMEBUFFER, t.B),
-    e.framebufferTexture2D(
-      e.FRAMEBUFFER,
-      e.COLOR_ATTACHMENT0,
-      e.TEXTURE_2D,
-      n,
-      0,
-    ));
-}
-function Aa(t) {
-  t.g?.bindFramebuffer(t.g.FRAMEBUFFER, null);
-}
-var ba = class {
-  H() {
-    return "\n  precision mediump float;\n  varying vec2 vTex;\n  uniform sampler2D inputTexture;\n  void main() {\n    gl_FragColor = texture2D(inputTexture, vTex);\n  }\n ";
-  }
-  m() {
-    const t = this.g;
-    if (
-      ((this.h = ga(t.createProgram(), "Failed to create WebGL program")),
-      (this.X = ya(
-        this,
-        "\n  attribute vec2 aVertex;\n  attribute vec2 aTex;\n  varying vec2 vTex;\n  void main(void) {\n    gl_Position = vec4(aVertex, 0.0, 1.0);\n    vTex = aTex;\n  }",
-        t.VERTEX_SHADER,
-      )),
-      (this.W = ya(this, this.H(), t.FRAGMENT_SHADER)),
-      t.linkProgram(this.h),
-      !t.getProgramParameter(this.h, t.LINK_STATUS))
-    )
-      throw Error(
-        `Error during program linking: ${t.getProgramInfoLog(this.h)}`,
-      );
-    ((this.O = t.getAttribLocation(this.h, "aVertex")),
-      (this.L = t.getAttribLocation(this.h, "aTex")));
-  }
-  D() {}
-  l() {}
-  close() {
-    if (this.h) {
-      const t = this.g;
-      (t.deleteProgram(this.h), t.deleteShader(this.X), t.deleteShader(this.W));
-    }
-    (this.B && this.g.deleteFramebuffer(this.B),
-      this.A && this.A.close(),
-      this.u && this.u.close());
-  }
-};
-var ka = class extends ba {
-    H() {
-      return "\n  precision mediump float;\n  uniform sampler2D backgroundTexture;\n  uniform sampler2D maskTexture;\n  uniform sampler2D colorMappingTexture;\n  varying vec2 vTex;\n  void main() {\n    vec4 backgroundColor = texture2D(backgroundTexture, vTex);\n    float category = texture2D(maskTexture, vTex).r;\n    vec4 categoryColor = texture2D(colorMappingTexture, vec2(category, 0.0));\n    gl_FragColor = mix(backgroundColor, categoryColor, categoryColor.a);\n  }\n ";
-    }
-    D() {
-      const t = this.g;
-      (t.activeTexture(t.TEXTURE1),
-        (this.C = wa(this, t, t.LINEAR)),
-        t.activeTexture(t.TEXTURE2),
-        (this.j = wa(this, t, t.NEAREST)));
-    }
-    m() {
-      super.m();
-      const t = this.g;
-      ((this.P = ga(
-        t.getUniformLocation(this.h, "backgroundTexture"),
-        "Uniform location",
-      )),
-        (this.U = ga(
-          t.getUniformLocation(this.h, "colorMappingTexture"),
-          "Uniform location",
-        )),
-        (this.M = ga(
-          t.getUniformLocation(this.h, "maskTexture"),
-          "Uniform location",
-        )));
-    }
-    l() {
-      super.l();
-      const t = this.g;
-      (t.uniform1i(this.M, 0), t.uniform1i(this.P, 1), t.uniform1i(this.U, 2));
-    }
-    close() {
-      (this.C && this.g.deleteTexture(this.C),
-        this.j && this.g.deleteTexture(this.j),
-        super.close());
-    }
-  },
-  Sa = class extends ba {
-    H() {
-      return "\n  precision mediump float;\n  uniform sampler2D maskTexture;\n  uniform sampler2D defaultTexture;\n  uniform sampler2D overlayTexture;\n  varying vec2 vTex;\n  void main() {\n    float confidence = texture2D(maskTexture, vTex).r;\n    vec4 defaultColor = texture2D(defaultTexture, vTex);\n    vec4 overlayColor = texture2D(overlayTexture, vTex);\n    // Apply the alpha from the overlay and merge in the default color\n    overlayColor = mix(defaultColor, overlayColor, overlayColor.a);\n    gl_FragColor = mix(defaultColor, overlayColor, confidence);\n  }\n ";
-    }
-    D() {
-      const t = this.g;
-      (t.activeTexture(t.TEXTURE1),
-        (this.j = wa(this, t)),
-        t.activeTexture(t.TEXTURE2),
-        (this.C = wa(this, t)));
-    }
-    m() {
-      super.m();
-      const t = this.g;
-      ((this.M = ga(
-        t.getUniformLocation(this.h, "defaultTexture"),
-        "Uniform location",
-      )),
-        (this.P = ga(
-          t.getUniformLocation(this.h, "overlayTexture"),
-          "Uniform location",
-        )),
-        (this.I = ga(
-          t.getUniformLocation(this.h, "maskTexture"),
-          "Uniform location",
-        )));
-    }
-    l() {
-      super.l();
-      const t = this.g;
-      (t.uniform1i(this.I, 0), t.uniform1i(this.M, 1), t.uniform1i(this.P, 2));
-    }
-    close() {
-      (this.j && this.g.deleteTexture(this.j),
-        this.C && this.g.deleteTexture(this.C),
-        super.close());
-    }
-  };
-function xa(t, e) {
-  switch (e) {
-    case 0:
-      return t.g.find((t) => t instanceof Uint8Array);
-    case 1:
-      return t.g.find((t) => t instanceof Float32Array);
-    case 2:
-      return t.g.find(
-        (t) => "undefined" != typeof WebGLTexture && t instanceof WebGLTexture,
-      );
-    default:
-      throw Error(`Type is not supported: ${e}`);
-  }
-}
-function La(t) {
-  var e = xa(t, 1);
-  if (!e) {
-    if ((e = xa(t, 0))) e = new Float32Array(e).map((t) => t / 255);
-    else {
-      e = new Float32Array(t.width * t.height);
-      const r = Ia(t);
-      var n = Ma(t);
-      if (
-        (Ta(n, r, Ra(t)),
-        "iPad Simulator;iPhone Simulator;iPod Simulator;iPad;iPhone;iPod"
-          .split(";")
-          .includes(navigator.platform) ||
-          (navigator.userAgent.includes("Mac") &&
-            "document" in self &&
-            "ontouchend" in self.document))
-      ) {
-        ((n = new Float32Array(t.width * t.height * 4)),
-          r.readPixels(0, 0, t.width, t.height, r.RGBA, r.FLOAT, n));
-        for (let t = 0, r = 0; t < e.length; ++t, r += 4) e[t] = n[r];
-      } else r.readPixels(0, 0, t.width, t.height, r.RED, r.FLOAT, e);
-    }
-    t.g.push(e);
-  }
-  return e;
-}
-function Ra(t) {
-  let e = xa(t, 2);
-  if (!e) {
-    const n = Ia(t);
-    e = Pa(t);
-    const r = La(t),
-      i = Fa(t);
-    (n.texImage2D(n.TEXTURE_2D, 0, i, t.width, t.height, 0, n.RED, n.FLOAT, r),
-      Ca(t));
-  }
-  return e;
-}
-function Ia(t) {
-  if (!t.canvas)
-    throw Error(
-      "Conversion to different image formats require that a canvas is passed when initializing the image.",
-    );
-  return (
-    t.h ||
-      (t.h = ga(
-        t.canvas.getContext("webgl2"),
-        "You cannot use a canvas that is already bound to a different type of rendering context.",
-      )),
-    t.h
-  );
-}
-function Fa(t) {
-  if (((t = Ia(t)), !Oa))
-    if (
-      t.getExtension("EXT_color_buffer_float") &&
-      t.getExtension("OES_texture_float_linear") &&
-      t.getExtension("EXT_float_blend")
-    )
-      Oa = t.R32F;
-    else {
-      if (!t.getExtension("EXT_color_buffer_half_float"))
-        throw Error(
-          "GPU does not fully support 4-channel float32 or float16 formats",
-        );
-      Oa = t.R16F;
-    }
-  return Oa;
-}
-function Ma(t) {
-  return (t.l || (t.l = new ba()), t.l);
-}
-function Pa(t) {
-  const e = Ia(t);
-  (e.viewport(0, 0, t.width, t.height), e.activeTexture(e.TEXTURE0));
-  let n = xa(t, 2);
-  return (
-    n ||
-      ((n = wa(Ma(t), e, t.m ? e.LINEAR : e.NEAREST)), t.g.push(n), (t.j = !0)),
-    e.bindTexture(e.TEXTURE_2D, n),
-    n
-  );
-}
-function Ca(t) {
-  t.h.bindTexture(t.h.TEXTURE_2D, null);
-}
-var Oa,
-  Na = class {
-    constructor(t, e, n, r, i, s, o) {
-      ((this.g = t),
-        (this.m = e),
-        (this.j = n),
-        (this.canvas = r),
-        (this.l = i),
-        (this.width = s),
-        (this.height = o),
-        this.j &&
-          0 === --Ua &&
-          console.error(
-            "You seem to be creating MPMask instances without invoking .close(). This leaks resources.",
-          ));
-    }
-    Fa() {
-      return !!xa(this, 0);
-    }
-    ka() {
-      return !!xa(this, 1);
-    }
-    R() {
-      return !!xa(this, 2);
-    }
-    ja() {
-      return (
-        (e = xa((t = this), 0)) ||
-          ((e = La(t)),
-          (e = new Uint8Array(e.map((t) => Math.round(255 * t)))),
-          t.g.push(e)),
-        e
-      );
-      var t, e;
-    }
-    ia() {
-      return La(this);
-    }
-    N() {
-      return Ra(this);
-    }
-    clone() {
-      const t = [];
-      for (const e of this.g) {
-        let n;
-        if (e instanceof Uint8Array) n = new Uint8Array(e);
-        else if (e instanceof Float32Array) n = new Float32Array(e);
-        else {
-          if (!(e instanceof WebGLTexture))
-            throw Error(`Type is not supported: ${e}`);
-          {
-            const t = Ia(this),
-              e = Ma(this);
-            (t.activeTexture(t.TEXTURE1),
-              (n = wa(e, t, this.m ? t.LINEAR : t.NEAREST)),
-              t.bindTexture(t.TEXTURE_2D, n));
-            const r = Fa(this);
-            (t.texImage2D(
-              t.TEXTURE_2D,
-              0,
-              r,
-              this.width,
-              this.height,
-              0,
-              t.RED,
-              t.FLOAT,
-              null,
-            ),
-              t.bindTexture(t.TEXTURE_2D, null),
-              Ta(e, t, n),
-              Ea(e, t, !1, () => {
-                (Pa(this),
-                  t.clearColor(0, 0, 0, 0),
-                  t.clear(t.COLOR_BUFFER_BIT),
-                  t.drawArrays(t.TRIANGLE_FAN, 0, 4),
-                  Ca(this));
-              }),
-              Aa(e),
-              Ca(this));
-          }
-        }
-        t.push(n);
-      }
-      return new Na(
-        t,
-        this.m,
-        this.R(),
-        this.canvas,
-        this.l,
-        this.width,
-        this.height,
-      );
-    }
-    close() {
-      (this.j && Ia(this).deleteTexture(xa(this, 2)), (Ua = -1));
-    }
-  };
-((Na.prototype.close = Na.prototype.close),
-  (Na.prototype.clone = Na.prototype.clone),
-  (Na.prototype.getAsWebGLTexture = Na.prototype.N),
-  (Na.prototype.getAsFloat32Array = Na.prototype.ia),
-  (Na.prototype.getAsUint8Array = Na.prototype.ja),
-  (Na.prototype.hasWebGLTexture = Na.prototype.R),
-  (Na.prototype.hasFloat32Array = Na.prototype.ka),
-  (Na.prototype.hasUint8Array = Na.prototype.Fa));
-var Ua = 250;
-const Da = { color: "white", lineWidth: 4, radius: 6 };
-function Ba(t) {
-  return { ...Da, fillColor: (t = t || {}).color, ...t };
-}
-function Ga(t, e) {
-  return t instanceof Function ? t(e) : t;
-}
-function ja(t, e, n) {
-  return Math.max(Math.min(e, n), Math.min(Math.max(e, n), t));
-}
-function Va(t) {
-  if (!t.l)
-    throw Error(
-      "CPU rendering requested but CanvasRenderingContext2D not provided.",
-    );
-  return t.l;
-}
-function Xa(t) {
-  if (!t.j)
-    throw Error(
-      "GPU rendering requested but WebGL2RenderingContext not provided.",
-    );
-  return t.j;
-}
-function Ha(t, e, n) {
-  if (e.R()) n(e.N());
-  else {
-    const r = e.ka() ? e.ia() : e.ja();
-    t.m = t.m ?? new ba();
-    const i = Xa(t);
-    (n((t = new Na([r], e.m, !1, i.canvas, t.m, e.width, e.height)).N()),
-      t.close());
-  }
-}
-function Wa(t, e, n, r) {
-  const i = (function (t) {
-      return (t.g || (t.g = new ka()), t.g);
-    })(t),
-    s = Xa(t),
-    o = Array.isArray(n) ? new ImageData(new Uint8ClampedArray(n), 1, 1) : n;
-  Ea(i, s, !0, () => {
-    (!(function (t, e, n, r) {
-      const i = t.g;
-      if (
-        (i.activeTexture(i.TEXTURE0),
-        i.bindTexture(i.TEXTURE_2D, e),
-        i.activeTexture(i.TEXTURE1),
-        i.bindTexture(i.TEXTURE_2D, t.C),
-        i.texImage2D(i.TEXTURE_2D, 0, i.RGBA, i.RGBA, i.UNSIGNED_BYTE, n),
-        t.I &&
-          (function (t, e) {
-            if (t !== e) return !1;
-            ((t = t.entries()), (e = e.entries()));
-            for (const [n, r] of t) {
-              t = n;
-              const i = r,
-                s = e.next();
-              if (s.done) return !1;
-              const [o, a] = s.value;
-              if (
-                t !== o ||
-                i[0] !== a[0] ||
-                i[1] !== a[1] ||
-                i[2] !== a[2] ||
-                i[3] !== a[3]
-              )
-                return !1;
-            }
-            return !!e.next().done;
-          })(t.I, r))
-      )
-        (i.activeTexture(i.TEXTURE2), i.bindTexture(i.TEXTURE_2D, t.j));
-      else {
-        t.I = r;
-        const e = Array(1024).fill(0);
-        (r.forEach((t, n) => {
-          if (4 !== t.length)
-            throw Error(`Color at index ${n} is not a four-channel value.`);
-          ((e[4 * n] = t[0]),
-            (e[4 * n + 1] = t[1]),
-            (e[4 * n + 2] = t[2]),
-            (e[4 * n + 3] = t[3]));
-        }),
-          i.activeTexture(i.TEXTURE2),
-          i.bindTexture(i.TEXTURE_2D, t.j),
-          i.texImage2D(
-            i.TEXTURE_2D,
-            0,
-            i.RGBA,
-            256,
-            1,
-            0,
-            i.RGBA,
-            i.UNSIGNED_BYTE,
-            new Uint8Array(e),
-          ));
-      }
-    })(i, e, o, r),
-      s.clearColor(0, 0, 0, 0),
-      s.clear(s.COLOR_BUFFER_BIT),
-      s.drawArrays(s.TRIANGLE_FAN, 0, 4));
-    const t = i.g;
-    (t.activeTexture(t.TEXTURE0),
-      t.bindTexture(t.TEXTURE_2D, null),
-      t.activeTexture(t.TEXTURE1),
-      t.bindTexture(t.TEXTURE_2D, null),
-      t.activeTexture(t.TEXTURE2),
-      t.bindTexture(t.TEXTURE_2D, null));
-  });
-}
-function za(t, e, n, r) {
-  const i = Xa(t),
-    s = (function (t) {
-      return (t.h || (t.h = new Sa()), t.h);
-    })(t),
-    o = Array.isArray(n) ? new ImageData(new Uint8ClampedArray(n), 1, 1) : n,
-    a = Array.isArray(r) ? new ImageData(new Uint8ClampedArray(r), 1, 1) : r;
-  Ea(s, i, !0, () => {
-    var t = s.g;
-    (t.activeTexture(t.TEXTURE0),
-      t.bindTexture(t.TEXTURE_2D, e),
-      t.activeTexture(t.TEXTURE1),
-      t.bindTexture(t.TEXTURE_2D, s.j),
-      t.texImage2D(t.TEXTURE_2D, 0, t.RGBA, t.RGBA, t.UNSIGNED_BYTE, o),
-      t.activeTexture(t.TEXTURE2),
-      t.bindTexture(t.TEXTURE_2D, s.C),
-      t.texImage2D(t.TEXTURE_2D, 0, t.RGBA, t.RGBA, t.UNSIGNED_BYTE, a),
-      i.clearColor(0, 0, 0, 0),
-      i.clear(i.COLOR_BUFFER_BIT),
-      i.drawArrays(i.TRIANGLE_FAN, 0, 4),
-      i.bindTexture(i.TEXTURE_2D, null),
-      (t = s.g).activeTexture(t.TEXTURE0),
-      t.bindTexture(t.TEXTURE_2D, null),
-      t.activeTexture(t.TEXTURE1),
-      t.bindTexture(t.TEXTURE_2D, null),
-      t.activeTexture(t.TEXTURE2),
-      t.bindTexture(t.TEXTURE_2D, null));
-  });
-}
-var Ka = class {
-  constructor(t, e) {
-    ("undefined" != typeof CanvasRenderingContext2D &&
-      t instanceof CanvasRenderingContext2D) ||
-    t instanceof OffscreenCanvasRenderingContext2D
-      ? ((this.l = t), (this.j = e))
-      : (this.j = t);
-  }
-  ya(t, e) {
-    if (t) {
-      var n = Va(this);
-      ((e = Ba(e)), n.save());
-      var r = n.canvas,
-        i = 0;
-      for (const s of t)
-        ((n.fillStyle = Ga(e.fillColor, { index: i, from: s })),
-          (n.strokeStyle = Ga(e.color, { index: i, from: s })),
-          (n.lineWidth = Ga(e.lineWidth, { index: i, from: s })),
-          (t = new Path2D()).arc(
-            s.x * r.width,
-            s.y * r.height,
-            Ga(e.radius, { index: i, from: s }),
-            0,
-            2 * Math.PI,
-          ),
-          n.fill(t),
-          n.stroke(t),
-          ++i);
-      n.restore();
-    }
-  }
-  xa(t, e, n) {
-    if (t && e) {
-      var r = Va(this);
-      ((n = Ba(n)), r.save());
-      var i = r.canvas,
-        s = 0;
-      for (const o of e) {
-        (r.beginPath(), (e = t[o.start]));
-        const a = t[o.end];
-        (e &&
-          a &&
-          ((r.strokeStyle = Ga(n.color, { index: s, from: e, to: a })),
-          (r.lineWidth = Ga(n.lineWidth, { index: s, from: e, to: a })),
-          r.moveTo(e.x * i.width, e.y * i.height),
-          r.lineTo(a.x * i.width, a.y * i.height)),
-          ++s,
-          r.stroke());
-      }
-      r.restore();
-    }
-  }
-  ua(t, e) {
-    const n = Va(this);
-    ((e = Ba(e)),
-      n.save(),
-      n.beginPath(),
-      (n.lineWidth = Ga(e.lineWidth, {})),
-      (n.strokeStyle = Ga(e.color, {})),
-      (n.fillStyle = Ga(e.fillColor, {})),
-      n.moveTo(t.originX, t.originY),
-      n.lineTo(t.originX + t.width, t.originY),
-      n.lineTo(t.originX + t.width, t.originY + t.height),
-      n.lineTo(t.originX, t.originY + t.height),
-      n.lineTo(t.originX, t.originY),
-      n.stroke(),
-      n.fill(),
-      n.restore());
-  }
-  va(t, e, n = [0, 0, 0, 255]) {
-    this.l
-      ? (function (t, e, n, r) {
-          const i = Xa(t);
-          Ha(t, e, (e) => {
-            (Wa(t, e, n, r),
-              (e = Va(t)).drawImage(
-                i.canvas,
-                0,
-                0,
-                e.canvas.width,
-                e.canvas.height,
-              ));
-          });
-        })(this, t, n, e)
-      : Wa(this, t.N(), n, e);
-  }
-  wa(t, e, n) {
-    this.l
-      ? (function (t, e, n, r) {
-          const i = Xa(t);
-          Ha(t, e, (e) => {
-            (za(t, e, n, r),
-              (e = Va(t)).drawImage(
-                i.canvas,
-                0,
-                0,
-                e.canvas.width,
-                e.canvas.height,
-              ));
-          });
-        })(this, t, e, n)
-      : za(this, t.N(), e, n);
-  }
-  close() {
-    (this.g?.close(),
-      (this.g = void 0),
-      this.h?.close(),
-      (this.h = void 0),
-      this.m?.close(),
-      (this.m = void 0));
-  }
-};
-function Ya(t, e) {
-  switch (e) {
-    case 0:
-      return t.g.find((t) => t instanceof ImageData);
-    case 1:
-      return t.g.find(
-        (t) => "undefined" != typeof ImageBitmap && t instanceof ImageBitmap,
-      );
-    case 2:
-      return t.g.find(
-        (t) => "undefined" != typeof WebGLTexture && t instanceof WebGLTexture,
-      );
-    default:
-      throw Error(`Type is not supported: ${e}`);
-  }
-}
-function qa(t) {
-  var e = Ya(t, 0);
-  if (!e) {
-    e = Ja(t);
-    const n = Za(t),
-      r = new Uint8Array(t.width * t.height * 4);
-    (Ta(n, e, $a(t)),
-      e.readPixels(0, 0, t.width, t.height, e.RGBA, e.UNSIGNED_BYTE, r),
-      Aa(n),
-      (e = new ImageData(new Uint8ClampedArray(r.buffer), t.width, t.height)),
-      t.g.push(e));
-  }
-  return e;
-}
-function $a(t) {
-  let e = Ya(t, 2);
-  if (!e) {
-    const n = Ja(t);
-    e = Qa(t);
-    const r = Ya(t, 1) || qa(t);
-    (n.texImage2D(n.TEXTURE_2D, 0, n.RGBA, n.RGBA, n.UNSIGNED_BYTE, r), tc(t));
-  }
-  return e;
-}
-function Ja(t) {
-  if (!t.canvas)
-    throw Error(
-      "Conversion to different image formats require that a canvas is passed when initializing the image.",
-    );
-  return (
-    t.h ||
-      (t.h = ga(
-        t.canvas.getContext("webgl2"),
-        "You cannot use a canvas that is already bound to a different type of rendering context.",
-      )),
-    t.h
-  );
-}
-function Za(t) {
-  return (t.l || (t.l = new ba()), t.l);
-}
-function Qa(t) {
-  const e = Ja(t);
-  (e.viewport(0, 0, t.width, t.height), e.activeTexture(e.TEXTURE0));
-  let n = Ya(t, 2);
-  return (
-    n || ((n = wa(Za(t), e)), t.g.push(n), (t.m = !0)),
-    e.bindTexture(e.TEXTURE_2D, n),
-    n
-  );
-}
-function tc(t) {
-  t.h.bindTexture(t.h.TEXTURE_2D, null);
-}
-function ec(t) {
-  const e = Ja(t);
-  return Ea(Za(t), e, !0, () =>
-    (function (t, e) {
-      const n = t.canvas;
-      if (n.width === t.width && n.height === t.height) return e();
-      const r = n.width,
-        i = n.height;
-      return (
-        (n.width = t.width),
-        (n.height = t.height),
-        (t = e()),
-        (n.width = r),
-        (n.height = i),
-        t
-      );
-    })(t, () => {
-      if (
-        (e.bindFramebuffer(e.FRAMEBUFFER, null),
-        e.clearColor(0, 0, 0, 0),
-        e.clear(e.COLOR_BUFFER_BIT),
-        e.drawArrays(e.TRIANGLE_FAN, 0, 4),
-        !(t.canvas instanceof OffscreenCanvas))
-      )
-        throw Error(
-          "Conversion to ImageBitmap requires that the MediaPipe Tasks is initialized with an OffscreenCanvas",
-        );
-      return t.canvas.transferToImageBitmap();
-    }),
-  );
-}
-((Ka.prototype.close = Ka.prototype.close),
-  (Ka.prototype.drawConfidenceMask = Ka.prototype.wa),
-  (Ka.prototype.drawCategoryMask = Ka.prototype.va),
-  (Ka.prototype.drawBoundingBox = Ka.prototype.ua),
-  (Ka.prototype.drawConnectors = Ka.prototype.xa),
-  (Ka.prototype.drawLandmarks = Ka.prototype.ya),
-  (Ka.lerp = function (t, e, n, r, i) {
-    return ja(r * (1 - (t - e) / (n - e)) + i * (1 - (n - t) / (n - e)), r, i);
-  }),
-  (Ka.clamp = ja));
-var nc = class {
-  constructor(t, e, n, r, i, s, o) {
-    ((this.g = t),
-      (this.j = e),
-      (this.m = n),
-      (this.canvas = r),
-      (this.l = i),
-      (this.width = s),
-      (this.height = o),
-      (this.j || this.m) &&
-        0 === --rc &&
-        console.error(
-          "You seem to be creating MPImage instances without invoking .close(). This leaks resources.",
-        ));
-  }
-  Ea() {
-    return !!Ya(this, 0);
-  }
-  la() {
-    return !!Ya(this, 1);
-  }
-  R() {
-    return !!Ya(this, 2);
-  }
-  Ca() {
-    return qa(this);
-  }
-  Ba() {
-    var t = Ya(this, 1);
-    return (
-      t ||
-        ($a(this),
-        Qa(this),
-        (t = ec(this)),
-        tc(this),
-        this.g.push(t),
-        (this.j = !0)),
-      t
-    );
-  }
-  N() {
-    return $a(this);
-  }
-  clone() {
-    const t = [];
-    for (const e of this.g) {
-      let n;
-      if (e instanceof ImageData)
-        n = new ImageData(e.data, this.width, this.height);
-      else if (e instanceof WebGLTexture) {
-        const t = Ja(this),
-          e = Za(this);
-        (t.activeTexture(t.TEXTURE1),
-          (n = wa(e, t)),
-          t.bindTexture(t.TEXTURE_2D, n),
-          t.texImage2D(
-            t.TEXTURE_2D,
-            0,
-            t.RGBA,
-            this.width,
-            this.height,
-            0,
-            t.RGBA,
-            t.UNSIGNED_BYTE,
-            null,
-          ),
-          t.bindTexture(t.TEXTURE_2D, null),
-          Ta(e, t, n),
-          Ea(e, t, !1, () => {
-            (Qa(this),
-              t.clearColor(0, 0, 0, 0),
-              t.clear(t.COLOR_BUFFER_BIT),
-              t.drawArrays(t.TRIANGLE_FAN, 0, 4),
-              tc(this));
-          }),
-          Aa(e),
-          tc(this));
-      } else {
-        if (!(e instanceof ImageBitmap))
-          throw Error(`Type is not supported: ${e}`);
-        ($a(this), Qa(this), (n = ec(this)), tc(this));
-      }
-      t.push(n);
-    }
-    return new nc(
-      t,
-      this.la(),
-      this.R(),
-      this.canvas,
-      this.l,
-      this.width,
-      this.height,
-    );
-  }
-  close() {
-    (this.j && Ya(this, 1).close(),
-      this.m && Ja(this).deleteTexture(Ya(this, 2)),
-      (rc = -1));
-  }
-};
-((nc.prototype.close = nc.prototype.close),
-  (nc.prototype.clone = nc.prototype.clone),
-  (nc.prototype.getAsWebGLTexture = nc.prototype.N),
-  (nc.prototype.getAsImageBitmap = nc.prototype.Ba),
-  (nc.prototype.getAsImageData = nc.prototype.Ca),
-  (nc.prototype.hasWebGLTexture = nc.prototype.R),
-  (nc.prototype.hasImageBitmap = nc.prototype.la),
-  (nc.prototype.hasImageData = nc.prototype.Ea));
-var rc = 250;
-function ic(...t) {
-  return t.map(([t, e]) => ({ start: t, end: e }));
-}
-const sc = (function (t) {
-  return class extends t {
-    Ja() {
-      this.i._registerModelResourcesGraphService();
-    }
-  };
-})(
-  ((oc = class {
-    constructor(t, e) {
-      ((this.l = !0),
-        (this.i = t),
-        (this.g = null),
-        (this.h = 0),
-        (this.m = "function" == typeof this.i._addIntToInputStream),
-        void 0 !== e
-          ? (this.i.canvas = e)
-          : Qo()
-            ? (this.i.canvas = new OffscreenCanvas(1, 1))
-            : (console.warn(
-                "OffscreenCanvas not supported and GraphRunner constructor glCanvas parameter is undefined. Creating backup canvas.",
-              ),
-              (this.i.canvas = document.createElement("canvas"))));
-    }
-    async initializeGraph(t) {
-      const e = await (await fetch(t)).arrayBuffer();
-      ((t = !(t.endsWith(".pbtxt") || t.endsWith(".textproto"))),
-        this.setGraph(new Uint8Array(e), t));
-    }
-    setGraphFromString(t) {
-      this.setGraph(new TextEncoder().encode(t), !1);
-    }
-    setGraph(t, e) {
-      const n = t.length,
-        r = this.i._malloc(n);
-      (this.i.HEAPU8.set(t, r),
-        e ? this.i._changeBinaryGraph(n, r) : this.i._changeTextGraph(n, r),
-        this.i._free(r));
-    }
-    configureAudio(t, e, n, r, i) {
-      (this.i._configureAudio ||
-        console.warn(
-          'Attempting to use configureAudio without support for input audio. Is build dep ":gl_graph_runner_audio" missing?',
-        ),
-        na(this, r || "input_audio", (r) => {
-          na(this, (i = i || "audio_header"), (i) => {
-            this.i._configureAudio(r, i, t, e ?? 0, n);
-          });
-        }));
-    }
-    setAutoResizeCanvas(t) {
-      this.l = t;
-    }
-    setAutoRenderToScreen(t) {
-      this.i._setAutoRenderToScreen(t);
-    }
-    setGpuBufferVerticalFlip(t) {
-      this.i.gpuOriginForWebTexturesIsBottomLeft = t;
-    }
-    ca(t) {
-      (sa(this, "__graph_config__", (e) => {
-        t(e);
-      }),
-        na(this, "__graph_config__", (t) => {
-          this.i._getGraphConfig(t, void 0);
-        }),
-        delete this.i.simpleListeners.__graph_config__);
-    }
-    attachErrorListener(t) {
-      this.i.errorListener = t;
-    }
-    attachEmptyPacketListener(t, e) {
-      ((this.i.emptyPacketListeners = this.i.emptyPacketListeners || {}),
-        (this.i.emptyPacketListeners[t] = e));
-    }
-    addAudioToStream(t, e, n) {
-      this.addAudioToStreamWithShape(t, 0, 0, e, n);
-    }
-    addAudioToStreamWithShape(t, e, n, r, i) {
-      const s = 4 * t.length;
-      (this.h !== s &&
-        (this.g && this.i._free(this.g),
-        (this.g = this.i._malloc(s)),
-        (this.h = s)),
-        this.i.HEAPF32.set(t, this.g / 4),
-        na(this, r, (t) => {
-          this.i._addAudioToInputStream(this.g, e, n, t, i);
-        }));
-    }
-    addGpuBufferToStream(t, e, n) {
-      na(this, e, (e) => {
-        const [r, i] = ra(this, t, e);
-        this.i._addBoundTextureToStream(e, r, i, n);
-      });
-    }
-    addBoolToStream(t, e, n) {
-      na(this, e, (e) => {
-        this.i._addBoolToInputStream(t, e, n);
-      });
-    }
-    addDoubleToStream(t, e, n) {
-      na(this, e, (e) => {
-        this.i._addDoubleToInputStream(t, e, n);
-      });
-    }
-    addFloatToStream(t, e, n) {
-      na(this, e, (e) => {
-        this.i._addFloatToInputStream(t, e, n);
-      });
-    }
-    addIntToStream(t, e, n) {
-      na(this, e, (e) => {
-        this.i._addIntToInputStream(t, e, n);
-      });
-    }
-    addUintToStream(t, e, n) {
-      na(this, e, (e) => {
-        this.i._addUintToInputStream(t, e, n);
-      });
-    }
-    addStringToStream(t, e, n) {
-      na(this, e, (e) => {
-        na(this, t, (t) => {
-          this.i._addStringToInputStream(t, e, n);
-        });
-      });
-    }
-    addStringRecordToStream(t, e, n) {
-      na(this, e, (e) => {
-        ia(this, Object.keys(t), (r) => {
-          ia(this, Object.values(t), (i) => {
-            this.i._addFlatHashMapToInputStream(
-              r,
-              i,
-              Object.keys(t).length,
-              e,
-              n,
-            );
-          });
-        });
-      });
-    }
-    addProtoToStream(t, e, n, r) {
-      na(this, n, (n) => {
-        na(this, e, (e) => {
-          const i = this.i._malloc(t.length);
-          (this.i.HEAPU8.set(t, i),
-            this.i._addProtoToInputStream(i, t.length, e, n, r),
-            this.i._free(i));
-        });
-      });
-    }
-    addEmptyPacketToStream(t, e) {
-      na(this, t, (t) => {
-        this.i._addEmptyPacketToInputStream(t, e);
-      });
-    }
-    addBoolVectorToStream(t, e, n) {
-      na(this, e, (e) => {
-        const r = this.i._allocateBoolVector(t.length);
-        if (!r) throw Error("Unable to allocate new bool vector on heap.");
-        for (const e of t) this.i._addBoolVectorEntry(r, e);
-        this.i._addBoolVectorToInputStream(r, e, n);
-      });
-    }
-    addDoubleVectorToStream(t, e, n) {
-      na(this, e, (e) => {
-        const r = this.i._allocateDoubleVector(t.length);
-        if (!r) throw Error("Unable to allocate new double vector on heap.");
-        for (const e of t) this.i._addDoubleVectorEntry(r, e);
-        this.i._addDoubleVectorToInputStream(r, e, n);
-      });
-    }
-    addFloatVectorToStream(t, e, n) {
-      na(this, e, (e) => {
-        const r = this.i._allocateFloatVector(t.length);
-        if (!r) throw Error("Unable to allocate new float vector on heap.");
-        for (const e of t) this.i._addFloatVectorEntry(r, e);
-        this.i._addFloatVectorToInputStream(r, e, n);
-      });
-    }
-    addIntVectorToStream(t, e, n) {
-      na(this, e, (e) => {
-        const r = this.i._allocateIntVector(t.length);
-        if (!r) throw Error("Unable to allocate new int vector on heap.");
-        for (const e of t) this.i._addIntVectorEntry(r, e);
-        this.i._addIntVectorToInputStream(r, e, n);
-      });
-    }
-    addUintVectorToStream(t, e, n) {
-      na(this, e, (e) => {
-        const r = this.i._allocateUintVector(t.length);
-        if (!r)
-          throw Error("Unable to allocate new unsigned int vector on heap.");
-        for (const e of t) this.i._addUintVectorEntry(r, e);
-        this.i._addUintVectorToInputStream(r, e, n);
-      });
-    }
-    addStringVectorToStream(t, e, n) {
-      na(this, e, (e) => {
-        const r = this.i._allocateStringVector(t.length);
-        if (!r) throw Error("Unable to allocate new string vector on heap.");
-        for (const e of t)
-          na(this, e, (t) => {
-            this.i._addStringVectorEntry(r, t);
-          });
-        this.i._addStringVectorToInputStream(r, e, n);
-      });
-    }
-    addBoolToInputSidePacket(t, e) {
-      na(this, e, (e) => {
-        this.i._addBoolToInputSidePacket(t, e);
-      });
-    }
-    addDoubleToInputSidePacket(t, e) {
-      na(this, e, (e) => {
-        this.i._addDoubleToInputSidePacket(t, e);
-      });
-    }
-    addFloatToInputSidePacket(t, e) {
-      na(this, e, (e) => {
-        this.i._addFloatToInputSidePacket(t, e);
-      });
-    }
-    addIntToInputSidePacket(t, e) {
-      na(this, e, (e) => {
-        this.i._addIntToInputSidePacket(t, e);
-      });
-    }
-    addUintToInputSidePacket(t, e) {
-      na(this, e, (e) => {
-        this.i._addUintToInputSidePacket(t, e);
-      });
-    }
-    addStringToInputSidePacket(t, e) {
-      na(this, e, (e) => {
-        na(this, t, (t) => {
-          this.i._addStringToInputSidePacket(t, e);
-        });
-      });
-    }
-    addProtoToInputSidePacket(t, e, n) {
-      na(this, n, (n) => {
-        na(this, e, (e) => {
-          const r = this.i._malloc(t.length);
-          (this.i.HEAPU8.set(t, r),
-            this.i._addProtoToInputSidePacket(r, t.length, e, n),
-            this.i._free(r));
-        });
-      });
-    }
-    addBoolVectorToInputSidePacket(t, e) {
-      na(this, e, (e) => {
-        const n = this.i._allocateBoolVector(t.length);
-        if (!n) throw Error("Unable to allocate new bool vector on heap.");
-        for (const e of t) this.i._addBoolVectorEntry(n, e);
-        this.i._addBoolVectorToInputSidePacket(n, e);
-      });
-    }
-    addDoubleVectorToInputSidePacket(t, e) {
-      na(this, e, (e) => {
-        const n = this.i._allocateDoubleVector(t.length);
-        if (!n) throw Error("Unable to allocate new double vector on heap.");
-        for (const e of t) this.i._addDoubleVectorEntry(n, e);
-        this.i._addDoubleVectorToInputSidePacket(n, e);
-      });
-    }
-    addFloatVectorToInputSidePacket(t, e) {
-      na(this, e, (e) => {
-        const n = this.i._allocateFloatVector(t.length);
-        if (!n) throw Error("Unable to allocate new float vector on heap.");
-        for (const e of t) this.i._addFloatVectorEntry(n, e);
-        this.i._addFloatVectorToInputSidePacket(n, e);
-      });
-    }
-    addIntVectorToInputSidePacket(t, e) {
-      na(this, e, (e) => {
-        const n = this.i._allocateIntVector(t.length);
-        if (!n) throw Error("Unable to allocate new int vector on heap.");
-        for (const e of t) this.i._addIntVectorEntry(n, e);
-        this.i._addIntVectorToInputSidePacket(n, e);
-      });
-    }
-    addUintVectorToInputSidePacket(t, e) {
-      na(this, e, (e) => {
-        const n = this.i._allocateUintVector(t.length);
-        if (!n)
-          throw Error("Unable to allocate new unsigned int vector on heap.");
-        for (const e of t) this.i._addUintVectorEntry(n, e);
-        this.i._addUintVectorToInputSidePacket(n, e);
-      });
-    }
-    addStringVectorToInputSidePacket(t, e) {
-      na(this, e, (e) => {
-        const n = this.i._allocateStringVector(t.length);
-        if (!n) throw Error("Unable to allocate new string vector on heap.");
-        for (const e of t)
-          na(this, e, (t) => {
-            this.i._addStringVectorEntry(n, t);
-          });
-        this.i._addStringVectorToInputSidePacket(n, e);
-      });
-    }
-    attachBoolListener(t, e) {
-      (sa(this, t, e),
-        na(this, t, (t) => {
-          this.i._attachBoolListener(t);
-        }));
-    }
-    attachBoolVectorListener(t, e) {
-      (oa(this, t, e),
-        na(this, t, (t) => {
-          this.i._attachBoolVectorListener(t);
-        }));
-    }
-    attachIntListener(t, e) {
-      (sa(this, t, e),
-        na(this, t, (t) => {
-          this.i._attachIntListener(t);
-        }));
-    }
-    attachIntVectorListener(t, e) {
-      (oa(this, t, e),
-        na(this, t, (t) => {
-          this.i._attachIntVectorListener(t);
-        }));
-    }
-    attachUintListener(t, e) {
-      (sa(this, t, e),
-        na(this, t, (t) => {
-          this.i._attachUintListener(t);
-        }));
-    }
-    attachUintVectorListener(t, e) {
-      (oa(this, t, e),
-        na(this, t, (t) => {
-          this.i._attachUintVectorListener(t);
-        }));
-    }
-    attachDoubleListener(t, e) {
-      (sa(this, t, e),
-        na(this, t, (t) => {
-          this.i._attachDoubleListener(t);
-        }));
-    }
-    attachDoubleVectorListener(t, e) {
-      (oa(this, t, e),
-        na(this, t, (t) => {
-          this.i._attachDoubleVectorListener(t);
-        }));
-    }
-    attachFloatListener(t, e) {
-      (sa(this, t, e),
-        na(this, t, (t) => {
-          this.i._attachFloatListener(t);
-        }));
-    }
-    attachFloatVectorListener(t, e) {
-      (oa(this, t, e),
-        na(this, t, (t) => {
-          this.i._attachFloatVectorListener(t);
-        }));
-    }
-    attachStringListener(t, e) {
-      (sa(this, t, e),
-        na(this, t, (t) => {
-          this.i._attachStringListener(t);
-        }));
-    }
-    attachStringVectorListener(t, e) {
-      (oa(this, t, e),
-        na(this, t, (t) => {
-          this.i._attachStringVectorListener(t);
-        }));
-    }
-    attachProtoListener(t, e, n) {
-      (sa(this, t, e),
-        na(this, t, (t) => {
-          this.i._attachProtoListener(t, n || !1);
-        }));
-    }
-    attachProtoVectorListener(t, e, n) {
-      (oa(this, t, e),
-        na(this, t, (t) => {
-          this.i._attachProtoVectorListener(t, n || !1);
-        }));
-    }
-    attachAudioListener(t, e, n) {
-      (this.i._attachAudioListener ||
-        console.warn(
-          'Attempting to use attachAudioListener without support for output audio. Is build dep ":gl_graph_runner_audio_out" missing?',
-        ),
-        sa(this, t, (t, n) => {
-          ((t = new Float32Array(t.buffer, t.byteOffset, t.length / 4)),
-            e(t, n));
-        }),
-        na(this, t, (t) => {
-          this.i._attachAudioListener(t, n || !1);
-        }));
-    }
-    finishProcessing() {
-      this.i._waitUntilIdle();
-    }
-    closeGraph() {
-      (this.i._closeGraph(),
-        (this.i.simpleListeners = void 0),
-        (this.i.emptyPacketListeners = void 0));
-    }
-  }),
-  class extends oc {
-    get ga() {
-      return this.i;
-    }
-    pa(t, e, n) {
-      na(this, e, (e) => {
-        const [r, i] = ra(this, t, e);
-        this.ga._addBoundTextureAsImageToStream(e, r, i, n);
-      });
-    }
-    Z(t, e) {
-      (sa(this, t, e),
-        na(this, t, (t) => {
-          this.ga._attachImageListener(t);
-        }));
-    }
-    aa(t, e) {
-      (oa(this, t, e),
-        na(this, t, (t) => {
-          this.ga._attachImageVectorListener(t);
-        }));
-    }
-  }),
-);
-var oc,
-  ac = class extends sc {};
-async function cc(t, e, n) {
-  return (async function (t, e, n, r) {
-    return aa(t, e, n, r);
-  })(t, n.canvas ?? (Qo() ? void 0 : document.createElement("canvas")), e, n);
-}
-function hc(t, e, n, r) {
-  if (t.U) {
-    const s = new Ls();
-    if (n?.regionOfInterest) {
-      if (!t.oa) throw Error("This task doesn't support region-of-interest.");
-      var i = n.regionOfInterest;
-      if (i.left >= i.right || i.top >= i.bottom)
-        throw Error("Expected RectF with left < right and top < bottom.");
-      if (i.left < 0 || i.top < 0 || i.right > 1 || i.bottom > 1)
-        throw Error("Expected RectF values to be in [0,1].");
-      (Ln(s, 1, (i.left + i.right) / 2),
-        Ln(s, 2, (i.top + i.bottom) / 2),
-        Ln(s, 4, i.right - i.left),
-        Ln(s, 3, i.bottom - i.top));
-    } else (Ln(s, 1, 0.5), Ln(s, 2, 0.5), Ln(s, 4, 1), Ln(s, 3, 1));
-    if (n?.rotationDegrees) {
-      if (n?.rotationDegrees % 90 != 0)
-        throw Error("Expected rotation to be a multiple of 90 degrees.");
-      if (
-        (Ln(s, 5, (-Math.PI * n.rotationDegrees) / 180),
-        n?.rotationDegrees % 180 != 0)
-      ) {
-        const [t, r] = ea(e);
-        ((n = (Sn(s, 3) * r) / t),
-          (i = (Sn(s, 4) * t) / r),
-          Ln(s, 4, n),
-          Ln(s, 3, i));
-      }
-    }
-    t.g.addProtoToStream(s.g(), "mediapipe.NormalizedRect", t.U, r);
-  }
-  (t.g.pa(e, t.X, r ?? performance.now()), t.finishProcessing());
-}
-function uc(t, e, n) {
-  if (t.baseOptions?.g())
-    throw Error(
-      "Task is not initialized with image mode. 'runningMode' must be set to 'IMAGE'.",
-    );
-  hc(t, e, n, t.C + 1);
-}
-function lc(t, e, n, r) {
-  if (!t.baseOptions?.g())
-    throw Error(
-      "Task is not initialized with video mode. 'runningMode' must be set to 'VIDEO'.",
-    );
-  hc(t, e, n, r);
-}
-function fc(t, e, n, r) {
-  var i = e.data;
-  const s = e.width,
-    o = s * (e = e.height);
-  if ((i instanceof Uint8Array || i instanceof Float32Array) && i.length !== o)
-    throw Error("Unsupported channel count: " + i.length / o);
-  return ((t = new Na([i], n, !1, t.g.i.canvas, t.P, s, e)), r ? t.clone() : t);
-}
-var dc = class extends pa {
-  constructor(t, e, n, r) {
-    (super(t),
-      (this.g = t),
-      (this.X = e),
-      (this.U = n),
-      (this.oa = r),
-      (this.P = new ba()));
-  }
-  l(t, e = !0) {
-    if (
-      ("runningMode" in t &&
-        Ze(
-          this.baseOptions,
-          2,
-          Jt(!!t.runningMode && "IMAGE" !== t.runningMode),
-        ),
-      void 0 !== t.canvas && this.g.i.canvas !== t.canvas)
-    )
-      throw Error("You must create a new task to reset the canvas.");
-    return super.l(t, e);
-  }
-  close() {
-    (this.P.close(), super.close());
-  }
-};
-dc.prototype.close = dc.prototype.close;
-var pc = class extends dc {
-  constructor(t, e) {
-    (super(new ac(t, e), "image_in", "norm_rect_in", !1),
-      (this.j = { detections: [] }),
-      wn((t = this.h = new zs()), 0, 1, (e = new Xs())),
-      Ln(this.h, 2, 0.5),
-      Ln(this.h, 3, 0.3));
-  }
-  get baseOptions() {
-    return yn(this.h, Xs, 1);
-  }
-  set baseOptions(t) {
-    wn(this.h, 0, 1, t);
-  }
-  o(t) {
-    return (
-      "minDetectionConfidence" in t &&
-        Ln(this.h, 2, t.minDetectionConfidence ?? 0.5),
-      "minSuppressionThreshold" in t &&
-        Ln(this.h, 3, t.minSuppressionThreshold ?? 0.3),
-      this.l(t)
-    );
-  }
-  F(t, e) {
-    return ((this.j = { detections: [] }), uc(this, t, e), this.j);
-  }
-  G(t, e, n) {
-    return ((this.j = { detections: [] }), lc(this, t, n, e), this.j);
-  }
-  m() {
-    var t = new ls();
-    (hs(t, "image_in"), hs(t, "norm_rect_in"), us(t, "detections"));
-    const e = new Qi();
-    xr(e, Ys, this.h);
-    const n = new is();
-    (Rn(n, 2, "mediapipe.tasks.vision.face_detector.FaceDetectorGraph"),
-      ns(n, "IMAGE:image_in"),
-      ns(n, "NORM_RECT:norm_rect_in"),
-      rs(n, "DETECTIONS:detections"),
-      n.o(e),
-      cs(t, n),
-      this.g.attachProtoVectorListener("detections", (t, e) => {
-        for (const e of t) ((t = ws(e)), this.j.detections.push(Xo(t)));
-        ua(this, e);
-      }),
-      this.g.attachEmptyPacketListener("detections", (t) => {
-        ua(this, t);
-      }),
-      (t = t.g()),
-      this.setGraph(new Uint8Array(t), !0));
-  }
-};
-((pc.prototype.detectForVideo = pc.prototype.G),
-  (pc.prototype.detect = pc.prototype.F),
-  (pc.prototype.setOptions = pc.prototype.o),
-  (pc.createFromModelPath = async function (t, e) {
-    return cc(pc, t, { baseOptions: { modelAssetPath: e } });
-  }),
-  (pc.createFromModelBuffer = function (t, e) {
-    return cc(pc, t, { baseOptions: { modelAssetBuffer: e } });
-  }),
-  (pc.createFromOptions = function (t, e) {
-    return cc(pc, t, e);
-  }));
-var gc = ic(
-    [61, 146],
-    [146, 91],
-    [91, 181],
-    [181, 84],
-    [84, 17],
-    [17, 314],
-    [314, 405],
-    [405, 321],
-    [321, 375],
-    [375, 291],
-    [61, 185],
-    [185, 40],
-    [40, 39],
-    [39, 37],
-    [37, 0],
-    [0, 267],
-    [267, 269],
-    [269, 270],
-    [270, 409],
-    [409, 291],
-    [78, 95],
-    [95, 88],
-    [88, 178],
-    [178, 87],
-    [87, 14],
-    [14, 317],
-    [317, 402],
-    [402, 318],
-    [318, 324],
-    [324, 308],
-    [78, 191],
-    [191, 80],
-    [80, 81],
-    [81, 82],
-    [82, 13],
-    [13, 312],
-    [312, 311],
-    [311, 310],
-    [310, 415],
-    [415, 308],
-  ),
-  mc = ic(
-    [263, 249],
-    [249, 390],
-    [390, 373],
-    [373, 374],
-    [374, 380],
-    [380, 381],
-    [381, 382],
-    [382, 362],
-    [263, 466],
-    [466, 388],
-    [388, 387],
-    [387, 386],
-    [386, 385],
-    [385, 384],
-    [384, 398],
-    [398, 362],
-  ),
-  yc = ic(
-    [276, 283],
-    [283, 282],
-    [282, 295],
-    [295, 285],
-    [300, 293],
-    [293, 334],
-    [334, 296],
-    [296, 336],
-  ),
-  _c = ic([474, 475], [475, 476], [476, 477], [477, 474]),
-  vc = ic(
-    [33, 7],
-    [7, 163],
-    [163, 144],
-    [144, 145],
-    [145, 153],
-    [153, 154],
-    [154, 155],
-    [155, 133],
-    [33, 246],
-    [246, 161],
-    [161, 160],
-    [160, 159],
-    [159, 158],
-    [158, 157],
-    [157, 173],
-    [173, 133],
-  ),
-  Ec = ic(
-    [46, 53],
-    [53, 52],
-    [52, 65],
-    [65, 55],
-    [70, 63],
-    [63, 105],
-    [105, 66],
-    [66, 107],
-  ),
-  wc = ic([469, 470], [470, 471], [471, 472], [472, 469]),
-  Tc = ic(
-    [10, 338],
-    [338, 297],
-    [297, 332],
-    [332, 284],
-    [284, 251],
-    [251, 389],
-    [389, 356],
-    [356, 454],
-    [454, 323],
-    [323, 361],
-    [361, 288],
-    [288, 397],
-    [397, 365],
-    [365, 379],
-    [379, 378],
-    [378, 400],
-    [400, 377],
-    [377, 152],
-    [152, 148],
-    [148, 176],
-    [176, 149],
-    [149, 150],
-    [150, 136],
-    [136, 172],
-    [172, 58],
-    [58, 132],
-    [132, 93],
-    [93, 234],
-    [234, 127],
-    [127, 162],
-    [162, 21],
-    [21, 54],
-    [54, 103],
-    [103, 67],
-    [67, 109],
-    [109, 10],
-  ),
-  Ac = [...gc, ...mc, ...yc, ...vc, ...Ec, ...Tc],
-  bc = ic(
-    [127, 34],
-    [34, 139],
-    [139, 127],
-    [11, 0],
-    [0, 37],
-    [37, 11],
-    [232, 231],
-    [231, 120],
-    [120, 232],
-    [72, 37],
-    [37, 39],
-    [39, 72],
-    [128, 121],
-    [121, 47],
-    [47, 128],
-    [232, 121],
-    [121, 128],
-    [128, 232],
-    [104, 69],
-    [69, 67],
-    [67, 104],
-    [175, 171],
-    [171, 148],
-    [148, 175],
-    [118, 50],
-    [50, 101],
-    [101, 118],
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-    [390, 339],
-    [339, 249],
-    [249, 390],
-    [339, 448],
-    [448, 255],
-    [255, 339],
-  );
-function kc(t) {
-  t.j = {
-    faceLandmarks: [],
-    faceBlendshapes: [],
-    facialTransformationMatrixes: [],
-  };
-}
-var Sc = class extends dc {
-  constructor(t, e) {
-    (super(new ac(t, e), "image_in", "norm_rect", !1),
-      (this.j = {
-        faceLandmarks: [],
-        faceBlendshapes: [],
-        facialTransformationMatrixes: [],
-      }),
-      (this.outputFacialTransformationMatrixes = this.outputFaceBlendshapes =
-        !1),
-      wn((t = this.h = new Js()), 0, 1, (e = new Xs())),
-      (this.A = new $s()),
-      wn(this.h, 0, 3, this.A),
-      (this.u = new zs()),
-      wn(this.h, 0, 2, this.u),
-      xn(this.u, 4, 1),
-      Ln(this.u, 2, 0.5),
-      Ln(this.A, 2, 0.5),
-      Ln(this.h, 4, 0.5));
-  }
-  get baseOptions() {
-    return yn(this.h, Xs, 1);
-  }
-  set baseOptions(t) {
-    wn(this.h, 0, 1, t);
-  }
-  o(t) {
-    return (
-      "numFaces" in t && xn(this.u, 4, t.numFaces ?? 1),
-      "minFaceDetectionConfidence" in t &&
-        Ln(this.u, 2, t.minFaceDetectionConfidence ?? 0.5),
-      "minTrackingConfidence" in t &&
-        Ln(this.h, 4, t.minTrackingConfidence ?? 0.5),
-      "minFacePresenceConfidence" in t &&
-        Ln(this.A, 2, t.minFacePresenceConfidence ?? 0.5),
-      "outputFaceBlendshapes" in t &&
-        (this.outputFaceBlendshapes = !!t.outputFaceBlendshapes),
-      "outputFacialTransformationMatrixes" in t &&
-        (this.outputFacialTransformationMatrixes =
-          !!t.outputFacialTransformationMatrixes),
-      this.l(t)
-    );
-  }
-  F(t, e) {
-    return (kc(this), uc(this, t, e), this.j);
-  }
-  G(t, e, n) {
-    return (kc(this), lc(this, t, n, e), this.j);
-  }
-  m() {
-    var t = new ls();
-    (hs(t, "image_in"), hs(t, "norm_rect"), us(t, "face_landmarks"));
-    const e = new Qi();
-    xr(e, Qs, this.h);
-    const n = new is();
-    (Rn(n, 2, "mediapipe.tasks.vision.face_landmarker.FaceLandmarkerGraph"),
-      ns(n, "IMAGE:image_in"),
-      ns(n, "NORM_RECT:norm_rect"),
-      rs(n, "NORM_LANDMARKS:face_landmarks"),
-      n.o(e),
-      cs(t, n),
-      this.g.attachProtoVectorListener("face_landmarks", (t, e) => {
-        for (const e of t) ((t = ks(e)), this.j.faceLandmarks.push(Ho(t)));
-        ua(this, e);
-      }),
-      this.g.attachEmptyPacketListener("face_landmarks", (t) => {
-        ua(this, t);
-      }),
-      this.outputFaceBlendshapes &&
-        (us(t, "blendshapes"),
-        rs(n, "BLENDSHAPES:blendshapes"),
-        this.g.attachProtoVectorListener("blendshapes", (t, e) => {
-          if (this.outputFaceBlendshapes)
-            for (const e of t)
-              ((t = ys(e)), this.j.faceBlendshapes.push(jo(t.g() ?? [])));
-          ua(this, e);
-        }),
-        this.g.attachEmptyPacketListener("blendshapes", (t) => {
-          ua(this, t);
-        })),
-      this.outputFacialTransformationMatrixes &&
-        (us(t, "face_geometry"),
-        rs(n, "FACE_GEOMETRY:face_geometry"),
-        this.g.attachProtoVectorListener("face_geometry", (t, e) => {
-          if (this.outputFacialTransformationMatrixes)
-            for (const e of t)
-              (t = yn((t = qs(e)), Ss, 2)) &&
-                this.j.facialTransformationMatrixes.push({
-                  rows: kn(t, 1) ?? 0 ?? 0,
-                  columns: kn(t, 2) ?? 0 ?? 0,
-                  data: en(t, 3, $t, tn()).slice() ?? [],
-                });
-          ua(this, e);
-        }),
-        this.g.attachEmptyPacketListener("face_geometry", (t) => {
-          ua(this, t);
-        })),
-      (t = t.g()),
-      this.setGraph(new Uint8Array(t), !0));
-  }
-};
-((Sc.prototype.detectForVideo = Sc.prototype.G),
-  (Sc.prototype.detect = Sc.prototype.F),
-  (Sc.prototype.setOptions = Sc.prototype.o),
-  (Sc.createFromModelPath = function (t, e) {
-    return cc(Sc, t, { baseOptions: { modelAssetPath: e } });
-  }),
-  (Sc.createFromModelBuffer = function (t, e) {
-    return cc(Sc, t, { baseOptions: { modelAssetBuffer: e } });
-  }),
-  (Sc.createFromOptions = function (t, e) {
-    return cc(Sc, t, e);
-  }),
-  (Sc.FACE_LANDMARKS_LIPS = gc),
-  (Sc.FACE_LANDMARKS_LEFT_EYE = mc),
-  (Sc.FACE_LANDMARKS_LEFT_EYEBROW = yc),
-  (Sc.FACE_LANDMARKS_LEFT_IRIS = _c),
-  (Sc.FACE_LANDMARKS_RIGHT_EYE = vc),
-  (Sc.FACE_LANDMARKS_RIGHT_EYEBROW = Ec),
-  (Sc.FACE_LANDMARKS_RIGHT_IRIS = wc),
-  (Sc.FACE_LANDMARKS_FACE_OVAL = Tc),
-  (Sc.FACE_LANDMARKS_CONTOURS = Ac),
-  (Sc.FACE_LANDMARKS_TESSELATION = bc));
-var xc = ic(
-  [0, 1],
-  [1, 2],
-  [2, 3],
-  [3, 4],
-  [0, 5],
-  [5, 6],
-  [6, 7],
-  [7, 8],
-  [5, 9],
-  [9, 10],
-  [10, 11],
-  [11, 12],
-  [9, 13],
-  [13, 14],
-  [14, 15],
-  [15, 16],
-  [13, 17],
-  [0, 17],
-  [17, 18],
-  [18, 19],
-  [19, 20],
-);
-function Lc(t) {
-  ((t.gestures = []),
-    (t.landmarks = []),
-    (t.worldLandmarks = []),
-    (t.handedness = []));
-}
-function Rc(t) {
-  return 0 === t.gestures.length
-    ? {
-        gestures: [],
-        landmarks: [],
-        worldLandmarks: [],
-        handedness: [],
-        handednesses: [],
-      }
-    : {
-        gestures: t.gestures,
-        landmarks: t.landmarks,
-        worldLandmarks: t.worldLandmarks,
-        handedness: t.handedness,
-        handednesses: t.handedness,
-      };
-}
-function Ic(t, e = !0) {
-  const n = [];
-  for (const i of t) {
-    var r = ys(i);
-    t = [];
-    for (const n of r.g())
-      ((r = e && null != kn(n, 1) ? (kn(n, 1) ?? 0) : -1),
-        t.push({
-          score: Sn(n, 2) ?? 0,
-          index: r,
-          categoryName: le($e(n, 3)) ?? "" ?? "",
-          displayName: le($e(n, 4)) ?? "" ?? "",
-        }));
-    n.push(t);
-  }
-  return n;
-}
-var Fc = class extends dc {
-  constructor(t, e) {
-    (super(new ac(t, e), "image_in", "norm_rect", !1),
-      (this.gestures = []),
-      (this.landmarks = []),
-      (this.worldLandmarks = []),
-      (this.handedness = []),
-      wn((t = this.j = new oo()), 0, 1, (e = new Xs())),
-      (this.u = new so()),
-      wn(this.j, 0, 2, this.u),
-      (this.D = new io()),
-      wn(this.u, 0, 3, this.D),
-      (this.A = new ro()),
-      wn(this.u, 0, 2, this.A),
-      (this.h = new no()),
-      wn(this.j, 0, 3, this.h),
-      Ln(this.A, 2, 0.5),
-      Ln(this.u, 4, 0.5),
-      Ln(this.D, 2, 0.5));
-  }
-  get baseOptions() {
-    return yn(this.j, Xs, 1);
-  }
-  set baseOptions(t) {
-    wn(this.j, 0, 1, t);
-  }
-  o(t) {
-    if (
-      (xn(this.A, 3, t.numHands ?? 1),
-      "minHandDetectionConfidence" in t &&
-        Ln(this.A, 2, t.minHandDetectionConfidence ?? 0.5),
-      "minTrackingConfidence" in t &&
-        Ln(this.u, 4, t.minTrackingConfidence ?? 0.5),
-      "minHandPresenceConfidence" in t &&
-        Ln(this.D, 2, t.minHandPresenceConfidence ?? 0.5),
-      t.cannedGesturesClassifierOptions)
-    ) {
-      var e = new to(),
-        n = e,
-        r = Bo(t.cannedGesturesClassifierOptions, yn(this.h, to, 3)?.l());
-      (wn(n, 0, 2, r), wn(this.h, 0, 3, e));
-    } else
-      void 0 === t.cannedGesturesClassifierOptions && yn(this.h, to, 3)?.g();
-    return (
-      t.customGesturesClassifierOptions
-        ? (wn(
-            (n = e = new to()),
-            0,
-            2,
-            (r = Bo(t.customGesturesClassifierOptions, yn(this.h, to, 4)?.l())),
-          ),
-          wn(this.h, 0, 4, e))
-        : void 0 === t.customGesturesClassifierOptions &&
-          yn(this.h, to, 4)?.g(),
-      this.l(t)
-    );
-  }
-  Ha(t, e) {
-    return (Lc(this), uc(this, t, e), Rc(this));
-  }
-  Ia(t, e, n) {
-    return (Lc(this), lc(this, t, n, e), Rc(this));
-  }
-  m() {
-    var t = new ls();
-    (hs(t, "image_in"),
-      hs(t, "norm_rect"),
-      us(t, "hand_gestures"),
-      us(t, "hand_landmarks"),
-      us(t, "world_hand_landmarks"),
-      us(t, "handedness"));
-    const e = new Qi();
-    xr(e, lo, this.j);
-    const n = new is();
-    (Rn(
-      n,
-      2,
-      "mediapipe.tasks.vision.gesture_recognizer.GestureRecognizerGraph",
-    ),
-      ns(n, "IMAGE:image_in"),
-      ns(n, "NORM_RECT:norm_rect"),
-      rs(n, "HAND_GESTURES:hand_gestures"),
-      rs(n, "LANDMARKS:hand_landmarks"),
-      rs(n, "WORLD_LANDMARKS:world_hand_landmarks"),
-      rs(n, "HANDEDNESS:handedness"),
-      n.o(e),
-      cs(t, n),
-      this.g.attachProtoVectorListener("hand_landmarks", (t, e) => {
-        for (const e of t) {
-          t = ks(e);
-          const n = [];
-          for (const e of vn(t, bs, 1))
-            n.push({
-              x: Sn(e, 1) ?? 0,
-              y: Sn(e, 2) ?? 0,
-              z: Sn(e, 3) ?? 0,
-              visibility: Sn(e, 4) ?? 0,
-            });
-          this.landmarks.push(n);
-        }
-        ua(this, e);
-      }),
-      this.g.attachEmptyPacketListener("hand_landmarks", (t) => {
-        ua(this, t);
-      }),
-      this.g.attachProtoVectorListener("world_hand_landmarks", (t, e) => {
-        for (const e of t) {
-          t = As(e);
-          const n = [];
-          for (const e of vn(t, Ts, 1))
-            n.push({
-              x: Sn(e, 1) ?? 0,
-              y: Sn(e, 2) ?? 0,
-              z: Sn(e, 3) ?? 0,
-              visibility: Sn(e, 4) ?? 0,
-            });
-          this.worldLandmarks.push(n);
-        }
-        ua(this, e);
-      }),
-      this.g.attachEmptyPacketListener("world_hand_landmarks", (t) => {
-        ua(this, t);
-      }),
-      this.g.attachProtoVectorListener("hand_gestures", (t, e) => {
-        (this.gestures.push(...Ic(t, !1)), ua(this, e));
-      }),
-      this.g.attachEmptyPacketListener("hand_gestures", (t) => {
-        ua(this, t);
-      }),
-      this.g.attachProtoVectorListener("handedness", (t, e) => {
-        (this.handedness.push(...Ic(t)), ua(this, e));
-      }),
-      this.g.attachEmptyPacketListener("handedness", (t) => {
-        ua(this, t);
-      }),
-      (t = t.g()),
-      this.setGraph(new Uint8Array(t), !0));
-  }
-};
-function Mc(t) {
-  return {
-    landmarks: t.landmarks,
-    worldLandmarks: t.worldLandmarks,
-    handednesses: t.handedness,
-    handedness: t.handedness,
-  };
-}
-((Fc.prototype.recognizeForVideo = Fc.prototype.Ia),
-  (Fc.prototype.recognize = Fc.prototype.Ha),
-  (Fc.prototype.setOptions = Fc.prototype.o),
-  (Fc.createFromModelPath = function (t, e) {
-    return cc(Fc, t, { baseOptions: { modelAssetPath: e } });
-  }),
-  (Fc.createFromModelBuffer = function (t, e) {
-    return cc(Fc, t, { baseOptions: { modelAssetBuffer: e } });
-  }),
-  (Fc.createFromOptions = function (t, e) {
-    return cc(Fc, t, e);
-  }),
-  (Fc.HAND_CONNECTIONS = xc));
-var Pc = class extends dc {
-  constructor(t, e) {
-    (super(new ac(t, e), "image_in", "norm_rect", !1),
-      (this.landmarks = []),
-      (this.worldLandmarks = []),
-      (this.handedness = []),
-      wn((t = this.h = new so()), 0, 1, (e = new Xs())),
-      (this.u = new io()),
-      wn(this.h, 0, 3, this.u),
-      (this.j = new ro()),
-      wn(this.h, 0, 2, this.j),
-      xn(this.j, 3, 1),
-      Ln(this.j, 2, 0.5),
-      Ln(this.u, 2, 0.5),
-      Ln(this.h, 4, 0.5));
-  }
-  get baseOptions() {
-    return yn(this.h, Xs, 1);
-  }
-  set baseOptions(t) {
-    wn(this.h, 0, 1, t);
-  }
-  o(t) {
-    return (
-      "numHands" in t && xn(this.j, 3, t.numHands ?? 1),
-      "minHandDetectionConfidence" in t &&
-        Ln(this.j, 2, t.minHandDetectionConfidence ?? 0.5),
-      "minTrackingConfidence" in t &&
-        Ln(this.h, 4, t.minTrackingConfidence ?? 0.5),
-      "minHandPresenceConfidence" in t &&
-        Ln(this.u, 2, t.minHandPresenceConfidence ?? 0.5),
-      this.l(t)
-    );
-  }
-  F(t, e) {
-    return (
-      (this.landmarks = []),
-      (this.worldLandmarks = []),
-      (this.handedness = []),
-      uc(this, t, e),
-      Mc(this)
-    );
-  }
-  G(t, e, n) {
-    return (
-      (this.landmarks = []),
-      (this.worldLandmarks = []),
-      (this.handedness = []),
-      lc(this, t, n, e),
-      Mc(this)
-    );
-  }
-  m() {
-    var t = new ls();
-    (hs(t, "image_in"),
-      hs(t, "norm_rect"),
-      us(t, "hand_landmarks"),
-      us(t, "world_hand_landmarks"),
-      us(t, "handedness"));
-    const e = new Qi();
-    xr(e, fo, this.h);
-    const n = new is();
-    (Rn(n, 2, "mediapipe.tasks.vision.hand_landmarker.HandLandmarkerGraph"),
-      ns(n, "IMAGE:image_in"),
-      ns(n, "NORM_RECT:norm_rect"),
-      rs(n, "LANDMARKS:hand_landmarks"),
-      rs(n, "WORLD_LANDMARKS:world_hand_landmarks"),
-      rs(n, "HANDEDNESS:handedness"),
-      n.o(e),
-      cs(t, n),
-      this.g.attachProtoVectorListener("hand_landmarks", (t, e) => {
-        for (const e of t) ((t = ks(e)), this.landmarks.push(Ho(t)));
-        ua(this, e);
-      }),
-      this.g.attachEmptyPacketListener("hand_landmarks", (t) => {
-        ua(this, t);
-      }),
-      this.g.attachProtoVectorListener("world_hand_landmarks", (t, e) => {
-        for (const e of t) ((t = As(e)), this.worldLandmarks.push(Wo(t)));
-        ua(this, e);
-      }),
-      this.g.attachEmptyPacketListener("world_hand_landmarks", (t) => {
-        ua(this, t);
-      }),
-      this.g.attachProtoVectorListener("handedness", (t, e) => {
-        var n = this.handedness,
-          r = n.push;
-        const i = [];
-        for (const e of t) {
-          t = ys(e);
-          const n = [];
-          for (const e of t.g())
-            n.push({
-              score: Sn(e, 2) ?? 0,
-              index: kn(e, 1) ?? 0 ?? -1,
-              categoryName: le($e(e, 3)) ?? "" ?? "",
-              displayName: le($e(e, 4)) ?? "" ?? "",
-            });
-          i.push(n);
-        }
-        (r.call(n, ...i), ua(this, e));
-      }),
-      this.g.attachEmptyPacketListener("handedness", (t) => {
-        ua(this, t);
-      }),
-      (t = t.g()),
-      this.setGraph(new Uint8Array(t), !0));
-  }
-};
-((Pc.prototype.detectForVideo = Pc.prototype.G),
-  (Pc.prototype.detect = Pc.prototype.F),
-  (Pc.prototype.setOptions = Pc.prototype.o),
-  (Pc.createFromModelPath = function (t, e) {
-    return cc(Pc, t, { baseOptions: { modelAssetPath: e } });
-  }),
-  (Pc.createFromModelBuffer = function (t, e) {
-    return cc(Pc, t, { baseOptions: { modelAssetBuffer: e } });
-  }),
-  (Pc.createFromOptions = function (t, e) {
-    return cc(Pc, t, e);
-  }),
-  (Pc.HAND_CONNECTIONS = xc));
-var Cc = ic(
-  [0, 1],
-  [1, 2],
-  [2, 3],
-  [3, 7],
-  [0, 4],
-  [4, 5],
-  [5, 6],
-  [6, 8],
-  [9, 10],
-  [11, 12],
-  [11, 13],
-  [13, 15],
-  [15, 17],
-  [15, 19],
-  [15, 21],
-  [17, 19],
-  [12, 14],
-  [14, 16],
-  [16, 18],
-  [16, 20],
-  [16, 22],
-  [18, 20],
-  [11, 23],
-  [12, 24],
-  [23, 24],
-  [23, 25],
-  [24, 26],
-  [25, 27],
-  [26, 28],
-  [27, 29],
-  [28, 30],
-  [29, 31],
-  [30, 32],
-  [27, 31],
-  [28, 32],
-);
-function Oc(t) {
-  t.h = {
-    faceLandmarks: [],
-    faceBlendshapes: [],
-    poseLandmarks: [],
-    poseWorldLandmarks: [],
-    poseSegmentationMasks: [],
-    leftHandLandmarks: [],
-    leftHandWorldLandmarks: [],
-    rightHandLandmarks: [],
-    rightHandWorldLandmarks: [],
-  };
-}
-function Nc(t) {
-  try {
-    if (!t.D) return t.h;
-    t.D(t.h);
-  } finally {
-    da(t);
-  }
-}
-function Uc(t, e) {
-  ((t = ks(t)), e.push(Ho(t)));
-}
-var Dc = class extends dc {
-  constructor(t, e) {
-    (super(new ac(t, e), "input_frames_image", null, !1),
-      (this.h = {
-        faceLandmarks: [],
-        faceBlendshapes: [],
-        poseLandmarks: [],
-        poseWorldLandmarks: [],
-        poseSegmentationMasks: [],
-        leftHandLandmarks: [],
-        leftHandWorldLandmarks: [],
-        rightHandLandmarks: [],
-        rightHandWorldLandmarks: [],
-      }),
-      (this.outputPoseSegmentationMasks = this.outputFaceBlendshapes = !1),
-      wn((t = this.j = new yo()), 0, 1, (e = new Xs())),
-      (this.I = new io()),
-      wn(this.j, 0, 2, this.I),
-      (this.W = new po()),
-      wn(this.j, 0, 3, this.W),
-      (this.u = new zs()),
-      wn(this.j, 0, 4, this.u),
-      (this.O = new $s()),
-      wn(this.j, 0, 5, this.O),
-      (this.A = new go()),
-      wn(this.j, 0, 6, this.A),
-      (this.M = new mo()),
-      wn(this.j, 0, 7, this.M),
-      Ln(this.u, 2, 0.5),
-      Ln(this.u, 3, 0.3),
-      Ln(this.O, 2, 0.5),
-      Ln(this.A, 2, 0.5),
-      Ln(this.A, 3, 0.3),
-      Ln(this.M, 2, 0.5),
-      Ln(this.I, 2, 0.5));
-  }
-  get baseOptions() {
-    return yn(this.j, Xs, 1);
-  }
-  set baseOptions(t) {
-    wn(this.j, 0, 1, t);
-  }
-  o(t) {
-    return (
-      "minFaceDetectionConfidence" in t &&
-        Ln(this.u, 2, t.minFaceDetectionConfidence ?? 0.5),
-      "minFaceSuppressionThreshold" in t &&
-        Ln(this.u, 3, t.minFaceSuppressionThreshold ?? 0.3),
-      "minFacePresenceConfidence" in t &&
-        Ln(this.O, 2, t.minFacePresenceConfidence ?? 0.5),
-      "outputFaceBlendshapes" in t &&
-        (this.outputFaceBlendshapes = !!t.outputFaceBlendshapes),
-      "minPoseDetectionConfidence" in t &&
-        Ln(this.A, 2, t.minPoseDetectionConfidence ?? 0.5),
-      "minPoseSuppressionThreshold" in t &&
-        Ln(this.A, 3, t.minPoseSuppressionThreshold ?? 0.3),
-      "minPosePresenceConfidence" in t &&
-        Ln(this.M, 2, t.minPosePresenceConfidence ?? 0.5),
-      "outputPoseSegmentationMasks" in t &&
-        (this.outputPoseSegmentationMasks = !!t.outputPoseSegmentationMasks),
-      "minHandLandmarksConfidence" in t &&
-        Ln(this.I, 2, t.minHandLandmarksConfidence ?? 0.5),
-      this.l(t)
-    );
-  }
-  F(t, e, n) {
-    const r = "function" != typeof e ? e : {};
-    return (
-      (this.D = "function" == typeof e ? e : n),
-      Oc(this),
-      uc(this, t, r),
-      Nc(this)
-    );
-  }
-  G(t, e, n, r) {
-    const i = "function" != typeof n ? n : {};
-    return (
-      (this.D = "function" == typeof n ? n : r),
-      Oc(this),
-      lc(this, t, i, e),
-      Nc(this)
-    );
-  }
-  m() {
-    var t = new ls();
-    (hs(t, "input_frames_image"),
-      us(t, "pose_landmarks"),
-      us(t, "pose_world_landmarks"),
-      us(t, "face_landmarks"),
-      us(t, "left_hand_landmarks"),
-      us(t, "left_hand_world_landmarks"),
-      us(t, "right_hand_landmarks"),
-      us(t, "right_hand_world_landmarks"));
-    const e = new Qi(),
-      n = new Bi();
-    (Rn(
-      n,
-      1,
-      "type.googleapis.com/mediapipe.tasks.vision.holistic_landmarker.proto.HolisticLandmarkerGraphOptions",
-    ),
-      (function (t, e) {
-        if (null != e)
-          if (Array.isArray(e)) Ze(t, 2, Ie(e, 0, Me));
-          else {
-            if (!("string" == typeof e || e instanceof F || x(e)))
-              throw Error(
-                "invalid value in Any.value field: " +
-                  e +
-                  " expected a ByteString, a base64 encoded string, a Uint8Array or a jspb array",
-              );
-            hn(t, 2, ht(e, !1), R());
-          }
-      })(n, this.j.g()));
-    const r = new is();
-    (Rn(
-      r,
-      2,
-      "mediapipe.tasks.vision.holistic_landmarker.HolisticLandmarkerGraph",
-    ),
-      bn(r, 8, Bi, n),
-      ns(r, "IMAGE:input_frames_image"),
-      rs(r, "POSE_LANDMARKS:pose_landmarks"),
-      rs(r, "POSE_WORLD_LANDMARKS:pose_world_landmarks"),
-      rs(r, "FACE_LANDMARKS:face_landmarks"),
-      rs(r, "LEFT_HAND_LANDMARKS:left_hand_landmarks"),
-      rs(r, "LEFT_HAND_WORLD_LANDMARKS:left_hand_world_landmarks"),
-      rs(r, "RIGHT_HAND_LANDMARKS:right_hand_landmarks"),
-      rs(r, "RIGHT_HAND_WORLD_LANDMARKS:right_hand_world_landmarks"),
-      r.o(e),
-      cs(t, r),
-      la(this, t),
-      this.g.attachProtoListener("pose_landmarks", (t, e) => {
-        (Uc(t, this.h.poseLandmarks), ua(this, e));
-      }),
-      this.g.attachEmptyPacketListener("pose_landmarks", (t) => {
-        ua(this, t);
-      }),
-      this.g.attachProtoListener("pose_world_landmarks", (t, e) => {
-        var n = this.h.poseWorldLandmarks;
-        ((t = As(t)), n.push(Wo(t)), ua(this, e));
-      }),
-      this.g.attachEmptyPacketListener("pose_world_landmarks", (t) => {
-        ua(this, t);
-      }),
-      this.outputPoseSegmentationMasks &&
-        (rs(r, "POSE_SEGMENTATION_MASK:pose_segmentation_mask"),
-        fa(this, "pose_segmentation_mask"),
-        this.g.Z("pose_segmentation_mask", (t, e) => {
-          ((this.h.poseSegmentationMasks = [fc(this, t, !0, !this.D)]),
-            ua(this, e));
-        }),
-        this.g.attachEmptyPacketListener("pose_segmentation_mask", (t) => {
-          ((this.h.poseSegmentationMasks = []), ua(this, t));
-        })),
-      this.g.attachProtoListener("face_landmarks", (t, e) => {
-        (Uc(t, this.h.faceLandmarks), ua(this, e));
-      }),
-      this.g.attachEmptyPacketListener("face_landmarks", (t) => {
-        ua(this, t);
-      }),
-      this.outputFaceBlendshapes &&
-        (us(t, "extra_blendshapes"),
-        rs(r, "FACE_BLENDSHAPES:extra_blendshapes"),
-        this.g.attachProtoListener("extra_blendshapes", (t, e) => {
-          var n = this.h.faceBlendshapes;
-          (this.outputFaceBlendshapes && ((t = ys(t)), n.push(jo(t.g() ?? []))),
-            ua(this, e));
-        }),
-        this.g.attachEmptyPacketListener("extra_blendshapes", (t) => {
-          ua(this, t);
-        })),
-      this.g.attachProtoListener("left_hand_landmarks", (t, e) => {
-        (Uc(t, this.h.leftHandLandmarks), ua(this, e));
-      }),
-      this.g.attachEmptyPacketListener("left_hand_landmarks", (t) => {
-        ua(this, t);
-      }),
-      this.g.attachProtoListener("left_hand_world_landmarks", (t, e) => {
-        var n = this.h.leftHandWorldLandmarks;
-        ((t = As(t)), n.push(Wo(t)), ua(this, e));
-      }),
-      this.g.attachEmptyPacketListener("left_hand_world_landmarks", (t) => {
-        ua(this, t);
-      }),
-      this.g.attachProtoListener("right_hand_landmarks", (t, e) => {
-        (Uc(t, this.h.rightHandLandmarks), ua(this, e));
-      }),
-      this.g.attachEmptyPacketListener("right_hand_landmarks", (t) => {
-        ua(this, t);
-      }),
-      this.g.attachProtoListener("right_hand_world_landmarks", (t, e) => {
-        var n = this.h.rightHandWorldLandmarks;
-        ((t = As(t)), n.push(Wo(t)), ua(this, e));
-      }),
-      this.g.attachEmptyPacketListener("right_hand_world_landmarks", (t) => {
-        ua(this, t);
-      }),
-      (t = t.g()),
-      this.setGraph(new Uint8Array(t), !0));
-  }
-};
-((Dc.prototype.detectForVideo = Dc.prototype.G),
-  (Dc.prototype.detect = Dc.prototype.F),
-  (Dc.prototype.setOptions = Dc.prototype.o),
-  (Dc.createFromModelPath = function (t, e) {
-    return cc(Dc, t, { baseOptions: { modelAssetPath: e } });
-  }),
-  (Dc.createFromModelBuffer = function (t, e) {
-    return cc(Dc, t, { baseOptions: { modelAssetBuffer: e } });
-  }),
-  (Dc.createFromOptions = function (t, e) {
-    return cc(Dc, t, e);
-  }),
-  (Dc.HAND_CONNECTIONS = xc),
-  (Dc.POSE_CONNECTIONS = Cc),
-  (Dc.FACE_LANDMARKS_LIPS = gc),
-  (Dc.FACE_LANDMARKS_LEFT_EYE = mc),
-  (Dc.FACE_LANDMARKS_LEFT_EYEBROW = yc),
-  (Dc.FACE_LANDMARKS_LEFT_IRIS = _c),
-  (Dc.FACE_LANDMARKS_RIGHT_EYE = vc),
-  (Dc.FACE_LANDMARKS_RIGHT_EYEBROW = Ec),
-  (Dc.FACE_LANDMARKS_RIGHT_IRIS = wc),
-  (Dc.FACE_LANDMARKS_FACE_OVAL = Tc),
-  (Dc.FACE_LANDMARKS_CONTOURS = Ac),
-  (Dc.FACE_LANDMARKS_TESSELATION = bc));
-var Bc = class extends dc {
-  constructor(t, e) {
-    (super(new ac(t, e), "input_image", "norm_rect", !0),
-      (this.j = { classifications: [] }),
-      wn((t = this.h = new Eo()), 0, 1, (e = new Xs())));
-  }
-  get baseOptions() {
-    return yn(this.h, Xs, 1);
-  }
-  set baseOptions(t) {
-    wn(this.h, 0, 1, t);
-  }
-  o(t) {
-    return (wn(this.h, 0, 2, Bo(t, yn(this.h, Ns, 2))), this.l(t));
-  }
-  sa(t, e) {
-    return ((this.j = { classifications: [] }), uc(this, t, e), this.j);
-  }
-  ta(t, e, n) {
-    return ((this.j = { classifications: [] }), lc(this, t, n, e), this.j);
-  }
-  m() {
-    var t = new ls();
-    (hs(t, "input_image"), hs(t, "norm_rect"), us(t, "classifications"));
-    const e = new Qi();
-    xr(e, wo, this.h);
-    const n = new is();
-    (Rn(n, 2, "mediapipe.tasks.vision.image_classifier.ImageClassifierGraph"),
-      ns(n, "IMAGE:input_image"),
-      ns(n, "NORM_RECT:norm_rect"),
-      rs(n, "CLASSIFICATIONS:classifications"),
-      n.o(e),
-      cs(t, n),
-      this.g.attachProtoListener("classifications", (t, e) => {
-        ((this.j = Vo(Is(t))), ua(this, e));
-      }),
-      this.g.attachEmptyPacketListener("classifications", (t) => {
-        ua(this, t);
-      }),
-      (t = t.g()),
-      this.setGraph(new Uint8Array(t), !0));
-  }
-};
-((Bc.prototype.classifyForVideo = Bc.prototype.ta),
-  (Bc.prototype.classify = Bc.prototype.sa),
-  (Bc.prototype.setOptions = Bc.prototype.o),
-  (Bc.createFromModelPath = function (t, e) {
-    return cc(Bc, t, { baseOptions: { modelAssetPath: e } });
-  }),
-  (Bc.createFromModelBuffer = function (t, e) {
-    return cc(Bc, t, { baseOptions: { modelAssetBuffer: e } });
-  }),
-  (Bc.createFromOptions = function (t, e) {
-    return cc(Bc, t, e);
-  }));
-var Gc = class extends dc {
-  constructor(t, e) {
-    (super(new ac(t, e), "image_in", "norm_rect", !0),
-      (this.h = new To()),
-      (this.embeddings = { embeddings: [] }),
-      wn((t = this.h), 0, 1, (e = new Xs())));
-  }
-  get baseOptions() {
-    return yn(this.h, Xs, 1);
-  }
-  set baseOptions(t) {
-    wn(this.h, 0, 1, t);
-  }
-  o(t) {
-    var e = this.h,
-      n = yn(this.h, Ds, 2);
-    return (
-      (n = n ? n.clone() : new Ds()),
-      void 0 !== t.l2Normalize
-        ? Ze(n, 1, Jt(t.l2Normalize))
-        : "l2Normalize" in t && Ze(n, 1),
-      void 0 !== t.quantize
-        ? Ze(n, 2, Jt(t.quantize))
-        : "quantize" in t && Ze(n, 2),
-      wn(e, 0, 2, n),
-      this.l(t)
-    );
-  }
-  za(t, e) {
-    return (uc(this, t, e), this.embeddings);
-  }
-  Aa(t, e, n) {
-    return (lc(this, t, n, e), this.embeddings);
-  }
-  m() {
-    var t = new ls();
-    (hs(t, "image_in"), hs(t, "norm_rect"), us(t, "embeddings_out"));
-    const e = new Qi();
-    xr(e, Ao, this.h);
-    const n = new is();
-    (Rn(n, 2, "mediapipe.tasks.vision.image_embedder.ImageEmbedderGraph"),
-      ns(n, "IMAGE:image_in"),
-      ns(n, "NORM_RECT:norm_rect"),
-      rs(n, "EMBEDDINGS:embeddings_out"),
-      n.o(e),
-      cs(t, n),
-      this.g.attachProtoListener("embeddings_out", (t, e) => {
-        ((t = Os(t)),
-          (this.embeddings = (function (t) {
-            return {
-              embeddings: vn(t, Ps, 1).map((t) => {
-                const e = {
-                  headIndex: kn(t, 3) ?? 0 ?? -1,
-                  headName: le($e(t, 4)) ?? "" ?? "",
-                };
-                var n = t.v;
-                return (
-                  void 0 !== mn(n, 0 | n[Q], Fs, ln(t, 1))
-                    ? ((t = en((t = yn(t, Fs, ln(t, 1), void 0)), 1, $t, tn())),
-                      (e.floatEmbedding = t.slice()))
-                    : ((n = new Uint8Array(0)),
-                      (e.quantizedEmbedding =
-                        yn(t, Ms, ln(t, 2), void 0)
-                          ?.na()
-                          ?.h() ?? n)),
-                  e
-                );
-              }),
-              timestampMs: Go($e(t, 2, void 0, void 0, ce) ?? Ye),
-            };
-          })(t)),
-          ua(this, e));
-      }),
-      this.g.attachEmptyPacketListener("embeddings_out", (t) => {
-        ua(this, t);
-      }),
-      (t = t.g()),
-      this.setGraph(new Uint8Array(t), !0));
-  }
-};
-((Gc.cosineSimilarity = function (t, e) {
-  if (t.floatEmbedding && e.floatEmbedding)
-    t = Ko(t.floatEmbedding, e.floatEmbedding);
-  else {
-    if (!t.quantizedEmbedding || !e.quantizedEmbedding)
-      throw Error(
-        "Cannot compute cosine similarity between quantized and float embeddings.",
-      );
-    t = Ko(zo(t.quantizedEmbedding), zo(e.quantizedEmbedding));
-  }
-  return t;
-}),
-  (Gc.prototype.embedForVideo = Gc.prototype.Aa),
-  (Gc.prototype.embed = Gc.prototype.za),
-  (Gc.prototype.setOptions = Gc.prototype.o),
-  (Gc.createFromModelPath = function (t, e) {
-    return cc(Gc, t, { baseOptions: { modelAssetPath: e } });
-  }),
-  (Gc.createFromModelBuffer = function (t, e) {
-    return cc(Gc, t, { baseOptions: { modelAssetBuffer: e } });
-  }),
-  (Gc.createFromOptions = function (t, e) {
-    return cc(Gc, t, e);
-  }));
-var jc = class {
-  constructor(t, e, n) {
-    ((this.confidenceMasks = t),
-      (this.categoryMask = e),
-      (this.qualityScores = n));
-  }
-  close() {
-    (this.confidenceMasks?.forEach((t) => {
-      t.close();
-    }),
-      this.categoryMask?.close());
-  }
-};
-function Vc(t) {
-  const e = (function (t) {
-    return vn(t, is, 1);
-  })(t.ca()).filter((t) =>
-    (le($e(t, 1)) ?? "").includes(
-      "mediapipe.tasks.TensorsToSegmentationCalculator",
-    ),
-  );
-  if (((t.u = []), e.length > 1))
-    throw Error(
-      "The graph has more than one mediapipe.tasks.TensorsToSegmentationCalculator.",
-    );
-  1 === e.length &&
-    (yn(e[0], Qi, 7)?.j()?.g() ?? new Map()).forEach((e, n) => {
-      t.u[Number(n)] = le($e(e, 1)) ?? "";
-    });
-}
-function Xc(t) {
-  ((t.categoryMask = void 0),
-    (t.confidenceMasks = void 0),
-    (t.qualityScores = void 0));
-}
-function Hc(t) {
-  try {
-    const e = new jc(t.confidenceMasks, t.categoryMask, t.qualityScores);
-    if (!t.j) return e;
-    t.j(e);
-  } finally {
-    da(t);
-  }
-}
-jc.prototype.close = jc.prototype.close;
-var Wc = class extends dc {
-  constructor(t, e) {
-    (super(new ac(t, e), "image_in", "norm_rect", !1),
-      (this.u = []),
-      (this.outputCategoryMask = !1),
-      (this.outputConfidenceMasks = !0),
-      (this.h = new Lo()),
-      (this.A = new bo()),
-      wn(this.h, 0, 3, this.A),
-      wn((t = this.h), 0, 1, (e = new Xs())));
-  }
-  get baseOptions() {
-    return yn(this.h, Xs, 1);
-  }
-  set baseOptions(t) {
-    wn(this.h, 0, 1, t);
-  }
-  o(t) {
-    return (
-      void 0 !== t.displayNamesLocale
-        ? Ze(this.h, 2, ue(t.displayNamesLocale))
-        : "displayNamesLocale" in t && Ze(this.h, 2),
-      "outputCategoryMask" in t &&
-        (this.outputCategoryMask = t.outputCategoryMask ?? !1),
-      "outputConfidenceMasks" in t &&
-        (this.outputConfidenceMasks = t.outputConfidenceMasks ?? !0),
-      super.l(t)
-    );
-  }
-  L() {
-    Vc(this);
-  }
-  segment(t, e, n) {
-    const r = "function" != typeof e ? e : {};
-    return (
-      (this.j = "function" == typeof e ? e : n),
-      Xc(this),
-      uc(this, t, r),
-      Hc(this)
-    );
-  }
-  La(t, e, n, r) {
-    const i = "function" != typeof n ? n : {};
-    return (
-      (this.j = "function" == typeof n ? n : r),
-      Xc(this),
-      lc(this, t, i, e),
-      Hc(this)
-    );
-  }
-  Da() {
-    return this.u;
-  }
-  m() {
-    var t = new ls();
-    (hs(t, "image_in"), hs(t, "norm_rect"));
-    const e = new Qi();
-    xr(e, Ro, this.h);
-    const n = new is();
-    (Rn(n, 2, "mediapipe.tasks.vision.image_segmenter.ImageSegmenterGraph"),
-      ns(n, "IMAGE:image_in"),
-      ns(n, "NORM_RECT:norm_rect"),
-      n.o(e),
-      cs(t, n),
-      la(this, t),
-      this.outputConfidenceMasks &&
-        (us(t, "confidence_masks"),
-        rs(n, "CONFIDENCE_MASKS:confidence_masks"),
-        fa(this, "confidence_masks"),
-        this.g.aa("confidence_masks", (t, e) => {
-          ((this.confidenceMasks = t.map((t) => fc(this, t, !0, !this.j))),
-            ua(this, e));
-        }),
-        this.g.attachEmptyPacketListener("confidence_masks", (t) => {
-          ((this.confidenceMasks = []), ua(this, t));
-        })),
-      this.outputCategoryMask &&
-        (us(t, "category_mask"),
-        rs(n, "CATEGORY_MASK:category_mask"),
-        fa(this, "category_mask"),
-        this.g.Z("category_mask", (t, e) => {
-          ((this.categoryMask = fc(this, t, !1, !this.j)), ua(this, e));
-        }),
-        this.g.attachEmptyPacketListener("category_mask", (t) => {
-          ((this.categoryMask = void 0), ua(this, t));
-        })),
-      us(t, "quality_scores"),
-      rs(n, "QUALITY_SCORES:quality_scores"),
-      this.g.attachFloatVectorListener("quality_scores", (t, e) => {
-        ((this.qualityScores = t), ua(this, e));
-      }),
-      this.g.attachEmptyPacketListener("quality_scores", (t) => {
-        ((this.categoryMask = void 0), ua(this, t));
-      }),
-      (t = t.g()),
-      this.setGraph(new Uint8Array(t), !0));
-  }
-};
-((Wc.prototype.getLabels = Wc.prototype.Da),
-  (Wc.prototype.segmentForVideo = Wc.prototype.La),
-  (Wc.prototype.segment = Wc.prototype.segment),
-  (Wc.prototype.setOptions = Wc.prototype.o),
-  (Wc.createFromModelPath = function (t, e) {
-    return cc(Wc, t, { baseOptions: { modelAssetPath: e } });
-  }),
-  (Wc.createFromModelBuffer = function (t, e) {
-    return cc(Wc, t, { baseOptions: { modelAssetBuffer: e } });
-  }),
-  (Wc.createFromOptions = function (t, e) {
-    return cc(Wc, t, e);
-  }));
-var zc = class {
-  constructor(t, e, n) {
-    ((this.confidenceMasks = t),
-      (this.categoryMask = e),
-      (this.qualityScores = n));
-  }
-  close() {
-    (this.confidenceMasks?.forEach((t) => {
-      t.close();
-    }),
-      this.categoryMask?.close());
-  }
-};
-zc.prototype.close = zc.prototype.close;
-var Kc = class extends dc {
-  constructor(t, e) {
-    (super(new ac(t, e), "image_in", "norm_rect_in", !1),
-      (this.outputCategoryMask = !1),
-      (this.outputConfidenceMasks = !0),
-      (this.h = new Lo()),
-      (this.u = new bo()),
-      wn(this.h, 0, 3, this.u),
-      wn((t = this.h), 0, 1, (e = new Xs())));
-  }
-  get baseOptions() {
-    return yn(this.h, Xs, 1);
-  }
-  set baseOptions(t) {
-    wn(this.h, 0, 1, t);
-  }
-  o(t) {
-    return (
-      "outputCategoryMask" in t &&
-        (this.outputCategoryMask = t.outputCategoryMask ?? !1),
-      "outputConfidenceMasks" in t &&
-        (this.outputConfidenceMasks = t.outputConfidenceMasks ?? !0),
-      super.l(t)
-    );
-  }
-  segment(t, e, n, r) {
-    const i = "function" != typeof n ? n : {};
-    if (
-      ((this.j = "function" == typeof n ? n : r),
-      (this.qualityScores = this.categoryMask = this.confidenceMasks = void 0),
-      (n = this.C + 1),
-      (r = new Po()),
-      e.keypoint && e.scribble)
-    )
-      throw Error("Cannot provide both keypoint and scribble.");
-    if (e.keypoint) {
-      var s = new Io();
-      (hn(s, 3, Jt(!0), !1),
-        hn(s, 1, qt(e.keypoint.x), 0),
-        hn(s, 2, qt(e.keypoint.y), 0),
-        Tn(r, 1, Co, s));
-    } else {
-      if (!e.scribble)
-        throw Error("Must provide either a keypoint or a scribble.");
-      {
-        const t = new Mo();
-        for (s of e.scribble)
-          (hn((e = new Io()), 3, Jt(!0), !1),
-            hn(e, 1, qt(s.x), 0),
-            hn(e, 2, qt(s.y), 0),
-            bn(t, 1, Io, e));
-        Tn(r, 2, Co, t);
-      }
-    }
-    (this.g.addProtoToStream(
-      r.g(),
-      "mediapipe.tasks.vision.interactive_segmenter.proto.RegionOfInterest",
-      "roi_in",
-      n,
-    ),
-      uc(this, t, i));
-    t: {
-      try {
-        const t = new zc(
-          this.confidenceMasks,
-          this.categoryMask,
-          this.qualityScores,
-        );
-        if (!this.j) {
-          var o = t;
-          break t;
-        }
-        this.j(t);
-      } finally {
-        da(this);
-      }
-      o = void 0;
-    }
-    return o;
-  }
-  m() {
-    var t = new ls();
-    (hs(t, "image_in"), hs(t, "roi_in"), hs(t, "norm_rect_in"));
-    const e = new Qi();
-    xr(e, Ro, this.h);
-    const n = new is();
-    (Rn(
-      n,
-      2,
-      "mediapipe.tasks.vision.interactive_segmenter.InteractiveSegmenterGraphV2",
-    ),
-      ns(n, "IMAGE:image_in"),
-      ns(n, "ROI:roi_in"),
-      ns(n, "NORM_RECT:norm_rect_in"),
-      n.o(e),
-      cs(t, n),
-      la(this, t),
-      this.outputConfidenceMasks &&
-        (us(t, "confidence_masks"),
-        rs(n, "CONFIDENCE_MASKS:confidence_masks"),
-        fa(this, "confidence_masks"),
-        this.g.aa("confidence_masks", (t, e) => {
-          ((this.confidenceMasks = t.map((t) => fc(this, t, !0, !this.j))),
-            ua(this, e));
-        }),
-        this.g.attachEmptyPacketListener("confidence_masks", (t) => {
-          ((this.confidenceMasks = []), ua(this, t));
-        })),
-      this.outputCategoryMask &&
-        (us(t, "category_mask"),
-        rs(n, "CATEGORY_MASK:category_mask"),
-        fa(this, "category_mask"),
-        this.g.Z("category_mask", (t, e) => {
-          ((this.categoryMask = fc(this, t, !1, !this.j)), ua(this, e));
-        }),
-        this.g.attachEmptyPacketListener("category_mask", (t) => {
-          ((this.categoryMask = void 0), ua(this, t));
-        })),
-      us(t, "quality_scores"),
-      rs(n, "QUALITY_SCORES:quality_scores"),
-      this.g.attachFloatVectorListener("quality_scores", (t, e) => {
-        ((this.qualityScores = t), ua(this, e));
-      }),
-      this.g.attachEmptyPacketListener("quality_scores", (t) => {
-        ((this.categoryMask = void 0), ua(this, t));
-      }),
-      (t = t.g()),
-      this.setGraph(new Uint8Array(t), !0));
-  }
-};
-((Kc.prototype.segment = Kc.prototype.segment),
-  (Kc.prototype.setOptions = Kc.prototype.o),
-  (Kc.createFromModelPath = function (t, e) {
-    return cc(Kc, t, { baseOptions: { modelAssetPath: e } });
-  }),
-  (Kc.createFromModelBuffer = function (t, e) {
-    return cc(Kc, t, { baseOptions: { modelAssetBuffer: e } });
-  }),
-  (Kc.createFromOptions = function (t, e) {
-    return cc(Kc, t, e);
-  }));
-var Yc = class extends dc {
-  constructor(t, e) {
-    (super(new ac(t, e), "input_frame_gpu", "norm_rect", !1),
-      (this.j = { detections: [] }),
-      wn((t = this.h = new Oo()), 0, 1, (e = new Xs())));
-  }
-  get baseOptions() {
-    return yn(this.h, Xs, 1);
-  }
-  set baseOptions(t) {
-    wn(this.h, 0, 1, t);
-  }
-  o(t) {
-    return (
-      void 0 !== t.displayNamesLocale
-        ? Ze(this.h, 2, ue(t.displayNamesLocale))
-        : "displayNamesLocale" in t && Ze(this.h, 2),
-      void 0 !== t.maxResults
-        ? xn(this.h, 3, t.maxResults)
-        : "maxResults" in t && Ze(this.h, 3),
-      void 0 !== t.scoreThreshold
-        ? Ln(this.h, 4, t.scoreThreshold)
-        : "scoreThreshold" in t && Ze(this.h, 4),
-      void 0 !== t.categoryAllowlist
-        ? In(this.h, 5, t.categoryAllowlist)
-        : "categoryAllowlist" in t && Ze(this.h, 5),
-      void 0 !== t.categoryDenylist
-        ? In(this.h, 6, t.categoryDenylist)
-        : "categoryDenylist" in t && Ze(this.h, 6),
-      this.l(t)
-    );
-  }
-  F(t, e) {
-    return ((this.j = { detections: [] }), uc(this, t, e), this.j);
-  }
-  G(t, e, n) {
-    return ((this.j = { detections: [] }), lc(this, t, n, e), this.j);
-  }
-  m() {
-    var t = new ls();
-    (hs(t, "input_frame_gpu"), hs(t, "norm_rect"), us(t, "detections"));
-    const e = new Qi();
-    xr(e, No, this.h);
-    const n = new is();
-    (Rn(n, 2, "mediapipe.tasks.vision.ObjectDetectorGraph"),
-      ns(n, "IMAGE:input_frame_gpu"),
-      ns(n, "NORM_RECT:norm_rect"),
-      rs(n, "DETECTIONS:detections"),
-      n.o(e),
-      cs(t, n),
-      this.g.attachProtoVectorListener("detections", (t, e) => {
-        for (const e of t) ((t = ws(e)), this.j.detections.push(Xo(t)));
-        ua(this, e);
-      }),
-      this.g.attachEmptyPacketListener("detections", (t) => {
-        ua(this, t);
-      }),
-      (t = t.g()),
-      this.setGraph(new Uint8Array(t), !0));
-  }
-};
-((Yc.prototype.detectForVideo = Yc.prototype.G),
-  (Yc.prototype.detect = Yc.prototype.F),
-  (Yc.prototype.setOptions = Yc.prototype.o),
-  (Yc.createFromModelPath = async function (t, e) {
-    return cc(Yc, t, { baseOptions: { modelAssetPath: e } });
-  }),
-  (Yc.createFromModelBuffer = function (t, e) {
-    return cc(Yc, t, { baseOptions: { modelAssetBuffer: e } });
-  }),
-  (Yc.createFromOptions = function (t, e) {
-    return cc(Yc, t, e);
-  }));
-var qc = class {
-  constructor(t, e, n) {
-    ((this.landmarks = t),
-      (this.worldLandmarks = e),
-      (this.segmentationMasks = n));
-  }
-  close() {
-    this.segmentationMasks?.forEach((t) => {
-      t.close();
-    });
-  }
-};
-function $c(t) {
-  ((t.landmarks = []), (t.worldLandmarks = []), (t.segmentationMasks = void 0));
-}
-function Jc(t) {
-  try {
-    const e = new qc(t.landmarks, t.worldLandmarks, t.segmentationMasks);
-    if (!t.u) return e;
-    t.u(e);
-  } finally {
-    da(t);
-  }
-}
-qc.prototype.close = qc.prototype.close;
-var Zc = class extends dc {
-  constructor(t, e) {
-    (super(new ac(t, e), "image_in", "norm_rect", !1),
-      (this.landmarks = []),
-      (this.worldLandmarks = []),
-      (this.outputSegmentationMasks = !1),
-      wn((t = this.h = new Uo()), 0, 1, (e = new Xs())),
-      (this.A = new mo()),
-      wn(this.h, 0, 3, this.A),
-      (this.j = new go()),
-      wn(this.h, 0, 2, this.j),
-      xn(this.j, 4, 1),
-      Ln(this.j, 2, 0.5),
-      Ln(this.A, 2, 0.5),
-      Ln(this.h, 4, 0.5));
-  }
-  get baseOptions() {
-    return yn(this.h, Xs, 1);
-  }
-  set baseOptions(t) {
-    wn(this.h, 0, 1, t);
-  }
-  o(t) {
-    return (
-      "numPoses" in t && xn(this.j, 4, t.numPoses ?? 1),
-      "minPoseDetectionConfidence" in t &&
-        Ln(this.j, 2, t.minPoseDetectionConfidence ?? 0.5),
-      "minTrackingConfidence" in t &&
-        Ln(this.h, 4, t.minTrackingConfidence ?? 0.5),
-      "minPosePresenceConfidence" in t &&
-        Ln(this.A, 2, t.minPosePresenceConfidence ?? 0.5),
-      "outputSegmentationMasks" in t &&
-        (this.outputSegmentationMasks = t.outputSegmentationMasks ?? !1),
-      this.l(t)
-    );
-  }
-  F(t, e, n) {
-    const r = "function" != typeof e ? e : {};
-    return (
-      (this.u = "function" == typeof e ? e : n),
-      $c(this),
-      uc(this, t, r),
-      Jc(this)
-    );
-  }
-  G(t, e, n, r) {
-    const i = "function" != typeof n ? n : {};
-    return (
-      (this.u = "function" == typeof n ? n : r),
-      $c(this),
-      lc(this, t, i, e),
-      Jc(this)
-    );
-  }
-  m() {
-    var t = new ls();
-    (hs(t, "image_in"),
-      hs(t, "norm_rect"),
-      us(t, "normalized_landmarks"),
-      us(t, "world_landmarks"),
-      us(t, "segmentation_masks"));
-    const e = new Qi();
-    xr(e, Do, this.h);
-    const n = new is();
-    (Rn(n, 2, "mediapipe.tasks.vision.pose_landmarker.PoseLandmarkerGraph"),
-      ns(n, "IMAGE:image_in"),
-      ns(n, "NORM_RECT:norm_rect"),
-      rs(n, "NORM_LANDMARKS:normalized_landmarks"),
-      rs(n, "WORLD_LANDMARKS:world_landmarks"),
-      n.o(e),
-      cs(t, n),
-      la(this, t),
-      this.g.attachProtoVectorListener("normalized_landmarks", (t, e) => {
-        this.landmarks = [];
-        for (const e of t) ((t = ks(e)), this.landmarks.push(Ho(t)));
-        ua(this, e);
-      }),
-      this.g.attachEmptyPacketListener("normalized_landmarks", (t) => {
-        ((this.landmarks = []), ua(this, t));
-      }),
-      this.g.attachProtoVectorListener("world_landmarks", (t, e) => {
-        this.worldLandmarks = [];
-        for (const e of t) ((t = As(e)), this.worldLandmarks.push(Wo(t)));
-        ua(this, e);
-      }),
-      this.g.attachEmptyPacketListener("world_landmarks", (t) => {
-        ((this.worldLandmarks = []), ua(this, t));
-      }),
-      this.outputSegmentationMasks &&
-        (rs(n, "SEGMENTATION_MASK:segmentation_masks"),
-        fa(this, "segmentation_masks"),
-        this.g.aa("segmentation_masks", (t, e) => {
-          ((this.segmentationMasks = t.map((t) => fc(this, t, !0, !this.u))),
-            ua(this, e));
-        }),
-        this.g.attachEmptyPacketListener("segmentation_masks", (t) => {
-          ((this.segmentationMasks = []), ua(this, t));
-        })),
-      (t = t.g()),
-      this.setGraph(new Uint8Array(t), !0));
-  }
-};
-((Zc.prototype.detectForVideo = Zc.prototype.G),
-  (Zc.prototype.detect = Zc.prototype.F),
-  (Zc.prototype.setOptions = Zc.prototype.o),
-  (Zc.createFromModelPath = function (t, e) {
-    return cc(Zc, t, { baseOptions: { modelAssetPath: e } });
-  }),
-  (Zc.createFromModelBuffer = function (t, e) {
-    return cc(Zc, t, { baseOptions: { modelAssetBuffer: e } });
-  }),
-  (Zc.createFromOptions = function (t, e) {
-    return cc(Zc, t, e);
-  }),
-  (Zc.POSE_CONNECTIONS = Cc));
-export {
-  Ka as DrawingUtils,
-  pc as FaceDetector,
-  Sc as FaceLandmarker,
-  Zo as FilesetResolver,
-  Fc as GestureRecognizer,
-  Pc as HandLandmarker,
-  Dc as HolisticLandmarker,
-  Bc as ImageClassifier,
-  Gc as ImageEmbedder,
-  Wc as ImageSegmenter,
-  jc as ImageSegmenterResult,
-  Kc as InteractiveSegmenter,
-  zc as InteractiveSegmenterResult,
-  nc as MPImage,
-  Na as MPMask,
-  Yc as ObjectDetector,
-  Zc as PoseLandmarker,
-  pa as TaskRunner,
-  dc as VisionTaskRunner,
-};
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This leaks resources."))}Ea(){return!!Ya(this,0)}la(){return!!Ya(this,1)}R(){return!!Ya(this,2)}Ca(){return qa(this)}Ba(){var t=Ya(this,1);return t||($a(this),Qa(this),t=ec(this),tc(this),this.g.push(t),this.j=!0),t}N(){return $a(this)}clone(){const t=[];for(const e of this.g){let n;if(e instanceof ImageData)n=new ImageData(e.data,this.width,this.height);else if(e instanceof WebGLTexture){const t=Ja(this),e=Za(this);t.activeTexture(t.TEXTURE1),n=wa(e,t),t.bindTexture(t.TEXTURE_2D,n),t.texImage2D(t.TEXTURE_2D,0,t.RGBA,this.width,this.height,0,t.RGBA,t.UNSIGNED_BYTE,null),t.bindTexture(t.TEXTURE_2D,null),Ta(e,t,n),Ea(e,t,!1,(()=>{Qa(this),t.clearColor(0,0,0,0),t.clear(t.COLOR_BUFFER_BIT),t.drawArrays(t.TRIANGLE_FAN,0,4),tc(this)})),Aa(e),tc(this)}else{if(!(e instanceof ImageBitmap))throw Error(`Type is not supported: ${e}`);$a(this),Qa(this),n=ec(this),tc(this)}t.push(n)}return new nc(t,this.la(),this.R(),this.canvas,this.l,this.width,this.height)}close(){this.j&&Ya(this,1).close(),this.m&&Ja(this).deleteTexture(Ya(this,2)),rc=-1}};nc.prototype.close=nc.prototype.close,nc.prototype.clone=nc.prototype.clone,nc.prototype.getAsWebGLTexture=nc.prototype.N,nc.prototype.getAsImageBitmap=nc.prototype.Ba,nc.prototype.getAsImageData=nc.prototype.Ca,nc.prototype.hasWebGLTexture=nc.prototype.R,nc.prototype.hasImageBitmap=nc.prototype.la,nc.prototype.hasImageData=nc.prototype.Ea;var rc=250;function ic(...t){return t.map((([t,e])=>({start:t,end:e})))}const sc=function(t){return class extends t{Ja(){this.i._registerModelResourcesGraphService()}}}((oc=class{constructor(t,e){this.l=!0,this.i=t,this.g=null,this.h=0,this.m="function"==typeof this.i._addIntToInputStream,void 0!==e?this.i.canvas=e:Qo()?this.i.canvas=new OffscreenCanvas(1,1):(console.warn("OffscreenCanvas not supported and GraphRunner constructor glCanvas parameter is undefined. Creating backup canvas."),this.i.canvas=document.createElement("canvas"))}async initializeGraph(t){const e=await(await fetch(t)).arrayBuffer();t=!(t.endsWith(".pbtxt")||t.endsWith(".textproto")),this.setGraph(new Uint8Array(e),t)}setGraphFromString(t){this.setGraph((new TextEncoder).encode(t),!1)}setGraph(t,e){const n=t.length,r=this.i._malloc(n);this.i.HEAPU8.set(t,r),e?this.i._changeBinaryGraph(n,r):this.i._changeTextGraph(n,r),this.i._free(r)}configureAudio(t,e,n,r,i){this.i._configureAudio||console.warn('Attempting to use configureAudio without support for input audio. Is build dep ":gl_graph_runner_audio" missing?'),na(this,r||"input_audio",(r=>{na(this,i=i||"audio_header",(i=>{this.i._configureAudio(r,i,t,e??0,n)}))}))}setAutoResizeCanvas(t){this.l=t}setAutoRenderToScreen(t){this.i._setAutoRenderToScreen(t)}setGpuBufferVerticalFlip(t){this.i.gpuOriginForWebTexturesIsBottomLeft=t}ca(t){sa(this,"__graph_config__",(e=>{t(e)})),na(this,"__graph_config__",(t=>{this.i._getGraphConfig(t,void 0)})),delete this.i.simpleListeners.__graph_config__}attachErrorListener(t){this.i.errorListener=t}attachEmptyPacketListener(t,e){this.i.emptyPacketListeners=this.i.emptyPacketListeners||{},this.i.emptyPacketListeners[t]=e}addAudioToStream(t,e,n){this.addAudioToStreamWithShape(t,0,0,e,n)}addAudioToStreamWithShape(t,e,n,r,i){const s=4*t.length;this.h!==s&&(this.g&&this.i._free(this.g),this.g=this.i._malloc(s),this.h=s),this.i.HEAPF32.set(t,this.g/4),na(this,r,(t=>{this.i._addAudioToInputStream(this.g,e,n,t,i)}))}addGpuBufferToStream(t,e,n){na(this,e,(e=>{const[r,i]=ra(this,t,e);this.i._addBoundTextureToStream(e,r,i,n)}))}addBoolToStream(t,e,n){na(this,e,(e=>{this.i._addBoolToInputStream(t,e,n)}))}addDoubleToStream(t,e,n){na(this,e,(e=>{this.i._addDoubleToInputStream(t,e,n)}))}addFloatToStream(t,e,n){na(this,e,(e=>{this.i._addFloatToInputStream(t,e,n)}))}addIntToStream(t,e,n){na(this,e,(e=>{this.i._addIntToInputStream(t,e,n)}))}addUintToStream(t,e,n){na(this,e,(e=>{this.i._addUintToInputStream(t,e,n)}))}addStringToStream(t,e,n){na(this,e,(e=>{na(this,t,(t=>{this.i._addStringToInputStream(t,e,n)}))}))}addStringRecordToStream(t,e,n){na(this,e,(e=>{ia(this,Object.keys(t),(r=>{ia(this,Object.values(t),(i=>{this.i._addFlatHashMapToInputStream(r,i,Object.keys(t).length,e,n)}))}))}))}addProtoToStream(t,e,n,r){na(this,n,(n=>{na(this,e,(e=>{const i=this.i._malloc(t.length);this.i.HEAPU8.set(t,i),this.i._addProtoToInputStream(i,t.length,e,n,r),this.i._free(i)}))}))}addEmptyPacketToStream(t,e){na(this,t,(t=>{this.i._addEmptyPacketToInputStream(t,e)}))}addBoolVectorToStream(t,e,n){na(this,e,(e=>{const r=this.i._allocateBoolVector(t.length);if(!r)throw Error("Unable to allocate new bool vector on heap.");for(const e of t)this.i._addBoolVectorEntry(r,e);this.i._addBoolVectorToInputStream(r,e,n)}))}addDoubleVectorToStream(t,e,n){na(this,e,(e=>{const r=this.i._allocateDoubleVector(t.length);if(!r)throw Error("Unable to allocate new double vector on heap.");for(const e of t)this.i._addDoubleVectorEntry(r,e);this.i._addDoubleVectorToInputStream(r,e,n)}))}addFloatVectorToStream(t,e,n){na(this,e,(e=>{const r=this.i._allocateFloatVector(t.length);if(!r)throw Error("Unable to allocate new float vector on heap.");for(const e of t)this.i._addFloatVectorEntry(r,e);this.i._addFloatVectorToInputStream(r,e,n)}))}addIntVectorToStream(t,e,n){na(this,e,(e=>{const r=this.i._allocateIntVector(t.length);if(!r)throw Error("Unable to allocate new int vector on heap.");for(const e of t)this.i._addIntVectorEntry(r,e);this.i._addIntVectorToInputStream(r,e,n)}))}addUintVectorToStream(t,e,n){na(this,e,(e=>{const r=this.i._allocateUintVector(t.length);if(!r)throw Error("Unable to allocate new unsigned int vector on heap.");for(const e of t)this.i._addUintVectorEntry(r,e);this.i._addUintVectorToInputStream(r,e,n)}))}addStringVectorToStream(t,e,n){na(this,e,(e=>{const r=this.i._allocateStringVector(t.length);if(!r)throw Error("Unable to allocate new string vector on heap.");for(const e of t)na(this,e,(t=>{this.i._addStringVectorEntry(r,t)}));this.i._addStringVectorToInputStream(r,e,n)}))}addBoolToInputSidePacket(t,e){na(this,e,(e=>{this.i._addBoolToInputSidePacket(t,e)}))}addDoubleToInputSidePacket(t,e){na(this,e,(e=>{this.i._addDoubleToInputSidePacket(t,e)}))}addFloatToInputSidePacket(t,e){na(this,e,(e=>{this.i._addFloatToInputSidePacket(t,e)}))}addIntToInputSidePacket(t,e){na(this,e,(e=>{this.i._addIntToInputSidePacket(t,e)}))}addUintToInputSidePacket(t,e){na(this,e,(e=>{this.i._addUintToInputSidePacket(t,e)}))}addStringToInputSidePacket(t,e){na(this,e,(e=>{na(this,t,(t=>{this.i._addStringToInputSidePacket(t,e)}))}))}addProtoToInputSidePacket(t,e,n){na(this,n,(n=>{na(this,e,(e=>{const r=this.i._malloc(t.length);this.i.HEAPU8.set(t,r),this.i._addProtoToInputSidePacket(r,t.length,e,n),this.i._free(r)}))}))}addBoolVectorToInputSidePacket(t,e){na(this,e,(e=>{const n=this.i._allocateBoolVector(t.length);if(!n)throw Error("Unable to allocate new bool vector on heap.");for(const e of t)this.i._addBoolVectorEntry(n,e);this.i._addBoolVectorToInputSidePacket(n,e)}))}addDoubleVectorToInputSidePacket(t,e){na(this,e,(e=>{const n=this.i._allocateDoubleVector(t.length);if(!n)throw Error("Unable to allocate new double vector on heap.");for(const e of t)this.i._addDoubleVectorEntry(n,e);this.i._addDoubleVectorToInputSidePacket(n,e)}))}addFloatVectorToInputSidePacket(t,e){na(this,e,(e=>{const n=this.i._allocateFloatVector(t.length);if(!n)throw Error("Unable to allocate new float vector on heap.");for(const e of t)this.i._addFloatVectorEntry(n,e);this.i._addFloatVectorToInputSidePacket(n,e)}))}addIntVectorToInputSidePacket(t,e){na(this,e,(e=>{const n=this.i._allocateIntVector(t.length);if(!n)throw Error("Unable to allocate new int vector on heap.");for(const e of t)this.i._addIntVectorEntry(n,e);this.i._addIntVectorToInputSidePacket(n,e)}))}addUintVectorToInputSidePacket(t,e){na(this,e,(e=>{const n=this.i._allocateUintVector(t.length);if(!n)throw Error("Unable to allocate new unsigned int vector on heap.");for(const e of t)this.i._addUintVectorEntry(n,e);this.i._addUintVectorToInputSidePacket(n,e)}))}addStringVectorToInputSidePacket(t,e){na(this,e,(e=>{const n=this.i._allocateStringVector(t.length);if(!n)throw Error("Unable to allocate new string vector on heap.");for(const e of t)na(this,e,(t=>{this.i._addStringVectorEntry(n,t)}));this.i._addStringVectorToInputSidePacket(n,e)}))}attachBoolListener(t,e){sa(this,t,e),na(this,t,(t=>{this.i._attachBoolListener(t)}))}attachBoolVectorListener(t,e){oa(this,t,e),na(this,t,(t=>{this.i._attachBoolVectorListener(t)}))}attachIntListener(t,e){sa(this,t,e),na(this,t,(t=>{this.i._attachIntListener(t)}))}attachIntVectorListener(t,e){oa(this,t,e),na(this,t,(t=>{this.i._attachIntVectorListener(t)}))}attachUintListener(t,e){sa(this,t,e),na(this,t,(t=>{this.i._attachUintListener(t)}))}attachUintVectorListener(t,e){oa(this,t,e),na(this,t,(t=>{this.i._attachUintVectorListener(t)}))}attachDoubleListener(t,e){sa(this,t,e),na(this,t,(t=>{this.i._attachDoubleListener(t)}))}attachDoubleVectorListener(t,e){oa(this,t,e),na(this,t,(t=>{this.i._attachDoubleVectorListener(t)}))}attachFloatListener(t,e){sa(this,t,e),na(this,t,(t=>{this.i._attachFloatListener(t)}))}attachFloatVectorListener(t,e){oa(this,t,e),na(this,t,(t=>{this.i._attachFloatVectorListener(t)}))}attachStringListener(t,e){sa(this,t,e),na(this,t,(t=>{this.i._attachStringListener(t)}))}attachStringVectorListener(t,e){oa(this,t,e),na(this,t,(t=>{this.i._attachStringVectorListener(t)}))}attachProtoListener(t,e,n){sa(this,t,e),na(this,t,(t=>{this.i._attachProtoListener(t,n||!1)}))}attachProtoVectorListener(t,e,n){oa(this,t,e),na(this,t,(t=>{this.i._attachProtoVectorListener(t,n||!1)}))}attachAudioListener(t,e,n){this.i._attachAudioListener||console.warn('Attempting to use attachAudioListener without support for output audio. Is build dep ":gl_graph_runner_audio_out" missing?'),sa(this,t,((t,n)=>{t=new Float32Array(t.buffer,t.byteOffset,t.length/4),e(t,n)})),na(this,t,(t=>{this.i._attachAudioListener(t,n||!1)}))}finishProcessing(){this.i._waitUntilIdle()}closeGraph(){this.i._closeGraph(),this.i.simpleListeners=void 0,this.i.emptyPacketListeners=void 0}},class extends oc{get ga(){return this.i}pa(t,e,n){na(this,e,(e=>{const[r,i]=ra(this,t,e);this.ga._addBoundTextureAsImageToStream(e,r,i,n)}))}Z(t,e){sa(this,t,e),na(this,t,(t=>{this.ga._attachImageListener(t)}))}aa(t,e){oa(this,t,e),na(this,t,(t=>{this.ga._attachImageVectorListener(t)}))}}));var oc,ac=class extends sc{};async function cc(t,e,n){return async function(t,e,n,r){return aa(t,e,n,r)}(t,n.canvas??(Qo()?void 0:document.createElement("canvas")),e,n)}function hc(t,e,n,r){if(t.U){const s=new Ls;if(n?.regionOfInterest){if(!t.oa)throw Error("This task doesn't support region-of-interest.");var i=n.regionOfInterest;if(i.left>=i.right||i.top>=i.bottom)throw Error("Expected RectF with left < right and top < bottom.");if(i.left<0||i.top<0||i.right>1||i.bottom>1)throw Error("Expected RectF values to be in [0,1].");Ln(s,1,(i.left+i.right)/2),Ln(s,2,(i.top+i.bottom)/2),Ln(s,4,i.right-i.left),Ln(s,3,i.bottom-i.top)}else Ln(s,1,.5),Ln(s,2,.5),Ln(s,4,1),Ln(s,3,1);if(n?.rotationDegrees){if(n?.rotationDegrees%90!=0)throw Error("Expected rotation to be a multiple of 90°.");if(Ln(s,5,-Math.PI*n.rotationDegrees/180),n?.rotationDegrees%180!=0){const[t,r]=ea(e);n=Sn(s,3)*r/t,i=Sn(s,4)*t/r,Ln(s,4,n),Ln(s,3,i)}}t.g.addProtoToStream(s.g(),"mediapipe.NormalizedRect",t.U,r)}t.g.pa(e,t.X,r??performance.now()),t.finishProcessing()}function uc(t,e,n){if(t.baseOptions?.g())throw Error("Task is not initialized with image mode. 'runningMode' must be set to 'IMAGE'.");hc(t,e,n,t.C+1)}function lc(t,e,n,r){if(!t.baseOptions?.g())throw Error("Task is not initialized with video mode. 'runningMode' must be set to 'VIDEO'.");hc(t,e,n,r)}function fc(t,e,n,r){var i=e.data;const s=e.width,o=s*(e=e.height);if((i instanceof Uint8Array||i instanceof Float32Array)&&i.length!==o)throw Error("Unsupported channel count: "+i.length/o);return t=new Na([i],n,!1,t.g.i.canvas,t.P,s,e),r?t.clone():t}var dc=class extends pa{constructor(t,e,n,r){super(t),this.g=t,this.X=e,this.U=n,this.oa=r,this.P=new ba}l(t,e=!0){if("runningMode"in t&&Ze(this.baseOptions,2,Jt(!!t.runningMode&&"IMAGE"!==t.runningMode)),void 0!==t.canvas&&this.g.i.canvas!==t.canvas)throw Error("You must create a new task to reset the canvas.");return super.l(t,e)}close(){this.P.close(),super.close()}};dc.prototype.close=dc.prototype.close;var pc=class extends dc{constructor(t,e){super(new ac(t,e),"image_in","norm_rect_in",!1),this.j={detections:[]},wn(t=this.h=new zs,0,1,e=new Xs),Ln(this.h,2,.5),Ln(this.h,3,.3)}get baseOptions(){return yn(this.h,Xs,1)}set 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t)t=ys(e),this.j.faceBlendshapes.push(jo(t.g()??[]));ua(this,e)})),this.g.attachEmptyPacketListener("blendshapes",(t=>{ua(this,t)}))),this.outputFacialTransformationMatrixes&&(us(t,"face_geometry"),rs(n,"FACE_GEOMETRY:face_geometry"),this.g.attachProtoVectorListener("face_geometry",((t,e)=>{if(this.outputFacialTransformationMatrixes)for(const e of t)(t=yn(t=qs(e),Ss,2))&&this.j.facialTransformationMatrixes.push({rows:kn(t,1)??0??0,columns:kn(t,2)??0??0,data:en(t,3,$t,tn()).slice()??[]});ua(this,e)})),this.g.attachEmptyPacketListener("face_geometry",(t=>{ua(this,t)}))),t=t.g(),this.setGraph(new Uint8Array(t),!0)}};Sc.prototype.detectForVideo=Sc.prototype.G,Sc.prototype.detect=Sc.prototype.F,Sc.prototype.setOptions=Sc.prototype.o,Sc.createFromModelPath=function(t,e){return cc(Sc,t,{baseOptions:{modelAssetPath:e}})},Sc.createFromModelBuffer=function(t,e){return cc(Sc,t,{baseOptions:{modelAssetBuffer:e}})},Sc.createFromOptions=function(t,e){return cc(Sc,t,e)},Sc.FACE_LANDMARKS_LIPS=gc,Sc.FACE_LANDMARKS_LEFT_EYE=mc,Sc.FACE_LANDMARKS_LEFT_EYEBROW=yc,Sc.FACE_LANDMARKS_LEFT_IRIS=_c,Sc.FACE_LANDMARKS_RIGHT_EYE=vc,Sc.FACE_LANDMARKS_RIGHT_EYEBROW=Ec,Sc.FACE_LANDMARKS_RIGHT_IRIS=wc,Sc.FACE_LANDMARKS_FACE_OVAL=Tc,Sc.FACE_LANDMARKS_CONTOURS=Ac,Sc.FACE_LANDMARKS_TESSELATION=bc;var xc=ic([0,1],[1,2],[2,3],[3,4],[0,5],[5,6],[6,7],[7,8],[5,9],[9,10],[10,11],[11,12],[9,13],[13,14],[14,15],[15,16],[13,17],[0,17],[17,18],[18,19],[19,20]);function Lc(t){t.gestures=[],t.landmarks=[],t.worldLandmarks=[],t.handedness=[]}function Rc(t){return 0===t.gestures.length?{gestures:[],landmarks:[],worldLandmarks:[],handedness:[],handednesses:[]}:{gestures:t.gestures,landmarks:t.landmarks,worldLandmarks:t.worldLandmarks,handedness:t.handedness,handednesses:t.handedness}}function Ic(t,e=!0){const n=[];for(const i of t){var r=ys(i);t=[];for(const n of r.g())r=e&&null!=kn(n,1)?kn(n,1)??0:-1,t.push({score:Sn(n,2)??0,index:r,categoryName:le($e(n,3))??""??"",displayName:le($e(n,4))??""??""});n.push(t)}return n}var Fc=class extends dc{constructor(t,e){super(new ac(t,e),"image_in","norm_rect",!1),this.gestures=[],this.landmarks=[],this.worldLandmarks=[],this.handedness=[],wn(t=this.j=new oo,0,1,e=new Xs),this.u=new so,wn(this.j,0,2,this.u),this.D=new io,wn(this.u,0,3,this.D),this.A=new ro,wn(this.u,0,2,this.A),this.h=new no,wn(this.j,0,3,this.h),Ln(this.A,2,.5),Ln(this.u,4,.5),Ln(this.D,2,.5)}get baseOptions(){return yn(this.j,Xs,1)}set baseOptions(t){wn(this.j,0,1,t)}o(t){if(xn(this.A,3,t.numHands??1),"minHandDetectionConfidence"in t&&Ln(this.A,2,t.minHandDetectionConfidence??.5),"minTrackingConfidence"in t&&Ln(this.u,4,t.minTrackingConfidence??.5),"minHandPresenceConfidence"in t&&Ln(this.D,2,t.minHandPresenceConfidence??.5),t.cannedGesturesClassifierOptions){var e=new to,n=e,r=Bo(t.cannedGesturesClassifierOptions,yn(this.h,to,3)?.l());wn(n,0,2,r),wn(this.h,0,3,e)}else void 0===t.cannedGesturesClassifierOptions&&yn(this.h,to,3)?.g();return t.customGesturesClassifierOptions?(wn(n=e=new to,0,2,r=Bo(t.customGesturesClassifierOptions,yn(this.h,to,4)?.l())),wn(this.h,0,4,e)):void 0===t.customGesturesClassifierOptions&&yn(this.h,to,4)?.g(),this.l(t)}Ha(t,e){return Lc(this),uc(this,t,e),Rc(this)}Ia(t,e,n){return Lc(this),lc(this,t,n,e),Rc(this)}m(){var t=new ls;hs(t,"image_in"),hs(t,"norm_rect"),us(t,"hand_gestures"),us(t,"hand_landmarks"),us(t,"world_hand_landmarks"),us(t,"handedness");const e=new Qi;xr(e,lo,this.j);const n=new is;Rn(n,2,"mediapipe.tasks.vision.gesture_recognizer.GestureRecognizerGraph"),ns(n,"IMAGE:image_in"),ns(n,"NORM_RECT:norm_rect"),rs(n,"HAND_GESTURES:hand_gestures"),rs(n,"LANDMARKS:hand_landmarks"),rs(n,"WORLD_LANDMARKS:world_hand_landmarks"),rs(n,"HANDEDNESS:handedness"),n.o(e),cs(t,n),this.g.attachProtoVectorListener("hand_landmarks",((t,e)=>{for(const e of t){t=ks(e);const n=[];for(const e of vn(t,bs,1))n.push({x:Sn(e,1)??0,y:Sn(e,2)??0,z:Sn(e,3)??0,visibility:Sn(e,4)??0});this.landmarks.push(n)}ua(this,e)})),this.g.attachEmptyPacketListener("hand_landmarks",(t=>{ua(this,t)})),this.g.attachProtoVectorListener("world_hand_landmarks",((t,e)=>{for(const e of t){t=As(e);const n=[];for(const e of vn(t,Ts,1))n.push({x:Sn(e,1)??0,y:Sn(e,2)??0,z:Sn(e,3)??0,visibility:Sn(e,4)??0});this.worldLandmarks.push(n)}ua(this,e)})),this.g.attachEmptyPacketListener("world_hand_landmarks",(t=>{ua(this,t)})),this.g.attachProtoVectorListener("hand_gestures",((t,e)=>{this.gestures.push(...Ic(t,!1)),ua(this,e)})),this.g.attachEmptyPacketListener("hand_gestures",(t=>{ua(this,t)})),this.g.attachProtoVectorListener("handedness",((t,e)=>{this.handedness.push(...Ic(t)),ua(this,e)})),this.g.attachEmptyPacketListener("handedness",(t=>{ua(this,t)})),t=t.g(),this.setGraph(new Uint8Array(t),!0)}};function Mc(t){return{landmarks:t.landmarks,worldLandmarks:t.worldLandmarks,handednesses:t.handedness,handedness:t.handedness}}Fc.prototype.recognizeForVideo=Fc.prototype.Ia,Fc.prototype.recognize=Fc.prototype.Ha,Fc.prototype.setOptions=Fc.prototype.o,Fc.createFromModelPath=function(t,e){return cc(Fc,t,{baseOptions:{modelAssetPath:e}})},Fc.createFromModelBuffer=function(t,e){return cc(Fc,t,{baseOptions:{modelAssetBuffer:e}})},Fc.createFromOptions=function(t,e){return cc(Fc,t,e)},Fc.HAND_CONNECTIONS=xc;var Pc=class extends dc{constructor(t,e){super(new ac(t,e),"image_in","norm_rect",!1),this.landmarks=[],this.worldLandmarks=[],this.handedness=[],wn(t=this.h=new so,0,1,e=new Xs),this.u=new io,wn(this.h,0,3,this.u),this.j=new ro,wn(this.h,0,2,this.j),xn(this.j,3,1),Ln(this.j,2,.5),Ln(this.u,2,.5),Ln(this.h,4,.5)}get baseOptions(){return yn(this.h,Xs,1)}set baseOptions(t){wn(this.h,0,1,t)}o(t){return"numHands"in t&&xn(this.j,3,t.numHands??1),"minHandDetectionConfidence"in t&&Ln(this.j,2,t.minHandDetectionConfidence??.5),"minTrackingConfidence"in t&&Ln(this.h,4,t.minTrackingConfidence??.5),"minHandPresenceConfidence"in t&&Ln(this.u,2,t.minHandPresenceConfidence??.5),this.l(t)}F(t,e){return this.landmarks=[],this.worldLandmarks=[],this.handedness=[],uc(this,t,e),Mc(this)}G(t,e,n){return this.landmarks=[],this.worldLandmarks=[],this.handedness=[],lc(this,t,n,e),Mc(this)}m(){var t=new ls;hs(t,"image_in"),hs(t,"norm_rect"),us(t,"hand_landmarks"),us(t,"world_hand_landmarks"),us(t,"handedness");const e=new Qi;xr(e,fo,this.h);const n=new is;Rn(n,2,"mediapipe.tasks.vision.hand_landmarker.HandLandmarkerGraph"),ns(n,"IMAGE:image_in"),ns(n,"NORM_RECT:norm_rect"),rs(n,"LANDMARKS:hand_landmarks"),rs(n,"WORLD_LANDMARKS:world_hand_landmarks"),rs(n,"HANDEDNESS:handedness"),n.o(e),cs(t,n),this.g.attachProtoVectorListener("hand_landmarks",((t,e)=>{for(const e of t)t=ks(e),this.landmarks.push(Ho(t));ua(this,e)})),this.g.attachEmptyPacketListener("hand_landmarks",(t=>{ua(this,t)})),this.g.attachProtoVectorListener("world_hand_landmarks",((t,e)=>{for(const e of t)t=As(e),this.worldLandmarks.push(Wo(t));ua(this,e)})),this.g.attachEmptyPacketListener("world_hand_landmarks",(t=>{ua(this,t)})),this.g.attachProtoVectorListener("handedness",((t,e)=>{var n=this.handedness,r=n.push;const i=[];for(const e of t){t=ys(e);const n=[];for(const e of t.g())n.push({score:Sn(e,2)??0,index:kn(e,1)??0??-1,categoryName:le($e(e,3))??""??"",displayName:le($e(e,4))??""??""});i.push(n)}r.call(n,...i),ua(this,e)})),this.g.attachEmptyPacketListener("handedness",(t=>{ua(this,t)})),t=t.g(),this.setGraph(new Uint8Array(t),!0)}};Pc.prototype.detectForVideo=Pc.prototype.G,Pc.prototype.detect=Pc.prototype.F,Pc.prototype.setOptions=Pc.prototype.o,Pc.createFromModelPath=function(t,e){return cc(Pc,t,{baseOptions:{modelAssetPath:e}})},Pc.createFromModelBuffer=function(t,e){return cc(Pc,t,{baseOptions:{modelAssetBuffer:e}})},Pc.createFromOptions=function(t,e){return cc(Pc,t,e)},Pc.HAND_CONNECTIONS=xc;var Cc=ic([0,1],[1,2],[2,3],[3,7],[0,4],[4,5],[5,6],[6,8],[9,10],[11,12],[11,13],[13,15],[15,17],[15,19],[15,21],[17,19],[12,14],[14,16],[16,18],[16,20],[16,22],[18,20],[11,23],[12,24],[23,24],[23,25],[24,26],[25,27],[26,28],[27,29],[28,30],[29,31],[30,32],[27,31],[28,32]);function Oc(t){t.h={faceLandmarks:[],faceBlendshapes:[],poseLandmarks:[],poseWorldLandmarks:[],poseSegmentationMasks:[],leftHandLandmarks:[],leftHandWorldLandmarks:[],rightHandLandmarks:[],rightHandWorldLandmarks:[]}}function Nc(t){try{if(!t.D)return t.h;t.D(t.h)}finally{da(t)}}function Uc(t,e){t=ks(t),e.push(Ho(t))}var Dc=class extends dc{constructor(t,e){super(new ac(t,e),"input_frames_image",null,!1),this.h={faceLandmarks:[],faceBlendshapes:[],poseLandmarks:[],poseWorldLandmarks:[],poseSegmentationMasks:[],leftHandLandmarks:[],leftHandWorldLandmarks:[],rightHandLandmarks:[],rightHandWorldLandmarks:[]},this.outputPoseSegmentationMasks=this.outputFaceBlendshapes=!1,wn(t=this.j=new yo,0,1,e=new Xs),this.I=new io,wn(this.j,0,2,this.I),this.W=new po,wn(this.j,0,3,this.W),this.u=new zs,wn(this.j,0,4,this.u),this.O=new $s,wn(this.j,0,5,this.O),this.A=new go,wn(this.j,0,6,this.A),this.M=new mo,wn(this.j,0,7,this.M),Ln(this.u,2,.5),Ln(this.u,3,.3),Ln(this.O,2,.5),Ln(this.A,2,.5),Ln(this.A,3,.3),Ln(this.M,2,.5),Ln(this.I,2,.5)}get baseOptions(){return yn(this.j,Xs,1)}set baseOptions(t){wn(this.j,0,1,t)}o(t){return"minFaceDetectionConfidence"in t&&Ln(this.u,2,t.minFaceDetectionConfidence??.5),"minFaceSuppressionThreshold"in t&&Ln(this.u,3,t.minFaceSuppressionThreshold??.3),"minFacePresenceConfidence"in t&&Ln(this.O,2,t.minFacePresenceConfidence??.5),"outputFaceBlendshapes"in t&&(this.outputFaceBlendshapes=!!t.outputFaceBlendshapes),"minPoseDetectionConfidence"in t&&Ln(this.A,2,t.minPoseDetectionConfidence??.5),"minPoseSuppressionThreshold"in t&&Ln(this.A,3,t.minPoseSuppressionThreshold??.3),"minPosePresenceConfidence"in t&&Ln(this.M,2,t.minPosePresenceConfidence??.5),"outputPoseSegmentationMasks"in t&&(this.outputPoseSegmentationMasks=!!t.outputPoseSegmentationMasks),"minHandLandmarksConfidence"in t&&Ln(this.I,2,t.minHandLandmarksConfidence??.5),this.l(t)}F(t,e,n){const r="function"!=typeof e?e:{};return this.D="function"==typeof e?e:n,Oc(this),uc(this,t,r),Nc(this)}G(t,e,n,r){const i="function"!=typeof n?n:{};return this.D="function"==typeof n?n:r,Oc(this),lc(this,t,i,e),Nc(this)}m(){var t=new ls;hs(t,"input_frames_image"),us(t,"pose_landmarks"),us(t,"pose_world_landmarks"),us(t,"face_landmarks"),us(t,"left_hand_landmarks"),us(t,"left_hand_world_landmarks"),us(t,"right_hand_landmarks"),us(t,"right_hand_world_landmarks");const e=new Qi,n=new Bi;Rn(n,1,"type.googleapis.com/mediapipe.tasks.vision.holistic_landmarker.proto.HolisticLandmarkerGraphOptions"),function(t,e){if(null!=e)if(Array.isArray(e))Ze(t,2,Ie(e,0,Me));else{if(!("string"==typeof e||e instanceof F||x(e)))throw Error("invalid value in Any.value field: "+e+" expected a ByteString, a base64 encoded string, a Uint8Array or a jspb array");hn(t,2,ht(e,!1),R())}}(n,this.j.g());const r=new is;Rn(r,2,"mediapipe.tasks.vision.holistic_landmarker.HolisticLandmarkerGraph"),bn(r,8,Bi,n),ns(r,"IMAGE:input_frames_image"),rs(r,"POSE_LANDMARKS:pose_landmarks"),rs(r,"POSE_WORLD_LANDMARKS:pose_world_landmarks"),rs(r,"FACE_LANDMARKS:face_landmarks"),rs(r,"LEFT_HAND_LANDMARKS:left_hand_landmarks"),rs(r,"LEFT_HAND_WORLD_LANDMARKS:left_hand_world_landmarks"),rs(r,"RIGHT_HAND_LANDMARKS:right_hand_landmarks"),rs(r,"RIGHT_HAND_WORLD_LANDMARKS:right_hand_world_landmarks"),r.o(e),cs(t,r),la(this,t),this.g.attachProtoListener("pose_landmarks",((t,e)=>{Uc(t,this.h.poseLandmarks),ua(this,e)})),this.g.attachEmptyPacketListener("pose_landmarks",(t=>{ua(this,t)})),this.g.attachProtoListener("pose_world_landmarks",((t,e)=>{var n=this.h.poseWorldLandmarks;t=As(t),n.push(Wo(t)),ua(this,e)})),this.g.attachEmptyPacketListener("pose_world_landmarks",(t=>{ua(this,t)})),this.outputPoseSegmentationMasks&&(rs(r,"POSE_SEGMENTATION_MASK:pose_segmentation_mask"),fa(this,"pose_segmentation_mask"),this.g.Z("pose_segmentation_mask",((t,e)=>{this.h.poseSegmentationMasks=[fc(this,t,!0,!this.D)],ua(this,e)})),this.g.attachEmptyPacketListener("pose_segmentation_mask",(t=>{this.h.poseSegmentationMasks=[],ua(this,t)}))),this.g.attachProtoListener("face_landmarks",((t,e)=>{Uc(t,this.h.faceLandmarks),ua(this,e)})),this.g.attachEmptyPacketListener("face_landmarks",(t=>{ua(this,t)})),this.outputFaceBlendshapes&&(us(t,"extra_blendshapes"),rs(r,"FACE_BLENDSHAPES:extra_blendshapes"),this.g.attachProtoListener("extra_blendshapes",((t,e)=>{var n=this.h.faceBlendshapes;this.outputFaceBlendshapes&&(t=ys(t),n.push(jo(t.g()??[]))),ua(this,e)})),this.g.attachEmptyPacketListener("extra_blendshapes",(t=>{ua(this,t)}))),this.g.attachProtoListener("left_hand_landmarks",((t,e)=>{Uc(t,this.h.leftHandLandmarks),ua(this,e)})),this.g.attachEmptyPacketListener("left_hand_landmarks",(t=>{ua(this,t)})),this.g.attachProtoListener("left_hand_world_landmarks",((t,e)=>{var n=this.h.leftHandWorldLandmarks;t=As(t),n.push(Wo(t)),ua(this,e)})),this.g.attachEmptyPacketListener("left_hand_world_landmarks",(t=>{ua(this,t)})),this.g.attachProtoListener("right_hand_landmarks",((t,e)=>{Uc(t,this.h.rightHandLandmarks),ua(this,e)})),this.g.attachEmptyPacketListener("right_hand_landmarks",(t=>{ua(this,t)})),this.g.attachProtoListener("right_hand_world_landmarks",((t,e)=>{var n=this.h.rightHandWorldLandmarks;t=As(t),n.push(Wo(t)),ua(this,e)})),this.g.attachEmptyPacketListener("right_hand_world_landmarks",(t=>{ua(this,t)})),t=t.g(),this.setGraph(new Uint8Array(t),!0)}};Dc.prototype.detectForVideo=Dc.prototype.G,Dc.prototype.detect=Dc.prototype.F,Dc.prototype.setOptions=Dc.prototype.o,Dc.createFromModelPath=function(t,e){return cc(Dc,t,{baseOptions:{modelAssetPath:e}})},Dc.createFromModelBuffer=function(t,e){return cc(Dc,t,{baseOptions:{modelAssetBuffer:e}})},Dc.createFromOptions=function(t,e){return cc(Dc,t,e)},Dc.HAND_CONNECTIONS=xc,Dc.POSE_CONNECTIONS=Cc,Dc.FACE_LANDMARKS_LIPS=gc,Dc.FACE_LANDMARKS_LEFT_EYE=mc,Dc.FACE_LANDMARKS_LEFT_EYEBROW=yc,Dc.FACE_LANDMARKS_LEFT_IRIS=_c,Dc.FACE_LANDMARKS_RIGHT_EYE=vc,Dc.FACE_LANDMARKS_RIGHT_EYEBROW=Ec,Dc.FACE_LANDMARKS_RIGHT_IRIS=wc,Dc.FACE_LANDMARKS_FACE_OVAL=Tc,Dc.FACE_LANDMARKS_CONTOURS=Ac,Dc.FACE_LANDMARKS_TESSELATION=bc;var Bc=class extends dc{constructor(t,e){super(new ac(t,e),"input_image","norm_rect",!0),this.j={classifications:[]},wn(t=this.h=new Eo,0,1,e=new Xs)}get baseOptions(){return yn(this.h,Xs,1)}set baseOptions(t){wn(this.h,0,1,t)}o(t){return wn(this.h,0,2,Bo(t,yn(this.h,Ns,2))),this.l(t)}sa(t,e){return this.j={classifications:[]},uc(this,t,e),this.j}ta(t,e,n){return this.j={classifications:[]},lc(this,t,n,e),this.j}m(){var t=new ls;hs(t,"input_image"),hs(t,"norm_rect"),us(t,"classifications");const e=new Qi;xr(e,wo,this.h);const n=new is;Rn(n,2,"mediapipe.tasks.vision.image_classifier.ImageClassifierGraph"),ns(n,"IMAGE:input_image"),ns(n,"NORM_RECT:norm_rect"),rs(n,"CLASSIFICATIONS:classifications"),n.o(e),cs(t,n),this.g.attachProtoListener("classifications",((t,e)=>{this.j=Vo(Is(t)),ua(this,e)})),this.g.attachEmptyPacketListener("classifications",(t=>{ua(this,t)})),t=t.g(),this.setGraph(new Uint8Array(t),!0)}};Bc.prototype.classifyForVideo=Bc.prototype.ta,Bc.prototype.classify=Bc.prototype.sa,Bc.prototype.setOptions=Bc.prototype.o,Bc.createFromModelPath=function(t,e){return cc(Bc,t,{baseOptions:{modelAssetPath:e}})},Bc.createFromModelBuffer=function(t,e){return cc(Bc,t,{baseOptions:{modelAssetBuffer:e}})},Bc.createFromOptions=function(t,e){return cc(Bc,t,e)};var Gc=class extends dc{constructor(t,e){super(new ac(t,e),"image_in","norm_rect",!0),this.h=new To,this.embeddings={embeddings:[]},wn(t=this.h,0,1,e=new Xs)}get baseOptions(){return yn(this.h,Xs,1)}set baseOptions(t){wn(this.h,0,1,t)}o(t){var e=this.h,n=yn(this.h,Ds,2);return n=n?n.clone():new Ds,void 0!==t.l2Normalize?Ze(n,1,Jt(t.l2Normalize)):"l2Normalize"in t&&Ze(n,1),void 0!==t.quantize?Ze(n,2,Jt(t.quantize)):"quantize"in t&&Ze(n,2),wn(e,0,2,n),this.l(t)}za(t,e){return uc(this,t,e),this.embeddings}Aa(t,e,n){return lc(this,t,n,e),this.embeddings}m(){var t=new ls;hs(t,"image_in"),hs(t,"norm_rect"),us(t,"embeddings_out");const e=new Qi;xr(e,Ao,this.h);const n=new is;Rn(n,2,"mediapipe.tasks.vision.image_embedder.ImageEmbedderGraph"),ns(n,"IMAGE:image_in"),ns(n,"NORM_RECT:norm_rect"),rs(n,"EMBEDDINGS:embeddings_out"),n.o(e),cs(t,n),this.g.attachProtoListener("embeddings_out",((t,e)=>{t=Os(t),this.embeddings=function(t){return{embeddings:vn(t,Ps,1).map((t=>{const e={headIndex:kn(t,3)??0??-1,headName:le($e(t,4))??""??""};var n=t.v;return void 0!==mn(n,0|n[Q],Fs,ln(t,1))?(t=en(t=yn(t,Fs,ln(t,1),void 0),1,$t,tn()),e.floatEmbedding=t.slice()):(n=new Uint8Array(0),e.quantizedEmbedding=yn(t,Ms,ln(t,2),void 0)?.na()?.h()??n),e})),timestampMs:Go($e(t,2,void 0,void 0,ce)??Ye)}}(t),ua(this,e)})),this.g.attachEmptyPacketListener("embeddings_out",(t=>{ua(this,t)})),t=t.g(),this.setGraph(new Uint8Array(t),!0)}};Gc.cosineSimilarity=function(t,e){if(t.floatEmbedding&&e.floatEmbedding)t=Ko(t.floatEmbedding,e.floatEmbedding);else{if(!t.quantizedEmbedding||!e.quantizedEmbedding)throw Error("Cannot compute cosine similarity between quantized and float embeddings.");t=Ko(zo(t.quantizedEmbedding),zo(e.quantizedEmbedding))}return t},Gc.prototype.embedForVideo=Gc.prototype.Aa,Gc.prototype.embed=Gc.prototype.za,Gc.prototype.setOptions=Gc.prototype.o,Gc.createFromModelPath=function(t,e){return cc(Gc,t,{baseOptions:{modelAssetPath:e}})},Gc.createFromModelBuffer=function(t,e){return cc(Gc,t,{baseOptions:{modelAssetBuffer:e}})},Gc.createFromOptions=function(t,e){return cc(Gc,t,e)};var jc=class{constructor(t,e,n){this.confidenceMasks=t,this.categoryMask=e,this.qualityScores=n}close(){this.confidenceMasks?.forEach((t=>{t.close()})),this.categoryMask?.close()}};function Vc(t){const e=function(t){return vn(t,is,1)}(t.ca()).filter((t=>(le($e(t,1))??"").includes("mediapipe.tasks.TensorsToSegmentationCalculator")));if(t.u=[],e.length>1)throw Error("The graph has more than one mediapipe.tasks.TensorsToSegmentationCalculator.");1===e.length&&(yn(e[0],Qi,7)?.j()?.g()??new Map).forEach(((e,n)=>{t.u[Number(n)]=le($e(e,1))??""}))}function Xc(t){t.categoryMask=void 0,t.confidenceMasks=void 0,t.qualityScores=void 0}function Hc(t){try{const e=new jc(t.confidenceMasks,t.categoryMask,t.qualityScores);if(!t.j)return e;t.j(e)}finally{da(t)}}jc.prototype.close=jc.prototype.close;var Wc=class extends dc{constructor(t,e){super(new ac(t,e),"image_in","norm_rect",!1),this.u=[],this.outputCategoryMask=!1,this.outputConfidenceMasks=!0,this.h=new Lo,this.A=new bo,wn(this.h,0,3,this.A),wn(t=this.h,0,1,e=new Xs)}get baseOptions(){return yn(this.h,Xs,1)}set baseOptions(t){wn(this.h,0,1,t)}o(t){return void 0!==t.displayNamesLocale?Ze(this.h,2,ue(t.displayNamesLocale)):"displayNamesLocale"in t&&Ze(this.h,2),"outputCategoryMask"in t&&(this.outputCategoryMask=t.outputCategoryMask??!1),"outputConfidenceMasks"in t&&(this.outputConfidenceMasks=t.outputConfidenceMasks??!0),super.l(t)}L(){Vc(this)}segment(t,e,n){const r="function"!=typeof e?e:{};return this.j="function"==typeof e?e:n,Xc(this),uc(this,t,r),Hc(this)}La(t,e,n,r){const 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Uint8Array(t),!0)}};Yc.prototype.detectForVideo=Yc.prototype.G,Yc.prototype.detect=Yc.prototype.F,Yc.prototype.setOptions=Yc.prototype.o,Yc.createFromModelPath=async function(t,e){return cc(Yc,t,{baseOptions:{modelAssetPath:e}})},Yc.createFromModelBuffer=function(t,e){return cc(Yc,t,{baseOptions:{modelAssetBuffer:e}})},Yc.createFromOptions=function(t,e){return cc(Yc,t,e)};var qc=class{constructor(t,e,n){this.landmarks=t,this.worldLandmarks=e,this.segmentationMasks=n}close(){this.segmentationMasks?.forEach((t=>{t.close()}))}};function $c(t){t.landmarks=[],t.worldLandmarks=[],t.segmentationMasks=void 0}function Jc(t){try{const e=new qc(t.landmarks,t.worldLandmarks,t.segmentationMasks);if(!t.u)return e;t.u(e)}finally{da(t)}}qc.prototype.close=qc.prototype.close;var Zc=class extends dc{constructor(t,e){super(new ac(t,e),"image_in","norm_rect",!1),this.landmarks=[],this.worldLandmarks=[],this.outputSegmentationMasks=!1,wn(t=this.h=new Uo,0,1,e=new Xs),this.A=new 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is;Rn(n,2,"mediapipe.tasks.vision.pose_landmarker.PoseLandmarkerGraph"),ns(n,"IMAGE:image_in"),ns(n,"NORM_RECT:norm_rect"),rs(n,"NORM_LANDMARKS:normalized_landmarks"),rs(n,"WORLD_LANDMARKS:world_landmarks"),n.o(e),cs(t,n),la(this,t),this.g.attachProtoVectorListener("normalized_landmarks",((t,e)=>{this.landmarks=[];for(const e of t)t=ks(e),this.landmarks.push(Ho(t));ua(this,e)})),this.g.attachEmptyPacketListener("normalized_landmarks",(t=>{this.landmarks=[],ua(this,t)})),this.g.attachProtoVectorListener("world_landmarks",((t,e)=>{this.worldLandmarks=[];for(const e of t)t=As(e),this.worldLandmarks.push(Wo(t));ua(this,e)})),this.g.attachEmptyPacketListener("world_landmarks",(t=>{this.worldLandmarks=[],ua(this,t)})),this.outputSegmentationMasks&&(rs(n,"SEGMENTATION_MASK:segmentation_masks"),fa(this,"segmentation_masks"),this.g.aa("segmentation_masks",((t,e)=>{this.segmentationMasks=t.map((t=>fc(this,t,!0,!this.u))),ua(this,e)})),this.g.attachEmptyPacketListener("segmentation_masks",(t=>{this.segmentationMasks=[],ua(this,t)}))),t=t.g(),this.setGraph(new Uint8Array(t),!0)}};Zc.prototype.detectForVideo=Zc.prototype.G,Zc.prototype.detect=Zc.prototype.F,Zc.prototype.setOptions=Zc.prototype.o,Zc.createFromModelPath=function(t,e){return cc(Zc,t,{baseOptions:{modelAssetPath:e}})},Zc.createFromModelBuffer=function(t,e){return cc(Zc,t,{baseOptions:{modelAssetBuffer:e}})},Zc.createFromOptions=function(t,e){return cc(Zc,t,e)},Zc.POSE_CONNECTIONS=Cc;export{Ka as DrawingUtils,pc as FaceDetector,Sc as FaceLandmarker,Zo as FilesetResolver,Fc as GestureRecognizer,Pc as HandLandmarker,Dc as HolisticLandmarker,Bc as ImageClassifier,Gc as ImageEmbedder,Wc as ImageSegmenter,jc as ImageSegmenterResult,Kc as InteractiveSegmenter,zc as InteractiveSegmenterResult,nc as MPImage,Na as MPMask,Yc as ObjectDetector,Zc as PoseLandmarker,pa as TaskRunner,dc as VisionTaskRunner};
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diff --git a/lab-js/vendor/vision_bundle_mjs.js.map b/lab-js/vendor/vision_bundle_mjs.js.map
new file mode 100644
index 0000000..2b45703
--- /dev/null
+++ b/lab-js/vendor/vision_bundle_mjs.js.map
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to use attachAudioListener without support for output audio. Is build dep \":gl_graph_runner_audio_out\" missing?');qi(this,a,(d,e)=>{d=new Float32Array(d.buffer,d.byteOffset,d.length/4);b(d,e)});T(this,a,d=>{this.i._attachAudioListener(d,c||!1)})}finishProcessing(){this.i._waitUntilIdle()}closeGraph(){this.i._closeGraph();\nthis.i.simpleListeners=void 0;this.i.emptyPacketListeners=void 0}},ti=async(a,b,c,d,e)=>{b&&await mi(b);if(!self.ModuleFactory)throw Error(\"ModuleFactory not set.\");if(c&&(await mi(c),!self.ModuleFactory))throw Error(\"ModuleFactory not set.\");self.Module&&e&&(b=self.Module,b.locateFile=e.locateFile,e.mainScriptUrlOrBlob&&(b.mainScriptUrlOrBlob=e.mainScriptUrlOrBlob));e=await self.ModuleFactory(self.Module||e);self.ModuleFactory=self.Module=void 0;return new a(e,d)};async function ui(a,b,c,d){a=await ti(a,c.wasmLoaderPath,c.assetLoaderPath,b,{locateFile(e){return e.endsWith(\".wasm\")?c.wasmBinaryPath.toString():c.assetBinaryPath&&e.endsWith(\".data\")?c.assetBinaryPath.toString():e}});await a.o(d);return a}async function vi(a,b,c,d){return ui(a,b,c,d)}function wi(a,b){let c=y(a.baseOptions,$g,3);if(!c){var d=c=new $g,e=new ng;Gd(d,4,ah,e)}\"delegate\"in b&&(b.delegate===\"GPU\"?(b=c,d=new jg,Gd(b,2,ah,d)):(b=c,d=new ng,Gd(b,4,ah,d)));z(a.baseOptions,$g,3,c)}\nfunction xi(a,b){const c=y(a.baseOptions,bh,1)||new bh;typeof b===\"string\"?(v(c,2,qc(b)),v(c,1)):b instanceof Uint8Array&&(v(c,1,mb(b,!1)),v(c,2));z(a.baseOptions,bh,1,c)}function yi(a){try{const b=a.H.length;if(b===1)throw Error(a.H[0].message);if(b>1)throw Error(\"Encountered multiple errors: \"+a.H.map(c=>c.message).join(\", \"));}finally{a.H=[]}}function U(a,b){a.C=Math.max(a.C,b)}\nfunction zi(a,b){a.B=new sg;Kd(a.B,2,\"PassThroughCalculator\");M(a.B,\"free_memory\");O(a.B,\"free_memory_unused_out\");P(b,\"free_memory\");xg(b,a.B)}function Ai(a,b){M(a.B,b);O(a.B,b+\"_unused_out\")}function Bi(a){a.g.addBoolToStream(!0,\"free_memory\",a.C)}\nvar Ci=class{constructor(a){this.g=a;this.H=[];this.C=0;this.g.setAutoRenderToScreen(!1)}l(a,b=!0){if(b){const c=a.baseOptions||{};if(a.baseOptions?.modelAssetBuffer&&a.baseOptions?.modelAssetPath)throw Error(\"Cannot set both baseOptions.modelAssetPath and baseOptions.modelAssetBuffer\");if(!(y(this.baseOptions,bh,1)?.g()||y(this.baseOptions,bh,1)?.l()||a.baseOptions?.modelAssetBuffer||a.baseOptions?.modelAssetPath))throw Error(\"Either baseOptions.modelAssetPath or baseOptions.modelAssetBuffer must be set\");\nwi(this,c);if(c.modelAssetPath)return fetch(c.modelAssetPath.toString()).then(d=>{if(d.ok)return d.arrayBuffer();throw Error(`Failed to fetch model: ${c.modelAssetPath} (${d.status})`);}).then(d=>{try{this.g.i.FS_unlink(\"/model.dat\")}catch{}this.g.i.FS_createDataFile(\"/\",\"model.dat\",new Uint8Array(d),!0,!1,!1);xi(this,\"/model.dat\");this.m();this.L()});if(c.modelAssetBuffer instanceof Uint8Array)xi(this,c.modelAssetBuffer);else if(c.modelAssetBuffer)return ji(c.modelAssetBuffer).then(d=>{xi(this,d);\nthis.m();this.L()})}this.m();this.L();return Promise.resolve()}L(){}ca(){let a;this.g.ca(b=>{a=Ag(b)});if(!a)throw Error(\"Failed to retrieve CalculatorGraphConfig\");return a}setGraph(a,b){this.g.attachErrorListener((c,d)=>{this.H.push(Error(d))});this.g.Ja();this.g.setGraph(a,b);this.B=void 0;yi(this)}finishProcessing(){this.g.finishProcessing();yi(this)}close(){this.B=void 0;this.g.closeGraph()}};Ci.prototype.close=Ci.prototype.close;export {Ci as TaskRunner};function Di(a,b){if(!a)throw Error(`Unable to obtain required WebGL resource: ${b}`);return a}class Ei{constructor(a,b,c,d){this.g=a;this.h=b;this.m=c;this.l=d}bind(){this.g.bindVertexArray(this.h)}close(){this.g.deleteVertexArray(this.h);this.g.deleteBuffer(this.m);this.g.deleteBuffer(this.l)}}\nfunction Fi(a,b,c){const d=a.g;c=Di(d.createShader(c),\"Failed to create WebGL shader\");d.shaderSource(c,b);d.compileShader(c);if(!d.getShaderParameter(c,d.COMPILE_STATUS))throw Error(`Could not compile WebGL shader: ${d.getShaderInfoLog(c)}`);d.attachShader(a.h,c);return c}\nfunction Gi(a,b){const c=a.g,d=Di(c.createVertexArray(),\"Failed to create vertex array\");c.bindVertexArray(d);const e=Di(c.createBuffer(),\"Failed to create buffer\");c.bindBuffer(c.ARRAY_BUFFER,e);c.enableVertexAttribArray(a.O);c.vertexAttribPointer(a.O,2,c.FLOAT,!1,0,0);c.bufferData(c.ARRAY_BUFFER,new Float32Array([-1,-1,-1,1,1,1,1,-1]),c.STATIC_DRAW);const f=Di(c.createBuffer(),\"Failed to create buffer\");c.bindBuffer(c.ARRAY_BUFFER,f);c.enableVertexAttribArray(a.L);c.vertexAttribPointer(a.L,2,c.FLOAT,\n!1,0,0);c.bufferData(c.ARRAY_BUFFER,new Float32Array(b?[0,1,0,0,1,0,1,1]:[0,0,0,1,1,1,1,0]),c.STATIC_DRAW);c.bindBuffer(c.ARRAY_BUFFER,null);c.bindVertexArray(null);return new Ei(c,d,e,f)}function Hi(a,b){if(!a.g)a.g=b;else if(b!==a.g)throw Error(\"Cannot change GL context once initialized\");}function Ii(a,b,c,d){Hi(a,b);a.h||(a.m(),a.D());c?(a.u||(a.u=Gi(a,!0)),c=a.u):(a.A||(a.A=Gi(a,!1)),c=a.A);b.useProgram(a.h);c.bind();a.l();a=d();c.g.bindVertexArray(null);return a}\nfunction Ji(a,b,c){Hi(a,b);a=Di(b.createTexture(),\"Failed to create texture\");b.bindTexture(b.TEXTURE_2D,a);b.texParameteri(b.TEXTURE_2D,b.TEXTURE_WRAP_S,b.CLAMP_TO_EDGE);b.texParameteri(b.TEXTURE_2D,b.TEXTURE_WRAP_T,b.CLAMP_TO_EDGE);b.texParameteri(b.TEXTURE_2D,b.TEXTURE_MIN_FILTER,c??b.LINEAR);b.texParameteri(b.TEXTURE_2D,b.TEXTURE_MAG_FILTER,c??b.LINEAR);b.bindTexture(b.TEXTURE_2D,null);return a}\nfunction Ki(a,b,c){Hi(a,b);a.B||(a.B=Di(b.createFramebuffer(),\"Failed to create framebuffe.\"));b.bindFramebuffer(b.FRAMEBUFFER,a.B);b.framebufferTexture2D(b.FRAMEBUFFER,b.COLOR_ATTACHMENT0,b.TEXTURE_2D,c,0)}function Li(a){a.g?.bindFramebuffer(a.g.FRAMEBUFFER,null)}\nvar Mi=class{H(){return\"\\n  precision mediump float;\\n  varying vec2 vTex;\\n  uniform sampler2D inputTexture;\\n  void main() {\\n    gl_FragColor = texture2D(inputTexture, vTex);\\n  }\\n \"}m(){const a=this.g;this.h=Di(a.createProgram(),\"Failed to create WebGL program\");this.X=Fi(this,\"\\n  attribute vec2 aVertex;\\n  attribute vec2 aTex;\\n  varying vec2 vTex;\\n  void main(void) {\\n    gl_Position = vec4(aVertex, 0.0, 1.0);\\n    vTex = aTex;\\n  }\",a.VERTEX_SHADER);this.W=Fi(this,this.H(),a.FRAGMENT_SHADER);\na.linkProgram(this.h);if(!a.getProgramParameter(this.h,a.LINK_STATUS))throw Error(`Error during program linking: ${a.getProgramInfoLog(this.h)}`);this.O=a.getAttribLocation(this.h,\"aVertex\");this.L=a.getAttribLocation(this.h,\"aTex\")}D(){}l(){}close(){if(this.h){const a=this.g;a.deleteProgram(this.h);a.deleteShader(this.X);a.deleteShader(this.W)}this.B&&this.g.deleteFramebuffer(this.B);this.A&&this.A.close();this.u&&this.u.close()}};function Ni(a,b){if(a!==b)return!1;a=a.entries();b=b.entries();for(const [c,d]of a){a=c;const e=d,f=b.next();if(f.done)return!1;const [g,h]=f.value;if(a!==g||e[0]!==h[0]||e[1]!==h[1]||e[2]!==h[2]||e[3]!==h[3])return!1}return!!b.next().done}\nfunction Oi(a,b,c,d){const e=a.g;e.activeTexture(e.TEXTURE0);e.bindTexture(e.TEXTURE_2D,b);e.activeTexture(e.TEXTURE1);e.bindTexture(e.TEXTURE_2D,a.C);e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,c);if(a.I&&Ni(a.I,d))e.activeTexture(e.TEXTURE2),e.bindTexture(e.TEXTURE_2D,a.j);else{a.I=d;const f=Array(1024).fill(0);d.forEach((g,h)=>{if(g.length!==4)throw Error(`Color at index ${h} is not a four-channel value.`);f[h*4]=g[0];f[h*4+1]=g[1];f[h*4+2]=g[2];f[h*4+3]=g[3]});e.activeTexture(e.TEXTURE2);\ne.bindTexture(e.TEXTURE_2D,a.j);e.texImage2D(e.TEXTURE_2D,0,e.RGBA,256,1,0,e.RGBA,e.UNSIGNED_BYTE,new Uint8Array(f))}}\nvar Pi=class extends Mi{H(){return\"\\n  precision mediump float;\\n  uniform sampler2D backgroundTexture;\\n  uniform sampler2D maskTexture;\\n  uniform sampler2D colorMappingTexture;\\n  varying vec2 vTex;\\n  void main() {\\n    vec4 backgroundColor = texture2D(backgroundTexture, vTex);\\n    float category = texture2D(maskTexture, vTex).r;\\n    vec4 categoryColor = texture2D(colorMappingTexture, vec2(category, 0.0));\\n    gl_FragColor = mix(backgroundColor, categoryColor, categoryColor.a);\\n  }\\n \"}D(){const a=this.g;\na.activeTexture(a.TEXTURE1);this.C=Ji(this,a,a.LINEAR);a.activeTexture(a.TEXTURE2);this.j=Ji(this,a,a.NEAREST)}m(){super.m();const a=this.g;this.P=Di(a.getUniformLocation(this.h,\"backgroundTexture\"),\"Uniform location\");this.U=Di(a.getUniformLocation(this.h,\"colorMappingTexture\"),\"Uniform location\");this.M=Di(a.getUniformLocation(this.h,\"maskTexture\"),\"Uniform location\")}l(){super.l();const a=this.g;a.uniform1i(this.M,0);a.uniform1i(this.P,1);a.uniform1i(this.U,2)}close(){this.C&&this.g.deleteTexture(this.C);\nthis.j&&this.g.deleteTexture(this.j);super.close()}};var Qi=class extends Mi{H(){return\"\\n  precision mediump float;\\n  uniform sampler2D maskTexture;\\n  uniform sampler2D defaultTexture;\\n  uniform sampler2D overlayTexture;\\n  varying vec2 vTex;\\n  void main() {\\n    float confidence = texture2D(maskTexture, vTex).r;\\n    vec4 defaultColor = texture2D(defaultTexture, vTex);\\n    vec4 overlayColor = texture2D(overlayTexture, vTex);\\n    // Apply the alpha from the overlay and merge in the default color\\n    overlayColor = mix(defaultColor, overlayColor, overlayColor.a);\\n    gl_FragColor = mix(defaultColor, overlayColor, confidence);\\n  }\\n \"}D(){const a=\nthis.g;a.activeTexture(a.TEXTURE1);this.j=Ji(this,a);a.activeTexture(a.TEXTURE2);this.C=Ji(this,a)}m(){super.m();const a=this.g;this.M=Di(a.getUniformLocation(this.h,\"defaultTexture\"),\"Uniform location\");this.P=Di(a.getUniformLocation(this.h,\"overlayTexture\"),\"Uniform location\");this.I=Di(a.getUniformLocation(this.h,\"maskTexture\"),\"Uniform location\")}l(){super.l();const a=this.g;a.uniform1i(this.I,0);a.uniform1i(this.M,1);a.uniform1i(this.P,2)}close(){this.j&&this.g.deleteTexture(this.j);this.C&&\nthis.g.deleteTexture(this.C);super.close()}};function Ri(a,b){switch(b){case 0:return a.g.find(c=>c instanceof Uint8Array);case 1:return a.g.find(c=>c instanceof Float32Array);case 2:return a.g.find(c=>typeof WebGLTexture!==\"undefined\"&&c instanceof WebGLTexture);default:throw Error(`Type is not supported: ${b}`);}}function Si(a){var b=Ri(a,0);b||(b=Ti(a),b=new Uint8Array(b.map(c=>Math.round(255*c))),a.g.push(b));return b}\nfunction Ti(a){var b=Ri(a,1);if(!b){if(b=Ri(a,0))b=(new Float32Array(b)).map(d=>d/255);else{b=new Float32Array(a.width*a.height);const d=Ui(a);var c=Vi(a);const e=Wi(a);Ki(c,d,e);if(\"iPad Simulator;iPhone Simulator;iPod Simulator;iPad;iPhone;iPod\".split(\";\").includes(navigator.platform)||navigator.userAgent.includes(\"Mac\")&&\"document\"in self&&\"ontouchend\"in self.document){c=new Float32Array(a.width*a.height*4);d.readPixels(0,0,a.width,a.height,d.RGBA,d.FLOAT,c);for(let f=0,g=0;f<b.length;++f,g+=4)b[f]=\nc[g]}else d.readPixels(0,0,a.width,a.height,d.RED,d.FLOAT,b)}a.g.push(b)}return b}function Wi(a){let b=Ri(a,2);if(!b){const c=Ui(a);b=Xi(a);const d=Ti(a),e=Yi(a);c.texImage2D(c.TEXTURE_2D,0,e,a.width,a.height,0,c.RED,c.FLOAT,d);Zi(a)}return b}\nfunction Ui(a){if(!a.canvas)throw Error(\"Conversion to different image formats require that a canvas is passed when initializing the image.\");a.h||(a.h=Di(a.canvas.getContext(\"webgl2\"),\"You cannot use a canvas that is already bound to a different type of rendering context.\"));return a.h}\nfunction Yi(a){a=Ui(a);if(!$i)if(a.getExtension(\"EXT_color_buffer_float\")&&a.getExtension(\"OES_texture_float_linear\")&&a.getExtension(\"EXT_float_blend\"))$i=a.R32F;else if(a.getExtension(\"EXT_color_buffer_half_float\"))$i=a.R16F;else throw Error(\"GPU does not fully support 4-channel float32 or float16 formats\");return $i}function Vi(a){a.l||(a.l=new Mi);return a.l}\nfunction Xi(a){const b=Ui(a);b.viewport(0,0,a.width,a.height);b.activeTexture(b.TEXTURE0);let c=Ri(a,2);c||(c=Ji(Vi(a),b,a.m?b.LINEAR:b.NEAREST),a.g.push(c),a.j=!0);b.bindTexture(b.TEXTURE_2D,c);return c}function Zi(a){a.h.bindTexture(a.h.TEXTURE_2D,null)}\nvar V=class{constructor(a,b,c,d,e,f,g){this.g=a;this.m=b;this.j=c;this.canvas=d;this.l=e;this.width=f;this.height=g;this.j&&(--aj,aj===0&&console.error(\"You seem to be creating MPMask instances without invoking .close(). This leaks resources.\"))}Fa(){return!!Ri(this,0)}ka(){return!!Ri(this,1)}R(){return!!Ri(this,2)}ja(){return Si(this)}ia(){return Ti(this)}N(){return Wi(this)}clone(){const a=[];for(const b of this.g){let c;if(b instanceof Uint8Array)c=new Uint8Array(b);else if(b instanceof Float32Array)c=\nnew Float32Array(b);else if(b instanceof WebGLTexture){const d=Ui(this),e=Vi(this);d.activeTexture(d.TEXTURE1);c=Ji(e,d,this.m?d.LINEAR:d.NEAREST);d.bindTexture(d.TEXTURE_2D,c);const f=Yi(this);d.texImage2D(d.TEXTURE_2D,0,f,this.width,this.height,0,d.RED,d.FLOAT,null);d.bindTexture(d.TEXTURE_2D,null);Ki(e,d,c);Ii(e,d,!1,()=>{Xi(this);d.clearColor(0,0,0,0);d.clear(d.COLOR_BUFFER_BIT);d.drawArrays(d.TRIANGLE_FAN,0,4);Zi(this)});Li(e);Zi(this)}else throw Error(`Type is not supported: ${b}`);a.push(c)}return new V(a,\nthis.m,this.R(),this.canvas,this.l,this.width,this.height)}close(){this.j&&Ui(this).deleteTexture(Ri(this,2));aj=-1}},$i;V.prototype.close=V.prototype.close;V.prototype.clone=V.prototype.clone;V.prototype.getAsWebGLTexture=V.prototype.N;V.prototype.getAsFloat32Array=V.prototype.ia;V.prototype.getAsUint8Array=V.prototype.ja;V.prototype.hasWebGLTexture=V.prototype.R;V.prototype.hasFloat32Array=V.prototype.ka;V.prototype.hasUint8Array=V.prototype.Fa;var aj=250;export {V as MPMask};const bj={color:\"white\",lineWidth:4,radius:6};function cj(a){a=a||{};return{...bj,fillColor:a.color,...a}}function dj(a,b){return a instanceof Function?a(b):a}function ej(a,b,c){return Math.max(Math.min(b,c),Math.min(Math.max(b,c),a))}function fj(a){if(!a.l)throw Error(\"CPU rendering requested but CanvasRenderingContext2D not provided.\");return a.l}function gj(a){if(!a.j)throw Error(\"GPU rendering requested but WebGL2RenderingContext not provided.\");return a.j}\nfunction hj(a){a.g||(a.g=new Pi);return a.g}function ij(a){a.h||(a.h=new Qi);return a.h}function jj(a,b,c){if(b.R())c(b.N());else{const d=b.ka()?b.ia():b.ja();a.m=a.m??new Mi;const e=gj(a);a=new V([d],b.m,!1,e.canvas,a.m,b.width,b.height);c(a.N());a.close()}}\nfunction kj(a,b,c,d){const e=hj(a),f=gj(a),g=Array.isArray(c)?new ImageData(new Uint8ClampedArray(c),1,1):c;Ii(e,f,!0,()=>{Oi(e,b,g,d);f.clearColor(0,0,0,0);f.clear(f.COLOR_BUFFER_BIT);f.drawArrays(f.TRIANGLE_FAN,0,4);const h=e.g;h.activeTexture(h.TEXTURE0);h.bindTexture(h.TEXTURE_2D,null);h.activeTexture(h.TEXTURE1);h.bindTexture(h.TEXTURE_2D,null);h.activeTexture(h.TEXTURE2);h.bindTexture(h.TEXTURE_2D,null)})}\nfunction lj(a,b,c,d){const e=gj(a);jj(a,b,f=>{kj(a,f,c,d);f=fj(a);f.drawImage(e.canvas,0,0,f.canvas.width,f.canvas.height)})}\nfunction mj(a,b,c,d){const e=gj(a),f=ij(a),g=Array.isArray(c)?new ImageData(new Uint8ClampedArray(c),1,1):c,h=Array.isArray(d)?new ImageData(new Uint8ClampedArray(d),1,1):d;Ii(f,e,!0,()=>{var k=f.g;k.activeTexture(k.TEXTURE0);k.bindTexture(k.TEXTURE_2D,b);k.activeTexture(k.TEXTURE1);k.bindTexture(k.TEXTURE_2D,f.j);k.texImage2D(k.TEXTURE_2D,0,k.RGBA,k.RGBA,k.UNSIGNED_BYTE,g);k.activeTexture(k.TEXTURE2);k.bindTexture(k.TEXTURE_2D,f.C);k.texImage2D(k.TEXTURE_2D,0,k.RGBA,k.RGBA,k.UNSIGNED_BYTE,h);e.clearColor(0,\n0,0,0);e.clear(e.COLOR_BUFFER_BIT);e.drawArrays(e.TRIANGLE_FAN,0,4);e.bindTexture(e.TEXTURE_2D,null);k=f.g;k.activeTexture(k.TEXTURE0);k.bindTexture(k.TEXTURE_2D,null);k.activeTexture(k.TEXTURE1);k.bindTexture(k.TEXTURE_2D,null);k.activeTexture(k.TEXTURE2);k.bindTexture(k.TEXTURE_2D,null)})}function nj(a,b,c,d){const e=gj(a);jj(a,b,f=>{mj(a,f,c,d);f=fj(a);f.drawImage(e.canvas,0,0,f.canvas.width,f.canvas.height)})}\nvar oj=class{constructor(a,b){typeof CanvasRenderingContext2D!==\"undefined\"&&a instanceof CanvasRenderingContext2D||a instanceof OffscreenCanvasRenderingContext2D?(this.l=a,this.j=b):this.j=a}ya(a,b){if(a){var c=fj(this);b=cj(b);c.save();var d=c.canvas,e=0;for(const f of a)c.fillStyle=dj(b.fillColor,{index:e,from:f}),c.strokeStyle=dj(b.color,{index:e,from:f}),c.lineWidth=dj(b.lineWidth,{index:e,from:f}),a=new Path2D,a.arc(f.x*d.width,f.y*d.height,dj(b.radius,{index:e,from:f}),0,2*Math.PI),c.fill(a),\nc.stroke(a),++e;c.restore()}}xa(a,b,c){if(a&&b){var d=fj(this);c=cj(c);d.save();var e=d.canvas,f=0;for(const g of b){d.beginPath();b=a[g.start];const h=a[g.end];b&&h&&(d.strokeStyle=dj(c.color,{index:f,from:b,to:h}),d.lineWidth=dj(c.lineWidth,{index:f,from:b,to:h}),d.moveTo(b.x*e.width,b.y*e.height),d.lineTo(h.x*e.width,h.y*e.height));++f;d.stroke()}d.restore()}}ua(a,b){const c=fj(this);b=cj(b);c.save();c.beginPath();c.lineWidth=dj(b.lineWidth,{});c.strokeStyle=dj(b.color,{});c.fillStyle=dj(b.fillColor,\n{});c.moveTo(a.originX,a.originY);c.lineTo(a.originX+a.width,a.originY);c.lineTo(a.originX+a.width,a.originY+a.height);c.lineTo(a.originX,a.originY+a.height);c.lineTo(a.originX,a.originY);c.stroke();c.fill();c.restore()}va(a,b,c=[0,0,0,255]){this.l?lj(this,a,c,b):kj(this,a.N(),c,b)}wa(a,b,c){this.l?nj(this,a,b,c):mj(this,a.N(),b,c)}close(){this.g?.close();this.g=void 0;this.h?.close();this.h=void 0;this.m?.close();this.m=void 0}};oj.prototype.close=oj.prototype.close;\noj.prototype.drawConfidenceMask=oj.prototype.wa;oj.prototype.drawCategoryMask=oj.prototype.va;oj.prototype.drawBoundingBox=oj.prototype.ua;oj.prototype.drawConnectors=oj.prototype.xa;oj.prototype.drawLandmarks=oj.prototype.ya;oj.lerp=function(a,b,c,d,e){return ej(d*(1-(a-b)/(c-b))+e*(1-(c-a)/(c-b)),d,e)};oj.clamp=ej;export {oj as DrawingUtils};function pj(a,b){switch(b){case 0:return a.g.find(c=>c instanceof ImageData);case 1:return a.g.find(c=>typeof ImageBitmap!==\"undefined\"&&c instanceof ImageBitmap);case 2:return a.g.find(c=>typeof WebGLTexture!==\"undefined\"&&c instanceof WebGLTexture);default:throw Error(`Type is not supported: ${b}`);}}\nfunction qj(a){var b=pj(a,0);if(!b){b=rj(a);const c=sj(a),d=new Uint8Array(a.width*a.height*4),e=tj(a);Ki(c,b,e);b.readPixels(0,0,a.width,a.height,b.RGBA,b.UNSIGNED_BYTE,d);Li(c);b=new ImageData(new Uint8ClampedArray(d.buffer),a.width,a.height);a.g.push(b)}return b}function tj(a){let b=pj(a,2);if(!b){const c=rj(a);b=uj(a);const d=pj(a,1)||qj(a);c.texImage2D(c.TEXTURE_2D,0,c.RGBA,c.RGBA,c.UNSIGNED_BYTE,d);vj(a)}return b}\nfunction rj(a){if(!a.canvas)throw Error(\"Conversion to different image formats require that a canvas is passed when initializing the image.\");a.h||(a.h=Di(a.canvas.getContext(\"webgl2\"),\"You cannot use a canvas that is already bound to a different type of rendering context.\"));return a.h}function sj(a){a.l||(a.l=new Mi);return a.l}\nfunction uj(a){const b=rj(a);b.viewport(0,0,a.width,a.height);b.activeTexture(b.TEXTURE0);let c=pj(a,2);c||(c=Ji(sj(a),b),a.g.push(c),a.m=!0);b.bindTexture(b.TEXTURE_2D,c);return c}function vj(a){a.h.bindTexture(a.h.TEXTURE_2D,null)}\nfunction wj(a){const b=rj(a);return Ii(sj(a),b,!0,()=>xj(a,()=>{b.bindFramebuffer(b.FRAMEBUFFER,null);b.clearColor(0,0,0,0);b.clear(b.COLOR_BUFFER_BIT);b.drawArrays(b.TRIANGLE_FAN,0,4);if(!(a.canvas instanceof OffscreenCanvas))throw Error(\"Conversion to ImageBitmap requires that the MediaPipe Tasks is initialized with an OffscreenCanvas\");return a.canvas.transferToImageBitmap()}))}\nfunction xj(a,b){const c=a.canvas;if(c.width===a.width&&c.height===a.height)return b();const d=c.width,e=c.height;c.width=a.width;c.height=a.height;a=b();c.width=d;c.height=e;return a}\nvar W=class{constructor(a,b,c,d,e,f,g){this.g=a;this.j=b;this.m=c;this.canvas=d;this.l=e;this.width=f;this.height=g;if(this.j||this.m)--yj,yj===0&&console.error(\"You seem to be creating MPImage instances without invoking .close(). 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a&&(this.outputFacialTransformationMatrixes=!!a.outputFacialTransformationMatrixes);return this.l(a)}F(a,b){Sj(this);Dj(this,a,b);return this.j}G(a,\nb,c){Sj(this);Ej(this,a,c,b);return this.j}m(){var a=new yg;P(a,\"image_in\");P(a,\"norm_rect\");Q(a,\"face_landmarks\");const b=new Wf;Oe(b,kh,this.h);const c=new sg;Kd(c,2,\"mediapipe.tasks.vision.face_landmarker.FaceLandmarkerGraph\");M(c,\"IMAGE:image_in\");M(c,\"NORM_RECT:norm_rect\");O(c,\"NORM_LANDMARKS:face_landmarks\");c.o(b);xg(a,c);this.g.attachProtoVectorListener(\"face_landmarks\",(d,e)=>{for(const f of d)d=Mg(f),this.j.faceLandmarks.push(ai(d));U(this,e)});this.g.attachEmptyPacketListener(\"face_landmarks\",\nd=>{U(this,d)});this.outputFaceBlendshapes&&(Q(a,\"blendshapes\"),O(c,\"BLENDSHAPES:blendshapes\"),this.g.attachProtoVectorListener(\"blendshapes\",(d,e)=>{if(this.outputFaceBlendshapes)for(const f of d)d=Eg(f),this.j.faceBlendshapes.push(Xh(d.g()??[]));U(this,e)}),this.g.attachEmptyPacketListener(\"blendshapes\",d=>{U(this,d)}));this.outputFacialTransformationMatrixes&&(Q(a,\"face_geometry\"),O(c,\"FACE_GEOMETRY:face_geometry\"),this.g.attachProtoVectorListener(\"face_geometry\",(d,e)=>{if(this.outputFacialTransformationMatrixes)for(const f of d)d=\ngh(f),(d=y(d,Ng,2))&&this.j.facialTransformationMatrixes.push({rows:Id(d,1)??0??0,columns:Id(d,2)??0??0,data:kd(d,3,Zb,jd()).slice()??[]});U(this,e)}),this.g.attachEmptyPacketListener(\"face_geometry\",d=>{U(this,d)}));a=a.g();this.setGraph(new Uint8Array(a),!0)}};Y.prototype.detectForVideo=Y.prototype.G;Y.prototype.detect=Y.prototype.F;Y.prototype.setOptions=Y.prototype.o;Y.createFromModelPath=function(a,b){return X(Y,a,{baseOptions:{modelAssetPath:b}})};\nY.createFromModelBuffer=function(a,b){return X(Y,a,{baseOptions:{modelAssetBuffer:b}})};Y.createFromOptions=function(a,b){return X(Y,a,b)};Y.FACE_LANDMARKS_LIPS=Ij;Y.FACE_LANDMARKS_LEFT_EYE=Jj;\nY.FACE_LANDMARKS_LEFT_EYEBROW=Kj;Y.FACE_LANDMARKS_LEFT_IRIS=Lj;Y.FACE_LANDMARKS_RIGHT_EYE=Mj;\nY.FACE_LANDMARKS_RIGHT_EYEBROW=Nj;Y.FACE_LANDMARKS_RIGHT_IRIS=Oj;\nY.FACE_LANDMARKS_FACE_OVAL=Pj;Y.FACE_LANDMARKS_CONTOURS=Qj;\nY.FACE_LANDMARKS_TESSELATION=Rj;export {Y as FaceLandmarker};var Tj=zj([0,1],[1,2],[2,3],[3,4],[0,5],[5,6],[6,7],[7,8],[5,9],[9,10],[10,11],[11,12],[9,13],[13,14],[14,15],[15,16],[13,17],[0,17],[17,18],[18,19],[19,20]);function Uj(a){a.gestures=[];a.landmarks=[];a.worldLandmarks=[];a.handedness=[]}function Vj(a){return a.gestures.length===0?{gestures:[],landmarks:[],worldLandmarks:[],handedness:[],handednesses:[]}:{gestures:a.gestures,landmarks:a.landmarks,worldLandmarks:a.worldLandmarks,handedness:a.handedness,handednesses:a.handedness}}\nfunction Wj(a,b=!0){const c=[];for(const e of a){var d=Eg(e);a=[];for(const f of d.g())d=b&&Id(f,1)!=null?Id(f,1)??0:-1,a.push({score:A(f,2)??0,index:d,categoryName:t(u(f,3))??\"\"??\"\",displayName:t(u(f,4))??\"\"??\"\"});c.push(a)}return c}\nvar Xj=class extends Gj{constructor(a,b){super(new Bj(a,b),\"image_in\",\"norm_rect\",!1);this.gestures=[];this.landmarks=[];this.worldLandmarks=[];this.handedness=[];a=this.j=new rh;b=new R;z(a,R,1,b);this.u=new qh;z(this.j,qh,2,this.u);this.D=new ph;z(this.u,ph,3,this.D);this.A=new oh;z(this.u,oh,2,this.A);this.h=new nh;z(this.j,nh,3,this.h);B(this.A,2,.5);B(this.u,4,.5);B(this.D,2,.5)}get baseOptions(){return y(this.j,R,1)}set baseOptions(a){z(this.j,R,1,a)}o(a){Jd(this.A,3,a.numHands??1);\"minHandDetectionConfidence\"in\na&&B(this.A,2,a.minHandDetectionConfidence??.5);\"minTrackingConfidence\"in a&&B(this.u,4,a.minTrackingConfidence??.5);\"minHandPresenceConfidence\"in a&&B(this.D,2,a.minHandPresenceConfidence??.5);if(a.cannedGesturesClassifierOptions){var b=new lh,c=b,d=Vh(a.cannedGesturesClassifierOptions,y(this.h,lh,3)?.l());z(c,Wg,2,d);z(this.h,lh,3,b)}else a.cannedGesturesClassifierOptions===void 0&&y(this.h,lh,3)?.g();a.customGesturesClassifierOptions?(c=b=new lh,d=Vh(a.customGesturesClassifierOptions,y(this.h,\nlh,4)?.l()),z(c,Wg,2,d),z(this.h,lh,4,b)):a.customGesturesClassifierOptions===void 0&&y(this.h,lh,4)?.g();return this.l(a)}Ha(a,b){Uj(this);Dj(this,a,b);return Vj(this)}Ia(a,b,c){Uj(this);Ej(this,a,c,b);return Vj(this)}m(){var a=new yg;P(a,\"image_in\");P(a,\"norm_rect\");Q(a,\"hand_gestures\");Q(a,\"hand_landmarks\");Q(a,\"world_hand_landmarks\");Q(a,\"handedness\");const b=new Wf;Oe(b,wh,this.j);const c=new sg;Kd(c,2,\"mediapipe.tasks.vision.gesture_recognizer.GestureRecognizerGraph\");M(c,\"IMAGE:image_in\");\nM(c,\"NORM_RECT:norm_rect\");O(c,\"HAND_GESTURES:hand_gestures\");O(c,\"LANDMARKS:hand_landmarks\");O(c,\"WORLD_LANDMARKS:world_hand_landmarks\");O(c,\"HANDEDNESS:handedness\");c.o(b);xg(a,c);this.g.attachProtoVectorListener(\"hand_landmarks\",(d,e)=>{for(const f of d){d=Mg(f);const g=[];for(const h of Ed(d,Lg,1))g.push({x:A(h,1)??0,y:A(h,2)??0,z:A(h,3)??0,visibility:A(h,4)??0});this.landmarks.push(g)}U(this,e)});this.g.attachEmptyPacketListener(\"hand_landmarks\",d=>{U(this,d)});this.g.attachProtoVectorListener(\"world_hand_landmarks\",\n(d,e)=>{for(const f of d){d=Kg(f);const g=[];for(const h of Ed(d,Jg,1))g.push({x:A(h,1)??0,y:A(h,2)??0,z:A(h,3)??0,visibility:A(h,4)??0});this.worldLandmarks.push(g)}U(this,e)});this.g.attachEmptyPacketListener(\"world_hand_landmarks\",d=>{U(this,d)});this.g.attachProtoVectorListener(\"hand_gestures\",(d,e)=>{this.gestures.push(...Wj(d,!1));U(this,e)});this.g.attachEmptyPacketListener(\"hand_gestures\",d=>{U(this,d)});this.g.attachProtoVectorListener(\"handedness\",(d,e)=>{this.handedness.push(...Wj(d));\nU(this,e)});this.g.attachEmptyPacketListener(\"handedness\",d=>{U(this,d)});a=a.g();this.setGraph(new Uint8Array(a),!0)}};Xj.prototype.recognizeForVideo=Xj.prototype.Ia;Xj.prototype.recognize=Xj.prototype.Ha;Xj.prototype.setOptions=Xj.prototype.o;Xj.createFromModelPath=function(a,b){return X(Xj,a,{baseOptions:{modelAssetPath:b}})};Xj.createFromModelBuffer=function(a,b){return X(Xj,a,{baseOptions:{modelAssetBuffer:b}})};Xj.createFromOptions=function(a,b){return X(Xj,a,b)};Xj.HAND_CONNECTIONS=Tj;\nexport {Xj as GestureRecognizer};function Yj(a){return{landmarks:a.landmarks,worldLandmarks:a.worldLandmarks,handednesses:a.handedness,handedness:a.handedness}}\nvar Zj=class extends Gj{constructor(a,b){super(new Bj(a,b),\"image_in\",\"norm_rect\",!1);this.landmarks=[];this.worldLandmarks=[];this.handedness=[];a=this.h=new qh;b=new R;z(a,R,1,b);this.u=new ph;z(this.h,ph,3,this.u);this.j=new oh;z(this.h,oh,2,this.j);Jd(this.j,3,1);B(this.j,2,.5);B(this.u,2,.5);B(this.h,4,.5)}get baseOptions(){return y(this.h,R,1)}set baseOptions(a){z(this.h,R,1,a)}o(a){\"numHands\"in a&&Jd(this.j,3,a.numHands??1);\"minHandDetectionConfidence\"in a&&B(this.j,2,a.minHandDetectionConfidence??\n.5);\"minTrackingConfidence\"in a&&B(this.h,4,a.minTrackingConfidence??.5);\"minHandPresenceConfidence\"in a&&B(this.u,2,a.minHandPresenceConfidence??.5);return this.l(a)}F(a,b){this.landmarks=[];this.worldLandmarks=[];this.handedness=[];Dj(this,a,b);return Yj(this)}G(a,b,c){this.landmarks=[];this.worldLandmarks=[];this.handedness=[];Ej(this,a,c,b);return Yj(this)}m(){var a=new yg;P(a,\"image_in\");P(a,\"norm_rect\");Q(a,\"hand_landmarks\");Q(a,\"world_hand_landmarks\");Q(a,\"handedness\");const b=new Wf;Oe(b,\nxh,this.h);const c=new sg;Kd(c,2,\"mediapipe.tasks.vision.hand_landmarker.HandLandmarkerGraph\");M(c,\"IMAGE:image_in\");M(c,\"NORM_RECT:norm_rect\");O(c,\"LANDMARKS:hand_landmarks\");O(c,\"WORLD_LANDMARKS:world_hand_landmarks\");O(c,\"HANDEDNESS:handedness\");c.o(b);xg(a,c);this.g.attachProtoVectorListener(\"hand_landmarks\",(d,e)=>{for(const f of d)d=Mg(f),this.landmarks.push(ai(d));U(this,e)});this.g.attachEmptyPacketListener(\"hand_landmarks\",d=>{U(this,d)});this.g.attachProtoVectorListener(\"world_hand_landmarks\",\n(d,e)=>{for(const f of d)d=Kg(f),this.worldLandmarks.push(bi(d));U(this,e)});this.g.attachEmptyPacketListener(\"world_hand_landmarks\",d=>{U(this,d)});this.g.attachProtoVectorListener(\"handedness\",(d,e)=>{var f=this.handedness,g=f.push;const h=[];for(const k of d){d=Eg(k);const w=[];for(const m of d.g())w.push({score:A(m,2)??0,index:Id(m,1)??0??-1,categoryName:t(u(m,3))??\"\"??\"\",displayName:t(u(m,4))??\"\"??\"\"});h.push(w)}g.call(f,...h);U(this,e)});this.g.attachEmptyPacketListener(\"handedness\",d=>{U(this,\nd)});a=a.g();this.setGraph(new Uint8Array(a),!0)}};Zj.prototype.detectForVideo=Zj.prototype.G;Zj.prototype.detect=Zj.prototype.F;Zj.prototype.setOptions=Zj.prototype.o;Zj.createFromModelPath=function(a,b){return X(Zj,a,{baseOptions:{modelAssetPath:b}})};Zj.createFromModelBuffer=function(a,b){return X(Zj,a,{baseOptions:{modelAssetBuffer:b}})};Zj.createFromOptions=function(a,b){return X(Zj,a,b)};Zj.HAND_CONNECTIONS=Tj;\nexport {Zj as HandLandmarker};var ak=zj([0,1],[1,2],[2,3],[3,7],[0,4],[4,5],[5,6],[6,8],[9,10],[11,12],[11,13],[13,15],[15,17],[15,19],[15,21],[17,19],[12,14],[14,16],[16,18],[16,20],[16,22],[18,20],[11,23],[12,24],[23,24],[23,25],[24,26],[25,27],[26,28],[27,29],[28,30],[29,31],[30,32],[27,31],[28,32]);function bk(a){a.h={faceLandmarks:[],faceBlendshapes:[],poseLandmarks:[],poseWorldLandmarks:[],poseSegmentationMasks:[],leftHandLandmarks:[],leftHandWorldLandmarks:[],rightHandLandmarks:[],rightHandWorldLandmarks:[]}}function ck(a){try{if(a.D)a.D(a.h);else return a.h}finally{Bi(a)}}function dk(a,b){a=Mg(a);b.push(ai(a))}\nvar Z=class extends Gj{constructor(a,b){super(new Bj(a,b),\"input_frames_image\",null,!1);this.h={faceLandmarks:[],faceBlendshapes:[],poseLandmarks:[],poseWorldLandmarks:[],poseSegmentationMasks:[],leftHandLandmarks:[],leftHandWorldLandmarks:[],rightHandLandmarks:[],rightHandWorldLandmarks:[]};this.outputPoseSegmentationMasks=this.outputFaceBlendshapes=!1;a=this.j=new Bh;b=new R;z(a,R,1,b);this.I=new ph;z(this.j,ph,2,this.I);this.W=new yh;z(this.j,yh,3,this.W);this.u=new dh;z(this.j,dh,4,this.u);this.O=\nnew hh;z(this.j,hh,5,this.O);this.A=new zh;z(this.j,zh,6,this.A);this.M=new Ah;z(this.j,Ah,7,this.M);B(this.u,2,.5);B(this.u,3,.3);B(this.O,2,.5);B(this.A,2,.5);B(this.A,3,.3);B(this.M,2,.5);B(this.I,2,.5)}get baseOptions(){return y(this.j,R,1)}set baseOptions(a){z(this.j,R,1,a)}o(a){\"minFaceDetectionConfidence\"in a&&B(this.u,2,a.minFaceDetectionConfidence??.5);\"minFaceSuppressionThreshold\"in a&&B(this.u,3,a.minFaceSuppressionThreshold??.3);\"minFacePresenceConfidence\"in a&&B(this.O,2,a.minFacePresenceConfidence??\n.5);\"outputFaceBlendshapes\"in a&&(this.outputFaceBlendshapes=!!a.outputFaceBlendshapes);\"minPoseDetectionConfidence\"in a&&B(this.A,2,a.minPoseDetectionConfidence??.5);\"minPoseSuppressionThreshold\"in a&&B(this.A,3,a.minPoseSuppressionThreshold??.3);\"minPosePresenceConfidence\"in a&&B(this.M,2,a.minPosePresenceConfidence??.5);\"outputPoseSegmentationMasks\"in a&&(this.outputPoseSegmentationMasks=!!a.outputPoseSegmentationMasks);\"minHandLandmarksConfidence\"in a&&B(this.I,2,a.minHandLandmarksConfidence??\n.5);return this.l(a)}F(a,b,c){const d=typeof b!==\"function\"?b:{};this.D=typeof b===\"function\"?b:c;bk(this);Dj(this,a,d);return ck(this)}G(a,b,c,d){const e=typeof c!==\"function\"?c:{};this.D=typeof c===\"function\"?c:d;bk(this);Ej(this,a,e,b);return ck(this)}m(){var a=new yg;P(a,\"input_frames_image\");Q(a,\"pose_landmarks\");Q(a,\"pose_world_landmarks\");Q(a,\"face_landmarks\");Q(a,\"left_hand_landmarks\");Q(a,\"left_hand_world_landmarks\");Q(a,\"right_hand_landmarks\");Q(a,\"right_hand_world_landmarks\");const b=new Wf,\nc=new ag;Kd(c,1,\"type.googleapis.com/mediapipe.tasks.vision.holistic_landmarker.proto.HolisticLandmarkerGraphOptions\");$f(c,this.j.g());const d=new sg;Kd(d,2,\"mediapipe.tasks.vision.holistic_landmarker.HolisticLandmarkerGraph\");Hd(d,8,ag,c);M(d,\"IMAGE:input_frames_image\");O(d,\"POSE_LANDMARKS:pose_landmarks\");O(d,\"POSE_WORLD_LANDMARKS:pose_world_landmarks\");O(d,\"FACE_LANDMARKS:face_landmarks\");O(d,\"LEFT_HAND_LANDMARKS:left_hand_landmarks\");O(d,\"LEFT_HAND_WORLD_LANDMARKS:left_hand_world_landmarks\");\nO(d,\"RIGHT_HAND_LANDMARKS:right_hand_landmarks\");O(d,\"RIGHT_HAND_WORLD_LANDMARKS:right_hand_world_landmarks\");d.o(b);xg(a,d);zi(this,a);this.g.attachProtoListener(\"pose_landmarks\",(e,f)=>{dk(e,this.h.poseLandmarks);U(this,f)});this.g.attachEmptyPacketListener(\"pose_landmarks\",e=>{U(this,e)});this.g.attachProtoListener(\"pose_world_landmarks\",(e,f)=>{var g=this.h.poseWorldLandmarks;e=Kg(e);g.push(bi(e));U(this,f)});this.g.attachEmptyPacketListener(\"pose_world_landmarks\",e=>{U(this,e)});this.outputPoseSegmentationMasks&&\n(O(d,\"POSE_SEGMENTATION_MASK:pose_segmentation_mask\"),Ai(this,\"pose_segmentation_mask\"),this.g.Z(\"pose_segmentation_mask\",(e,f)=>{this.h.poseSegmentationMasks=[Fj(this,e,!0,!this.D)];U(this,f)}),this.g.attachEmptyPacketListener(\"pose_segmentation_mask\",e=>{this.h.poseSegmentationMasks=[];U(this,e)}));this.g.attachProtoListener(\"face_landmarks\",(e,f)=>{dk(e,this.h.faceLandmarks);U(this,f)});this.g.attachEmptyPacketListener(\"face_landmarks\",e=>{U(this,e)});this.outputFaceBlendshapes&&(Q(a,\"extra_blendshapes\"),\nO(d,\"FACE_BLENDSHAPES:extra_blendshapes\"),this.g.attachProtoListener(\"extra_blendshapes\",(e,f)=>{var g=this.h.faceBlendshapes;this.outputFaceBlendshapes&&(e=Eg(e),g.push(Xh(e.g()??[])));U(this,f)}),this.g.attachEmptyPacketListener(\"extra_blendshapes\",e=>{U(this,e)}));this.g.attachProtoListener(\"left_hand_landmarks\",(e,f)=>{dk(e,this.h.leftHandLandmarks);U(this,f)});this.g.attachEmptyPacketListener(\"left_hand_landmarks\",e=>{U(this,e)});this.g.attachProtoListener(\"left_hand_world_landmarks\",(e,f)=>\n{var g=this.h.leftHandWorldLandmarks;e=Kg(e);g.push(bi(e));U(this,f)});this.g.attachEmptyPacketListener(\"left_hand_world_landmarks\",e=>{U(this,e)});this.g.attachProtoListener(\"right_hand_landmarks\",(e,f)=>{dk(e,this.h.rightHandLandmarks);U(this,f)});this.g.attachEmptyPacketListener(\"right_hand_landmarks\",e=>{U(this,e)});this.g.attachProtoListener(\"right_hand_world_landmarks\",(e,f)=>{var g=this.h.rightHandWorldLandmarks;e=Kg(e);g.push(bi(e));U(this,f)});this.g.attachEmptyPacketListener(\"right_hand_world_landmarks\",\ne=>{U(this,e)});a=a.g();this.setGraph(new Uint8Array(a),!0)}};Z.prototype.detectForVideo=Z.prototype.G;Z.prototype.detect=Z.prototype.F;Z.prototype.setOptions=Z.prototype.o;Z.createFromModelPath=function(a,b){return X(Z,a,{baseOptions:{modelAssetPath:b}})};Z.createFromModelBuffer=function(a,b){return X(Z,a,{baseOptions:{modelAssetBuffer:b}})};Z.createFromOptions=function(a,b){return X(Z,a,b)};Z.HAND_CONNECTIONS=Tj;\nZ.POSE_CONNECTIONS=ak;Z.FACE_LANDMARKS_LIPS=Ij;\nZ.FACE_LANDMARKS_LEFT_EYE=Jj;Z.FACE_LANDMARKS_LEFT_EYEBROW=Kj;\nZ.FACE_LANDMARKS_LEFT_IRIS=Lj;Z.FACE_LANDMARKS_RIGHT_EYE=Mj;\nZ.FACE_LANDMARKS_RIGHT_EYEBROW=Nj;Z.FACE_LANDMARKS_RIGHT_IRIS=Oj;\nZ.FACE_LANDMARKS_FACE_OVAL=Pj;Z.FACE_LANDMARKS_CONTOURS=Qj;\nZ.FACE_LANDMARKS_TESSELATION=Rj;export {Z as HolisticLandmarker};var ek=class extends Gj{constructor(a,b){super(new Bj(a,b),\"input_image\",\"norm_rect\",!0);this.j={classifications:[]};a=this.h=new Eh;b=new R;z(a,R,1,b)}get baseOptions(){return y(this.h,R,1)}set baseOptions(a){z(this.h,R,1,a)}o(a){var b=this.h,c=Vh(a,y(this.h,Wg,2));z(b,Wg,2,c);return this.l(a)}sa(a,b){this.j={classifications:[]};Dj(this,a,b);return this.j}ta(a,b,c){this.j={classifications:[]};Ej(this,a,c,b);return this.j}m(){var a=new yg;P(a,\"input_image\");P(a,\"norm_rect\");Q(a,\"classifications\");\nconst b=new Wf;Oe(b,Fh,this.h);const c=new sg;Kd(c,2,\"mediapipe.tasks.vision.image_classifier.ImageClassifierGraph\");M(c,\"IMAGE:input_image\");M(c,\"NORM_RECT:norm_rect\");O(c,\"CLASSIFICATIONS:classifications\");c.o(b);xg(a,c);this.g.attachProtoListener(\"classifications\",(d,e)=>{this.j=Yh(Rg(d));U(this,e)});this.g.attachEmptyPacketListener(\"classifications\",d=>{U(this,d)});a=a.g();this.setGraph(new Uint8Array(a),!0)}};ek.prototype.classifyForVideo=ek.prototype.ta;ek.prototype.classify=ek.prototype.sa;\nek.prototype.setOptions=ek.prototype.o;ek.createFromModelPath=function(a,b){return X(ek,a,{baseOptions:{modelAssetPath:b}})};ek.createFromModelBuffer=function(a,b){return X(ek,a,{baseOptions:{modelAssetBuffer:b}})};ek.createFromOptions=function(a,b){return X(ek,a,b)};export {ek as ImageClassifier};var fk=class extends Gj{constructor(a,b){super(new Bj(a,b),\"image_in\",\"norm_rect\",!0);this.h=new Gh;this.embeddings={embeddings:[]};a=this.h;b=new R;z(a,R,1,b)}get baseOptions(){return y(this.h,R,1)}set baseOptions(a){z(this.h,R,1,a)}o(a){var b=this.h,c=y(this.h,Yg,2);c=c?c.clone():new Yg;a.l2Normalize!==void 0?v(c,1,$b(a.l2Normalize)):\"l2Normalize\"in a&&v(c,1);a.quantize!==void 0?v(c,2,$b(a.quantize)):\"quantize\"in a&&v(c,2);z(b,Yg,2,c);return this.l(a)}za(a,b){Dj(this,a,b);return this.embeddings}Aa(a,\nb,c){Ej(this,a,c,b);return this.embeddings}m(){var a=new yg;P(a,\"image_in\");P(a,\"norm_rect\");Q(a,\"embeddings_out\");const b=new Wf;Oe(b,Hh,this.h);const c=new sg;Kd(c,2,\"mediapipe.tasks.vision.image_embedder.ImageEmbedderGraph\");M(c,\"IMAGE:image_in\");M(c,\"NORM_RECT:norm_rect\");O(c,\"EMBEDDINGS:embeddings_out\");c.o(b);xg(a,c);this.g.attachProtoListener(\"embeddings_out\",(d,e)=>{d=Vg(d);this.embeddings=$h(d);U(this,e)});this.g.attachEmptyPacketListener(\"embeddings_out\",d=>{U(this,d)});a=a.g();this.setGraph(new Uint8Array(a),\n!0)}};fk.cosineSimilarity=function(a,b){if(a.floatEmbedding&&b.floatEmbedding)a=di(a.floatEmbedding,b.floatEmbedding);else if(a.quantizedEmbedding&&b.quantizedEmbedding)a=di(ci(a.quantizedEmbedding),ci(b.quantizedEmbedding));else throw Error(\"Cannot compute cosine similarity between quantized and float embeddings.\");return a};fk.prototype.embedForVideo=fk.prototype.Aa;fk.prototype.embed=fk.prototype.za;fk.prototype.setOptions=fk.prototype.o;fk.createFromModelPath=function(a,b){return X(fk,a,{baseOptions:{modelAssetPath:b}})};\nfk.createFromModelBuffer=function(a,b){return 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\ No newline at end of file

From bea89363583f46fce9861cbaf9975cc09184710c Mon Sep 17 00:00:00 2001
From: vecna <vecna@globaleaks.org>
Date: Mon, 12 Oct 2026 13:51:11 +0200
Subject: [PATCH 5/9] large revision of video-loader and CLI tool

---
 .gitignore                                |  14 +-
 lab-js/loader.js                          | 529 ++++++++++++++++--
 lab-js/video-loader/FOLDER-DESCRIPTION.md |   5 +-
 lab-js/video-loader/analysis.js           |  80 ++-
 lab-js/video-loader/controller.js         | 430 ++++++++++++---
 lab-js/video-loader/session.js            | 104 +++-
 loader.html                               |  93 ++--
 scripts-dev/README.md                     |  18 +-
 scripts-dev/video-loader.cjs              | 249 +++++++--
 styles/loader.css                         | 269 +++++++++-
 tests/e2e/loader.spec.js                  |  86 ++-
 tests/fixtures/FOLDER-DESCRIPTION.md      |   8 +-
 tests/unit/loader.test.js                 | 343 +++++++++++-
 tests/unit/video-loader-analysis.test.js  |  64 +++
 tests/unit/video-loader-session.test.js   |  88 ++-
 tutorials/video-loader.md                 | 623 ++++++++++++++++++++--
 16 files changed, 2721 insertions(+), 282 deletions(-)

diff --git a/.gitignore b/.gitignore
index 31657c6..200996b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -23,13 +23,13 @@ EXTRACTED-text-*.md
 # Generated technical reference
 /docs/jsdoc/
 
-# Local long-form research videos. Small committed fixtures remain available to CI.
-/tests/fixtures/screencast-with-overlay.mp4
-/tests/fixtures/*[[]hpAi9PM0uAM[]].webm
-/tests/fixtures/*[[]SJWOErYvxVI[]].webm
-/tests/fixtures/*[[]PD-Cc5XpY5w[]].webm
-/tests/fixtures/*[[]suny-SfWufU[]].webm
-/tests/fixtures/*[[]HilDlfMxQKQ[]].mkv
+# Local research videos. The small synthetic MP4 remains committed for CI.
+/tests/fixtures/*.mp4
+/tests/fixtures/*.webm
+/tests/fixtures/*.mkv
+/tests/fixtures/*.mov
+/tests/fixtures/*.m4v
+!/tests/fixtures/my-moving-face.mp4
 
 # Local measurements produced while calibrating model thresholds.
 /tests/fixtures/synthetic-faces/lab-measurements.json
diff --git a/lab-js/loader.js b/lab-js/loader.js
index c7e2532..9523919 100644
--- a/lab-js/loader.js
+++ b/lab-js/loader.js
@@ -18,6 +18,13 @@ import {
 const elements = {
    file: document.getElementById('videoFile'),
    video: document.getElementById('video'),
+   viewer: document.getElementById('viewer'),
+   viewerLock: document.getElementById('analysisLockNotice'),
+   sourceControls: document.querySelector('.loader-source-controls'),
+   analysisSettings: document.getElementById('analysisSettings'),
+   sourceFileAction: document.getElementById('sourceFileAction'),
+   sourceFileName: document.getElementById('sourceFileName'),
+   sourceFileMeta: document.getElementById('sourceFileMeta'),
    placeholder: document.getElementById('placeholder'),
    seek: document.getElementById('videoSeek'),
    currentTime: document.getElementById('currentTimeLabel'),
@@ -27,10 +34,14 @@ const elements = {
    start: document.getElementById('startAnalysisBtn'),
    cancel: document.getElementById('cancelAnalysisBtn'),
    sampleInterval: document.getElementById('sampleInterval'),
-   baselineTime: document.getElementById('baselineTime'),
-   useCurrentTime: document.getElementById('useCurrentTimeBtn'),
+   selectBase: document.getElementById('selectBaseBtn'),
+   selectedBaseLabel: document.getElementById('selectedBaseLabel'),
+   analyzeFrame: document.getElementById('analyzeFrameBtn'),
    progress: document.getElementById('analysisProgress'),
    progressLabel: document.getElementById('analysisProgressLabel'),
+   analysisMessage: document.getElementById('analysisMessage'),
+   analysisMessageTitle: document.getElementById('analysisMessageTitle'),
+   analysisMessageBody: document.getElementById('analysisMessageBody'),
    baselineSummary: document.getElementById('baselineSummary'),
    timeline: document.getElementById('analysisTimeline'),
    eventList: document.getElementById('eventList'),
@@ -46,6 +57,8 @@ const controller = createVideoAnalysisController(elements.video);
 let pendingSourceMetadata = {};
 let sourceLoaded = false;
 let analysisRunning = false;
+let selectedBaseTime = null;
+let lastProgressState = { scanSampleIndex: 0, scanSampleTotal: null, mediaTime: null };
 
 /**
  * Formats a media timestamp as `mm:ss.mmm` or `hh:mm:ss.mmm`.
@@ -62,6 +75,163 @@ function formatMediaTime(seconds) {
    return `${prefix}${String(minutes).padStart(2, '0')}:${rest.toFixed(3).padStart(6, '0')}`;
 }
 
+/**
+ * Formats source byte size for the compact file identity line.
+ *
+ * @param {number} bytes Source file size.
+ * @returns {string} Human-readable binary size.
+ */
+function formatFileSize(bytes) {
+   if (!Number.isFinite(bytes) || bytes < 0) return 'unknown size';
+   const units = ['B', 'KiB', 'MiB', 'GiB'];
+   let value = bytes;
+   let unitIndex = 0;
+   while (value >= 1024 && unitIndex < units.length - 1) {
+      value /= 1024;
+      unitIndex += 1;
+   }
+   return `${value.toFixed(unitIndex === 0 ? 0 : 1)} ${units[unitIndex]}`;
+}
+
+/**
+ * Shows or clears the persistent analysis notice beside the run controls.
+ *
+ * @param {'error'|'success'|null} kind Notice kind, or null to clear it.
+ * @param {string} [title] Short notice heading.
+ * @param {string} [body] Actionable explanation.
+ * @returns {void}
+ */
+function renderAnalysisMessage(kind, title = '', body = '') {
+   elements.analysisMessage.hidden = kind === null;
+   if (kind === null) {
+      elements.analysisMessage.removeAttribute('data-kind');
+      elements.analysisMessageTitle.textContent = '';
+      elements.analysisMessageBody.textContent = '';
+      return;
+   }
+   elements.analysisMessage.dataset.kind = kind;
+   elements.analysisMessageTitle.textContent = title;
+   elements.analysisMessageBody.textContent = body;
+}
+
+/**
+ * Converts a stable controller failure reason into baseline guidance that can
+ * be understood without reading the technical log.
+ *
+ * @param {object} update Controller failure update.
+ * @returns {{title: string, body: string}} Visible baseline diagnostic.
+ */
+function baselineFailureCopy(update) {
+   const time = formatMediaTime(update.baselineTime);
+   if (update.reason === 'selected-frame-neighbours-insufficient') {
+      const windowSeconds = Math.max(0, update.baselineSearchEnd - update.baselineTime).toFixed(1);
+      return {
+         title: `Baseline not created at ${time}`,
+         body: `The selected frame may be usable, but only ${update.acceptedEmbeddings} of ${update.requiredEmbeddings} comparable nearby frames were found within ${windowSeconds} seconds. Rapid pose, hand, crop, or makeup changes prevented stable local calibration. Select a steadier moment and retry. Makeup already being present is not itself an error.`,
+      };
+   }
+   if (update.reason === 'selected-frame-is-not-comparable') {
+      return {
+         title: `Baseline frame is not comparable at ${time}`,
+         body: 'MediaPipe could not produce a usable aligned face crop. Select a frame with the complete face visible, sufficiently large, evenly exposed, and closer to a frontal pose.',
+      };
+   }
+   if (update.reason === 'face-api-could-not-describe-the-selected-frame') {
+      return {
+         title: `No face-api reference at ${time}`,
+         body: 'MediaPipe found usable face geometry, but face-api could not create a descriptor for the selected frame. Select a clearer frame with less occlusion or motion and retry.',
+      };
+   }
+   if (update.reason === 'selected-frame-descriptors-insufficient') {
+      return {
+         title: `Not enough face-api references near ${time}`,
+         body: `MediaPipe calibration succeeded, but only ${update.acceptedDescriptors} of ${update.requiredDescriptors} nearby frames returned a face-api descriptor. The selected frame may be usable; choose an interval with a clearer, less occluded face and retry.`,
+      };
+   }
+   if (update.reason === 'selected-frame-descriptors-are-not-stable') {
+      return {
+         title: `Baseline readings are unstable at ${time}`,
+         body: 'Nearby face-api descriptors varied too much to define one trustworthy reference. Select a steadier interval and retry.',
+      };
+   }
+   return {
+      title: `Baseline not created at ${time}`,
+      body: 'The selected point did not provide enough stable, comparable face observations. Choose a clearer and steadier moment; the technical log retains the precise rejection reasons.',
+   };
+}
+
+/**
+ * Seeks the source player from report evidence when analysis is idle. During
+ * analysis the controller owns media time, so evidence links remain disabled.
+ *
+ * @param {number} mediaTime Source timestamp.
+ * @returns {void}
+ */
+function seekToMediaTime(mediaTime) {
+   if (analysisRunning || !sourceLoaded) return;
+   elements.video.pause();
+   elements.video.currentTime = Math.max(0, Math.min(elements.video.duration || mediaTime, mediaTime));
+   syncVideoTimeline();
+}
+
+/**
+ * Creates a timestamp that acts as a source-video seek control.
+ *
+ * @param {number} mediaTime Source timestamp.
+ * @param {string} [extraClass] Optional presentation class.
+ * @returns {HTMLButtonElement} Clickable media-time control.
+ */
+function createMediaTimeButton(mediaTime, extraClass = '') {
+   const button = document.createElement('button');
+   button.type = 'button';
+   button.className = `loader-time-link ${extraClass}`.trim();
+   button.dataset.mediaTime = String(mediaTime);
+   button.textContent = formatMediaTime(mediaTime);
+   button.setAttribute('aria-label', `Show source video at ${formatMediaTime(mediaTime)}`);
+   button.disabled = analysisRunning;
+   button.addEventListener('click', () => seekToMediaTime(mediaTime));
+   return button;
+}
+
+/**
+ * Renders baseline calibration or scan-sample progress without pretending
+ * that browser media time is a reliable encoded-frame number.
+ *
+ * @param {object} update Controller update.
+ * @returns {void}
+ */
+function renderAnalysisProgress(update) {
+   if (update.phase === 'baseline') {
+      const accepted = update.baselineAcceptedSamples ?? 0;
+      const required = update.baselineRequiredSamples ?? 0;
+      const acceptedDescriptors = update.baselineAcceptedDescriptors ?? 0;
+      const requiredDescriptors = update.baselineRequiredDescriptors ?? 0;
+      const descriptorStage = required > 0 && accepted >= required;
+      elements.progress.max = Math.max(1, descriptorStage ? requiredDescriptors : required);
+      elements.progress.value = descriptorStage ? acceptedDescriptors : Math.min(accepted, required);
+      elements.progressLabel.textContent = descriptorStage
+         ? `baseline face-api ${acceptedDescriptors} / ${requiredDescriptors}`
+         : `baseline MediaPipe ${accepted} / ${required}`;
+      return;
+   }
+
+   const index = update.scanSampleIndex ?? lastProgressState.scanSampleIndex;
+   const total = update.scanSampleTotal ?? lastProgressState.scanSampleTotal;
+   const mediaTime = update.mediaTime ?? update.sample?.mediaTime ?? lastProgressState.mediaTime;
+   lastProgressState = { scanSampleIndex: index, scanSampleTotal: total, mediaTime };
+   elements.progress.max = Math.max(1, total || 1);
+   elements.progress.value = Math.min(index, total || index);
+   const percentage = Math.max(0, Math.min(100, Math.round((update.progress ?? 0) * 100)));
+   const count = total ? `${index} / ${total} samples` : `${index} samples`;
+   if (update.type === 'analysis-cancelled') {
+      elements.progressLabel.textContent = `${count} · paused at ${formatMediaTime(mediaTime)}`;
+   } else if (update.type === 'analysis-complete') {
+      elements.progressLabel.textContent = `${count} · complete`;
+   } else {
+      elements.progressLabel.textContent = `${count} · ${percentage}%`;
+   }
+}
+
 /**
  * Updates the visible application status.
  *
@@ -130,6 +300,45 @@ function syncVideoTimeline() {
    elements.duration.textContent = formatMediaTime(duration);
    elements.seek.max = String(duration);
    if (!elements.seek.matches(':active')) elements.seek.value = String(elements.video.currentTime || 0);
+   elements.analyzeFrame.textContent = `Analyse frame at ${formatMediaTime(elements.video.currentTime || 0)}`;
+   renderBaseSelection();
+}
+
+/**
+ * Shows the current scrub position as an explicit baseline action and keeps
+ * the already selected reference visible while the person reviews the video.
+ *
+ * @returns {void}
+ */
+function renderBaseSelection() {
+   const currentTime = Number(elements.video.currentTime) || 0;
+   const currentLabel = formatMediaTime(currentTime);
+   if (selectedBaseTime === null) {
+      elements.selectBase.textContent = `Select base at ${currentLabel}`;
+      elements.selectedBaseLabel.textContent = 'No base selected. Scrub the video, then select the displayed frame.';
+      return;
+   }
+
+   const selectedLabel = formatMediaTime(selectedBaseTime);
+   const isAtSelectedFrame = Math.abs(currentTime - selectedBaseTime) < 0.001;
+   elements.selectBase.textContent = isAtSelectedFrame
+      ? `Base selected at ${selectedLabel}`
+      : `Replace base with ${currentLabel}`;
+   elements.selectedBaseLabel.textContent = `Distance reference selected at ${selectedLabel}. It will be the first evidence-story entry.`;
+}
+
+/**
+ * Selects the currently displayed video position as the exact computational
+ * and visual baseline. Nearby frames may calibrate noise but cannot replace
+ * the selected source timestamp.
+ *
+ * @returns {void}
+ */
+function selectCurrentBase() {
+   selectedBaseTime = Number(elements.video.currentTime) || 0;
+   renderAnalysisMessage(null);
+   renderBaseSelection();
+   syncControls();
 }
 
 /**
@@ -154,14 +363,16 @@ function drawTimeline(session) {
    context.fillStyle = '#17130f';
    context.fillRect(0, 0, cssWidth, cssHeight);
 
-   const left = 112;
+   const left = 150;
    const right = 14;
    const plotWidth = cssWidth - left - right;
-   const duration = session?.source?.duration || 1;
-   const x = (mediaTime) => left + (mediaTime / duration) * plotWidth;
+   const rangeStart = session?.config?.analysisRange?.start ?? 0;
+   const rangeEnd = session?.config?.analysisRange?.end ?? session?.source?.duration ?? 1;
+   const duration = Math.max(0.001, rangeEnd - rangeStart);
+   const x = (mediaTime) => left + ((mediaTime - rangeStart) / duration) * plotWidth;
    const lanes = {
-      presence: { top: 18, height: 34, label: 'MediaPipe face' },
-      drift: { top: 74, height: 50, label: 'Visual drift' },
+      presence: { top: 18, height: 34, label: 'MediaPipe coverage' },
+      drift: { top: 74, height: 50, label: 'ImageEmbedder drift' },
       face: { top: 146, height: 48, label: 'face-api L2' },
    };
 
@@ -186,11 +397,22 @@ function drawTimeline(session) {
    context.beginPath();
    timelineSamples.forEach((sample, index) => {
       const px = x(sample.mediaTime);
-      const py = lanes.presence.top + (sample.facePresent ? 7 : lanes.presence.height - 7);
+      const py = lanes.presence.top + (sample.comparable
+         ? 7
+         : sample.facePresent ? lanes.presence.height / 2 : lanes.presence.height - 7);
       if (index === 0) context.moveTo(px, py);
       else context.lineTo(px, py);
    });
    context.stroke();
+   for (const sample of timelineSamples) {
+      context.fillStyle = sample.comparable ? '#7fe3b0' : sample.facePresent ? '#f3c747' : '#756b60';
+      const py = lanes.presence.top + (sample.comparable
+         ? 7
+         : sample.facePresent ? lanes.presence.height / 2 : lanes.presence.height - 7);
+      context.beginPath();
+      context.arc(x(sample.mediaTime), py, 2.25, 0, Math.PI * 2);
+      context.fill();
+   }
 
    context.strokeStyle = '#f3c747';
    context.beginPath();
@@ -218,19 +440,44 @@ function drawTimeline(session) {
       context.lineTo(left + plotWidth, thresholdY);
       context.stroke();
       context.setLineDash([]);
+      context.fillStyle = '#d8c9b5';
+      context.font = '700 9px ui-monospace, monospace';
+      context.textAlign = 'right';
+      context.fillText(
+         `trigger ${session.baseline.visualTriggerThreshold.toFixed(4)}`,
+         left + plotWidth - 6,
+         thresholdY - 7,
+      );
+      context.textAlign = 'left';
    }
 
    const faceApiEntries = session.journal.filter((entry) => entry.type === 'face-api-result');
+   const numericFaceDistances = faceApiEntries
+      .map((entry) => entry.faceDistance)
+      .filter(Number.isFinite);
+   const faceThreshold = session.config?.faceDistanceThreshold ?? 0.6;
+   const faceMax = Math.max(0.8, faceThreshold * 1.25, ...numericFaceDistances.map((value) => value * 1.1));
    context.fillStyle = '#ff8e72';
    for (const entry of faceApiEntries) {
-      const value = entry.faceDistance === null ? 0.8 : Math.min(1, entry.faceDistance);
       const px = x(entry.mediaTime);
-      const py = lanes.face.top + lanes.face.height - value * lanes.face.height;
-      context.beginPath();
-      context.arc(px, py, 3.5, 0, Math.PI * 2);
-      context.fill();
+      if (Number.isFinite(entry.faceDistance)) {
+         const py = lanes.face.top + lanes.face.height - (entry.faceDistance / faceMax) * lanes.face.height;
+         context.beginPath();
+         context.arc(px, py, 3.5, 0, Math.PI * 2);
+         context.fill();
+      } else {
+         const py = lanes.face.top + 7;
+         context.strokeStyle = '#ff8e72';
+         context.lineWidth = 1.6;
+         context.beginPath();
+         context.moveTo(px - 3, py - 3);
+         context.lineTo(px + 3, py + 3);
+         context.moveTo(px + 3, py - 3);
+         context.lineTo(px - 3, py + 3);
+         context.stroke();
+      }
    }
-   const faceThresholdY = lanes.face.top + lanes.face.height - 0.6 * lanes.face.height;
+   const faceThresholdY = lanes.face.top + lanes.face.height - (faceThreshold / faceMax) * lanes.face.height;
    context.strokeStyle = 'rgba(255, 142, 114, 0.7)';
    context.setLineDash([4, 4]);
    context.beginPath();
@@ -238,6 +485,12 @@ function drawTimeline(session) {
    context.lineTo(left + plotWidth, faceThresholdY);
    context.stroke();
    context.setLineDash([]);
+   context.fillStyle = '#d8c9b5';
+   context.font = '700 9px ui-monospace, monospace';
+   context.textAlign = 'right';
+   context.fillText('× no 2D face', left + plotWidth - 6, faceThresholdY - 7);
+   context.fillText(`threshold ${faceThreshold.toFixed(4)}`, left + plotWidth - 6, faceThresholdY + 12);
+   context.textAlign = 'left';
 }
 
 /**
@@ -249,7 +502,11 @@ function drawTimeline(session) {
  */
 function createEventCard(event, session) {
    const card = document.createElement('article');
-   card.className = `loader-event loader-event--${event.status}`;
+   card.className = [
+      'loader-event',
+      `loader-event--${event.status}`,
+      event.origin === 'manual' ? 'loader-event--manual' : '',
+   ].filter(Boolean).join(' ');
    card.dataset.eventId = event.id;
 
    if (event.selectedFrame) {
@@ -262,12 +519,14 @@ function createEventCard(event, session) {
    const body = document.createElement('div');
    body.className = 'loader-event__body';
    const title = document.createElement('h3');
-   const eventLabel = event.classification === 'identity-distance'
+   const eventLabel = event.classification === 'manual-frame-check'
+      ? 'Manual frame check'
+      : event.classification === 'identity-distance'
       ? 'Identity-distance transition'
       : event.classification === 'detector-divergence'
          ? 'Detector divergence — review'
          : event.status === 'confirmed' ? 'Mixed transition — review' : 'Candidate';
-   title.textContent = `${eventLabel} · ${formatMediaTime(event.anchorMediaTime)}`;
+   title.append(`${eventLabel} · `, createMediaTimeButton(event.anchorMediaTime));
    body.appendChild(title);
 
    const best = event.observations.at(-1);
@@ -275,15 +534,30 @@ function createEventCard(event, session) {
    metrics.className = 'loader-event__metrics';
    metrics.textContent = [
       best?.faceDistance === null ? 'face-api: no face' : `face-api: ${best?.faceDistance?.toFixed(4) ?? 'n/a'}`,
-      best?.visualDrift === null ? 'visual drift: n/a' : `visual drift: ${best.visualDrift.toFixed(4)}`,
+      best?.visualDrift === null ? 'ImageEmbedder drift: n/a' : `ImageEmbedder drift: ${best.visualDrift.toFixed(4)}`,
    ].join(' · ');
    body.appendChild(metrics);
 
    const reasons = document.createElement('p');
    reasons.className = 'loader-event__reasons';
-   reasons.textContent = `Trigger: ${(best?.reasons || []).join(', ') || 'verification'}`;
+   reasons.textContent = event.origin === 'manual'
+      ? 'Requested manually from the source position. This is one explicit check, not a repeated automatic transition.'
+      : `Trigger: ${(best?.reasons || []).join(', ') || 'verification'}`;
    body.appendChild(reasons);
 
+   if (event.origin === 'manual') {
+      const interpretation = document.createElement('p');
+      interpretation.className = 'loader-event__warning';
+      interpretation.textContent = event.interpretation === 'above-threshold'
+         ? `The face-api distance is at or above the ${(best?.faceThreshold ?? 0.6).toFixed(4)} threshold.`
+         : event.interpretation === 'below-threshold'
+            ? `The face-api distance is below the ${(best?.faceThreshold ?? 0.6).toFixed(4)} threshold.`
+            : event.interpretation === 'no-2d-face'
+               ? 'MediaPipe retained a comparable crop, but face-api returned no 2D face.'
+               : 'This frame did not meet MediaPipe comparability limits; any face-api value is shown only as diagnostic context.';
+      body.appendChild(interpretation);
+   }
+
    const caption = document.createElement('textarea');
    caption.value = event.caption;
    caption.placeholder = 'Explain why this before/after moment matters…';
@@ -360,7 +634,7 @@ function orderedStoryItems(session) {
  * Adds reversible story-order controls to one evidence card.
  *
  * @param {HTMLElement} actions Action container.
- * @param {string} itemId Event identifier or the reserved baseline identifier.
+ * @param {string} itemId Event identifier.
  * @param {object} session Current session.
  * @returns {void}
  */
@@ -370,7 +644,7 @@ function appendStoryOrderButtons(actions, itemId, session) {
    const earlier = document.createElement('button');
    earlier.type = 'button';
    earlier.textContent = 'Move earlier';
-   earlier.disabled = index <= 0;
+   earlier.disabled = index <= 0 || order[index - 1] === 'baseline';
    earlier.addEventListener('click', () => {
       applyEditorialCommand(session, { type: 'move-earlier', eventId: itemId });
       renderSession(session);
@@ -389,8 +663,8 @@ function appendStoryOrderButtons(actions, itemId, session) {
 }
 
 /**
- * Builds the computational reference as a reorderable story item. Its source
- * timestamp and role in the distance calculation remain unchanged.
+ * Builds the pinned computational reference as the first story item. Its
+ * source timestamp and role in the distance calculation remain unchanged.
  *
  * @param {object} baseline Session baseline.
  * @param {object} session Current session.
@@ -411,22 +685,32 @@ function createBaselineCard(baseline, session) {
    const body = document.createElement('div');
    body.className = 'loader-event__body';
    const title = document.createElement('h3');
-   title.textContent = `Computational reference · ${formatMediaTime(baseline.mediaTime)}`;
+   title.append('Baseline · distance reference · ', createMediaTimeButton(baseline.mediaTime));
    body.appendChild(title);
 
    const metrics = document.createElement('p');
    metrics.className = 'loader-event__metrics';
-   metrics.textContent = `visual trigger ${baseline.visualTriggerThreshold.toFixed(4)} · face noise ${baseline.faceSpread.toFixed(4)}`;
+   metrics.textContent = [
+      `ImageEmbedder trigger ${baseline.visualTriggerThreshold.toFixed(4)}`,
+      `face-api baseline spread ${baseline.faceSpread.toFixed(4)}`,
+      `face-api decision threshold ${(session.config?.faceDistanceThreshold ?? 0.6).toFixed(4)}`,
+   ].join(' · ');
    body.appendChild(metrics);
 
    const explanation = document.createElement('p');
    explanation.className = 'loader-event__reasons';
-   explanation.textContent = 'This frame anchors the distances. Move its story position without changing its source timestamp or computational role.';
+   explanation.textContent = 'This frame anchors the distances and remains the first story entry. Its source timestamp and computational role never change.';
    body.appendChild(explanation);
 
+   if (baseline.faceSpread > (session.config?.maximumBaselineFaceSpread ?? 0.3)) {
+      const warning = document.createElement('p');
+      warning.className = 'loader-event__warning';
+      warning.textContent = 'The selected baseline has elevated face-api variation. Treat distances near the decision threshold as review evidence.';
+      body.appendChild(warning);
+   }
+
    const actions = document.createElement('div');
    actions.className = 'loader-event__actions';
-   appendStoryOrderButtons(actions, 'baseline', session);
    const save = document.createElement('button');
    save.type = 'button';
    save.textContent = 'Download frame';
@@ -458,11 +742,14 @@ function renderSession(session) {
    }
 
    if (session?.baseline) {
-      elements.baselineSummary.textContent = [
-         `Reference ${formatMediaTime(session.baseline.mediaTime)}`,
-         `visual trigger ${session.baseline.visualTriggerThreshold.toFixed(4)}`,
-         `face noise ${session.baseline.faceSpread.toFixed(4)}`,
-      ].join(' · ');
+      elements.baselineSummary.replaceChildren(
+         document.createTextNode('Reference '),
+         createMediaTimeButton(session.baseline.mediaTime, 'loader-time-link--summary'),
+         document.createTextNode([
+            ` · visual trigger ${session.baseline.visualTriggerThreshold.toFixed(4)}`,
+            `face noise ${session.baseline.faceSpread.toFixed(4)}`,
+         ].join(' · ')),
+      );
    } else {
       elements.baselineSummary.textContent = 'Distance reference not acquired yet.';
    }
@@ -472,6 +759,7 @@ function renderSession(session) {
          `${session.summary.sampledFrames} samples`,
          `${session.summary.faceApiRuns} face-api checks`,
          `${session.summary.confirmedEvents} confirmed events`,
+         `${session.summary.manualChecks || 0} manual checks`,
       ].join(' · ');
       elements.downloadReport.disabled = session.status !== 'complete';
       elements.downloadLog.disabled = session.samples.length === 0;
@@ -480,18 +768,77 @@ function renderSession(session) {
    }
 }
 
+/**
+ * Compares paused-run settings with the editable UI values. It prevents a
+ * changed cadence or reference from being presented as a lossless resume.
+ *
+ * @param {object|null} session Current analysis session.
+ * @param {number} requestedSampleInterval Sampling interval requested by the UI or API.
+ * @param {number|null} requestedBaselineTime Baseline timestamp requested by the UI or API.
+ * @returns {{samplingChanged: boolean, baselineChanged: boolean}} Restart reasons.
+ */
+function getPausedSettingsChanges(session, requestedSampleInterval, requestedBaselineTime) {
+   if (session?.status !== 'cancelled') return { samplingChanged: false, baselineChanged: false };
+   const pausedSampleInterval = Number(session.config?.sampleIntervalSeconds);
+   const pausedBaselineTime = Number(
+      session.baseline?.selectedMediaTime
+      ?? session.baseline?.mediaTime
+      ?? session.config?.baselineSearchStart,
+   );
+   return {
+      samplingChanged: Number.isFinite(pausedSampleInterval)
+         && requestedSampleInterval !== pausedSampleInterval,
+      baselineChanged: requestedBaselineTime !== null
+         && Number.isFinite(pausedBaselineTime)
+         && Math.abs(Number(requestedBaselineTime) - pausedBaselineTime) >= 0.001,
+   };
+}
+
 /**
  * Updates controls after model, file, and analysis state changes.
  *
  * @returns {void}
  */
 function syncControls() {
-   elements.start.disabled = !sourceLoaded || !controller.isModelsReady() || analysisRunning;
+   const session = controller.getSession();
+   const resumable = session?.status === 'cancelled';
+   const changes = getPausedSettingsChanges(
+      session,
+      Number(elements.sampleInterval.value),
+      selectedBaseTime,
+   );
+   const restartLabel = changes.samplingChanged && changes.baselineChanged
+      ? 'Restart with new settings'
+      : changes.samplingChanged
+         ? 'Restart with new sampling'
+         : 'Restart with new baseline';
+   const restartRequired = changes.samplingChanged || changes.baselineChanged;
+   elements.start.disabled = !sourceLoaded
+      || !controller.isModelsReady()
+      || (!resumable && selectedBaseTime === null)
+      || analysisRunning;
+   elements.start.textContent = resumable
+      ? restartRequired ? restartLabel : 'Resume analysis'
+      : session?.status === 'complete'
+         ? 'Analyse again'
+         : session?.status === 'failed' ? 'Retry analysis' : 'Analyse video';
    elements.cancel.disabled = !analysisRunning;
+   elements.start.hidden = analysisRunning;
+   elements.cancel.hidden = !analysisRunning;
    elements.file.disabled = analysisRunning;
    elements.sampleInterval.disabled = analysisRunning;
-   elements.baselineTime.disabled = analysisRunning;
-   elements.useCurrentTime.disabled = !sourceLoaded || analysisRunning;
+   elements.selectBase.disabled = !sourceLoaded || analysisRunning;
+   elements.analyzeFrame.disabled = !sourceLoaded || !session?.baseline || analysisRunning;
+   elements.seek.disabled = !sourceLoaded || analysisRunning;
+   elements.video.inert = analysisRunning;
+   elements.video.setAttribute('aria-disabled', String(analysisRunning));
+   elements.viewer.classList.toggle('is-analysis-locked', analysisRunning);
+   elements.viewerLock.hidden = !analysisRunning;
+   elements.sourceControls.classList.toggle('is-muted', analysisRunning);
+   elements.analysisSettings.classList.toggle('is-muted', analysisRunning);
+   document.querySelectorAll('.loader-time-link').forEach((button) => {
+      button.disabled = analysisRunning;
+   });
 }
 
 /**
@@ -506,6 +853,16 @@ async function loadSelectedFile() {
    const session = await controller.loadFile(file, pendingSourceMetadata);
    pendingSourceMetadata = {};
    sourceLoaded = true;
+   selectedBaseTime = null;
+   elements.sourceFileAction.textContent = 'Replace local video';
+   elements.sourceFileName.textContent = session.source.name;
+   elements.sourceFileName.title = session.source.name;
+   elements.sourceFileMeta.textContent = [
+      session.source.width && session.source.height ? `${session.source.width} × ${session.source.height}` : null,
+      formatMediaTime(session.source.duration),
+      formatFileSize(session.source.size),
+   ].filter(Boolean).join(' · ');
+   renderAnalysisMessage(null);
    elements.placeholder.hidden = true;
    elements.seek.disabled = false;
    syncVideoTimeline();
@@ -523,23 +880,71 @@ async function loadSelectedFile() {
  * @returns {Promise<object>} Completed session.
  */
 async function startAnalysis(options = {}) {
+   const currentSession = controller.getSession();
+   const requestedSampleInterval = Number(options.sampleIntervalSeconds ?? elements.sampleInterval.value);
+   const requestedBaselineValue = options.baselineTime ?? selectedBaseTime;
+   const changes = getPausedSettingsChanges(currentSession, requestedSampleInterval, requestedBaselineValue);
+   const restartRequired = changes.samplingChanged || changes.baselineChanged;
+   const resume = options.resume ?? (currentSession?.status === 'cancelled' && !restartRequired);
+   const baselineWasExplicitlySelected = options.baselineTime !== undefined && options.baselineTime !== null
+      || selectedBaseTime !== null;
    analysisRunning = true;
+   renderAnalysisMessage(null);
    syncControls();
-   setStatus('working', 'analysing video');
-   elements.progress.value = 0;
-   elements.progressLabel.textContent = '0%';
+   setStatus('working', resume ? 'resuming analysis' : 'analysing video');
+   if (!resume) {
+      lastProgressState = { scanSampleIndex: 0, scanSampleTotal: null, mediaTime: null };
+      elements.progress.max = 1;
+      elements.progress.value = 0;
+      elements.progressLabel.textContent = 'building baseline';
+   }
    try {
       const session = await controller.analyze({
-         sampleIntervalSeconds: Number(options.sampleIntervalSeconds ?? elements.sampleInterval.value),
-         baselineTime: Number(options.baselineTime ?? elements.baselineTime.value),
+         resume,
+         sampleIntervalSeconds: requestedSampleInterval,
+         baselineTime: Number(options.baselineTime ?? selectedBaseTime ?? options.start ?? 0),
+         baselineSelectionMode: options.baselineSelectionMode
+            ?? (baselineWasExplicitlySelected ? 'anchored' : 'search'),
          start: options.start,
          end: options.end,
       });
       renderSession(session);
-      setStatus(session.status === 'complete' ? 'ready' : 'init', session.status);
+      setStatus('ready', session.status === 'complete' ? 'analysis complete' : 'analysis paused');
       return session;
    } catch (error) {
-      setStatus('error', error.message || String(error));
+      const failedBaseline = controller.getSession()?.status === 'failed';
+      setStatus('error', failedBaseline ? 'baseline not created' : error.message || String(error));
+      if (!failedBaseline) {
+         renderAnalysisMessage('error', 'Analysis stopped', error.message || String(error));
+      }
+      dispatchLoaderEvent('error', { message: error.message || String(error) });
+      throw error;
+   } finally {
+      analysisRunning = false;
+      syncControls();
+   }
+}
+
+/**
+ * Runs an explicit comparison for the frame currently shown by the source
+ * player and adds the result to the editable evidence story.
+ *
+ * @param {number} [mediaTime] Optional timestamp supplied by automation.
+ * @returns {Promise<object>} Created manual evidence event.
+ */
+async function analyzeCurrentFrame(mediaTime = Number(elements.video.currentTime) || 0) {
+   analysisRunning = true;
+   renderAnalysisMessage(null);
+   syncControls();
+   setStatus('working', 'analysing selected frame');
+   try {
+      const event = await controller.analyzeFrame(mediaTime);
+      renderSession(controller.getSession());
+      setStatus('ready', 'manual frame check added');
+      return event;
+   } catch (error) {
+      setStatus('error', 'frame check failed');
+      renderAnalysisMessage('error', 'Frame check failed', error.message || String(error));
       dispatchLoaderEvent('error', { message: error.message || String(error) });
       throw error;
    } finally {
@@ -555,13 +960,33 @@ controller.subscribe((update) => {
       dispatchLoaderEvent('models-ready');
       return;
    }
+   if (update.type === 'sample' || update.type === 'analysis-cancelled' || update.type === 'analysis-complete') {
+      renderAnalysisProgress(update);
+   }
    if (update.type === 'sample') {
-      const percentage = Math.max(0, Math.min(100, Math.round(update.progress * 100)));
-      elements.progress.value = percentage;
-      elements.progressLabel.textContent = `${percentage}%`;
       renderSession(update.session);
    }
-   if (update.type === 'baseline-ready' || update.type === 'event-confirmed') renderSession(update.session);
+   if (update.type === 'analysis-failed') {
+      const copy = baselineFailureCopy(update);
+      renderAnalysisMessage('error', copy.title, copy.body);
+      setStatus('error', 'baseline not created');
+   }
+   if (update.type === 'manual-frame-checked') {
+      const observation = update.event.observations.at(-1);
+      const distance = Number.isFinite(observation?.faceDistance)
+         ? `face-api distance ${observation.faceDistance.toFixed(4)}`
+         : 'face-api returned no numeric distance';
+      renderAnalysisMessage(
+         'success',
+         `Frame added at ${formatMediaTime(update.event.anchorMediaTime)}`,
+         `${distance}. Review the new manual card in the evidence story.`,
+      );
+      renderSession(update.session);
+   }
+   if (update.type === 'baseline-ready' || update.type === 'event-confirmed') {
+      renderSession(update.session);
+      syncControls();
+   }
    dispatchLoaderEvent(update.type, {
       progress: update.progress,
       eventId: update.event?.id,
@@ -578,11 +1003,11 @@ elements.seek.addEventListener('input', () => {
    elements.video.currentTime = Number(elements.seek.value) || 0;
    syncVideoTimeline();
 });
-elements.useCurrentTime.addEventListener('click', () => {
-   elements.baselineTime.value = elements.video.currentTime.toFixed(3);
-});
+elements.sampleInterval.addEventListener('change', syncControls);
+elements.selectBase.addEventListener('click', selectCurrentBase);
 elements.start.addEventListener('click', () => startAnalysis().catch(() => {}));
 elements.cancel.addEventListener('click', () => controller.cancel());
+elements.analyzeFrame.addEventListener('click', () => analyzeCurrentFrame().catch(() => {}));
 elements.undo.addEventListener('click', () => {
    const session = controller.getSession();
    if (session && undoEditorialCommand(session)) renderSession(session);
@@ -620,7 +1045,11 @@ window.gstmxxVideoLoader = {
    setSourceMetadata(metadata) { pendingSourceMetadata = { ...metadata }; },
    /** @param {object} options Analysis range and cadence. */
    analyze: (options = {}) => startAnalysis(options),
-   /** @returns {void} Requests cancellation at the next sample boundary. */
+   /** @param {number} mediaTime Source timestamp to compare with the baseline. */
+   analyzeFrame: (mediaTime) => analyzeCurrentFrame(mediaTime),
+   /** @returns {Promise<object>} Resumes the current cancelled analysis. */
+   resume: () => startAnalysis({ resume: true }),
+   /** @returns {void} Requests a resumable pause at the next sample boundary. */
    cancel: () => controller.cancel(),
 };
 
diff --git a/lab-js/video-loader/FOLDER-DESCRIPTION.md b/lab-js/video-loader/FOLDER-DESCRIPTION.md
index 3849dfb..e4e482f 100644
--- a/lab-js/video-loader/FOLDER-DESCRIPTION.md
+++ b/lab-js/video-loader/FOLDER-DESCRIPTION.md
@@ -8,7 +8,10 @@ It is intentionally independent from the live-camera state owned by
 FaceLandmarker and ImageEmbedder sampling, sparse face-api verification,
 baseline acquisition, and native-resolution evidence capture. It retains no
 pixel queue: only selected evidence frames survive, while neighbouring frames
-can be recovered later by seeking the original source.
+can be recovered later by seeking the original source. A person can also ask
+for an immediate comparison at the current source position after the baseline
+exists; that manual check is stored separately from automatically confirmed
+events.
 
 `analysis.js` contains pure numeric helpers and the trigger state machine. Its
 default face-api identity distance is `0.6`; ImageEmbedder drift is maintained
diff --git a/lab-js/video-loader/analysis.js b/lab-js/video-loader/analysis.js
index e593019..0901627 100644
--- a/lab-js/video-loader/analysis.js
+++ b/lab-js/video-loader/analysis.js
@@ -11,7 +11,11 @@
 export const DEFAULT_FACE_DISTANCE_THRESHOLD = 0.6;
 
 /**
- * Computes Euclidean distance between equal-length vectors.
+ * Computes Euclidean distance between equal-length vectors. The Video Loader
+ * uses this metric for face-api descriptors: `buildFaceBaseline()` uses it to
+ * choose the representative descriptor and measure baseline spread, while the
+ * controller uses it to compare later face-api checks with that baseline and
+ * the configured `0.6` identity-distance threshold.
  *
  * @param {ArrayLike<number>} left First vector.
  * @param {ArrayLike<number>} right Second vector.
@@ -29,7 +33,10 @@ export function euclideanDistance(left, right) {
 }
 
 /**
- * Computes cosine similarity between equal-length vectors.
+ * Computes cosine similarity between equal-length vectors. It is used only
+ * for MediaPipe ImageEmbedder vectors from aligned face crops. The baseline
+ * medoid and every later visual-drift sample use `1 - cosineSimilarity(...)`;
+ * this value is a generic appearance-change signal, not an identity score.
  *
  * @param {ArrayLike<number>} left First vector.
  * @param {ArrayLike<number>} right Second vector.
@@ -51,7 +58,10 @@ export function cosineSimilarity(left, right) {
 }
 
 /**
- * Returns the median without mutating the caller's collection.
+ * Returns the median without mutating the caller's collection. Baseline
+ * construction uses it to summarise face-api descriptor spread and the centre
+ * of ImageEmbedder drift; `medianAbsoluteDeviation()` calls it again to derive
+ * the adaptive visual-change threshold from baseline noise.
  *
  * @param {number[]} values Numeric values.
  * @returns {number} Median, or zero for an empty collection.
@@ -75,7 +85,12 @@ export function medianAbsoluteDeviation(values) {
 }
 
 /**
- * Picks the real vector with the smallest total distance to its peers.
+ * Picks the real vector with the smallest total distance to its peers. Both
+ * baseline builders use this helper: cosine distance selects a representative
+ * ImageEmbedder vector, and Euclidean distance selects a representative
+ * face-api descriptor. Keeping an observed vector, instead of an arithmetic
+ * mean, also lets the controller associate the visual baseline with a real
+ * sampled timestamp.
  *
  * @param {Array<ArrayLike<number>>} vectors Candidate vectors.
  * @param {function(ArrayLike<number>, ArrayLike<number>): number} metric Distance metric.
@@ -148,6 +163,59 @@ export function buildFaceBaseline(descriptors) {
    };
 }
 
+/**
+ * Uses the human-selected ImageEmbedder vector as the actual baseline while
+ * nearby vectors estimate normal visual noise. The Video Loader uses this
+ * variant only for an explicitly anchored baseline, ensuring that later
+ * visual drift is computed from the frame the person selected rather than a
+ * medoid several seconds later.
+ *
+ * @param {Array<ArrayLike<number>>} embeddings Nearby stable embeddings.
+ * @param {number} referenceIndex Index of the explicitly selected frame.
+ * @param {object} [options] Adaptive-trigger tuning.
+ * @param {number} [options.minimumDelta] Minimum drift above baseline noise.
+ * @param {number} [options.madMultiplier] Robust-noise multiplier.
+ * @returns {{embedding: number[], referenceIndex: number, medianDrift: number, mad: number, triggerThreshold: number, drifts: number[]}} Anchored baseline model.
+ */
+export function buildAnchoredEmbeddingBaseline(embeddings, referenceIndex, options = {}) {
+   if (!embeddings[referenceIndex]) throw new Error('Anchored embedding baseline requires a valid reference index.');
+   const minimumDelta = options.minimumDelta ?? 0.025;
+   const madMultiplier = options.madMultiplier ?? 6;
+   const embedding = Array.from(embeddings[referenceIndex]);
+   const drifts = embeddings.map((candidate) => visualDrift(candidate, embedding));
+   const medianDrift = median(drifts);
+   const mad = medianAbsoluteDeviation(drifts);
+   return {
+      embedding,
+      referenceIndex,
+      medianDrift,
+      mad,
+      triggerThreshold: medianDrift + Math.max(minimumDelta, mad * madMultiplier),
+      drifts,
+   };
+}
+
+/**
+ * Uses the face-api descriptor from the human-selected frame as the identity
+ * baseline and measures nearby descriptor spread around it. The Video Loader
+ * uses this for explicit baseline selection so every later L2 distance truly
+ * originates from the visible baseline card.
+ *
+ * @param {Array<ArrayLike<number>>} descriptors Nearby stable descriptors.
+ * @param {number} referenceIndex Index of the selected frame descriptor.
+ * @returns {{descriptor: number[], referenceIndex: number, spread: number}} Anchored face-api baseline.
+ */
+export function buildAnchoredFaceBaseline(descriptors, referenceIndex) {
+   if (!descriptors[referenceIndex]) throw new Error('Anchored face baseline requires a valid reference index.');
+   const descriptor = Array.from(descriptors[referenceIndex]);
+   const distances = descriptors.map((candidate) => euclideanDistance(candidate, descriptor));
+   return {
+      descriptor,
+      referenceIndex,
+      spread: median(distances),
+   };
+}
+
 /**
  * Computes ImageEmbedder visual drift from a baseline embedding.
  *
@@ -215,14 +283,14 @@ export function createTriggerDetector(options = {}) {
             recentVotes = [];
          }
 
-         if (sample.facePresent && sample.mediaTime - lastFaceApiAt >= heartbeatSeconds) {
+         if (sample.comparable && sample.mediaTime - lastFaceApiAt >= heartbeatSeconds) {
             reasons.push('identity-heartbeat');
          }
 
          previousPresence = sample.facePresent;
          const outsideCooldown = sample.mediaTime - lastFaceApiAt >= minimumFaceApiGapSeconds;
          return {
-            shouldVerify: sample.facePresent && reasons.length > 0 && outsideCooldown,
+            shouldVerify: sample.facePresent && sample.comparable && reasons.length > 0 && outsideCooldown,
             reasons: [...new Set(reasons)],
             observations,
          };
diff --git a/lab-js/video-loader/controller.js b/lab-js/video-loader/controller.js
index c4a328e..4192f22 100644
--- a/lab-js/video-loader/controller.js
+++ b/lab-js/video-loader/controller.js
@@ -16,6 +16,8 @@ import {
 } from '../config.js';
 import {
    DEFAULT_FACE_DISTANCE_THRESHOLD,
+   buildAnchoredEmbeddingBaseline,
+   buildAnchoredFaceBaseline,
    buildEmbeddingBaseline,
    buildFaceBaseline,
    createTriggerDetector,
@@ -28,6 +30,7 @@ import {
    buildReport,
    createAnalysisSession,
    recordEvidenceObservation,
+   recordManualFrameCheck,
    selectEventFrame,
    serializeTechnicalLog,
 } from './session.js';
@@ -37,6 +40,8 @@ import {
  * @property {number} sampleIntervalSeconds Media-time interval between lightweight samples.
  * @property {number} baselineEmbeddingSamples Stable ImageEmbedder samples required.
  * @property {number} baselineFaceSamples Stable face-api descriptors required.
+ * @property {number} anchoredBaselineEmbeddingSamples Comparable neighbours used around a selected frame.
+ * @property {number} anchoredBaselineFaceSamples Face descriptors used around a selected frame.
  * @property {number} faceDistanceThreshold Face-api L2 distance threshold.
  * @property {number} heartbeatSeconds Maximum media-time gap between face-api checks.
  * @property {number} minimumFaceApiGapSeconds Minimum media-time gap between face-api checks.
@@ -48,6 +53,8 @@ import {
  * @property {number} maximumBaselineYawStep Largest accepted approximate yaw change.
  * @property {number} maximumBaselineScaleStep Largest accepted face-width ratio change.
  * @property {number} maximumBaselineFaceSpread Largest accepted face-api baseline spread.
+ * @property {number} maximumAnchoredBaselineFaceSpread Largest spread accepted around a human-selected frame.
+ * @property {number} anchoredBaselineWindowSeconds Maximum local calibration span after a selected frame.
  * @property {number} alignedCropSize ImageEmbedder crop edge in pixels.
  * @property {number} evidenceRefinementRadiusSeconds Neighbourhood around a confirmed event.
  * @property {number} evidenceRefinementStepSeconds MediaPipe-only refinement cadence.
@@ -59,6 +66,8 @@ export const DEFAULT_VIDEO_LOADER_CONFIG = Object.freeze({
    sampleIntervalSeconds: 0.5,
    baselineEmbeddingSamples: 8,
    baselineFaceSamples: 3,
+   anchoredBaselineEmbeddingSamples: 4,
+   anchoredBaselineFaceSamples: 3,
    faceDistanceThreshold: DEFAULT_FACE_DISTANCE_THRESHOLD,
    heartbeatSeconds: 4,
    minimumFaceApiGapSeconds: 0.75,
@@ -70,6 +79,8 @@ export const DEFAULT_VIDEO_LOADER_CONFIG = Object.freeze({
    maximumBaselineYawStep: 0.15,
    maximumBaselineScaleStep: 0.15,
    maximumBaselineFaceSpread: 0.3,
+   maximumAnchoredBaselineFaceSpread: 0.5,
+   anchoredBaselineWindowSeconds: 3,
    alignedCropSize: 224,
    evidenceRefinementRadiusSeconds: 1,
    evidenceRefinementStepSeconds: 0.2,
@@ -418,6 +429,23 @@ export function createVideoAnalysisController(video, overrides = {}) {
       return session;
    }
 
+   /**
+    * Creates an empty analysis state for the already loaded source. This is
+    * used when a failed or completed run is intentionally started again; a
+    * resume operation keeps the existing session instead.
+    *
+    * @returns {object} Fresh session for the current source.
+    */
+   function resetLoadedSourceSession() {
+      const source = structuredClone(session.source);
+      const models = structuredClone(session.models);
+      session = createAnalysisSession(source, config);
+      session.models = models;
+      appendJournal(session, 'source-reused', { source: session.source });
+      triggerDetector.reset();
+      return session;
+   }
+
    /**
     * Runs face-api recognition against the current decoded frame.
     *
@@ -429,6 +457,96 @@ export function createVideoAnalysisController(video, overrides = {}) {
       return faceapi.detectSingleFace(video, options).withFaceLandmarks().withFaceDescriptor();
    }
 
+   /**
+    * Compares one person-selected source timestamp with the established
+    * baseline. The explicit request runs both detector families immediately,
+    * stores one lightweight sample, captures the native frame, and creates a
+    * manual story entry without pretending that it passed automatic voting.
+    *
+    * @param {number} mediaTime Source timestamp requested by the person.
+    * @returns {Promise<object>} Created manual evidence event.
+    */
+   async function analyzeFrame(mediaTime) {
+      if (!sourceFile || !session) throw new Error('Load a local video before analysing a frame.');
+      if (!modelsReady) await loadModels();
+      if (!session.baseline) throw new Error('Complete baseline acquisition before analysing an individual frame.');
+      if (session.status === 'analysing') throw new Error('Pause the running analysis before inspecting an individual frame.');
+
+      const target = Math.max(0, Math.min(video.duration, Number(mediaTime)));
+      if (!Number.isFinite(target)) throw new Error('The requested frame timestamp is invalid.');
+      await seekVideo(video, target);
+      const mediaPipeResult = faceLandmarker.detect(video);
+      const landmarks = mediaPipeResult?.faceLandmarks?.[0] || null;
+      let geometry = null;
+      let quality = null;
+      let embedding = null;
+      let comparable = false;
+      if (landmarks) {
+         geometry = drawAlignedFaceCrop(video, landmarks, cropCanvas);
+         const measured = measureCropQuality(cropCanvas);
+         quality = {
+            sharpness: measured.sharpness,
+            luma: measured.luma,
+            clippedRatio: measured.clippedRatio,
+            exposed: measured.exposed,
+         };
+         comparable = geometry.comparable && quality.exposed;
+         if (comparable) embedding = unpackEmbedding(imageEmbedder.embed(cropCanvas));
+      }
+      const drift = comparable && embedding
+         ? visualDrift(embedding, session.baseline.embedding)
+         : null;
+      const detection = await runFaceApi();
+      const faceDistance = detection?.descriptor
+         ? euclideanDistance(detection.descriptor, session.baseline.descriptor)
+         : null;
+      const interpretation = !comparable
+         ? 'not-comparable'
+         : !detection
+            ? 'no-2d-face'
+            : faceDistance >= config.faceDistanceThreshold
+               ? 'above-threshold'
+               : 'below-threshold';
+      const sample = appendSample(session, {
+         phase: 'manual',
+         mediaTime: target,
+         facePresent: !!landmarks,
+         comparable,
+         visualDrift: drift,
+         visualThreshold: session.baseline.visualTriggerThreshold,
+         geometry,
+         quality,
+      });
+      const observation = {
+         mediaTime: target,
+         outcome: interpretation === 'above-threshold' || interpretation === 'no-2d-face'
+            ? 'divergent'
+            : interpretation === 'below-threshold' ? 'matched' : 'not-comparable',
+         mediaPipeComparable: comparable,
+         faceApiDetected: !!detection,
+         faceDistance,
+         faceThreshold: config.faceDistanceThreshold,
+         visualDrift: drift,
+         reasons: ['manual-frame-check'],
+         quality,
+      };
+      appendJournal(session, 'face-api-result', { ...observation, origin: 'manual' });
+      const frame = captureSourceFrame(
+         video,
+         target,
+         quality?.sharpness || 0,
+         'manual-frame-check',
+      );
+      const event = recordManualFrameCheck(session, {
+         mediaTime: target,
+         interpretation,
+         observation,
+         frame,
+      });
+      notify('manual-frame-checked', { event, sample });
+      return event;
+   }
+
    /**
     * Finds a clear frame near a confirmed event without retaining a frame
     * queue. MediaPipe ranks timestamps first; face-api then verifies only the
@@ -540,20 +658,35 @@ export function createVideoAnalysisController(video, overrides = {}) {
     *
     * @param {Array<object>} embeddingSamples Accepted embedding samples.
     * @param {Array<object>} faceSamples Accepted face-api samples.
-    * @param {object} representativeFrame Frame nearest the embedding medoid.
+    * @param {object} representativeFrame Visible baseline frame.
+    * @param {number|null} anchoredMediaTime Human-selected timestamp, or null for automatic search.
     * @returns {object} Stored session baseline.
     */
-   function finaliseBaseline(embeddingSamples, faceSamples, representativeFrame) {
-      const embeddingBaseline = buildEmbeddingBaseline(
-         embeddingSamples.map((sample) => sample.embedding),
-         {
+   function finaliseBaseline(embeddingSamples, faceSamples, representativeFrame, anchoredMediaTime = null) {
+      const embeddingVectors = embeddingSamples.map((sample) => sample.embedding);
+      const faceDescriptors = faceSamples.map((sample) => sample.descriptor);
+      const embeddingReferenceIndex = anchoredMediaTime === null
+         ? null
+         : embeddingSamples.findIndex((sample) => Math.abs(sample.mediaTime - anchoredMediaTime) < 0.001);
+      const faceReferenceIndex = anchoredMediaTime === null
+         ? null
+         : faceSamples.findIndex((sample) => Math.abs(sample.mediaTime - anchoredMediaTime) < 0.001);
+      const embeddingBaseline = anchoredMediaTime === null
+         ? buildEmbeddingBaseline(embeddingVectors, {
             minimumDelta: config.minimumVisualDelta,
             madMultiplier: config.visualMadMultiplier,
-         },
-      );
-      const faceBaseline = buildFaceBaseline(faceSamples.map((sample) => sample.descriptor));
+         })
+         : buildAnchoredEmbeddingBaseline(embeddingVectors, embeddingReferenceIndex, {
+            minimumDelta: config.minimumVisualDelta,
+            madMultiplier: config.visualMadMultiplier,
+         });
+      const faceBaseline = anchoredMediaTime === null
+         ? buildFaceBaseline(faceDescriptors)
+         : buildAnchoredFaceBaseline(faceDescriptors, faceReferenceIndex);
       session.baseline = {
          mediaTime: representativeFrame.mediaTime,
+         selectionMode: anchoredMediaTime === null ? 'automatic-search' : 'selected-frame',
+         selectedMediaTime: anchoredMediaTime,
          embedding: embeddingBaseline.embedding,
          descriptor: faceBaseline.descriptor,
          faceSpread: faceBaseline.spread,
@@ -564,6 +697,8 @@ export function createVideoAnalysisController(video, overrides = {}) {
       };
       appendJournal(session, 'baseline-ready', {
          mediaTime: session.baseline.mediaTime,
+         selectionMode: session.baseline.selectionMode,
+         selectedMediaTime: session.baseline.selectedMediaTime,
          faceSpread: session.baseline.faceSpread,
          embeddingMedianDrift: session.baseline.embeddingMedianDrift,
          embeddingMad: session.baseline.embeddingMad,
@@ -578,38 +713,88 @@ export function createVideoAnalysisController(video, overrides = {}) {
     * @param {object} [options] Scan range and baseline search start.
     * @param {number} [options.start] First source timestamp.
     * @param {number} [options.end] Last source timestamp.
-    * @param {number} [options.baselineTime] Baseline search start.
+    * @param {number} [options.baselineTime] Baseline timestamp or automatic-search start.
+    * @param {'anchored'|'search'} [options.baselineSelectionMode] Whether baselineTime is exact or a search start.
     * @param {number} [options.sampleIntervalSeconds] Coarse scan cadence.
+    * @param {boolean} [options.resume] Continue a previously cancelled scan.
     * @returns {Promise<object>} Completed or cancelled session.
     */
    async function analyze(options = {}) {
       if (!sourceFile || !session) throw new Error('Load a local video before starting analysis.');
       if (!modelsReady) await loadModels();
-      const start = Math.max(0, Number(options.start ?? 0));
-      const end = Math.min(video.duration, Number(options.end ?? video.duration));
-      const baselineTime = Math.max(start, Math.min(end, Number(options.baselineTime ?? start)));
-      const step = Math.max(0.1, Number(options.sampleIntervalSeconds ?? config.sampleIntervalSeconds));
+      const resumeRequested = options.resume === true;
+      if (resumeRequested && session.status !== 'cancelled') {
+         throw new Error('Only a cancelled analysis can be resumed.');
+      }
+      if (!resumeRequested && session.status !== 'idle') resetLoadedSourceSession();
+      const previousRange = session.config.analysisRange;
+      const start = Math.max(0, Number(resumeRequested ? previousRange?.start ?? 0 : options.start ?? 0));
+      const end = Math.min(
+         video.duration,
+         Number(resumeRequested ? previousRange?.end ?? video.duration : options.end ?? video.duration),
+      );
+      const baselineTime = Math.max(start, Math.min(
+         end,
+         Number(resumeRequested ? session.config.baselineSearchStart ?? start : options.baselineTime ?? start),
+      ));
+      const baselineSelectionMode = resumeRequested
+         ? session.config.baselineSelectionMode ?? 'search'
+         : options.baselineSelectionMode === 'anchored' ? 'anchored' : 'search';
+      const anchoredBaseline = baselineSelectionMode === 'anchored';
+      const baselineSearchEnd = anchoredBaseline
+         ? Math.min(end, baselineTime + config.anchoredBaselineWindowSeconds)
+         : end;
+      const step = Math.max(0.1, Number(
+         resumeRequested ? session.config.sampleIntervalSeconds : options.sampleIntervalSeconds ?? config.sampleIntervalSeconds,
+      ));
       if (!(end > start)) throw new Error('Analysis end time must be greater than start time.');
 
+      const previousScanSamples = session.samples.filter((sample) => sample.phase === 'scan');
+      const lastScanSample = previousScanSamples.at(-1) || null;
+      const resumeWithBaseline = resumeRequested && !!session.baseline;
+      const resumeMediaTime = resumeWithBaseline && lastScanSample
+         ? Math.min(end, lastScanSample.mediaTime + step)
+         : start;
+      const scanSampleTotal = Math.ceil((end - start) / step) + 1;
+      let scanSampleIndex = previousScanSamples.length;
+      const baselineEmbeddingSamplesRequired = anchoredBaseline
+         ? config.anchoredBaselineEmbeddingSamples
+         : config.baselineEmbeddingSamples;
+      const baselineFaceSamplesRequired = anchoredBaseline
+         ? config.anchoredBaselineFaceSamples
+         : config.baselineFaceSamples;
+
       cancelRequested = false;
       video.pause();
       session.status = 'analysing';
       session.config.sampleIntervalSeconds = step;
       session.config.analysisRange = { start, end };
       session.config.baselineSearchStart = baselineTime;
+      session.config.baselineSelectionMode = baselineSelectionMode;
+      session.config.baselineSearchEnd = baselineSearchEnd;
       session.config.baselineSampleIntervalSeconds = Math.min(step, 0.25);
-      appendJournal(session, 'analysis-started', {
+      appendJournal(session, resumeRequested ? 'analysis-resumed' : 'analysis-started', {
          start,
          end,
          baselineTime,
+         baselineSelectionMode,
+         baselineSearchEnd,
          sampleIntervalSeconds: step,
+         resumeMediaTime: resumeRequested ? resumeMediaTime : null,
+      });
+      notify(resumeRequested ? 'analysis-resumed' : 'analysis-started', {
+         start,
+         end,
+         resumeMediaTime: resumeRequested ? resumeMediaTime : null,
+         scanSampleIndex,
+         scanSampleTotal,
       });
-      notify('analysis-started', { start, end });
 
       /** @type {Array<{mediaTime: number, embedding: number[], quality: object, signature: number[], geometry: object}>} */
       const embeddingSamples = [];
       /** @type {Array<{mediaTime: number, descriptor: number[]}>} */
       const faceSamples = [];
+      let anchoredBaselineFailure = null;
 
       /**
        * Drops an unstable baseline run while retaining the reason in the
@@ -634,10 +819,11 @@ export function createVideoAnalysisController(video, overrides = {}) {
          faceSamples.length = 0;
       }
 
-      let phase = 'baseline';
-      let mediaTime = baselineTime;
+      let phase = resumeWithBaseline ? 'scan' : 'baseline';
+      let mediaTime = resumeWithBaseline ? resumeMediaTime : baselineTime;
       while (mediaTime <= end + 0.0001) {
          if (cancelRequested) break;
+         if (phase === 'baseline' && mediaTime > baselineSearchEnd + 0.0001) break;
          const currentTime = Math.min(mediaTime, end);
          await seekVideo(video, currentTime);
          const mediaPipeResult = faceLandmarker.detect(video);
@@ -670,26 +856,41 @@ export function createVideoAnalysisController(video, overrides = {}) {
                || yawStep > config.maximumBaselineYawStep
                || scaleStep > config.maximumBaselineScaleStep;
             if (unstable) {
-               resetBaselineCandidate('appearance-or-pose-discontinuity', currentTime, {
-                  appearanceStep,
-                  continuityDrift,
-                  rollStep,
-                  yawStep,
-                  scaleStep,
+               const detail = { appearanceStep, continuityDrift, rollStep, yawStep, scaleStep };
+               if (anchoredBaseline) {
+                  appendJournal(session, 'anchored-baseline-neighbour-skipped', {
+                     reason: 'appearance-or-pose-discontinuity',
+                     mediaTime: currentTime,
+                     ...detail,
+                  });
+               } else {
+                  resetBaselineCandidate('appearance-or-pose-discontinuity', currentTime, detail);
+               }
+            }
+            if (!unstable || !anchoredBaseline) {
+               embeddingSamples.push({
+                  mediaTime: currentTime,
+                  embedding,
+                  quality,
+                  signature: appearanceSignature,
+                  geometry,
                });
             }
-            embeddingSamples.push({
-               mediaTime: currentTime,
-               embedding,
-               quality,
-               signature: appearanceSignature,
-               geometry,
-            });
-            if (embeddingSamples.length >= config.baselineEmbeddingSamples) {
-               const verificationSamples = [...embeddingSamples]
+            if (embeddingSamples.length >= baselineEmbeddingSamplesRequired) {
+               const rankedVerificationSamples = [...embeddingSamples]
                   .sort((left, right) => right.quality.sharpness - left.quality.sharpness)
-                  .slice(0, config.baselineFaceSamples)
+                  .filter((sample) => !anchoredBaseline
+                     || Math.abs(sample.mediaTime - baselineTime) >= 0.001);
+               const selectedSample = anchoredBaseline
+                  ? embeddingSamples.find((sample) => Math.abs(sample.mediaTime - baselineTime) < 0.001)
+                  : null;
+               const verificationSamples = [
+                  ...(selectedSample ? [selectedSample] : []),
+                  ...rankedVerificationSamples,
+               ]
+                  .slice(0, baselineFaceSamplesRequired)
                   .sort((left, right) => left.mediaTime - right.mediaTime);
+               faceSamples.length = 0;
                for (const candidate of verificationSamples) {
                   await seekVideo(video, candidate.mediaTime);
                   const detection = await runFaceApi();
@@ -701,38 +902,85 @@ export function createVideoAnalysisController(video, overrides = {}) {
                   }
                }
 
-               if (faceSamples.length >= config.baselineFaceSamples) {
-                  const provisionalFaceBaseline = buildFaceBaseline(faceSamples.map((sample) => sample.descriptor));
-                  if (provisionalFaceBaseline.spread <= config.maximumBaselineFaceSpread) {
-                     const provisionalEmbeddingBaseline = buildEmbeddingBaseline(
-                        embeddingSamples.map((sample) => sample.embedding),
-                        {
-                           minimumDelta: config.minimumVisualDelta,
-                           madMultiplier: config.visualMadMultiplier,
-                        },
-                     );
-                     const representative = embeddingSamples[provisionalEmbeddingBaseline.medoidIndex];
-                     await seekVideo(video, representative.mediaTime);
-                     const representativeFrame = captureSourceFrame(
-                        video,
-                        representative.mediaTime,
-                        representative.quality.sharpness,
-                        'baseline-embedding-medoid',
-                     );
-                     finaliseBaseline(embeddingSamples, faceSamples, representativeFrame);
-                     triggerDetector.markFaceApi(currentTime);
-                     notify('baseline-ready');
+               if (faceSamples.length >= baselineFaceSamplesRequired) {
+                  const selectedFaceIndex = anchoredBaseline
+                     ? faceSamples.findIndex((sample) => Math.abs(sample.mediaTime - baselineTime) < 0.001)
+                     : -1;
+                  if (anchoredBaseline && selectedFaceIndex < 0) {
+                     resetBaselineCandidate('selected-frame-face-api-missing', currentTime);
+                     anchoredBaselineFailure = 'face-api-could-not-describe-the-selected-frame';
                   } else {
-                     resetBaselineCandidate('face-descriptor-spread', currentTime, {
-                        faceSpread: provisionalFaceBaseline.spread,
-                     });
+                     const provisionalFaceBaseline = anchoredBaseline
+                        ? buildAnchoredFaceBaseline(
+                           faceSamples.map((sample) => sample.descriptor),
+                           selectedFaceIndex,
+                        )
+                        : buildFaceBaseline(faceSamples.map((sample) => sample.descriptor));
+                     const maximumFaceSpread = anchoredBaseline
+                        ? config.maximumAnchoredBaselineFaceSpread
+                        : config.maximumBaselineFaceSpread;
+                     if (provisionalFaceBaseline.spread <= maximumFaceSpread) {
+                        const provisionalEmbeddingBaseline = buildEmbeddingBaseline(
+                           embeddingSamples.map((sample) => sample.embedding),
+                           {
+                              minimumDelta: config.minimumVisualDelta,
+                              madMultiplier: config.visualMadMultiplier,
+                           },
+                        );
+                        const representative = anchoredBaseline
+                           ? selectedSample
+                           : embeddingSamples[provisionalEmbeddingBaseline.medoidIndex];
+                        await seekVideo(video, representative.mediaTime);
+                        const representativeFrame = captureSourceFrame(
+                           video,
+                           representative.mediaTime,
+                           representative.quality.sharpness,
+                           anchoredBaseline ? 'baseline-selected-frame' : 'baseline-embedding-medoid',
+                        );
+                        finaliseBaseline(
+                           embeddingSamples,
+                           faceSamples,
+                           representativeFrame,
+                           anchoredBaseline ? baselineTime : null,
+                        );
+                        triggerDetector.markFaceApi(currentTime);
+                        notify('baseline-ready');
+                     } else {
+                        resetBaselineCandidate('face-descriptor-spread', currentTime, {
+                           faceSpread: provisionalFaceBaseline.spread,
+                        });
+                        if (anchoredBaseline) anchoredBaselineFailure = 'selected-frame-descriptors-are-not-stable';
+                     }
                   }
                } else {
-                  resetBaselineCandidate('face-api-baseline-incomplete', currentTime);
+                  const selectedFaceAvailable = faceSamples.some(
+                     (sample) => Math.abs(sample.mediaTime - baselineTime) < 0.001,
+                  );
+                  if (anchoredBaseline && selectedFaceAvailable) {
+                     appendJournal(session, 'anchored-baseline-face-sample-incomplete', {
+                        mediaTime: currentTime,
+                        acceptedDescriptors: faceSamples.length,
+                        requiredDescriptors: baselineFaceSamplesRequired,
+                     });
+                  } else {
+                     resetBaselineCandidate('face-api-baseline-incomplete', currentTime);
+                     if (anchoredBaseline) {
+                        anchoredBaselineFailure = 'face-api-could-not-describe-the-selected-frame';
+                     }
+                  }
                }
             }
          } else if (!session.baseline && embeddingSamples.length) {
-            resetBaselineCandidate('non-comparable-frame', currentTime);
+            if (anchoredBaseline) {
+               appendJournal(session, 'anchored-baseline-neighbour-skipped', {
+                  reason: 'non-comparable-frame',
+                  mediaTime: currentTime,
+               });
+            } else {
+               resetBaselineCandidate('non-comparable-frame', currentTime);
+            }
+         } else if (!session.baseline && anchoredBaseline && currentTime === baselineTime) {
+            anchoredBaselineFailure = 'selected-frame-is-not-comparable';
          }
 
          const drift = session.baseline && embedding
@@ -748,6 +996,10 @@ export function createVideoAnalysisController(video, overrides = {}) {
             geometry,
             quality,
          });
+         if (phase === 'scan') {
+            scanSampleIndex += 1;
+            session.config.lastScanMediaTime = currentTime;
+         }
 
          if (session.baseline && phase === 'scan') {
             const decision = triggerDetector.evaluate(sample);
@@ -764,6 +1016,7 @@ export function createVideoAnalysisController(video, overrides = {}) {
                const observation = {
                   mediaTime: currentTime,
                   outcome: divergent ? 'divergent' : 'matched',
+                  mediaPipeComparable: comparable,
                   faceApiDetected: !!detection,
                   faceDistance,
                   faceThreshold: config.faceDistanceThreshold,
@@ -789,7 +1042,14 @@ export function createVideoAnalysisController(video, overrides = {}) {
             sample,
             phase,
             progress: phase === 'baseline' ? 0 : (currentTime - start) / (end - start),
+            baselineAcceptedSamples: phase === 'baseline' ? embeddingSamples.length : null,
+            baselineRequiredSamples: baselineEmbeddingSamplesRequired,
+            baselineAcceptedDescriptors: phase === 'baseline' ? faceSamples.length : null,
+            baselineRequiredDescriptors: baselineFaceSamplesRequired,
+            scanSampleIndex,
+            scanSampleTotal,
          });
+         if (anchoredBaselineFailure && phase === 'baseline') break;
          if (session.baseline && phase === 'baseline') {
             phase = 'scan';
             mediaTime = start;
@@ -807,17 +1067,62 @@ export function createVideoAnalysisController(video, overrides = {}) {
 
       if (!session.baseline && !cancelRequested) {
          session.status = 'failed';
-         appendJournal(session, 'analysis-failed', { reason: 'baseline-not-found' });
-         notify('analysis-failed', { reason: 'baseline-not-found' });
+         const selectedEmbeddingAvailable = embeddingSamples.some(
+            (sample) => Math.abs(sample.mediaTime - baselineTime) < 0.001,
+         );
+         const selectedDescriptorAvailable = faceSamples.some(
+            (sample) => Math.abs(sample.mediaTime - baselineTime) < 0.001,
+         );
+         const reason = anchoredBaselineFailure || (anchoredBaseline
+            ? selectedEmbeddingAvailable && embeddingSamples.length < baselineEmbeddingSamplesRequired
+               ? 'selected-frame-neighbours-insufficient'
+               : selectedDescriptorAvailable && faceSamples.length < baselineFaceSamplesRequired
+                  ? 'selected-frame-descriptors-insufficient'
+               : 'selected-frame-window-incomplete'
+            : 'baseline-not-found');
+         const failureDetail = {
+            reason,
+            baselineTime,
+            baselineSearchEnd,
+            acceptedEmbeddings: embeddingSamples.length,
+            requiredEmbeddings: baselineEmbeddingSamplesRequired,
+            acceptedDescriptors: faceSamples.length,
+            requiredDescriptors: baselineFaceSamplesRequired,
+         };
+         appendJournal(session, 'analysis-failed', failureDetail);
+         notify('analysis-failed', failureDetail);
+         if (anchoredBaseline) {
+            if (reason === 'selected-frame-neighbours-insufficient') {
+               throw new Error(`The selected baseline at ${baselineTime.toFixed(3)} s had only ${embeddingSamples.length} of ${baselineEmbeddingSamplesRequired} comparable nearby frames. Choose a steadier moment and try again.`);
+            }
+            if (reason === 'selected-frame-descriptors-insufficient') {
+               throw new Error(`The selected baseline at ${baselineTime.toFixed(3)} s had only ${faceSamples.length} of ${baselineFaceSamplesRequired} nearby face-api descriptors. Choose a clearer interval and try again.`);
+            }
+            throw new Error(`The selected baseline at ${baselineTime.toFixed(3)} s could not produce a stable local reference (${reason}). Choose a clearer frame and try again.`);
+         }
          throw new Error('No stable face baseline was found in the selected interval.');
       }
 
       session.status = cancelRequested ? 'cancelled' : 'complete';
       session.updatedAt = new Date().toISOString();
+      if (cancelRequested && Number.isFinite(session.config.lastScanMediaTime)) {
+         await seekVideo(video, session.config.lastScanMediaTime);
+      }
       appendJournal(session, cancelRequested ? 'analysis-cancelled' : 'analysis-complete', {
          summary: session.summary,
+         lastScanMediaTime: session.config.lastScanMediaTime ?? null,
+         scanSampleIndex,
+         scanSampleTotal,
+      });
+      const cancelledProgress = Number.isFinite(session.config.lastScanMediaTime)
+         ? Math.max(0, Math.min(1, (session.config.lastScanMediaTime - start) / (end - start)))
+         : 0;
+      notify(session.status === 'complete' ? 'analysis-complete' : 'analysis-cancelled', {
+         progress: session.status === 'complete' ? 1 : cancelledProgress,
+         mediaTime: session.config.lastScanMediaTime ?? null,
+         scanSampleIndex,
+         scanSampleTotal,
       });
-      notify(session.status === 'complete' ? 'analysis-complete' : 'analysis-cancelled');
       return session;
    }
 
@@ -856,6 +1161,7 @@ export function createVideoAnalysisController(video, overrides = {}) {
       loadModels,
       loadFile,
       analyze,
+      analyzeFrame,
       cancel,
       exportBundle,
       getSession: () => session,
diff --git a/lab-js/video-loader/session.js b/lab-js/video-loader/session.js
index 900d17a..c8a25f4 100644
--- a/lab-js/video-loader/session.js
+++ b/lab-js/video-loader/session.js
@@ -8,7 +8,7 @@
  */
 
 /** Current machine-readable session and log schema version. */
-export const VIDEO_ANALYSIS_SCHEMA_VERSION = 2;
+export const VIDEO_ANALYSIS_SCHEMA_VERSION = 4;
 
 /**
  * Creates a stable random identifier with a deterministic fallback for older
@@ -62,6 +62,7 @@ export function createAnalysisSession(source, config) {
          mediaPipeFaces: 0,
          faceApiRuns: 0,
          confirmedEvents: 0,
+         manualChecks: 0,
       },
    };
 }
@@ -117,10 +118,12 @@ export function appendSample(session, sample) {
 export function recordEvidenceObservation(session, observation, mergeWindowSeconds = 3) {
    const latest = session.events.at(-1);
    const mayMerge = latest
+      && latest.origin !== 'manual'
       && latest.status !== 'rejected'
       && observation.mediaTime - latest.endMediaTime <= mergeWindowSeconds;
    const event = mayMerge ? latest : {
       id: createId('event'),
+      origin: 'automatic',
       startMediaTime: observation.mediaTime,
       endMediaTime: observation.mediaTime,
       anchorMediaTime: observation.mediaTime,
@@ -144,29 +147,35 @@ export function recordEvidenceObservation(session, observation, mergeWindowSecon
    event.endMediaTime = observation.mediaTime;
    event.recommendedClip.end = observation.mediaTime + 3;
 
-   const divergent = event.observations.filter((item) => item.outcome === 'divergent');
+   const divergent = event.observations.filter((item) => (
+      item.outcome === 'divergent'
+      && (Number.isFinite(item.faceDistance) || item.mediaPipeComparable === true)
+   ));
    if (divergent.length >= 2 && event.status !== 'confirmed') {
       event.status = 'confirmed';
       event.anchorMediaTime = divergent.reduce((best, item) => (
          (item.quality?.sharpness || 0) > (best.quality?.sharpness || 0) ? item : best
       )).mediaTime;
       session.editorial.storyOrder.push(event.id);
-      session.editorial.storyOrder.sort((leftId, rightId) => {
-         const leftTime = leftId === 'baseline'
-            ? session.baseline?.mediaTime ?? Number.POSITIVE_INFINITY
-            : session.events.find((candidate) => candidate.id === leftId)?.anchorMediaTime ?? Number.POSITIVE_INFINITY;
-         const rightTime = rightId === 'baseline'
-            ? session.baseline?.mediaTime ?? Number.POSITIVE_INFINITY
-            : session.events.find((candidate) => candidate.id === rightId)?.anchorMediaTime ?? Number.POSITIVE_INFINITY;
-         return leftTime - rightTime;
-      });
+      const chronologicalEvents = session.editorial.storyOrder
+         .filter((itemId) => itemId !== 'baseline')
+         .sort((leftId, rightId) => {
+            const leftTime = session.events.find((candidate) => candidate.id === leftId)?.anchorMediaTime
+               ?? Number.POSITIVE_INFINITY;
+            const rightTime = session.events.find((candidate) => candidate.id === rightId)?.anchorMediaTime
+               ?? Number.POSITIVE_INFINITY;
+            return leftTime - rightTime;
+         });
+      session.editorial.storyOrder = ['baseline', ...chronologicalEvents];
       session.summary.confirmedEvents += 1;
    }
    const distanceDivergences = divergent.filter((item) => (
       Number.isFinite(item.faceDistance)
       && item.faceDistance >= (item.faceThreshold ?? session.config.faceDistanceThreshold)
    ));
-   const missingDetections = divergent.filter((item) => item.faceApiDetected === false);
+   const missingDetections = divergent.filter((item) => (
+      item.mediaPipeComparable === true && item.faceApiDetected === false
+   ));
    if (distanceDivergences.length >= 2) {
       event.classification = 'identity-distance';
       event.reviewRequired = false;
@@ -180,6 +189,64 @@ export function recordEvidenceObservation(session, observation, mergeWindowSecon
    return event;
 }
 
+/**
+ * Adds one person-requested source-frame comparison to the evidence story.
+ * Unlike automatic events, a manual check needs no repeated divergence vote:
+ * its meaning is that a person explicitly asked to inspect this timestamp.
+ * Image bytes remain on the session event and are omitted from the log.
+ *
+ * @param {object} session Analysis session with an acquired baseline.
+ * @param {object} check Manual comparison result.
+ * @param {number} check.mediaTime Exact source timestamp.
+ * @param {'below-threshold'|'above-threshold'|'no-2d-face'|'not-comparable'} check.interpretation Human-facing result category.
+ * @param {object} check.observation Model result stored with the event.
+ * @param {{mediaTime: number, dataUrl: string, width: number, height: number, sharpness: number, reason: string}} check.frame Native source frame.
+ * @returns {object} Created manual evidence event.
+ */
+export function recordManualFrameCheck(session, check) {
+   if (!session.baseline) throw new Error('A baseline is required before a frame can be compared.');
+   const event = {
+      id: createId('event'),
+      origin: 'manual',
+      startMediaTime: check.mediaTime,
+      endMediaTime: check.mediaTime,
+      anchorMediaTime: check.mediaTime,
+      status: 'confirmed',
+      classification: 'manual-frame-check',
+      interpretation: check.interpretation,
+      reviewRequired: true,
+      promoted: false,
+      caption: '',
+      observations: [structuredClone(check.observation)],
+      selectedFrame: { ...check.frame },
+      recommendedClip: {
+         start: Math.max(0, check.mediaTime - 2),
+         end: check.mediaTime + 3,
+      },
+   };
+   session.events.push(event);
+   session.editorial.storyOrder.push(event.id);
+   session.summary.manualChecks = (session.summary.manualChecks || 0) + 1;
+   appendJournal(session, 'manual-frame-checked', {
+      eventId: event.id,
+      mediaTime: check.mediaTime,
+      interpretation: check.interpretation,
+      mediaPipeComparable: check.observation.mediaPipeComparable,
+      faceApiDetected: check.observation.faceApiDetected,
+      faceDistance: check.observation.faceDistance,
+      faceThreshold: check.observation.faceThreshold,
+      visualDrift: check.observation.visualDrift,
+      frame: {
+         mediaTime: check.frame.mediaTime,
+         width: check.frame.width,
+         height: check.frame.height,
+         sharpness: check.frame.sharpness,
+         reason: check.frame.reason,
+      },
+   });
+   return event;
+}
+
 /**
  * Attaches or replaces the selected high-resolution frame for an event.
  *
@@ -199,14 +266,14 @@ export function selectEventFrame(event, frame) {
  * @param {object} session Analysis session.
  * @param {object} command Editorial command.
  * @param {'promote'|'reject'|'caption'|'move-earlier'|'move-later'} command.type Command type.
- * @param {string} command.eventId Event identifier or the reserved `baseline` story item.
+ * @param {string} command.eventId Event identifier. The baseline is pinned and cannot receive editorial commands.
  * @param {string} [command.value] Caption value.
  * @returns {boolean} Whether the command changed an event.
  */
 export function applyEditorialCommand(session, command) {
    const event = session.events.find((candidate) => candidate.id === command.eventId);
    const isMovement = command.type === 'move-earlier' || command.type === 'move-later';
-   if (!event && !(isMovement && command.eventId === 'baseline')) return false;
+   if (!event) return false;
    const before = {
       event: event ? {
          status: event.status,
@@ -226,6 +293,7 @@ export function applyEditorialCommand(session, command) {
       const offset = command.type === 'move-earlier' ? -1 : 1;
       const target = index + offset;
       if (index < 0 || target < 0 || target >= session.editorial.storyOrder.length) return false;
+      if (session.editorial.storyOrder[target] === 'baseline') return false;
       [session.editorial.storyOrder[index], session.editorial.storyOrder[target]] = [
          session.editorial.storyOrder[target],
          session.editorial.storyOrder[index],
@@ -299,6 +367,8 @@ export function buildReport(session) {
          endMediaTime: event.endMediaTime,
          anchorMediaTime: event.anchorMediaTime,
          classification: event.classification,
+         origin: event.origin || 'automatic',
+         interpretation: event.interpretation || null,
          reviewRequired: event.reviewRequired,
          promoted: event.promoted,
          caption: event.caption,
@@ -336,6 +406,8 @@ export function buildReport(session) {
       storyOrder,
       baseline: session.baseline ? {
          mediaTime: session.baseline.mediaTime,
+         selectionMode: session.baseline.selectionMode,
+         selectedMediaTime: session.baseline.selectedMediaTime,
          faceSpread: session.baseline.faceSpread,
          embeddingMedianDrift: session.baseline.embeddingMedianDrift,
          embeddingMad: session.baseline.embeddingMad,
@@ -370,6 +442,8 @@ export function serializeTechnicalLog(session) {
       models: session.models,
       baseline: session.baseline ? {
          mediaTime: session.baseline.mediaTime,
+         selectionMode: session.baseline.selectionMode,
+         selectedMediaTime: session.baseline.selectedMediaTime,
          faceSpread: session.baseline.faceSpread,
          embeddingMedianDrift: session.baseline.embeddingMedianDrift,
          embeddingMad: session.baseline.embeddingMad,
@@ -385,6 +459,8 @@ export function serializeTechnicalLog(session) {
       anchorMediaTime: event.anchorMediaTime,
       status: event.status,
       classification: event.classification,
+      origin: event.origin || 'automatic',
+      interpretation: event.interpretation || null,
       reviewRequired: event.reviewRequired,
       promoted: event.promoted,
       caption: event.caption,
diff --git a/loader.html b/loader.html
index 86d527e..f05f9b6 100644
--- a/loader.html
+++ b/loader.html
@@ -56,41 +56,64 @@ <h1 id="loader-title" class="loader-title">Makeup video analyser</h1>
       <div id="viewer" class="loader-viewer">
         <div id="placeholder" class="loader-placeholder">Select a local MP4, WebM, or compatible video to begin.</div>
         <video id="video" controls playsinline preload="metadata"></video>
+        <div id="analysisLockNotice" class="loader-viewer-lock" hidden>Analysis controls the video position</div>
       </div>
 
       <div class="loader-source-controls">
-        <label class="file-control">
-          <span>Select local video</span>
-          <input id="videoFile" type="file" accept="video/*,.mkv" />
-        </label>
-        <div class="loader-seek" aria-label="Video position">
+        <div class="loader-source-file">
+          <span class="loader-control-label">Source</span>
+          <label class="file-control">
+            <span id="sourceFileAction">Select local video</span>
+            <input id="videoFile" type="file" accept="video/*,.mkv" />
+          </label>
+          <div class="loader-source-file__identity">
+            <strong id="sourceFileName">No video selected</strong>
+            <span id="sourceFileMeta">Local files remain on this device.</span>
+          </div>
+        </div>
+        <div class="loader-seek" aria-label="Precise source position">
+          <span class="loader-seek__name">Viewing</span>
           <span id="currentTimeLabel">00:00.000</span>
           <input id="videoSeek" type="range" min="0" max="0" step="0.001" value="0" disabled />
           <span id="durationLabel">00:00.000</span>
         </div>
+        <button id="analyzeFrameBtn" type="button" disabled>Analyse this frame</button>
       </div>
 
       <div class="loader-analysis-controls" aria-label="Analysis controls">
-        <label>
-          <span>Sample every</span>
-          <select id="sampleInterval">
-            <option value="0.25">0.25 seconds</option>
-            <option value="0.5" selected>0.5 seconds</option>
-            <option value="1">1 second</option>
-            <option value="2">2 seconds</option>
-          </select>
-        </label>
-        <label>
-          <span>Find distance reference after</span>
-          <input id="baselineTime" type="number" min="0" step="0.1" value="0" inputmode="decimal" />
-          <span>seconds</span>
-        </label>
-        <button id="useCurrentTimeBtn" type="button" disabled>Use current video time</button>
-        <button id="startAnalysisBtn" class="loader-primary" type="button" disabled>Analyse video</button>
-        <button id="cancelAnalysisBtn" type="button" disabled>Stop after this sample</button>
-        <div class="loader-progress">
-          <progress id="analysisProgress" max="100" value="0"></progress>
-          <span id="analysisProgressLabel">0%</span>
+        <div class="loader-control-group loader-analysis-plan">
+          <span class="loader-control-label">Analysis plan</span>
+          <div id="analysisSettings" class="loader-analysis-settings">
+            <label>
+              <span>Sample every</span>
+              <select id="sampleInterval">
+                <option value="0.25">0.25 seconds</option>
+                <option value="0.5" selected>0.5 seconds</option>
+                <option value="1">1 second</option>
+                <option value="2">2 seconds</option>
+              </select>
+            </label>
+            <div class="loader-base-picker">
+              <button id="selectBaseBtn" type="button" disabled>Select base at 00:00.000</button>
+              <span id="selectedBaseLabel" aria-live="polite">No base selected. Scrub the video, then select the displayed frame.</span>
+            </div>
+          </div>
+        </div>
+        <div class="loader-control-group loader-run-state">
+          <span class="loader-control-label">Run</span>
+          <div class="loader-run-state__action">
+            <button id="startAnalysisBtn" class="loader-primary" type="button" disabled>Analyse video</button>
+            <button id="cancelAnalysisBtn" type="button" disabled hidden>Pause after this sample</button>
+          </div>
+          <div class="loader-progress" aria-label="Analysis sample progress">
+            <span class="loader-progress__name">Analysis samples</span>
+            <progress id="analysisProgress" max="100" value="0"></progress>
+            <span id="analysisProgressLabel" aria-live="polite">0 / —</span>
+          </div>
+        </div>
+        <div id="analysisMessage" class="loader-analysis-message" role="alert" hidden>
+          <strong id="analysisMessageTitle"></strong>
+          <span id="analysisMessageBody"></span>
         </div>
       </div>
     </section>
@@ -103,10 +126,16 @@ <h2 id="report-title">Evidence and story order</h2>
           <p id="reportSummary" class="loader-report__summary">No video analysed yet.</p>
         </div>
         <div class="loader-report__actions">
-          <button id="undoEditBtn" type="button" disabled>Undo</button>
-          <button id="redoEditBtn" type="button" disabled>Redo</button>
-          <button id="downloadReportBtn" type="button" disabled>Download report</button>
-          <button id="downloadLogBtn" type="button" disabled>Download technical log</button>
+          <div class="loader-action-group" aria-label="Edit history">
+            <span>History</span>
+            <button id="undoEditBtn" type="button" disabled>Undo</button>
+            <button id="redoEditBtn" type="button" disabled>Redo</button>
+          </div>
+          <div class="loader-action-group" aria-label="Export analysis">
+            <span>Export</span>
+            <button id="downloadReportBtn" type="button" disabled>Download report</button>
+            <button id="downloadLogBtn" type="button" disabled>Download technical log</button>
+          </div>
         </div>
       </header>
 
@@ -116,13 +145,13 @@ <h2 id="report-title">Evidence and story order</h2>
       </div>
 
       <p class="loader-story-note">
-        Source timestamps stay factual. Use “Move earlier” and “Move later” to choose the narrative order independently—for example, when a finished-look teaser appears before the tutorial itself.
+        The baseline stays first because every distance starts there. Source timestamps remain factual; use “Move earlier” and “Move later” to arrange the later evidence independently of source chronology.
       </p>
 
       <figure class="loader-timeline-figure">
-        <canvas id="analysisTimeline" aria-label="Analysis timeline with separate MediaPipe, ImageEmbedder, and face-api lanes"></canvas>
+        <canvas id="analysisTimeline" aria-label="Analysis timeline with separate MediaPipe coverage, ImageEmbedder, and face-api lanes"></canvas>
         <figcaption>
-          MediaPipe presence and visual drift are frequent signals. face-api distance is sampled only at verification points; its dashed line is the 0.6 threshold.
+          MediaPipe coverage is diagnostic context, not evidence by itself. ImageEmbedder drift is frequent generic-image change measured as 1 − cosine similarity. face-api L2 distance is sampled only on comparable frames; × means no 2D face was returned. Each dashed line prints the active numeric trigger or decision threshold beside it.
         </figcaption>
       </figure>
 
diff --git a/scripts-dev/README.md b/scripts-dev/README.md
index 3dbd697..859c6f5 100644
--- a/scripts-dev/README.md
+++ b/scripts-dev/README.md
@@ -371,6 +371,12 @@ Source: `scripts-dev/video-loader.cjs`. This Playwright script controls the
 same file input and public API as `loader.html`; it does not emulate a webcam or
 convert the source to Y4M.
 
+Passing `--baseline-time` anchors the visible frame and both model references
+to that exact decoded timestamp; nearby comparable frames only calibrate noise.
+If the selected frame cannot produce a valid local reference, analysis fails
+with diagnostics rather than drifting to a later scene. Omitting the option
+keeps the CLI's automatic stable-run search.
+
 ```sh
 npm run analyze:video -- video.webm \
   --output /tmp/video-analysis \
@@ -387,9 +393,15 @@ node scripts-dev/video-loader.cjs extract-clips \
   --output /tmp/video-analysis/clips
 ```
 
-`analyze` writes `report.json`, portable `report.html`, selected JPEG evidence
-under `frames/`, and `analysis.ndjson`. Report images and technical telemetry
-remain separate artifacts. `replay-log` needs no video or model inference.
+`analyze` writes `report.json`, portable `report.html`, `timeline.svg`, selected
+JPEG evidence under `frames/`, and `analysis.ndjson`. Report images and
+technical telemetry remain separate artifacts. `replay-log` does not run an
+analysis and does not generate another log: it reads the existing NDJSON and
+writes a diagnostic `report.replayed.json` plus `timeline.svg`. This verifies
+that archived telemetry remains usable without the source video or model
+inference, and provides an input for future report renderers. The replayed JSON
+also retains candidate events that the editorial report omits, so it is a
+diagnostic projection rather than a replacement for the original report.
 `extract-clips` verifies the source byte size and sampled fingerprint recorded
 in the log, then uses ffmpeg to re-encode logged event windows as H.264/AAC MP4
 files. The source is read in place and never overwritten. Detailed signal semantics, baseline
diff --git a/scripts-dev/video-loader.cjs b/scripts-dev/video-loader.cjs
index 8fc9177..daefc31 100644
--- a/scripts-dev/video-loader.cjs
+++ b/scripts-dev/video-loader.cjs
@@ -34,7 +34,7 @@ Ghostmaxxing seekable-video analyser
 
 Commands:
   analyze <video>             Analyse a local seekable video through loader.html
-  replay-log <log>            Rebuild a text-only report from a technical log
+  replay-log <log>            Derive diagnostic JSON and an SVG timeline from a log
   extract-clips <log> <video> Extract event windows from the original source
 
 Analyze options:
@@ -50,7 +50,7 @@ Analyze options:
   --clip-after <seconds>      Clip post-roll (default: 3)
 
 Replay options:
-  --output <directory>        Output directory
+  --output <directory>        Derived-artifact directory; no new log is written
 
 Clip options:
   --output <directory>        Clip directory (default: <log directory>/clips)
@@ -266,58 +266,238 @@ function formatTime(seconds) {
    return `${hours ? `${String(hours).padStart(2, '0')}:` : ''}${String(minutes).padStart(2, '0')}:${rest.toFixed(3).padStart(6, '0')}`;
 }
 
+/**
+ * Describes an automatic or person-requested event without claiming that
+ * makeup caused it.
+ * A person's edited caption always replaces this classification-specific
+ * fallback in the portable report.
+ *
+ * @param {object} event Report event.
+ * @param {number} threshold Active face-api descriptor threshold.
+ * @returns {string} Conservative default report caption.
+ */
+function defaultEventCaption(event, threshold) {
+   if (event.classification === 'manual-frame-check') {
+      const evidence = event.evidence?.at(-1);
+      if (event.interpretation === 'above-threshold') {
+         return `A person requested this comparison. Its face-api distance ${Number(evidence?.faceDistance).toFixed(4)} is at or above the ${threshold} threshold; this is one explicit check, not a repeated automatic transition.`;
+      }
+      if (event.interpretation === 'below-threshold') {
+         return `A person requested this comparison. Its face-api distance ${Number(evidence?.faceDistance).toFixed(4)} is below the ${threshold} threshold.`;
+      }
+      if (event.interpretation === 'no-2d-face') {
+         return 'A person requested this comparison. MediaPipe retained a comparable crop, but face-api returned no 2D face.';
+      }
+      return 'A person requested this comparison, but the frame did not meet MediaPipe comparability limits. Any face-api result is diagnostic context only.';
+   }
+   if (event.classification === 'identity-distance') {
+      return `Repeated comparable frames returned face-api descriptor distances at or above ${threshold}.`;
+   }
+   if (event.classification === 'detector-divergence') {
+      return 'MediaPipe retained a comparable face mesh while face-api returned no 2D face in repeated checks. Review pose and occlusion before attributing this to makeup.';
+   }
+   return 'Different model checks diverged in this interval. Review the selected frame and source clip before interpreting the result.';
+}
+
+/**
+ * Parses a newline-delimited technical log from memory.
+ *
+ * @param {string} contents NDJSON document.
+ * @param {string} sourceLabel Human-readable source used in parse errors.
+ * @returns {object[]} Parsed records.
+ */
+function parseTechnicalLog(contents, sourceLabel) {
+   return contents
+      .split(/\r?\n/)
+      .filter(Boolean)
+      .map((line, index) => {
+         try {
+            return JSON.parse(line);
+         } catch (error) {
+            throw new Error(`Invalid JSON on ${sourceLabel}:${index + 1}: ${error.message}`);
+         }
+      });
+}
+
+/**
+ * Renders the three analysis signals as a scalable report artifact. Missing
+ * face-api detections use an × marker rather than a fabricated numeric L2
+ * value, and numeric distances receive a data-aware vertical scale.
+ *
+ * @param {object[]} records Parsed technical-log records.
+ * @returns {string} Standalone SVG document.
+ */
+function renderTimelineSvg(records) {
+   const header = records.find((record) => record.type === 'analysis-header');
+   if (!header) throw new Error('Technical log has no analysis-header record.');
+   const samples = records
+      .filter((record) => record.type === 'sample' && record.phase === 'scan')
+      .sort((left, right) => left.mediaTime - right.mediaTime);
+   const faceEntries = records
+      .filter((record) => record.type === 'face-api-result')
+      .sort((left, right) => left.mediaTime - right.mediaTime);
+   const width = 1160;
+   const height = 230;
+   const left = 150;
+   const right = 18;
+   const plotWidth = width - left - right;
+   const rangeStart = header.config?.analysisRange?.start ?? 0;
+   const rangeEnd = header.config?.analysisRange?.end ?? header.source?.duration ?? 1;
+   const duration = Math.max(0.001, rangeEnd - rangeStart);
+   const x = (mediaTime) => left + ((mediaTime - rangeStart) / duration) * plotWidth;
+   const lanes = {
+      coverage: { top: 18, height: 34, label: 'MediaPipe coverage' },
+      drift: { top: 74, height: 50, label: 'ImageEmbedder drift' },
+      face: { top: 146, height: 48, label: 'face-api L2' },
+   };
+   const visualThreshold = header.baseline?.visualTriggerThreshold ?? 0.1;
+   const driftValues = samples.map((sample) => sample.visualDrift).filter(Number.isFinite);
+   const driftMax = Math.max(0.1, visualThreshold * 3, ...driftValues);
+   const faceThreshold = header.config?.faceDistanceThreshold ?? 0.6;
+   const faceValues = faceEntries.map((entry) => entry.faceDistance).filter(Number.isFinite);
+   const faceMax = Math.max(0.8, faceThreshold * 1.25, ...faceValues.map((value) => value * 1.1));
+   const coveragePoints = samples.map((sample) => {
+      const y = lanes.coverage.top + (sample.comparable
+         ? 7
+         : sample.facePresent ? lanes.coverage.height / 2 : lanes.coverage.height - 7);
+      return `${x(sample.mediaTime).toFixed(2)},${y.toFixed(2)}`;
+   }).join(' ');
+   const coverageDots = samples.map((sample) => {
+      const y = lanes.coverage.top + (sample.comparable
+         ? 7
+         : sample.facePresent ? lanes.coverage.height / 2 : lanes.coverage.height - 7);
+      const fill = sample.comparable ? '#7fe3b0' : sample.facePresent ? '#f3c747' : '#756b60';
+      return `<circle cx="${x(sample.mediaTime).toFixed(2)}" cy="${y.toFixed(2)}" r="2.25" fill="${fill}"/>`;
+   }).join('');
+   const driftSegments = [];
+   let currentSegment = [];
+   for (const sample of samples) {
+      if (!Number.isFinite(sample.visualDrift)) {
+         if (currentSegment.length) driftSegments.push(currentSegment);
+         currentSegment = [];
+         continue;
+      }
+      const y = lanes.drift.top + lanes.drift.height - (sample.visualDrift / driftMax) * lanes.drift.height;
+      currentSegment.push(`${x(sample.mediaTime).toFixed(2)},${y.toFixed(2)}`);
+   }
+   if (currentSegment.length) driftSegments.push(currentSegment);
+   const driftLines = driftSegments
+      .map((points) => `<polyline points="${points.join(' ')}" fill="none" stroke="#f3c747" stroke-width="2"/>`)
+      .join('');
+   const driftThresholdY = lanes.drift.top + lanes.drift.height
+      - (visualThreshold / driftMax) * lanes.drift.height;
+   const faceMarks = faceEntries.map((entry) => {
+      const px = x(entry.mediaTime);
+      if (Number.isFinite(entry.faceDistance)) {
+         const py = lanes.face.top + lanes.face.height - (entry.faceDistance / faceMax) * lanes.face.height;
+         return `<circle cx="${px.toFixed(2)}" cy="${py.toFixed(2)}" r="3.5" fill="#ff8e72"/>`;
+      }
+      const py = lanes.face.top + 7;
+      return `<path d="M ${(px - 3).toFixed(2)} ${py - 3} L ${(px + 3).toFixed(2)} ${py + 3} M ${(px + 3).toFixed(2)} ${py - 3} L ${(px - 3).toFixed(2)} ${py + 3}" fill="none" stroke="#ff8e72" stroke-width="1.6"/>`;
+   }).join('');
+   const faceThresholdY = lanes.face.top + lanes.face.height
+      - (faceThreshold / faceMax) * lanes.face.height;
+   const laneMarkup = Object.values(lanes).map((lane) => `
+      <text class="lane-label" x="10" y="${lane.top + lane.height / 2}">${lane.label}</text>
+      <rect class="lane" x="${left}" y="${lane.top}" width="${plotWidth}" height="${lane.height}"/>`).join('');
+
+   return `<svg xmlns="http://www.w3.org/2000/svg" width="${width}" height="${height}" viewBox="0 0 ${width} ${height}" role="img" aria-labelledby="timeline-title timeline-desc">
+   <title id="timeline-title">Video analysis timeline</title>
+   <desc id="timeline-desc">MediaPipe coverage, generic ImageEmbedder drift, and sparse face-api descriptor distances. Crosses mean face-api returned no two-dimensional face.</desc>
+   <style>
+      .bg{fill:#17130f}.lane{fill:none;stroke:rgba(255,231,168,.12)}
+      text{font-family:ui-monospace,SFMono-Regular,Menlo,monospace;fill:#b8aa96}
+      .lane-label{font-size:11px;font-weight:700;dominant-baseline:middle}
+      .note{font-size:9px;font-weight:700}.time{font-size:10px}
+   </style>
+   <rect class="bg" width="${width}" height="${height}"/>
+   ${laneMarkup}
+   ${coveragePoints ? `<polyline points="${coveragePoints}" fill="none" stroke="#7fe3b0" stroke-width="2"/>${coverageDots}` : ''}
+   ${driftLines}
+   <line x1="${left}" y1="${driftThresholdY}" x2="${left + plotWidth}" y2="${driftThresholdY}" stroke="rgba(243,199,71,.65)" stroke-dasharray="4 4"/>
+   ${faceMarks}
+   <line x1="${left}" y1="${faceThresholdY}" x2="${left + plotWidth}" y2="${faceThresholdY}" stroke="rgba(255,142,114,.7)" stroke-dasharray="4 4"/>
+   <text class="note" x="${left + 6}" y="${lanes.coverage.top + 11}">high usable · middle mesh only · low absent</text>
+   <text class="note" x="${left + plotWidth - 6}" y="${driftThresholdY - 7}" text-anchor="end">trigger ${visualThreshold.toFixed(4)}</text>
+   <text class="note" x="${left + plotWidth - 6}" y="${faceThresholdY - 7}" text-anchor="end">× no 2D face</text>
+   <text class="note" x="${left + plotWidth - 6}" y="${faceThresholdY + 12}" text-anchor="end">threshold ${faceThreshold.toFixed(4)}</text>
+   <text class="time" x="${left}" y="218">${escapeHtml(formatTime(rangeStart))}</text>
+   <text class="time" x="${left + plotWidth}" y="218" text-anchor="end">${escapeHtml(formatTime(rangeEnd))}</text>
+</svg>`;
+}
+
 /**
  * Renders a portable report that references separately written JPEG evidence.
  *
  * @param {object} report Browser report model.
  * @param {Map<string, string>} frameFiles Artifact ID to relative filename.
+ * @param {string|null} timelineFile Relative timeline artifact filename.
  * @returns {string} Standalone HTML.
  */
-function renderReportHtml(report, frameFiles) {
+function renderReportHtml(report, frameFiles, timelineFile = null) {
    const baseline = frameFiles.get('baseline');
    const eventMarkup = new Map(report.events.map((event) => {
       const image = frameFiles.get(event.id);
       const evidence = event.evidence.at(-1) || {};
       const eventLabel = event.promoted
          ? 'Selected story moment'
-         : event.classification === 'identity-distance'
-            ? 'Identity-distance transition'
-            : 'Detector divergence — review';
+         : event.classification === 'manual-frame-check'
+            ? 'Manual frame check'
+            : event.classification === 'identity-distance'
+               ? 'Identity-distance transition'
+               : event.classification === 'detector-divergence'
+                  ? 'Detector divergence — review'
+                  : 'Mixed transition — review';
       return [event.id, `<article class="event">
         ${image ? `<img src="${escapeHtml(image)}" alt="Selected frame at ${escapeHtml(formatTime(event.anchorMediaTime))}">` : ''}
         <div>
           <p class="kicker">${eventLabel}</p>
           <h2>${escapeHtml(formatTime(event.anchorMediaTime))}</h2>
-          <p>${escapeHtml(event.caption || 'A repeatable model divergence was detected at this point in the source video.')}</p>
+          <p>${escapeHtml(event.caption || defaultEventCaption(event, report.config.faceDistanceThreshold))}</p>
           <dl>
             <dt>face-api distance</dt><dd>${evidence.faceDistance == null ? 'no face returned' : Number(evidence.faceDistance).toFixed(4)}</dd>
-            <dt>visual drift</dt><dd>${evidence.visualDrift == null ? 'n/a' : Number(evidence.visualDrift).toFixed(4)}</dd>
+            <dt>ImageEmbedder drift</dt><dd>${evidence.visualDrift == null ? 'n/a' : Number(evidence.visualDrift).toFixed(4)}</dd>
             <dt>clip window</dt><dd>${escapeHtml(formatTime(event.recommendedClip.start))}–${escapeHtml(formatTime(event.recommendedClip.end))}</dd>
           </dl>
         </div>
       </article>`];
    }));
+   const baselineVariationWarning = report.baseline?.faceSpread
+      > (report.config.maximumBaselineFaceSpread ?? 0.3)
+      ? '<p class="warning">The selected baseline has elevated face-api variation. Treat distances near the decision threshold as review evidence.</p>'
+      : '';
    const baselineMarkup = baseline ? `<article class="event reference">
       <img src="${escapeHtml(baseline)}" alt="Computational reference frame at ${escapeHtml(formatTime(report.baseline.mediaTime))}">
-      <div><p class="kicker">Computational distance reference</p>
-      <h2>${escapeHtml(formatTime(report.baseline.mediaTime))}</h2>
-      <p>This source frame anchors the reported distances. Its position here is editorial and does not change its timestamp or computational role.</p></div>
+      <div><p class="kicker">Baseline · distance reference</p>
+      <h2>Baseline · ${escapeHtml(formatTime(report.baseline.mediaTime))}</h2>
+      <p>This source frame anchors the reported distances and remains the first story entry. Its timestamp and computational role do not change.</p>
+      ${baselineVariationWarning}
+      <dl>
+        <dt>ImageEmbedder trigger</dt><dd>${Number(report.baseline.visualTriggerThreshold).toFixed(4)}</dd>
+        <dt>face-api baseline spread</dt><dd>${Number(report.baseline.faceSpread).toFixed(4)}</dd>
+        <dt>face-api decision threshold</dt><dd>${Number(report.config.faceDistanceThreshold).toFixed(4)}</dd>
+      </dl></div>
    </article>` : '';
    const storyMarkup = (report.storyOrder || ['baseline', ...report.events.map((event) => event.id)])
       .map((id) => id === 'baseline' ? baselineMarkup : eventMarkup.get(id) || '')
       .filter(Boolean)
       .join('\n');
+   const timelineMarkup = timelineFile ? `<figure class="timeline">
+      <img src="${escapeHtml(timelineFile)}" alt="Timeline of MediaPipe coverage, ImageEmbedder drift, and sparse face-api checks">
+      <figcaption>MediaPipe coverage is context only. ImageEmbedder drift is generic image change measured as 1 − cosine similarity. face-api circles are numeric L2 distances; × means no 2D face was returned.</figcaption>
+   </figure>` : '';
 
    return `<!doctype html>
 <html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1">
 <title>Ghostmaxxing video analysis — ${escapeHtml(report.source.name)}</title>
 <style>
-body{margin:0;background:#f26a1b;color:#0a0a08;font:17px/1.5 system-ui,sans-serif}main{width:min(1100px,calc(100% - 2rem));margin:auto;padding:3rem 0}h1,h2{font-family:Georgia,serif;line-height:1}.meta,.event{border:2px solid #0a0a08;background:#fff1cf}.meta{padding:1rem}.event{display:grid;grid-template-columns:minmax(260px,1fr) 1fr;margin:1.5rem 0}.event.reference{border-color:#0b4a3c}.event img{width:100%;height:100%;object-fit:contain;background:#050505}.event>div{padding:1.25rem}.kicker,dt{font-size:.75rem;font-weight:800;text-transform:uppercase;letter-spacing:.08em;color:#0b4a3c}dd{margin:0 0 .6rem}@media(max-width:700px){.event{grid-template-columns:1fr}}
+body{margin:0;background:#f26a1b;color:#0a0a08;font:17px/1.5 system-ui,sans-serif}main{width:min(1100px,calc(100% - 2rem));margin:auto;padding:3rem 0}h1,h2{font-family:Georgia,serif;line-height:1}.meta,.event,.timeline{border:2px solid #0a0a08;background:#fff1cf}.meta{padding:1rem}.timeline{margin:1.5rem 0}.timeline img{display:block;width:100%;height:auto;background:#17130f}.timeline figcaption{padding:.8rem 1rem;border-top:2px solid #0a0a08;font-size:.82rem}.event{display:grid;grid-template-columns:minmax(260px,1fr) 1fr;margin:1.5rem 0}.event.reference{border-color:#0b4a3c}.event img{width:100%;height:100%;object-fit:contain;background:#050505}.event>div{padding:1.25rem}.warning{padding:.65rem .75rem;border-left:.3rem solid #a54b14;background:rgba(243,199,71,.22);font-weight:750}.kicker,dt{font-size:.75rem;font-weight:800;text-transform:uppercase;letter-spacing:.08em;color:#0b4a3c}dd{margin:0 0 .6rem}@media(max-width:700px){.event{grid-template-columns:1fr}}
 </style></head><body><main>
 <p class="kicker">Ghostmaxxing · seekable video report</p>
 <h1>${escapeHtml(report.source.name)}</h1>
-<div class="meta">${report.summary.sampledFrames} samples · ${report.summary.faceApiRuns} face-api checks · ${report.summary.confirmedEvents} confirmed events · threshold ${escapeHtml(report.config.faceDistanceThreshold)}</div>
+<div class="meta">${report.summary.sampledFrames} samples · ${report.summary.faceApiRuns} face-api checks · ${report.summary.confirmedEvents} confirmed events · ${report.summary.manualChecks || 0} manual checks · threshold ${escapeHtml(report.config.faceDistanceThreshold)}</div>
 <p>Story order is editorial. Every card retains its original source timestamp.</p>
+${timelineMarkup}
 ${storyMarkup || '<p>No event passed the confirmation policy.</p>'}
 </main></body></html>`;
 }
@@ -327,7 +507,7 @@ ${storyMarkup || '<p>No event passed the confirmation policy.</p>'}
  *
  * @param {string} outputDirectory Destination directory.
  * @param {{report: object, technicalLog: string, frames: object[]}} bundle Browser export bundle.
- * @returns {Promise<{logFile: string, reportFile: string}>} Key output paths.
+ * @returns {Promise<{logFile: string, reportFile: string, timelineFile: string}>} Key output paths.
  */
 async function writeBundle(outputDirectory, bundle) {
    const framesDirectory = path.join(outputDirectory, 'frames');
@@ -341,12 +521,23 @@ async function writeBundle(outputDirectory, bundle) {
    }
    const logFile = path.join(outputDirectory, 'analysis.ndjson');
    const reportFile = path.join(outputDirectory, 'report.json');
+   const timelineName = 'timeline.svg';
+   const timelineFile = path.join(outputDirectory, timelineName);
+   const timeline = renderTimelineSvg(parseTechnicalLog(bundle.technicalLog, 'browser export'));
+   const report = {
+      ...bundle.report,
+      artifacts: {
+         timeline: timelineName,
+         frames: Object.fromEntries(frameFiles),
+      },
+   };
    await Promise.all([
       fs.promises.writeFile(logFile, bundle.technicalLog),
-      fs.promises.writeFile(reportFile, `${JSON.stringify(bundle.report, null, 2)}\n`),
-      fs.promises.writeFile(path.join(outputDirectory, 'report.html'), renderReportHtml(bundle.report, frameFiles)),
+      fs.promises.writeFile(reportFile, `${JSON.stringify(report, null, 2)}\n`),
+      fs.promises.writeFile(timelineFile, timeline),
+      fs.promises.writeFile(path.join(outputDirectory, 'report.html'), renderReportHtml(report, frameFiles, timelineName)),
    ]);
-   return { logFile, reportFile };
+   return { logFile, reportFile, timelineFile };
 }
 
 /**
@@ -356,16 +547,7 @@ async function writeBundle(outputDirectory, bundle) {
  * @returns {object[]} Records.
  */
 function readTechnicalLog(logFile) {
-   return fs.readFileSync(logFile, 'utf8')
-      .split(/\r?\n/)
-      .filter(Boolean)
-      .map((line, index) => {
-         try {
-            return JSON.parse(line);
-         } catch (error) {
-            throw new Error(`Invalid JSON on ${logFile}:${index + 1}: ${error.message}`);
-         }
-      });
+   return parseTechnicalLog(fs.readFileSync(logFile, 'utf8'), logFile);
 }
 
 /**
@@ -459,7 +641,7 @@ async function analyze(options) {
       }
       const bundle = await page.evaluate(() => window.gstmxxVideoLoader.exportBundle());
       const outputs = await writeBundle(output, bundle);
-      process.stdout.write(`Report: ${outputs.reportFile}\nTechnical log: ${outputs.logFile}\n`);
+      process.stdout.write(`Report: ${outputs.reportFile}\nTimeline: ${outputs.timelineFile}\nTechnical log: ${outputs.logFile}\n`);
       if (analysisError) {
          throw new Error(`Browser analysis failed; diagnostic artifacts were preserved in ${output}: ${analysisError.message}`);
       }
@@ -478,7 +660,8 @@ async function analyze(options) {
 }
 
 /**
- * Rebuilds a text-only report from the independent technical log.
+ * Rebuilds a diagnostic JSON projection and timeline from the independent
+ * technical log. It never writes another NDJSON log or reruns model inference.
  *
  * @param {object} options Parsed options.
  * @returns {Promise<void>}
@@ -492,6 +675,7 @@ async function replayLog(options) {
    if (!header || !footer) throw new Error('Technical log is missing its header or footer.');
    const output = options.output || path.join(path.dirname(logFile), 'replayed');
    await fs.promises.mkdir(output, { recursive: true });
+   const timelineName = 'timeline.svg';
    const report = {
       schemaVersion: header.schemaVersion,
       sessionId: header.sessionId,
@@ -504,9 +688,14 @@ async function replayLog(options) {
       storyOrder: footer.storyOrder || [],
       events,
       replayedFrom: logFile,
+      artifacts: { timeline: timelineName },
    };
-   await fs.promises.writeFile(path.join(output, 'report.replayed.json'), `${JSON.stringify(report, null, 2)}\n`);
+   await Promise.all([
+      fs.promises.writeFile(path.join(output, 'report.replayed.json'), `${JSON.stringify(report, null, 2)}\n`),
+      fs.promises.writeFile(path.join(output, timelineName), renderTimelineSvg(records)),
+   ]);
    process.stdout.write(`Replayed report: ${path.join(output, 'report.replayed.json')}\n`);
+   process.stdout.write(`Replayed timeline: ${path.join(output, timelineName)}\n`);
 }
 
 /**
diff --git a/styles/loader.css b/styles/loader.css
index b50d7f8..59093d2 100644
--- a/styles/loader.css
+++ b/styles/loader.css
@@ -152,6 +152,32 @@
   display: none;
 }
 
+.loader-viewer-lock {
+  position: absolute;
+  z-index: 3;
+  top: 0.8rem;
+  right: 0.8rem;
+  padding: 0.55rem 0.75rem;
+  border: 1px solid rgba(255, 231, 168, 0.55);
+  background: rgba(20, 16, 12, 0.88);
+  color: var(--gm-cream);
+  font: 800 0.72rem var(--sans);
+  letter-spacing: 0.04em;
+  text-transform: uppercase;
+}
+
+.loader-viewer-lock[hidden] {
+  display: none;
+}
+
+.loader-viewer.is-analysis-locked video {
+  pointer-events: none;
+}
+
+.loader-viewer.is-analysis-locked video::-webkit-media-controls {
+  opacity: 0.38;
+}
+
 .loader-source-controls,
 .loader-analysis-controls {
   gap: 0.85rem;
@@ -159,11 +185,95 @@
   border-top: 1px solid var(--gm-border);
 }
 
+.loader-source-controls {
+  display: grid;
+  grid-template-columns: minmax(24rem, 1.1fr) minmax(20rem, 1.3fr) auto;
+  align-items: end;
+}
+
 .loader-analysis-controls {
+  display: grid;
+  grid-template-columns: minmax(26rem, 1.35fr) minmax(24rem, 1fr);
+  align-items: stretch;
+}
+
+.loader-source-file,
+.loader-control-group {
+  display: grid;
+  gap: 0.55rem;
+}
+
+.loader-source-file {
+  grid-template-columns: auto minmax(0, 1fr);
+  align-items: center;
+}
+
+.loader-control-label {
+  grid-column: 1 / -1;
+  color: var(--gm-green);
+  font: 900 0.68rem var(--sans);
+  letter-spacing: 0.12em;
+  text-transform: uppercase;
+}
+
+.loader-source-file__identity {
+  min-width: 0;
+  display: grid;
+  gap: 0.15rem;
+  color: var(--gm-text);
+  font-family: var(--sans);
+}
+
+.loader-source-file__identity strong {
+  font-size: 0.82rem;
+  line-height: 1.25;
+  overflow-wrap: anywhere;
+}
+
+.loader-source-file__identity span {
+  font-size: 0.7rem;
+  line-height: 1.3;
+}
+
+.loader-control-group {
+  padding: 0.75rem;
+  border: 1px solid rgba(10, 10, 8, 0.24);
+  background: rgba(255, 231, 168, 0.12);
+}
+
+.loader-run-state {
+  grid-template-columns: auto minmax(0, 1fr);
+  align-content: start;
+  align-items: center;
+}
+
+.loader-run-state > .loader-control-label {
+  grid-column: 1 / -1;
+}
+
+.loader-run-state__action {
+  min-width: max-content;
+}
+
+.loader-run-state__action button {
+  width: 100%;
+}
+
+.loader-analysis-settings {
+  display: flex;
+  align-items: center;
   flex-wrap: wrap;
+  gap: 0.85rem;
+  transition: opacity 160ms ease;
+}
+
+.loader-analysis-settings.is-muted,
+.loader-source-controls.is-muted {
+  opacity: 0.42;
 }
 
 .file-control,
+.loader-source-controls button,
 .loader-analysis-controls button,
 .loader-report button {
   min-height: 2.8rem;
@@ -197,7 +307,9 @@
 }
 
 button:disabled,
-.file-control:has(input:disabled) {
+.file-control:has(input:disabled),
+select:disabled,
+input[type="range"]:disabled {
   opacity: 0.42;
   cursor: not-allowed;
 }
@@ -212,15 +324,21 @@ input[type="range"]:focus-visible {
 }
 
 .loader-seek {
-  flex: 1;
   display: grid;
-  grid-template-columns: 7rem minmax(8rem, 1fr) 7rem;
+  grid-template-columns: auto 7rem minmax(8rem, 1fr) 7rem;
   align-items: center;
   gap: 0.75rem;
   color: var(--gm-ink);
   font: 750 0.78rem ui-monospace, SFMono-Regular, Menlo, monospace;
 }
 
+.loader-seek__name,
+.loader-progress__name {
+  color: var(--gm-text);
+  font-family: var(--sans);
+  letter-spacing: 0.02em;
+}
+
 .loader-seek span:last-child {
   text-align: right;
 }
@@ -246,18 +364,20 @@ input[type="range"]:focus-visible {
   font: 700 0.82rem var(--sans);
 }
 
-.loader-analysis-controls input[type="number"] {
-  width: 6rem;
-  min-height: 2.6rem;
-  padding: 0.45rem 0.55rem;
-  border: 1.5px solid var(--gm-border-strong);
-  background: var(--gm-cream);
-  color: var(--gm-ink);
-  font: 700 0.82rem var(--sans);
+.loader-base-picker {
+  display: flex;
+  align-items: center;
+  flex-wrap: wrap;
+  gap: 0.6rem;
+}
+
+.loader-base-picker span {
+  max-width: 24rem;
+  color: var(--gm-text);
+  font: 650 0.76rem/1.35 var(--sans);
 }
 
 .loader-progress {
-  flex: 1;
   min-width: 10rem;
   display: flex;
   align-items: center;
@@ -266,12 +386,45 @@ input[type="range"]:focus-visible {
   font: 800 0.78rem ui-monospace, SFMono-Regular, Menlo, monospace;
 }
 
+.loader-progress__name {
+  white-space: nowrap;
+}
+
 .loader-progress progress {
   width: 100%;
   height: 0.8rem;
   accent-color: var(--gm-green);
 }
 
+.loader-analysis-message {
+  grid-column: 1 / -1;
+  display: grid;
+  gap: 0.3rem;
+  padding: 0.8rem 1rem;
+  border: 1.5px solid var(--gm-border-strong);
+  border-left-width: 0.4rem;
+  background: var(--gm-cream);
+  color: var(--gm-text);
+  font: 650 0.82rem/1.45 var(--sans);
+}
+
+.loader-analysis-message[hidden] {
+  display: none;
+}
+
+.loader-analysis-message[data-kind="error"] {
+  border-left-color: #b4212b;
+}
+
+.loader-analysis-message[data-kind="success"] {
+  border-left-color: var(--gm-green);
+}
+
+.loader-analysis-message strong {
+  color: var(--gm-ink);
+  font-size: 0.9rem;
+}
+
 .loader-report {
   padding: clamp(1rem, 2vw, 1.5rem);
   display: grid;
@@ -300,6 +453,25 @@ input[type="range"]:focus-visible {
   gap: 0.55rem;
 }
 
+.loader-report__actions {
+  align-items: flex-end;
+}
+
+.loader-action-group {
+  display: flex;
+  align-items: center;
+  flex-wrap: wrap;
+  gap: 0.5rem;
+}
+
+.loader-action-group > span {
+  width: 100%;
+  color: var(--gm-green);
+  font: 900 0.66rem var(--sans);
+  letter-spacing: 0.1em;
+  text-transform: uppercase;
+}
+
 .loader-baseline {
   display: flex;
   gap: 0.75rem;
@@ -363,6 +535,11 @@ input[type="range"]:focus-visible {
   box-shadow: inset 0.4rem 0 0 var(--gm-green);
 }
 
+.loader-event--manual {
+  border-color: #a54b14;
+  box-shadow: inset 0.4rem 0 0 #f3c747;
+}
+
 .loader-event img {
   width: 100%;
   height: 100%;
@@ -388,6 +565,33 @@ input[type="range"]:focus-visible {
   font: 800 1.1rem var(--sans);
 }
 
+.loader-time-link {
+  display: inline;
+  min-height: 0;
+  margin: 0;
+  padding: 0;
+  border: 0;
+  border-bottom: 1px solid currentColor;
+  background: transparent;
+  color: inherit;
+  font: inherit;
+  cursor: pointer;
+}
+
+.loader-time-link:hover:not(:disabled) {
+  color: var(--gm-green);
+  border-bottom-width: 2px;
+}
+
+.loader-time-link:disabled {
+  opacity: 0.42;
+}
+
+.loader-time-link--summary {
+  font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
+  font-weight: 800;
+}
+
 .loader-event__metrics {
   color: var(--gm-green);
   font: 800 0.8rem ui-monospace, SFMono-Regular, Menlo, monospace;
@@ -398,6 +602,14 @@ input[type="range"]:focus-visible {
   font: 650 0.8rem/1.45 var(--sans);
 }
 
+.loader-event__warning {
+  padding: 0.65rem 0.75rem;
+  border-left: 0.3rem solid #a54b14;
+  background: rgba(243, 199, 71, 0.22);
+  color: var(--gm-ink);
+  font: 750 0.8rem/1.45 var(--sans);
+}
+
 .loader-event textarea {
   width: 100%;
   min-height: 6rem;
@@ -424,8 +636,6 @@ input[type="range"]:focus-visible {
 @media (max-width: 820px) {
   .loader-header,
   .loader-intro,
-  .loader-source-controls,
-  .loader-analysis-controls,
   .loader-report__header {
     align-items: stretch;
     flex-direction: column;
@@ -440,7 +650,36 @@ input[type="range"]:focus-visible {
   }
 
   .loader-seek {
-    grid-template-columns: 5.7rem minmax(5rem, 1fr) 5.7rem;
+    grid-template-columns: 1fr 1fr;
+    width: 100%;
+  }
+
+  .loader-source-controls,
+  .loader-analysis-controls {
+    grid-template-columns: 1fr;
+  }
+
+  .loader-source-file {
+    grid-template-columns: 1fr;
+  }
+
+  .loader-source-file__identity strong {
+    overflow: visible;
+    text-overflow: clip;
+    white-space: normal;
+  }
+
+  .loader-run-state {
+    grid-template-columns: 1fr;
+  }
+
+  .loader-seek__name {
+    grid-column: 1 / -1;
+  }
+
+  #videoSeek {
+    grid-column: 1 / -1;
+    grid-row: 3;
   }
 
   .loader-event {
diff --git a/tests/e2e/loader.spec.js b/tests/e2e/loader.spec.js
index a82f057..fc1a008 100644
--- a/tests/e2e/loader.spec.js
+++ b/tests/e2e/loader.spec.js
@@ -114,14 +114,21 @@ test.describe('seekable Video Loader', () => {
     await page.setInputFiles('#videoFile', path.join(process.cwd(), 'tests/fixtures/my-moving-face.mp4'));
 
     await expect.poll(() => page.evaluate(() => window.gstmxxVideoLoader?.isSourceLoaded())).toBe(true);
+    await expect(page.locator('#startAnalysisBtn')).toBeDisabled();
+    await page.locator('#selectBaseBtn').click();
     await expect(page.locator('#startAnalysisBtn')).toBeEnabled();
     await page.evaluate(() => window.gstmxxVideoLoader.analyze({ end: 6, sampleIntervalSeconds: 0.5 }));
 
-    await expect(page.locator('#analysisProgressLabel')).toHaveText('100%');
+    await expect(page.locator('#analysisProgressLabel')).toContainText('complete');
     await expect(page.locator('#reportSummary')).toContainText('confirmed event');
+    await expect(page.locator('.loader-event--reference h3')).toContainText('Baseline · distance reference');
     await expect(page.locator('.loader-event--confirmed')).toHaveCount(1);
     await expect(page.locator('#downloadReportBtn')).toBeEnabled();
     await expect(page.locator('#downloadLogBtn')).toBeEnabled();
+    const baselineTimeLink = page.locator('.loader-event--reference .loader-time-link');
+    await expect(baselineTimeLink).toBeEnabled();
+    await baselineTimeLink.click();
+    await expect.poll(() => page.locator('#video').evaluate(video => video.currentTime)).toBeCloseTo(0, 2);
 
     const exported = await page.evaluate(() => {
       const bundle = window.gstmxxVideoLoader.exportBundle();
@@ -134,6 +141,7 @@ test.describe('seekable Video Loader', () => {
         fingerprint: bundle.report.source.fingerprint,
         eventFrameReason: bundle.report.events[0]?.selectedFrame?.reason,
         storyOrder: bundle.report.storyOrder,
+        baselineTime: bundle.report.baseline?.mediaTime,
       };
     });
     expect(exported).toMatchObject({
@@ -149,6 +157,80 @@ test.describe('seekable Video Loader', () => {
     expect(exported.frames).toContain('baseline');
     expect(exported.frames).toHaveLength(2);
     expect(exported.eventFrameReason).toMatch(/^refined-/);
-    expect(exported.storyOrder).toContain('baseline');
+    expect(exported.baselineTime).toBe(0);
+    expect(exported.storyOrder[0]).toBe('baseline');
+
+    await page.locator('#videoSeek').fill('3');
+    await page.locator('#analyzeFrameBtn').click();
+    await expect(page.locator('.loader-event--manual h3')).toContainText('Manual frame check');
+    await expect(page.locator('#analysisMessage')).toContainText('Frame added at 00:03.000');
+    const manualExport = await page.evaluate(() => {
+      const bundle = window.gstmxxVideoLoader.exportBundle();
+      return {
+        manualChecks: bundle.report.summary.manualChecks,
+        manualOrigin: bundle.report.events.find(event => event.origin === 'manual')?.origin,
+        logHasManualCheck: bundle.technicalLog.includes('manual-frame-checked'),
+      };
+    });
+    expect(manualExport).toEqual({
+      manualChecks: 1,
+      manualOrigin: 'manual',
+      logHasManualCheck: true,
+    });
+  });
+
+  test('pauses at a scan checkpoint and resumes without rebuilding the baseline', async ({ page }) => {
+    await page.addInitScript(() => {
+      CanvasRenderingContext2D.prototype.drawImage = function drawImage() {};
+      CanvasRenderingContext2D.prototype.getImageData = function getImageData(x, y, width, height) {
+        const data = new Uint8ClampedArray(width * height * 4);
+        for (let offset = 0; offset < data.length; offset += 4) {
+          data[offset] = 128;
+          data[offset + 1] = 128;
+          data[offset + 2] = 128;
+          data[offset + 3] = 255;
+        }
+        return { data, width, height };
+      };
+    });
+    await page.goto('/loader.html');
+    await expect.poll(() => page.evaluate(() => window.gstmxxVideoLoader?.isModelsReady())).toBe(true);
+    await page.setInputFiles('#videoFile', path.join(process.cwd(), 'tests/fixtures/my-moving-face.mp4'));
+    await expect.poll(() => page.evaluate(() => window.gstmxxVideoLoader?.isSourceLoaded())).toBe(true);
+    await page.locator('#selectBaseBtn').click();
+
+    const result = await page.evaluate(async () => {
+      let pauseRequested = false;
+      const onSample = event => {
+        if (!pauseRequested && event.detail.progress >= 0.25) {
+          pauseRequested = true;
+          window.gstmxxVideoLoader.cancel();
+        }
+      };
+      window.addEventListener('gstmxx-video-loader:sample', onSample);
+      const paused = await window.gstmxxVideoLoader.analyze({ end: 6, sampleIntervalSeconds: 0.5 });
+      window.removeEventListener('gstmxx-video-loader:sample', onSample);
+      const pausedSnapshot = {
+        status: paused.status,
+        baselineTime: paused.baseline.mediaTime,
+        samples: paused.samples.filter(sample => sample.phase === 'scan').length,
+        lastScanMediaTime: paused.config.lastScanMediaTime,
+      };
+      const completed = await window.gstmxxVideoLoader.resume();
+      return {
+        paused: pausedSnapshot,
+        completedStatus: completed.status,
+        completedSamples: completed.samples.filter(sample => sample.phase === 'scan').length,
+        resumedJournal: completed.journal.some(entry => entry.type === 'analysis-resumed'),
+      };
+    });
+
+    expect(result.paused.status).toBe('cancelled');
+    expect(result.paused.baselineTime).toBe(0);
+    expect(result.paused.lastScanMediaTime).toBeGreaterThan(0);
+    expect(result.completedStatus).toBe('complete');
+    expect(result.completedSamples).toBeGreaterThan(result.paused.samples);
+    expect(result.resumedJournal).toBe(true);
+    await expect(page.locator('#analysisProgressLabel')).toContainText('complete');
   });
 });
diff --git a/tests/fixtures/FOLDER-DESCRIPTION.md b/tests/fixtures/FOLDER-DESCRIPTION.md
index c492541..623bc53 100644
--- a/tests/fixtures/FOLDER-DESCRIPTION.md
+++ b/tests/fixtures/FOLDER-DESCRIPTION.md
@@ -28,6 +28,8 @@ changes test inputs and re-encodes JPEGs; restore unmarked files from Git before
 changing badge sizes or comparing against an earlier unmarked baseline.
 
 Large makeup tutorials and event recordings may be placed directly in this
-folder for local Video Loader calibration. They are git-ignored by source ID or
-filename, are never copied or modified by the analyser, and are not required by
-CI. See `tutorials/video-loader.md` for the manual validation order.
+folder for local Video Loader calibration. Common video extensions are
+git-ignored regardless of source ID or filename; the small committed
+`my-moving-face.mp4` is the explicit CI exception. Local research videos are
+never copied or modified by the analyser and are not required by CI. See
+`tutorials/video-loader.md` for the manual validation order.
diff --git a/tests/unit/loader.test.js b/tests/unit/loader.test.js
index 3cdcd45..8eedbd6 100644
--- a/tests/unit/loader.test.js
+++ b/tests/unit/loader.test.js
@@ -8,6 +8,7 @@ const runtime = vi.hoisted(() => ({
   loadModels: vi.fn(),
   loadFile: vi.fn(),
   analyze: vi.fn(),
+  analyzeFrame: vi.fn(),
   cancel: vi.fn(),
   exportBundle: vi.fn(),
 }));
@@ -23,6 +24,7 @@ vi.mock('../../lab-js/video-loader/controller.js', () => ({
     loadModels: runtime.loadModels,
     loadFile: runtime.loadFile,
     analyze: runtime.analyze,
+    analyzeFrame: runtime.analyzeFrame,
     cancel: runtime.cancel,
     exportBundle: runtime.exportBundle,
     getSession: () => runtime.session,
@@ -44,13 +46,14 @@ function loaderMarkup() {
 
 function makeSession(status = 'idle') {
   return {
-    source: { name: 'tutorial.webm', duration: 20 },
+    source: { name: 'tutorial.webm', size: 2048, duration: 20, width: 1280, height: 720 },
     status,
+    config: { sampleIntervalSeconds: 0.5 },
     baseline: null,
     samples: [],
     events: [],
     journal: [],
-    summary: { sampledFrames: 0, faceApiRuns: 0, confirmedEvents: 0 },
+    summary: { sampledFrames: 0, faceApiRuns: 0, confirmedEvents: 0, manualChecks: 0 },
     editorial: { storyOrder: ['baseline'], undo: [], redo: [] },
   };
 }
@@ -80,9 +83,17 @@ describe('loader UI adapter', () => {
       runtime.session.status = 'complete';
       runtime.session.samples.push({ mediaTime: 1, facePresent: true, visualDrift: null });
       runtime.session.summary.sampledFrames = 1;
-      runtime.listener?.({ type: 'sample', session: runtime.session, progress: 1 });
+      runtime.listener?.({
+        type: 'sample',
+        session: runtime.session,
+        phase: 'scan',
+        progress: 1,
+        scanSampleIndex: 1,
+        scanSampleTotal: 1,
+      });
       return runtime.session;
     });
+    runtime.analyzeFrame.mockReset();
     runtime.exportBundle.mockReturnValue({ report: {}, technicalLog: '{}\n', frames: [] });
 
     const video = document.getElementById('video');
@@ -112,15 +123,39 @@ describe('loader UI adapter', () => {
       fingerprint: 'sampled-sha256:test',
     });
     expect(window.gstmxxVideoLoader.isSourceLoaded()).toBe(true);
-    expect(document.getElementById('startAnalysisBtn').disabled).toBe(false);
+    expect(document.getElementById('sourceFileName').textContent).toBe('tutorial.webm');
+    expect(document.getElementById('sourceFileMeta').textContent).toContain('1280 × 720');
+    expect(document.getElementById('startAnalysisBtn').disabled).toBe(true);
+
+    runtime.session.baseline = {
+      mediaTime: 12.5,
+      visualTriggerThreshold: 0.1234,
+      faceSpread: 0.0456,
+      frame: {
+        mediaTime: 12.5,
+        dataUrl: 'data:image/jpeg;base64,AA==',
+      },
+    };
+    runtime.listener({ type: 'baseline-ready', session: runtime.session });
+    const baselineCard = document.querySelector('.loader-event--reference[data-event-id="baseline"]');
+    expect(baselineCard).not.toBeNull();
+    expect(baselineCard.querySelector('h3').textContent).toBe('Baseline · distance reference · 00:12.500');
+    expect(baselineCard.querySelector('.loader-event__metrics').textContent).toContain('face-api decision threshold 0.6000');
 
     document.getElementById('video').currentTime = 12.5;
-    document.getElementById('useCurrentTimeBtn').click();
-    expect(document.getElementById('baselineTime').value).toBe('12.500');
+    document.getElementById('video').dispatchEvent(new Event('timeupdate'));
+    expect(document.getElementById('selectBaseBtn').textContent).toBe('Select base at 00:12.500');
+    document.getElementById('selectBaseBtn').click();
+    expect(document.getElementById('selectedBaseLabel').textContent).toContain('selected at 00:12.500');
+    expect(document.getElementById('startAnalysisBtn').disabled).toBe(false);
 
     await window.gstmxxVideoLoader.analyze({ sampleIntervalSeconds: 1 });
-    expect(runtime.analyze).toHaveBeenCalledWith(expect.objectContaining({ sampleIntervalSeconds: 1 }));
-    expect(document.getElementById('analysisProgressLabel').textContent).toBe('100%');
+    expect(runtime.analyze).toHaveBeenCalledWith(expect.objectContaining({
+      sampleIntervalSeconds: 1,
+      baselineTime: 12.5,
+      baselineSelectionMode: 'anchored',
+    }));
+    expect(document.getElementById('analysisProgressLabel').textContent).toBe('1 / 1 samples · 100%');
     expect(document.getElementById('downloadReportBtn').disabled).toBe(false);
 
     window.gstmxxVideoLoader.cancel();
@@ -128,6 +163,298 @@ describe('loader UI adapter', () => {
     expect(window.gstmxxVideoLoader.exportBundle()).toEqual({ report: {}, technicalLog: '{}\n', frames: [] });
   });
 
+  it('turns a cancelled run into a resumable sample checkpoint', async () => {
+    let run = 0;
+    runtime.analyze.mockImplementation(async options => {
+      run += 1;
+      if (run === 1) {
+        runtime.session.status = 'cancelled';
+        runtime.listener?.({
+          type: 'sample',
+          session: runtime.session,
+          phase: 'scan',
+          progress: 0.4,
+          scanSampleIndex: 4,
+          scanSampleTotal: 10,
+          sample: { mediaTime: 8 },
+        });
+        runtime.listener?.({
+          type: 'analysis-cancelled',
+          session: runtime.session,
+          progress: 0.4,
+          scanSampleIndex: 4,
+          scanSampleTotal: 10,
+          mediaTime: 8,
+        });
+      } else {
+        expect(options.resume).toBe(true);
+        runtime.session.status = 'complete';
+        runtime.listener?.({
+          type: 'analysis-complete',
+          session: runtime.session,
+          progress: 1,
+          scanSampleIndex: 10,
+          scanSampleTotal: 10,
+          mediaTime: 20,
+        });
+      }
+      return runtime.session;
+    });
+
+    await import('../../lab-js/loader.js?loader-resume');
+    await flushPromises();
+    const input = document.getElementById('videoFile');
+    Object.defineProperty(input, 'files', {
+      configurable: true,
+      value: [new File(['video'], 'tutorial.webm', { type: 'video/webm' })],
+    });
+    input.dispatchEvent(new Event('change'));
+    await flushPromises();
+    document.getElementById('selectBaseBtn').click();
+    document.getElementById('startAnalysisBtn').click();
+    await flushPromises();
+
+    expect(document.getElementById('startAnalysisBtn').textContent).toBe('Resume analysis');
+    expect(document.getElementById('analysisProgressLabel').textContent).toBe('4 / 10 samples · paused at 00:08.000');
+    expect(document.getElementById('videoSeek').disabled).toBe(false);
+    expect(document.querySelector('.loader-source-controls').classList.contains('is-muted')).toBe(false);
+
+    document.getElementById('startAnalysisBtn').click();
+    await flushPromises();
+    expect(runtime.analyze).toHaveBeenLastCalledWith(expect.objectContaining({ resume: true }));
+    expect(document.getElementById('analysisProgressLabel').textContent).toBe('10 / 10 samples · complete');
+  });
+
+  it('restarts and recalculates the scan when paused sampling changes', async () => {
+    let run = 0;
+    runtime.analyze.mockImplementation(async options => {
+      run += 1;
+      if (run === 1) {
+        runtime.session.status = 'cancelled';
+        runtime.session.config.sampleIntervalSeconds = options.sampleIntervalSeconds;
+        runtime.listener?.({
+          type: 'analysis-cancelled',
+          session: runtime.session,
+          progress: 0.4,
+          scanSampleIndex: 4,
+          scanSampleTotal: 10,
+          mediaTime: 8,
+        });
+      } else {
+        runtime.session.status = 'complete';
+      }
+      return runtime.session;
+    });
+
+    await import('../../lab-js/loader.js?loader-restart-cadence');
+    await flushPromises();
+    const input = document.getElementById('videoFile');
+    Object.defineProperty(input, 'files', {
+      configurable: true,
+      value: [new File(['video'], 'tutorial.webm', { type: 'video/webm' })],
+    });
+    input.dispatchEvent(new Event('change'));
+    await flushPromises();
+    document.getElementById('selectBaseBtn').click();
+    document.getElementById('startAnalysisBtn').click();
+    await flushPromises();
+
+    const sampleInterval = document.getElementById('sampleInterval');
+    sampleInterval.value = '2';
+    sampleInterval.dispatchEvent(new Event('change'));
+    expect(document.getElementById('startAnalysisBtn').textContent).toBe('Restart with new sampling');
+
+    document.getElementById('startAnalysisBtn').click();
+    await flushPromises();
+    expect(runtime.analyze).toHaveBeenLastCalledWith(expect.objectContaining({
+      resume: false,
+      sampleIntervalSeconds: 2,
+    }));
+  });
+
+  it('restarts instead of resuming when the paused baseline changes', async () => {
+    let run = 0;
+    runtime.analyze.mockImplementation(async options => {
+      run += 1;
+      if (run === 1) {
+        runtime.session.status = 'cancelled';
+        runtime.session.baseline = { mediaTime: options.baselineTime };
+        runtime.listener?.({
+          type: 'analysis-cancelled',
+          session: runtime.session,
+          progress: 0.4,
+          scanSampleIndex: 4,
+          scanSampleTotal: 10,
+          mediaTime: 8,
+        });
+      } else {
+        runtime.session.status = 'complete';
+      }
+      return runtime.session;
+    });
+
+    await import('../../lab-js/loader.js?loader-restart-baseline');
+    await flushPromises();
+    const input = document.getElementById('videoFile');
+    Object.defineProperty(input, 'files', {
+      configurable: true,
+      value: [new File(['video'], 'tutorial.webm', { type: 'video/webm' })],
+    });
+    input.dispatchEvent(new Event('change'));
+    await flushPromises();
+    document.getElementById('selectBaseBtn').click();
+    document.getElementById('startAnalysisBtn').click();
+    await flushPromises();
+
+    document.getElementById('video').currentTime = 9;
+    document.getElementById('selectBaseBtn').click();
+    expect(document.getElementById('startAnalysisBtn').textContent).toBe('Restart with new baseline');
+
+    document.getElementById('startAnalysisBtn').click();
+    await flushPromises();
+    expect(runtime.analyze).toHaveBeenLastCalledWith(expect.objectContaining({
+      resume: false,
+      baselineTime: 9,
+    }));
+  });
+
+  it('mutes source navigation while the controller owns video time', async () => {
+    let finishAnalysis;
+    runtime.analyze.mockImplementation(() => new Promise(resolve => {
+      finishAnalysis = () => {
+        runtime.session.status = 'complete';
+        resolve(runtime.session);
+      };
+    }));
+
+    await import('../../lab-js/loader.js?loader-muted-controls');
+    await flushPromises();
+    const input = document.getElementById('videoFile');
+    Object.defineProperty(input, 'files', {
+      configurable: true,
+      value: [new File(['video'], 'tutorial.webm', { type: 'video/webm' })],
+    });
+    input.dispatchEvent(new Event('change'));
+    await flushPromises();
+    document.getElementById('selectBaseBtn').click();
+    document.getElementById('startAnalysisBtn').click();
+    await flushPromises();
+
+    expect(document.querySelector('.loader-source-controls').classList.contains('is-muted')).toBe(true);
+    expect(document.getElementById('analysisSettings').classList.contains('is-muted')).toBe(true);
+    expect(document.getElementById('videoSeek').disabled).toBe(true);
+    expect(document.getElementById('video').inert).toBe(true);
+    expect(document.getElementById('analysisLockNotice').hidden).toBe(false);
+    expect(document.getElementById('cancelAnalysisBtn').disabled).toBe(false);
+
+    finishAnalysis();
+    await flushPromises();
+    expect(document.querySelector('.loader-source-controls').classList.contains('is-muted')).toBe(false);
+    expect(document.getElementById('video').inert).toBe(false);
+  });
+
+  it('adds a person-requested frame check after a baseline exists', async () => {
+    runtime.analyzeFrame.mockImplementation(async mediaTime => {
+      const event = {
+        id: 'event-manual',
+        origin: 'manual',
+        status: 'confirmed',
+        classification: 'manual-frame-check',
+        interpretation: 'above-threshold',
+        anchorMediaTime: mediaTime,
+        caption: '',
+        promoted: false,
+        observations: [{
+          mediaTime,
+          faceDistance: 0.71,
+          faceThreshold: 0.6,
+          visualDrift: 0.12,
+          reasons: ['manual-frame-check'],
+        }],
+        selectedFrame: { mediaTime, dataUrl: 'data:image/jpeg;base64,AA==', sharpness: 0.2 },
+      };
+      runtime.session.events.push(event);
+      runtime.session.editorial.storyOrder.push(event.id);
+      runtime.session.summary.manualChecks += 1;
+      runtime.listener?.({ type: 'manual-frame-checked', session: runtime.session, event });
+      return event;
+    });
+
+    await import('../../lab-js/loader.js?loader-manual-frame');
+    await flushPromises();
+    const input = document.getElementById('videoFile');
+    Object.defineProperty(input, 'files', {
+      configurable: true,
+      value: [new File(['video'], 'rainbow.webm', { type: 'video/webm' })],
+    });
+    input.dispatchEvent(new Event('change'));
+    await flushPromises();
+    runtime.session.baseline = {
+      mediaTime: 0,
+      visualTriggerThreshold: 0.04,
+      faceSpread: 0.02,
+      frame: { mediaTime: 0, dataUrl: 'data:image/jpeg;base64,AA==' },
+    };
+    runtime.listener?.({ type: 'baseline-ready', session: runtime.session });
+    document.getElementById('video').currentTime = 7;
+    document.getElementById('video').dispatchEvent(new Event('timeupdate'));
+
+    expect(document.getElementById('analyzeFrameBtn').disabled).toBe(false);
+    expect(document.getElementById('analyzeFrameBtn').textContent).toBe('Analyse frame at 00:07.000');
+    document.getElementById('analyzeFrameBtn').click();
+    await flushPromises();
+
+    expect(runtime.analyzeFrame).toHaveBeenCalledWith(7);
+    expect(document.querySelector('.loader-event--manual h3').textContent).toContain('Manual frame check');
+    expect(document.getElementById('analysisMessage').textContent).toContain('face-api distance 0.7100');
+  });
+
+  it('keeps an actionable baseline failure beside the run controls', async () => {
+    runtime.analyze.mockImplementation(async options => {
+      runtime.session.status = 'failed';
+      runtime.listener?.({
+        type: 'sample',
+        session: runtime.session,
+        phase: 'baseline',
+        baselineAcceptedSamples: 6,
+        baselineRequiredSamples: 4,
+        baselineAcceptedDescriptors: 2,
+        baselineRequiredDescriptors: 3,
+      });
+      runtime.listener?.({
+        type: 'analysis-failed',
+        session: runtime.session,
+        reason: 'selected-frame-neighbours-insufficient',
+        baselineTime: options.baselineTime,
+        baselineSearchEnd: options.baselineTime + 3,
+        acceptedEmbeddings: 2,
+        requiredEmbeddings: 4,
+        acceptedDescriptors: 0,
+        requiredDescriptors: 3,
+      });
+      throw new Error('baseline failed');
+    });
+
+    await import('../../lab-js/loader.js?loader-baseline-failure');
+    await flushPromises();
+    const input = document.getElementById('videoFile');
+    Object.defineProperty(input, 'files', {
+      configurable: true,
+      value: [new File(['video'], 'short.webm', { type: 'video/webm' })],
+    });
+    input.dispatchEvent(new Event('change'));
+    await flushPromises();
+    document.getElementById('selectBaseBtn').click();
+    document.getElementById('startAnalysisBtn').click();
+    await flushPromises();
+
+    expect(document.getElementById('analysisMessage').hidden).toBe(false);
+    expect(document.getElementById('analysisProgressLabel').textContent).toBe('baseline face-api 2 / 3');
+    expect(document.getElementById('analysisMessage').textContent).toContain('only 2 of 4 comparable nearby frames');
+    expect(document.getElementById('analysisMessage').textContent).toContain('Makeup already being present is not itself an error');
+    expect(document.getElementById('statusText').textContent).toBe('baseline not created');
+  });
+
   it('reports model-loading failures and keeps analysis unavailable', async () => {
     runtime.loadModels.mockRejectedValueOnce(new Error('model unavailable'));
     await import('../../lab-js/loader.js?new-loader-failure');
diff --git a/tests/unit/video-loader-analysis.test.js b/tests/unit/video-loader-analysis.test.js
index 3d14711..a47a9b9 100644
--- a/tests/unit/video-loader-analysis.test.js
+++ b/tests/unit/video-loader-analysis.test.js
@@ -1,5 +1,7 @@
 import { describe, expect, it } from 'vitest';
 import {
+  buildAnchoredEmbeddingBaseline,
+  buildAnchoredFaceBaseline,
   buildEmbeddingBaseline,
   buildFaceBaseline,
   cosineSimilarity,
@@ -40,6 +42,23 @@ describe('video-loader analysis policy', () => {
     expect(faceBaseline.spread).toBeGreaterThanOrEqual(0);
   });
 
+  it('keeps the explicitly selected frame as the anchored model reference', () => {
+    const embeddings = [[1, 0], [0.99, 0.1], [0.98, -0.1]];
+    const embeddingBaseline = buildAnchoredEmbeddingBaseline(embeddings, 0, {
+      minimumDelta: 0.02,
+      madMultiplier: 4,
+    });
+    expect(embeddingBaseline.embedding).toEqual(embeddings[0]);
+    expect(embeddingBaseline.referenceIndex).toBe(0);
+    expect(embeddingBaseline.triggerThreshold).toBeGreaterThanOrEqual(0.02);
+
+    const descriptors = [[0, 0], [0.02, 0], [0.04, 0]];
+    const faceBaseline = buildAnchoredFaceBaseline(descriptors, 0);
+    expect(faceBaseline.descriptor).toEqual(descriptors[0]);
+    expect(faceBaseline.referenceIndex).toBe(0);
+    expect(faceBaseline.spread).toBeCloseTo(0.02);
+  });
+
   it('triggers on sustained drift, face return, jump, and heartbeat without queueing frames', () => {
     const detector = createTriggerDetector({
       heartbeatSeconds: 4,
@@ -70,4 +89,49 @@ describe('video-loader analysis policy', () => {
     expect(sustained.reasons).toContain('visual-drift-sustained');
     expect(sustained.reasons).toContain('visual-drift-jump');
   });
+
+  it('keeps MediaPipe gaps and unusable crops as diagnostics rather than face-api evidence', () => {
+    const detector = createTriggerDetector({
+      heartbeatSeconds: 4,
+      minimumFaceApiGapSeconds: 0.5,
+    });
+    detector.evaluate({
+      mediaTime: 0,
+      facePresent: true,
+      comparable: true,
+      visualDrift: 0.01,
+      visualThreshold: 0.05,
+    });
+    detector.markFaceApi(0);
+
+    const lost = detector.evaluate({
+      mediaTime: 1,
+      facePresent: false,
+      comparable: false,
+      visualDrift: null,
+      visualThreshold: 0.05,
+    });
+    expect(lost.shouldVerify).toBe(false);
+    expect(lost.observations).toContain('mediapipe-face-lost');
+
+    const unusableReturn = detector.evaluate({
+      mediaTime: 5,
+      facePresent: true,
+      comparable: false,
+      visualDrift: null,
+      visualThreshold: 0.05,
+    });
+    expect(unusableReturn.reasons).toContain('mediapipe-face-returned');
+    expect(unusableReturn.shouldVerify).toBe(false);
+
+    const usableHeartbeat = detector.evaluate({
+      mediaTime: 6,
+      facePresent: true,
+      comparable: true,
+      visualDrift: 0.01,
+      visualThreshold: 0.05,
+    });
+    expect(usableHeartbeat.reasons).toContain('identity-heartbeat');
+    expect(usableHeartbeat.shouldVerify).toBe(true);
+  });
 });
diff --git a/tests/unit/video-loader-session.test.js b/tests/unit/video-loader-session.test.js
index f759cdb..57b655a 100644
--- a/tests/unit/video-loader-session.test.js
+++ b/tests/unit/video-loader-session.test.js
@@ -6,6 +6,7 @@ import {
   buildReport,
   createAnalysisSession,
   recordEvidenceObservation,
+  recordManualFrameCheck,
   redoEditorialCommand,
   selectEventFrame,
   serializeTechnicalLog,
@@ -79,6 +80,7 @@ describe('video-loader session', () => {
     recordEvidenceObservation(current, {
       mediaTime: 2,
       outcome: 'divergent',
+      mediaPipeComparable: true,
       faceApiDetected: false,
       faceDistance: null,
       faceThreshold: 0.6,
@@ -87,6 +89,7 @@ describe('video-loader session', () => {
     const event = recordEvidenceObservation(current, {
       mediaTime: 3,
       outcome: 'divergent',
+      mediaPipeComparable: true,
       faceApiDetected: false,
       faceDistance: null,
       faceThreshold: 0.6,
@@ -97,6 +100,31 @@ describe('video-loader session', () => {
     expect(event.reviewRequired).toBe(true);
   });
 
+  it('does not confirm missing detections from unusable MediaPipe crops', () => {
+    const current = session();
+    recordEvidenceObservation(current, {
+      mediaTime: 2,
+      outcome: 'divergent',
+      mediaPipeComparable: false,
+      faceApiDetected: false,
+      faceDistance: null,
+      faceThreshold: 0.6,
+      reasons: ['identity-heartbeat'],
+    });
+    const event = recordEvidenceObservation(current, {
+      mediaTime: 3,
+      outcome: 'divergent',
+      mediaPipeComparable: false,
+      faceApiDetected: false,
+      faceDistance: null,
+      faceThreshold: 0.6,
+      reasons: ['identity-heartbeat'],
+    });
+    expect(event.status).toBe('candidate');
+    expect(event.classification).toBe('unclassified');
+    expect(current.summary.confirmedEvents).toBe(0);
+  });
+
   it('supports reversible editorial choices', () => {
     const current = session();
     const event = recordEvidenceObservation(current, {
@@ -114,6 +142,57 @@ describe('video-loader session', () => {
     expect(event.promoted).toBe(true);
   });
 
+  it('stores a requested frame as a distinct manual story check', () => {
+    const current = session();
+    current.baseline = {
+      mediaTime: 1,
+      descriptor: [0, 0],
+      faceSpread: 0.02,
+      embeddingMedianDrift: 0.01,
+      embeddingMad: 0.002,
+      visualTriggerThreshold: 0.04,
+      frame: null,
+    };
+    const event = recordManualFrameCheck(current, {
+      mediaTime: 6,
+      interpretation: 'above-threshold',
+      observation: {
+        mediaTime: 6,
+        outcome: 'divergent',
+        mediaPipeComparable: true,
+        faceApiDetected: true,
+        faceDistance: 0.72,
+        faceThreshold: 0.6,
+        visualDrift: 0.11,
+        reasons: ['manual-frame-check'],
+      },
+      frame: {
+        mediaTime: 6,
+        dataUrl: 'data:image/jpeg;base64,manual',
+        width: 1920,
+        height: 1080,
+        sharpness: 0.2,
+        reason: 'manual-frame-check',
+      },
+    });
+
+    expect(event).toMatchObject({
+      origin: 'manual',
+      classification: 'manual-frame-check',
+      status: 'confirmed',
+    });
+    expect(current.summary.manualChecks).toBe(1);
+    expect(current.summary.confirmedEvents).toBe(0);
+    expect(current.editorial.storyOrder).toEqual(['baseline', event.id]);
+    expect(buildReport(current).events[0]).toMatchObject({
+      origin: 'manual',
+      interpretation: 'above-threshold',
+    });
+    const log = serializeTechnicalLog(current);
+    expect(log).toContain('manual-frame-checked');
+    expect(log).not.toContain('base64,manual');
+  });
+
   it('keeps source time fixed while a person reorders the story', () => {
     const current = session();
     current.baseline = {
@@ -127,6 +206,7 @@ describe('video-loader session', () => {
     recordEvidenceObservation(current, {
       mediaTime: 1,
       outcome: 'divergent',
+      mediaPipeComparable: true,
       faceApiDetected: false,
       faceDistance: null,
       faceThreshold: 0.6,
@@ -135,17 +215,17 @@ describe('video-loader session', () => {
     const event = recordEvidenceObservation(current, {
       mediaTime: 2,
       outcome: 'divergent',
+      mediaPipeComparable: true,
       faceApiDetected: false,
       faceDistance: null,
       faceThreshold: 0.6,
       reasons: [],
     });
-    expect(current.editorial.storyOrder).toEqual([event.id, 'baseline']);
-    expect(applyEditorialCommand(current, { type: 'move-earlier', eventId: 'baseline' })).toBe(true);
+    expect(current.editorial.storyOrder).toEqual(['baseline', event.id]);
+    expect(applyEditorialCommand(current, { type: 'move-later', eventId: 'baseline' })).toBe(false);
+    expect(applyEditorialCommand(current, { type: 'move-earlier', eventId: event.id })).toBe(false);
     expect(current.editorial.storyOrder).toEqual(['baseline', event.id]);
     expect(event.anchorMediaTime).toBe(1);
     expect(buildReport(current).storyOrder).toEqual(['baseline', event.id]);
-    expect(undoEditorialCommand(current)).toBe(true);
-    expect(current.editorial.storyOrder).toEqual([event.id, 'baseline']);
   });
 });
diff --git a/tutorials/video-loader.md b/tutorials/video-loader.md
index 000ec7c..4adcd6f 100644
--- a/tutorials/video-loader.md
+++ b/tutorials/video-loader.md
@@ -71,9 +71,12 @@ await window.gstmxxVideoLoader.analyze({
   start: 0,
   end: 120,
   baselineTime: 18,
+  baselineSelectionMode: 'anchored',
   sampleIntervalSeconds: 0.5,
 });
 window.gstmxxVideoLoader.cancel();
+await window.gstmxxVideoLoader.resume();
+await window.gstmxxVideoLoader.analyzeFrame(83.417);
 window.gstmxxVideoLoader.getSession();
 window.gstmxxVideoLoader.exportBundle();
 ```
@@ -83,6 +86,186 @@ source readiness, samples, baseline readiness, confirmed events, completion,
 and errors. Automation should prefer this contract over button text or screen
 coordinates.
 
+## Event-to-function call map
+
+The following trees describe the important call paths from their external
+trigger. They are deliberately written like `pstree`: indentation means a
+direct call, while `[...]` marks a conditional branch or a repeated sampling
+loop. Browser events and the public Playwright API converge before analysis,
+so interactive and automated runs exercise the same controller.
+
+```text
+<script type="module"> loads lab-js/loader.js
+└─ init()
+   ├─ initI18n()
+   ├─ setupLocaleSelect()
+   ├─ applyI18n()
+   ├─ renderSession(null)
+   │  └─ drawTimeline(null)
+   ├─ syncControls()
+   └─ controller.loadModels()
+      ├─ faceapi.nets.*.loadFromUri()                 [three face-api models]
+      ├─ import(MEDIAPIPE_TASKS_VISION_URL)
+      ├─ FilesetResolver.forVisionTasks()
+      ├─ FaceLandmarker.createFromOptions()
+      ├─ ImageEmbedder.createFromOptions()
+      └─ notify('models-ready')
+         └─ controller subscriber in loader.js
+            ├─ setStatus()
+            ├─ syncControls()
+            └─ dispatchLoaderEvent('models-ready')
+
+<input type="file"> change
+or Playwright page.setInputFiles('#videoFile', source)
+└─ loadSelectedFile()
+   └─ controller.loadFile()
+      ├─ URL.createObjectURL()
+      ├─ waitForMediaEvent(video, 'loadedmetadata')
+      ├─ createAnalysisSession()
+      ├─ appendJournal('source-loaded')
+      ├─ triggerDetector.reset()
+      └─ notify('source-loaded')
+   ├─ syncVideoTimeline()
+   │  └─ renderBaseSelection()
+   ├─ renderSession()
+   │  ├─ drawTimeline()
+   │  └─ orderedStoryItems()
+   ├─ syncControls()
+   └─ dispatchLoaderEvent('source-loaded')
+
+<video> timeupdate | seeked | loadedmetadata
+or #videoSeek input
+└─ syncVideoTimeline()
+   └─ renderBaseSelection()
+
+#selectBaseBtn click
+└─ selectCurrentBase()
+   ├─ selectedBaseTime = video.currentTime
+   ├─ renderBaseSelection()
+   └─ syncControls()                                  [enables Analyse]
+
+#analyzeFrameBtn click
+or window.gstmxxVideoLoader.analyzeFrame(mediaTime)
+└─ analyzeCurrentFrame(mediaTime)
+   └─ controller.analyzeFrame(mediaTime)
+      ├─ seekVideo()
+      ├─ faceLandmarker.detect()
+      ├─ drawAlignedFaceCrop() + measureCropQuality()
+      ├─ imageEmbedder.embed()                        [only if comparable]
+      ├─ runFaceApi()                                 [explicit human request]
+      ├─ euclideanDistance()                          [descriptor vs baseline]
+      ├─ appendSample({ phase: 'manual' })
+      ├─ appendJournal('face-api-result')
+      ├─ captureSourceFrame()                         [exact native frame]
+      ├─ recordManualFrameCheck()
+      └─ notify('manual-frame-checked')
+         ├─ renderSession()
+         ├─ renderAnalysisMessage()
+         └─ dispatchLoaderEvent('manual-frame-checked')
+
+#startAnalysisBtn click
+or window.gstmxxVideoLoader.analyze(options)
+└─ startAnalysis(options)
+   └─ controller.analyze(options)
+      ├─ appendJournal('analysis-started')
+      ├─ notify('analysis-started')
+      ├─ [anchored baseline calibration after selectedBaseTime]
+      │  ├─ seekVideo()
+      │  │  └─ waitForMediaEvent(video, 'seeked')
+      │  ├─ faceLandmarker.detect()
+      │  ├─ drawAlignedFaceCrop()
+      │  ├─ measureCropQuality()
+      │  ├─ imageEmbedder.embed()
+      │  │  └─ unpackEmbedding()
+      │  ├─ appearanceDistance() + visualDrift()      [neighbour eligibility]
+      │  ├─ [enough comparable local samples]
+      │  │  ├─ runFaceApi()                           [selected + sharp neighbours]
+      │  │  ├─ buildAnchoredFaceBaseline()            [selected descriptor]
+      │  │  │  └─ median(euclideanDistance)
+      │  │  ├─ buildAnchoredEmbeddingBaseline()       [selected embedding]
+      │  │  │  ├─ median()
+      │  │  │  └─ medianAbsoluteDeviation()
+      │  │  ├─ captureSourceFrame()                   [selected native JPEG]
+      │  │  ├─ finaliseBaseline()
+      │  │  └─ notify('baseline-ready')
+      │  ├─ appendSample()
+      │  └─ notify('sample')
+      ├─ [coarse scan loop, rewound to analysis start]
+      │  ├─ seekVideo()
+      │  ├─ faceLandmarker.detect()
+      │  ├─ drawAlignedFaceCrop()
+      │  ├─ measureCropQuality()
+      │  ├─ imageEmbedder.embed()
+      │  ├─ visualDrift()
+      │  ├─ appendSample()
+      │  ├─ triggerDetector.evaluate()
+      │  ├─ [verification requested]
+      │  │  ├─ runFaceApi()
+      │  │  ├─ euclideanDistance()                    [descriptor vs baseline]
+      │  │  ├─ appendJournal('face-api-result')
+      │  │  └─ recordEvidenceObservation()
+      │  │     └─ [event newly confirmed]
+      │  │        ├─ refineEvidenceFrame()
+      │  │        │  ├─ [neighbourhood loop]
+      │  │        │  │  ├─ seekVideo()
+      │  │        │  │  ├─ faceLandmarker.detect()
+      │  │        │  │  ├─ drawAlignedFaceCrop()
+      │  │        │  │  └─ measureCropQuality()
+      │  │        │  ├─ runFaceApi()                  [at most three candidates]
+      │  │        │  ├─ euclideanDistance()
+      │  │        │  ├─ captureSourceFrame()          [one selected JPEG]
+      │  │        │  └─ selectEventFrame()
+      │  │        └─ notify('event-confirmed')
+      │  └─ notify('sample')
+      ├─ appendJournal('analysis-complete' | 'analysis-cancelled')
+      └─ notify('analysis-complete' | 'analysis-cancelled')
+         └─ controller subscriber in loader.js
+            ├─ renderSession()
+            │  ├─ drawTimeline()
+            │  ├─ orderedStoryItems()
+            │  ├─ createBaselineCard()
+            │  └─ createEventCard()
+            └─ dispatchLoaderEvent(update.type)
+
+#cancelAnalysisBtn click | window.gstmxxVideoLoader.cancel()
+└─ controller.cancel()                                [sets boundary flag]
+   └─ analyze() finishes the current sample
+      ├─ seekVideo(lastScanMediaTime)                  [visible checkpoint]
+      ├─ appendJournal('analysis-cancelled')
+      └─ notify('analysis-cancelled')
+
+#startAnalysisBtn click while cancelled
+or window.gstmxxVideoLoader.resume()
+└─ startAnalysis({ resume: true })
+   └─ controller.analyze({ resume: true })
+      ├─ appendJournal('analysis-resumed')
+      ├─ preserve baseline, samples, events, and journal
+      └─ resume coarse scan at lastScanMediaTime + sample interval
+
+editorial button click
+├─ applyEditorialCommand()                            [caption/promote/reject/order]
+├─ undoEditorialCommand() | redoEditorialCommand()   [Undo/Redo]
+└─ renderSession()
+
+Download report click | Download technical log click
+└─ controller.exportBundle()
+   ├─ buildReport()
+   ├─ serializeTechnicalLog()
+   └─ downloadText()                                  [chosen artifact only]
+
+scripts-dev/video-loader.cjs analyze
+└─ main()
+   └─ analyze()
+      ├─ sampledFingerprint()
+      ├─ startServer()                                [unless --base-url]
+      ├─ chromium.launch()
+      ├─ page.setInputFiles('#videoFile', source)
+      ├─ window.gstmxxVideoLoader.analyze(options)    [browser tree above]
+      ├─ window.gstmxxVideoLoader.exportBundle()
+      ├─ writeBundle()
+      └─ extractClips()                               [only with --clips]
+```
+
 ## Model provenance
 
 The repository vendors the following browser assets:
@@ -116,6 +299,12 @@ FaceLandmarker is the light, frequent sensor. For every sampled media timestamp
 it answers whether a mesh is present and supplies geometry for crop alignment.
 It is not used as an identity matcher.
 
+A missing mesh is coverage information, not evidence that makeup affected a
+recogniser. Hands, brushes, pose, framing, motion, and ordinary cuts can all
+remove the mesh temporarily. The technical log and coverage lane retain these
+gaps so a person can understand what the analyser did while unattended, but a
+gap cannot create an evidence event or invoke face-api.
+
 The Video Loader uses MediaPipe in `IMAGE` mode rather than `VIDEO` mode. A
 seekable analysis may revisit timestamps during refinement. `VIDEO` mode
 requires monotonically increasing timestamps, while `IMAGE` mode describes the
@@ -153,9 +342,10 @@ the face-api L2 scale and is displayed in a separate timeline lane.
 ### face-api: sparse identity verification
 
 face-api performs detection, 68-point alignment, and a 128-dimensional face
-descriptor. It runs only for:
+descriptor. It first runs on the bounded baseline candidates. During the later
+scan it runs only when MediaPipe returns a geometrically and photometrically
+comparable crop and one of these reasons exists:
 
-- baseline acquisition;
 - a MediaPipe face returning after an absence;
 - sustained ImageEmbedder drift;
 - a sudden ImageEmbedder drift jump;
@@ -171,18 +361,58 @@ yields between samples; this is sufficient for an offline, paused, seekable
 video. A Web Worker should only be introduced after measuring actual UI stalls
 and verifying the vendored backend in a worker environment.
 
-## Baseline acquisition
+### Multi-person sources: current limitation
+
+The current pipeline is intentionally single-subject but does not yet enforce
+that contract. FaceLandmarker is configured with `numFaces: 1` and the scan
+reads `faceLandmarks[0]`. The vendored face-api call is
+`detectSingleFace(...)`: despite the underlying detector finding candidates,
+that API returns only the candidate with the highest detection score, or
+`null`, rather than an array. The two models select independently from the
+full source frame. With two visible people, MediaPipe's chosen mesh and
+face-api's highest-score detection may therefore refer to different people.
+No bounding-box association, subject identifier, or cross-sample track
+currently distinguishes them, so an apparent descriptor transition in a
+multi-person interval is not trustworthy.
+
+The safest first multi-person policy is to configure the frequent detector to
+report more than one mesh, store only compact face counts and boxes, and mark
+every multi-face sample as non-comparable. Such samples would remain technical
+coverage context and could not confirm an event. A later subject-lock design
+could let a person choose the baseline subject and associate candidates by
+box continuity across samples and cuts. Descriptor distance must not be the
+only association rule because that is the very signal the Video Loader is
+trying to measure. This decision is deliberately left out of the current
+implementation rather than silently guessing a subject.
 
-The first decoded frame is not automatically trusted. The scan advances until
-it collects enough comparable aligned face crops and face-api descriptors.
+## Baseline acquisition
 
-Makeup tutorials often open with a finished-look teaser. The UI and CLI expose
-`baselineTime`, the timestamp from which baseline search begins. After a stable
-baseline is acquired, the controller rewinds and scans the complete requested
-range, so a teaser before the bare-face baseline can still appear as evidence.
-In the UI, a person can scrub the normal video player to the start of the
-bare-face section and press **Use current video time** instead of transcribing a
-timestamp. The CLI equivalent is `--baseline-time`.
+The first decoded frame is not automatically trusted. Browser interaction and
+automatic CLI analysis deliberately use two different baseline modes.
+
+Makeup tutorials often open with a finished-look teaser. The UI asks a person
+to scrub the normal video player and press **Select base at…** on the frame
+that must become the reference. **Analyse video** remains unavailable
+until that deliberate choice has been made. The selected base is reserved as
+the first evidence-story item and cannot be moved behind derived evidence. The
+button becomes
+**Replace base with…** after the person moves to another frame, so correcting
+the choice never requires synchronising a numeric field with the player.
+
+Internally and in the Playwright API this value is `baselineTime` with
+`baselineSelectionMode: 'anchored'`. The visible JPEG, ImageEmbedder vector,
+and face-api descriptor all come from that exact decoded timestamp. The
+controller may inspect nearby frames for noise calibration, but it cannot move
+the baseline. An unusable selected frame produces an actionable error instead
+of silently selecting a later scene. After acquisition, the controller rewinds
+and scans the complete requested range, so a teaser before the bare-face
+baseline can still appear as evidence.
+
+The CLI equivalent is `--baseline-time`, which also selects anchored mode.
+When automation omits that option, the CLI uses automatic-search mode from the
+analysis-range start. Automatic search still requires a continuous stable run
+and chooses medoid vectors because no person has identified an authoritative
+frame.
 
 A sample is comparable when:
 
@@ -192,8 +422,16 @@ A sample is comparable when:
 - exposure is neither nearly black nor heavily clipped;
 - ImageEmbedder returns a float vector.
 
-The default baseline requires eight ImageEmbedder vectors and three face-api
-descriptors. Baseline candidates are sampled at no more than 0.25-second
+An anchored baseline requires the selected ImageEmbedder vector plus three
+comparable neighbours and three face-api descriptors. Neighbours are sampled
+within the following three seconds at no more than 0.25-second intervals.
+Abrupt pose, appearance, or comparability changes skip that neighbour and are
+recorded as `anchored-baseline-neighbour-skipped`; they never replace the
+selected frame. The selected frame itself must be comparable and must return a
+face-api descriptor.
+
+Automatic search requires eight ImageEmbedder vectors and three face-api
+descriptors. Its candidates are sampled at no more than 0.25-second
 intervals even when the coarse scan uses a wider interval; this prevents a
 two-second long-video cadence from stretching one baseline across many scene
 changes. A candidate run is reset when an 8 × 8 RGB crop signature changes too
@@ -203,11 +441,18 @@ ImageEmbedder distance is deliberately not the stability gate: calibration on
 real tutorial footage showed that it can vary strongly between neighbouring
 frames that still depict the same face. face-api
 does not run for every failed candidate: it verifies the three sharpest frames
-only after eight lightweight samples have formed a continuous run. Both
+only after eight lightweight samples have formed a continuous run. Automatic
 baselines are medoids: real observed vectors with the
 smallest total distance to their peers. A medoid is preferred to a mean because
 it remains a genuine model output and is robust to one unusual sample.
 
+For an anchored baseline, the selected vectors remain the actual references.
+Nearby vectors only estimate noise. Automatic descriptor spread must be at
+most `0.3000`; an explicitly selected baseline can be accepted up to `0.5000`,
+but the UI displays an elevated-variation warning above `0.3000`. The identity
+decision threshold remains `0.6000`; the looser anchored calibration limit does
+not change it.
+
 For baseline visual drifts `d_i`, the adaptive trigger threshold is:
 
 ```text
@@ -218,11 +463,105 @@ median(d_i) + max(minimumDelta, MAD(d_i) × madMultiplier)
 validated against real makeup videos; they are configuration, not scientific
 constants.
 
-The frame attached to the baseline is captured at the ImageEmbedder medoid's
-timestamp, rather than simply choosing the sharpest candidate. This keeps the
-visible baseline and its numeric visual representative aligned, and avoids
-systematically preferring unusually high-contrast hand or product edges. It is
-retained at native source resolution. All other baseline pixels are discarded.
+The anchored frame is captured at the selected timestamp, keeping the visible
+baseline and both numeric references aligned. In automatic mode the frame is
+captured at the ImageEmbedder medoid timestamp. Either way, one native-resolution
+JPEG is retained and all neighbouring baseline pixels are discarded.
+
+Baseline failure is a visible product state, not only a header status or log
+record. The run controls retain a `role="alert"` explanation that translates
+stable failure codes into an action. In particular,
+`selected-frame-neighbours-insufficient` states how many of the four required
+comparable local frames were found. This distinguishes “the selected frame is
+unusable” from “the selected frame may be usable, but the following three
+seconds change too rapidly to estimate local noise”. Makeup already being
+present is not itself a baseline error: stability and detector availability
+are the requirements. `selected-frame-descriptors-insufficient` separately
+reports a usable MediaPipe calibration for which fewer than three nearby
+frames returned face-api descriptors. The NDJSON record keeps
+accepted/required embedding and descriptor counts for later diagnosis.
+
+## Control ownership, seeking, and pausing
+
+The controls have three explicit responsibilities. **Source** shows the full
+local filename, decoded dimensions, duration, file size, and current seek
+position. **Analysis plan** owns baseline selection and sampling cadence.
+**Run** owns the mutually exclusive Analyse/Resume/Restart and Pause actions,
+plus sample progress. The visible filename matters when several locally
+downloaded tutorials have similar content; it is not inferred from a video ID.
+
+Pause replaces the primary run action instead of leaving a disabled Pause and
+another active action side by side. Report actions are grouped by intent:
+Undo/Redo are edit history, while report/log downloads are exports.
+
+While analysis runs, the controller owns `video.currentTime`. Native video
+interaction, file replacement, the precise source slider, baseline selection,
+sampling cadence, and report timestamp links are therefore inert and visually
+muted. The player remains visible because watching decoded checkpoints is
+useful; a notice explains why its controls are unavailable. The pause button
+remains active.
+
+The native player bar and the custom source slider have different jobs. The
+native controls provide familiar playback. The custom slider provides a
+millisecond-labelled position for baseline selection and later evidence review.
+It does not claim to show encoded frame numbers: HTML video does not expose a
+stable random-access frame index, and the source may use variable frame rate.
+The separate analysis bar therefore reports the truthful unit available to the
+analyser: processed scan samples, for example `42 / 601 samples · 7%`.
+
+Pausing sets a cancellation flag and finishes the current sample. The
+controller stores `lastScanMediaTime`, seeks the visible player to that
+checkpoint, and leaves both the source slider and sample-progress bar there.
+The primary action becomes **Resume analysis**. Resume preserves the baseline,
+observations, events, and journal, then continues at one sampling interval
+after the checkpoint. A pause during initial baseline calibration restarts that
+small bounded calibration window because no image-vector queue is retained.
+
+Sampling cadence is part of a scan's identity. Changing **Sample every** while
+a run is paused changes the primary action to **Restart with new sampling**.
+That action starts a fresh scan, rebuilds the baseline at the selected source
+timestamp, and recalculates the total as
+`ceil((rangeEnd - rangeStart) / interval) + 1`. A true resume deliberately keeps
+the original interval, total, journal, and next checkpoint; mixing two cadences
+inside one progress denominator would make both the count and technical log
+ambiguous.
+
+Replacing the selected baseline while paused follows the same rule. The action
+becomes **Restart with new baseline**, or **Restart with new settings** when the
+baseline and cadence both changed. A resume cannot reuse old observations with
+a new reference because every stored descriptor distance and visual drift was
+computed from the previous baseline.
+
+Every baseline or evidence-card timestamp is a seek button when analysis is
+idle. Clicking it pauses ordinary playback and moves the source player to the
+recorded frame. These links become inert and muted while the controller owns
+media time.
+
+## Person-requested frame checks
+
+After a baseline exists, **Analyse frame at…** compares the source position
+currently shown by the player. This differs from automatic event confirmation:
+a person asked a question about one exact frame, so no second divergence vote
+is required and the card is labelled **Manual frame check**. It never claims
+to be a repeated automatic transition.
+
+The explicit check runs MediaPipe and face-api immediately. MediaPipe still
+controls whether the frame is considered comparable; face-api is also run when
+it is not, because a direct request should return the available diagnostic
+distance rather than silently do nothing. The interpretation is one of:
+
+- `below-threshold`: comparable frame and face-api L2 below `0.6000`;
+- `above-threshold`: comparable frame and L2 at or above `0.6000`;
+- `no-2d-face`: comparable MediaPipe crop but no face-api descriptor;
+- `not-comparable`: MediaPipe geometry or exposure rejected the crop, so any
+  face-api value is diagnostic context only.
+
+The exact native-resolution frame becomes an editable story card with source
+timestamp, caption, ordering, rejection, download, and recommended clip
+window. A `phase: "manual"` sample, a sparse `face-api-result`, and a
+`manual-frame-checked` journal record make the decision reprocessable without
+placing JPEG bytes in the log. Manual cards increment `manualChecks`, not
+`confirmedEvents`, and automatic observations never merge into them.
 
 ## Sampling and memory policy
 
@@ -263,11 +602,14 @@ one trigger reason exists:
 
 Sustained drift currently means at least two above-threshold samples in a
 three-sample window. A lost MediaPipe face is logged immediately but does not
-invoke face-api when no crop exists. The return transition does invoke it.
+invoke face-api when no crop exists. A return transition invokes face-api only
+after the returned crop is comparable; a mesh with excessive pose, unusable
+exposure, or invalid geometry remains diagnostic coverage rather than report
+evidence.
 
 A face-api observation is divergent when either:
 
-- MediaPipe has a face but face-api returns no detection; or
+- MediaPipe has a comparable face crop but face-api returns no detection; or
 - face-api returns a descriptor with L2 distance greater than or equal to 0.6.
 
 One divergent result creates a candidate. Two divergent observations within a
@@ -279,8 +621,8 @@ confirmed event receives one of these classifications:
 
 - `identity-distance`: at least two descriptors meet or exceed the configured
   L2 threshold. This is the strongest current identity-change signal;
-- `detector-divergence`: at least two frames contain a MediaPipe mesh while
-  face-api returns no face. This requires human review because a hand,
+- `detector-divergence`: at least two comparable frames contain a MediaPipe
+  mesh while face-api returns no face. This requires human review because a hand,
   applicator, pose, or partial occlusion can cause it;
 - `mixed-divergence`: two divergent checks exist, but they do not form either
   homogeneous case above. This also requires review.
@@ -308,12 +650,148 @@ so a later tool can reconstruct why that image was chosen.
 
 The report timeline has three independent lanes:
 
-1. **MediaPipe face** — a binary presence trace.
-2. **Visual drift** — frequent `1 - cosine` samples and an adaptive dashed threshold.
-3. **face-api L2** — sparse verification points and the fixed 0.6 dashed threshold.
+1. **MediaPipe coverage** — high means a comparable mesh, middle means a mesh
+   exists but its crop is unusable, and low means no mesh. This is context, not
+   evidence by itself.
+2. **ImageEmbedder drift** — frequent `1 - cosine` samples and an adaptive
+   dashed threshold. It is generic aligned-crop change, not identity distance.
+3. **face-api L2** — sparse numeric verification points and the fixed 0.6
+   dashed threshold. An × above the threshold label at the right is
+   categorical: face-api returned no 2D face, so no descriptor and no numeric
+   distance exist.
+
+Missing values create gaps; they are never converted into zeros or invented
+L2 values. Numeric face-api points use a data-aware vertical range instead of
+being clamped at `1.0`. ImageEmbedder and face-api values are not overlaid or
+normalised into one synthetic score. The dashed ImageEmbedder line is labelled
+with the session's four-decimal adaptive trigger; the dashed face-api line is
+labelled with the four-decimal identity threshold. The browser canvas and the
+exported SVG use the same labels, so a screenshot remains interpretable when
+removed from the surrounding page.
+
+## Support-image condition catalogue
+
+The analysis UI must distinguish a useful identity signal from a frame that
+cannot support any conclusion. These are not interchangeable warning styles:
+each condition has a different meaning, and the future photographic support
+manual must show a real source-frame example beside the explanation.
+
+The baseline is always rendered as an ordinary story entry with
+`<article class="loader-event loader-event--reference" data-event-id="baseline">`.
+Its searchable heading is:
+
+```text
+Baseline · distance reference · <timestamp>
+```
+
+The card shows the source-resolution reference image and names all three
+numbers needed to interpret later entries:
+
+```text
+ImageEmbedder trigger <value>
+face-api baseline spread <value>
+face-api decision threshold <value>
+```
+
+The first value controls lightweight verification requests, the second reports
+how internally consistent the accepted baseline descriptors were, and the
+third controls the later identity-distance decision. The baseline card is
+pinned first because every later distance derives from it.
+
+Confirmed entries use the following searchable headings. A future support page
+can use these strings to pair actual Video Loader cards with explanatory copy:
+
+| Searchable heading | Condition that created it | What the support photograph must make clear |
+|---|---|---|
+| `Identity-distance transition · <timestamp>` | At least two comparable checks return numeric face-api L2 distances at or above the active threshold, `0.6000` by default. | MediaPipe still supplies a usable face crop and face-api still detects a face; the signal concerns descriptor distance, not detector failure. |
+| `Detector divergence — review · <timestamp>` | At least two comparable MediaPipe frames exist while face-api returns no 2D face. | The whole face must still be sufficiently visible for MediaPipe comparison. A hand, applicator, pose, or partial occlusion can still explain the divergence, so this is review evidence rather than an automatic makeup-success claim. |
+| `Mixed transition — review · <timestamp>` | The confirmation window contains divergent checks of different kinds and does not satisfy either homogeneous rule above. | The photograph and nearby source clip must be reviewed because one summary number cannot explain the interval. |
 
-Missing values create gaps; they are never converted into zeros. ImageEmbedder
-and face-api values are not overlaid or normalised into one synthetic score.
+Every event card also contains one of these metric messages:
+
+```text
+face-api: <numeric L2 distance>
+face-api: no face
+ImageEmbedder drift: <numeric 1 − cosine value>
+ImageEmbedder drift: n/a
+Trigger: identity-heartbeat, mediapipe-face-returned,
+         visual-drift-sustained, visual-drift-jump
+```
+
+The trigger line explains why face-api was scheduled; it is not the result of
+that check. In particular, an ImageEmbedder value above its adaptive trigger
+does not itself establish an identity change.
+
+The portable HTML report provides classification-specific fallback prose when
+a person has not written a caption. These strings are deliberately explicit
+and must all be illustrated in the support manual:
+
+```text
+Repeated comparable frames returned face-api descriptor distances at or above <threshold>.
+
+MediaPipe retained a comparable face mesh while face-api returned no 2D face
+in repeated checks. Review pose and occlusion before attributing this to makeup.
+
+Different model checks diverged in this interval. Review the selected frame
+and source clip before interpreting the result.
+```
+
+MediaPipe absence is intentionally not an event card. The coverage lane records
+three diagnostic states instead:
+
+| Coverage state | Stored condition | Interpretation |
+|---|---|---|
+| `high usable` | `facePresent: true`, `comparable: true` | One mesh exists and its aligned crop is suitable for ImageEmbedder and possible face-api verification. |
+| `middle mesh only` | `facePresent: true`, `comparable: false` | A mesh exists, but the crop is too small, too rolled, too yawed, too dark, too bright, or too clipped. No model-divergence conclusion is allowed. |
+| `low absent` | `facePresent: false`, `comparable: false` | FaceLandmarker returned no mesh. This can be an edit, occlusion, profile, or absent face; it is context only and never evidence of successful makeup. |
+
+The current comparability limits are a face width of at least `0.12` of the
+video width, absolute roll no greater than `25` degrees, normalised yaw offset
+no greater than `0.42`, crop luma from `0.12` through `0.92`, and clipped-pixel
+ratio no greater than `0.18`. These values belong in the support explanation
+because “not comparable” otherwise looks like an unexplained rejection.
+Sharpness ranks candidate photographs but does not make a frame comparable or
+non-comparable on its own.
+
+Baseline search failures remain technical-log diagnostics rather than story
+cards. The exact `baseline-candidate-reset.reason` values are:
+
+- `appearance-or-pose-discontinuity`: the candidate run crossed a cut or made
+  too large a colour, roll, yaw, or scale step;
+- `face-descriptor-spread`: the candidate descriptors were not internally
+  consistent enough to establish one reference;
+- `face-api-baseline-incomplete`: too few of the sharpest candidates produced
+  face-api descriptors;
+- `non-comparable-frame`: an unusable frame interrupted the stable run.
+
+Anchored calibration does not reset away from the selected timestamp. It uses
+`anchored-baseline-neighbour-skipped` with reason
+`appearance-or-pose-discontinuity` or `non-comparable-frame`, and
+`anchored-baseline-face-sample-incomplete` when the selected descriptor exists
+but too few neighbouring descriptors were returned. These are calibration
+diagnostics, not evidence events.
+
+The first photographic support pass should use the local tutorial whose source
+ID is `hpAi9PM0uAM`. It should collect actual frames for every condition that
+the analysis genuinely produces, and then use another fixture for missing
+conditions rather than staging a misleading example. A useful capture set is:
+
+1. the chosen baseline card;
+2. one clear, comparable below-threshold match;
+3. one numeric identity-distance transition at or above `0.6000`;
+4. one detector-divergence review frame where the MediaPipe crop remains
+   comparable;
+5. one `middle mesh only` frame, preferably with a visible hand, applicator,
+   pose, or exposure problem;
+6. one `low absent` frame caused by a cut or genuinely missing/hidden face;
+7. one mixed transition if the corpus naturally contains it;
+8. one above-trigger ImageEmbedder drift that does not become a confirmed
+   identity event.
+
+That future page is informational documentation, not the reusable graphic
+component itself. Extracting a shared threshold bar and updating
+`visual-styleguide.html` remain a separate phase after Video Loader semantics
+and the photographic vocabulary are stable.
 
 ## Session, report, and technical log
 
@@ -329,10 +807,22 @@ The report contains:
 - baseline metrics;
 - confirmed, non-rejected events;
 - captions and promotion state;
-- compact face-api and visual-drift evidence;
+- compact face-api and ImageEmbedder-drift evidence;
 - event classification and whether human review is required;
 - references to separately written image files.
 
+An empty human caption receives a conservative fallback chosen from the event
+classification. Identity-distance text names the threshold crossing;
+detector-divergence text states that MediaPipe retained a comparable mesh while
+face-api returned no 2D face and explicitly asks for pose/occlusion review.
+The fallback is fixed explanatory copy, not another model inference, and an
+edited caption always replaces it.
+
+CLI output also includes `timeline.svg`, referenced by `report.html` and by the
+`artifacts.timeline` field in `report.json`. It is regenerated from the
+technical records rather than captured from the browser canvas, so it remains
+scalable and preserves categorical × markers for missing face-api detections.
+
 It deliberately excludes raw embeddings, raw image data URLs, and diagnostic
 errors unrelated to the story.
 
@@ -342,6 +832,14 @@ errors unrelated to the story.
 records, journal records, evidence-event records, and a footer. It excludes
 image bytes but keeps exact media timestamps and recommended clip windows.
 
+Schema version 4 records whether a baseline was an exact selected frame or an
+automatic medoid, its selected timestamp, skipped anchored neighbours,
+`lastScanMediaTime`, and `analysis-resumed` journal entries. These fields let a
+later processor distinguish deliberate human reference selection from
+automatic search and reconstruct pause boundaries without video pixels. It
+also records person-requested frame checks, their four-way interpretation,
+compact model results, and native-frame metadata without image bytes.
+
 The technical log is downloaded separately from the report. This separation
 allows a user to share the report without accidentally sharing detailed model
 telemetry, while retaining a machine-readable research artifact locally.
@@ -353,6 +851,13 @@ sample, and model diagnostics are most valuable.
 The log can be replayed even when the UI layout changes. Its source fingerprint
 lets tooling warn if a different video is supplied later.
 
+`replay-log` is intentionally a derivation operation, not another analysis. It
+reads `analysis.ndjson` and writes `report.replayed.json` plus `timeline.svg` in
+the chosen output directory. It does not write a second NDJSON file, seek the
+video, or run any model. The replayed JSON includes diagnostic candidates as
+well as confirmed events, so it demonstrates that archived telemetry remains
+processable but does not replace the original editorial report.
+
 ## Reversible editorial choices
 
 Automatic observations are append-only. User choices are reversible commands:
@@ -376,12 +881,12 @@ then show the transformation. Media timestamps therefore cannot define
 - story order is a human-editable sequence used by the browser report and the
   portable HTML export.
 
-The computational baseline is included as a reorderable story card, but moving
-it never changes the descriptor used for distance calculations. Confirmed
-events enter the story in source-time order by default; candidates remain only
-in the technical log until a second divergent observation confirms them. The
-person can then move the teaser after the reference, or construct another
-sequence, without falsifying where either frame came from.
+The computational baseline is pinned as the first story card. Confirmed
+events enter after it in source-time order; an early finished-look teaser no
+longer displaces the reference merely because its source timestamp is smaller.
+Events remain reorderable among themselves, but no event can cross the
+baseline boundary. Candidates remain only in the technical log until a second
+divergent observation confirms them.
 
 ## CLI workflow
 
@@ -414,6 +919,14 @@ node scripts-dev/video-loader.cjs analyze video.webm \
   --clip-after 3
 ```
 
+Rebuild diagnostic artifacts without the source video or model inference:
+
+```sh
+node scripts-dev/video-loader.cjs replay-log \
+  /tmp/video-report/analysis.ndjson \
+  --output /tmp/video-report/replayed
+```
+
 Or reprocess a log later:
 
 ```sh
@@ -452,7 +965,9 @@ rerunning MediaPipe, ImageEmbedder, or face-api.
 
 ## Real-video test corpus
 
-Large research videos under `tests/fixtures/` are local-only and git-ignored.
+Large research videos under `tests/fixtures/` are local-only and git-ignored by
+video extension rather than source ID or title. `my-moving-face.mp4` is an
+explicit exception because it is the small committed browser fixture.
 They include makeup tutorials and event recordings. Automated CI must continue
 to use the small committed fixture; the large corpus is for manual calibration,
 performance tests, and qualitative review.
@@ -471,8 +986,25 @@ pass. That run also demonstrated why the reference cannot be inferred from
 chronology alone: the default search selected the stable finished-look title
 card at six seconds. Later detector divergences included a product held in
 front of the face and application in progress, so they were correctly marked
-for review. A person should scrub past the intro and set the reference-search
-time before a substantive analysis.
+for review. A person should scrub past the intro and select an exact reference
+frame before a substantive analysis.
+
+The `hpAi9PM0uAM` fixture was also analysed with an explicitly selected
+baseline at `00:06.000`. The exact 1920 × 1080 frame, ImageEmbedder vector, and
+face-api descriptor remained at `6.000`; two unstable neighbours were skipped,
+the accepted face-api spread was reported as `0.3919`, and the story order was
+`baseline` followed by the confirmed event. This case is the regression target
+for both “do not drift to 16 seconds” and “do not let a one-second teaser
+automatically precede the baseline”.
+
+The 17.361-second `ye3W-_tr4fw` fixture demonstrates two distinct baseline
+failures. At exactly `00:00.000`, FaceLandmarker returns no mesh even though a
+face is visible shortly afterwards, so the selected frame itself is reported
+as non-comparable. At `00:03.000`, MediaPipe supplies enough comparable local
+samples, but only two of the required three nearby frames return face-api
+descriptors while pose, hands, and appearance change rapidly. The latter is
+reported as `selected-frame-descriptors-insufficient`, not as a claim that
+makeup was already present or that no face existed throughout the clip.
 
 ## Known limitations
 
@@ -483,12 +1015,10 @@ time before a substantive analysis.
 - Generic ImageEmbedder drift has not yet been calibrated as a reliable makeup
   signal; it remains a trigger hypothesis.
 - Lighting and expression can still affect the aligned crop.
-- `baselineTime` is a search start, not semantic knowledge of “before”. In the
-  tested beginner tutorial, `18` seconds begins while cream is already visible;
-  the medoid correctly represents that window but cannot infer that an earlier
-  bare-face moment is editorially preferable. The UI therefore exposes the
-  timestamp instead of pretending the first stable face is always the desired
-  before image.
+- A selected baseline is exact but still cannot provide semantic knowledge of
+  “before”. If a person chooses a frame where makeup is already visible, the
+  models correctly use that frame and cannot infer that an earlier moment was
+  editorially preferable.
 - face-api work is serialised but currently runs in the page's execution
   context, not a dedicated worker.
 - The current report exports single evidence frames and portable HTML/JSON.
@@ -501,6 +1031,7 @@ Pure unit tests cover:
 
 - Euclidean and cosine metrics;
 - medoid selection and robust noise;
+- anchored-reference calibration;
 - trigger cooldown and sustained-vote logic;
 - event merging and confirmation;
 - technical-log exclusion of image bytes;
@@ -512,6 +1043,8 @@ Browser tests mock model runtimes to verify:
 - Playwright can use `setInputFiles()`;
 - the public automation API becomes ready;
 - source metadata, progress, report, and log stay separately addressable.
+- cancellation preserves a visible checkpoint and resume does not rebuild the
+  baseline.
 
 Manual real-video validation records duration, sampling interval, wall-clock
 time, sample count, face-api count, confirmed events, false positives, and

From c621b3c358f5bc93bcfa1031c77f6532a5a56fb2 Mon Sep 17 00:00:00 2001
From: vecna <vecna@globaleaks.org>
Date: Mon, 12 Oct 2026 14:36:25 +0200
Subject: [PATCH 6/9] revisited video-loader tutorial order and logic

---
 tutorials/video-loader.md | 1070 ++++++++++++++++++-------------------
 1 file changed, 535 insertions(+), 535 deletions(-)

diff --git a/tutorials/video-loader.md b/tutorials/video-loader.md
index 4adcd6f..4974186 100644
--- a/tutorials/video-loader.md
+++ b/tutorials/video-loader.md
@@ -24,272 +24,301 @@ The future Ghostyle Studio is a third application. It consumes one or more
 selected frames to reconstruct, edit, and export a Ghostyle. It does not run
 the long-video scan.
 
-## Files and responsibilities
+## Baseline acquisition
 
-| File | Responsibility |
-|---|---|
-| `loader.html` | Accessible video, analysis controls, timeline, and report shell. |
-| `styles/loader.css` | Video Loader layout only. No Realtime selectors. |
-| `lab-js/loader.js` | UI rendering, downloads, editing, and public Playwright API. |
-| `lab-js/video-loader/controller.js` | Model lifecycle, seeking, sampling, baseline acquisition, and verification. |
-| `lab-js/video-loader/analysis.js` | Pure metrics, robust baselines, and trigger policy. |
-| `lab-js/video-loader/session.js` | Versioned session, report projection, technical journal, undo/redo. |
-| `scripts-dev/video-loader.cjs` | Specialized Playwright CLI and optional clip post-processing. |
+The first decoded frame is not automatically trusted. Browser interaction and
+automatic CLI analysis deliberately use two different baseline modes.
 
-Every JavaScript and CommonJS function added for this application uses English
-JSDoc. Inline comments explain reasons and constraints rather than restating
-syntax. This tutorial records architectural decisions that would be too broad
-for local source comments.
+Makeup tutorials often open with a finished-look teaser. The UI asks a person
+to scrub the normal video player and press **Select base at…** on the frame
+that must become the reference. **Analyse video** remains unavailable
+until that deliberate choice has been made. The selected base is reserved as
+the first evidence-story item and cannot be moved behind derived evidence. The
+button becomes
+**Replace base with…** after the person moves to another frame, so correcting
+the choice never requires synchronising a numeric field with the player.
 
-## Why the source remains a normal video
+Internally and in the Playwright API this value is `baselineTime` with
+`baselineSelectionMode: 'anchored'`. The visible JPEG, ImageEmbedder vector,
+and face-api descriptor all come from that exact decoded timestamp. The
+controller may inspect nearby frames for noise calibration, but it cannot move
+the baseline. An unusable selected frame produces an actionable error instead
+of silently selecting a later scene. After acquisition, the controller rewinds
+and scans the complete requested range, so a teaser before the bare-face
+baseline can still appear as evidence.
 
-Chromium can decode MP4 and WebM files assigned to an ordinary
-`HTMLVideoElement`. Playwright can populate the page's real file input with
-`page.setInputFiles()`. There is therefore no reason to convert a long video to
-Y4M or present it as a fake webcam.
+The CLI equivalent is `--baseline-time`, which also selects anchored mode.
+When automation omits that option, the CLI uses automatic-search mode from the
+analysis-range start. Automatic search still requires a continuous stable run
+and chooses medoid vectors because no person has identified an authoritative
+frame.
 
-The source is represented by a Blob URL in the browser. The Blob URL is local
-to the page and is revoked when another file is selected. The analysis session
-stores only source metadata and a bounded provenance fingerprint; it does not
-copy the source into the output directory.
+A sample is comparable when:
 
-The CLI fingerprint is `sampled-sha256`: SHA-256 over the file size, first MiB,
-and last MiB. This is intentionally bounded for multi-gigabyte recordings. It
-is a provenance guard, not a cryptographic claim over every source byte.
+- FaceLandmarker returns one mesh;
+- the face occupies a useful portion of the frame;
+- roll and approximate yaw remain within broad bounds;
+- exposure is neither nearly black nor heavily clipped;
+- ImageEmbedder returns a float vector.
 
-## Browser automation contract
+An anchored baseline requires the selected ImageEmbedder vector plus three
+comparable neighbours and three face-api descriptors. Neighbours are sampled
+within the following three seconds at no more than 0.25-second intervals.
+Abrupt pose, appearance, or comparability changes skip that neighbour and are
+recorded as `anchored-baseline-neighbour-skipped`; they never replace the
+selected frame. The selected frame itself must be comparable and must return a
+face-api descriptor.
 
-The UI and Playwright use the same file input and the same browser API. The
-stable entry point is `window.gstmxxVideoLoader`:
+Automatic search requires eight ImageEmbedder vectors and three face-api
+descriptors. Its candidates are sampled at no more than 0.25-second
+intervals even when the coarse scan uses a wider interval; this prevents a
+two-second long-video cadence from stretching one baseline across many scene
+changes. A candidate run is reset when an 8 × 8 RGB crop signature changes too
+abruptly, when pose or scale jumps, when a non-comparable frame breaks the run,
+or when the eventual face descriptors have excessive spread. Generic
+ImageEmbedder distance is deliberately not the stability gate: calibration on
+real tutorial footage showed that it can vary strongly between neighbouring
+frames that still depict the same face. face-api
+does not run for every failed candidate: it verifies the three sharpest frames
+only after eight lightweight samples have formed a continuous run. Automatic
+baselines are medoids: real observed vectors with the
+smallest total distance to their peers. A medoid is preferred to a mean because
+it remains a genuine model output and is robust to one unusual sample.
 
-```js
-window.gstmxxVideoLoader.isModelsReady();
-window.gstmxxVideoLoader.isSourceLoaded();
-window.gstmxxVideoLoader.isAnalysisRunning();
-window.gstmxxVideoLoader.setSourceMetadata({ fingerprint });
-await window.gstmxxVideoLoader.analyze({
-  start: 0,
-  end: 120,
-  baselineTime: 18,
-  baselineSelectionMode: 'anchored',
-  sampleIntervalSeconds: 0.5,
-});
-window.gstmxxVideoLoader.cancel();
-await window.gstmxxVideoLoader.resume();
-await window.gstmxxVideoLoader.analyzeFrame(83.417);
-window.gstmxxVideoLoader.getSession();
-window.gstmxxVideoLoader.exportBundle();
+For an anchored baseline, the selected vectors remain the actual references.
+Nearby vectors only estimate noise. Automatic descriptor spread must be at
+most `0.3000`; an explicitly selected baseline can be accepted up to `0.5000`,
+but the UI displays an elevated-variation warning above `0.3000`. The identity
+decision threshold remains `0.6000`; the looser anchored calibration limit does
+not change it.
+
+For baseline visual drifts `d_i`, the adaptive trigger threshold is:
+
+```text
+median(d_i) + max(minimumDelta, MAD(d_i) × madMultiplier)
 ```
 
-The page also dispatches `gstmxx-video-loader:*` events for model readiness,
-source readiness, samples, baseline readiness, confirmed events, completion,
-and errors. Automation should prefer this contract over button text or screen
-coordinates.
+`MAD` is median absolute deviation. The defaults are hypotheses that must be
+validated against real makeup videos; they are configuration, not scientific
+constants.
 
-## Event-to-function call map
+The anchored frame is captured at the selected timestamp, keeping the visible
+baseline and both numeric references aligned. In automatic mode the frame is
+captured at the ImageEmbedder medoid timestamp. Either way, one native-resolution
+JPEG is retained and all neighbouring baseline pixels are discarded.
 
-The following trees describe the important call paths from their external
-trigger. They are deliberately written like `pstree`: indentation means a
-direct call, while `[...]` marks a conditional branch or a repeated sampling
-loop. Browser events and the public Playwright API converge before analysis,
-so interactive and automated runs exercise the same controller.
+Baseline failure is a visible product state, not only a header status or log
+record. The run controls retain a `role="alert"` explanation that translates
+stable failure codes into an action. In particular,
+`selected-frame-neighbours-insufficient` states how many of the four required
+comparable local frames were found. This distinguishes “the selected frame is
+unusable” from “the selected frame may be usable, but the following three
+seconds change too rapidly to estimate local noise”. Makeup already being
+present is not itself a baseline error: stability and detector availability
+are the requirements. `selected-frame-descriptors-insufficient` separately
+reports a usable MediaPipe calibration for which fewer than three nearby
+frames returned face-api descriptors. The NDJSON record keeps
+accepted/required embedding and descriptor counts for later diagnosis.
 
-```text
-<script type="module"> loads lab-js/loader.js
-└─ init()
-   ├─ initI18n()
-   ├─ setupLocaleSelect()
-   ├─ applyI18n()
-   ├─ renderSession(null)
-   │  └─ drawTimeline(null)
-   ├─ syncControls()
-   └─ controller.loadModels()
-      ├─ faceapi.nets.*.loadFromUri()                 [three face-api models]
-      ├─ import(MEDIAPIPE_TASKS_VISION_URL)
-      ├─ FilesetResolver.forVisionTasks()
-      ├─ FaceLandmarker.createFromOptions()
-      ├─ ImageEmbedder.createFromOptions()
-      └─ notify('models-ready')
-         └─ controller subscriber in loader.js
-            ├─ setStatus()
-            ├─ syncControls()
-            └─ dispatchLoaderEvent('models-ready')
+## Control ownership, seeking, and pausing
 
-<input type="file"> change
-or Playwright page.setInputFiles('#videoFile', source)
-└─ loadSelectedFile()
-   └─ controller.loadFile()
-      ├─ URL.createObjectURL()
-      ├─ waitForMediaEvent(video, 'loadedmetadata')
-      ├─ createAnalysisSession()
-      ├─ appendJournal('source-loaded')
-      ├─ triggerDetector.reset()
-      └─ notify('source-loaded')
-   ├─ syncVideoTimeline()
-   │  └─ renderBaseSelection()
-   ├─ renderSession()
-   │  ├─ drawTimeline()
-   │  └─ orderedStoryItems()
-   ├─ syncControls()
-   └─ dispatchLoaderEvent('source-loaded')
+The controls have three explicit responsibilities. **Source** shows the full
+local filename, decoded dimensions, duration, file size, and current seek
+position. **Analysis plan** owns baseline selection and sampling cadence.
+**Run** owns the mutually exclusive Analyse/Resume/Restart and Pause actions,
+plus sample progress. The visible filename matters when several locally
+downloaded tutorials have similar content; it is not inferred from a video ID.
 
-<video> timeupdate | seeked | loadedmetadata
-or #videoSeek input
-└─ syncVideoTimeline()
-   └─ renderBaseSelection()
+Pause replaces the primary run action instead of leaving a disabled Pause and
+another active action side by side. Report actions are grouped by intent:
+Undo/Redo are edit history, while report/log downloads are exports.
 
-#selectBaseBtn click
-└─ selectCurrentBase()
-   ├─ selectedBaseTime = video.currentTime
-   ├─ renderBaseSelection()
-   └─ syncControls()                                  [enables Analyse]
+While analysis runs, the controller owns `video.currentTime`. Native video
+interaction, file replacement, the precise source slider, baseline selection,
+sampling cadence, and report timestamp links are therefore inert and visually
+muted. The player remains visible because watching decoded checkpoints is
+useful; a notice explains why its controls are unavailable. The pause button
+remains active.
 
-#analyzeFrameBtn click
-or window.gstmxxVideoLoader.analyzeFrame(mediaTime)
-└─ analyzeCurrentFrame(mediaTime)
-   └─ controller.analyzeFrame(mediaTime)
-      ├─ seekVideo()
-      ├─ faceLandmarker.detect()
-      ├─ drawAlignedFaceCrop() + measureCropQuality()
-      ├─ imageEmbedder.embed()                        [only if comparable]
-      ├─ runFaceApi()                                 [explicit human request]
-      ├─ euclideanDistance()                          [descriptor vs baseline]
-      ├─ appendSample({ phase: 'manual' })
-      ├─ appendJournal('face-api-result')
-      ├─ captureSourceFrame()                         [exact native frame]
-      ├─ recordManualFrameCheck()
-      └─ notify('manual-frame-checked')
-         ├─ renderSession()
-         ├─ renderAnalysisMessage()
-         └─ dispatchLoaderEvent('manual-frame-checked')
+The native player bar and the custom source slider have different jobs. The
+native controls provide familiar playback. The custom slider provides a
+millisecond-labelled position for baseline selection and later evidence review.
+It does not claim to show encoded frame numbers: HTML video does not expose a
+stable random-access frame index, and the source may use variable frame rate.
+The separate analysis bar therefore reports the truthful unit available to the
+analyser: processed scan samples, for example `42 / 601 samples · 7%`.
 
-#startAnalysisBtn click
-or window.gstmxxVideoLoader.analyze(options)
-└─ startAnalysis(options)
-   └─ controller.analyze(options)
-      ├─ appendJournal('analysis-started')
-      ├─ notify('analysis-started')
-      ├─ [anchored baseline calibration after selectedBaseTime]
-      │  ├─ seekVideo()
-      │  │  └─ waitForMediaEvent(video, 'seeked')
-      │  ├─ faceLandmarker.detect()
-      │  ├─ drawAlignedFaceCrop()
-      │  ├─ measureCropQuality()
-      │  ├─ imageEmbedder.embed()
-      │  │  └─ unpackEmbedding()
-      │  ├─ appearanceDistance() + visualDrift()      [neighbour eligibility]
-      │  ├─ [enough comparable local samples]
-      │  │  ├─ runFaceApi()                           [selected + sharp neighbours]
-      │  │  ├─ buildAnchoredFaceBaseline()            [selected descriptor]
-      │  │  │  └─ median(euclideanDistance)
-      │  │  ├─ buildAnchoredEmbeddingBaseline()       [selected embedding]
-      │  │  │  ├─ median()
-      │  │  │  └─ medianAbsoluteDeviation()
-      │  │  ├─ captureSourceFrame()                   [selected native JPEG]
-      │  │  ├─ finaliseBaseline()
-      │  │  └─ notify('baseline-ready')
-      │  ├─ appendSample()
-      │  └─ notify('sample')
-      ├─ [coarse scan loop, rewound to analysis start]
-      │  ├─ seekVideo()
-      │  ├─ faceLandmarker.detect()
-      │  ├─ drawAlignedFaceCrop()
-      │  ├─ measureCropQuality()
-      │  ├─ imageEmbedder.embed()
-      │  ├─ visualDrift()
-      │  ├─ appendSample()
-      │  ├─ triggerDetector.evaluate()
-      │  ├─ [verification requested]
-      │  │  ├─ runFaceApi()
-      │  │  ├─ euclideanDistance()                    [descriptor vs baseline]
-      │  │  ├─ appendJournal('face-api-result')
-      │  │  └─ recordEvidenceObservation()
-      │  │     └─ [event newly confirmed]
-      │  │        ├─ refineEvidenceFrame()
-      │  │        │  ├─ [neighbourhood loop]
-      │  │        │  │  ├─ seekVideo()
-      │  │        │  │  ├─ faceLandmarker.detect()
-      │  │        │  │  ├─ drawAlignedFaceCrop()
-      │  │        │  │  └─ measureCropQuality()
-      │  │        │  ├─ runFaceApi()                  [at most three candidates]
-      │  │        │  ├─ euclideanDistance()
-      │  │        │  ├─ captureSourceFrame()          [one selected JPEG]
-      │  │        │  └─ selectEventFrame()
-      │  │        └─ notify('event-confirmed')
-      │  └─ notify('sample')
-      ├─ appendJournal('analysis-complete' | 'analysis-cancelled')
-      └─ notify('analysis-complete' | 'analysis-cancelled')
-         └─ controller subscriber in loader.js
-            ├─ renderSession()
-            │  ├─ drawTimeline()
-            │  ├─ orderedStoryItems()
-            │  ├─ createBaselineCard()
-            │  └─ createEventCard()
-            └─ dispatchLoaderEvent(update.type)
+Pausing sets a cancellation flag and finishes the current sample. The
+controller stores `lastScanMediaTime`, seeks the visible player to that
+checkpoint, and leaves both the source slider and sample-progress bar there.
+The primary action becomes **Resume analysis**. Resume preserves the baseline,
+observations, events, and journal, then continues at one sampling interval
+after the checkpoint. A pause during initial baseline calibration restarts that
+small bounded calibration window because no image-vector queue is retained.
 
-#cancelAnalysisBtn click | window.gstmxxVideoLoader.cancel()
-└─ controller.cancel()                                [sets boundary flag]
-   └─ analyze() finishes the current sample
-      ├─ seekVideo(lastScanMediaTime)                  [visible checkpoint]
-      ├─ appendJournal('analysis-cancelled')
-      └─ notify('analysis-cancelled')
+Sampling cadence is part of a scan's identity. Changing **Sample every** while
+a run is paused changes the primary action to **Restart with new sampling**.
+That action starts a fresh scan, rebuilds the baseline at the selected source
+timestamp, and recalculates the total as
+`ceil((rangeEnd - rangeStart) / interval) + 1`. A true resume deliberately keeps
+the original interval, total, journal, and next checkpoint; mixing two cadences
+inside one progress denominator would make both the count and technical log
+ambiguous.
 
-#startAnalysisBtn click while cancelled
-or window.gstmxxVideoLoader.resume()
-└─ startAnalysis({ resume: true })
-   └─ controller.analyze({ resume: true })
-      ├─ appendJournal('analysis-resumed')
-      ├─ preserve baseline, samples, events, and journal
-      └─ resume coarse scan at lastScanMediaTime + sample interval
+Replacing the selected baseline while paused follows the same rule. The action
+becomes **Restart with new baseline**, or **Restart with new settings** when the
+baseline and cadence both changed. A resume cannot reuse old observations with
+a new reference because every stored descriptor distance and visual drift was
+computed from the previous baseline.
 
-editorial button click
-├─ applyEditorialCommand()                            [caption/promote/reject/order]
-├─ undoEditorialCommand() | redoEditorialCommand()   [Undo/Redo]
-└─ renderSession()
+Every baseline or evidence-card timestamp is a seek button when analysis is
+idle. Clicking it pauses ordinary playback and moves the source player to the
+recorded frame. These links become inert and muted while the controller owns
+media time.
 
-Download report click | Download technical log click
-└─ controller.exportBundle()
-   ├─ buildReport()
-   ├─ serializeTechnicalLog()
-   └─ downloadText()                                  [chosen artifact only]
+## Timeline semantics
 
-scripts-dev/video-loader.cjs analyze
-└─ main()
-   └─ analyze()
-      ├─ sampledFingerprint()
-      ├─ startServer()                                [unless --base-url]
-      ├─ chromium.launch()
-      ├─ page.setInputFiles('#videoFile', source)
-      ├─ window.gstmxxVideoLoader.analyze(options)    [browser tree above]
-      ├─ window.gstmxxVideoLoader.exportBundle()
-      ├─ writeBundle()
-      └─ extractClips()                               [only with --clips]
-```
+The report timeline has three independent lanes:
 
-## Model provenance
+1. **MediaPipe coverage** — high means a comparable mesh, middle means a mesh
+   exists but its crop is unusable, and low means no mesh. This is context, not
+   evidence by itself.
+2. **ImageEmbedder drift** — frequent `1 - cosine` samples and an adaptive
+   dashed threshold. It is generic aligned-crop change, not identity distance.
+3. **face-api L2** — sparse numeric verification points and the fixed 0.6
+   dashed threshold. An × above the threshold label at the right is
+   categorical: face-api returned no 2D face, so no descriptor and no numeric
+   distance exist.
 
-The repository vendors the following browser assets:
+Missing values create gaps; they are never converted into zeros or invented
+L2 values. Numeric face-api points use a data-aware vertical range instead of
+being clamped at `1.0`. ImageEmbedder and face-api values are not overlaid or
+normalised into one synthetic score. The dashed ImageEmbedder line is labelled
+with the session's four-decimal adaptive trigger; the dashed face-api line is
+labelled with the four-decimal identity threshold. The browser canvas and the
+exported SVG use the same labels, so a screenshot remains interpretable when
+removed from the surrounding page.
 
-- `@vladmandic/face-api` as a classic global `faceapi` bundle;
-- MediaPipe Tasks Vision `0.10.35`;
-- FaceLandmarker `float16/1` with 478 landmarks;
-- ImageEmbedder MobileNetV3 Small `float32/1`.
+## Person-requested frame checks
 
-Both MediaPipe tasks explicitly use the CPU delegate. The sampled offline
-workflow does not need continuous GPU throughput, and the CPU path remains
-reliable in headless Playwright, software-rendered documentation captures, and
-machines without a usable WebGL delegate. The chosen delegate is recorded with
-the session model provenance.
+After a baseline exists, **Analyse frame at…** compares the source position
+currently shown by the player. This differs from automatic event confirmation:
+a person asked a question about one exact frame, so no second divergence vote
+is required and the card is labelled **Manual frame check**. It never claims
+to be a repeated automatic transition.
 
-The face-api bundle currently comes from a floating package URL in the vendor
-refresh documentation. A future vendor refresh should pin the package version
-or record a digest. Reports record model labels so results are not silently
-presented as timeless or implementation-independent.
+The explicit check runs MediaPipe and face-api immediately. MediaPipe still
+controls whether the frame is considered comparable; face-api is also run when
+it is not, because a direct request should return the available diagnostic
+distance rather than silently do nothing. The interpretation is one of:
 
-The vendored FaceMatcher default is `0.6` and accepts a match when the L2
-distance is strictly below that threshold. The Video Loader therefore uses
-`0.6` as its default identity-distance threshold. The threshold and the
-baseline spread are recorded in each report and technical log.
+- `below-threshold`: comparable frame and face-api L2 below `0.6000`;
+- `above-threshold`: comparable frame and L2 at or above `0.6000`;
+- `no-2d-face`: comparable MediaPipe crop but no face-api descriptor;
+- `not-comparable`: MediaPipe geometry or exposure rejected the crop, so any
+  face-api value is diagnostic context only.
+
+The exact native-resolution frame becomes an editable story card with source
+timestamp, caption, ordering, rejection, download, and recommended clip
+window. A `phase: "manual"` sample, a sparse `face-api-result`, and a
+`manual-frame-checked` journal record make the decision reprocessable without
+placing JPEG bytes in the log. Manual cards increment `manualChecks`, not
+`confirmedEvents`, and automatic observations never merge into them.
+
+## Reversible editorial choices
+
+Automatic observations are append-only. User choices are reversible commands:
+
+- promote an event into the story;
+- reject an event;
+- edit its caption.
+- move an evidence frame earlier or later in the story.
+
+Each command records before/after state. Undo and redo change editorial state;
+they do not delete automatic observations or rewrite the technical history.
+
+### Source chronology is not story chronology
+
+Makeup videos often open with the finished look, cut back to a bare face, and
+then show the transformation. Media timestamps therefore cannot define
+“before” and “after”. The Video Loader preserves two independent orders:
+
+- source time is immutable evidence and remains attached to every frame,
+  observation, log record, and clip window;
+- story order is a human-editable sequence used by the browser report and the
+  portable HTML export.
+
+The computational baseline is pinned as the first story card. Confirmed
+events enter after it in source-time order; an early finished-look teaser no
+longer displaces the reference merely because its source timestamp is smaller.
+Events remain reorderable among themselves, but no event can cross the
+baseline boundary. Candidates remain only in the technical log until a second
+divergent observation confirms them.
+
+## Session, report, and technical log
+
+The mutable browser session owns all observations and editorial decisions.
+The report and technical log are projections with different audiences.
+
+### Editorial report
+
+The report contains:
+
+- source and model provenance;
+- configuration;
+- baseline metrics;
+- confirmed, non-rejected events;
+- captions and promotion state;
+- compact face-api and ImageEmbedder-drift evidence;
+- event classification and whether human review is required;
+- references to separately written image files.
+
+An empty human caption receives a conservative fallback chosen from the event
+classification. Identity-distance text names the threshold crossing;
+detector-divergence text states that MediaPipe retained a comparable mesh while
+face-api returned no 2D face and explicitly asks for pose/occlusion review.
+The fallback is fixed explanatory copy, not another model inference, and an
+edited caption always replaces it.
+
+CLI output also includes `timeline.svg`, referenced by `report.html` and by the
+`artifacts.timeline` field in `report.json`. It is regenerated from the
+technical records rather than captured from the browser canvas, so it remains
+scalable and preserves categorical × markers for missing face-api detections.
+
+It deliberately excludes raw embeddings, raw image data URLs, and diagnostic
+errors unrelated to the story.
+
+### Technical log
+
+`analysis.ndjson` is newline-delimited JSON with a versioned header, sample
+records, journal records, evidence-event records, and a footer. It excludes
+image bytes but keeps exact media timestamps and recommended clip windows.
+
+Schema version 4 records whether a baseline was an exact selected frame or an
+automatic medoid, its selected timestamp, skipped anchored neighbours,
+`lastScanMediaTime`, and `analysis-resumed` journal entries. These fields let a
+later processor distinguish deliberate human reference selection from
+automatic search and reconstruct pause boundaries without video pixels. It
+also records person-requested frame checks, their four-way interpretation,
+compact model results, and native-frame metadata without image bytes.
+
+The technical log is downloaded separately from the report. This separation
+allows a user to share the report without accidentally sharing detailed model
+telemetry, while retaining a machine-readable research artifact locally.
+
+The CLI writes both artifacts even when analysis fails after the source has
+loaded. A failed run is precisely when the reset reasons, last successful
+sample, and model diagnostics are most valuable.
+
+The log can be replayed even when the UI layout changes. Its source fingerprint
+lets tooling warn if a different video is supplied later.
+
+`replay-log` is intentionally a derivation operation, not another analysis. It
+reads `analysis.ndjson` and writes `report.replayed.json` plus `timeline.svg` in
+the chosen output directory. It does not write a second NDJSON file, seek the
+video, or run any model. The replayed JSON includes diagnostic candidates as
+well as confirmed events, so it demonstrates that archived telemetry remains
+processable but does not replace the original editorial report.
 
 ## Three distinct model signals
 
@@ -385,215 +414,10 @@ only association rule because that is the very signal the Video Loader is
 trying to measure. This decision is deliberately left out of the current
 implementation rather than silently guessing a subject.
 
-## Baseline acquisition
+## Trigger state and event confirmation
 
-The first decoded frame is not automatically trusted. Browser interaction and
-automatic CLI analysis deliberately use two different baseline modes.
-
-Makeup tutorials often open with a finished-look teaser. The UI asks a person
-to scrub the normal video player and press **Select base at…** on the frame
-that must become the reference. **Analyse video** remains unavailable
-until that deliberate choice has been made. The selected base is reserved as
-the first evidence-story item and cannot be moved behind derived evidence. The
-button becomes
-**Replace base with…** after the person moves to another frame, so correcting
-the choice never requires synchronising a numeric field with the player.
-
-Internally and in the Playwright API this value is `baselineTime` with
-`baselineSelectionMode: 'anchored'`. The visible JPEG, ImageEmbedder vector,
-and face-api descriptor all come from that exact decoded timestamp. The
-controller may inspect nearby frames for noise calibration, but it cannot move
-the baseline. An unusable selected frame produces an actionable error instead
-of silently selecting a later scene. After acquisition, the controller rewinds
-and scans the complete requested range, so a teaser before the bare-face
-baseline can still appear as evidence.
-
-The CLI equivalent is `--baseline-time`, which also selects anchored mode.
-When automation omits that option, the CLI uses automatic-search mode from the
-analysis-range start. Automatic search still requires a continuous stable run
-and chooses medoid vectors because no person has identified an authoritative
-frame.
-
-A sample is comparable when:
-
-- FaceLandmarker returns one mesh;
-- the face occupies a useful portion of the frame;
-- roll and approximate yaw remain within broad bounds;
-- exposure is neither nearly black nor heavily clipped;
-- ImageEmbedder returns a float vector.
-
-An anchored baseline requires the selected ImageEmbedder vector plus three
-comparable neighbours and three face-api descriptors. Neighbours are sampled
-within the following three seconds at no more than 0.25-second intervals.
-Abrupt pose, appearance, or comparability changes skip that neighbour and are
-recorded as `anchored-baseline-neighbour-skipped`; they never replace the
-selected frame. The selected frame itself must be comparable and must return a
-face-api descriptor.
-
-Automatic search requires eight ImageEmbedder vectors and three face-api
-descriptors. Its candidates are sampled at no more than 0.25-second
-intervals even when the coarse scan uses a wider interval; this prevents a
-two-second long-video cadence from stretching one baseline across many scene
-changes. A candidate run is reset when an 8 × 8 RGB crop signature changes too
-abruptly, when pose or scale jumps, when a non-comparable frame breaks the run,
-or when the eventual face descriptors have excessive spread. Generic
-ImageEmbedder distance is deliberately not the stability gate: calibration on
-real tutorial footage showed that it can vary strongly between neighbouring
-frames that still depict the same face. face-api
-does not run for every failed candidate: it verifies the three sharpest frames
-only after eight lightweight samples have formed a continuous run. Automatic
-baselines are medoids: real observed vectors with the
-smallest total distance to their peers. A medoid is preferred to a mean because
-it remains a genuine model output and is robust to one unusual sample.
-
-For an anchored baseline, the selected vectors remain the actual references.
-Nearby vectors only estimate noise. Automatic descriptor spread must be at
-most `0.3000`; an explicitly selected baseline can be accepted up to `0.5000`,
-but the UI displays an elevated-variation warning above `0.3000`. The identity
-decision threshold remains `0.6000`; the looser anchored calibration limit does
-not change it.
-
-For baseline visual drifts `d_i`, the adaptive trigger threshold is:
-
-```text
-median(d_i) + max(minimumDelta, MAD(d_i) × madMultiplier)
-```
-
-`MAD` is median absolute deviation. The defaults are hypotheses that must be
-validated against real makeup videos; they are configuration, not scientific
-constants.
-
-The anchored frame is captured at the selected timestamp, keeping the visible
-baseline and both numeric references aligned. In automatic mode the frame is
-captured at the ImageEmbedder medoid timestamp. Either way, one native-resolution
-JPEG is retained and all neighbouring baseline pixels are discarded.
-
-Baseline failure is a visible product state, not only a header status or log
-record. The run controls retain a `role="alert"` explanation that translates
-stable failure codes into an action. In particular,
-`selected-frame-neighbours-insufficient` states how many of the four required
-comparable local frames were found. This distinguishes “the selected frame is
-unusable” from “the selected frame may be usable, but the following three
-seconds change too rapidly to estimate local noise”. Makeup already being
-present is not itself a baseline error: stability and detector availability
-are the requirements. `selected-frame-descriptors-insufficient` separately
-reports a usable MediaPipe calibration for which fewer than three nearby
-frames returned face-api descriptors. The NDJSON record keeps
-accepted/required embedding and descriptor counts for later diagnosis.
-
-## Control ownership, seeking, and pausing
-
-The controls have three explicit responsibilities. **Source** shows the full
-local filename, decoded dimensions, duration, file size, and current seek
-position. **Analysis plan** owns baseline selection and sampling cadence.
-**Run** owns the mutually exclusive Analyse/Resume/Restart and Pause actions,
-plus sample progress. The visible filename matters when several locally
-downloaded tutorials have similar content; it is not inferred from a video ID.
-
-Pause replaces the primary run action instead of leaving a disabled Pause and
-another active action side by side. Report actions are grouped by intent:
-Undo/Redo are edit history, while report/log downloads are exports.
-
-While analysis runs, the controller owns `video.currentTime`. Native video
-interaction, file replacement, the precise source slider, baseline selection,
-sampling cadence, and report timestamp links are therefore inert and visually
-muted. The player remains visible because watching decoded checkpoints is
-useful; a notice explains why its controls are unavailable. The pause button
-remains active.
-
-The native player bar and the custom source slider have different jobs. The
-native controls provide familiar playback. The custom slider provides a
-millisecond-labelled position for baseline selection and later evidence review.
-It does not claim to show encoded frame numbers: HTML video does not expose a
-stable random-access frame index, and the source may use variable frame rate.
-The separate analysis bar therefore reports the truthful unit available to the
-analyser: processed scan samples, for example `42 / 601 samples · 7%`.
-
-Pausing sets a cancellation flag and finishes the current sample. The
-controller stores `lastScanMediaTime`, seeks the visible player to that
-checkpoint, and leaves both the source slider and sample-progress bar there.
-The primary action becomes **Resume analysis**. Resume preserves the baseline,
-observations, events, and journal, then continues at one sampling interval
-after the checkpoint. A pause during initial baseline calibration restarts that
-small bounded calibration window because no image-vector queue is retained.
-
-Sampling cadence is part of a scan's identity. Changing **Sample every** while
-a run is paused changes the primary action to **Restart with new sampling**.
-That action starts a fresh scan, rebuilds the baseline at the selected source
-timestamp, and recalculates the total as
-`ceil((rangeEnd - rangeStart) / interval) + 1`. A true resume deliberately keeps
-the original interval, total, journal, and next checkpoint; mixing two cadences
-inside one progress denominator would make both the count and technical log
-ambiguous.
-
-Replacing the selected baseline while paused follows the same rule. The action
-becomes **Restart with new baseline**, or **Restart with new settings** when the
-baseline and cadence both changed. A resume cannot reuse old observations with
-a new reference because every stored descriptor distance and visual drift was
-computed from the previous baseline.
-
-Every baseline or evidence-card timestamp is a seek button when analysis is
-idle. Clicking it pauses ordinary playback and moves the source player to the
-recorded frame. These links become inert and muted while the controller owns
-media time.
-
-## Person-requested frame checks
-
-After a baseline exists, **Analyse frame at…** compares the source position
-currently shown by the player. This differs from automatic event confirmation:
-a person asked a question about one exact frame, so no second divergence vote
-is required and the card is labelled **Manual frame check**. It never claims
-to be a repeated automatic transition.
-
-The explicit check runs MediaPipe and face-api immediately. MediaPipe still
-controls whether the frame is considered comparable; face-api is also run when
-it is not, because a direct request should return the available diagnostic
-distance rather than silently do nothing. The interpretation is one of:
-
-- `below-threshold`: comparable frame and face-api L2 below `0.6000`;
-- `above-threshold`: comparable frame and L2 at or above `0.6000`;
-- `no-2d-face`: comparable MediaPipe crop but no face-api descriptor;
-- `not-comparable`: MediaPipe geometry or exposure rejected the crop, so any
-  face-api value is diagnostic context only.
-
-The exact native-resolution frame becomes an editable story card with source
-timestamp, caption, ordering, rejection, download, and recommended clip
-window. A `phase: "manual"` sample, a sparse `face-api-result`, and a
-`manual-frame-checked` journal record make the decision reprocessable without
-placing JPEG bytes in the log. Manual cards increment `manualChecks`, not
-`confirmedEvents`, and automatic observations never merge into them.
-
-## Sampling and memory policy
-
-The scan seeks through media time at a configurable interval. The default is
-0.5 seconds, with UI choices from 0.25 to 2 seconds. This is a coarse analysis,
-not a frame-perfect decoder.
-
-For every sample, the session stores only:
-
-- media timestamp;
-- face presence;
-- comparison eligibility;
-- visual drift and its active threshold;
-- compact geometry metrics;
-- compact quality metrics.
-
-It does not store raw landmarks, model embeddings, source pixels, canvases,
-`ImageBitmap` objects, or a ring buffer. Because a local source is seekable,
-the video itself is the recoverable frame store.
-
-Native-resolution JPEG pixels are retained only for:
-
-- the selected baseline;
-- confirmed divergence events.
-
-If a later pass needs neighbouring frames, it should seek to neighbouring
-timestamps rather than cache the original scan.
-
-## Trigger state and event confirmation
-
-A lightweight sample becomes eligible for face-api verification when at least
-one trigger reason exists:
+A lightweight sample becomes eligible for face-api verification when at least
+one trigger reason exists:
 
 - `identity-heartbeat`;
 - `mediapipe-face-returned`;
@@ -646,28 +470,32 @@ Candidate events remain in the technical log even when they never reach the
 report. Refinement checks and the final selection reason are journal records,
 so a later tool can reconstruct why that image was chosen.
 
-## Timeline semantics
+## Sampling and memory policy
 
-The report timeline has three independent lanes:
+The scan seeks through media time at a configurable interval. The default is
+0.5 seconds, with UI choices from 0.25 to 2 seconds. This is a coarse analysis,
+not a frame-perfect decoder.
 
-1. **MediaPipe coverage** — high means a comparable mesh, middle means a mesh
-   exists but its crop is unusable, and low means no mesh. This is context, not
-   evidence by itself.
-2. **ImageEmbedder drift** — frequent `1 - cosine` samples and an adaptive
-   dashed threshold. It is generic aligned-crop change, not identity distance.
-3. **face-api L2** — sparse numeric verification points and the fixed 0.6
-   dashed threshold. An × above the threshold label at the right is
-   categorical: face-api returned no 2D face, so no descriptor and no numeric
-   distance exist.
+For every sample, the session stores only:
 
-Missing values create gaps; they are never converted into zeros or invented
-L2 values. Numeric face-api points use a data-aware vertical range instead of
-being clamped at `1.0`. ImageEmbedder and face-api values are not overlaid or
-normalised into one synthetic score. The dashed ImageEmbedder line is labelled
-with the session's four-decimal adaptive trigger; the dashed face-api line is
-labelled with the four-decimal identity threshold. The browser canvas and the
-exported SVG use the same labels, so a screenshot remains interpretable when
-removed from the surrounding page.
+- media timestamp;
+- face presence;
+- comparison eligibility;
+- visual drift and its active threshold;
+- compact geometry metrics;
+- compact quality metrics.
+
+It does not store raw landmarks, model embeddings, source pixels, canvases,
+`ImageBitmap` objects, or a ring buffer. Because a local source is seekable,
+the video itself is the recoverable frame store.
+
+Native-resolution JPEG pixels are retained only for:
+
+- the selected baseline;
+- confirmed divergence events.
+
+If a later pass needs neighbouring frames, it should seek to neighbouring
+timestamps rather than cache the original scan.
 
 ## Support-image condition catalogue
 
@@ -793,100 +621,272 @@ component itself. Extracting a shared threshold bar and updating
 `visual-styleguide.html` remain a separate phase after Video Loader semantics
 and the photographic vocabulary are stable.
 
-## Session, report, and technical log
+## Files and responsibilities
 
-The mutable browser session owns all observations and editorial decisions.
-The report and technical log are projections with different audiences.
+| File | Responsibility |
+|---|---|
+| `loader.html` | Accessible video, analysis controls, timeline, and report shell. |
+| `styles/loader.css` | Video Loader layout only. No Realtime selectors. |
+| `lab-js/loader.js` | UI rendering, downloads, editing, and public Playwright API. |
+| `lab-js/video-loader/controller.js` | Model lifecycle, seeking, sampling, baseline acquisition, and verification. |
+| `lab-js/video-loader/analysis.js` | Pure metrics, robust baselines, and trigger policy. |
+| `lab-js/video-loader/session.js` | Versioned session, report projection, technical journal, undo/redo. |
+| `scripts-dev/video-loader.cjs` | Specialized Playwright CLI and optional clip post-processing. |
 
-### Editorial report
+Every JavaScript and CommonJS function added for this application uses English
+JSDoc. Inline comments explain reasons and constraints rather than restating
+syntax. This tutorial records architectural decisions that would be too broad
+for local source comments.
 
-The report contains:
+## Why the source remains a normal video
 
-- source and model provenance;
-- configuration;
-- baseline metrics;
-- confirmed, non-rejected events;
-- captions and promotion state;
-- compact face-api and ImageEmbedder-drift evidence;
-- event classification and whether human review is required;
-- references to separately written image files.
+Chromium can decode MP4 and WebM files assigned to an ordinary
+`HTMLVideoElement`. Playwright can populate the page's real file input with
+`page.setInputFiles()`. There is therefore no reason to convert a long video to
+Y4M or present it as a fake webcam.
 
-An empty human caption receives a conservative fallback chosen from the event
-classification. Identity-distance text names the threshold crossing;
-detector-divergence text states that MediaPipe retained a comparable mesh while
-face-api returned no 2D face and explicitly asks for pose/occlusion review.
-The fallback is fixed explanatory copy, not another model inference, and an
-edited caption always replaces it.
+The source is represented by a Blob URL in the browser. The Blob URL is local
+to the page and is revoked when another file is selected. The analysis session
+stores only source metadata and a bounded provenance fingerprint; it does not
+copy the source into the output directory.
 
-CLI output also includes `timeline.svg`, referenced by `report.html` and by the
-`artifacts.timeline` field in `report.json`. It is regenerated from the
-technical records rather than captured from the browser canvas, so it remains
-scalable and preserves categorical × markers for missing face-api detections.
+The CLI fingerprint is `sampled-sha256`: SHA-256 over the file size, first MiB,
+and last MiB. This is intentionally bounded for multi-gigabyte recordings. It
+is a provenance guard, not a cryptographic claim over every source byte.
 
-It deliberately excludes raw embeddings, raw image data URLs, and diagnostic
-errors unrelated to the story.
+## Model provenance
 
-### Technical log
+The repository vendors the following browser assets:
 
-`analysis.ndjson` is newline-delimited JSON with a versioned header, sample
-records, journal records, evidence-event records, and a footer. It excludes
-image bytes but keeps exact media timestamps and recommended clip windows.
+- `@vladmandic/face-api` as a classic global `faceapi` bundle;
+- MediaPipe Tasks Vision `0.10.35`;
+- FaceLandmarker `float16/1` with 478 landmarks;
+- ImageEmbedder MobileNetV3 Small `float32/1`.
 
-Schema version 4 records whether a baseline was an exact selected frame or an
-automatic medoid, its selected timestamp, skipped anchored neighbours,
-`lastScanMediaTime`, and `analysis-resumed` journal entries. These fields let a
-later processor distinguish deliberate human reference selection from
-automatic search and reconstruct pause boundaries without video pixels. It
-also records person-requested frame checks, their four-way interpretation,
-compact model results, and native-frame metadata without image bytes.
+Both MediaPipe tasks explicitly use the CPU delegate. The sampled offline
+workflow does not need continuous GPU throughput, and the CPU path remains
+reliable in headless Playwright, software-rendered documentation captures, and
+machines without a usable WebGL delegate. The chosen delegate is recorded with
+the session model provenance.
 
-The technical log is downloaded separately from the report. This separation
-allows a user to share the report without accidentally sharing detailed model
-telemetry, while retaining a machine-readable research artifact locally.
+The face-api bundle currently comes from a floating package URL in the vendor
+refresh documentation. A future vendor refresh should pin the package version
+or record a digest. Reports record model labels so results are not silently
+presented as timeless or implementation-independent.
 
-The CLI writes both artifacts even when analysis fails after the source has
-loaded. A failed run is precisely when the reset reasons, last successful
-sample, and model diagnostics are most valuable.
+The vendored FaceMatcher default is `0.6` and accepts a match when the L2
+distance is strictly below that threshold. The Video Loader therefore uses
+`0.6` as its default identity-distance threshold. The threshold and the
+baseline spread are recorded in each report and technical log.
 
-The log can be replayed even when the UI layout changes. Its source fingerprint
-lets tooling warn if a different video is supplied later.
+## Browser automation contract
 
-`replay-log` is intentionally a derivation operation, not another analysis. It
-reads `analysis.ndjson` and writes `report.replayed.json` plus `timeline.svg` in
-the chosen output directory. It does not write a second NDJSON file, seek the
-video, or run any model. The replayed JSON includes diagnostic candidates as
-well as confirmed events, so it demonstrates that archived telemetry remains
-processable but does not replace the original editorial report.
+The UI and Playwright use the same file input and the same browser API. The
+stable entry point is `window.gstmxxVideoLoader`:
 
-## Reversible editorial choices
+```js
+window.gstmxxVideoLoader.isModelsReady();
+window.gstmxxVideoLoader.isSourceLoaded();
+window.gstmxxVideoLoader.isAnalysisRunning();
+window.gstmxxVideoLoader.setSourceMetadata({ fingerprint });
+await window.gstmxxVideoLoader.analyze({
+  start: 0,
+  end: 120,
+  baselineTime: 18,
+  baselineSelectionMode: 'anchored',
+  sampleIntervalSeconds: 0.5,
+});
+window.gstmxxVideoLoader.cancel();
+await window.gstmxxVideoLoader.resume();
+await window.gstmxxVideoLoader.analyzeFrame(83.417);
+window.gstmxxVideoLoader.getSession();
+window.gstmxxVideoLoader.exportBundle();
+```
 
-Automatic observations are append-only. User choices are reversible commands:
+The page also dispatches `gstmxx-video-loader:*` events for model readiness,
+source readiness, samples, baseline readiness, confirmed events, completion,
+and errors. Automation should prefer this contract over button text or screen
+coordinates.
 
-- promote an event into the story;
-- reject an event;
-- edit its caption.
-- move an evidence frame earlier or later in the story.
+## Event-to-function call map
 
-Each command records before/after state. Undo and redo change editorial state;
-they do not delete automatic observations or rewrite the technical history.
+The following trees describe the important call paths from their external
+trigger. They are deliberately written like `pstree`: indentation means a
+direct call, while `[...]` marks a conditional branch or a repeated sampling
+loop. Browser events and the public Playwright API converge before analysis,
+so interactive and automated runs exercise the same controller.
 
-### Source chronology is not story chronology
+```text
+<script type="module"> loads lab-js/loader.js
+└─ init()
+   ├─ initI18n()
+   ├─ setupLocaleSelect()
+   ├─ applyI18n()
+   ├─ renderSession(null)
+   │  └─ drawTimeline(null)
+   ├─ syncControls()
+   └─ controller.loadModels()
+      ├─ faceapi.nets.*.loadFromUri()                 [three face-api models]
+      ├─ import(MEDIAPIPE_TASKS_VISION_URL)
+      ├─ FilesetResolver.forVisionTasks()
+      ├─ FaceLandmarker.createFromOptions()
+      ├─ ImageEmbedder.createFromOptions()
+      └─ notify('models-ready')
+         └─ controller subscriber in loader.js
+            ├─ setStatus()
+            ├─ syncControls()
+            └─ dispatchLoaderEvent('models-ready')
 
-Makeup videos often open with the finished look, cut back to a bare face, and
-then show the transformation. Media timestamps therefore cannot define
-“before” and “after”. The Video Loader preserves two independent orders:
+<input type="file"> change
+or Playwright page.setInputFiles('#videoFile', source)
+└─ loadSelectedFile()
+   └─ controller.loadFile()
+      ├─ URL.createObjectURL()
+      ├─ waitForMediaEvent(video, 'loadedmetadata')
+      ├─ createAnalysisSession()
+      ├─ appendJournal('source-loaded')
+      ├─ triggerDetector.reset()
+      └─ notify('source-loaded')
+   ├─ syncVideoTimeline()
+   │  └─ renderBaseSelection()
+   ├─ renderSession()
+   │  ├─ drawTimeline()
+   │  └─ orderedStoryItems()
+   ├─ syncControls()
+   └─ dispatchLoaderEvent('source-loaded')
 
-- source time is immutable evidence and remains attached to every frame,
-  observation, log record, and clip window;
-- story order is a human-editable sequence used by the browser report and the
-  portable HTML export.
+<video> timeupdate | seeked | loadedmetadata
+or #videoSeek input
+└─ syncVideoTimeline()
+   └─ renderBaseSelection()
 
-The computational baseline is pinned as the first story card. Confirmed
-events enter after it in source-time order; an early finished-look teaser no
-longer displaces the reference merely because its source timestamp is smaller.
-Events remain reorderable among themselves, but no event can cross the
-baseline boundary. Candidates remain only in the technical log until a second
-divergent observation confirms them.
+#selectBaseBtn click
+└─ selectCurrentBase()
+   ├─ selectedBaseTime = video.currentTime
+   ├─ renderBaseSelection()
+   └─ syncControls()                                  [enables Analyse]
+
+#analyzeFrameBtn click
+or window.gstmxxVideoLoader.analyzeFrame(mediaTime)
+└─ analyzeCurrentFrame(mediaTime)
+   └─ controller.analyzeFrame(mediaTime)
+      ├─ seekVideo()
+      ├─ faceLandmarker.detect()
+      ├─ drawAlignedFaceCrop() + measureCropQuality()
+      ├─ imageEmbedder.embed()                        [only if comparable]
+      ├─ runFaceApi()                                 [explicit human request]
+      ├─ euclideanDistance()                          [descriptor vs baseline]
+      ├─ appendSample({ phase: 'manual' })
+      ├─ appendJournal('face-api-result')
+      ├─ captureSourceFrame()                         [exact native frame]
+      ├─ recordManualFrameCheck()
+      └─ notify('manual-frame-checked')
+         ├─ renderSession()
+         ├─ renderAnalysisMessage()
+         └─ dispatchLoaderEvent('manual-frame-checked')
+
+#startAnalysisBtn click
+or window.gstmxxVideoLoader.analyze(options)
+└─ startAnalysis(options)
+   └─ controller.analyze(options)
+      ├─ appendJournal('analysis-started')
+      ├─ notify('analysis-started')
+      ├─ [anchored baseline calibration after selectedBaseTime]
+      │  ├─ seekVideo()
+      │  │  └─ waitForMediaEvent(video, 'seeked')
+      │  ├─ faceLandmarker.detect()
+      │  ├─ drawAlignedFaceCrop()
+      │  ├─ measureCropQuality()
+      │  ├─ imageEmbedder.embed()
+      │  │  └─ unpackEmbedding()
+      │  ├─ appearanceDistance() + visualDrift()      [neighbour eligibility]
+      │  ├─ [enough comparable local samples]
+      │  │  ├─ runFaceApi()                           [selected + sharp neighbours]
+      │  │  ├─ buildAnchoredFaceBaseline()            [selected descriptor]
+      │  │  │  └─ median(euclideanDistance)
+      │  │  ├─ buildAnchoredEmbeddingBaseline()       [selected embedding]
+      │  │  │  ├─ median()
+      │  │  │  └─ medianAbsoluteDeviation()
+      │  │  ├─ captureSourceFrame()                   [selected native JPEG]
+      │  │  ├─ finaliseBaseline()
+      │  │  └─ notify('baseline-ready')
+      │  ├─ appendSample()
+      │  └─ notify('sample')
+      ├─ [coarse scan loop, rewound to analysis start]
+      │  ├─ seekVideo()
+      │  ├─ faceLandmarker.detect()
+      │  ├─ drawAlignedFaceCrop()
+      │  ├─ measureCropQuality()
+      │  ├─ imageEmbedder.embed()
+      │  ├─ visualDrift()
+      │  ├─ appendSample()
+      │  ├─ triggerDetector.evaluate()
+      │  ├─ [verification requested]
+      │  │  ├─ runFaceApi()
+      │  │  ├─ euclideanDistance()                    [descriptor vs baseline]
+      │  │  ├─ appendJournal('face-api-result')
+      │  │  └─ recordEvidenceObservation()
+      │  │     └─ [event newly confirmed]
+      │  │        ├─ refineEvidenceFrame()
+      │  │        │  ├─ [neighbourhood loop]
+      │  │        │  │  ├─ seekVideo()
+      │  │        │  │  ├─ faceLandmarker.detect()
+      │  │        │  │  ├─ drawAlignedFaceCrop()
+      │  │        │  │  └─ measureCropQuality()
+      │  │        │  ├─ runFaceApi()                  [at most three candidates]
+      │  │        │  ├─ euclideanDistance()
+      │  │        │  ├─ captureSourceFrame()          [one selected JPEG]
+      │  │        │  └─ selectEventFrame()
+      │  │        └─ notify('event-confirmed')
+      │  └─ notify('sample')
+      ├─ appendJournal('analysis-complete' | 'analysis-cancelled')
+      └─ notify('analysis-complete' | 'analysis-cancelled')
+         └─ controller subscriber in loader.js
+            ├─ renderSession()
+            │  ├─ drawTimeline()
+            │  ├─ orderedStoryItems()
+            │  ├─ createBaselineCard()
+            │  └─ createEventCard()
+            └─ dispatchLoaderEvent(update.type)
+
+#cancelAnalysisBtn click | window.gstmxxVideoLoader.cancel()
+└─ controller.cancel()                                [sets boundary flag]
+   └─ analyze() finishes the current sample
+      ├─ seekVideo(lastScanMediaTime)                  [visible checkpoint]
+      ├─ appendJournal('analysis-cancelled')
+      └─ notify('analysis-cancelled')
+
+#startAnalysisBtn click while cancelled
+or window.gstmxxVideoLoader.resume()
+└─ startAnalysis({ resume: true })
+   └─ controller.analyze({ resume: true })
+      ├─ appendJournal('analysis-resumed')
+      ├─ preserve baseline, samples, events, and journal
+      └─ resume coarse scan at lastScanMediaTime + sample interval
+
+editorial button click
+├─ applyEditorialCommand()                            [caption/promote/reject/order]
+├─ undoEditorialCommand() | redoEditorialCommand()   [Undo/Redo]
+└─ renderSession()
+
+Download report click | Download technical log click
+└─ controller.exportBundle()
+   ├─ buildReport()
+   ├─ serializeTechnicalLog()
+   └─ downloadText()                                  [chosen artifact only]
+
+scripts-dev/video-loader.cjs analyze
+└─ main()
+   └─ analyze()
+      ├─ sampledFingerprint()
+      ├─ startServer()                                [unless --base-url]
+      ├─ chromium.launch()
+      ├─ page.setInputFiles('#videoFile', source)
+      ├─ window.gstmxxVideoLoader.analyze(options)    [browser tree above]
+      ├─ window.gstmxxVideoLoader.exportBundle()
+      ├─ writeBundle()
+      └─ extractClips()                               [only with --clips]
+```
 
 ## CLI workflow
 

From a2599c6e820cf8a158764f58ac89d238b06ae9f1 Mon Sep 17 00:00:00 2001
From: vecna <vecna@globaleaks.org>
Date: Mon, 12 Oct 2026 17:47:22 +0200
Subject: [PATCH 7/9] maxxi improvement and review of realtime analyzer

---
 lab-js/realtime/realtime-capture.js  | 179 +++++++++-
 lab-js/realtime/realtime-graph.js    | 107 +++++-
 lab-js/realtime/realtime-radial.js   | 145 +++++++++
 lab-js/realtime/realtime-settings.js |  18 +-
 lab-js/realtime/realtime.js          | 466 +++++++++++++++++++++++++--
 realtime.html                        |  45 ++-
 styles/realtime.css                  | 211 +++++++++++-
 tests/e2e/realtime.spec.js           | 203 +++++++++++-
 tests/unit/realtime-capture.test.js  | 133 ++++++++
 tests/unit/realtime-graph.test.js    |  78 +++++
 tests/unit/realtime-radial.test.js   |  75 +++++
 tests/unit/realtime-settings.test.js |  68 ++++
 12 files changed, 1654 insertions(+), 74 deletions(-)
 create mode 100644 lab-js/realtime/realtime-radial.js
 create mode 100644 tests/unit/realtime-capture.test.js
 create mode 100644 tests/unit/realtime-graph.test.js
 create mode 100644 tests/unit/realtime-radial.test.js
 create mode 100644 tests/unit/realtime-settings.test.js

diff --git a/lab-js/realtime/realtime-capture.js b/lab-js/realtime/realtime-capture.js
index 2ce4c15..f3bb7de 100644
--- a/lab-js/realtime/realtime-capture.js
+++ b/lab-js/realtime/realtime-capture.js
@@ -20,7 +20,7 @@ const LM_RIGHT_EYE_OUTER = 45;
 const LM_NOSE_TIP = 30;
 
 const DEFAULT_GATE = {
-  minScore: 0.9,
+  minScore: 0.85,
   minBoxWidthRatio: 0.25,
   edgeMarginRatio: 0.02,
   maxRollDegrees: 8,
@@ -30,6 +30,71 @@ const DEFAULT_GATE = {
   maxClippedRatio: 0.02,
 };
 
+const DEFAULT_COMPARISON = {
+  maxRollDelta: 4,
+  maxYawDelta: 0.055,
+  maxScaleDelta: 0.08,
+  maxCenterDelta: 0.065,
+  maxLumaDelta: 0.1,
+};
+
+const CONDITION_KEYS = [
+  'rollDegrees',
+  'yawOffset',
+  'boxWidthRatio',
+  'centerXRatio',
+  'centerYRatio',
+  'luma',
+];
+
+/**
+ * Averages the capture conditions accepted during one temporal window.
+ *
+ * @param {object[]} readings Per-frame 2D pose, framing and light metrics.
+ * @returns {object|null} Mean condition profile, or null when unavailable.
+ */
+export function summarizeConditions(readings) {
+  const usable = readings.filter(Boolean);
+  if (!usable.length) return null;
+  const profile = {};
+  for (const key of CONDITION_KEYS) {
+    const values = usable.map((reading) => reading[key]).filter(Number.isFinite);
+    if (values.length) profile[key] = values.reduce((sum, value) => sum + value, 0) / values.length;
+  }
+  return Object.keys(profile).length ? profile : null;
+}
+
+/**
+ * Checks whether a live frame is comparable with the baseline conditions.
+ * This is deliberately a 2D nuisance gate, not pose correction: rejected
+ * frames are omitted rather than mathematically transformed into a result.
+ *
+ * @param {object|null} baseline Baseline condition profile.
+ * @param {object|null} current Current frame conditions.
+ * @param {object} [overrides] Partial tolerance overrides.
+ * @returns {{ok: boolean, reason: string, differences: object}} Comparison verdict.
+ */
+export function compareConditions(baseline, current, overrides = {}) {
+  if (!baseline || !current) return { ok: true, reason: '', differences: {} };
+  const limits = { ...DEFAULT_COMPARISON, ...overrides };
+  const difference = (key) => Math.abs((current[key] ?? 0) - (baseline[key] ?? 0));
+  const differences = Object.fromEntries(CONDITION_KEYS.map((key) => [key, difference(key)]));
+
+  if (
+    differences.rollDegrees > limits.maxRollDelta ||
+    differences.yawOffset > limits.maxYawDelta ||
+    differences.boxWidthRatio > limits.maxScaleDelta ||
+    differences.centerXRatio > limits.maxCenterDelta ||
+    differences.centerYRatio > limits.maxCenterDelta
+  ) {
+    return { ok: false, reason: 'Return to the baseline pose and framing.', differences };
+  }
+  if (differences.luma > limits.maxLumaDelta) {
+    return { ok: false, reason: 'Lighting differs too much from the baseline.', differences };
+  }
+  return { ok: true, reason: '', differences };
+}
+
 /**
  * Euclidean distance between two descriptors.
  *
@@ -166,6 +231,38 @@ function analyseCrop(video, box, scratch) {
   };
 }
 
+/**
+ * Estimates illumination from pixels outside the detected face box. Makeup can
+ * legitimately darken the face crop; using the surrounding frame makes the
+ * light comparison less likely to erase the very intervention being studied.
+ *
+ * @param {HTMLVideoElement} video Source video element.
+ * @param {{x: number, y: number, width: number, height: number}} box Face box.
+ * @param {{canvas: HTMLCanvasElement, ctx: CanvasRenderingContext2D}} scratch Scratch surface.
+ * @returns {number} Mean normalized luma outside the face box.
+ */
+function analyseAmbientLuma(video, box, scratch) {
+  const size = scratch.canvas.width;
+  scratch.ctx.drawImage(video, 0, 0, video.videoWidth, video.videoHeight, 0, 0, size, size);
+  const { data } = scratch.ctx.getImageData(0, 0, size, size);
+  const left = (box.x / video.videoWidth) * size;
+  const right = ((box.x + box.width) / video.videoWidth) * size;
+  const top = (box.y / video.videoHeight) * size;
+  const bottom = ((box.y + box.height) / video.videoHeight) * size;
+  let sum = 0;
+  let count = 0;
+
+  for (let y = 0; y < size; y += 1) {
+    for (let x = 0; x < size; x += 1) {
+      if (x >= left && x <= right && y >= top && y <= bottom) continue;
+      const offset = (y * size + x) * 4;
+      sum += (data[offset] * 0.299 + data[offset + 1] * 0.587 + data[offset + 2] * 0.114) / 255;
+      count += 1;
+    }
+  }
+  return count ? sum / count : Number.NaN;
+}
+
 /**
  * Creates the frame quality gate.
  *
@@ -182,9 +279,13 @@ export function createQualityGate(video, overrides = {}) {
      * Judges one detection against every framing and quality rule.
      *
      * @param {*} detection face-api detection with landmarks and descriptor.
+     * @param {object} [options] Evaluation mode.
+     * @param {boolean} [options.allowAppearanceChanges=false] Do not let face-crop
+     *   exposure or detector-score changes reject an intervention frame.
      * @returns {{ok: boolean, reason: string, sharpness: number}} Verdict.
      */
-    evaluate(detection) {
+    evaluate(detection, options = {}) {
+      const { allowAppearanceChanges = false } = options;
       const reject = (reason) => ({ ok: false, reason, sharpness: 0 });
 
       const box = detection?.detection?.box;
@@ -195,7 +296,11 @@ export function createQualityGate(video, overrides = {}) {
       const videoW = video.videoWidth || 1;
       const videoH = video.videoHeight || 1;
 
-      if (score < limits.minScore) return reject('Low confidence — improve the lighting.');
+      if (!allowAppearanceChanges && score < limits.minScore) {
+        return reject(
+          `Detector confidence is low (${score.toFixed(2)}). Hold a frontal, steady pose and check glare or shadows.`,
+        );
+      }
       if (box.width < videoW * limits.minBoxWidthRatio) return reject('Move closer to the camera.');
 
       const marginX = videoW * limits.edgeMarginRatio;
@@ -212,23 +317,37 @@ export function createQualityGate(video, overrides = {}) {
       const nose = points[LM_NOSE_TIP];
       if (!leftEye || !rightEye || !nose) return reject('Landmarks incomplete.');
 
-      const rollDegrees = Math.abs(
-        (Math.atan2(rightEye.y - leftEye.y, rightEye.x - leftEye.x) * 180) / Math.PI,
-      );
-      if (rollDegrees > limits.maxRollDegrees) return reject('Level your head.');
+      const rollDegrees =
+        (Math.atan2(rightEye.y - leftEye.y, rightEye.x - leftEye.x) * 180) / Math.PI;
+      if (Math.abs(rollDegrees) > limits.maxRollDegrees) return reject('Level your head.');
 
       const interocular = Math.hypot(rightEye.x - leftEye.x, rightEye.y - leftEye.y) || 1;
       const eyeMidX = (leftEye.x + rightEye.x) / 2;
-      const yawOffset = Math.abs(nose.x - eyeMidX) / interocular;
-      if (yawOffset > limits.maxYawOffset) return reject('Look straight at the camera.');
+      const yawOffset = (nose.x - eyeMidX) / interocular;
+      if (Math.abs(yawOffset) > limits.maxYawOffset) return reject('Look straight at the camera.');
 
+      const ambientLuma = analyseAmbientLuma(video, box, scratch);
       const crop = analyseCrop(video, box, scratch);
-      if (crop.luma < limits.minLuma) return reject('Too dark — add some light.');
-      if (crop.luma > limits.maxLuma || crop.clipped > limits.maxClippedRatio) {
-        return reject('Overexposed — reduce the light.');
+      if (!allowAppearanceChanges) {
+        if (crop.luma < limits.minLuma) return reject('Too dark — add some light.');
+        if (crop.luma > limits.maxLuma || crop.clipped > limits.maxClippedRatio) {
+          return reject('Overexposed — reduce the light.');
+        }
       }
 
-      return { ok: true, reason: '', sharpness: crop.sharpness };
+      return {
+        ok: true,
+        reason: '',
+        sharpness: crop.sharpness,
+        metrics: {
+          rollDegrees,
+          yawOffset,
+          boxWidthRatio: box.width / videoW,
+          centerXRatio: (box.x + box.width / 2) / videoW,
+          centerYRatio: (box.y + box.height / 2) / videoH,
+          luma: Number.isFinite(ambientLuma) ? ambientLuma : crop.luma,
+        },
+      };
     },
   };
 }
@@ -251,7 +370,9 @@ export function createBaselineCollector(options) {
   const { targetSamples, minDurationMs, timeoutMs, maxSpread } = options;
 
   let descriptors = [];
+  let conditions = [];
   let startedAt = 0;
+  let lastAcceptedAt = 0;
   let active = false;
 
   return {
@@ -262,7 +383,9 @@ export function createBaselineCollector(options) {
      */
     start() {
       descriptors = [];
+      conditions = [];
       startedAt = Date.now();
+      lastAcceptedAt = 0;
       active = true;
     },
 
@@ -274,6 +397,8 @@ export function createBaselineCollector(options) {
     cancel() {
       active = false;
       descriptors = [];
+      conditions = [];
+      lastAcceptedAt = 0;
     },
 
     /**
@@ -288,14 +413,15 @@ export function createBaselineCollector(options) {
     /**
      * Offers one gated frame to the collector.
      *
-     * @param {{ok: boolean, reason: string}} verdict Quality gate verdict.
+     * @param {{ok: boolean, reason: string, metrics?: object}} verdict Quality gate verdict.
      * @param {Float32Array|number[]} descriptor Descriptor for the frame.
      * @returns {{status: string, accepted: number, target: number, reason?: string, baseline?: object}} Progress.
      */
     offer(verdict, descriptor) {
       if (!active) return { status: 'idle', accepted: 0, target: targetSamples };
 
-      const elapsed = Date.now() - startedAt;
+      const now = Date.now();
+      const elapsed = now - startedAt;
 
       if (!verdict.ok) {
         if (elapsed > timeoutMs) {
@@ -304,7 +430,7 @@ export function createBaselineCollector(options) {
             status: 'failed',
             accepted: descriptors.length,
             target: targetSamples,
-            reason: verdict.reason,
+            reason: `Not enough clean frames before the capture timed out. Last rejection: ${verdict.reason}`,
           };
         }
         return {
@@ -315,12 +441,30 @@ export function createBaselineCollector(options) {
         };
       }
 
-      if (descriptors.length < targetSamples) descriptors.push(Array.from(descriptor));
+      // Space observations across the whole requested window. Without this,
+      // a fast detector could fill the array in one short burst and merely
+      // wait out minDurationMs without observing the rest of the interval.
+      const sampleIntervalMs = targetSamples > 1 ? minDurationMs / (targetSamples - 1) : 0;
+      const sampleDue = descriptors.length === 0 || now - lastAcceptedAt >= sampleIntervalMs;
+      if (descriptors.length < targetSamples && sampleDue) {
+        descriptors.push(Array.from(descriptor));
+        if (verdict.metrics) conditions.push(verdict.metrics);
+        lastAcceptedAt = now;
+      }
 
       const enoughSamples = descriptors.length >= targetSamples;
       const enoughTime = elapsed >= minDurationMs;
 
       if (!enoughSamples || !enoughTime) {
+        if (elapsed > timeoutMs) {
+          active = false;
+          return {
+            status: 'failed',
+            accepted: descriptors.length,
+            target: targetSamples,
+            reason: 'Not enough clean frames before the capture timed out.',
+          };
+        }
         return { status: 'collecting', accepted: descriptors.length, target: targetSamples };
       }
 
@@ -348,6 +492,7 @@ export function createBaselineCollector(options) {
           noiseFloor: result.spread,
           capturedAt: Date.now(),
           samples: descriptors.length,
+          conditions: summarizeConditions(conditions),
         },
       };
     },
diff --git a/lab-js/realtime/realtime-graph.js b/lab-js/realtime/realtime-graph.js
index 9780814..feaa634 100644
--- a/lab-js/realtime/realtime-graph.js
+++ b/lab-js/realtime/realtime-graph.js
@@ -19,12 +19,13 @@
  *
  * Rendering is virtualised: the canvas is sticky and sized to the scroll
  * viewport, a spacer supplies the scroll height, and only the visible slice is
- * drawn. Memory is constant, so session length is unbounded. Growing one tall
- * canvas instead would hit the ~32k px dimension cap after ~45 minutes and
- * hold ~30MB on the way there.
+ * drawn. Render work and canvas memory stay bounded; compact timeline entries
+ * grow with the session. Growing one tall canvas instead would hit the ~32k px
+ * dimension cap after ~45 minutes and hold ~30MB on the way there.
  */
 
 const GAP_BLOCK_PX = 30;
+const EVENT_BLOCK_PX = 24;
 const AXIS_STRIP_PX = 17;
 const PAD_LEFT = 6;
 const GUTTER_RIGHT = 24;
@@ -128,7 +129,9 @@ export function createGraph(options) {
    * @returns {number} Entry height.
    */
   function entryHeight(entry) {
-    return entry.kind === 'gap' ? GAP_BLOCK_PX : pxPerSample;
+    if (entry.kind === 'gap') return GAP_BLOCK_PX;
+    if (entry.kind === 'event') return EVENT_BLOCK_PX;
+    return pxPerSample;
   }
 
   /**
@@ -228,6 +231,9 @@ export function createGraph(options) {
       grid: token(canvasEl, '--rt-grid', 'rgba(255,231,168,0.08)'),
       gap: token(canvasEl, '--rt-gap', '#8a7b6a'),
       faint: token(canvasEl, '--rt-gap', '#8a7b6a'),
+      applying: token(canvasEl, '--rt-phase-applying', 'rgba(242,106,27,0.13)'),
+      measuring: token(canvasEl, '--rt-phase-measuring', 'rgba(127,227,176,0.13)'),
+      event: token(canvasEl, '--rt-event', '#f3c747'),
     };
 
     const scrollTop = scrollEl.scrollTop;
@@ -275,6 +281,24 @@ export function createGraph(options) {
       const firstIndex = indexAt(Math.max(0, scrollTop - GAP_BLOCK_PX));
       const limit = scrollTop + viewportH;
 
+      // Session phases remain visible behind the trace. They are declared by
+      // the operator and therefore describe workflow, not an inferred cause.
+      for (let i = firstIndex; i < entries.length; i += 1) {
+        const top = tops[i];
+        if (top > limit) break;
+        const entry = entries[i];
+        const y = top - scrollTop;
+        if (entry.kind === 'sample' && entry.phase === 'applying') {
+          ctx.fillStyle = colors.applying;
+          ctx.fillRect(PAD_LEFT, y, plotWidth, pxPerSample);
+        } else if (entry.kind === 'sample' && entry.phase === 'measuring') {
+          ctx.fillStyle = colors.measuring;
+          ctx.fillRect(PAD_LEFT, y, plotWidth, pxPerSample);
+        } else if (entry.kind === 'event') {
+          drawEvent(entry, y, colors, plotWidth);
+        }
+      }
+
       ctx.lineWidth = 2;
       ctx.strokeStyle = colors.trace;
       ctx.lineJoin = 'round';
@@ -288,13 +312,15 @@ export function createGraph(options) {
         const entry = entries[i];
         const y = top - scrollTop;
 
-        if (entry.kind === 'gap') {
+        if (entry.kind === 'gap' || entry.kind === 'event') {
           if (penDown) {
             ctx.stroke();
             ctx.beginPath();
             penDown = false;
           }
-          drawGap(entry, y, colors, plotWidth);
+          // Events were already painted in the phase-background pass. They
+          // interrupt the trace, but they are not absence blocks.
+          if (entry.kind === 'gap') drawGap(entry, y, colors, plotWidth);
           continue;
         }
 
@@ -376,7 +402,7 @@ export function createGraph(options) {
     ctx.lineTo(PAD_LEFT + plotWidth, Math.round(y + GAP_BLOCK_PX) + 0.5);
     ctx.stroke();
 
-    const label = `no face · ${formatDuration(endT - entry.t)}`;
+    const label = `${entry.label || 'no face'} · ${formatDuration(endT - entry.t)}`;
     ctx.font = '9px ui-monospace, SFMono-Regular, Menlo, monospace';
     ctx.textAlign = 'center';
     ctx.textBaseline = 'middle';
@@ -390,6 +416,31 @@ export function createGraph(options) {
     ctx.fillText(label, cx, cy);
   }
 
+  /**
+   * Draws an operator-declared session event.
+   *
+   * @param {object} entry Timeline event.
+   * @param {number} y Canvas y.
+   * @param {object} colors Resolved palette.
+   * @param {number} plotWidth Plot width.
+   * @returns {void}
+   */
+  function drawEvent(entry, y, colors, plotWidth) {
+    ctx.fillStyle = entry.phase === 'measuring' ? colors.measuring : colors.applying;
+    ctx.fillRect(PAD_LEFT, y, plotWidth, EVENT_BLOCK_PX);
+    ctx.strokeStyle = colors.event;
+    ctx.lineWidth = 1;
+    ctx.beginPath();
+    ctx.moveTo(PAD_LEFT, Math.round(y) + 0.5);
+    ctx.lineTo(PAD_LEFT + plotWidth, Math.round(y) + 0.5);
+    ctx.stroke();
+    ctx.fillStyle = colors.event;
+    ctx.font = '9px ui-monospace, SFMono-Regular, Menlo, monospace';
+    ctx.textAlign = 'center';
+    ctx.textBaseline = 'middle';
+    ctx.fillText(entry.label, PAD_LEFT + plotWidth / 2, y + EVENT_BLOCK_PX / 2);
+  }
+
   /**
    * Draws the off-axis indicator for a sample beyond the plotted ceiling.
    *
@@ -506,11 +557,12 @@ export function createGraph(options) {
    *
    * @param {number} t Epoch milliseconds of the sample.
    * @param {number} value Averaged distance for the tick.
+   * @param {string} [phase] Operator-declared session phase.
    * @returns {number} Index of the appended entry.
    */
-  function pushSample(t, value) {
+  function pushSample(t, value, phase = 'ready') {
     openGapIndex = -1;
-    const entry = { kind: 'sample', t, value, seq: sampleSeq };
+    const entry = { kind: 'sample', t, value, phase, seq: sampleSeq };
     sampleSeq += 1;
     tops.push(contentHeight);
     entries.push(entry);
@@ -525,14 +577,21 @@ export function createGraph(options) {
    * Opens an absence block, or extends the one already open.
    *
    * @param {number} t Epoch milliseconds when the face was last seen.
+   * @param {string} [label] Human-readable exclusion reason.
    * @returns {void}
    */
-  function beginGap(t) {
+  function beginGap(t, label = 'no face') {
     if (openGapIndex !== -1) {
+      const open = entries[openGapIndex];
+      if (open.label !== label) {
+        endGap(t);
+        beginGap(t, label);
+        return;
+      }
       draw();
       return;
     }
-    const entry = { kind: 'gap', t, endT: null };
+    const entry = { kind: 'gap', t, endT: null, label };
     tops.push(contentHeight);
     entries.push(entry);
     contentHeight += GAP_BLOCK_PX;
@@ -555,6 +614,25 @@ export function createGraph(options) {
     draw();
   }
 
+  /**
+   * Adds an operator-declared phase marker to the timeline.
+   *
+   * @param {number} t Epoch milliseconds.
+   * @param {string} phase Session phase.
+   * @param {string} label Visible marker label.
+   * @returns {void}
+   */
+  function addEvent(t, phase, label) {
+    endGap(t);
+    const entry = { kind: 'event', t, phase, label };
+    tops.push(contentHeight);
+    entries.push(entry);
+    contentHeight += EVENT_BLOCK_PX;
+    syncSpacer();
+    if (follow) scrollToNow();
+    draw();
+  }
+
   /**
    * Attaches a saved capture to the most recent sample.
    *
@@ -674,22 +752,25 @@ export function createGraph(options) {
     pushSample,
     beginGap,
     endGap,
+    addEvent,
     markLatestCapture,
     clearCapture,
     setNoiseFloor,
     /**
      * Reports timeline size for diagnostics and tests.
      *
-     * @returns {{samples: number, gaps: number, following: boolean}} Snapshot.
+     * @returns {{samples: number, gaps: number, events: number, following: boolean}} Snapshot.
      */
     stats() {
       let samples = 0;
       let gaps = 0;
+      let events = 0;
       for (const entry of entries) {
         if (entry.kind === 'gap') gaps += 1;
+        else if (entry.kind === 'event') events += 1;
         else samples += 1;
       }
-      return { samples, gaps, following: follow };
+      return { samples, gaps, events, following: follow };
     },
     /**
      * Disconnects observers held by the graph.
diff --git a/lab-js/realtime/realtime-radial.js b/lab-js/realtime/realtime-radial.js
new file mode 100644
index 0000000..dc7b818
--- /dev/null
+++ b/lab-js/realtime/realtime-radial.js
@@ -0,0 +1,145 @@
+/**
+ * @module realtime-radial
+ * @description
+ * A compact fingerprint of all 128 descriptor deltas. The spokes are latent
+ * coordinates, not facial regions: the plot can compare the shape of two
+ * changes, but it cannot say that one spoke belongs to an eyebrow or cheek.
+ */
+
+/**
+ * Converts descriptor deltas to radial points.
+ *
+ * @param {Float32Array|number[]} values Signed descriptor deltas.
+ * @param {object} [options] Geometry and scale.
+ * @param {number} [options.centerX=0] Plot centre x.
+ * @param {number} [options.centerY=0] Plot centre y.
+ * @param {number} [options.radius=1] Maximum radius.
+ * @param {number} [options.maxValue=0.15] Delta mapped to the outer ring.
+ * @returns {Array<{x: number, y: number, value: number, index: number}>} Points in descriptor order.
+ */
+export function radialPoints(values, options = {}) {
+  const {
+    centerX = 0,
+    centerY = 0,
+    radius = 1,
+    maxValue = 0.15,
+  } = options;
+  const count = values?.length || 0;
+  if (!count) return [];
+
+  return Array.from(values, (raw, index) => {
+    const value = Number.isFinite(raw) ? Math.abs(raw) : 0;
+    const magnitude = Math.min(1, value / maxValue) * radius;
+    const angle = -Math.PI / 2 + (index / count) * Math.PI * 2;
+    return {
+      x: centerX + Math.cos(angle) * magnitude,
+      y: centerY + Math.sin(angle) * magnitude,
+      value,
+      index,
+    };
+  });
+}
+
+/**
+ * Creates the live/snapshot radial descriptor plot.
+ *
+ * @param {object} options Controller options.
+ * @param {HTMLCanvasElement} options.canvasEl Plot canvas.
+ * @param {number} [options.maxDelta=0.15] Delta mapped to the outer ring.
+ * @returns {object} Plot controller.
+ */
+export function createRadialGraph({ canvasEl, maxDelta = 0.15 }) {
+  const ctx = canvasEl.getContext('2d');
+  let live = null;
+  let measured = null;
+
+  function css(name, fallback) {
+    const value = getComputedStyle(canvasEl).getPropertyValue(name).trim();
+    return value || fallback;
+  }
+
+  function path(points) {
+    if (!points.length) return;
+    ctx.beginPath();
+    ctx.moveTo(points[0].x, points[0].y);
+    for (let index = 1; index < points.length; index += 1) {
+      ctx.lineTo(points[index].x, points[index].y);
+    }
+    ctx.closePath();
+  }
+
+  /** Repaints the plot from the current live and measured deltas. */
+  function draw() {
+    const rect = canvasEl.getBoundingClientRect();
+    const width = Math.max(1, Math.floor(rect.width));
+    const height = Math.max(1, Math.floor(rect.height));
+    const dpr = Math.max(1, window.devicePixelRatio || 1);
+    if (canvasEl.width !== Math.floor(width * dpr) || canvasEl.height !== Math.floor(height * dpr)) {
+      canvasEl.width = Math.floor(width * dpr);
+      canvasEl.height = Math.floor(height * dpr);
+    }
+    ctx.setTransform(dpr, 0, 0, dpr, 0, 0);
+    ctx.clearRect(0, 0, width, height);
+
+    const centerX = width / 2;
+    const centerY = height / 2;
+    const radius = Math.max(4, Math.min(width, height) / 2 - 10);
+    const grid = css('--rt-grid', 'rgba(255,231,168,0.12)');
+
+    ctx.strokeStyle = grid;
+    ctx.lineWidth = 1;
+    for (const fraction of [0.25, 0.5, 0.75, 1]) {
+      ctx.beginPath();
+      ctx.arc(centerX, centerY, radius * fraction, 0, Math.PI * 2);
+      ctx.stroke();
+    }
+    for (let index = 0; index < 16; index += 1) {
+      const angle = -Math.PI / 2 + (index / 16) * Math.PI * 2;
+      ctx.beginPath();
+      ctx.moveTo(centerX, centerY);
+      ctx.lineTo(centerX + Math.cos(angle) * radius, centerY + Math.sin(angle) * radius);
+      ctx.stroke();
+    }
+
+    if (measured?.length) {
+      const points = radialPoints(measured, { centerX, centerY, radius, maxValue: maxDelta });
+      path(points);
+      ctx.fillStyle = css('--rt-radial-measured-fill', 'rgba(242,106,27,0.25)');
+      ctx.strokeStyle = css('--rt-radial-measured', '#f26a1b');
+      ctx.lineWidth = 1.5;
+      ctx.fill();
+      ctx.stroke();
+    }
+
+    if (live?.length) {
+      const points = radialPoints(live, { centerX, centerY, radius, maxValue: maxDelta });
+      path(points);
+      ctx.strokeStyle = css('--rt-radial-live', '#ffe7a8');
+      ctx.lineWidth = 1;
+      ctx.stroke();
+    }
+  }
+
+  const observer = new ResizeObserver(draw);
+  observer.observe(canvasEl);
+
+  return {
+    draw,
+    setLive(values) {
+      live = values ? Array.from(values) : null;
+      draw();
+    },
+    setMeasured(values) {
+      measured = values ? Array.from(values) : null;
+      draw();
+    },
+    reset() {
+      live = null;
+      measured = null;
+      draw();
+    },
+    destroy() {
+      observer.disconnect();
+    },
+  };
+}
diff --git a/lab-js/realtime/realtime-settings.js b/lab-js/realtime/realtime-settings.js
index 712a75d..6583c16 100644
--- a/lab-js/realtime/realtime-settings.js
+++ b/lab-js/realtime/realtime-settings.js
@@ -17,7 +17,15 @@
 
 const SETTINGS_KEY = 'gstmxx.realtime.settings';
 const BASELINE_KEY = 'gstmxx.realtime.baseline';
-const BASELINE_FORMAT = 1;
+const BASELINE_FORMAT = 2;
+const CONDITION_KEYS = [
+  'rollDegrees',
+  'yawOffset',
+  'boxWidthRatio',
+  'centerXRatio',
+  'centerYRatio',
+  'luma',
+];
 
 const DEFAULT_SETTINGS = {
   brightness: 0.85,
@@ -63,7 +71,7 @@ export function saveSettings(settings) {
  * The baseline is a biometric template. It never leaves the device, and
  * "Delete baseline" removes it outright rather than flipping a flag.
  *
- * @returns {{descriptor: number[], noiseFloor: number, capturedAt: number}|null} Stored baseline.
+ * @returns {{descriptor: number[], noiseFloor: number, capturedAt: number, sourceKind: 'camera', conditions: object}|null} Stored baseline.
  */
 export function loadBaseline() {
   try {
@@ -72,7 +80,13 @@ export function loadBaseline() {
     const parsed = JSON.parse(raw);
     if (parsed.format !== BASELINE_FORMAT) return null;
     if (!Array.isArray(parsed.descriptor) || parsed.descriptor.length !== 128) return null;
+    if (parsed.descriptor.some((value) => !Number.isFinite(value))) return null;
     if (!Number.isFinite(parsed.noiseFloor)) return null;
+    if (!Number.isFinite(parsed.capturedAt)) return null;
+    if (parsed.sourceKind !== 'camera') return null;
+    if (!parsed.conditions || CONDITION_KEYS.some((key) => !Number.isFinite(parsed.conditions[key]))) {
+      return null;
+    }
     return parsed;
   } catch {
     return null;
diff --git a/lab-js/realtime/realtime.js b/lab-js/realtime/realtime.js
index 1bc634b..9f54c7c 100644
--- a/lab-js/realtime/realtime.js
+++ b/lab-js/realtime/realtime.js
@@ -20,9 +20,11 @@ import {
   createBaselineCollector,
   createSpikeDetector,
   createFrameBuffer,
+  compareConditions,
   euclidean,
 } from './realtime-capture.js';
 import { createGallery } from './realtime-gallery.js';
+import { createRadialGraph } from './realtime-radial.js';
 import {
   createSettings,
   guardDestructive,
@@ -37,6 +39,13 @@ const APP_STATES = {
   TRACKING: 'TRACKING',
 };
 
+const SESSION_PHASES = {
+  IDLE: 'idle',
+  READY: 'ready',
+  APPLYING: 'applying',
+  MEASURING: 'measuring',
+};
+
 const DESCRIPTOR_SIZE = 128;
 const CELLS_PER_ROW = 10;
 const MAX_DELTA = 0.15;
@@ -52,10 +61,14 @@ const SAMPLES_PER_SECOND = 3;
 /** Long enough that a blink or a head turn does not punch a hole in the trace. */
 const NO_FACE_DEBOUNCE_MS = 1500;
 
-const BASELINE_TARGET_SAMPLES = 12;
+const BASELINE_TARGET_SAMPLES = 8;
 const BASELINE_MIN_MS = 3000;
-const BASELINE_TIMEOUT_MS = 15000;
+const BASELINE_TIMEOUT_MS = 30000;
 const BASELINE_MAX_SPREAD = 0.2;
+const MEASUREMENT_TARGET_SAMPLES = 8;
+const MEASUREMENT_MIN_MS = 3000;
+const MEASUREMENT_TIMEOUT_MS = 30000;
+const MEASUREMENT_MAX_SPREAD = 0.2;
 
 const SPIKE_SUSTAIN_MS = 1500;
 const SPIKE_BEST_MARGIN = 0.05;
@@ -85,6 +98,15 @@ const runtime = {
   faceMissing: false,
   lastFaceAt: 0,
   stream: null,
+  sourceKind: 'camera',
+  sourceEpoch: 0,
+  sourceObjectUrl: null,
+  lastDetectionScore: null,
+  lastDescriptorLength: 0,
+  comparisonExcluded: false,
+  sessionPhase: SESSION_PHASES.IDLE,
+  sessionStep: 0,
+  lastMeasurement: null,
 };
 
 const els = {
@@ -96,6 +118,7 @@ const els = {
   graphSpacer: document.getElementById('graph-spacer'),
   graphCanvas: document.getElementById('distance-graph'),
   graphFollow: document.getElementById('graph-follow'),
+  radialCanvas: document.getElementById('radial-graph'),
   legendNoise: document.getElementById('legend-noise'),
   calibrationUi: document.getElementById('calibration-ui'),
   captureRing: document.getElementById('capture-ring'),
@@ -111,6 +134,15 @@ const els = {
   brightness: document.getElementById('brightness-input'),
   brightnessValue: document.getElementById('brightness-value'),
   toggleEqualizer: document.getElementById('toggle-equalizer'),
+  sessionBar: document.getElementById('session-bar'),
+  sessionStep: document.getElementById('session-step'),
+  sessionResult: document.getElementById('session-result'),
+  btnStartChange: document.getElementById('btn-start-change'),
+  btnMeasureChange: document.getElementById('btn-measure-change'),
+  videoUpload: document.getElementById('video-upload'),
+  btnOpenVideo: document.getElementById('btn-open-video'),
+  btnUseCamera: document.getElementById('btn-use-camera'),
+  sourceState: document.getElementById('source-state'),
   statusPill: document.getElementById('status-pill'),
   distanceValue: document.getElementById('distance-value'),
   trackingState: document.getElementById('tracking-state'),
@@ -124,9 +156,11 @@ const els = {
 };
 
 let graph = null;
+let radialGraph = null;
 let gallery = null;
 let qualityGate = null;
 let baselineCollector = null;
+let measurementCollector = null;
 let spikeDetector = null;
 let frameBuffer = null;
 
@@ -161,7 +195,67 @@ function setTrackingState(state) {
  */
 function setDistanceValue(value) {
   if (!els.distanceValue) return;
-  els.distanceValue.textContent = Number.isFinite(value) ? value.toFixed(3) : '0.000';
+  els.distanceValue.textContent = Number.isFinite(value) ? value.toFixed(3) : '—';
+}
+
+/**
+ * Computes signed per-coordinate descriptor deltas.
+ *
+ * @param {Float32Array|number[]} baseline Baseline descriptor.
+ * @param {Float32Array|number[]} current Current descriptor.
+ * @returns {Float32Array} Signed deltas in descriptor order.
+ */
+function descriptorDelta(baseline, current) {
+  const delta = new Float32Array(DESCRIPTOR_SIZE);
+  for (let index = 0; index < DESCRIPTOR_SIZE; index += 1) {
+    delta[index] = current[index] - baseline[index];
+  }
+  return delta;
+}
+
+/** Returns a compact local-video time suffix for timeline events. */
+function mediaTimeSuffix() {
+  if (runtime.sourceKind !== 'video' || !Number.isFinite(els.video.currentTime)) return '';
+  const total = Math.max(0, els.video.currentTime);
+  const minutes = Math.floor(total / 60);
+  const seconds = (total % 60).toFixed(1).padStart(4, '0');
+  return ` · ${minutes}:${seconds}`;
+}
+
+/** Reflects the declared experiment phase in the workflow controls. */
+function syncSessionControls() {
+  const hasBaseline = Boolean(runtime.baseline);
+  els.sessionBar.hidden = !hasBaseline;
+  els.btnStartChange.disabled = !hasBaseline || runtime.sessionPhase === SESSION_PHASES.MEASURING;
+  els.btnMeasureChange.disabled = runtime.sessionPhase !== SESSION_PHASES.APPLYING;
+
+  if (!hasBaseline) {
+    els.sessionStep.textContent = 'Ready';
+    els.sessionResult.textContent = 'Capture a clean baseline first.';
+    return;
+  }
+
+  if (runtime.sessionPhase === SESSION_PHASES.APPLYING) {
+    els.sessionStep.textContent = `Step ${runtime.sessionStep + 1} · applying`;
+    els.sessionResult.textContent = 'Live readings continue, but this interval is marked as application time.';
+    els.btnStartChange.textContent = 'Applying…';
+  } else if (runtime.sessionPhase === SESSION_PHASES.MEASURING) {
+    els.sessionStep.textContent = `Step ${runtime.sessionStep + 1} · measuring`;
+    els.sessionResult.textContent = 'Collecting a comparable temporal window…';
+    els.btnStartChange.textContent = 'Start makeup step';
+  } else {
+    els.sessionStep.textContent = runtime.sessionStep ? `Step ${runtime.sessionStep} measured` : 'Ready';
+    els.sessionResult.textContent = runtime.lastMeasurement
+      ? `Snapshot d=${runtime.lastMeasurement.distance.toFixed(3)} · spread ±${runtime.lastMeasurement.spread.toFixed(3)}`
+      : 'Mark when you begin applying a change.';
+    els.btnStartChange.textContent = runtime.sessionStep ? 'Start next step' : 'Start makeup step';
+  }
+}
+
+/** Changes the declared experiment phase and redraws its controls. */
+function setSessionPhase(phase) {
+  runtime.sessionPhase = phase;
+  syncSessionControls();
 }
 
 /**
@@ -222,6 +316,7 @@ function updateEqualizer(baseline, current) {
     }
     runtime.equalizerLit[i] = lit;
   }
+  if (radialGraph) radialGraph.setLive(descriptorDelta(baseline, current));
 }
 
 /**
@@ -235,6 +330,7 @@ function resetEqualizer() {
     for (const cell of cells) cell.classList.remove('is-on');
     runtime.equalizerLit[i] = 0;
   }
+  if (radialGraph) radialGraph.setLive(null);
 }
 
 /**
@@ -279,17 +375,44 @@ function syncBaselineControls() {
     els.legendNoise.textContent = hasBaseline ? runtime.baseline.noiseFloor.toFixed(3) : '—';
   }
   if (graph) graph.setNoiseFloor(hasBaseline ? runtime.baseline.noiseFloor : null);
+  syncSessionControls();
+}
+
+/**
+ * Starts or resumes a selected local video before a declared session action.
+ *
+ * @param {string} failureMessage Status shown when autoplay is rejected.
+ * @returns {Promise<boolean>} Whether the action may continue.
+ */
+async function ensureLocalVideoPlaying(failureMessage) {
+  if (runtime.sourceKind !== 'video' || !els.video.paused) return true;
+  try {
+    if (els.video.ended) els.video.currentTime = 0;
+    await els.video.play();
+    return true;
+  } catch {
+    setStatus(failureMessage, true);
+    return false;
+  }
 }
 
 /**
  * Begins a baseline capture run.
  *
- * @returns {void}
+ * @returns {Promise<void>}
  */
-function startBaselineCapture() {
+async function startBaselineCapture() {
+  const playing = await ensureLocalVideoPlaying(
+    'Play the local video before capturing a baseline.',
+  );
+  if (!playing) return;
   runtime.appState = APP_STATES.CAPTURING;
   runtime.baseline = null;
   runtime.distanceBuffer = [];
+  runtime.comparisonExcluded = false;
+  runtime.sessionStep = 0;
+  runtime.lastMeasurement = null;
+  setSessionPhase(SESSION_PHASES.IDLE);
   clearBaseline();
 
   setTrackingState(APP_STATES.CAPTURING);
@@ -299,6 +422,7 @@ function startBaselineCapture() {
   graph.setNoiseFloor(null);
   spikeDetector.reset();
   frameBuffer.clear();
+  if (radialGraph) radialGraph.reset();
 
   setCalibrationVisible(true);
   if (els.captureRing) els.captureRing.classList.add('is-active');
@@ -338,12 +462,17 @@ function adoptBaseline(baseline, restored = false) {
   runtime.appState = APP_STATES.TRACKING;
   runtime.distanceBuffer = [];
   runtime.faceMissing = false;
+  runtime.comparisonExcluded = false;
+  runtime.sessionStep = 0;
+  runtime.lastMeasurement = null;
+  setSessionPhase(SESSION_PHASES.READY);
 
   setTrackingState(APP_STATES.TRACKING);
   setDistanceValue(0);
   resetEqualizer();
   spikeDetector.reset();
   frameBuffer.clear();
+  if (radialGraph) radialGraph.reset();
 
   if (els.captureRing) els.captureRing.classList.remove('is-active');
   syncBaselineControls();
@@ -364,8 +493,13 @@ function adoptBaseline(baseline, restored = false) {
 function deleteBaseline() {
   runtime.baseline = null;
   runtime.appState = APP_STATES.NO_BASELINE;
+  runtime.comparisonExcluded = false;
+  runtime.sessionStep = 0;
+  runtime.lastMeasurement = null;
+  setSessionPhase(SESSION_PHASES.IDLE);
   clearBaseline();
   baselineCollector.cancel();
+  measurementCollector.cancel();
 
   setTrackingState(APP_STATES.NO_BASELINE);
   setDistanceValue(0);
@@ -373,6 +507,7 @@ function deleteBaseline() {
   graph.reset();
   spikeDetector.reset();
   frameBuffer.clear();
+  if (radialGraph) radialGraph.reset();
 
   if (els.captureRing) els.captureRing.classList.remove('is-active');
   syncBaselineControls();
@@ -410,11 +545,89 @@ function processBaselineFrame(detection) {
     return;
   }
   if (progress.status === 'done') {
-    saveBaseline(progress.baseline);
+    // Camera baselines can be restored against the same physical source. A
+    // local video's blob URL disappears on reload, so persisting that template
+    // would silently compare the next camera session with the wrong source.
+    if (runtime.sourceKind === 'camera') {
+      saveBaseline({ ...progress.baseline, sourceKind: 'camera' });
+    } else {
+      clearBaseline();
+    }
     adoptBaseline(progress.baseline, false);
   }
 }
 
+/** Marks the beginning of one operator-declared makeup change. */
+async function startMakeupStep() {
+  if (!runtime.baseline || runtime.appState !== APP_STATES.TRACKING) return;
+  const playing = await ensureLocalVideoPlaying(
+    'Play the local video before marking the makeup step.',
+  );
+  if (!playing) return;
+  measurementCollector.cancel();
+  frameBuffer.clear();
+  setSessionPhase(SESSION_PHASES.APPLYING);
+  graph.addEvent(
+    Date.now(),
+    SESSION_PHASES.APPLYING,
+    `step ${runtime.sessionStep + 1}: applying${mediaTimeSuffix()}`,
+  );
+  setStatus('Application interval marked. When this piece is finished, measure the result.');
+}
+
+/** Starts a temporal measurement window for the current declared change. */
+async function startStepMeasurement() {
+  if (!runtime.baseline || runtime.sessionPhase !== SESSION_PHASES.APPLYING) return;
+  const playing = await ensureLocalVideoPlaying(
+    'Play the local video before measuring this step.',
+  );
+  if (!playing) return;
+  measurementCollector.start();
+  frameBuffer.clear();
+  setSessionPhase(SESSION_PHASES.MEASURING);
+  graph.addEvent(
+    Date.now(),
+    SESSION_PHASES.MEASURING,
+    `step ${runtime.sessionStep + 1}: measuring${mediaTimeSuffix()}`,
+  );
+  setStatus('Measuring this step — hold the baseline pose and lighting.');
+}
+
+/**
+ * Publishes an aggregated step measurement and saves its sharpest frame.
+ *
+ * @param {object} snapshot Temporal-window result returned by the collector.
+ * @returns {Promise<void>}
+ */
+async function completeStepMeasurement(snapshot) {
+  const distance = euclidean(runtime.baseline.descriptor, snapshot.descriptor);
+  const delta = descriptorDelta(runtime.baseline.descriptor, snapshot.descriptor);
+  runtime.sessionStep += 1;
+  runtime.lastMeasurement = {
+    distance,
+    spread: snapshot.noiseFloor,
+    descriptor: snapshot.descriptor,
+    conditions: snapshot.conditions,
+    capturedAt: snapshot.capturedAt,
+  };
+
+  if (radialGraph) radialGraph.setMeasured(delta);
+  updateEqualizer(runtime.baseline.descriptor, snapshot.descriptor);
+  graph.addEvent(
+    Date.now(),
+    SESSION_PHASES.MEASURING,
+    `step ${runtime.sessionStep}: snapshot d=${distance.toFixed(3)}${mediaTimeSuffix()}`,
+  );
+
+  const best = frameBuffer.best();
+  if (best) await storeCapture(best.canvas, distance, `step-${runtime.sessionStep}`);
+  frameBuffer.clear();
+  setSessionPhase(SESSION_PHASES.READY);
+  setStatus(
+    `Step ${runtime.sessionStep} measured: d=${distance.toFixed(3)}, temporal spread ±${snapshot.noiseFloor.toFixed(3)}.`,
+  );
+}
+
 /**
  * Measures one tracking frame and evaluates it for a spike capture.
  *
@@ -422,11 +635,64 @@ function processBaselineFrame(detection) {
  * @returns {Promise<void>}
  */
 async function processTrackingFrame(detection) {
+  // Makeup may legitimately change detector confidence and the exposure inside
+  // the face crop. Comparability is based on pose, framing and ambient light so
+  // the intervention itself is not filtered away.
+  const quality = qualityGate.evaluate(detection, { allowAppearanceChanges: true });
+  const comparable = quality.ok
+    ? compareConditions(runtime.baseline.conditions, quality.metrics)
+    : { ok: false, reason: quality.reason };
+
+  if (!comparable.ok) {
+    runtime.distanceBuffer = [];
+    runtime.comparisonExcluded = true;
+    resetEqualizer();
+    setDistanceValue(Number.NaN);
+    els.btnShutter.disabled = true;
+    graph.beginGap(Date.now(), 'pose/light excluded');
+
+    if (runtime.sessionPhase === SESSION_PHASES.MEASURING) {
+      const progress = measurementCollector.offer(
+        { ok: false, reason: comparable.reason },
+        detection.descriptor,
+      );
+      els.sessionResult.textContent = `Waiting for comparable frames: ${comparable.reason}`;
+      if (progress.status === 'failed') {
+        measurementCollector.cancel();
+        setSessionPhase(SESSION_PHASES.APPLYING);
+        setStatus(`Measurement paused. ${progress.reason}`, true);
+      }
+    } else {
+      setStatus(`${comparable.reason} This frame is excluded from the measurement.`, true);
+    }
+    return;
+  }
+
+  if (runtime.comparisonExcluded) {
+    runtime.comparisonExcluded = false;
+    graph.endGap(Date.now());
+  }
+  els.btnShutter.disabled = false;
+
   const distance = euclidean(runtime.baseline.descriptor, detection.descriptor);
   runtime.distanceBuffer.push(distance);
   setDistanceValue(distance);
   updateEqualizer(runtime.baseline.descriptor, detection.descriptor);
 
+  if (runtime.sessionPhase === SESSION_PHASES.MEASURING) {
+    const progress = measurementCollector.offer(quality, detection.descriptor);
+    if (quality.ok) frameBuffer.push(quality.sharpness, distance);
+    if (progress.status === 'collecting') {
+      els.sessionResult.textContent = `Comparable samples ${progress.accepted}/${progress.target}`;
+    } else if (progress.status === 'done') {
+      await completeStepMeasurement(progress.baseline);
+    } else if (progress.status === 'failed') {
+      setSessionPhase(SESSION_PHASES.APPLYING);
+      setStatus(`Could not measure this step. ${progress.reason}`, true);
+    }
+    return;
+  }
+
   if (distance > MATCH_THRESHOLD) {
     setStatus(`Past the 0.60 match threshold — reads as a different person.`);
   } else if (distance <= runtime.baseline.noiseFloor) {
@@ -435,12 +701,13 @@ async function processTrackingFrame(detection) {
     setStatus(`Above the noise floor, below 0.60 — still reads as a match.`);
   }
 
+  // Declared application intervals use an explicit temporal snapshot. Automatic
+  // "best" captures remain available only while the experiment is otherwise idle.
+  if (runtime.sessionPhase === SESSION_PHASES.APPLYING) return;
+
   const verdict = spikeDetector.update(distance);
   if (!verdict.candidate) return;
 
-  // The gate's crop analysis only runs while a spike is live, so ordinary
-  // tracking never pays for the getImageData.
-  const quality = qualityGate.evaluate(detection);
   if (quality.ok) frameBuffer.push(quality.sharpness, distance);
 
   if (!verdict.capture) return;
@@ -516,11 +783,16 @@ function processNoFace() {
     runtime.distanceBuffer = [];
     resetEqualizer();
     setDistanceValue(0);
+    if (els.btnShutter) els.btnShutter.disabled = true;
     setStatus('No face — measurement paused.');
   }
 
   if (runtime.appState === APP_STATES.TRACKING) {
-    graph.beginGap(runtime.lastFaceAt);
+    graph.beginGap(runtime.lastFaceAt, 'no face');
+  }
+
+  if (runtime.sessionPhase === SESSION_PHASES.MEASURING) {
+    els.sessionResult.textContent = 'Waiting for the face to return.';
   }
 
   if (runtime.appState === APP_STATES.CAPTURING) {
@@ -555,17 +827,27 @@ async function onFrame() {
   if (!els.video || els.video.readyState < 2) return;
 
   runtime.detectorBusy = true;
+  const sourceEpoch = runtime.sourceEpoch;
   try {
     const detection = await faceapi
       .detectSingleFace(els.video, DETECTOR_OPTIONS)
       .withFaceLandmarks()
       .withFaceDescriptor();
 
+    // A camera detection can still be resolving while a local file is being
+    // selected. Never publish results produced for the previous source.
+    if (sourceEpoch !== runtime.sourceEpoch) return;
+
     if (!detection || !detection.descriptor) {
+      runtime.lastDetectionScore = null;
+      runtime.lastDescriptorLength = 0;
       processNoFace();
       return;
     }
 
+    runtime.lastDetectionScore = detection.detection?.score ?? null;
+    runtime.lastDescriptorLength = detection.descriptor.length;
+
     processFaceReturned();
 
     if (runtime.appState === APP_STATES.CAPTURING) {
@@ -597,7 +879,7 @@ function flushDistanceBuffer() {
   const sum = runtime.distanceBuffer.reduce((acc, value) => acc + value, 0);
   const average = sum / runtime.distanceBuffer.length;
   runtime.distanceBuffer = [];
-  graph.pushSample(Date.now(), average);
+  graph.pushSample(Date.now(), average, runtime.sessionPhase);
 }
 
 /**
@@ -636,11 +918,18 @@ function stopLoops() {
  * @returns {void}
  */
 function stopStream() {
-  if (!runtime.stream) return;
-  for (const track of runtime.stream.getTracks()) track.stop();
+  if (runtime.stream) {
+    for (const track of runtime.stream.getTracks()) track.stop();
+  }
   runtime.stream = null;
   runtime.streamStopped = true;
-  if (els.video) els.video.srcObject = null;
+  if (els.video?.srcObject) els.video.srcObject = null;
+}
+
+/** Releases the object URL used by a local-video source. */
+function releaseLocalVideo() {
+  if (runtime.sourceObjectUrl) URL.revokeObjectURL(runtime.sourceObjectUrl);
+  runtime.sourceObjectUrl = null;
 }
 
 /**
@@ -651,6 +940,17 @@ function stopStream() {
 async function initWebcam() {
   if (!els.video) throw new Error('Missing #webcam element');
 
+  runtime.sourceEpoch += 1;
+  runtime.lastDetectionScore = null;
+  runtime.lastDescriptorLength = 0;
+  releaseLocalVideo();
+  els.video.pause();
+  els.video.removeAttribute('src');
+  els.video.load();
+  els.video.controls = false;
+  els.video.classList.remove('source-video');
+  els.video.classList.add('source-camera');
+
   runtime.stream = await navigator.mediaDevices.getUserMedia({
     video: { facingMode: 'user',
              width: { ideal: 1080 },
@@ -659,8 +959,76 @@ async function initWebcam() {
     audio: false,
   });
   runtime.streamStopped = false;
+  runtime.sourceKind = 'camera';
   els.video.srcObject = runtime.stream;
   await els.video.play();
+  els.sourceState.textContent = 'camera';
+  els.btnUseCamera.hidden = true;
+}
+
+/**
+ * Replaces the camera with a local, seekable video. The file never leaves the
+ * browser; changing source invalidates the source-specific baseline.
+ *
+ * @param {File} file Local video chosen by the operator.
+ * @returns {Promise<void>}
+ */
+async function useLocalVideo(file) {
+  if (!file) return;
+  runtime.sourceEpoch += 1;
+  runtime.lastDetectionScore = null;
+  runtime.lastDescriptorLength = 0;
+  stopLoops();
+  stopStream();
+  releaseLocalVideo();
+  deleteBaseline();
+
+  runtime.sourceKind = 'video';
+  runtime.sourceObjectUrl = URL.createObjectURL(file);
+  els.video.srcObject = null;
+  els.video.src = runtime.sourceObjectUrl;
+  els.video.controls = true;
+  els.video.loop = false;
+  els.video.classList.remove('source-camera');
+  els.video.classList.add('source-video');
+  els.sourceState.textContent = `local video · ${file.name}`;
+  els.btnUseCamera.hidden = false;
+
+  await new Promise((resolve, reject) => {
+    const ready = () => {
+      cleanup();
+      resolve();
+    };
+    const failed = () => {
+      cleanup();
+      reject(new Error('The selected video could not be decoded.'));
+    };
+    const cleanup = () => {
+      els.video.removeEventListener('loadeddata', ready);
+      els.video.removeEventListener('error', failed);
+    };
+    els.video.addEventListener('loadeddata', ready, { once: true });
+    els.video.addEventListener('error', failed, { once: true });
+    els.video.load();
+  });
+
+  startLoops();
+  setStatus('Local video ready. Play or seek it, then capture a clean baseline.');
+}
+
+/** Returns from a local-video experiment to the live camera. */
+async function useCameraSource() {
+  stopLoops();
+  deleteBaseline();
+  setStatus('Connecting the camera…');
+  try {
+    await initWebcam();
+    startLoops();
+    setStatus('Camera ready. Capture a baseline to start measuring.');
+  } catch (err) {
+    console.error('failed to switch to camera:', err);
+    setStatus('Camera unavailable. You can still open a local video.', true);
+  }
 }
 
 /**
@@ -710,15 +1078,15 @@ async function handleVisible() {
   }
 
   try {
-    if (runtime.streamStopped) {
+    if (runtime.sourceKind === 'camera' && runtime.streamStopped) {
       setStatus('Reconnecting the camera…');
       await initWebcam();
-    } else if (els.video) {
+    } else if (runtime.sourceKind === 'camera' && els.video) {
       await els.video.play();
     }
     if (runtime.appState === APP_STATES.TRACKING) runtime.lastFaceAt = Date.now();
     startLoops();
-    setStatus('Camera live.');
+    setStatus(runtime.sourceKind === 'camera' ? 'Camera live.' : 'Local video ready.');
   } catch (err) {
     console.error('failed to resume camera:', err);
     setStatus('Could not reconnect the camera.', true);
@@ -736,6 +1104,8 @@ function teardown() {
   if (runtime.hiddenStopTimerId) window.clearTimeout(runtime.hiddenStopTimerId);
   runtime.hiddenStopTimerId = null;
   if (graph) graph.destroy();
+  if (radialGraph) radialGraph.destroy();
+  releaseLocalVideo();
 }
 
 /**
@@ -745,6 +1115,23 @@ function teardown() {
  */
 function bindListeners() {
   els.btnBaselineStart.addEventListener('click', startBaselineCapture);
+  els.btnStartChange.addEventListener('click', startMakeupStep);
+  els.btnMeasureChange.addEventListener('click', startStepMeasurement);
+  els.btnOpenVideo.addEventListener('click', () => els.videoUpload.click());
+
+  els.videoUpload.addEventListener('change', async () => {
+    const [file] = els.videoUpload.files || [];
+    if (!file) return;
+    try {
+      await useLocalVideo(file);
+    } catch (err) {
+      console.error('failed to open local video:', err);
+      setStatus(err.message || 'Could not open the selected video.', true);
+    } finally {
+      els.videoUpload.value = '';
+    }
+  });
+  els.btnUseCamera.addEventListener('click', useCameraSource);
 
   guardDestructive(els.btnBaseline, 'Delete baseline', 'Delete — sure?', deleteBaseline);
   guardDestructive(els.btnDeleteAll, 'Delete all', 'Delete all — sure?', () => gallery.deleteAll());
@@ -781,7 +1168,7 @@ window.gstmxxRealtime = {
    * @returns {object} State snapshot.
    */
   getState() {
-    const stats = graph ? graph.stats() : { samples: 0, gaps: 0, following: true };
+    const stats = graph ? graph.stats() : { samples: 0, gaps: 0, events: 0, following: true };
     return {
       appState: runtime.appState,
       hasBaseline: Boolean(runtime.baseline),
@@ -793,10 +1180,23 @@ window.gstmxxRealtime = {
       distanceBufferLength: runtime.distanceBuffer.length,
       sampleCount: stats.samples,
       gapCount: stats.gaps,
+      eventCount: stats.events,
       following: stats.following,
       captureCount: gallery ? gallery.size() : 0,
       running: runtime.running,
       streamStopped: runtime.streamStopped,
+      sourceKind: runtime.sourceKind,
+      lastDetectionScore: runtime.lastDetectionScore,
+      lastDescriptorLength: runtime.lastDescriptorLength,
+      sessionPhase: runtime.sessionPhase,
+      sessionStep: runtime.sessionStep,
+      lastMeasurement: runtime.lastMeasurement
+        ? {
+            distance: runtime.lastMeasurement.distance,
+            spread: runtime.lastMeasurement.spread,
+            capturedAt: runtime.lastMeasurement.capturedAt,
+          }
+        : null,
     };
   },
   /**
@@ -807,6 +1207,10 @@ window.gstmxxRealtime = {
   triggerCalibration() {
     startBaselineCapture();
   },
+  /** Marks the beginning of a declared makeup step. */
+  startMakeupStep,
+  /** Starts the temporal snapshot for the declared makeup step. */
+  measureStep: startStepMeasurement,
   /**
    * Releases camera and loops.
    *
@@ -846,6 +1250,11 @@ export async function initRealtime() {
     onMarkerClick: (captureId) => gallery.open(captureId),
   });
 
+  radialGraph = createRadialGraph({
+    canvasEl: els.radialCanvas,
+    maxDelta: MAX_DELTA,
+  });
+
   gallery = createGallery({
     viewerEl: els.viewer,
     imageEl: els.viewerImage,
@@ -878,6 +1287,12 @@ export async function initRealtime() {
     timeoutMs: BASELINE_TIMEOUT_MS,
     maxSpread: BASELINE_MAX_SPREAD,
   });
+  measurementCollector = createBaselineCollector({
+    targetSamples: MEASUREMENT_TARGET_SAMPLES,
+    minDurationMs: MEASUREMENT_MIN_MS,
+    timeoutMs: MEASUREMENT_TIMEOUT_MS,
+    maxSpread: MEASUREMENT_MAX_SPREAD,
+  });
   spikeDetector = createSpikeDetector({
     sustainMs: SPIKE_SUSTAIN_MS,
     bestMargin: SPIKE_BEST_MARGIN,
@@ -897,16 +1312,25 @@ export async function initRealtime() {
   }
 
   await loadModels();
-  await initWebcam();
+  let sourceReady = false;
+  try {
+    await initWebcam();
+    sourceReady = true;
+  } catch (err) {
+    runtime.streamStopped = true;
+    console.error('camera unavailable:', err);
+    els.sourceState.textContent = 'camera unavailable';
+    setStatus('Camera unavailable. Open a local video from Source.', true);
+  }
 
   // A stored baseline skips straight to tracking. The trace itself is not
   // restored: replaying old history and appending new samples would draw one
   // continuous line across a discontinuity that could be days wide.
-  const stored = loadBaseline();
+  const stored = sourceReady ? loadBaseline() : null;
   if (stored) adoptBaseline(stored, true);
   else {
     syncBaselineControls();
-    setStatus('Camera ready. Capture a baseline to start measuring.');
+    if (sourceReady) setStatus('Camera ready. Capture a baseline to start measuring.');
   }
 
   startLoops();
diff --git a/realtime.html b/realtime.html
index b09ddf9..57bf6f0 100644
--- a/realtime.html
+++ b/realtime.html
@@ -75,13 +75,13 @@
       </div>
     </section>
 
-    <section id="center-view" aria-label="Webcam, baseline capture and controls">
-      <video id="webcam" autoplay muted playsinline></video>
+    <section id="center-view" aria-label="Camera or local video, baseline capture and controls">
+      <video id="webcam" class="source-camera" autoplay muted playsinline></video>
       <div class="video-scrim"></div>
       <div id="status-pill" aria-live="polite">Loading models…</div>
 
       <div id="calibration-ui">
-        <div id="capture-ring" class="capture-ring" role="progressbar" aria-valuemin="0" aria-valuemax="12" aria-valuenow="0" aria-label="Baseline samples collected">
+        <div id="capture-ring" class="capture-ring" role="progressbar" aria-valuemin="0" aria-valuemax="8" aria-valuenow="0" aria-label="Baseline samples collected">
           <svg viewBox="0 0 116 116" aria-hidden="true">
             <circle class="capture-ring__track" cx="58" cy="58" r="54" />
             <circle class="capture-ring__arc" id="capture-ring-arc" cx="58" cy="58" r="54" />
@@ -105,9 +105,32 @@
         </div>
       </div>
 
+      <div id="session-bar" class="sessionbar" aria-label="Makeup experiment phases" hidden>
+        <div class="sessionbar__state">
+          <span class="sb-label">Experiment</span>
+          <strong id="session-step">Ready</strong>
+          <span id="session-result">Mark when you begin applying a change.</span>
+        </div>
+        <button id="btn-start-change" class="sb-btn" type="button" disabled>Start makeup step</button>
+        <button id="btn-measure-change" class="sb-btn sessionbar__measure" type="button" disabled>Measure this step</button>
+      </div>
+
       <div id="settings-bar" class="settingsbar" aria-label="View and capture controls">
         <div class="sb-group">
-          <label class="sb-label" for="brightness-input">View</label>
+          <details class="source-menu">
+            <summary class="sb-btn">Source</summary>
+            <div class="source-menu__popover">
+              <span class="source-menu__state">Using <strong id="source-state">camera</strong></span>
+              <button id="btn-open-video" class="sb-btn" type="button">Open local video</button>
+              <input id="video-upload" class="source-file" type="file" accept="video/*,.mkv" />
+              <button id="btn-use-camera" class="sb-btn" type="button" hidden>Use camera</button>
+              <span class="source-menu__note">The file stays on this device.</span>
+            </div>
+          </details>
+        </div>
+
+        <div class="sb-group">
+          <label class="sb-label" for="brightness-input">Display</label>
           <input
             id="brightness-input"
             type="range"
@@ -152,8 +175,18 @@
         <p class="panel-title">Descriptor Delta</p>
         <p class="panel-value"><span id="tracking-state">NO BASELINE</span></p>
       </header>
-      <div class="panel-body">
-        <div id="embedding-bar" aria-label="128 descriptor rows"></div>
+      <div class="panel-body descriptor-body">
+        <section class="radial-panel" aria-label="All 128 descriptor deltas as a radial fingerprint">
+          <canvas id="radial-graph"></canvas>
+          <div class="radial-legend">
+            <span><i class="radial-live"></i>live</span>
+            <span><i class="radial-measured"></i>measured step</span>
+          </div>
+          <p>128 latent coordinates; not facial regions.</p>
+        </section>
+        <div class="equalizer-panel">
+          <div id="embedding-bar" aria-label="128 descriptor rows"></div>
+        </div>
       </div>
     </section>
 
diff --git a/styles/realtime.css b/styles/realtime.css
index e46698b..d371bc4 100644
--- a/styles/realtime.css
+++ b/styles/realtime.css
@@ -57,6 +57,12 @@
   --rt-delta-low: var(--faint);
   --rt-delta-med: var(--yellow);
   --rt-delta-high: var(--gm-bg);
+  --rt-radial-live: var(--cream);
+  --rt-radial-measured: var(--gm-bg);
+  --rt-radial-measured-fill: rgba(242, 106, 27, 0.25);
+  --rt-phase-applying: rgba(242, 106, 27, 0.13);
+  --rt-phase-measuring: rgba(127, 227, 176, 0.13);
+  --rt-event: var(--yellow);
 
   --rt-surface: rgba(12, 9, 6, 0.52);
   --rt-surface-solid: rgba(12, 9, 6, 0.86);
@@ -67,6 +73,8 @@
   --rt-brightness: 0.85;
 
   --rt-bar-h: 44px;
+  --rt-session-h: 54px;
+  --rt-controls-stack: calc(var(--rt-bar-h) + var(--rt-session-h));
   --rt-gutter: 24px;
 }
 
@@ -284,10 +292,19 @@
   width: 100%;
   height: 100%;
   object-fit: cover;
-  transform: scaleX(-1);
   filter: brightness(var(--rt-brightness));
 }
 
+#webcam.source-camera {
+  transform: scaleX(-1);
+}
+
+#webcam.source-video {
+  height: calc(100% - var(--rt-controls-stack));
+  object-fit: contain;
+  transform: none;
+}
+
 /* Scrim recedes as exposure rises, so turning the view up actually turns it
    up. Negative opacity clamps to 0 in every engine. */
 .video-scrim {
@@ -336,7 +353,7 @@
 
 #calibration-ui {
   position: absolute;
-  inset: 0 0 var(--rt-bar-h) 0;
+  inset: 0 0 var(--rt-controls-stack) 0;
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -444,6 +461,55 @@
 
 /* --- Settings bar --------------------------------------------------------- */
 
+.sessionbar {
+  position: absolute;
+  left: 0;
+  right: 0;
+  bottom: var(--rt-bar-h);
+  z-index: 6;
+  min-height: var(--rt-session-h);
+  display: flex;
+  align-items: center;
+  gap: 10px;
+  padding: 7px 12px;
+  border-top: 1px solid var(--line-soft);
+  background: rgba(12, 9, 6, 0.82);
+  backdrop-filter: blur(10px);
+}
+
+.sessionbar[hidden] {
+  display: none;
+}
+
+.sessionbar__state {
+  flex: 1;
+  min-width: 0;
+  display: grid;
+  grid-template-columns: auto 1fr;
+  gap: 2px 8px;
+  align-items: baseline;
+}
+
+.sessionbar__state strong {
+  color: var(--yellow);
+  font: 600 11px var(--mono);
+  text-transform: uppercase;
+}
+
+.sessionbar__state > span:last-child {
+  grid-column: 1 / -1;
+  overflow: hidden;
+  color: var(--muted);
+  font: 10px/1.35 var(--mono);
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+.sessionbar__measure {
+  border-color: rgba(127, 227, 176, 0.45);
+  color: var(--dev);
+}
+
 .settingsbar {
   position: absolute;
   left: 0;
@@ -506,6 +572,61 @@
   cursor: pointer;
 }
 
+.sb-btn[hidden] {
+  display: none;
+}
+
+.source-menu {
+  position: relative;
+}
+
+.source-menu > summary {
+  list-style: none;
+}
+
+.source-menu > summary::-webkit-details-marker {
+  display: none;
+}
+
+.source-menu[open] > summary {
+  background: rgba(33, 26, 18, 0.86);
+}
+
+.source-menu__popover {
+  position: absolute;
+  left: 0;
+  bottom: calc(100% + 10px);
+  z-index: 12;
+  width: 210px;
+  display: grid;
+  gap: 8px;
+  padding: 10px;
+  border: 1px solid var(--line);
+  border-radius: 10px;
+  background: var(--rt-surface-solid);
+  box-shadow: 0 10px 28px rgba(0, 0, 0, 0.4);
+}
+
+.source-menu__state,
+.source-menu__note {
+  color: var(--muted);
+  font: 10px/1.4 var(--mono);
+}
+
+.source-menu__state strong {
+  color: var(--fg);
+}
+
+.source-file {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  overflow: hidden;
+  clip: rect(0 0 0 0);
+  clip-path: inset(50%);
+  white-space: nowrap;
+}
+
 .sb-btn:hover:not(:disabled) {
   border-color: rgba(255, 231, 168, 0.26);
   background: rgba(33, 26, 18, 0.7);
@@ -599,6 +720,64 @@
    a steady face writes nothing to the DOM. The prototype rewrote all 1,280
    className strings on every detection frame. */
 
+.descriptor-body {
+  display: grid;
+  grid-template-rows: minmax(150px, 0.45fr) minmax(0, 1fr);
+}
+
+.radial-panel {
+  min-height: 0;
+  display: grid;
+  grid-template-rows: minmax(0, 1fr) auto auto;
+  padding: 8px 8px 6px;
+  border-bottom: 1px solid var(--line-soft);
+}
+
+#radial-graph {
+  width: 100%;
+  height: 100%;
+  min-height: 120px;
+}
+
+.radial-legend {
+  display: flex;
+  justify-content: center;
+  gap: 10px;
+  color: var(--muted);
+  font: 8.5px var(--mono);
+}
+
+.radial-legend span {
+  display: inline-flex;
+  align-items: center;
+  gap: 4px;
+}
+
+.radial-legend i {
+  width: 12px;
+  height: 2px;
+}
+
+.radial-live {
+  background: var(--rt-radial-live);
+}
+
+.radial-measured {
+  background: var(--rt-radial-measured);
+}
+
+.radial-panel p {
+  margin: 4px 0 0;
+  color: var(--faint);
+  font: 8.5px/1.35 var(--mono);
+  text-align: center;
+}
+
+.equalizer-panel {
+  position: relative;
+  min-height: 0;
+}
+
 #embedding-bar {
   position: absolute;
   inset: 0;
@@ -655,12 +834,14 @@
 
   #ui-layer {
     --rt-bar-h: 52px;
+    --rt-session-h: 66px;
   }
 
   .settingsbar {
-    flex-wrap: wrap;
+    flex-wrap: nowrap;
     gap: 8px 12px;
     padding: 7px 10px;
+    overflow-x: auto;
   }
 
   .sb-spacer {
@@ -671,6 +852,30 @@
     width: 96px;
     height: 96px;
   }
+
+  .sessionbar {
+    align-items: stretch;
+    flex-wrap: wrap;
+    gap: 5px 8px;
+  }
+
+  .sessionbar__state {
+    flex-basis: 100%;
+  }
+
+  .descriptor-body {
+    grid-template-columns: 180px minmax(0, 1fr);
+    grid-template-rows: minmax(0, 1fr);
+  }
+
+  .radial-panel {
+    border-right: 1px solid var(--line-soft);
+    border-bottom: 0;
+  }
+
+  #radial-graph {
+    min-height: 110px;
+  }
 }
 
 @media (pointer: coarse) {
diff --git a/tests/e2e/realtime.spec.js b/tests/e2e/realtime.spec.js
index 3959922..2633597 100644
--- a/tests/e2e/realtime.spec.js
+++ b/tests/e2e/realtime.spec.js
@@ -1,20 +1,89 @@
 import { test, expect } from '@playwright/test';
+import path from 'node:path';
 
 /**
- * These run without a camera. getUserMedia rejects in headless Chromium unless
- * --use-fake-device-for-media-stream is set, so anything past initWebcam() is
- * out of scope here: the page is asserted up to the point where it needs a
- * camera, plus the pure functions that do not.
+ * The default project uses a Y4M fake camera. The workflow test additionally
+ * selects a seekable local video with setInputFiles and deterministic model
+ * output, which exercises the same advanced source a researcher can use.
  */
 
 const MODULE_PATHS = [
-  '/lab-js/realtime.js',
-  '/lab-js/realtime-graph.js',
-  '/lab-js/realtime-capture.js',
-  '/lab-js/realtime-gallery.js',
-  '/lab-js/realtime-settings.js',
+  '/lab-js/realtime/realtime.js',
+  '/lab-js/realtime/realtime-graph.js',
+  '/lab-js/realtime/realtime-capture.js',
+  '/lab-js/realtime/realtime-gallery.js',
+  '/lab-js/realtime/realtime-radial.js',
+  '/lab-js/realtime/realtime-settings.js',
 ];
 
+async function mockRealtimeModels(page) {
+  await page.addInitScript(() => {
+    window.__rtDescriptorShift = 0;
+    window.__rtAppearanceChange = false;
+    window.__rtPoseShift = 0;
+    window.__rtImageReads = 0;
+    const originalGetImageData = CanvasRenderingContext2D.prototype.getImageData;
+    CanvasRenderingContext2D.prototype.getImageData = function getImageData(x, y, width, height) {
+      if (!width || !height) return originalGetImageData.call(this, x, y, width, height);
+      const data = new Uint8ClampedArray(width * height * 4);
+      window.__rtImageReads += 1;
+      // Each quality pass reads ambient pixels first and the face crop second.
+      // During the intervention only the face is darkened; ambient light stays
+      // comparable, so the appearance change must not be filtered away.
+      const value = window.__rtAppearanceChange && window.__rtImageReads % 2 === 0 ? 5 : 128;
+      for (let offset = 0; offset < data.length; offset += 4) {
+        data[offset] = value;
+        data[offset + 1] = value;
+        data[offset + 2] = value;
+        data[offset + 3] = 255;
+      }
+      return { data, width, height };
+    };
+  });
+  await page.route('/lab-js/vendor/face-api.js', route => {
+    route.fulfill({
+      contentType: 'application/javascript',
+      body: `
+        (() => {
+          window.faceapi = {
+            TinyFaceDetectorOptions: class TinyFaceDetectorOptions { constructor(options) { this.options = options; } },
+            nets: {
+              tinyFaceDetector: { loadFromUri: async () => {} },
+              faceLandmark68Net: { loadFromUri: async () => {} },
+              faceRecognitionNet: { loadFromUri: async () => {} },
+            },
+            detectSingleFace: input => {
+              const width = input.videoWidth || 640;
+              const height = input.videoHeight || 480;
+              const faceSize = width * 0.5;
+              const positions = Array.from({ length: 68 }, () => ({ x: width * 0.5, y: height * 0.42 }));
+              positions[36] = { x: width * 0.4, y: height * 0.4 };
+              positions[45] = { x: width * 0.6, y: height * 0.4 };
+              positions[30] = { x: width * (0.5 + (window.__rtPoseShift || 0)), y: height * 0.45 };
+              return {
+                withFaceLandmarks: () => ({
+                  withFaceDescriptor: async () => {
+                  const descriptor = new Float32Array(128);
+                  descriptor[0] = window.__rtDescriptorShift || 0;
+                  return {
+                    landmarks: { positions },
+                    descriptor,
+                    detection: {
+                      score: window.__rtAppearanceChange ? 0.4 : 0.99,
+                      box: { x: width * 0.25, y: height * 0.22, width: faceSize, height: faceSize },
+                    },
+                  };
+                  },
+                }),
+              };
+            },
+          };
+        })();
+      `,
+    });
+  });
+}
+
 test.describe('Realtime page', () => {
   test('loads html, css and every module without 404 responses', async ({ page }) => {
     const targetPaths = new Set(['/realtime.html', '/styles/realtime.css', ...MODULE_PATHS]);
@@ -51,6 +120,8 @@ test.describe('Realtime page', () => {
       triggerCalibration: typeof window.gstmxxRealtime.triggerCalibration,
       teardown: typeof window.gstmxxRealtime.teardown,
       resume: typeof window.gstmxxRealtime.resume,
+      startMakeupStep: typeof window.gstmxxRealtime.startMakeupStep,
+      measureStep: typeof window.gstmxxRealtime.measureStep,
     }));
 
     expect(shape).toEqual({
@@ -58,6 +129,8 @@ test.describe('Realtime page', () => {
       triggerCalibration: 'function',
       teardown: 'function',
       resume: 'function',
+      startMakeupStep: 'function',
+      measureStep: 'function',
     });
   });
 
@@ -160,7 +233,7 @@ test.describe('Realtime page', () => {
     await page.goto('/realtime.html');
 
     const result = await page.evaluate(async () => {
-      const { medoid, euclidean } = await import('/lab-js/realtime-capture.js');
+      const { medoid, euclidean } = await import('/lab-js/realtime/realtime-capture.js');
       const tight = [
         [0, 0, 0],
         [0.01, 0, 0],
@@ -185,7 +258,7 @@ test.describe('Realtime page', () => {
     await page.goto('/realtime.html');
 
     const header = await page.evaluate(async () => {
-      const { buildZip } = await import('/lab-js/realtime-gallery.js');
+      const { buildZip } = await import('/lab-js/realtime/realtime-gallery.js');
       const blob = buildZip([
         { name: 'a.jpg', bytes: new Uint8Array([1, 2, 3]), date: new Date(2026, 0, 1) },
       ]);
@@ -201,10 +274,116 @@ test.describe('Realtime page', () => {
     await page.goto('/realtime.html');
 
     const labels = await page.evaluate(async () => {
-      const { formatDuration } = await import('/lab-js/realtime-graph.js');
+      const { formatDuration } = await import('/lab-js/realtime/realtime-graph.js');
       return [formatDuration(4000), formatDuration(492000), formatDuration(7500000)];
     });
 
     expect(labels).toEqual(['4s', '8m 12s', '2h 5m']);
   });
+
+  test('extracts a real 128D face-api descriptor from an uploaded video', async ({ page }) => {
+    await page.goto('/realtime.html');
+    await expect.poll(() => page.evaluate(() => window.gstmxxRealtime?.getState().running)).toBe(true);
+
+    await page.setInputFiles(
+      '#video-upload',
+      path.join(process.cwd(), 'tests/fixtures/my-moving-face.mp4'),
+    );
+    await expect.poll(() => page.evaluate(() => window.gstmxxRealtime.getState().sourceKind)).toBe('video');
+    await expect.poll(
+      () => page.evaluate(() => {
+        const state = window.gstmxxRealtime.getState();
+        return { descriptorLength: state.lastDescriptorLength, score: state.lastDetectionScore };
+      }),
+      { timeout: 45000 },
+    ).toEqual({ descriptorLength: 128, score: expect.any(Number) });
+    expect(await page.evaluate(() => window.gstmxxRealtime.getState().lastDetectionScore)).toBeGreaterThan(0.5);
+  });
+
+  test('measures a declared makeup step from a seekable local video', async ({ page }) => {
+    await mockRealtimeModels(page);
+    await page.goto('/realtime.html');
+    await expect.poll(() => page.evaluate(() => window.gstmxxRealtime?.getState().running)).toBe(true);
+
+    await page.setInputFiles(
+      '#video-upload',
+      path.join(process.cwd(), 'tests/fixtures/my-moving-face.mp4'),
+    );
+    await expect.poll(() => page.evaluate(() => window.gstmxxRealtime.getState().sourceKind)).toBe('video');
+    await expect(page.locator('#webcam')).toHaveJSProperty('controls', true);
+
+    await page.locator('#btn-baseline-start').click();
+    await expect.poll(
+      () => page.evaluate(() => window.gstmxxRealtime.getState().appState),
+      { timeout: 20000 },
+    ).toBe('TRACKING');
+
+    await expect.poll(
+      () => page.evaluate(() => window.gstmxxRealtime.getState().sampleCount),
+    ).toBeGreaterThan(0);
+    const pausedAt = await page.evaluate(() => {
+      const video = document.getElementById('webcam');
+      video.pause();
+      return video.currentTime;
+    });
+    await page.locator('#btn-start-change').click();
+    await expect(page.locator('#webcam')).toHaveJSProperty('paused', false);
+    await expect.poll(
+      () => page.evaluate(() => document.getElementById('webcam').currentTime),
+    ).toBeGreaterThan(pausedAt + 0.05);
+    const applyingSampleCount = await page.evaluate(
+      () => window.gstmxxRealtime.getState().sampleCount,
+    );
+    await expect.poll(
+      () => page.evaluate(() => window.gstmxxRealtime.getState().sampleCount),
+    ).toBeGreaterThan(applyingSampleCount);
+
+    await page.evaluate(() => { window.__rtPoseShift = 0.02; });
+    await page.locator('#btn-measure-change').click();
+    await expect(page.locator('#distance-value')).toHaveText('—');
+    await expect(page.locator('#session-result')).toContainText('baseline pose');
+
+    await page.evaluate(() => {
+      window.__rtPoseShift = 0;
+      window.__rtDescriptorShift = 0.2;
+      window.__rtAppearanceChange = true;
+    });
+
+    await expect.poll(
+      () => page.evaluate(() => {
+        const state = window.gstmxxRealtime.getState();
+        return { step: state.sessionStep, phase: state.sessionPhase, captures: state.captureCount };
+      }),
+      { timeout: 20000 },
+    ).toEqual({ step: 1, phase: 'ready', captures: 1 });
+    const state = await page.evaluate(() => window.gstmxxRealtime.getState());
+    expect(state.sessionPhase).toBe('ready');
+    expect(state.lastMeasurement.distance).toBeCloseTo(0.2, 3);
+    expect(state.lastMeasurement.spread).toBeCloseTo(0, 3);
+    expect(state.eventCount).toBeGreaterThanOrEqual(3);
+    expect(state.captureCount).toBe(1);
+    await expect(page.locator('#session-result')).toContainText('Snapshot d=0.200');
+    await expect(page.locator('#radial-graph')).toBeVisible();
+
+    // Local-video baselines are intentionally session-only: after reload the
+    // original blob is gone and must never be confused with the camera source.
+    await page.reload();
+    await expect.poll(() => page.evaluate(() => window.gstmxxRealtime?.getState().running)).toBe(true);
+    expect(await page.evaluate(() => window.gstmxxRealtime.getState().hasBaseline)).toBe(false);
+  });
+
+  test('keeps local-video analysis available when camera permission fails', async ({ page }) => {
+    await mockRealtimeModels(page);
+    await page.addInitScript(() => {
+      Object.defineProperty(navigator, 'mediaDevices', {
+        configurable: true,
+        value: { getUserMedia: async () => { throw new Error('permission denied'); } },
+      });
+    });
+    await page.goto('/realtime.html');
+    await expect(page.locator('#status-pill')).toContainText('Camera unavailable');
+    await expect(page.locator('#video-upload')).toBeAttached();
+    await expect.poll(() => page.evaluate(() => window.gstmxxRealtime.getState().running)).toBe(true);
+    await expect(page.locator('#btn-baseline-start')).toBeVisible();
+  });
 });
diff --git a/tests/unit/realtime-capture.test.js b/tests/unit/realtime-capture.test.js
new file mode 100644
index 0000000..3c024a1
--- /dev/null
+++ b/tests/unit/realtime-capture.test.js
@@ -0,0 +1,133 @@
+import { afterEach, describe, expect, it, vi } from 'vitest';
+import {
+  compareConditions,
+  createBaselineCollector,
+  createQualityGate,
+  euclidean,
+  medoid,
+  summarizeConditions,
+} from '../../lab-js/realtime/realtime-capture.js';
+
+describe('realtime temporal measurement helpers', () => {
+  afterEach(() => vi.useRealTimers());
+
+  it('uses a real descriptor as medoid and reports Euclidean spread', () => {
+    const descriptors = [[0, 0], [0.1, 0], [0.2, 0]];
+    const result = medoid(descriptors);
+    expect(result.descriptor).toEqual([0.1, 0]);
+    expect(result.spread).toBeCloseTo(0.1);
+    expect(euclidean(result.descriptor, descriptors[0])).toBeCloseTo(0.1);
+  });
+
+  it('averages accepted 2D capture conditions', () => {
+    const profile = summarizeConditions([
+      { rollDegrees: -2, yawOffset: -0.02, boxWidthRatio: 0.4, centerXRatio: 0.48, centerYRatio: 0.5, luma: 0.4 },
+      { rollDegrees: 2, yawOffset: 0.02, boxWidthRatio: 0.5, centerXRatio: 0.52, centerYRatio: 0.6, luma: 0.6 },
+    ]);
+    expect(profile).toEqual({
+      rollDegrees: 0,
+      yawOffset: 0,
+      boxWidthRatio: 0.45,
+      centerXRatio: 0.5,
+      centerYRatio: 0.55,
+      luma: 0.5,
+    });
+  });
+
+  it('excludes pose and light changes but accepts comparable frames', () => {
+    const baseline = {
+      rollDegrees: 0,
+      yawOffset: 0,
+      boxWidthRatio: 0.45,
+      centerXRatio: 0.5,
+      centerYRatio: 0.5,
+      luma: 0.5,
+    };
+    expect(compareConditions(baseline, { ...baseline, yawOffset: 0.03 }).ok).toBe(true);
+    expect(compareConditions(baseline, { ...baseline, rollDegrees: 7 })).toMatchObject({
+      ok: false,
+      reason: 'Return to the baseline pose and framing.',
+    });
+    expect(compareConditions(baseline, { ...baseline, luma: 0.7 })).toMatchObject({
+      ok: false,
+      reason: 'Lighting differs too much from the baseline.',
+    });
+  });
+
+  it('builds a temporal medoid and condition profile after time and sample requirements', () => {
+    vi.useFakeTimers();
+    vi.setSystemTime(new Date('2026-10-12T10:00:00Z'));
+    const collector = createBaselineCollector({
+      targetSamples: 3,
+      minDurationMs: 1000,
+      timeoutMs: 5000,
+      maxSpread: 0.2,
+    });
+    const metrics = {
+      rollDegrees: 0,
+      yawOffset: 0,
+      boxWidthRatio: 0.4,
+      centerXRatio: 0.5,
+      centerYRatio: 0.5,
+      luma: 0.5,
+    };
+    collector.start();
+    expect(collector.offer({ ok: true, reason: '', metrics }, [0, 0]).status).toBe('collecting');
+    vi.advanceTimersByTime(500);
+    expect(collector.offer({ ok: true, reason: '', metrics }, [0.01, 0]).status).toBe('collecting');
+    vi.advanceTimersByTime(500);
+    const result = collector.offer({ ok: true, reason: '', metrics }, [0.02, 0]);
+    expect(result.status).toBe('done');
+    expect(result.baseline.descriptor).toEqual([0.01, 0]);
+    for (const [key, value] of Object.entries(metrics)) {
+      expect(result.baseline.conditions[key]).toBeCloseTo(value);
+    }
+  });
+
+  it('keeps post-baseline appearance changes measurable', () => {
+    const getContext = HTMLCanvasElement.prototype.getContext;
+    const previous = getContext.getMockImplementation();
+    const pixels = new Uint8ClampedArray(96 * 96 * 4);
+    for (let offset = 0; offset < pixels.length; offset += 4) {
+      pixels[offset] = 5;
+      pixels[offset + 1] = 5;
+      pixels[offset + 2] = 5;
+      pixels[offset + 3] = 255;
+    }
+    getContext.mockImplementation(() => ({
+      drawImage: vi.fn(),
+      getImageData: vi.fn(() => ({ data: pixels })),
+    }));
+
+    try {
+      const video = { videoWidth: 640, videoHeight: 480 };
+      const positions = Array.from({ length: 68 }, () => ({ x: 320, y: 220 }));
+      positions[36] = { x: 256, y: 200 };
+      positions[45] = { x: 384, y: 200 };
+      positions[30] = { x: 320, y: 230 };
+      const detection = {
+        detection: {
+          score: 0.4,
+          box: { x: 160, y: 100, width: 320, height: 300 },
+        },
+        landmarks: { positions },
+      };
+      const gate = createQualityGate(video);
+
+      expect(gate.evaluate(detection)).toMatchObject({
+        ok: false,
+        reason: 'Detector confidence is low (0.40). Hold a frontal, steady pose and check glare or shadows.',
+      });
+      expect(gate.evaluate(detection, { allowAppearanceChanges: true })).toMatchObject({
+        ok: true,
+        metrics: {
+          rollDegrees: 0,
+          yawOffset: 0,
+          boxWidthRatio: 0.5,
+        },
+      });
+    } finally {
+      getContext.mockImplementation(previous);
+    }
+  });
+});
diff --git a/tests/unit/realtime-graph.test.js b/tests/unit/realtime-graph.test.js
new file mode 100644
index 0000000..4011936
--- /dev/null
+++ b/tests/unit/realtime-graph.test.js
@@ -0,0 +1,78 @@
+import { afterEach, describe, expect, it, vi } from 'vitest';
+
+import { createGraph } from '../../lab-js/realtime/realtime-graph.js';
+
+describe('realtime timeline phases', () => {
+  afterEach(() => vi.unstubAllGlobals());
+
+  it('counts phase events separately and does not render them as absence gaps', () => {
+    const disconnect = vi.fn();
+    vi.stubGlobal('ResizeObserver', class ResizeObserver {
+      constructor(callback) {
+        this.callback = callback;
+      }
+
+      observe() {
+        this.callback();
+      }
+
+      disconnect() {
+        disconnect();
+      }
+    });
+
+    const ctx = {
+      setTransform: vi.fn(),
+      clearRect: vi.fn(),
+      fillRect: vi.fn(),
+      beginPath: vi.fn(),
+      moveTo: vi.fn(),
+      lineTo: vi.fn(),
+      stroke: vi.fn(),
+      fill: vi.fn(),
+      arc: vi.fn(),
+      closePath: vi.fn(),
+      setLineDash: vi.fn(),
+      save: vi.fn(),
+      restore: vi.fn(),
+      rect: vi.fn(),
+      clip: vi.fn(),
+      measureText: vi.fn(() => ({ width: 40 })),
+      fillText: vi.fn(),
+    };
+    const scroll = document.createElement('div');
+    scroll.getBoundingClientRect = vi.fn(() => ({ width: 260, height: 300 }));
+    const spacer = document.createElement('div');
+    const canvas = document.createElement('canvas');
+    canvas.getContext = vi.fn(() => ctx);
+    canvas.getBoundingClientRect = vi.fn(() => ({ left: 0, top: 0, width: 260, height: 300 }));
+    const follow = document.createElement('button');
+
+    const graph = createGraph({
+      scrollEl: scroll,
+      spacerEl: spacer,
+      canvasEl: canvas,
+      followEl: follow,
+      windowSeconds: 60,
+      samplesPerSecond: 2,
+      maxDistance: 1.25,
+      threshold: 0.6,
+      onMarkerClick: vi.fn(),
+    });
+
+    graph.pushSample(1000, 0.1, 'applying');
+    graph.addEvent(2000, 'measuring', 'step one');
+    graph.pushSample(3000, 0.2, 'measuring');
+    graph.beginGap(4000, 'pose/light excluded');
+    graph.beginGap(4500, 'pose/light excluded');
+    graph.endGap(5000);
+
+    expect(graph.stats()).toEqual({ samples: 2, gaps: 1, events: 1, following: true });
+    const labels = ctx.fillText.mock.calls.map(([label]) => label);
+    expect(labels).toContain('step one');
+    expect(labels.some((label) => String(label).startsWith('step one ·'))).toBe(false);
+
+    graph.destroy();
+    expect(disconnect).toHaveBeenCalledOnce();
+  });
+});
diff --git a/tests/unit/realtime-radial.test.js b/tests/unit/realtime-radial.test.js
new file mode 100644
index 0000000..fdf6a22
--- /dev/null
+++ b/tests/unit/realtime-radial.test.js
@@ -0,0 +1,75 @@
+import { afterEach, describe, expect, it, vi } from 'vitest';
+import { createRadialGraph, radialPoints } from '../../lab-js/realtime/realtime-radial.js';
+
+describe('realtime radial fingerprint', () => {
+  afterEach(() => vi.unstubAllGlobals());
+
+  it('maps every descriptor coordinate in order', () => {
+    const values = new Float32Array(128);
+    values[0] = 0.15;
+    values[32] = -0.075;
+    const points = radialPoints(values, { centerX: 10, centerY: 10, radius: 8, maxValue: 0.15 });
+    expect(points).toHaveLength(128);
+    expect(points[0]).toMatchObject({ index: 0 });
+    expect(points[0].x).toBeCloseTo(10);
+    expect(points[0].y).toBeCloseTo(2);
+    expect(points[0].value).toBeCloseTo(0.15);
+    expect(points[32]).toMatchObject({ index: 32 });
+    expect(points[32].x).toBeCloseTo(14);
+    expect(points[32].y).toBeCloseTo(10);
+    expect(points[32].value).toBeCloseTo(0.075);
+    expect(points[127].index).toBe(127);
+  });
+
+  it('clamps outlying deltas and accepts empty input', () => {
+    const [point] = radialPoints([1], { radius: 5, maxValue: 0.1 });
+    expect(Math.hypot(point.x, point.y)).toBeCloseTo(5);
+    expect(radialPoints([])).toEqual([]);
+  });
+
+  it('draws live and measured 128-coordinate fingerprints and releases its observer', () => {
+    const disconnect = vi.fn();
+    const observe = vi.fn();
+    vi.stubGlobal('ResizeObserver', class ResizeObserver {
+      constructor(callback) {
+        this.callback = callback;
+      }
+
+      observe(element) {
+        observe(element);
+        this.callback();
+      }
+
+      disconnect() {
+        disconnect();
+      }
+    });
+
+    const ctx = {
+      setTransform: vi.fn(),
+      clearRect: vi.fn(),
+      beginPath: vi.fn(),
+      arc: vi.fn(),
+      stroke: vi.fn(),
+      moveTo: vi.fn(),
+      lineTo: vi.fn(),
+      closePath: vi.fn(),
+      fill: vi.fn(),
+    };
+    const canvas = document.createElement('canvas');
+    canvas.getContext = vi.fn(() => ctx);
+    canvas.getBoundingClientRect = vi.fn(() => ({ width: 200, height: 180 }));
+
+    const graph = createRadialGraph({ canvasEl: canvas, maxDelta: 0.2 });
+    graph.setLive(new Float32Array(128).fill(0.05));
+    graph.setMeasured(new Float32Array(128).fill(0.1));
+
+    expect(observe).toHaveBeenCalledWith(canvas);
+    expect(ctx.lineTo).toHaveBeenCalled();
+    expect(ctx.fill).toHaveBeenCalled();
+
+    graph.reset();
+    graph.destroy();
+    expect(disconnect).toHaveBeenCalledOnce();
+  });
+});
diff --git a/tests/unit/realtime-settings.test.js b/tests/unit/realtime-settings.test.js
new file mode 100644
index 0000000..a91af5b
--- /dev/null
+++ b/tests/unit/realtime-settings.test.js
@@ -0,0 +1,68 @@
+import { beforeEach, describe, expect, it } from 'vitest';
+
+import {
+  clearBaseline,
+  loadBaseline,
+  saveBaseline,
+} from '../../lab-js/realtime/realtime-settings.js';
+
+const descriptor = Array.from({ length: 128 }, (_, index) => index / 128);
+const conditions = {
+  rollDegrees: 0,
+  yawOffset: 0.01,
+  boxWidthRatio: 0.4,
+  centerXRatio: 0.5,
+  centerYRatio: 0.5,
+  luma: 0.6,
+};
+
+describe('realtime baseline persistence', () => {
+  beforeEach(() => localStorage.clear());
+
+  it('round-trips a versioned camera baseline with comparison conditions', () => {
+    const baseline = {
+      descriptor,
+      conditions,
+      noiseFloor: 0.01,
+      capturedAt: 1234,
+      sourceKind: 'camera',
+    };
+
+    saveBaseline(baseline);
+
+    expect(loadBaseline()).toMatchObject(baseline);
+  });
+
+  it('rejects old, source-less, video, and condition-less baselines', () => {
+    const key = 'gstmxx.realtime.baseline';
+    const valid = {
+      descriptor,
+      conditions,
+      noiseFloor: 0.01,
+      capturedAt: 1234,
+      sourceKind: 'camera',
+      format: 2,
+    };
+
+    localStorage.setItem(key, JSON.stringify({ ...valid, format: 1 }));
+    expect(loadBaseline()).toBeNull();
+
+    localStorage.setItem(key, JSON.stringify({ ...valid, sourceKind: 'video' }));
+    expect(loadBaseline()).toBeNull();
+
+    localStorage.setItem(key, JSON.stringify({ ...valid, conditions: undefined }));
+    expect(loadBaseline()).toBeNull();
+
+    localStorage.setItem(key, JSON.stringify({ ...valid, sourceKind: undefined }));
+    expect(loadBaseline()).toBeNull();
+
+    localStorage.setItem(key, JSON.stringify({ ...valid, capturedAt: undefined }));
+    expect(loadBaseline()).toBeNull();
+  });
+
+  it('deletes the stored biometric template', () => {
+    saveBaseline({ descriptor, conditions, noiseFloor: 0.01, sourceKind: 'camera' });
+    clearBaseline();
+    expect(loadBaseline()).toBeNull();
+  });
+});

From 8b467c8660cf8ce7a5e96eaf3fbaeaf19b5ffb7c Mon Sep 17 00:00:00 2001
From: vecna <vecna@globaleaks.org>
Date: Wed, 14 Oct 2026 14:41:53 +0200
Subject: [PATCH 8/9] work in progress with realtime latent space visualizer

---
 JSDOC_index.md                                |  25 ++-
 README.md                                     |   3 +-
 about.html                                    |   1 +
 coverage/FOLDER-DESCRIPTION.md                |   7 -
 docs-src/en/develop.body.html                 |   9 +-
 docs-src/en/index.body.html                   |  10 +
 docs-src/en/maintain.body.html                |   1 +
 docs-src/en/pages.json                        |   7 +
 docs-src/en/realtime.body.html                |  88 +++++++++
 fediverse.html                                |   1 +
 genealogy.html                                |   1 +
 index.html                                    |   1 +
 lab-js/realtime/realtime-capture.js           |   4 +-
 lab-js/realtime/realtime-gallery.js           |   8 +-
 lab-js/realtime/realtime-graph.js             |   6 +-
 lab-js/realtime/realtime-settings.js          |   6 +-
 package.json                                  |   4 +-
 references/index.html                         |   1 +
 references/templates/references.template.html |   1 +
 report.html                                   |   1 +
 scripts-dev/README.md                         |   1 +
 scripts-dev/build-functional-docs.cjs         |   1 +
 scripts-dev/build-genealogy.py                |   1 +
 styles/FOLDER-DESCRIPTION.md                  |   9 +-
 .../realtime-fr-method.md                     | 148 +++++++++++----
 tutorials/realtime.md                         | 171 +++++++++++-------
 tutorials/tutorials.json                      |   6 +
 workshops.html                                |   1 +
 28 files changed, 389 insertions(+), 134 deletions(-)
 delete mode 100644 coverage/FOLDER-DESCRIPTION.md
 create mode 100644 docs-src/en/realtime.body.html
 rename realtime-fr-method.md => tutorials/realtime-fr-method.md (85%)

diff --git a/JSDOC_index.md b/JSDOC_index.md
index 21d82b0..bf2bf78 100644
--- a/JSDOC_index.md
+++ b/JSDOC_index.md
@@ -1,7 +1,7 @@
 # Ghostmaxxing technical reference
 
-> Version 0.9.11  
-> Last updated: September 2026  
+> Version 0.9.11
+> Last updated: October 2026
 > Functional documentation: [use the tools and understand the results](/docs/)
 
 Ghostmaxxing is a static browser lab for testing how selected face-analysis models respond to visible interventions. This site is the generated developer reference. It documents runtime modules, functions, parameters, events, and the Ghostyle extension contract.
@@ -12,7 +12,7 @@ If you want to operate an interface, begin with the functional guides. If you ar
 
 | Question | Documentation |
 |---|---|
-| How do I use the Lab, Video Loader, Transfer, or Face Brush? | [Functional guides](/docs/) |
+| How do I use the Lab, Video Loader, Latent Space Visualizer, or Face Brush? | [Functional guides](/docs/) |
 | What does a match state, threshold, descriptor, or landmark mean? | [Understand the results](/docs/understand/) and [technical glossary](/docs/glossary/) |
 | How do I create a Ghostyle? | [Ghostyle Authoring Guide](tutorial-ghostyle-authoring.html), then [`ghostyles/00-template.js`](module-ghostyles_00-template.html) |
 | Which module, function, parameter, or event do I need? | Generated API reference in this site's sidebar |
@@ -34,7 +34,17 @@ Use it when exact media time matters more than a live webcam.
 
 Use it to explore appearance transfer. A visually successful transfer is not evidence that the target image evades recognition.
 
-`realtime.html` is not part of the initial released documentation set.
+### Latent Space Visualizer
+
+`realtime.html` loads `lab-js/realtime/realtime.js`. It captures a temporal 2D
+face-api baseline, filters frames whose 2D pose, framing, or ambient light is not
+comparable, and lets the operator mark and aggregate successive makeup steps.
+The input may be a live camera or a local video. The timeline, equalizer, and
+radial fingerprint are different views of the same descriptor-distance session.
+
+Begin with the [functional guide](/docs/guides/realtime.html), then read the
+[methodology and limits](/docs/jsdoc/tutorials/realtime-fr-method) before interpreting
+the threshold, noise floor, or per-coordinate plots.
 
 ## Runtime architecture
 
@@ -53,6 +63,13 @@ lab.html
 loader.html
   └─ lab-js/loader.js
 
+realtime.html
+  └─ lab-js/realtime/realtime.js
+       ├─ realtime-capture.js (quality, temporal collectors, spike/frame buffer)
+       ├─ realtime-graph.js + realtime-radial.js
+       ├─ realtime-settings.js
+       └─ realtime-gallery.js
+
 ```
 
 ### Core state and lifecycle
diff --git a/README.md b/README.md
index 4eec4f3..4d1fac1 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # Ghostmaxxing
 <!-- coverage-badge:start -->
-[![Unit Test Coverage](https://img.shields.io/badge/coverage-96.79%25-green)](coverage/)
+[![Unit Test Coverage](https://img.shields.io/badge/coverage-88.94%25-green)](coverage/)
 <!-- coverage-badge:end -->
 
 [![CI](https://github.com/vecna/ghostmaxxing/actions/workflows/ci.yml/badge.svg)](https://github.com/vecna/ghostmaxxing/actions/workflows/ci.yml)
@@ -76,6 +76,7 @@ npm run check              # validator + unit tests (run before proposing change
 npm run test:unit          # Vitest unit tests
 npm run test:unit -- --coverage
 npm run test:e2e           # Playwright browser tests
+npm run test:e2e:realtime  # Focused realtime camera/video workflow
 npm run docs               # generate the JSDoc site into docs/jsdoc/
 npm run validate:ghostyles # validate every ghostyle in ghostyles/
 npm run validate:ghostyle -- ghostyles/your-slug.js   # validate one
diff --git a/about.html b/about.html
index f341307..4e485fa 100644
--- a/about.html
+++ b/about.html
@@ -472,6 +472,7 @@ <h2>About the project</h2>
           <a href="/docs/">Technical documentation</a>
           <a href="https://github.com/vecna/ghostmaxxing">Code</a>
           <a href="/loader.html">Video Loader</a>
+          <a href="/realtime.html">Latent Space Visualizer</a>
         </div>
       </div>
     </footer>
diff --git a/coverage/FOLDER-DESCRIPTION.md b/coverage/FOLDER-DESCRIPTION.md
deleted file mode 100644
index 01a2f99..0000000
--- a/coverage/FOLDER-DESCRIPTION.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# coverage
-
-This folder contains generated Vitest coverage output.
-
-It is produced by `npm run test:coverage` and includes HTML reports, coverage JSON, `lcov.info`, and static report assets. The README coverage badge links here for local inspection.
-
-Coverage output is excluded from code2prompt because it is generated test-report data. The chatbot should infer that coverage exists from this summary and from the package scripts.
diff --git a/docs-src/en/develop.body.html b/docs-src/en/develop.body.html
index db83df0..7cd174d 100644
--- a/docs-src/en/develop.body.html
+++ b/docs-src/en/develop.body.html
@@ -22,6 +22,7 @@ <h2>Choose a starting point</h2>
                 <tr><td>Call or integrate a runtime module</td><td><a href="/docs/jsdoc/">Generated JSDoc</a></td><td><a href="https://github.com/vecna/ghostmaxxing/tree/main/lab-js"><code>lab-js/</code> source</a></td></tr>
                 <tr><td>Understand imports, pages, and events</td><td><a href="/codemap/codemap.html">Code map</a></td><td>Module headers and event-bus source</td></tr>
                 <tr><td>Use the public browser API</td><td><a href="https://github.com/vecna/ghostmaxxing/blob/main/lab-js/Ghostmaxxing.d.ts"><code>Ghostmaxxing.d.ts</code></a></td><td><code>window.gstmxx</code> in <code>main.js</code></td></tr>
+                <tr><td>Inspect the realtime measurement method</td><td><a href="/docs/jsdoc/tutorials/realtime-fr-method">Realtime methodology</a></td><td><a href="https://github.com/vecna/ghostmaxxing/tree/main/lab-js/realtime"><code>lab-js/realtime/</code> source</a></td></tr>
                 <tr><td>Regenerate or package the project</td><td><a href="/docs/maintain/">Maintainer guide</a></td><td><a href="https://github.com/vecna/ghostmaxxing/tree/main/scripts-dev"><code>scripts-dev/</code></a></td></tr>
               </tbody>
             </table>
@@ -36,14 +37,9 @@ <h2>Available interfaces</h2>
               <tbody>
                 <tr><td><code>lab.html</code></td><td><code>lab-js/main.js</code></td><td>Live camera, local baseline, 2D and mesh views, Ghostyles, composite analysis.</td></tr>
                 <tr><td><code>loader.html</code></td><td><code>lab-js/loader.js</code></td><td>Repeatable comparison of frames from a local MP4.</td></tr>
+                <tr><td><code>realtime.html</code></td><td><code>lab-js/realtime/realtime.js</code></td><td>Temporal baseline and operator-declared makeup-step measurements from a camera or local video.</td></tr>
               </tbody>
             </table>
-            <!--
-            <div class="docs-note">
-              <p>
-                <code>realtime.html</code> is not part of the initial released documentation set. Add it only when its release contract and user guide are ready.
-              </p>
-            </div> -->
           </section>
 
           <section id="lab-runtime">
@@ -58,6 +54,7 @@ <h2>Runtime modules</h2>
                 <tr><td>Mesh and visual embedding</td><td><code>mediapipe-loop.js</code>, <code>engine-3d.js</code></td><td>478-point geometry and the separate experimental ImageEmbedder path.</td></tr>
                 <tr><td>Composite feedback</td><td><code>auto-find-loop.js</code>, <code>bbox-overlay.js</code>, <code>analyze-panel.js</code></td><td>Slower modified-image pass, live scaffold, status, metrics, report text.</td></tr>
                 <tr><td>Extensions</td><td><code>ghostyles-manager.js</code>, <code>plugins3d-loader.js</code>, <code>ghostyle3d-uv-renderer.js</code></td><td>Load 2D, UV, and hybrid Ghostyles and expose their controls.</td></tr>
+                <tr><td>Realtime experiment</td><td><code>realtime.js</code>, <code>realtime-capture.js</code>, <code>realtime-graph.js</code>, <code>realtime-radial.js</code></td><td>2D-only temporal capture, comparable-condition filtering, phase timeline, and 128-value descriptor views.</td></tr>
               </tbody>
             </table>
           </section>
diff --git a/docs-src/en/index.body.html b/docs-src/en/index.body.html
index 14c53d6..2a90cea 100644
--- a/docs-src/en/index.body.html
+++ b/docs-src/en/index.body.html
@@ -33,6 +33,16 @@ <h3>Video Loader</h3>
             </div>
           </article>
 
+          <article class="docs-card">
+            <span class="docs-card__number">Tool 03 [Experimental!]</span>
+            <h3>Latent Space Visualizer</h3>
+            <p>Mark makeup steps and measure face-descriptor drift over short, comparable time windows using a camera or local video.</p>
+            <div class="docs-card__actions">
+              <a class="docs-link docs-link--primary" href="/realtime.html">Open the Visualizer &#8599;</a>
+              <a class="docs-link" href="/docs/guides/realtime.html">Read the guide</a>
+            </div>
+          </article>
+
         </div>
 
         <div class="docs-note">
diff --git a/docs-src/en/maintain.body.html b/docs-src/en/maintain.body.html
index 8431f46..6ef46f3 100644
--- a/docs-src/en/maintain.body.html
+++ b/docs-src/en/maintain.body.html
@@ -24,6 +24,7 @@ <h2>Implemented in <code>packages.json</code></h2>
                 <tr><td>Validate all Ghostyles</td><td><code>npm run validate:ghostyles</code></td><td>Reports errors and warnings; stops on the first failed file.</td></tr>
                 <tr><td>Run fast checks</td><td><code>npm run check</code></td><td>Validates Ghostyles, then runs unit tests.</td></tr>
                 <tr><td>Run all tests</td><td><code>npm test</code></td><td>Runs unit tests, then Playwright end-to-end tests.</td></tr>
+                <tr><td>Test the realtime workflow</td><td><code>npm run test:e2e:realtime</code></td><td>Runs the focused Chromium suite, including local-video upload and declared-step measurement.</td></tr>
                 <tr><td>Build architecture map</td><td><code>npm run codemap</code></td><td>Writes the JSON graph and standalone HTML viewer.</td></tr>
                 <tr><td>Extract translations</td><td><code>npm run i18n:extract</code></td><td>Overwrites POT and pipe-delimited summary files.</td></tr>
               </tbody>
diff --git a/docs-src/en/pages.json b/docs-src/en/pages.json
index 87d9d2d..270ed81 100644
--- a/docs-src/en/pages.json
+++ b/docs-src/en/pages.json
@@ -21,6 +21,13 @@
     "description": "Use a local MP4 and exact timestamps to record and compare faces with the Ghostmaxxing Video Loader.",
     "section": "tools"
   },
+  {
+    "source": "realtime.body.html",
+    "output": "docs/guides/realtime.html",
+    "title": "Measure a makeup session over time — Ghostmaxxing",
+    "description": "Capture a temporal baseline, mark makeup steps, and inspect local face-descriptor drift with the Ghostmaxxing Latent Space Visualizer.",
+    "section": "tools"
+  },
   {
     "source": "face-brush.body.html",
     "output": "docs/guides/face-brush.html",
diff --git a/docs-src/en/realtime.body.html b/docs-src/en/realtime.body.html
new file mode 100644
index 0000000..bf3402d
--- /dev/null
+++ b/docs-src/en/realtime.body.html
@@ -0,0 +1,88 @@
+      <section class="content-page__header-area gm-prose" aria-labelledby="realtime-guide-title">
+        <span class="content-page__kicker">Functional guide · Latent Space Visualizer</span>
+        <h1 id="realtime-guide-title">Measure a makeup session over time.</h1>
+        <p class="content-page__lead">Capture a clean baseline, mark each application interval, and aggregate the descriptor change after every finished step.</p>
+        <p class="content-page__deck">The visualizer uses only the 2D face-api/dlib path. It excludes sufficiently different pose, framing, and ambient-light frames; it does not mathematically correct them or predict another recognition system.</p>
+        <div class="content-page__actions">
+          <a class="docs-link docs-link--primary" href="/realtime.html">Open the Visualizer &#8599;</a>
+          <a class="docs-link" href="/docs/jsdoc/tutorials/realtime-fr-method">Read the methodology</a>
+        </div>
+      </section>
+
+      <div class="docs-reader">
+        <article class="docs-article gm-prose">
+          <section id="source">
+            <span class="docs-eyebrow">Input</span>
+            <h2>Use the camera or a local video.</h2>
+            <p>The camera is the default. Open <strong>Source</strong> to select a local video; the file remains on this device and can be paused, sought, and replayed. Changing source invalidates the current baseline.</p>
+            <div class="docs-note"><p><strong>Display</strong> changes only how bright the video looks on screen. The model reads the original video pixels, so this control cannot improve lighting or change a measurement.</p></div>
+          </section>
+
+          <section id="procedure">
+            <span class="docs-eyebrow">Session workflow</span>
+            <h2>Baseline, application, measurement.</h2>
+            <div class="docs-step-list">
+              <div class="docs-step"><span class="docs-step__number">01</span><div><h3>Set the baseline</h3><p>Use a neutral frontal pose and stable light, then press <strong>Set baseline</strong>. Hold still while eight accepted samples are distributed across at least three seconds.</p></div></div>
+              <div class="docs-step"><span class="docs-step__number">02</span><div><h3>Start one makeup step</h3><p>Press <strong>Start makeup step</strong> before applying one change. The timeline marks the event and shades the application interval.</p></div></div>
+              <div class="docs-step"><span class="docs-step__number">03</span><div><h3>Return to comparable conditions</h3><p>Finish the change, then return to the baseline pose, framing, and room light. Leaving the frame during application produces one collapsed gap in the timeline.</p></div></div>
+              <div class="docs-step"><span class="docs-step__number">04</span><div><h3>Measure the step</h3><p>Press <strong>Measure this step</strong> and hold still. Eight comparable frames over at least three seconds become one temporal snapshot.</p></div></div>
+              <div class="docs-step"><span class="docs-step__number">05</span><div><h3>Read and repeat</h3><p>Record the distance, baseline noise floor, and step temporal spread together. Use <strong>Start next step</strong> for the next cumulative change.</p></div></div>
+            </div>
+          </section>
+
+          <section id="exclusions">
+            <span class="docs-eyebrow">Nuisance control</span>
+            <h2>Different pose and light are omitted, not normalised.</h2>
+            <p>Baseline capture checks confidence, scale, framing, 2D roll/yaw, and face-crop exposure. After makeup starts, confidence and face-crop exposure may legitimately change, so they no longer reject a frame. Scale, framing, roll, and yaw still apply, followed by a comparison with the baseline's averaged 2D conditions and ambient luminance outside the face box.</p>
+            <div class="docs-warning"><p>The gate reduces obvious confounds; it cannot prove that makeup caused the remaining distance. It does not estimate pitch and cannot separate every intervention from every shadow.</p></div>
+          </section>
+
+          <section id="views">
+            <span class="docs-eyebrow">Feedback</span>
+            <h2>Read the trace and the 128-value fingerprints.</h2>
+            <table>
+              <thead><tr><th>View</th><th>What it shows</th><th>Do not infer</th></tr></thead>
+              <tbody>
+                <tr><td><strong>Distance timeline</strong></td><td>Smoothed live distance, gaps, phase backgrounds, event lines, measured noise floor, and the cited 0.60 dlib reference.</td><td>That the space between the two reference lines has a calibrated meaning.</td></tr>
+                <tr><td><strong>Equalizer</strong></td><td>Absolute change for each of 128 descriptor coordinates.</td><td>That one row corresponds to a facial region.</td></tr>
+                <tr><td><strong>Radial fingerprint</strong></td><td>All 128 deltas as a live outline and the latest measured step as a filled shape.</td><td>That a spoke or direction identifies a makeup line, cheek, or eyebrow.</td></tr>
+                <tr><td><strong>Step capture</strong></td><td>The sharpest accepted frame from the measurement window, watermarked with local result metadata.</td><td>That one model-local result transfers to a deployed system.</td></tr>
+              </tbody>
+            </table>
+          </section>
+
+          <section id="video">
+            <span class="docs-eyebrow">Recorded sessions</span>
+            <h2>Use video as a repeatable fixture.</h2>
+            <p>Local-video event labels include media time. Pause or seek to inspect the recording, then mark application and measurement moments. Starting either interval resumes a paused video so the temporal collector does not repeatedly inspect one frozen frame.</p>
+          </section>
+
+          <section id="privacy">
+            <span class="docs-eyebrow">Data</span>
+            <h2>Local does not mean insignificant.</h2>
+            <ul>
+              <li>Camera baselines persist in localStorage; local-video baselines exist only for the current page session.</li>
+              <li>Manual, sustained-spike, and declared-step JPEG captures use browser IndexedDB.</li>
+              <li>The selected video is neither uploaded nor persisted by the page.</li>
+              <li><strong>Delete baseline</strong> does not erase the gallery. Delete captures separately when they are no longer needed.</li>
+            </ul>
+          </section>
+
+          <nav class="docs-next" aria-label="Continue reading">
+            <a class="docs-link" href="/docs/">&#8592; Documentation home</a>
+            <a class="docs-link" href="/docs/understand/">Next: understand the results &#8594;</a>
+          </nav>
+        </article>
+
+        <aside class="docs-toc">
+          <h2>On this page</h2>
+          <nav>
+            <a href="#source">Choose a source</a>
+            <a href="#procedure">Session workflow</a>
+            <a href="#exclusions">Excluded frames</a>
+            <a href="#views">Feedback views</a>
+            <a href="#video">Recorded sessions</a>
+            <a href="#privacy">Local data</a>
+          </nav>
+        </aside>
+      </div>
diff --git a/fediverse.html b/fediverse.html
index ded72f3..1b0f4e5 100644
--- a/fediverse.html
+++ b/fediverse.html
@@ -407,6 +407,7 @@ <h2>On this page</h2>
           <a href="/docs/">Technical documentation</a>
           <a href="https://github.com/vecna/ghostmaxxing">Code</a>
           <a href="/loader.html">Video Loader</a>
+          <a href="/realtime.html">Latent Space Visualizer</a>
         </div>
       </div>
     </footer>
diff --git a/genealogy.html b/genealogy.html
index fb29741..66bd9f3 100644
--- a/genealogy.html
+++ b/genealogy.html
@@ -1099,6 +1099,7 @@ <h2 class="gen-row__title">Wearable readers</h2>
         <a href="/docs/">Technical documentation</a>
         <a href="https://github.com/vecna/ghostmaxxing">Code</a>
         <a href="/loader.html">Video Loader</a>
+        <a href="/realtime.html">Latent Space Visualizer</a>
       </div>
     </div>
   </footer>
diff --git a/index.html b/index.html
index 4529af3..898f0d9 100644
--- a/index.html
+++ b/index.html
@@ -542,6 +542,7 @@ <h2 class="story__title" id="storyTitle">Disrupt Face Recognition, one Ghostyle
           <a href="/docs/">Technical documentation</a>
           <a href="https://github.com/vecna/ghostmaxxing">Code</a>
           <a href="/loader.html">Video Loader</a>
+          <a href="/realtime.html">Latent Space Visualizer</a>
         </div>
       </div>
     </footer>
diff --git a/lab-js/realtime/realtime-capture.js b/lab-js/realtime/realtime-capture.js
index f3bb7de..5847682 100644
--- a/lab-js/realtime/realtime-capture.js
+++ b/lab-js/realtime/realtime-capture.js
@@ -413,9 +413,9 @@ export function createBaselineCollector(options) {
     /**
      * Offers one gated frame to the collector.
      *
-     * @param {{ok: boolean, reason: string, metrics?: object}} verdict Quality gate verdict.
+     * @param {object} verdict Quality gate verdict.
      * @param {Float32Array|number[]} descriptor Descriptor for the frame.
-     * @returns {{status: string, accepted: number, target: number, reason?: string, baseline?: object}} Progress.
+     * @returns {object} Collector progress and, when complete, the temporal medoid.
      */
     offer(verdict, descriptor) {
       if (!active) return { status: 'idle', accepted: 0, target: targetSamples };
diff --git a/lab-js/realtime/realtime-gallery.js b/lab-js/realtime/realtime-gallery.js
index 202b828..00fa5a6 100644
--- a/lab-js/realtime/realtime-gallery.js
+++ b/lab-js/realtime/realtime-gallery.js
@@ -50,7 +50,7 @@ function openDb() {
  *
  * @param {IDBDatabase} db Open database.
  * @param {IDBTransactionMode} mode Transaction mode.
- * @param {(store: IDBObjectStore) => IDBRequest} work Callback issuing the request.
+ * @param {function(IDBObjectStore): IDBRequest} work Callback issuing the request.
  * @returns {Promise<*>} Request result.
  */
 function run(db, mode, work) {
@@ -275,9 +275,9 @@ function download(blob, filename) {
  * @param {HTMLImageElement} options.imageEl Viewer image element.
  * @param {HTMLElement} options.metaEl Viewer metadata line.
  * @param {HTMLElement} options.countEl Settings-bar count element.
- * @param {(count: number) => void} options.onCountChange Count change callback.
- * @param {(captureId: string) => void} options.onDelete Deletion callback.
- * @param {(message: string) => void} options.onStatus Status message callback.
+ * @param {function(number): void} options.onCountChange Count change callback.
+ * @param {function(string): void} options.onDelete Deletion callback.
+ * @param {function(string): void} options.onStatus Status message callback.
  * @returns {object} Gallery controller.
  */
 export function createGallery(options) {
diff --git a/lab-js/realtime/realtime-graph.js b/lab-js/realtime/realtime-graph.js
index feaa634..d060798 100644
--- a/lab-js/realtime/realtime-graph.js
+++ b/lab-js/realtime/realtime-graph.js
@@ -87,7 +87,7 @@ function token(el, name, fallback) {
  * @param {number} options.samplesPerSecond Sample cadence used for layout.
  * @param {number} options.maxDistance Right edge of the distance axis.
  * @param {number} options.threshold Cited match threshold line.
- * @param {(captureId: string) => void} options.onMarkerClick Marker click handler.
+ * @param {function(string): void} options.onMarkerClick Marker click handler.
  * @returns {object} Graph controller.
  */
 export function createGraph(options) {
@@ -105,9 +105,9 @@ export function createGraph(options) {
 
   const ctx = canvasEl.getContext('2d');
 
-  /** @type {Array<{kind: string, t: number, value?: number, endT?: number, seq?: number, captureId?: string}>} */
+  /** @type {Array<object>} */
   let entries = [];
-  /** @type {number[]} Virtual top offset of each entry, in CSS pixels. */
+  /** @type {number[]} */
   let tops = [];
   let contentHeight = 0;
   let sampleSeq = 0;
diff --git a/lab-js/realtime/realtime-settings.js b/lab-js/realtime/realtime-settings.js
index 6583c16..7bfbdd8 100644
--- a/lab-js/realtime/realtime-settings.js
+++ b/lab-js/realtime/realtime-settings.js
@@ -132,9 +132,9 @@ export function clearBaseline() {
  * @param {HTMLButtonElement} button Button to guard.
  * @param {string} idleLabel Label in the resting state.
  * @param {string} confirmLabel Label while awaiting confirmation.
- * @param {() => void} onConfirm Action to run on the second click.
+ * @param {function(): void} onConfirm Action to run on the second click.
  * @param {number} [timeoutMs] Time before the button reverts.
- * @returns {{reset: () => void}} Guard handle.
+ * @returns {object} Guard handle exposing `reset()`.
  */
 export function guardDestructive(button, idleLabel, confirmLabel, onConfirm, timeoutMs = 4000) {
   let armed = false;
@@ -177,7 +177,7 @@ export function guardDestructive(button, idleLabel, confirmLabel, onConfirm, tim
  * @param {HTMLElement} options.brightnessValueEl Brightness readout.
  * @param {HTMLButtonElement} options.equalizerEl Equalizer switch.
  * @param {HTMLElement} options.layoutEl Grid root toggled by the equalizer switch.
- * @param {() => void} options.onLayoutChange Called after a layout-affecting change.
+ * @param {function(): void} options.onLayoutChange Called after a layout-affecting change.
  * @returns {object} Settings controller.
  */
 export function createSettings(options) {
diff --git a/package.json b/package.json
index 08a3e13..548aa8a 100644
--- a/package.json
+++ b/package.json
@@ -33,13 +33,14 @@
     "test:unit:watch": "vitest",
     "test:coverage": "vitest run --coverage",
     "test:e2e": "playwright test",
+    "test:e2e:realtime": "playwright test tests/e2e/realtime.spec.js",
     "test:e2e:ui": "playwright test --ui",
     "docs": "jsdoc -c jsdoc.clean.json && node scripts-dev/enrich-jsdoc-seo.cjs",
     "docs:functional": "node scripts-dev/build-functional-docs.cjs",
     "docs:clean": "node -e \"require('node:fs').rmSync('docs/jsdoc', { recursive: true, force: true })\"",
     "docs:rebuild": "npm run docs:clean && npm run docs",
     "htmls": "npm run docs:functional && npm run docs:rebuild && npm run test:coverage && npm run update:coverage-badge && npm run update:projects && npm run update:references && npm run update:genealogy && npm run codemap",
-    "htmls:install": "npm run docs:functional && npm run docs:rebuild && npm run test:coverage && npm run update:coverage-badge && npm run update:projects && npm run update:references && npm run update:genealogy && npm run codemap && npm run check:seo && npm run web-client-setup",
+    "htmls:install": "npm run docs:functional && npm run docs:rebuild && npm run test:coverage && npm run update:coverage-badge && npm run update:projects && npm run update:references && npm run update:genealogy && npm run codemap && npm run web-client-setup",
     "docs:screenshots": "node scripts-dev/capture-doc-screenshots.cjs",
     "analyze:video": "node scripts-dev/video-loader.cjs analyze",
     "capture:render": "node scripts-dev/lab-capture.cjs render",
@@ -53,7 +54,6 @@
     "update:projects": "node projects/build-projects-page.js",
     "update:references": "node references/build-references-page.js",
     "update:genealogy": "python3 scripts-dev/build-genealogy.py",
-    "check:seo": "node scripts-dev/check-seo.cjs",
     "i18n:extract": "node scripts-dev/extract-i18n-pot.cjs",
     "build:client-interface": "node scripts-dev/install-client-interface.cjs",
     "web-client-setup": "npm run build:client-interface",
diff --git a/references/index.html b/references/index.html
index 0993e97..036f4c3 100644
--- a/references/index.html
+++ b/references/index.html
@@ -1299,6 +1299,7 @@ <h2>Jump to</h2>
           <a href="/docs/">Technical documentation</a>
           <a href="https://github.com/vecna/ghostmaxxing">Code</a>
           <a href="/loader.html">Video Loader</a>
+          <a href="/realtime.html">Latent Space Visualizer</a>
         </div>
       </div>
     </footer>
diff --git a/references/templates/references.template.html b/references/templates/references.template.html
index f0c736e..b6a3e8d 100644
--- a/references/templates/references.template.html
+++ b/references/templates/references.template.html
@@ -235,6 +235,7 @@ <h2>Jump to</h2>
           <a href="/docs/">Technical documentation</a>
           <a href="https://github.com/vecna/ghostmaxxing">Code</a>
           <a href="/loader.html">Video Loader</a>
+          <a href="/realtime.html">Latent Space Visualizer</a>
         </div>
       </div>
     </footer>
diff --git a/report.html b/report.html
index 137468e..43479fd 100644
--- a/report.html
+++ b/report.html
@@ -616,6 +616,7 @@ <h2>Jump to</h2>
           <a href="/docs/">Technical documentation</a>
           <a href="https://github.com/vecna/ghostmaxxing">Code</a>
           <a href="/loader.html">Video Loader</a>
+          <a href="/realtime.html">Latent Space Visualizer</a>
         </div>
       </div>
     </footer>
diff --git a/scripts-dev/README.md b/scripts-dev/README.md
index 859c6f5..6c7c41f 100644
--- a/scripts-dev/README.md
+++ b/scripts-dev/README.md
@@ -356,6 +356,7 @@ The package provides the following additional commands:
 | `npm run test:unit:watch` | Starts interactive Vitest |
 | `npm run prepare:e2e` | Installs Playwright Chromium |
 | `npm run test:e2e` | Runs Playwright tests |
+| `npm run test:e2e:realtime` | Runs only the Latent Space Visualizer Playwright suite, including local-video measurement |
 | `npm run test:e2e:ui` | Opens the Playwright test UI |
 | `npm test` | Runs unit tests, then end-to-end tests |
 
diff --git a/scripts-dev/build-functional-docs.cjs b/scripts-dev/build-functional-docs.cjs
index 5a9b858..8cdc544 100644
--- a/scripts-dev/build-functional-docs.cjs
+++ b/scripts-dev/build-functional-docs.cjs
@@ -112,6 +112,7 @@ function siteFooter() {
         <a href="/docs/jsdoc/">API reference</a>
         <a href="https://github.com/vecna/ghostmaxxing">Code</a>
         <a href="/loader.html">Video Loader</a>
+        <a href="/realtime.html">Latent Space Visualizer</a>
       </div>
       <p class="docs-footer-meta">Version ${PROJECT_VERSION} · Last updated: ${LAST_UPDATED}</p>
     </div>
diff --git a/scripts-dev/build-genealogy.py b/scripts-dev/build-genealogy.py
index e096bac..09a7d80 100644
--- a/scripts-dev/build-genealogy.py
+++ b/scripts-dev/build-genealogy.py
@@ -687,6 +687,7 @@ def x(year):
         <a href="/docs/">Technical documentation</a>
         <a href="https://github.com/vecna/ghostmaxxing">Code</a>
         <a href="/loader.html">Video Loader</a>
+        <a href="/realtime.html">Latent Space Visualizer</a>
       </div>
     </div>
   </footer>"""
diff --git a/styles/FOLDER-DESCRIPTION.md b/styles/FOLDER-DESCRIPTION.md
index c6f21e7..ae0df2e 100644
--- a/styles/FOLDER-DESCRIPTION.md
+++ b/styles/FOLDER-DESCRIPTION.md
@@ -7,7 +7,7 @@ The architecture is described in the header comment of `styles/styles.css`: ever
 ## Shared architecture
 
 - `styles.css` — root import list. This is the main shared stylesheet the site loads on most pages. It imports the fonts, tokens, reset, and reusable component styles. It is the canonical layer-order file.
-  - Included directly by: `index.html`, `genealogy.html`, `about.html`, `lab.html`, `loader.html`, `references/index.html`, `report.html`, `workshops.html`, `visual-styleguide.html`
+  - Included directly by: `index.html`, `genealogy.html`, `about.html`, `lab.html`, `loader.html`, `realtime.html`, `references/index.html`, `report.html`, `workshops.html`, `visual-styleguide.html`
 
 - `tokens.css` — design tokens for the palette, type scale, spacing, border, and shared CSS custom properties. This is the source of the site-wide theme values.
   - Loaded by: `styles.css`
@@ -50,9 +50,10 @@ These files are linked directly by HTML pages and provide page layout or unique
   - Included by: `about.html`, `report.html`, `references/index.html`, `references/templates/references.template.html`, `workshops.html`
 
 - `lab.css` — lab and live-analysis page styling; used for full-screen camera surfaces and tooling chrome.
-  - Included by: `lab.html`
+  - Included by: `lab.html`, `realtime.html`
 
-- `realtime.css` — retained stylesheet for unreleased realtime calibration/tracking work; no current page entry point is present.
+- `realtime.css` — layout, timeline, experiment phases, descriptor equalizer, and radial plot for the Latent Space Visualizer.
+  - Included by: `realtime.html`
 
 - `loader.css` — behavior and layout for the loader/fixture-analysis page.
   - Included by: `loader.html`
@@ -79,6 +80,6 @@ Most pages follow this basic pattern:
 - `styles.css` is the universal layer and is always linked first.
 - One page stylesheet is then added only when the page needs unique layout or a page-specific composition.
 - For the long-form content pages, `pages.css` and `content-pages.css` are often used together.
-- For the lab UX, `lab.css` is the primary page layer. The retained `realtime.css` stylesheet belongs to unreleased calibration/tracking work and has no current page entry point.
+- For the Lab UX, `lab.css` is the primary page layer. `realtime.html` reuses that chrome and adds `realtime.css` for its three-panel measurement workspace.
 
 This makes the stylesheet folder a layered CSS system rather than a flat set of unrelated files: shared tokens and components first, page placement last, with the HTML deciding which page layer to attach.
diff --git a/realtime-fr-method.md b/tutorials/realtime-fr-method.md
similarity index 85%
rename from realtime-fr-method.md
rename to tutorials/realtime-fr-method.md
index 202bf1c..fb4ebea 100644
--- a/realtime-fr-method.md
+++ b/tutorials/realtime-fr-method.md
@@ -6,8 +6,9 @@ the surrounding research does and does not support. It is written so that
 someone who has never opened the code can judge whether a reading from this tool
 means anything.
 
-**Scope.** Covers `realtime.html`, `realtime.css`, and the `realtime-*.js`
-modules. The 2D/3D effect pipelines in `lab.html` use different models
+**Scope.** Covers `realtime.html`, `styles/realtime.css`,
+`lab-js/realtime/realtime.js`, and its `realtime-*.js` modules. The 2D/3D effect
+pipelines in `lab.html` use different models
 (MediaPipe FaceLandmarker, MobileNetV3 ImageEmbedder) and are out of scope here.
 
 **Status of claims.** Every threshold in this document is tagged with its
@@ -55,8 +56,8 @@ descriptor of your face captured before a makeup session, and a descriptor of
 your face right now, both produced by dlib's face recognition ResNet as shipped
 in face-api.js.
 
-That number tells you how far one specific, seven-year-old, publicly-available
-model has moved you in its own embedding space. It does **not** tell you:
+That number tells you how far one specific, publicly available 2017 model has
+moved you in its own embedding space. It does **not** tell you:
 
 - whether you would be recognised by any deployed surveillance system
 - whether the makeup would work on a different model
@@ -66,7 +67,9 @@ model has moved you in its own embedding space. It does **not** tell you:
 What it is genuinely good for is **iteration**. If you are developing a makeup
 technique, you need a fast local signal to compare variant A against variant B.
 This gives you that, cheaply, offline, on your own device, without sending a
-face anywhere. Treat it as a rehearsal instrument, not a verdict.
+face anywhere. It can take either a live camera or a local video, mark the
+operator-declared application intervals, and measure each finished step over a
+short temporal window. Treat it as a rehearsal instrument, not a verdict.
 
 The rest of this document explains why that framing is the correct one.
 
@@ -74,6 +77,10 @@ The rest of this document explains why that framing is the correct one.
 
 ## 2. The pipeline
 
+The input is either the live camera or a video file selected from the device.
+The local file is read through a temporary object URL; it is not uploaded or
+persisted. Both inputs go through the same 2D face-api pipeline below.
+
 Three separate models run in sequence on each frame. They are frequently
 conflated in public discussion, and the distinction matters because
 countermeasures that defeat one do not necessarily touch the others.
@@ -440,12 +447,17 @@ Rationale: raw per-frame distance is noisy, and the graph's job is trend
 legibility, not maximum temporal resolution. The numeric readout is unsmoothed
 and updates per frame, so instantaneous values remain visible.
 
+Baseline and declared-step measurements use a different aggregation path. Each
+collects eight accepted descriptors distributed over at least three seconds,
+then selects their medoid. A step is therefore represented by a temporal window,
+not by the frame that happened to be visible when the button was pressed.
+
 ### 9.2 The quality gate
 
-`realtime-capture.js → createQualityGate()`. Every frame considered for a
-baseline or a saved capture must clear all of:
+`realtime-capture.js → createQualityGate()`. Baseline frames use the strict gate
+below:
 
-**Confidence.** Detector score ≥ 0.90, well above the 0.5 reporting floor in
+**Confidence.** Detector score ≥ 0.85, above the 0.5 reporting floor in
 `DETECTOR_OPTIONS`. A frame that barely registered as a face produces a
 correspondingly unreliable descriptor.
 
@@ -494,6 +506,20 @@ that false-negative inequities are substantially caused by under-exposure of
 dark-skinned subjects. Refusing to build a baseline from an under-exposed frame
 is a small, concrete mitigation.
 
+Intervention frames deliberately use a different mode. Detector confidence and
+face-crop exposure are not rejection criteria, because makeup may change exactly
+those signals. Scale, framing, roll and yaw must still pass. The frame is then
+compared with the baseline's averaged 2D conditions: roll, yaw proxy, face-box
+scale and centre, plus ambient luminance sampled **outside** the face box. A
+frame beyond those relative tolerances is omitted from both the timeline and a
+declared-step measurement.
+
+This is exclusion, not correction. The software does not rotate, relight or
+otherwise normalise a changed frame back to the baseline. The checks reduce
+obvious pose, framing and room-light confounds, but they cannot prove that the
+remaining distance came only from makeup. Pitch is not estimated, and ambient
+luminance is a deliberately simple 2D proxy.
+
 ### 9.3 Medoid baseline
 
 Given N accepted descriptors, compute the pairwise distance matrix and select the
@@ -511,8 +537,8 @@ spread = (1/(N−1)) Σ_j d(x_medoid, xⱼ)
    — no systematic offset gets baked into the reference.
 3. It is naturally robust to one marginal frame slipping the gate. The mean is not.
 
-Cost is O(N²) in distance computations. At N = 12 that is 66 comparisons of
-128-element vectors, computed once. Irrelevant.
+Cost is O(N²) in distance computations. At N = 8 that is 28 comparisons of
+128-element vectors, computed once for the baseline and once per declared step.
 
 ### 9.4 The noise floor
 
@@ -520,9 +546,12 @@ Cost is O(N²) in distance computations. At N = 12 that is 66 comparisons of
 about the face has changed. It is the tool's only empirical reference line, and
 the most useful number it produces.
 
-Without it you cannot distinguish "the makeup did something" from "I moved my
-head." With it, a reading of 0.09 against a measured floor of 0.06 is visibly
-nothing, and a reading of 0.55 against the same floor is visibly something.
+Without it you cannot tell whether a small movement in the descriptor exceeds
+the variability of the baseline window. With it, a reading of 0.09 against a
+measured floor of 0.06 is weak evidence, while a reading of 0.55 against the
+same floor is a much larger model-local effect. The relative-condition gate in
+§9.2 removes obvious head and light changes; neither mechanism can guarantee
+that all nuisance variation has been removed.
 
 If spread exceeds 0.20 (**UNTUNED**) the batch is rejected outright — the subject
 moved or the lighting flickered, and accepting it would set a floor so high that
@@ -538,6 +567,10 @@ triggers:
 
 Plus a manual shutter, because no heuristic catches everything.
 
+Automatic spike capture is active only while the session is in its ready state.
+During an operator-declared application interval and its measurement window,
+the explicit step snapshot is the record of interest.
+
 The frame written to disk is the **sharpest** observed across the sustain window,
 not whichever frame tripped the trigger. Rationale: a single frame at 0.50 is as
 likely to be motion blur as a result, and "the makeup is working" should not rest
@@ -557,6 +590,27 @@ diffuse across the embedding, and for nothing else. Work such as
 (which uses this exact dlib model) examines what structure embeddings do carry;
 per-dimension interpretation is not part of it.
 
+### 9.7 Declared makeup steps and the radial fingerprint
+
+After setting a baseline, the operator controls the experiment semantics:
+
+1. **Start makeup step** marks the beginning of an application interval.
+2. **Measure this step** begins an eight-sample, minimum-three-second window.
+3. The tool stores the window medoid as the step snapshot, reports its distance
+   and temporal spread, and keeps the sharpest accepted thumbnail.
+
+The timeline changes background during application and measurement and draws a
+labelled event line for each transition. With a local video, events also include
+the media time, so pause, seek and replay can be used as an experiment fixture.
+The controls resume a paused local video before beginning a marked interval.
+
+The radial plot uses all 128 absolute component deltas. The live reading is an
+outline; the most recent measured step is a filled shape. It can make two
+descriptor-change patterns easier to compare than 128 equalizer rows, but it
+does not add new information and it is not a map of facial regions. Spoke order
+is merely descriptor index order, and rotation or local peaks have no semantic
+interpretation.
+
 ---
 
 ## 10. Every threshold, and where it came from
@@ -573,7 +627,8 @@ That is the complete list. One number.
 
 | Quantity | Source |
 |---|---|
-| Noise floor | Medoid spread over 12 gated frames, this session |
+| Baseline noise floor | Medoid spread over 8 gated frames, this session |
+| Step temporal spread | Medoid spread over 8 comparable frames, this step |
 
 ### Chosen
 
@@ -584,24 +639,33 @@ That is the complete list. One number.
 | `SAMPLES_PER_SECOND` | 3 | Smoothing vs. responsiveness. |
 | `NO_FACE_DEBOUNCE_MS` | 1500 | Longer than a blink or head turn. |
 | `GAP_BLOCK_PX` | 30 | Fixed height for a collapsed absence. |
-| `BASELINE_TARGET_SAMPLES` | 12 | Enough for a stable medoid; ~4s at typical detector rates. |
-| `BASELINE_MIN_MS` | 3000 | Prevents 12 frames of a single instant. |
-| `BASELINE_TIMEOUT_MS` | 15000 | Fails loudly rather than hanging. |
+| `BASELINE_TARGET_SAMPLES` | 8 | A compact temporal medoid rather than one frame. |
+| `BASELINE_MIN_MS` | 3000 | Distributes accepted frames across the interval. |
+| `BASELINE_TIMEOUT_MS` | 30000 | Leaves time to respond to a diagnostic rejection. |
+| `MEASUREMENT_TARGET_SAMPLES` | 8 | Uses the same temporal aggregation for every declared step. |
+| `MEASUREMENT_MIN_MS` | 3000 | Distributes accepted comparison frames across the interval. |
+| `MEASUREMENT_TIMEOUT_MS` | 30000 | Fails explicitly when comparable frames cannot be collected. |
 | `SPIKE_SUSTAIN_MS` | 1500 | ~4–5 samples at 3 Hz. |
 | `SPIKE_BEST_MARGIN` | 0.05 | Avoids capturing noise as a personal best. |
 | `SPIKE_COOLDOWN_MS` | 8000 | Keeps the gallery to keepers. |
-| `minScore` | 0.90 | Detector floor is 0.5; the baseline deserves better. |
+| `minScore` | 0.85 | Detector floor is 0.5; used only for strict baseline capture. |
 | `minBoxWidthRatio` | 0.25 | Recognition net input is 150×150. |
 | `maxRollDegrees` | 8 | Tight enough to matter, loose enough to achieve. |
 | `maxYawOffset` | 0.12 | As above; proxy metric, see §9.2. |
-| `MAX_DELTA` | 0.15 | Equalizer scaling only. Purely visual. |
+| `maxRollDelta` | 4° | Relative 2D comparison tolerance; frames beyond it are omitted. |
+| `maxYawDelta` | 0.055 | Relative yaw-proxy tolerance. |
+| `maxScaleDelta` | 0.08 | Relative face-box width tolerance. |
+| `maxCenterDelta` | 0.065 | Relative horizontal or vertical centre tolerance. |
+| `maxLumaDelta` | 0.10 | Relative ambient-luminance tolerance, measured outside the face box. |
+| `MAX_DELTA` | 0.15 | Equalizer and radial scaling only. Purely visual. |
 
 ### Untuned — needs empirical calibration
 
 | Constant | Value | Why it needs work |
 |---|---|---|
-| `BASELINE_MAX_SPREAD` | 0.20 | Should be derived from observed spread distributions across cameras and subjects. Currently a guess. |
+| `BASELINE_MAX_SPREAD` / `MEASUREMENT_MAX_SPREAD` | 0.20 | Should be derived from observed spread distributions across cameras and subjects. Currently a guess. |
 | `minLuma` / `maxLuma` | 0.18 / 0.86 | Should be validated across skin tones specifically, given §6.2. A gate tuned on light skin would systematically reject dark-skinned subjects and is exactly the failure NIST documents. **This is the highest-priority calibration task in the codebase.** |
+| Relative condition tolerances | values above | Need repeated-camera and repeated-subject studies. They reduce confounds but are not calibrated guarantees of pose or illumination invariance. |
 
 ### Inherited from `config.js`
 
@@ -792,18 +856,22 @@ project, not something the current page does.
   own measured noise floor
 - Let you **compare two makeup variants** on the same face, same session, same
   camera — the comparison it is actually built for
-- Show **which direction** a change pushes the descriptor, and whether the change
-  is diffuse or concentrated
-- Give you a **timestamped, watermarked record** with the measurement burned in,
-  so a result is reproducible and citable rather than anecdotal
+- Mark operator-declared application intervals and aggregate each finished step
+  over time instead of reporting only one clicked frame
+- Show whether component changes are diffuse or concentrated, with an equalizer
+  and a radial fingerprint that preserve all 128 absolute deltas
+- Give you a **timestamped, watermarked record** with the measurement burned in;
+  local-video events also retain their media time
 - Do all of this **offline, on-device**, with no face leaving the machine
 
 ### It cannot
 
 - Tell you whether any deployed system would recognise you
 - Tell you whether the technique transfers to another model
-- Account for camera, angle, distance or lighting conditions other than the ones
-  you are sitting in
+- Prove invariance to pose or lighting. It omits frames with sufficiently large
+  2D pose, framing or ambient-light differences; it does not correct them and
+  does not estimate pitch
+- Map a descriptor dimension or radial spoke to a facial feature or makeup line
 - Tell you anything about **detection** evasion — if the detector loses you, the
   tool records a gap, not a success
 - Establish that a technique works for anyone other than you
@@ -811,28 +879,36 @@ project, not something the current page does.
 
 ### Methodological hygiene, if you want the results to mean something
 
-1. **Re-baseline whenever lighting or camera changes.** A baseline captured under
-   different light is a different experiment.
+1. **Re-baseline whenever source, lighting or camera setup changes.** A baseline
+   captured under different conditions is a different experiment.
 2. **Report the noise floor with every result.** A distance without its noise
    floor is uninterpretable.
-3. **Compare within a session.** Cross-session comparison confounds makeup with
+3. **Use the declared-step controls.** Mark the start of application, then
+   measure only after returning to the baseline pose and lighting.
+4. **Compare within a session.** Cross-session comparison confounds makeup with
    lighting, camera position and time of day.
-4. **Vary pose deliberately and record the envelope.** If a result only holds at
-   one exact angle, it is not a result.
-5. **Never report a peak.** Report a sustained value with its duration.
+5. **Repeat the step.** One accepted temporal window is stronger than a peak,
+   but it is still one small sample. Report distance, baseline noise floor and
+   step temporal spread together.
+6. **Test pose separately.** Deliberately vary yaw, roll and pitch in a separate
+   protocol rather than treating excluded frames as proof of invariance.
 
 ---
 
 ## 14. Privacy and data handling
 
-The tool stores two categories of data, both local, both deletable.
+The tool keeps the following local records. None is transmitted.
 
 | Data | Store | Deletion |
 |---|---|---|
-| Baseline descriptor + noise floor + timestamp | `localStorage` (`gstmxx.realtime.baseline`) | "Delete baseline" — removes the key |
-| Spike captures (watermarked JPEGs) | IndexedDB (`gstmxx-realtime` / `captures`) | "Delete all", or per-capture in the viewer |
+| Camera baseline descriptor + noise floor + averaged conditions + timestamp | `localStorage` (`gstmxx.realtime.baseline`, format 2) | "Delete baseline" — removes the key |
+| Local-video baseline | Memory for the current page session | Change source, delete baseline, or close the page |
+| Spike, manual and declared-step captures (watermarked JPEGs) | IndexedDB (`gstmxx-realtime` / `captures`) | "Delete all", or per-capture in the viewer |
 | View settings | `localStorage` (`gstmxx.realtime.settings`) | Browser storage clear |
 
+The selected video is exposed to the page through a temporary browser object
+URL. The file and that URL are not stored in IndexedDB or `localStorage`.
+
 **Why IndexedDB for captures.** `localStorage` caps near 5MB per origin, is
 shared with everything else the site stores, and inflates binary by ~33% through
 base64. Photographs belong in a store that takes Blobs natively.
@@ -998,7 +1074,7 @@ commit SHAs and record the SHA alongside results.
 
 ---
 
-*Sources current as of August 2026. The EU AI Act sections describe recent and
+*Sources current as of October 2026. The EU AI Act sections describe recent and
 evolving provisions — verify against the consolidated text before relying on
 them. If you re-run `fetch-sources.sh`, record the resulting commit SHAs: the
 upstream model references are unpinned, and an unnoticed model change invalidates
diff --git a/tutorials/realtime.md b/tutorials/realtime.md
index f2fe099..e5bf571 100644
--- a/tutorials/realtime.md
+++ b/tutorials/realtime.md
@@ -1,65 +1,112 @@
 # Latent Space Visualizer
 
 **Project:** Ghostmaxxing
-**Audience:** researchers and facilitators studying how a face descriptor moves
-**Status:** ⚠️ NOT RELEASED YET — this page is written ahead of launch and is
-deliberately kept out of the docs build. It is not registered in
-`tutorials/tutorials.json` and is not linked from the site. See the RELEASE
-TOGGLE in `docs/index.html` for how to publish it.
-
----
-
-## 1. What it is
-
-The Latent Space Visualizer (`realtime.html`) is a real-time debugger for **face
-descriptor drift**. You record a baseline face signature from the webcam, and the
-tool then shows, live, how far the current face has moved from that baseline —
-numerically, historically, and per-dimension.
-
-It answers a question the main lab abstracts away: *when a look changes the match
-result, what exactly is moving in the descriptor?*
-
-## 2. The three panels
-
-- **Distance (left).** The live descriptor distance to your baseline, plus a
-  scrolling history graph. The reference line sits at the match threshold
-  (`0.6` in this tool).
-- **Webcam + calibration (centre).** The live video, a status pill, a countdown
-  used when setting the baseline, and a **Set Baseline** button.
-- **Descriptor Delta (right).** A 128-cell "equalizer": one cell per dimension of
-  the face descriptor, coloured by how much that dimension has shifted from the
-  baseline. This is where you can *see* which parts of the signature a given
-  makeup stroke, head turn, or lighting change disturbs.
-
-## 3. How to use it
-
-1. Open the page and allow camera access; models load automatically.
-2. Sit in a neutral pose and press **Set Baseline**. A short countdown captures a
-   clean baseline descriptor. The state moves from `CALIBRATING` to `TRACKING`.
-3. Change something — apply makeup, turn your head, change the light — and watch:
-   - the **Distance** value and graph move;
-   - the **Descriptor Delta** equalizer light up on the dimensions that shifted.
-4. Re-press **Set Baseline** any time to re-anchor.
-
-## 4. Reading the numbers
-
-- Distance is a descriptor distance: **lower means more similar** to the
-  baseline. Crossing the threshold line is the moment the pipeline would stop
-  treating the current face as the baseline face.
-- The equalizer is a *diagnostic*, not a score. A large delta on many dimensions
-  usually corresponds to a large distance, but the point is to build intuition
-  about which changes matter, not to read an exact value off a single cell.
-
-## 5. Limits
-
-- This is a **local, conditional** view of one browser pipeline's descriptor.
-  Movement here is not proof of real-world protection, and the threshold shown is
-  this tool's own reference, not a universal cut-off.
-- The visualizer studies the descriptor path; it does not bake or export a
-  Ghostyle. When a look you found here is worth keeping, reproduce and bake it in
-  the main lab (`lab.html`).
-
-## 6. Privacy
-
-The baseline descriptor and all per-frame computation stay in the browser.
-Nothing is uploaded.
+
+**Audience:** people iterating on physical makeup and researchers inspecting
+face-descriptor drift
+
+**Status:** experimental
+
+The Latent Space Visualizer (`realtime.html`) is a local, real-time instrument
+for comparing a face-api/dlib descriptor with a baseline. It is designed around
+a makeup session: capture the untreated state, tell the tool when application
+starts, then ask it to measure each finished step over a short window.
+
+It reports what this one browser model did under the tested conditions. It does
+not establish protection from another recognition system.
+
+## 1. Choose a source
+
+The default source is the camera. Open **Source → Open local video** to use a
+recording instead. The file remains on the device and can be paused, sought and
+replayed with the browser's video controls.
+
+Changing source invalidates the current baseline. Camera baselines can be
+restored after reload; local-video baselines last only for the current page
+session because the temporary file URL does not survive a reload.
+
+## 2. Capture a baseline
+
+1. Return to a neutral, frontal pose and keep the room light steady.
+2. Press **Set baseline**.
+3. Hold the pose while the ring collects eight accepted samples across at least
+   three seconds.
+
+The tool rejects baseline frames when detection confidence is low, the face is
+too small or clipped, the head roll/yaw is too large, or the face crop is badly
+under- or overexposed. The message in the pill names the current rejection; it
+is not a generic lighting warning.
+
+The resulting medoid becomes the baseline. Its average spread is shown as the
+session noise floor.
+
+## 3. Measure a makeup step
+
+1. Press **Start makeup step** before applying one change. The timeline marks
+   the event and shades the application interval.
+2. Apply the change. You may leave the frame; a sustained absence becomes one
+   collapsed gap rather than a long run of false zeroes.
+3. Return to the baseline pose and light, then press **Measure this step**.
+4. Hold still while eight comparable samples are collected across at least
+   three seconds.
+
+The result is the medoid of that window, not a single clicked frame. The tool
+shows its distance from the original baseline and its temporal spread, updates
+the measured radial shape, and saves the sharpest accepted frame as a step
+thumbnail. Repeat with **Start next step** for cumulative changes.
+
+With a local video, phase markers include the media time. Starting or measuring
+a step resumes a paused video, so the collection window can advance instead of
+silently sampling one frozen frame.
+
+## 4. What gets excluded
+
+After baseline capture, the software permits appearance changes that may lower
+detector confidence or darken the face crop. It still excludes frames whose 2D
+pose, face-box size/position, or ambient luminance differs too much from the
+baseline. Ambient light is estimated outside the face box so dark makeup is
+less likely to be mistaken for a room-light change.
+
+This is a nuisance gate, not full pose or lighting normalisation. It does not
+measure pitch, cannot know that makeup caused the remaining change, and cannot
+separate every shadow from every intervention.
+
+## 5. Read the three panels
+
+- **Distance.** The live Euclidean distance and a scrolling timeline. The noise
+  floor is measured from this baseline; `0.60` is dlib's documented LFW
+  operating point. The space between them is deliberately uncalibrated.
+- **Source and experiment.** Camera or local video, baseline progress, phase
+  controls, status, capture controls and local gallery actions. **Display** only
+  changes CSS brightness; it does not change pixels seen by the model.
+- **Descriptor Delta.** The equalizer and radial fingerprint show the absolute
+  change in all 128 descriptor coordinates. The live radial outline can be
+  compared with the filled shape from the latest measured step.
+
+Descriptor dimensions are not facial regions. A radial spoke cannot identify
+which cheek, eyebrow or makeup line caused a change. These views can show
+whether two 128-value patterns differ; they cannot explain the face semantically.
+
+## 6. Captures and privacy
+
+Manual, sustained-spike and declared-step captures are saved as watermarked
+JPEGs in browser IndexedDB. Camera baselines and view settings use localStorage.
+The selected video itself is neither uploaded nor persisted by the page.
+
+**Delete baseline** removes the persisted camera descriptor but deliberately
+does not erase the gallery. Use **Delete all** or delete individual captures in
+the viewer when those records are no longer needed.
+
+A 128-value face descriptor is biometric data. Treat exported images and local
+browser storage accordingly, even though this page does not transmit them.
+
+## 7. Interpreting a result
+
+Report the measured step distance together with the baseline noise floor and
+the step's temporal spread. Compare steps within one session, re-baseline after
+changing the source or setup, and repeat promising measurements. A high value is
+evidence of movement in this specific 2017 dlib embedding under these conditions;
+it is not evidence of evasion against a deployed system.
+
+For thresholds, algorithms, research provenance and limitations, read
+[Realtime Visualizer: Methodology, Sources and Limits](/docs/jsdoc/tutorials/realtime-fr-method).
diff --git a/tutorials/tutorials.json b/tutorials/tutorials.json
index 32519ae..c0855bb 100644
--- a/tutorials/tutorials.json
+++ b/tutorials/tutorials.json
@@ -32,6 +32,12 @@
   "video-loader": {
      "title": "(internal) Seekable Video Loader architecture"
   },
+  "realtime": {
+     "title": "Latent Space Visualizer"
+  },
+  "realtime-fr-method": {
+     "title": "Realtime Visualizer: Methodology, Sources and Limits"
+  },
   "aigen-image-labeler": {
      "title": "(internal) AI Gen image labeler"
   },
diff --git a/workshops.html b/workshops.html
index ae31b3a..037bd48 100644
--- a/workshops.html
+++ b/workshops.html
@@ -288,6 +288,7 @@ <h2>Run one where you are</h2>
           <a href="/docs/">Technical documentation</a>
           <a href="https://github.com/vecna/ghostmaxxing">Code</a>
           <a href="/loader.html">Video Loader</a>
+          <a href="/realtime.html">Latent Space Visualizer</a>
         </div>
       </div>
     </footer>

From 2129783d8bb4fc3c06a98149912e65ba527e6467 Mon Sep 17 00:00:00 2001
From: vecna <vecna@globaleaks.org>
Date: Thu, 15 Oct 2026 00:20:31 +0200
Subject: [PATCH 9/9] good enough for a release is now

---
 JSDOC_index.md                       |    6 +-
 docs-src/en/develop.body.html        |    2 +-
 docs-src/en/realtime.body.html       |   10 +-
 lab-js/realtime/realtime-capture.js  |  275 ++++--
 lab-js/realtime/realtime-gallery.js  |  121 ++-
 lab-js/realtime/realtime-graph.js    |   98 +-
 lab-js/realtime/realtime-guide.js    |  159 ++++
 lab-js/realtime/realtime-radial.js   |   31 +-
 lab-js/realtime/realtime-settings.js |   17 +-
 lab-js/realtime/realtime.js          |  373 ++++++--
 realtime.html                        |   95 +-
 styles/realtime.css                  |  351 ++++++-
 tests/e2e/realtime.spec.js           |  122 ++-
 tests/unit/realtime-capture.test.js  |  111 ++-
 tests/unit/realtime-gallery.test.js  |   16 +
 tests/unit/realtime-graph.test.js    |   18 +-
 tests/unit/realtime-guide.test.js    |   21 +
 tests/unit/realtime-radial.test.js   |   17 +-
 tests/unit/realtime-settings.test.js |   16 +-
 tutorials/realtime-fr-method.md      | 1081 ---------------------
 tutorials/realtime.md                | 1290 ++++++++++++++++++++++++--
 tutorials/tutorials.json             |    5 +-
 22 files changed, 2865 insertions(+), 1370 deletions(-)
 create mode 100644 lab-js/realtime/realtime-guide.js
 create mode 100644 tests/unit/realtime-gallery.test.js
 create mode 100644 tests/unit/realtime-guide.test.js
 delete mode 100644 tutorials/realtime-fr-method.md

diff --git a/JSDOC_index.md b/JSDOC_index.md
index bf2bf78..0bebdf5 100644
--- a/JSDOC_index.md
+++ b/JSDOC_index.md
@@ -42,9 +42,9 @@ comparable, and lets the operator mark and aggregate successive makeup steps.
 The input may be a live camera or a local video. The timeline, equalizer, and
 radial fingerprint are different views of the same descriptor-distance session.
 
-Begin with the [functional guide](/docs/guides/realtime.html), then read the
-[methodology and limits](/docs/jsdoc/tutorials/realtime-fr-method) before interpreting
-the threshold, noise floor, or per-coordinate plots.
+Begin with the [functional guide](/docs/guides/realtime.html), then use the
+[combined operating guide and methodology](/docs/jsdoc/tutorials/realtime) before
+interpreting the threshold, noise floor, or per-coordinate plots.
 
 ## Runtime architecture
 
diff --git a/docs-src/en/develop.body.html b/docs-src/en/develop.body.html
index 7cd174d..0446702 100644
--- a/docs-src/en/develop.body.html
+++ b/docs-src/en/develop.body.html
@@ -22,7 +22,7 @@ <h2>Choose a starting point</h2>
                 <tr><td>Call or integrate a runtime module</td><td><a href="/docs/jsdoc/">Generated JSDoc</a></td><td><a href="https://github.com/vecna/ghostmaxxing/tree/main/lab-js"><code>lab-js/</code> source</a></td></tr>
                 <tr><td>Understand imports, pages, and events</td><td><a href="/codemap/codemap.html">Code map</a></td><td>Module headers and event-bus source</td></tr>
                 <tr><td>Use the public browser API</td><td><a href="https://github.com/vecna/ghostmaxxing/blob/main/lab-js/Ghostmaxxing.d.ts"><code>Ghostmaxxing.d.ts</code></a></td><td><code>window.gstmxx</code> in <code>main.js</code></td></tr>
-                <tr><td>Inspect the realtime measurement method</td><td><a href="/docs/jsdoc/tutorials/realtime-fr-method">Realtime methodology</a></td><td><a href="https://github.com/vecna/ghostmaxxing/tree/main/lab-js/realtime"><code>lab-js/realtime/</code> source</a></td></tr>
+                <tr><td>Run and inspect the realtime measurement method</td><td><a href="/docs/jsdoc/tutorials/realtime">Realtime guide and methodology</a></td><td><a href="https://github.com/vecna/ghostmaxxing/tree/main/lab-js/realtime"><code>lab-js/realtime/</code> source</a></td></tr>
                 <tr><td>Regenerate or package the project</td><td><a href="/docs/maintain/">Maintainer guide</a></td><td><a href="https://github.com/vecna/ghostmaxxing/tree/main/scripts-dev"><code>scripts-dev/</code></a></td></tr>
               </tbody>
             </table>
diff --git a/docs-src/en/realtime.body.html b/docs-src/en/realtime.body.html
index bf3402d..7d9f4c1 100644
--- a/docs-src/en/realtime.body.html
+++ b/docs-src/en/realtime.body.html
@@ -5,7 +5,7 @@ <h1 id="realtime-guide-title">Measure a makeup session over time.</h1>
         <p class="content-page__deck">The visualizer uses only the 2D face-api/dlib path. It excludes sufficiently different pose, framing, and ambient-light frames; it does not mathematically correct them or predict another recognition system.</p>
         <div class="content-page__actions">
           <a class="docs-link docs-link--primary" href="/realtime.html">Open the Visualizer &#8599;</a>
-          <a class="docs-link" href="/docs/jsdoc/tutorials/realtime-fr-method">Read the methodology</a>
+          <a class="docs-link" href="/docs/jsdoc/tutorials/realtime">Read how it works and why</a>
         </div>
       </section>
 
@@ -15,16 +15,16 @@ <h1 id="realtime-guide-title">Measure a makeup session over time.</h1>
             <span class="docs-eyebrow">Input</span>
             <h2>Use the camera or a local video.</h2>
             <p>The camera is the default. Open <strong>Source</strong> to select a local video; the file remains on this device and can be paused, sought, and replayed. Changing source invalidates the current baseline.</p>
-            <div class="docs-note"><p><strong>Display</strong> changes only how bright the video looks on screen. The model reads the original video pixels, so this control cannot improve lighting or change a measurement.</p></div>
+            <div class="docs-note"><p><strong>Source → Brightness</strong> changes only how bright the video looks on screen. The model reads the original video pixels, so this control cannot improve lighting or change a measurement.</p></div>
           </section>
 
           <section id="procedure">
             <span class="docs-eyebrow">Session workflow</span>
             <h2>Baseline, application, measurement.</h2>
             <div class="docs-step-list">
-              <div class="docs-step"><span class="docs-step__number">01</span><div><h3>Set the baseline</h3><p>Use a neutral frontal pose and stable light, then press <strong>Set baseline</strong>. Hold still while eight accepted samples are distributed across at least three seconds.</p></div></div>
+              <div class="docs-step"><span class="docs-step__number">01</span><div><h3>Set the baseline</h3><p>Use a neutral frontal pose and stable light, then press <strong>Set baseline</strong>. Hold still and follow the centre target while five accepted samples are distributed across at least two seconds.</p></div></div>
               <div class="docs-step"><span class="docs-step__number">02</span><div><h3>Start one makeup step</h3><p>Press <strong>Start makeup step</strong> before applying one change. The timeline marks the event and shades the application interval.</p></div></div>
-              <div class="docs-step"><span class="docs-step__number">03</span><div><h3>Return to comparable conditions</h3><p>Finish the change, then return to the baseline pose, framing, and room light. Leaving the frame during application produces one collapsed gap in the timeline.</p></div></div>
+              <div class="docs-step"><span class="docs-step__number">03</span><div><h3>Return to comparable conditions</h3><p>Finish the change, then return to the baseline pose, framing, and room light. Leaving the frame during application produces a marked no-face interval without an invented distance line.</p></div></div>
               <div class="docs-step"><span class="docs-step__number">04</span><div><h3>Measure the step</h3><p>Press <strong>Measure this step</strong> and hold still. Eight comparable frames over at least three seconds become one temporal snapshot.</p></div></div>
               <div class="docs-step"><span class="docs-step__number">05</span><div><h3>Read and repeat</h3><p>Record the distance, baseline noise floor, and step temporal spread together. Use <strong>Start next step</strong> for the next cumulative change.</p></div></div>
             </div>
@@ -45,7 +45,7 @@ <h2>Read the trace and the 128-value fingerprints.</h2>
               <tbody>
                 <tr><td><strong>Distance timeline</strong></td><td>Smoothed live distance, gaps, phase backgrounds, event lines, measured noise floor, and the cited 0.60 dlib reference.</td><td>That the space between the two reference lines has a calibrated meaning.</td></tr>
                 <tr><td><strong>Equalizer</strong></td><td>Absolute change for each of 128 descriptor coordinates.</td><td>That one row corresponds to a facial region.</td></tr>
-                <tr><td><strong>Radial fingerprint</strong></td><td>All 128 deltas as a live outline and the latest measured step as a filled shape.</td><td>That a spoke or direction identifies a makeup line, cheek, or eyebrow.</td></tr>
+                <tr><td><strong>Radial distribution</strong></td><td>All 128 deltas sorted into 16 ranked RMS bands, with the live reading as an outline and the latest measured step as a filled shape.</td><td>That a spoke or direction identifies a makeup line, cheek, or eyebrow.</td></tr>
                 <tr><td><strong>Step capture</strong></td><td>The sharpest accepted frame from the measurement window, watermarked with local result metadata.</td><td>That one model-local result transfers to a deployed system.</td></tr>
               </tbody>
             </table>
diff --git a/lab-js/realtime/realtime-capture.js b/lab-js/realtime/realtime-capture.js
index 5847682..f50ea0f 100644
--- a/lab-js/realtime/realtime-capture.js
+++ b/lab-js/realtime/realtime-capture.js
@@ -64,6 +64,48 @@ export function summarizeConditions(readings) {
   return Object.keys(profile).length ? profile : null;
 }
 
+/** Returns the median finite value from a list, or zero for an empty list. */
+function median(values) {
+  const sorted = values.filter(Number.isFinite).sort((a, b) => a - b);
+  if (!sorted.length) return 0;
+  const middle = Math.floor(sorted.length / 2);
+  return sorted.length % 2
+    ? sorted[middle]
+    : (sorted[middle - 1] + sorted[middle]) / 2;
+}
+
+/**
+ * Builds one robust, normalized 2D reference guide from accepted frames.
+ * This geometry is deliberately separate from the descriptor medoid: it is
+ * only a visual alignment aid and never participates in distance arithmetic.
+ *
+ * @param {object[]} guides Per-frame normalized face boxes and 68 landmarks.
+ * @returns {object|null} Coordinate-wise median guide.
+ */
+export function summarizeGuides(guides) {
+  const usable = guides.filter((guide) =>
+    guide &&
+    Number.isFinite(guide.aspectRatio) &&
+    Array.isArray(guide.landmarks) &&
+    guide.landmarks.length === 68,
+  );
+  if (!usable.length) return null;
+  const coordinate = (read) => median(usable.map(read));
+  return {
+    aspectRatio: coordinate((guide) => guide.aspectRatio),
+    box: {
+      x: coordinate((guide) => guide.box.x),
+      y: coordinate((guide) => guide.box.y),
+      width: coordinate((guide) => guide.box.width),
+      height: coordinate((guide) => guide.box.height),
+    },
+    landmarks: Array.from({ length: 68 }, (_, index) => ({
+      x: coordinate((guide) => guide.landmarks[index].x),
+      y: coordinate((guide) => guide.landmarks[index].y),
+    })),
+  };
+}
+
 /**
  * Checks whether a live frame is comparable with the baseline conditions.
  * This is deliberately a 2D nuisance gate, not pose correction: rejected
@@ -72,27 +114,52 @@ export function summarizeConditions(readings) {
  * @param {object|null} baseline Baseline condition profile.
  * @param {object|null} current Current frame conditions.
  * @param {object} [overrides] Partial tolerance overrides.
- * @returns {{ok: boolean, reason: string, differences: object}} Comparison verdict.
+ * @returns {{ok: boolean, reason: string, reasons: string[], differences: object}} Comparison verdict.
  */
 export function compareConditions(baseline, current, overrides = {}) {
-  if (!baseline || !current) return { ok: true, reason: '', differences: {} };
+  if (!baseline || !current) return { ok: true, reason: '', reasons: [], differences: {} };
   const limits = { ...DEFAULT_COMPARISON, ...overrides };
   const difference = (key) => Math.abs((current[key] ?? 0) - (baseline[key] ?? 0));
   const differences = Object.fromEntries(CONDITION_KEYS.map((key) => [key, difference(key)]));
+  const reasons = [];
 
   if (
     differences.rollDegrees > limits.maxRollDelta ||
-    differences.yawOffset > limits.maxYawDelta ||
-    differences.boxWidthRatio > limits.maxScaleDelta ||
+    differences.yawOffset > limits.maxYawDelta
+  ) {
+    const details = [];
+    if (differences.rollDegrees > limits.maxRollDelta) {
+      details.push(`roll Δ${differences.rollDegrees.toFixed(1)}°; limit ${limits.maxRollDelta}°`);
+    }
+    if (differences.yawOffset > limits.maxYawDelta) {
+      details.push(
+        `yaw proxy Δ${differences.yawOffset.toFixed(3)}; limit ${limits.maxYawDelta.toFixed(3)}`,
+      );
+    }
+    reasons.push(`Head angle differs from the baseline pose (${details.join(', ')}).`);
+  }
+  if (differences.boxWidthRatio > limits.maxScaleDelta) {
+    reasons.push(
+      current.boxWidthRatio > baseline.boxWidthRatio
+        ? 'Face size differs from the baseline framing. Move slightly farther from the camera.'
+        : 'Face size differs from the baseline framing. Move slightly closer to the camera.',
+    );
+  }
+  if (
     differences.centerXRatio > limits.maxCenterDelta ||
     differences.centerYRatio > limits.maxCenterDelta
   ) {
-    return { ok: false, reason: 'Return to the baseline pose and framing.', differences };
+    reasons.push('Face position differs from the baseline framing. Re-centre it in the frame.');
   }
   if (differences.luma > limits.maxLumaDelta) {
-    return { ok: false, reason: 'Lighting differs too much from the baseline.', differences };
+    reasons.push(
+      `Room lighting differs from the baseline (luma Δ${differences.luma.toFixed(2)}; limit ${limits.maxLumaDelta.toFixed(2)}).`,
+    );
+  }
+  if (reasons.length) {
+    return { ok: false, reason: reasons[0], reasons, differences };
   }
-  return { ok: true, reason: '', differences };
+  return { ok: true, reason: '', reasons: [], differences };
 }
 
 /**
@@ -282,26 +349,49 @@ export function createQualityGate(video, overrides = {}) {
      * @param {object} [options] Evaluation mode.
      * @param {boolean} [options.allowAppearanceChanges=false] Do not let face-crop
      *   exposure or detector-score changes reject an intervention frame.
-     * @returns {{ok: boolean, reason: string, sharpness: number}} Verdict.
+     * @returns {{ok: boolean, reason: string, reasons: string[], code: string, sharpness: number}} Verdict.
      */
     evaluate(detection, options = {}) {
       const { allowAppearanceChanges = false } = options;
-      const reject = (reason) => ({ ok: false, reason, sharpness: 0 });
+      const reject = (code, reason) => ({
+        ok: false,
+        code,
+        reason,
+        reasons: [reason],
+        failures: [{ code, reason }],
+        sharpness: 0,
+      });
 
       const box = detection?.detection?.box;
       const score = detection?.detection?.score ?? 0;
       const points = detection?.landmarks?.positions;
-      if (!box || !points) return reject('No usable detection.');
+      if (!box || !points) return reject('detection', 'No usable face detection.');
 
       const videoW = video.videoWidth || 1;
       const videoH = video.videoHeight || 1;
+      const guide = {
+        aspectRatio: videoW / videoH,
+        box: {
+          x: box.x / videoW,
+          y: box.y / videoH,
+          width: box.width / videoW,
+          height: box.height / videoH,
+        },
+        landmarks: points.map((point) => ({ x: point.x / videoW, y: point.y / videoH })),
+      };
+
+      const failures = [];
+      const fail = (code, reason) => failures.push({ code, reason });
 
       if (!allowAppearanceChanges && score < limits.minScore) {
-        return reject(
-          `Detector confidence is low (${score.toFixed(2)}). Hold a frontal, steady pose and check glare or shadows.`,
+        fail(
+          'confidence',
+          `Face detected, but confidence is low (${score.toFixed(2)}; target ${limits.minScore.toFixed(2)}). Hold still and check glare or shadows.`,
         );
       }
-      if (box.width < videoW * limits.minBoxWidthRatio) return reject('Move closer to the camera.');
+      if (box.width < videoW * limits.minBoxWidthRatio) {
+        fail('scale', 'Face detected, but it is too small in the frame. Move closer.');
+      }
 
       const marginX = videoW * limits.edgeMarginRatio;
       const marginY = videoH * limits.edgeMarginRatio;
@@ -310,43 +400,80 @@ export function createQualityGate(video, overrides = {}) {
         box.y < marginY ||
         box.x + box.width > videoW - marginX ||
         box.y + box.height > videoH - marginY;
-      if (clipsFrame) return reject('Centre your face in the frame.');
+      if (clipsFrame) fail('framing', 'Face detected, but it is too close to an edge. Centre it in the frame.');
 
       const leftEye = points[LM_LEFT_EYE_OUTER];
       const rightEye = points[LM_RIGHT_EYE_OUTER];
       const nose = points[LM_NOSE_TIP];
-      if (!leftEye || !rightEye || !nose) return reject('Landmarks incomplete.');
+      if (!leftEye || !rightEye || !nose) return reject('landmarks', 'Face detected, but its landmarks are incomplete.');
 
       const rollDegrees =
         (Math.atan2(rightEye.y - leftEye.y, rightEye.x - leftEye.x) * 180) / Math.PI;
-      if (Math.abs(rollDegrees) > limits.maxRollDegrees) return reject('Level your head.');
+      if (Math.abs(rollDegrees) > limits.maxRollDegrees) {
+        fail(
+          'roll',
+          `Face detected, but the head is tilted (${Math.abs(rollDegrees).toFixed(1)}°; target ≤ ${limits.maxRollDegrees}°). Level it and hold still.`,
+        );
+      }
 
       const interocular = Math.hypot(rightEye.x - leftEye.x, rightEye.y - leftEye.y) || 1;
       const eyeMidX = (leftEye.x + rightEye.x) / 2;
       const yawOffset = (nose.x - eyeMidX) / interocular;
-      if (Math.abs(yawOffset) > limits.maxYawOffset) return reject('Look straight at the camera.');
+      if (Math.abs(yawOffset) > limits.maxYawOffset) {
+        fail(
+          'yaw',
+          `Face detected, but the head is turned (yaw proxy ${Math.abs(yawOffset).toFixed(2)}; target ≤ ${limits.maxYawOffset.toFixed(2)}). Face the camera and hold still.`,
+        );
+      }
 
       const ambientLuma = analyseAmbientLuma(video, box, scratch);
       const crop = analyseCrop(video, box, scratch);
       if (!allowAppearanceChanges) {
-        if (crop.luma < limits.minLuma) return reject('Too dark — add some light.');
+        if (crop.luma < limits.minLuma) {
+          fail('underexposed', 'Face detected, but the face crop is too dark. Add some light.');
+        }
         if (crop.luma > limits.maxLuma || crop.clipped > limits.maxClippedRatio) {
-          return reject('Overexposed — reduce the light.');
+          fail(
+            'overexposed',
+            `Face detected, but the face crop is overexposed (clipped ${(crop.clipped * 100).toFixed(1)}%; target ≤ ${(limits.maxClippedRatio * 100).toFixed(1)}%). Reduce glare or direct light.`,
+          );
         }
       }
 
+      const metrics = {
+        rollDegrees,
+        yawOffset,
+        boxWidthRatio: box.width / videoW,
+        centerXRatio: (box.x + box.width / 2) / videoW,
+        centerYRatio: (box.y + box.height / 2) / videoH,
+        luma: Number.isFinite(ambientLuma) ? ambientLuma : crop.luma,
+        faceLuma: crop.luma,
+        clippedRatio: crop.clipped,
+        detectorScore: score,
+      };
+
+      if (failures.length) {
+        return {
+          ok: false,
+          code: failures[0].code,
+          reason: failures[0].reason,
+          reasons: failures.map((failure) => failure.reason),
+          failures,
+          sharpness: crop.sharpness,
+          metrics,
+          guide,
+        };
+      }
+
       return {
         ok: true,
+        code: '',
         reason: '',
+        reasons: [],
+        failures: [],
         sharpness: crop.sharpness,
-        metrics: {
-          rollDegrees,
-          yawOffset,
-          boxWidthRatio: box.width / videoW,
-          centerXRatio: (box.x + box.width / 2) / videoW,
-          centerYRatio: (box.y + box.height / 2) / videoH,
-          luma: Number.isFinite(ambientLuma) ? ambientLuma : crop.luma,
-        },
+        metrics,
+        guide,
       };
     },
   };
@@ -371,8 +498,14 @@ export function createBaselineCollector(options) {
 
   let descriptors = [];
   let conditions = [];
+  let guides = [];
   let startedAt = 0;
   let lastAcceptedAt = 0;
+  let firstAcceptedAt = 0;
+  let lastObservationId = null;
+  let attempted = 0;
+  let rejected = 0;
+  let duplicates = 0;
   let active = false;
 
   return {
@@ -384,8 +517,14 @@ export function createBaselineCollector(options) {
     start() {
       descriptors = [];
       conditions = [];
+      guides = [];
       startedAt = Date.now();
       lastAcceptedAt = 0;
+      firstAcceptedAt = 0;
+      lastObservationId = null;
+      attempted = 0;
+      rejected = 0;
+      duplicates = 0;
       active = true;
     },
 
@@ -398,7 +537,13 @@ export function createBaselineCollector(options) {
       active = false;
       descriptors = [];
       conditions = [];
+      guides = [];
       lastAcceptedAt = 0;
+      firstAcceptedAt = 0;
+      lastObservationId = null;
+      attempted = 0;
+      rejected = 0;
+      duplicates = 0;
     },
 
     /**
@@ -415,57 +560,78 @@ export function createBaselineCollector(options) {
      *
      * @param {object} verdict Quality gate verdict.
      * @param {Float32Array|number[]} descriptor Descriptor for the frame.
+     * @param {object} [observation] Source timing and identity.
+     * @param {number} [observation.sourceTimeMs] Media time for a local video.
+     * @param {string|number} [observation.id] Stable id for one source frame.
      * @returns {object} Collector progress and, when complete, the temporal medoid.
      */
-    offer(verdict, descriptor) {
+    offer(verdict, descriptor, observation = {}) {
       if (!active) return { status: 'idle', accepted: 0, target: targetSamples };
 
       const now = Date.now();
       const elapsed = now - startedAt;
+      attempted += 1;
+
+      const progress = (status, extra = {}) => ({
+        status,
+        accepted: descriptors.length,
+        target: targetSamples,
+        attempted,
+        rejected,
+        duplicates,
+        ...extra,
+      });
 
       if (!verdict.ok) {
+        rejected += 1;
         if (elapsed > timeoutMs) {
           active = false;
-          return {
-            status: 'failed',
-            accepted: descriptors.length,
-            target: targetSamples,
+          return progress('failed', {
             reason: `Not enough clean frames before the capture timed out. Last rejection: ${verdict.reason}`,
-          };
+            reasons: verdict.reasons || [verdict.reason],
+          });
         }
-        return {
-          status: 'blocked',
-          accepted: descriptors.length,
-          target: targetSamples,
+        return progress('blocked', {
           reason: verdict.reason,
-        };
+          reasons: verdict.reasons || [verdict.reason],
+        });
       }
 
+      if (observation.id != null && observation.id === lastObservationId) {
+        duplicates += 1;
+        return progress('waiting', { reason: 'Waiting for the video to advance to a new frame.' });
+      }
+      if (observation.id != null) lastObservationId = observation.id;
+
+      const sampleTime = Number.isFinite(observation.sourceTimeMs)
+        ? observation.sourceTimeMs
+        : now;
+
       // Space observations across the whole requested window. Without this,
       // a fast detector could fill the array in one short burst and merely
       // wait out minDurationMs without observing the rest of the interval.
       const sampleIntervalMs = targetSamples > 1 ? minDurationMs / (targetSamples - 1) : 0;
-      const sampleDue = descriptors.length === 0 || now - lastAcceptedAt >= sampleIntervalMs;
+      const sampleDue = descriptors.length === 0 || sampleTime - lastAcceptedAt >= sampleIntervalMs;
       if (descriptors.length < targetSamples && sampleDue) {
         descriptors.push(Array.from(descriptor));
         if (verdict.metrics) conditions.push(verdict.metrics);
-        lastAcceptedAt = now;
+        if (verdict.guide) guides.push(verdict.guide);
+        lastAcceptedAt = sampleTime;
+        if (descriptors.length === 1) firstAcceptedAt = sampleTime;
       }
 
       const enoughSamples = descriptors.length >= targetSamples;
-      const enoughTime = elapsed >= minDurationMs;
+      const enoughTime =
+        descriptors.length > 0 && lastAcceptedAt - firstAcceptedAt >= minDurationMs;
 
       if (!enoughSamples || !enoughTime) {
         if (elapsed > timeoutMs) {
           active = false;
-          return {
-            status: 'failed',
-            accepted: descriptors.length,
-            target: targetSamples,
+          return progress('failed', {
             reason: 'Not enough clean frames before the capture timed out.',
-          };
+          });
         }
-        return { status: 'collecting', accepted: descriptors.length, target: targetSamples };
+        return progress('collecting');
       }
 
       const result = medoid(descriptors);
@@ -475,26 +641,21 @@ export function createBaselineCollector(options) {
       // during collection. Accepting it would set a noise floor so high that
       // real results disappear underneath it.
       if (result.spread > maxSpread) {
-        return {
-          status: 'failed',
-          accepted: descriptors.length,
-          target: targetSamples,
+        return progress('failed', {
           reason: 'Too much movement during capture.',
-        };
+        });
       }
 
-      return {
-        status: 'done',
-        accepted: descriptors.length,
-        target: targetSamples,
+      return progress('done', {
         baseline: {
           descriptor: result.descriptor,
           noiseFloor: result.spread,
           capturedAt: Date.now(),
           samples: descriptors.length,
           conditions: summarizeConditions(conditions),
+          guide: summarizeGuides(guides),
         },
-      };
+      });
     },
   };
 }
diff --git a/lab-js/realtime/realtime-gallery.js b/lab-js/realtime/realtime-gallery.js
index 00fa5a6..dbe958e 100644
--- a/lab-js/realtime/realtime-gallery.js
+++ b/lab-js/realtime/realtime-gallery.js
@@ -63,7 +63,7 @@ function run(db, mode, work) {
 }
 
 /**
- * Formats a timestamp for watermarks and filenames.
+ * Formats a timestamp for filenames.
  *
  * @param {number} timestamp Epoch milliseconds.
  * @param {boolean} [fileSafe] Whether to avoid characters illegal in filenames.
@@ -78,17 +78,95 @@ function stamp(timestamp, fileSafe = false) {
 }
 
 /**
- * Burns provenance into the frame.
+ * Formats the only measurement claim shown with a saved frame.
  *
- * The measurement travels with the image on purpose. An exported frame carrying
- * only a date is a selfie; one carrying the distance, the measured noise floor
- * and the threshold it was judged against is a record.
+ * @param {object} meta Measurement metadata.
+ * @param {number} meta.distance Distance at capture.
+ * @param {number} meta.threshold Match threshold used for the verdict.
+ * @returns {string} Human-readable distance and verdict.
+ */
+export function formatCaptureMeasurement(meta) {
+  const threshold = Number.isFinite(meta.threshold) ? meta.threshold : 0.6;
+  const distance = Number.isFinite(meta.distance) ? meta.distance : NaN;
+  const formattedDistance = Number.isFinite(distance) ? distance.toFixed(3) : '—';
+  const verdict = Number.isFinite(distance) && distance <= threshold
+    ? 'still match'
+    : 'different person';
+  return `distance ${formattedDistance}/${threshold.toFixed(2)} · ${verdict}`;
+}
+
+/** Draws the project mark using the same geometry as mark-small-mono-cream.svg. */
+function drawLogoMark(ctx, x, y, size) {
+  ctx.save();
+  ctx.translate(x, y);
+  ctx.scale(size / 64, size / 64);
+  ctx.strokeStyle = '#fff1cf';
+  ctx.fillStyle = '#fff1cf';
+  ctx.lineCap = 'round';
+  ctx.lineJoin = 'round';
+
+  ctx.save();
+  ctx.translate(1, 10);
+  ctx.scale(0.45, 0.45);
+  ctx.lineWidth = 7.5;
+  ctx.beginPath();
+  ctx.moveTo(8, 44);
+  ctx.lineTo(2, 32);
+  ctx.moveTo(92, 44);
+  ctx.lineTo(98, 32);
+  ctx.stroke();
+  ctx.beginPath();
+  ctx.moveTo(50, 45);
+  ctx.bezierCurveTo(65, 25, 90, 25, 90, 45);
+  ctx.bezierCurveTo(90, 65, 65, 65, 50, 45);
+  ctx.bezierCurveTo(35, 25, 10, 25, 10, 45);
+  ctx.bezierCurveTo(10, 65, 35, 65, 50, 45);
+  ctx.closePath();
+  ctx.stroke();
+  for (const cx of [28, 72]) {
+    ctx.beginPath();
+    ctx.arc(cx, 45, 8, 0, Math.PI * 2);
+    ctx.fill();
+  }
+  ctx.restore();
+
+  ctx.save();
+  ctx.translate(13, 36);
+  ctx.scale(0.4, 0.4);
+  ctx.lineWidth = 9;
+  ctx.beginPath();
+  ctx.moveTo(80, 26);
+  ctx.lineTo(80, 84);
+  ctx.moveTo(80, 30);
+  ctx.lineTo(44, 30);
+  ctx.moveTo(52, 30);
+  ctx.lineTo(52, 40);
+  ctx.stroke();
+  ctx.beginPath();
+  ctx.moveTo(17, 40);
+  ctx.lineTo(75, 40);
+  ctx.arcTo(82, 40, 82, 47, 7);
+  ctx.lineTo(82, 63);
+  ctx.arcTo(82, 70, 75, 70, 7);
+  ctx.lineTo(17, 70);
+  ctx.arcTo(10, 70, 10, 63, 7);
+  ctx.lineTo(10, 47);
+  ctx.arcTo(10, 40, 17, 40, 7);
+  ctx.closePath();
+  ctx.stroke();
+  ctx.beginPath();
+  ctx.arc(32, 55, 11.5, 0, Math.PI * 2);
+  ctx.fill();
+  ctx.restore();
+  ctx.restore();
+}
+
+/**
+ * Burns the project mark and the distance verdict into the frame.
  *
  * @param {HTMLCanvasElement} source Frame to watermark.
  * @param {object} meta Measurement metadata.
- * @param {number} meta.t Capture timestamp.
  * @param {number} meta.distance Distance at capture.
- * @param {number} meta.noiseFloor Measured session noise floor.
  * @param {number} meta.threshold Cited match threshold.
  * @returns {HTMLCanvasElement} Watermarked canvas.
  */
@@ -104,25 +182,26 @@ export function watermark(source, meta) {
   ctx.fillStyle = '#0c0906';
   ctx.fillRect(0, source.height, canvas.width, barHeight);
 
-  const fontSize = Math.max(10, Math.round(barHeight * 0.42));
+  const markSize = Math.max(24, Math.round(barHeight * 0.82));
+  const markX = 12;
+  const markY = source.height + (barHeight - markSize) / 2;
+  drawLogoMark(ctx, markX, markY, markSize);
+
+  const detail = formatCaptureMeasurement(meta);
+  const textX = markX + markSize + 12;
+  const availableWidth = Math.max(0, canvas.width - textX - 12);
+  let fontSize = Math.max(9, Math.round(barHeight * 0.36));
   ctx.font = `${fontSize}px ui-monospace, SFMono-Regular, Menlo, monospace`;
+  while (fontSize > 8 && ctx.measureText(detail).width > availableWidth) {
+    fontSize -= 1;
+    ctx.font = `${fontSize}px ui-monospace, SFMono-Regular, Menlo, monospace`;
+  }
   ctx.textBaseline = 'middle';
   ctx.textAlign = 'left';
 
   const y = source.height + barHeight / 2;
   ctx.fillStyle = '#ffe7a8';
-  ctx.fillText('ghostmaxxing', 12, y);
-
-  const detail = [
-    stamp(meta.t),
-    `d=${meta.distance.toFixed(3)}`,
-    `noise ±${meta.noiseFloor.toFixed(3)}`,
-    `dlib t=${meta.threshold.toFixed(2)}`,
-  ].join('  ·  ');
-
-  ctx.fillStyle = '#b9a892';
-  ctx.textAlign = 'right';
-  ctx.fillText(detail, canvas.width - 12, y);
+  ctx.fillText(detail, textX, y, availableWidth);
 
   return canvas;
 }
@@ -355,7 +434,7 @@ export function createGallery(options) {
     openId = id;
 
     imageEl.src = openUrl;
-    metaEl.textContent = `${stamp(record.t)}  ·  d=${record.distance.toFixed(3)}  ·  noise ±${record.noiseFloor.toFixed(3)}  ·  ${record.kind}`;
+    metaEl.textContent = formatCaptureMeasurement(record);
     viewerEl.hidden = false;
   }
 
diff --git a/lab-js/realtime/realtime-graph.js b/lab-js/realtime/realtime-graph.js
index d060798..a2a8a9a 100644
--- a/lab-js/realtime/realtime-graph.js
+++ b/lab-js/realtime/realtime-graph.js
@@ -10,12 +10,10 @@
  *    panel and a trace's steepness meant nothing. Every sample now occupies
  *    the same number of pixels. The visible window is 60 seconds, always.
  *
- * 2. COLLAPSED GAPS. An absence is drawn as a fixed-height break labelled with
- *    its real duration, not as a proportional stretch of empty axis. The
- *    working session is: measure, walk to the mirror, apply makeup for eight
- *    minutes, come back. Drawn to scale that is ~5,700px of nothing wedged
- *    between the two measurements you are actually trying to compare. The
- *    elapsed time is not lost, it just isn't spatialised.
+ * 2. CONTINUOUS QUALITY STATES. Descriptor values keep flowing when pose,
+ *    framing or light are not comparable, but their background says why they
+ *    are excluded from measurements. No-face ticks keep time without inventing
+ *    a value. Each state can be hidden non-destructively from the legend.
  *
  * Rendering is virtualised: the canvas is sticky and sized to the scroll
  * viewport, a spacer supplies the scroll height, and only the visible slice is
@@ -114,6 +112,12 @@ export function createGraph(options) {
   let openGapIndex = -1;
 
   let noiseFloor = null;
+  const qualityVisible = new Map([
+    ['valid', true],
+    ['outside-guide', true],
+    ['lighting', true],
+    ['no-face', true],
+  ]);
   let follow = true;
   let programmaticScroll = false;
 
@@ -131,6 +135,7 @@ export function createGraph(options) {
   function entryHeight(entry) {
     if (entry.kind === 'gap') return GAP_BLOCK_PX;
     if (entry.kind === 'event') return EVENT_BLOCK_PX;
+    if (qualityVisible.get(entry.status || 'valid') === false) return 0;
     return pxPerSample;
   }
 
@@ -233,7 +238,11 @@ export function createGraph(options) {
       faint: token(canvasEl, '--rt-gap', '#8a7b6a'),
       applying: token(canvasEl, '--rt-phase-applying', 'rgba(242,106,27,0.13)'),
       measuring: token(canvasEl, '--rt-phase-measuring', 'rgba(127,227,176,0.13)'),
-      event: token(canvasEl, '--rt-event', '#f3c747'),
+      eventApplying: token(canvasEl, '--rt-event-applying', '#f26a1b'),
+      eventMeasuring: token(canvasEl, '--rt-event-measuring', '#7fe3b0'),
+      outsideGuide: token(canvasEl, '--rt-quality-outside', 'rgba(242,106,27,0.22)'),
+      lighting: token(canvasEl, '--rt-quality-light', 'rgba(243,199,71,0.20)'),
+      noFace: token(canvasEl, '--rt-quality-no-face', 'rgba(138,123,106,0.24)'),
     };
 
     const scrollTop = scrollEl.scrollTop;
@@ -288,6 +297,9 @@ export function createGraph(options) {
         if (top > limit) break;
         const entry = entries[i];
         const y = top - scrollTop;
+        if (entry.kind === 'sample' && qualityVisible.get(entry.status || 'valid') === false) {
+          continue;
+        }
         if (entry.kind === 'sample' && entry.phase === 'applying') {
           ctx.fillStyle = colors.applying;
           ctx.fillRect(PAD_LEFT, y, plotWidth, pxPerSample);
@@ -297,6 +309,17 @@ export function createGraph(options) {
         } else if (entry.kind === 'event') {
           drawEvent(entry, y, colors, plotWidth);
         }
+        if (entry.kind === 'sample' && qualityVisible.get(entry.status) !== false) {
+          const qualityColor = {
+            'outside-guide': colors.outsideGuide,
+            lighting: colors.lighting,
+            'no-face': colors.noFace,
+          }[entry.status];
+          if (qualityColor) {
+            ctx.fillStyle = qualityColor;
+            ctx.fillRect(PAD_LEFT, y, plotWidth, pxPerSample);
+          }
+        }
       }
 
       ctx.lineWidth = 2;
@@ -312,7 +335,9 @@ export function createGraph(options) {
         const entry = entries[i];
         const y = top - scrollTop;
 
-        if (entry.kind === 'gap' || entry.kind === 'event') {
+        const hiddenQuality = entry.kind === 'sample' && qualityVisible.get(entry.status || 'valid') === false;
+        if (hiddenQuality) continue;
+        if (entry.kind === 'gap' || entry.kind === 'event' || entry.value == null) {
           if (penDown) {
             ctx.stroke();
             ctx.beginPath();
@@ -349,6 +374,7 @@ export function createGraph(options) {
         if (top > limit) break;
         const entry = entries[i];
         if (entry.kind !== 'sample') continue;
+        if (qualityVisible.get(entry.status || 'valid') === false) continue;
         const yMid = top - scrollTop + pxPerSample / 2;
 
         if (entry.seq % labelEvery === 0) {
@@ -359,7 +385,9 @@ export function createGraph(options) {
           ctx.fillText(formatClock(entry.t), PAD_LEFT + 2, yMid);
         }
 
-        if (entry.captureId) drawMarker(yMid, colors);
+        if (entry.captureId && entry.value != null && qualityVisible.get(entry.status) !== false) {
+          drawMarker(yMid, colors);
+        }
       }
     }
 
@@ -426,15 +454,24 @@ export function createGraph(options) {
    * @returns {void}
    */
   function drawEvent(entry, y, colors, plotWidth) {
+    // Opaque base deliberately masks the vertical threshold/noise/grid lines;
+    // event labels must remain readable where those references cross them.
+    ctx.fillStyle = colors.plotBg;
+    ctx.fillRect(PAD_LEFT, y, plotWidth, EVENT_BLOCK_PX);
     ctx.fillStyle = entry.phase === 'measuring' ? colors.measuring : colors.applying;
     ctx.fillRect(PAD_LEFT, y, plotWidth, EVENT_BLOCK_PX);
-    ctx.strokeStyle = colors.event;
+    const eventColor = entry.phase === 'measuring'
+      ? colors.eventMeasuring
+      : colors.eventApplying;
+    ctx.strokeStyle = eventColor;
     ctx.lineWidth = 1;
     ctx.beginPath();
     ctx.moveTo(PAD_LEFT, Math.round(y) + 0.5);
     ctx.lineTo(PAD_LEFT + plotWidth, Math.round(y) + 0.5);
+    ctx.moveTo(PAD_LEFT, Math.round(y + EVENT_BLOCK_PX) + 0.5);
+    ctx.lineTo(PAD_LEFT + plotWidth, Math.round(y + EVENT_BLOCK_PX) + 0.5);
     ctx.stroke();
-    ctx.fillStyle = colors.event;
+    ctx.fillStyle = eventColor;
     ctx.font = '9px ui-monospace, SFMono-Regular, Menlo, monospace';
     ctx.textAlign = 'center';
     ctx.textBaseline = 'middle';
@@ -556,13 +593,14 @@ export function createGraph(options) {
    * Appends one measured sample at the live edge.
    *
    * @param {number} t Epoch milliseconds of the sample.
-   * @param {number} value Averaged distance for the tick.
+   * @param {number|null} value Averaged raw distance, or null when no face exists.
    * @param {string} [phase] Operator-declared session phase.
+   * @param {'valid'|'outside-guide'|'lighting'|'no-face'} [status] Comparability state.
    * @returns {number} Index of the appended entry.
    */
-  function pushSample(t, value, phase = 'ready') {
+  function pushSample(t, value, phase = 'ready', status = 'valid') {
     openGapIndex = -1;
-    const entry = { kind: 'sample', t, value, phase, seq: sampleSeq };
+    const entry = { kind: 'sample', t, value, phase, status, seq: sampleSeq };
     sampleSeq += 1;
     tops.push(contentHeight);
     entries.push(entry);
@@ -673,6 +711,15 @@ export function createGraph(options) {
     draw();
   }
 
+  /** Shows or hides one quality state without deleting its samples. */
+  function setQualityVisible(status, visible) {
+    if (!qualityVisible.has(status)) return;
+    qualityVisible.set(status, Boolean(visible));
+    relayout();
+    if (follow) scrollToNow();
+    draw();
+  }
+
   /**
    * Clears the timeline and returns to the live edge.
    *
@@ -708,6 +755,7 @@ export function createGraph(options) {
     for (let i = Math.max(0, index - 3); i <= Math.min(entries.length - 1, index + 3); i += 1) {
       const entry = entries[i];
       if (entry.kind !== 'sample' || !entry.captureId) continue;
+      if (qualityVisible.get(entry.status || 'valid') === false) continue;
       const centre = tops[i] + pxPerSample / 2;
       if (Math.abs(centre - virtualY) <= MARKER_RADIUS + 3) return entry.captureId;
     }
@@ -756,21 +804,37 @@ export function createGraph(options) {
     markLatestCapture,
     clearCapture,
     setNoiseFloor,
+    setQualityVisible,
     /**
      * Reports timeline size for diagnostics and tests.
      *
-     * @returns {{samples: number, gaps: number, events: number, following: boolean}} Snapshot.
+     * @returns {object} Timeline counts and filter state.
      */
     stats() {
       let samples = 0;
+      let visibleSamples = 0;
       let gaps = 0;
       let events = 0;
+      const quality = { valid: 0, 'outside-guide': 0, lighting: 0, 'no-face': 0 };
       for (const entry of entries) {
         if (entry.kind === 'gap') gaps += 1;
         else if (entry.kind === 'event') events += 1;
-        else samples += 1;
+        else {
+          samples += 1;
+          if (qualityVisible.get(entry.status || 'valid') !== false) visibleSamples += 1;
+          quality[entry.status || 'valid'] = (quality[entry.status || 'valid'] || 0) + 1;
+        }
       }
-      return { samples, gaps, events, following: follow };
+      return {
+        samples,
+        visibleSamples,
+        virtualHeight: contentHeight,
+        gaps,
+        events,
+        following: follow,
+        quality,
+        qualityVisible: Object.fromEntries(qualityVisible),
+      };
     },
     /**
      * Disconnects observers held by the graph.
diff --git a/lab-js/realtime/realtime-guide.js b/lab-js/realtime/realtime-guide.js
new file mode 100644
index 0000000..e578915
--- /dev/null
+++ b/lab-js/realtime/realtime-guide.js
@@ -0,0 +1,159 @@
+/**
+ * @module realtime-guide
+ * @description Draws the baseline's normalized face geometry over the video.
+ * The overlay is a positioning memory aid, not a mask or a measurement.
+ */
+
+const JAW = Array.from({ length: 17 }, (_, index) => index);
+const LEFT_EYE = [36, 37, 38, 39, 40, 41];
+const RIGHT_EYE = [42, 43, 44, 45, 46, 47];
+
+/** True when Space belongs to the page rather than a focused control. */
+export function canUseGuideShortcut(target) {
+  if (!(target instanceof Element)) return true;
+  if (target.closest('button, input, select, textarea, summary, video, [contenteditable="true"], [role="dialog"]')) {
+    return false;
+  }
+  return true;
+}
+
+/**
+ * Creates a temporary baseline-guide overlay.
+ *
+ * @param {object} options Controller wiring.
+ * @param {HTMLCanvasElement} options.canvasEl Overlay canvas.
+ * @param {HTMLVideoElement} options.videoEl Video whose object-fit is mirrored.
+ * @param {HTMLElement} options.stageEl Canvas positioning container.
+ * @returns {object} Guide controller.
+ */
+export function createBaselineGuide({ canvasEl, videoEl, stageEl }) {
+  const ctx = canvasEl.getContext('2d');
+  let guide = null;
+  let fadeTimer = null;
+  let hideTimer = null;
+
+  function clearTimers() {
+    if (fadeTimer) window.clearTimeout(fadeTimer);
+    if (hideTimer) window.clearTimeout(hideTimer);
+    fadeTimer = null;
+    hideTimer = null;
+  }
+
+  function pointMapper(width, height) {
+    const stageRect = stageEl.getBoundingClientRect();
+    const videoRect = videoEl.getBoundingClientRect();
+    const sourceW = guide.aspectRatio;
+    const sourceH = 1;
+    const fit = getComputedStyle(videoEl).objectFit || 'fill';
+    const scale = fit === 'cover'
+      ? Math.max(videoRect.width / sourceW, videoRect.height / sourceH)
+      : Math.min(videoRect.width / sourceW, videoRect.height / sourceH);
+    const renderedW = sourceW * scale;
+    const renderedH = sourceH * scale;
+    const offsetX = videoRect.left - stageRect.left + (videoRect.width - renderedW) / 2;
+    const offsetY = videoRect.top - stageRect.top + (videoRect.height - renderedH) / 2;
+    const mirrored = videoEl.classList.contains('source-camera');
+    return ({ x, y }) => ({
+      x: mirrored ? width - (offsetX + x * sourceW * scale) : offsetX + x * sourceW * scale,
+      y: Math.min(height, Math.max(0, offsetY + y * sourceH * scale)),
+    });
+  }
+
+  function strokePath(points, close = false) {
+    if (!points.length) return;
+    ctx.beginPath();
+    ctx.moveTo(points[0].x, points[0].y);
+    for (let index = 1; index < points.length; index += 1) ctx.lineTo(points[index].x, points[index].y);
+    if (close) ctx.closePath();
+    ctx.stroke();
+  }
+
+  /** Paints the guide using the current stage geometry. */
+  function draw() {
+    const rect = stageEl.getBoundingClientRect();
+    const width = Math.max(1, Math.floor(rect.width));
+    const height = Math.max(1, Math.floor(rect.height));
+    const dpr = Math.max(1, window.devicePixelRatio || 1);
+    canvasEl.width = Math.floor(width * dpr);
+    canvasEl.height = Math.floor(height * dpr);
+    ctx.setTransform(dpr, 0, 0, dpr, 0, 0);
+    ctx.clearRect(0, 0, width, height);
+    if (!guide) return;
+
+    const map = pointMapper(width, height);
+    const landmarks = guide.landmarks.map(map);
+    const box = guide.box;
+    const boxTopLeft = map({ x: box.x, y: box.y });
+    const boxTopRight = map({ x: box.x + box.width, y: box.y });
+    const jaw = JAW.map((index) => landmarks[index]);
+    const leftEye = LEFT_EYE.map((index) => landmarks[index]);
+    const rightEye = RIGHT_EYE.map((index) => landmarks[index]);
+    const nose = landmarks[30];
+
+    ctx.strokeStyle = getComputedStyle(canvasEl).getPropertyValue('--rt-guide').trim() || '#ffe7a8';
+    ctx.fillStyle = ctx.strokeStyle;
+    ctx.lineWidth = 1.5;
+    ctx.setLineDash([6, 5]);
+    strokePath(jaw);
+
+    // face-api has no scalp landmarks. The upper reference is therefore an
+    // explicit detector-box arc, connected to the jaw endpoints.
+    const leftJaw = jaw[0];
+    const rightJaw = jaw[jaw.length - 1];
+    const topY = Math.min(boxTopLeft.y, boxTopRight.y);
+    const centerX = (leftJaw.x + rightJaw.x) / 2;
+    const radiusX = Math.abs(rightJaw.x - leftJaw.x) / 2;
+    const radiusY = Math.max(12, Math.max(leftJaw.y, rightJaw.y) - topY);
+    ctx.beginPath();
+    ctx.ellipse(centerX, Math.max(leftJaw.y, rightJaw.y), radiusX, radiusY, 0, Math.PI, Math.PI * 2);
+    ctx.stroke();
+
+    ctx.setLineDash([]);
+    strokePath(leftEye, true);
+    strokePath(rightEye, true);
+    const eyeA = landmarks[36];
+    const eyeB = landmarks[45];
+    strokePath([eyeA, eyeB]);
+    ctx.beginPath();
+    ctx.arc(nose.x, nose.y, 3.5, 0, Math.PI * 2);
+    ctx.fill();
+  }
+
+  function hide() {
+    clearTimers();
+    canvasEl.classList.remove('is-visible', 'is-fading');
+  }
+
+  function show() {
+    if (!guide) return false;
+    clearTimers();
+    draw();
+    canvasEl.classList.remove('is-fading');
+    canvasEl.classList.add('is-visible');
+    fadeTimer = window.setTimeout(() => canvasEl.classList.add('is-fading'), 1000);
+    hideTimer = window.setTimeout(hide, 1600);
+    return true;
+  }
+
+  const observer = new ResizeObserver(() => {
+    if (canvasEl.classList.contains('is-visible')) draw();
+  });
+  observer.observe(stageEl);
+
+  return {
+    draw,
+    show,
+    hide,
+    setGuide(value) {
+      guide = value || null;
+      if (!guide) hide();
+    },
+    hasGuide() {
+      return Boolean(guide);
+    },
+    destroy() {
+      clearTimers();
+      observer.disconnect();
+    },
+  };
+}
diff --git a/lab-js/realtime/realtime-radial.js b/lab-js/realtime/realtime-radial.js
index dc7b818..d3a24c7 100644
--- a/lab-js/realtime/realtime-radial.js
+++ b/lab-js/realtime/realtime-radial.js
@@ -1,11 +1,32 @@
 /**
  * @module realtime-radial
  * @description
- * A compact fingerprint of all 128 descriptor deltas. The spokes are latent
- * coordinates, not facial regions: the plot can compare the shape of two
- * changes, but it cannot say that one spoke belongs to an eyebrow or cheek.
+ * A compact distribution of descriptor change. Absolute deltas are ranked,
+ * then reduced into 16 RMS bands. The spokes are magnitudes, not stable latent
+ * coordinates or facial regions.
  */
 
+/**
+ * Sorts absolute descriptor deltas and reduces them to equal-sized RMS bands.
+ * Ranking makes the plot legible while deliberately discarding coordinate
+ * identity: it compares the distribution of change, not where change lives.
+ *
+ * @param {Float32Array|number[]} values Descriptor deltas.
+ * @param {number} [bandCount=16] Number of ranked bands.
+ * @returns {number[]} Ascending RMS magnitudes.
+ */
+export function rankedDeltaBands(values, bandCount = 16) {
+  const ranked = Array.from(values || [], (value) => Number.isFinite(value) ? Math.abs(value) : 0)
+    .sort((a, b) => a - b);
+  if (!ranked.length || bandCount < 1) return [];
+  return Array.from({ length: Math.min(bandCount, ranked.length) }, (_, index) => {
+    const start = Math.floor((index * ranked.length) / bandCount);
+    const end = Math.max(start + 1, Math.floor(((index + 1) * ranked.length) / bandCount));
+    const band = ranked.slice(start, end);
+    return Math.sqrt(band.reduce((sum, value) => sum + value * value, 0) / band.length);
+  });
+}
+
 /**
  * Converts descriptor deltas to radial points.
  *
@@ -126,11 +147,11 @@ export function createRadialGraph({ canvasEl, maxDelta = 0.15 }) {
   return {
     draw,
     setLive(values) {
-      live = values ? Array.from(values) : null;
+      live = values ? rankedDeltaBands(values) : null;
       draw();
     },
     setMeasured(values) {
-      measured = values ? Array.from(values) : null;
+      measured = values ? rankedDeltaBands(values) : null;
       draw();
     },
     reset() {
diff --git a/lab-js/realtime/realtime-settings.js b/lab-js/realtime/realtime-settings.js
index 7bfbdd8..0716a53 100644
--- a/lab-js/realtime/realtime-settings.js
+++ b/lab-js/realtime/realtime-settings.js
@@ -17,7 +17,7 @@
 
 const SETTINGS_KEY = 'gstmxx.realtime.settings';
 const BASELINE_KEY = 'gstmxx.realtime.baseline';
-const BASELINE_FORMAT = 2;
+const BASELINE_FORMAT = 3;
 const CONDITION_KEYS = [
   'rollDegrees',
   'yawOffset',
@@ -69,9 +69,9 @@ export function saveSettings(settings) {
  * Reads a persisted baseline.
  *
  * The baseline is a biometric template. It never leaves the device, and
- * "Delete baseline" removes it outright rather than flipping a flag.
+ * "Reset baseline" removes it outright rather than flipping a flag.
  *
- * @returns {{descriptor: number[], noiseFloor: number, capturedAt: number, sourceKind: 'camera', conditions: object}|null} Stored baseline.
+ * @returns {{descriptor: number[], noiseFloor: number, capturedAt: number, sourceKind: 'camera', conditions: object, guide: object}|null} Stored baseline.
  */
 export function loadBaseline() {
   try {
@@ -87,6 +87,17 @@ export function loadBaseline() {
     if (!parsed.conditions || CONDITION_KEYS.some((key) => !Number.isFinite(parsed.conditions[key]))) {
       return null;
     }
+    if (
+      !parsed.guide ||
+      !Number.isFinite(parsed.guide.aspectRatio) ||
+      !parsed.guide.box ||
+      ['x', 'y', 'width', 'height'].some((key) => !Number.isFinite(parsed.guide.box[key])) ||
+      !Array.isArray(parsed.guide.landmarks) ||
+      parsed.guide.landmarks.length !== 68 ||
+      parsed.guide.landmarks.some((point) => !Number.isFinite(point?.x) || !Number.isFinite(point?.y))
+    ) {
+      return null;
+    }
     return parsed;
   } catch {
     return null;
diff --git a/lab-js/realtime/realtime.js b/lab-js/realtime/realtime.js
index 9f54c7c..d854814 100644
--- a/lab-js/realtime/realtime.js
+++ b/lab-js/realtime/realtime.js
@@ -25,6 +25,7 @@ import {
 } from './realtime-capture.js';
 import { createGallery } from './realtime-gallery.js';
 import { createRadialGraph } from './realtime-radial.js';
+import { canUseGuideShortcut, createBaselineGuide } from './realtime-guide.js';
 import {
   createSettings,
   guardDestructive,
@@ -58,11 +59,11 @@ const GRAPH_MAX_DISTANCE = 1.25;
 const GRAPH_WINDOW_SECONDS = 60;
 const SAMPLES_PER_SECOND = 3;
 
-/** Long enough that a blink or a head turn does not punch a hole in the trace. */
+/** Long enough that a blink does not put controls into a paused UI state. */
 const NO_FACE_DEBOUNCE_MS = 1500;
 
-const BASELINE_TARGET_SAMPLES = 8;
-const BASELINE_MIN_MS = 3000;
+const BASELINE_TARGET_SAMPLES = 5;
+const BASELINE_MIN_MS = 2000;
 const BASELINE_TIMEOUT_MS = 30000;
 const BASELINE_MAX_SPREAD = 0.2;
 const MEASUREMENT_TARGET_SAMPLES = 8;
@@ -75,6 +76,7 @@ const SPIKE_BEST_MARGIN = 0.05;
 const SPIKE_COOLDOWN_MS = 8000;
 const FRAME_BUFFER_SLOTS = 5;
 const CAPTURE_MAX_WIDTH = 960;
+const STATUS_MIN_DWELL_MS = 1500;
 
 /** Grace period before a hidden tab loses the camera entirely. */
 const HIDDEN_STOP_DELAY_MS = 45000;
@@ -96,6 +98,7 @@ const runtime = {
   running: false,
   streamStopped: false,
   faceMissing: false,
+  timelineNoFace: false,
   lastFaceAt: 0,
   stream: null,
   sourceKind: 'camera',
@@ -107,6 +110,7 @@ const runtime = {
   sessionPhase: SESSION_PHASES.IDLE,
   sessionStep: 0,
   lastMeasurement: null,
+  hasValidDescriptorView: false,
 };
 
 const els = {
@@ -119,12 +123,15 @@ const els = {
   graphCanvas: document.getElementById('distance-graph'),
   graphFollow: document.getElementById('graph-follow'),
   radialCanvas: document.getElementById('radial-graph'),
+  radialValidity: document.getElementById('radial-validity'),
+  guideCanvas: document.getElementById('baseline-guide'),
   legendNoise: document.getElementById('legend-noise'),
   calibrationUi: document.getElementById('calibration-ui'),
   captureRing: document.getElementById('capture-ring'),
   captureRingArc: document.getElementById('capture-ring-arc'),
   captureRingCount: document.getElementById('capture-ring-count'),
   captureHint: document.getElementById('capture-hint'),
+  captureProgressDetail: document.getElementById('capture-progress-detail'),
   btnBaselineStart: document.getElementById('btn-baseline-start'),
   btnBaseline: document.getElementById('btn-baseline'),
   btnShutter: document.getElementById('btn-shutter'),
@@ -139,12 +146,20 @@ const els = {
   sessionResult: document.getElementById('session-result'),
   btnStartChange: document.getElementById('btn-start-change'),
   btnMeasureChange: document.getElementById('btn-measure-change'),
+  btnShowGuide: document.getElementById('btn-show-guide'),
   videoUpload: document.getElementById('video-upload'),
   btnOpenVideo: document.getElementById('btn-open-video'),
   btnUseCamera: document.getElementById('btn-use-camera'),
   sourceState: document.getElementById('source-state'),
   statusPill: document.getElementById('status-pill'),
+  statusText: document.getElementById('status-text'),
+  statusSwatch: document.getElementById('status-swatch'),
+  thresholdHelp: document.getElementById('threshold-help'),
+  thresholdTooltip: document.getElementById('threshold-tooltip'),
   distanceValue: document.getElementById('distance-value'),
+  distanceQuality: document.getElementById('distance-quality'),
+  distanceQualityMain: document.getElementById('distance-quality-main'),
+  distanceQualityDetail: document.getElementById('distance-quality-detail'),
   trackingState: document.getElementById('tracking-state'),
   viewer: document.getElementById('photo-viewer'),
   viewerImage: document.getElementById('viewer-image'),
@@ -163,18 +178,72 @@ let baselineCollector = null;
 let measurementCollector = null;
 let spikeDetector = null;
 let frameBuffer = null;
+let baselineGuide = null;
+
+const statusDisplay = {
+  key: '',
+  quality: 'neutral',
+  shownAt: 0,
+  pending: null,
+  timer: null,
+};
 
 /**
  * Writes the floating status message over the video stage.
  *
  * @param {string} message Message to display.
  * @param {boolean} [warn] Whether the message needs emphasis.
+ * @param {'neutral'|'valid'|'outside-guide'|'lighting'|'no-face'} [quality] Timeline state.
+ * @param {string} [key] Stable state identity; numeric details may vary per frame.
+ * @param {string} [details] Full text exposed as a native tooltip.
  * @returns {void}
  */
-function setStatus(message, warn = false) {
-  if (!els.statusPill) return;
-  els.statusPill.textContent = message;
-  els.statusPill.classList.toggle('is-warn', warn);
+function setStatus(message, warn = false, quality = 'neutral', key = message, details = message) {
+  if (!els.statusPill || !els.statusText || !els.statusSwatch) return;
+
+  const render = (next) => {
+    statusDisplay.key = next.key;
+    statusDisplay.quality = next.quality;
+    statusDisplay.shownAt = Date.now();
+    statusDisplay.pending = null;
+    statusDisplay.timer = null;
+    els.statusText.textContent = next.message;
+    els.statusPill.title = next.details;
+    els.statusPill.classList.toggle('is-warn', next.warn);
+    els.statusSwatch.dataset.quality = next.quality;
+  };
+
+  const next = { message, warn, quality, key, details };
+  if (quality === 'neutral' || !statusDisplay.key || statusDisplay.quality === 'neutral') {
+    if (statusDisplay.timer) window.clearTimeout(statusDisplay.timer);
+    render(next);
+    return;
+  }
+
+  if (statusDisplay.key === key && statusDisplay.quality === quality) {
+    return;
+  }
+
+  const remaining = STATUS_MIN_DWELL_MS - (Date.now() - statusDisplay.shownAt);
+  if (remaining <= 0) {
+    if (statusDisplay.timer) window.clearTimeout(statusDisplay.timer);
+    render(next);
+    return;
+  }
+
+  statusDisplay.pending = next;
+  if (statusDisplay.timer) window.clearTimeout(statusDisplay.timer);
+  statusDisplay.timer = window.setTimeout(() => {
+    if (statusDisplay.pending) render(statusDisplay.pending);
+  }, remaining);
+}
+
+/** Shows or hides the threshold explainer without occupying persistent space. */
+function setThresholdTooltipVisible(visible) {
+  if (!els.thresholdHelp || !els.thresholdTooltip) return;
+  els.thresholdTooltip.hidden = !visible;
+  els.thresholdHelp.setAttribute('aria-expanded', String(visible));
+  if (visible) els.thresholdTooltip.focus();
 }
 
 /**
@@ -198,6 +267,38 @@ function setDistanceValue(value) {
   els.distanceValue.textContent = Number.isFinite(value) ? value.toFixed(3) : '—';
 }
 
+/** Labels whether the numeric readout is measurement-grade or raw context. */
+function setDistanceQuality(status) {
+  if (!els.distanceQuality || !els.distanceQualityMain || !els.distanceQualityDetail) return;
+  const labels = {
+    valid: { main: 'VALID DISTANCE', detail: '' },
+    'outside-guide': { main: 'NO DISTANCE', detail: 'OUTSIDE GUIDE' },
+    lighting: { main: 'NO DISTANCE', detail: 'LIGHTING CHANGED' },
+    'no-face': { main: 'NO DISTANCE', detail: 'NO FACE' },
+  };
+  const label = labels[status] || labels.valid;
+  els.distanceQualityMain.textContent = label.main;
+  els.distanceQualityDetail.textContent = label.detail;
+  els.distanceQualityDetail.hidden = !label.detail;
+  els.distanceQuality.dataset.quality = status;
+}
+
+/** Marks whether the descriptor panels show a current or frozen valid frame. */
+function setDescriptorValidity(valid) {
+  if (!els.radialValidity) return;
+  if (valid) {
+    runtime.hasValidDescriptorView = true;
+    els.radialValidity.textContent = 'LIVE VALID';
+    els.radialValidity.dataset.validity = 'live';
+  } else if (runtime.hasValidDescriptorView) {
+    els.radialValidity.textContent = 'LAST VALID FRAME';
+    els.radialValidity.dataset.validity = 'stale';
+  } else {
+    els.radialValidity.textContent = 'WAITING FOR VALID FRAME';
+    delete els.radialValidity.dataset.validity;
+  }
+}
+
 /**
  * Computes signed per-coordinate descriptor deltas.
  *
@@ -225,9 +326,12 @@ function mediaTimeSuffix() {
 /** Reflects the declared experiment phase in the workflow controls. */
 function syncSessionControls() {
   const hasBaseline = Boolean(runtime.baseline);
+  const applying = runtime.sessionPhase === SESSION_PHASES.APPLYING;
+  const measuring = runtime.sessionPhase === SESSION_PHASES.MEASURING;
   els.sessionBar.hidden = !hasBaseline;
-  els.btnStartChange.disabled = !hasBaseline || runtime.sessionPhase === SESSION_PHASES.MEASURING;
-  els.btnMeasureChange.disabled = runtime.sessionPhase !== SESSION_PHASES.APPLYING;
+  els.btnStartChange.disabled = !hasBaseline || applying || measuring;
+  els.btnStartChange.classList.toggle('is-status', applying);
+  els.btnMeasureChange.disabled = !applying;
 
   if (!hasBaseline) {
     els.sessionStep.textContent = 'Ready';
@@ -345,8 +449,46 @@ function setRingProgress(accepted, target) {
   const ratio = Math.max(0, Math.min(1, accepted / target));
   els.captureRingArc.style.strokeDasharray = String(RING_CIRCUMFERENCE);
   els.captureRingArc.style.strokeDashoffset = String(RING_CIRCUMFERENCE * (1 - ratio));
-  if (els.captureRingCount) els.captureRingCount.textContent = String(accepted);
-  if (els.captureRing) els.captureRing.setAttribute('aria-valuenow', String(accepted));
+  if (els.captureRingCount) els.captureRingCount.textContent = `${accepted}/${target}`;
+  if (els.captureRing) {
+    const previous = Number(els.captureRing.dataset.accepted || 0);
+    els.captureRing.setAttribute('aria-valuenow', String(accepted));
+    els.captureRing.setAttribute('aria-valuemax', String(target));
+    els.captureRing.dataset.accepted = String(accepted);
+    if (accepted > previous) {
+      els.captureRing.classList.remove('is-sampled');
+      void els.captureRing.offsetWidth;
+      els.captureRing.classList.add('is-sampled');
+    }
+  }
+}
+
+/** Returns source-relative timing so a paused video cannot be sampled twice. */
+function currentObservation() {
+  if (runtime.sourceKind !== 'video' || !Number.isFinite(els.video.currentTime)) return {};
+  const sourceTimeMs = els.video.currentTime * 1000;
+  return {
+    sourceTimeMs,
+    id: `${runtime.sourceEpoch}:${sourceTimeMs.toFixed(3)}`,
+  };
+}
+
+/** Updates the diagnostic line below the baseline target. */
+function setCaptureProgressDetail(progress, faceDetected = true) {
+  if (!els.captureProgressDetail || !progress) return;
+  els.captureProgressDetail.hidden = false;
+  const state = faceDetected ? 'face detected' : 'no face';
+  const excluded = progress.rejected || 0;
+  const duplicate = progress.duplicates ? ` · ${progress.duplicates} repeated video frames ignored` : '';
+  els.captureProgressDetail.textContent =
+    `${state} · ${progress.accepted}/${progress.target} clean · ${excluded} excluded${duplicate}`;
+}
+
+/** Joins every failed quality check instead of hiding all but the first one. */
+function qualityMessage(progress) {
+  const reasons = progress?.reasons?.filter(Boolean) || [];
+  if (!reasons.length) return progress?.reason || 'This frame is not usable.';
+  return reasons.join(' Also: ');
 }
 
 /**
@@ -368,6 +510,7 @@ function syncBaselineControls() {
   const hasBaseline = Boolean(runtime.baseline);
   if (els.btnBaseline) els.btnBaseline.hidden = !hasBaseline;
   if (els.btnShutter) els.btnShutter.disabled = !hasBaseline;
+  if (els.btnShowGuide) els.btnShowGuide.disabled = !hasBaseline || !baselineGuide?.hasGuide();
   setCalibrationVisible(!hasBaseline);
   if (els.btnBaselineStart) els.btnBaselineStart.hidden = false;
 
@@ -412,6 +555,7 @@ async function startBaselineCapture() {
   runtime.comparisonExcluded = false;
   runtime.sessionStep = 0;
   runtime.lastMeasurement = null;
+  runtime.hasValidDescriptorView = false;
   setSessionPhase(SESSION_PHASES.IDLE);
   clearBaseline();
 
@@ -423,6 +567,9 @@ async function startBaselineCapture() {
   spikeDetector.reset();
   frameBuffer.clear();
   if (radialGraph) radialGraph.reset();
+  if (baselineGuide) baselineGuide.setGuide(null);
+  setDistanceQuality('valid');
+  setDescriptorValidity(false);
 
   setCalibrationVisible(true);
   if (els.captureRing) els.captureRing.classList.add('is-active');
@@ -432,7 +579,11 @@ async function startBaselineCapture() {
   if (els.legendNoise) els.legendNoise.textContent = '—';
 
   setRingProgress(0, BASELINE_TARGET_SAMPLES);
-  setHint('Hold still and look at the camera.', true);
+  if (els.captureProgressDetail) {
+    els.captureProgressDetail.hidden = false;
+    els.captureProgressDetail.textContent = `face not sampled yet · 0/${BASELINE_TARGET_SAMPLES} clean · 0 excluded`;
+  }
+  setHint('Keep your face still and look at the centre dot for 2 seconds.', true);
   setStatus('Capturing baseline…');
   baselineCollector.start();
 }
@@ -462,9 +613,11 @@ function adoptBaseline(baseline, restored = false) {
   runtime.appState = APP_STATES.TRACKING;
   runtime.distanceBuffer = [];
   runtime.faceMissing = false;
+  runtime.timelineNoFace = false;
   runtime.comparisonExcluded = false;
   runtime.sessionStep = 0;
   runtime.lastMeasurement = null;
+  runtime.hasValidDescriptorView = false;
   setSessionPhase(SESSION_PHASES.READY);
 
   setTrackingState(APP_STATES.TRACKING);
@@ -473,15 +626,22 @@ function adoptBaseline(baseline, restored = false) {
   spikeDetector.reset();
   frameBuffer.clear();
   if (radialGraph) radialGraph.reset();
+  if (baselineGuide) {
+    baselineGuide.setGuide(baseline.guide);
+    baselineGuide.show();
+  }
+  setDistanceQuality('valid');
+  setDescriptorValidity(false);
 
   if (els.captureRing) els.captureRing.classList.remove('is-active');
+  if (els.captureProgressDetail) els.captureProgressDetail.hidden = true;
   syncBaselineControls();
 
   if (restored) {
     const age = formatDuration(Date.now() - baseline.capturedAt);
     setStatus(`Baseline restored, captured ${age} ago. Noise floor ${baseline.noiseFloor.toFixed(3)}.`);
   } else {
-    setStatus(`Baseline set. Noise floor ${baseline.noiseFloor.toFixed(3)} — anything below that is not a result.`);
+    setStatus(`Baseline set. Noise floor ${baseline.noiseFloor.toFixed(3)} — readings below it are within baseline variation.`);
   }
 }
 
@@ -496,6 +656,7 @@ function deleteBaseline() {
   runtime.comparisonExcluded = false;
   runtime.sessionStep = 0;
   runtime.lastMeasurement = null;
+  runtime.hasValidDescriptorView = false;
   setSessionPhase(SESSION_PHASES.IDLE);
   clearBaseline();
   baselineCollector.cancel();
@@ -508,8 +669,12 @@ function deleteBaseline() {
   spikeDetector.reset();
   frameBuffer.clear();
   if (radialGraph) radialGraph.reset();
+  if (baselineGuide) baselineGuide.setGuide(null);
+  setDistanceQuality('valid');
+  setDescriptorValidity(false);
 
   if (els.captureRing) els.captureRing.classList.remove('is-active');
+  if (els.captureProgressDetail) els.captureProgressDetail.hidden = true;
   syncBaselineControls();
   setHint('Capture a reference of your bare face before you start.');
   setStatus('Baseline deleted. Saved captures are kept.');
@@ -523,16 +688,21 @@ function deleteBaseline() {
  */
 function processBaselineFrame(detection) {
   const verdict = qualityGate.evaluate(detection);
-  const progress = baselineCollector.offer(verdict, detection.descriptor);
+  const progress = baselineCollector.offer(verdict, detection.descriptor, currentObservation());
 
   setRingProgress(progress.accepted, progress.target);
+  setCaptureProgressDetail(progress, true);
 
   if (progress.status === 'blocked') {
+    setHint(qualityMessage(progress), true);
+    return;
+  }
+  if (progress.status === 'waiting') {
     setHint(progress.reason, true);
     return;
   }
   if (progress.status === 'collecting') {
-    setHint('Hold still and look at the camera.', true);
+    setHint('Keep your face still and look at the centre dot.', true);
     return;
   }
   if (progress.status === 'failed') {
@@ -641,46 +811,61 @@ async function processTrackingFrame(detection) {
   const quality = qualityGate.evaluate(detection, { allowAppearanceChanges: true });
   const comparable = quality.ok
     ? compareConditions(runtime.baseline.conditions, quality.metrics)
-    : { ok: false, reason: quality.reason };
+    : quality;
+  const distance = euclidean(runtime.baseline.descriptor, detection.descriptor);
 
   if (!comparable.ok) {
-    runtime.distanceBuffer = [];
+    const exclusionReason = qualityMessage(comparable);
+    const hasLightDifference = comparable.reasons?.some((reason) =>
+      /light|luma/i.test(reason),
+    );
+    const hasGeometryDifference = comparable.reasons?.some((reason) =>
+      !/light|luma/i.test(reason),
+    );
+    const timelineStatus = hasLightDifference && !hasGeometryDifference
+      ? 'lighting'
+      : 'outside-guide';
+    runtime.distanceBuffer.push({ value: distance, status: timelineStatus });
     runtime.comparisonExcluded = true;
-    resetEqualizer();
-    setDistanceValue(Number.NaN);
+    setDistanceValue(distance);
+    setDistanceQuality(timelineStatus);
+    setDescriptorValidity(false);
     els.btnShutter.disabled = true;
-    graph.beginGap(Date.now(), 'pose/light excluded');
+    setStatus(
+      comparable.reason || exclusionReason,
+      true,
+      timelineStatus,
+      `excluded:${timelineStatus}`,
+      `${exclusionReason} Raw distance is shown, but this frame is excluded from measurements.`,
+    );
 
     if (runtime.sessionPhase === SESSION_PHASES.MEASURING) {
       const progress = measurementCollector.offer(
-        { ok: false, reason: comparable.reason },
+        { ok: false, reason: comparable.reason, reasons: comparable.reasons },
         detection.descriptor,
+        currentObservation(),
       );
-      els.sessionResult.textContent = `Waiting for comparable frames: ${comparable.reason}`;
+      els.sessionResult.textContent = `Waiting for comparable frames: ${exclusionReason}`;
       if (progress.status === 'failed') {
         measurementCollector.cancel();
         setSessionPhase(SESSION_PHASES.APPLYING);
         setStatus(`Measurement paused. ${progress.reason}`, true);
       }
-    } else {
-      setStatus(`${comparable.reason} This frame is excluded from the measurement.`, true);
     }
     return;
   }
 
-  if (runtime.comparisonExcluded) {
-    runtime.comparisonExcluded = false;
-    graph.endGap(Date.now());
-  }
+  runtime.comparisonExcluded = false;
   els.btnShutter.disabled = false;
 
-  const distance = euclidean(runtime.baseline.descriptor, detection.descriptor);
-  runtime.distanceBuffer.push(distance);
+  runtime.distanceBuffer.push({ value: distance, status: 'valid' });
   setDistanceValue(distance);
+  setDistanceQuality('valid');
   updateEqualizer(runtime.baseline.descriptor, detection.descriptor);
+  setDescriptorValidity(true);
 
   if (runtime.sessionPhase === SESSION_PHASES.MEASURING) {
-    const progress = measurementCollector.offer(quality, detection.descriptor);
+    const progress = measurementCollector.offer(quality, detection.descriptor, currentObservation());
     if (quality.ok) frameBuffer.push(quality.sharpness, distance);
     if (progress.status === 'collecting') {
       els.sessionResult.textContent = `Comparable samples ${progress.accepted}/${progress.target}`;
@@ -694,11 +879,26 @@ async function processTrackingFrame(detection) {
   }
 
   if (distance > MATCH_THRESHOLD) {
-    setStatus(`Past the 0.60 match threshold — reads as a different person.`);
+    setStatus(
+      'Past the 0.60 match threshold — reads as a different person.',
+      false,
+      'valid',
+      'valid:threshold',
+    );
   } else if (distance <= runtime.baseline.noiseFloor) {
-    setStatus('Inside the noise floor — nothing has changed yet.');
+    setStatus(
+      'Inside baseline variation — no change is distinguishable from the baseline noise.',
+      false,
+      'valid',
+      'valid:noise',
+    );
   } else {
-    setStatus(`Above the noise floor, below 0.60 — still reads as a match.`);
+    setStatus(
+      'Above the noise floor, below 0.60 — still reads as a match.',
+      false,
+      'valid',
+      'valid:between',
+    );
   }
 
   // Declared application intervals use an explicit temporal snapshot. Automatic
@@ -767,28 +967,32 @@ async function captureNow() {
 /**
  * Handles a frame in which no face was found.
  *
- * After the debounce the timeline stops advancing entirely and the absence is
- * drawn as one collapsed block. Nothing is sampled until a face returns, so
- * stepping away to apply makeup costs no scroll and produces no readings.
+ * The timeline marks absence immediately with a no-value quality tick. The UI
+ * waits through a short debounce before disabling controls or announcing a
+ * pause, so a blink remains harmless without erasing elapsed time.
  *
  * @returns {void}
  */
 function processNoFace() {
   const now = Date.now();
+  runtime.timelineNoFace = true;
+  runtime.distanceBuffer = [];
   if (!runtime.lastFaceAt) runtime.lastFaceAt = now;
   if (now - runtime.lastFaceAt < NO_FACE_DEBOUNCE_MS) return;
 
   if (!runtime.faceMissing) {
     runtime.faceMissing = true;
-    runtime.distanceBuffer = [];
-    resetEqualizer();
-    setDistanceValue(0);
+    setDistanceValue(Number.NaN);
+    setDistanceQuality('no-face');
+    setDescriptorValidity(false);
     if (els.btnShutter) els.btnShutter.disabled = true;
-    setStatus('No face — measurement paused.');
-  }
-
-  if (runtime.appState === APP_STATES.TRACKING) {
-    graph.beginGap(runtime.lastFaceAt, 'no face');
+    if (runtime.appState === APP_STATES.CAPTURING) {
+      setStatus('No face detected — baseline capture is waiting.');
+    } else if (runtime.appState === APP_STATES.TRACKING) {
+      setStatus('No face — measurement paused.', false, 'no-face', 'no-face');
+    } else {
+      setStatus('No face detected. Enter the frame, then set a baseline.');
+    }
   }
 
   if (runtime.sessionPhase === SESSION_PHASES.MEASURING) {
@@ -796,7 +1000,23 @@ function processNoFace() {
   }
 
   if (runtime.appState === APP_STATES.CAPTURING) {
-    setHint('Step into frame.', true);
+    const progress = baselineCollector.offer(
+      { ok: false, reason: 'No face detected.', reasons: ['No face detected.'] },
+      null,
+      currentObservation(),
+    );
+    setRingProgress(progress.accepted, progress.target);
+    setCaptureProgressDetail(progress, false);
+    if (progress.status === 'failed') {
+      if (els.captureRing) els.captureRing.classList.remove('is-active');
+      if (els.btnBaselineStart) els.btnBaselineStart.hidden = false;
+      runtime.appState = APP_STATES.NO_BASELINE;
+      setTrackingState(APP_STATES.NO_BASELINE);
+      setHint(`Could not get a clean read. ${progress.reason}`, true);
+      setStatus('Baseline capture abandoned.', true);
+    } else {
+      setHint('No face detected. Centre your face on the target and hold still.', true);
+    }
   }
 }
 
@@ -807,10 +1027,10 @@ function processNoFace() {
  */
 function processFaceReturned() {
   const now = Date.now();
+  runtime.timelineNoFace = false;
   if (runtime.faceMissing) {
     runtime.faceMissing = false;
-    graph.endGap(now);
-    setStatus('Face back — reading resuming.');
+    setStatus('Face back — reading resuming.', false, 'valid', 'face-returned');
   }
   runtime.lastFaceAt = now;
 }
@@ -870,16 +1090,18 @@ async function onFrame() {
  */
 function flushDistanceBuffer() {
   if (runtime.appState !== APP_STATES.TRACKING) return;
-  if (runtime.faceMissing) {
-    graph.draw();
+  if (runtime.timelineNoFace) {
+    graph.pushSample(Date.now(), null, runtime.sessionPhase, 'no-face');
     return;
   }
   if (!runtime.distanceBuffer.length) return;
 
-  const sum = runtime.distanceBuffer.reduce((acc, value) => acc + value, 0);
-  const average = sum / runtime.distanceBuffer.length;
+  const status = runtime.distanceBuffer[runtime.distanceBuffer.length - 1].status;
+  const matching = runtime.distanceBuffer.filter((reading) => reading.status === status);
+  const sum = matching.reduce((acc, reading) => acc + reading.value, 0);
+  const average = sum / matching.length;
   runtime.distanceBuffer = [];
-  graph.pushSample(Date.now(), average, runtime.sessionPhase);
+  graph.pushSample(Date.now(), average, runtime.sessionPhase, status);
 }
 
 /**
@@ -1101,10 +1323,14 @@ async function handleVisible() {
 function teardown() {
   stopLoops();
   stopStream();
+  if (statusDisplay.timer) window.clearTimeout(statusDisplay.timer);
+  statusDisplay.timer = null;
+  statusDisplay.pending = null;
   if (runtime.hiddenStopTimerId) window.clearTimeout(runtime.hiddenStopTimerId);
   runtime.hiddenStopTimerId = null;
   if (graph) graph.destroy();
   if (radialGraph) radialGraph.destroy();
+  if (baselineGuide) baselineGuide.destroy();
   releaseLocalVideo();
 }
 
@@ -1118,6 +1344,15 @@ function bindListeners() {
   els.btnStartChange.addEventListener('click', startMakeupStep);
   els.btnMeasureChange.addEventListener('click', startStepMeasurement);
   els.btnOpenVideo.addEventListener('click', () => els.videoUpload.click());
+  els.btnShowGuide.addEventListener('click', () => baselineGuide?.show());
+
+  for (const toggle of document.querySelectorAll('.quality-toggle')) {
+    toggle.addEventListener('click', () => {
+      const visible = toggle.getAttribute('aria-pressed') !== 'true';
+      toggle.setAttribute('aria-pressed', String(visible));
+      graph.setQualityVisible(toggle.dataset.quality, visible);
+    });
+  }
 
   els.videoUpload.addEventListener('change', async () => {
     const [file] = els.videoUpload.files || [];
@@ -1133,9 +1368,17 @@ function bindListeners() {
   });
   els.btnUseCamera.addEventListener('click', useCameraSource);
 
-  guardDestructive(els.btnBaseline, 'Delete baseline', 'Delete — sure?', deleteBaseline);
+  guardDestructive(els.btnBaseline, 'Reset baseline', 'Reset — sure?', deleteBaseline);
   guardDestructive(els.btnDeleteAll, 'Delete all', 'Delete all — sure?', () => gallery.deleteAll());
 
+  els.thresholdHelp.addEventListener('click', () => {
+    setThresholdTooltipVisible(els.thresholdTooltip.hidden);
+  });
+  els.thresholdTooltip.addEventListener('click', () => setThresholdTooltipVisible(false));
+  els.thresholdTooltip.addEventListener('keydown', (event) => {
+    if (event.key === 'Enter' || event.key === ' ') setThresholdTooltipVisible(false);
+  });
+
   els.btnShutter.addEventListener('click', captureNow);
   els.btnDownloadAll.addEventListener('click', () => gallery.downloadAll());
 
@@ -1146,6 +1389,16 @@ function bindListeners() {
 
   document.addEventListener('keydown', (event) => {
     if (event.key === 'Escape' && gallery.isOpen()) gallery.close();
+    if (event.key === 'Escape') setThresholdTooltipVisible(false);
+    if (
+      event.code === 'Space' &&
+      !event.repeat &&
+      runtime.baseline &&
+      canUseGuideShortcut(event.target)
+    ) {
+      event.preventDefault();
+      baselineGuide?.show();
+    }
   });
 
   document.addEventListener('visibilitychange', () => {
@@ -1179,8 +1432,14 @@ window.gstmxxRealtime = {
       faceMissing: runtime.faceMissing,
       distanceBufferLength: runtime.distanceBuffer.length,
       sampleCount: stats.samples,
+      visibleSampleCount: stats.visibleSamples,
+      timelineVirtualHeight: stats.virtualHeight,
       gapCount: stats.gaps,
       eventCount: stats.events,
+      qualityCounts: stats.quality,
+      qualityVisible: stats.qualityVisible,
+      statusQuality: statusDisplay.quality,
+      statusKey: statusDisplay.key,
       following: stats.following,
       captureCount: gallery ? gallery.size() : 0,
       running: runtime.running,
@@ -1255,6 +1514,12 @@ export async function initRealtime() {
     maxDelta: MAX_DELTA,
   });
 
+  baselineGuide = createBaselineGuide({
+    canvasEl: els.guideCanvas,
+    videoEl: els.video,
+    stageEl: els.centerView,
+  });
+
   gallery = createGallery({
     viewerEl: els.viewer,
     imageEl: els.viewerImage,
diff --git a/realtime.html b/realtime.html
index 57bf6f0..6e3d021 100644
--- a/realtime.html
+++ b/realtime.html
@@ -42,7 +42,10 @@
 
     <section id="graph-container" aria-label="Descriptor distance over time">
       <header class="panel-header">
-        <p class="panel-title">Distance</p>
+        <div id="distance-quality" class="distance-quality" data-quality="valid">
+          <strong id="distance-quality-main">VALID DISTANCE</strong>
+          <small id="distance-quality-detail" hidden></small>
+        </div>
         <p class="panel-value"><span id="distance-value">0.000</span></p>
       </header>
 
@@ -62,33 +65,66 @@
           <span class="legend-swatch legend-swatch--noise" aria-hidden="true"></span>
           <span>Noise floor <span class="legend-value" id="legend-noise">—</span></span>
         </div>
+        <div class="quality-legend" aria-label="Timeline quality filters">
+          <button class="quality-toggle" type="button" data-quality="outside-guide" aria-pressed="true">
+            <i class="quality-swatch quality-swatch--outside" aria-hidden="true"></i>Outside baseline guide
+          </button>
+          <button class="quality-toggle" type="button" data-quality="lighting" aria-pressed="true">
+            <i class="quality-swatch quality-swatch--lighting" aria-hidden="true"></i>Lighting changed
+          </button>
+          <button class="quality-toggle" type="button" data-quality="no-face" aria-pressed="true">
+            <i class="quality-swatch quality-swatch--no-face" aria-hidden="true"></i>No face
+          </button>
+        </div>
         <div class="legend-row">
           <span class="legend-swatch legend-swatch--threshold" aria-hidden="true"></span>
           <span>Match threshold <span class="legend-value">0.60</span></span>
+          <button
+            id="threshold-help"
+            class="legend-help"
+            type="button"
+            aria-label="Explain the match threshold and noise floor"
+            aria-controls="threshold-tooltip"
+            aria-expanded="false"
+          >?</button>
+        </div>
+        <div
+          class="threshold-tooltip"
+          id="threshold-tooltip"
+          role="tooltip"
+          tabindex="0"
+          title="Click to close"
+          hidden
+        >
+          The noise floor is measured from your own baseline capture: a reading
+          below it is within the variation observed during that capture. 0.60
+          is dlib's default on the LFW benchmark — a benchmark's number, not a
+          property of your face. The space between them is not calibrated.
+          <span class="threshold-tooltip__close">Click to close</span>
         </div>
-        <span class="legend-note" id="legend-explainer">
-          The noise floor is measured from your own baseline capture: below it,
-          nothing changed. 0.60 is dlib's default on the LFW benchmark — a
-          benchmark's number, not a property of your face. The space between
-          them is not calibrated.
-        </span>
       </div>
     </section>
 
     <section id="center-view" aria-label="Camera or local video, baseline capture and controls">
       <video id="webcam" class="source-camera" autoplay muted playsinline></video>
+      <canvas id="baseline-guide" aria-hidden="true"></canvas>
       <div class="video-scrim"></div>
-      <div id="status-pill" aria-live="polite">Loading models…</div>
+      <div id="status-pill" aria-live="polite">
+        <i id="status-swatch" data-quality="neutral" aria-hidden="true"></i>
+        <span id="status-text">Loading models…</span>
+      </div>
 
       <div id="calibration-ui">
-        <div id="capture-ring" class="capture-ring" role="progressbar" aria-valuemin="0" aria-valuemax="8" aria-valuenow="0" aria-label="Baseline samples collected">
+        <div id="capture-ring" class="capture-ring" role="progressbar" aria-valuemin="0" aria-valuemax="5" aria-valuenow="0" aria-label="Clean baseline samples collected">
           <svg viewBox="0 0 116 116" aria-hidden="true">
             <circle class="capture-ring__track" cx="58" cy="58" r="54" />
             <circle class="capture-ring__arc" id="capture-ring-arc" cx="58" cy="58" r="54" />
           </svg>
-          <span class="capture-ring__count" id="capture-ring-count">0</span>
+          <span class="capture-ring__target" aria-hidden="true"><i></i></span>
+          <span class="capture-ring__count" id="capture-ring-count">0/5</span>
         </div>
         <p class="capture-hint" id="capture-hint">Capture a reference of your bare face before you start.</p>
+        <p class="capture-meta" id="capture-progress-detail" aria-live="polite" hidden></p>
         <button id="btn-baseline-start" class="stage-btn" type="button">Set baseline</button>
       </div>
 
@@ -113,6 +149,7 @@
         </div>
         <button id="btn-start-change" class="sb-btn" type="button" disabled>Start makeup step</button>
         <button id="btn-measure-change" class="sb-btn sessionbar__measure" type="button" disabled>Measure this step</button>
+        <button id="btn-show-guide" class="sb-btn sessionbar__guide" type="button" aria-keyshortcuts="Space" disabled>Show guide <kbd>Space</kbd></button>
       </div>
 
       <div id="settings-bar" class="settingsbar" aria-label="View and capture controls">
@@ -121,28 +158,29 @@
             <summary class="sb-btn">Source</summary>
             <div class="source-menu__popover">
               <span class="source-menu__state">Using <strong id="source-state">camera</strong></span>
+              <div class="source-menu__control">
+                <label class="source-menu__control-label" for="brightness-input">
+                  Brightness <output id="brightness-value">0.85</output>
+                </label>
+                <input
+                  id="brightness-input"
+                  type="range"
+                  min="0.4"
+                  max="1.4"
+                  step="0.05"
+                  value="0.85"
+                  title="Display brightness only. This does not reach the detector and cannot change a measurement."
+                />
+              </div>
               <button id="btn-open-video" class="sb-btn" type="button">Open local video</button>
               <input id="video-upload" class="source-file" type="file" accept="video/*,.mkv" />
               <button id="btn-use-camera" class="sb-btn" type="button" hidden>Use camera</button>
+              <button id="btn-baseline" class="sb-btn sb-btn--warn" type="button" hidden>Reset baseline</button>
               <span class="source-menu__note">The file stays on this device.</span>
             </div>
           </details>
         </div>
 
-        <div class="sb-group">
-          <label class="sb-label" for="brightness-input">Display</label>
-          <input
-            id="brightness-input"
-            type="range"
-            min="0.4"
-            max="1.4"
-            step="0.05"
-            value="0.85"
-            title="Display brightness only. This does not reach the detector and cannot change a measurement."
-          />
-          <span class="sb-value" id="brightness-value">0.85</span>
-        </div>
-
         <div class="sb-group">
           <span class="sb-label" id="equalizer-label">Equalizer</span>
           <button
@@ -163,10 +201,6 @@
           <button id="btn-download-all" class="sb-btn" type="button" disabled>Download all</button>
           <button id="btn-delete-all" class="sb-btn sb-btn--warn" type="button" disabled>Delete all</button>
         </div>
-
-        <div class="sb-group">
-          <button id="btn-baseline" class="sb-btn sb-btn--warn" type="button" hidden>Delete baseline</button>
-        </div>
       </div>
     </section>
 
@@ -176,13 +210,14 @@
         <p class="panel-value"><span id="tracking-state">NO BASELINE</span></p>
       </header>
       <div class="panel-body descriptor-body">
-        <section class="radial-panel" aria-label="All 128 descriptor deltas as a radial fingerprint">
+        <section class="radial-panel" aria-label="Ranked distribution of all 128 descriptor deltas">
+          <span id="radial-validity" class="radial-validity">WAITING FOR VALID FRAME</span>
           <canvas id="radial-graph"></canvas>
           <div class="radial-legend">
             <span><i class="radial-live"></i>live</span>
             <span><i class="radial-measured"></i>measured step</span>
           </div>
-          <p>128 latent coordinates; not facial regions.</p>
+          <p>16 ranked RMS bands from 128 latent deltas; distribution, not facial regions.</p>
         </section>
         <div class="equalizer-panel">
           <div id="embedding-bar" aria-label="128 descriptor rows"></div>
diff --git a/styles/realtime.css b/styles/realtime.css
index d371bc4..ca36736 100644
--- a/styles/realtime.css
+++ b/styles/realtime.css
@@ -62,7 +62,12 @@
   --rt-radial-measured-fill: rgba(242, 106, 27, 0.25);
   --rt-phase-applying: rgba(242, 106, 27, 0.13);
   --rt-phase-measuring: rgba(127, 227, 176, 0.13);
-  --rt-event: var(--yellow);
+  --rt-event-applying: var(--gm-bg);
+  --rt-event-measuring: var(--dev);
+  --rt-quality-outside: rgba(242, 106, 27, 0.22);
+  --rt-quality-light: rgba(243, 199, 71, 0.2);
+  --rt-quality-no-face: rgba(138, 123, 106, 0.26);
+  --rt-guide: var(--cream);
 
   --rt-surface: rgba(12, 9, 6, 0.52);
   --rt-surface-solid: rgba(12, 9, 6, 0.86);
@@ -110,6 +115,12 @@
   z-index: var(--z-chrome);
 }
 
+/* The fixed home control occupies the graph header's top-left corner. Reserve
+   that space so it never sits on top of the distance-quality label or value. */
+#graph-container .panel-header {
+  padding-left: 54px;
+}
+
 
 /* --- Panels --------------------------------------------------------------- */
 
@@ -143,6 +154,7 @@
 .panel-title,
 .panel-value,
 .panel-note {
+  position: relative;
   margin: 0;
 }
 
@@ -160,6 +172,44 @@
   font-variant-numeric: tabular-nums;
 }
 
+.distance-quality {
+  display: flex;
+  align-items: baseline;
+  gap: 7px;
+  min-width: 0;
+  width: 100%;
+  padding: 4px 7px;
+  border: 1px solid var(--line);
+  border-radius: 999px;
+  color: var(--muted);
+  white-space: nowrap;
+  overflow: hidden;
+}
+
+.distance-quality strong {
+  flex: none;
+  font: 700 9px/1 var(--mono);
+  letter-spacing: 0.08em;
+}
+
+.distance-quality small {
+  min-width: 0;
+  overflow: hidden;
+  color: currentColor;
+  font: 600 8px/1 var(--mono);
+  letter-spacing: 0.06em;
+  text-overflow: ellipsis;
+}
+
+.distance-quality small[hidden] {
+  display: none;
+}
+
+.distance-quality[data-quality="valid"] { color: var(--rt-noise); }
+.distance-quality[data-quality="outside-guide"] { color: #ffb98b; }
+.distance-quality[data-quality="lighting"] { color: var(--yellow); }
+.distance-quality[data-quality="no-face"] { color: var(--faint); }
+
 .panel-note {
   flex: none;
   padding: 9px 12px;
@@ -199,14 +249,104 @@
   font-variant-numeric: tabular-nums;
 }
 
-.legend-note {
+.legend-help {
+  display: inline-grid;
+  width: 17px;
+  height: 17px;
+  place-items: center;
+  padding: 0;
+  border: 1px solid var(--line);
+  border-radius: 50%;
+  background: rgba(33, 26, 18, 0.72);
+  color: var(--cream);
+  font: 700 10px/1 var(--mono);
+  cursor: pointer;
+}
+
+.legend-help:hover,
+.legend-help[aria-expanded="true"] {
+  border-color: rgba(255, 231, 168, 0.36);
+  background: var(--panel-2);
+}
+
+.legend-help:focus-visible,
+.threshold-tooltip:focus-visible {
+  outline: 2px solid var(--yellow);
+  outline-offset: 2px;
+}
+
+.threshold-tooltip {
+  position: absolute;
+  right: 8px;
+  bottom: calc(100% + 8px);
+  left: 8px;
+  z-index: 4;
+  padding: 10px 11px;
+  border: 1px solid rgba(255, 231, 168, 0.24);
+  border-radius: 8px;
+  background: rgba(33, 26, 18, 0.96);
+  box-shadow: 0 10px 28px rgba(0, 0, 0, 0.42);
+  color: var(--fg);
+  font: 10px/1.5 var(--mono);
+  cursor: pointer;
+}
+
+.threshold-tooltip[hidden] {
+  display: none;
+}
+
+.threshold-tooltip__close {
   display: block;
   margin-top: 6px;
   color: var(--faint);
-  font-size: 10px;
-  line-height: 1.45;
+  font-size: 9px;
+  text-transform: uppercase;
+  letter-spacing: 0.08em;
+}
+
+.quality-legend {
+  display: grid;
+  gap: 3px;
+  margin-top: 7px;
+  padding-top: 7px;
+  border-top: 1px solid var(--line-soft);
+}
+
+.quality-toggle {
+  display: flex;
+  align-items: center;
+  gap: 7px;
+  width: 100%;
+  padding: 2px 0;
+  border: 0;
+  background: transparent;
+  color: var(--muted);
+  font: 9px/1.25 var(--mono);
+  text-align: left;
+  cursor: pointer;
+}
+
+.quality-toggle[aria-pressed="false"] {
+  opacity: 0.42;
+  text-decoration: line-through;
+}
+
+.quality-toggle:focus-visible {
+  outline: 2px solid var(--yellow);
+  outline-offset: 2px;
+}
+
+.quality-swatch {
+  flex: none;
+  width: 14px;
+  height: 8px;
+  border: 1px solid rgba(255, 231, 168, 0.12);
 }
 
+.quality-swatch--outside { background: var(--rt-quality-outside); }
+.quality-swatch--lighting { background: var(--rt-quality-light); }
+.quality-swatch--no-face { background: var(--rt-quality-no-face); }
+
 
 /* --- Graph: virtualised scroll ---------------------------------------------
    The canvas is sticky inside a tall spacer, so it always covers the scroll
@@ -305,6 +445,26 @@
   transform: none;
 }
 
+#baseline-guide {
+  position: absolute;
+  inset: 0;
+  z-index: 3;
+  width: 100%;
+  height: 100%;
+  pointer-events: none;
+  opacity: 0;
+  filter: drop-shadow(0 1px 3px rgba(12, 9, 6, 0.8));
+}
+
+#baseline-guide.is-visible {
+  opacity: 1;
+}
+
+#baseline-guide.is-fading {
+  opacity: 0;
+  transition: opacity 600ms ease;
+}
+
 /* Scrim recedes as exposure rises, so turning the view up actually turns it
    up. Negative opacity clamps to 0 in every engine. */
 .video-scrim {
@@ -331,8 +491,8 @@
 #status-pill {
   position: absolute;
   top: 12px;
-  left: 12px;
-  max-width: calc(100% - 24px);
+  left: 60px;
+  max-width: calc(100% - 72px);
   padding: 7px 12px;
   border: 1px solid var(--line);
   border-radius: 999px;
@@ -341,8 +501,34 @@
   font: 11px var(--mono);
   letter-spacing: 0.04em;
   backdrop-filter: blur(8px);
+  z-index: 4;
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  white-space: nowrap;
+  overflow: hidden;
 }
 
+#status-pill > span {
+  min-width: 0;
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
+
+#status-swatch {
+  flex: none;
+  width: 8px;
+  height: 8px;
+  border: 1px solid rgba(255, 231, 168, 0.28);
+  border-radius: 1px;
+  background: transparent;
+}
+
+#status-swatch[data-quality="valid"] { background: var(--rt-trace); }
+#status-swatch[data-quality="outside-guide"] { background: var(--gm-bg); }
+#status-swatch[data-quality="lighting"] { background: var(--yellow); }
+#status-swatch[data-quality="no-face"] { background: var(--faint); }
+
 #status-pill.is-warn {
   color: var(--cream);
   border-color: rgba(255, 231, 168, 0.28);
@@ -360,6 +546,7 @@
   justify-content: center;
   gap: 16px;
   padding: 16px;
+  z-index: 4;
 }
 
 #calibration-ui[hidden] {
@@ -403,10 +590,66 @@
 }
 
 .capture-ring__count {
-  position: relative;
+  position: absolute;
+  top: calc(50% + 23px);
+  left: 50%;
+  transform: translateX(-50%);
   color: var(--cream);
-  font: 400 30px/1 var(--serif);
+  font: 600 10px/1 var(--mono);
   font-variant-numeric: tabular-nums;
+  letter-spacing: 0.06em;
+}
+
+.capture-ring__target {
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  width: 24px;
+  height: 24px;
+  border: 1px solid rgba(255, 231, 168, 0.72);
+  border-radius: 50%;
+  transform: translate(-50%, -50%);
+}
+
+.capture-ring__target::before,
+.capture-ring__target::after {
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  background: rgba(255, 231, 168, 0.5);
+  content: '';
+  transform: translate(-50%, -50%);
+}
+
+.capture-ring__target::before {
+  width: 34px;
+  height: 1px;
+}
+
+.capture-ring__target::after {
+  width: 1px;
+  height: 34px;
+}
+
+.capture-ring__target i {
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  width: 5px;
+  height: 5px;
+  border-radius: 50%;
+  background: var(--cream);
+  box-shadow: 0 0 12px rgba(255, 231, 168, 0.75);
+  transform: translate(-50%, -50%);
+}
+
+.capture-ring.is-sampled .capture-ring__target {
+  animation: capture-accepted 0.34s ease-out;
+}
+
+@keyframes capture-accepted {
+  0% { box-shadow: 0 0 0 0 rgba(127, 227, 176, 0.8); }
+  100% { box-shadow: 0 0 0 24px rgba(127, 227, 176, 0); }
 }
 
 .capture-hint {
@@ -422,6 +665,25 @@
   color: var(--cream);
 }
 
+.capture-meta {
+  min-height: 1em;
+  margin: -10px 0 0;
+  padding: 6px 9px;
+  border: 1px solid rgba(255, 231, 168, 0.2);
+  border-radius: 999px;
+  background: rgba(33, 26, 18, 0.86);
+  box-shadow: 0 5px 16px rgba(0, 0, 0, 0.24);
+  color: var(--cream);
+  font: 10px/1.4 var(--mono);
+  font-variant-numeric: tabular-nums;
+  letter-spacing: 0.03em;
+  text-align: center;
+}
+
+.capture-meta[hidden] {
+  display: none;
+}
+
 .stage-btn {
   display: inline-flex;
   align-items: center;
@@ -510,6 +772,23 @@
   color: var(--dev);
 }
 
+.sessionbar .sb-btn.is-status,
+.sessionbar .sb-btn.is-status:disabled {
+  border-color: transparent;
+  background: transparent;
+  color: var(--muted);
+  opacity: 1;
+  cursor: default;
+}
+
+.sessionbar__guide kbd {
+  padding: 1px 4px;
+  border: 1px solid var(--line);
+  border-radius: 4px;
+  color: var(--faint);
+  font: 8px/1.2 var(--mono);
+}
+
 .settingsbar {
   position: absolute;
   left: 0;
@@ -613,10 +892,43 @@
   font: 10px/1.4 var(--mono);
 }
 
+.source-menu__control {
+  display: grid;
+  gap: 6px;
+  padding: 8px 0;
+  border-top: 1px solid var(--line-soft);
+  border-bottom: 1px solid var(--line-soft);
+}
+
+.source-menu__control-label {
+  display: flex;
+  justify-content: space-between;
+  color: var(--faint);
+  font: 700 9px var(--mono);
+  letter-spacing: 0.12em;
+  text-transform: uppercase;
+}
+
+.source-menu__control-label output {
+  color: var(--fg);
+  font-variant-numeric: tabular-nums;
+  letter-spacing: 0;
+}
+
+.source-menu__control input[type="range"] {
+  width: 100%;
+  margin: 0;
+}
+
 .source-menu__state strong {
   color: var(--fg);
 }
 
+.source-menu__popover .sb-btn {
+  justify-content: center;
+  width: 100%;
+}
+
 .source-file {
   position: absolute;
   width: 1px;
@@ -728,11 +1040,24 @@
 .radial-panel {
   min-height: 0;
   display: grid;
-  grid-template-rows: minmax(0, 1fr) auto auto;
+  grid-template-rows: auto minmax(0, 1fr) auto auto;
   padding: 8px 8px 6px;
   border-bottom: 1px solid var(--line-soft);
 }
 
+.radial-validity {
+  justify-self: center;
+  padding: 3px 7px;
+  border: 1px solid var(--line-soft);
+  border-radius: 999px;
+  color: var(--faint);
+  font: 700 7.5px/1 var(--mono);
+  letter-spacing: 0.08em;
+}
+
+.radial-validity[data-validity="live"] { color: var(--dev); }
+.radial-validity[data-validity="stale"] { color: var(--yellow); }
+
 #radial-graph {
   width: 100%;
   height: 100%;
@@ -890,4 +1215,12 @@
   .stage-btn {
     transition: none;
   }
+
+  .capture-ring.is-sampled .capture-ring__target {
+    animation: none;
+  }
+
+  #baseline-guide.is-fading {
+    transition-duration: 1ms;
+  }
 }
diff --git a/tests/e2e/realtime.spec.js b/tests/e2e/realtime.spec.js
index 2633597..ce15445 100644
--- a/tests/e2e/realtime.spec.js
+++ b/tests/e2e/realtime.spec.js
@@ -12,6 +12,7 @@ const MODULE_PATHS = [
   '/lab-js/realtime/realtime-graph.js',
   '/lab-js/realtime/realtime-capture.js',
   '/lab-js/realtime/realtime-gallery.js',
+  '/lab-js/realtime/realtime-guide.js',
   '/lab-js/realtime/realtime-radial.js',
   '/lab-js/realtime/realtime-settings.js',
 ];
@@ -21,6 +22,7 @@ async function mockRealtimeModels(page) {
     window.__rtDescriptorShift = 0;
     window.__rtAppearanceChange = false;
     window.__rtPoseShift = 0;
+    window.__rtNoFace = false;
     window.__rtImageReads = 0;
     const originalGetImageData = CanvasRenderingContext2D.prototype.getImageData;
     CanvasRenderingContext2D.prototype.getImageData = function getImageData(x, y, width, height) {
@@ -63,6 +65,7 @@ async function mockRealtimeModels(page) {
               return {
                 withFaceLandmarks: () => ({
                   withFaceDescriptor: async () => {
+                  if (window.__rtNoFace) return null;
                   const descriptor = new Float32Array(128);
                   descriptor[0] = window.__rtDescriptorShift || 0;
                   return {
@@ -148,6 +151,7 @@ test.describe('Realtime page', () => {
     // No control may move the threshold: it is the one number here with a
     // citable provenance, and a moved threshold means nothing.
     await expect(page.locator('#settings-bar input[type="range"]')).toHaveCount(1);
+    await page.locator('.source-menu > summary').click();
     await expect(page.locator('#settings-bar #brightness-input')).toBeVisible();
   });
 
@@ -158,8 +162,52 @@ test.describe('Realtime page', () => {
     await expect(legend).toContainText('Noise floor');
     await expect(legend).toContainText('Match threshold');
     await expect(legend).toContainText('0.60');
-    await expect(legend).toContainText('LFW');
     await expect(page.locator('#legend-noise')).toHaveText('—');
+
+    const help = page.locator('#threshold-help');
+    const tooltip = page.locator('#threshold-tooltip');
+    await expect(tooltip).toBeHidden();
+    await help.click();
+    await expect(help).toHaveAttribute('aria-expanded', 'true');
+    await expect(tooltip).toBeVisible();
+    await expect(tooltip).toContainText('LFW');
+    await tooltip.click();
+    await expect(help).toHaveAttribute('aria-expanded', 'false');
+    await expect(tooltip).toBeHidden();
+
+    const outsideGuide = page.locator('[data-quality="outside-guide"]');
+    await expect(outsideGuide).toHaveAttribute('aria-pressed', 'true');
+    await outsideGuide.click();
+    await expect(outsideGuide).toHaveAttribute('aria-pressed', 'false');
+    expect(
+      await page.evaluate(() => window.gstmxxRealtime.getState().qualityVisible['outside-guide']),
+    ).toBe(false);
+    await outsideGuide.click();
+  });
+
+  test('keeps the home control clear of the distance status', async ({ page }) => {
+    await page.goto('/realtime.html');
+
+    const [homeBox, qualityBox] = await Promise.all([
+      page.locator('.realtime-home').boundingBox(),
+      page.locator('#distance-quality').boundingBox(),
+    ]);
+    expect(homeBox).not.toBeNull();
+    expect(qualityBox).not.toBeNull();
+    expect(homeBox.x + homeBox.width).toBeLessThanOrEqual(qualityBox.x);
+
+    const statusLayout = await page.locator('#status-pill').evaluate((element) => ({
+      whiteSpace: getComputedStyle(element).whiteSpace,
+      overflow: getComputedStyle(element).overflow,
+      lines: element.querySelectorAll('#status-text').length,
+    }));
+    expect(statusLayout).toEqual({ whiteSpace: 'nowrap', overflow: 'hidden', lines: 1 });
+
+    const distanceLayout = await page.locator('#distance-quality').evaluate((element) => ({
+      whiteSpace: getComputedStyle(element).whiteSpace,
+      fitsOneLine: element.scrollHeight <= element.clientHeight,
+    }));
+    expect(distanceLayout).toEqual({ whiteSpace: 'nowrap', fitsOneLine: true });
   });
 
   test('equalizer toggle collapses the right column and persists', async ({ page }) => {
@@ -186,12 +234,15 @@ test.describe('Realtime page', () => {
 
   test('brightness control moves only the display variable', async ({ page }) => {
     await page.goto('/realtime.html');
+    await page.locator('.source-menu > summary').click();
 
     const slider = page.locator('#brightness-input');
     await slider.fill('1.2');
     await slider.dispatchEvent('input');
 
     await expect(page.locator('#brightness-value')).toHaveText('1.20');
+    await expect(page.locator('.source-menu__popover #brightness-input')).toHaveCount(1);
+    expect(await slider.evaluate((element) => Boolean(element.closest('.source-menu__popover')))).toBe(true);
 
     const applied = await page.evaluate(() =>
       document.getElementById('ui-layer').style.getPropertyValue('--rt-brightness'),
@@ -207,7 +258,10 @@ test.describe('Realtime page', () => {
     await expect(deleteAll).toHaveText('Delete all');
 
     // Baseline controls stay hidden until a baseline exists.
-    await expect(page.locator('#btn-baseline')).toBeHidden();
+    const resetBaseline = page.locator('.source-menu__popover #btn-baseline');
+    await expect(resetBaseline).toHaveCount(1);
+    await expect(resetBaseline).toBeHidden();
+    await expect(resetBaseline).toHaveText('Reset baseline');
     await expect(page.locator('#btn-shutter')).toBeDisabled();
     await expect(page.locator('#calibration-ui')).toBeVisible();
     await expect(page.locator('#btn-baseline-start')).toBeVisible();
@@ -312,21 +366,60 @@ test.describe('Realtime page', () => {
     await expect.poll(() => page.evaluate(() => window.gstmxxRealtime.getState().sourceKind)).toBe('video');
     await expect(page.locator('#webcam')).toHaveJSProperty('controls', true);
 
+    await expect(page.locator('#capture-ring')).toHaveAttribute('aria-valuemax', '5');
     await page.locator('#btn-baseline-start').click();
+    await expect(page.locator('.capture-ring__target')).toBeVisible();
+    await expect(page.locator('#capture-hint')).toContainText('centre dot');
+    await expect(page.locator('#capture-ring-count')).toHaveText(/[0-5]\/5/);
+    await expect(page.locator('#capture-progress-detail')).toContainText('clean');
+    const captureMetaBackground = await page.locator('#capture-progress-detail').evaluate(
+      (element) => getComputedStyle(element).backgroundColor,
+    );
+    expect(captureMetaBackground).not.toBe('rgba(0, 0, 0, 0)');
     await expect.poll(
       () => page.evaluate(() => window.gstmxxRealtime.getState().appState),
       { timeout: 20000 },
     ).toBe('TRACKING');
 
+    const showGuide = page.locator('#btn-show-guide');
+    await expect(showGuide).toBeEnabled();
+    await showGuide.click();
+    await expect(page.locator('#baseline-guide')).toHaveClass(/is-visible/);
+    await expect(page.locator('#baseline-guide')).toHaveClass(/is-fading/, { timeout: 1400 });
+    await expect(page.locator('#baseline-guide')).not.toHaveClass(/is-visible/, { timeout: 2200 });
+
+    await page.locator('.source-menu > summary').click();
+    await expect(page.locator('.source-menu__popover #btn-baseline')).toBeVisible();
+    await page.locator('.source-menu > summary').click();
+
     await expect.poll(
       () => page.evaluate(() => window.gstmxxRealtime.getState().sampleCount),
     ).toBeGreaterThan(0);
+    await page.evaluate(() => { window.__rtNoFace = true; });
+    await expect.poll(
+      () => page.evaluate(() => window.gstmxxRealtime.getState().qualityCounts['no-face']),
+    ).toBeGreaterThan(0);
+    const beforeNoFaceFilter = await page.evaluate(() => window.gstmxxRealtime.getState());
+    const noFaceFilter = page.locator('[data-quality="no-face"]');
+    await noFaceFilter.click();
+    const afterNoFaceFilter = await page.evaluate(() => window.gstmxxRealtime.getState());
+    expect(afterNoFaceFilter.visibleSampleCount).toBe(
+      afterNoFaceFilter.sampleCount - afterNoFaceFilter.qualityCounts['no-face'],
+    );
+    expect(afterNoFaceFilter.visibleSampleCount).toBeLessThan(beforeNoFaceFilter.visibleSampleCount);
+    expect(afterNoFaceFilter.timelineVirtualHeight).toBeLessThan(
+      beforeNoFaceFilter.timelineVirtualHeight,
+    );
+    await noFaceFilter.click();
+    await page.evaluate(() => { window.__rtNoFace = false; });
     const pausedAt = await page.evaluate(() => {
       const video = document.getElementById('webcam');
       video.pause();
       return video.currentTime;
     });
     await page.locator('#btn-start-change').click();
+    await expect(page.locator('#btn-start-change')).toHaveClass(/is-status/);
+    await expect(page.locator('#btn-start-change')).toBeDisabled();
     await expect(page.locator('#webcam')).toHaveJSProperty('paused', false);
     await expect.poll(
       () => page.evaluate(() => document.getElementById('webcam').currentTime),
@@ -340,14 +433,37 @@ test.describe('Realtime page', () => {
 
     await page.evaluate(() => { window.__rtPoseShift = 0.02; });
     await page.locator('#btn-measure-change').click();
-    await expect(page.locator('#distance-value')).toHaveText('—');
+    await expect(page.locator('#distance-value')).toHaveText('0.000');
+    await expect(page.locator('#distance-quality-main')).toHaveText('NO DISTANCE');
+    await expect(page.locator('#distance-quality-detail')).toHaveText('OUTSIDE GUIDE');
     await expect(page.locator('#session-result')).toContainText('baseline pose');
+    await expect.poll(
+      () => page.evaluate(() => window.gstmxxRealtime.getState().statusQuality),
+      { timeout: 4000 },
+    ).toBe('outside-guide');
+    await expect(page.locator('#status-swatch')).toHaveAttribute('data-quality', 'outside-guide');
+    await expect(page.locator('#status-text')).toContainText('yaw proxy');
+    const excludedSampleCount = await page.evaluate(
+      () => window.gstmxxRealtime.getState().sampleCount,
+    );
+    await expect.poll(
+      () => page.evaluate(() => window.gstmxxRealtime.getState().sampleCount),
+    ).toBeGreaterThan(excludedSampleCount);
+    expect(
+      await page.evaluate(() => window.gstmxxRealtime.getState().qualityCounts['outside-guide']),
+    ).toBeGreaterThan(0);
 
     await page.evaluate(() => {
       window.__rtPoseShift = 0;
       window.__rtDescriptorShift = 0.2;
       window.__rtAppearanceChange = true;
     });
+    await expect(page.locator('#distance-value')).toHaveText('0.200');
+    // The raw frame state has already recovered, but the readable projection
+    // keeps the previous instruction for its minimum dwell interval.
+    expect(await page.evaluate(() => window.gstmxxRealtime.getState().statusQuality)).toBe(
+      'outside-guide',
+    );
 
     await expect.poll(
       () => page.evaluate(() => {
diff --git a/tests/unit/realtime-capture.test.js b/tests/unit/realtime-capture.test.js
index 3c024a1..db19cf8 100644
--- a/tests/unit/realtime-capture.test.js
+++ b/tests/unit/realtime-capture.test.js
@@ -6,6 +6,7 @@ import {
   euclidean,
   medoid,
   summarizeConditions,
+  summarizeGuides,
 } from '../../lab-js/realtime/realtime-capture.js';
 
 describe('realtime temporal measurement helpers', () => {
@@ -34,6 +35,21 @@ describe('realtime temporal measurement helpers', () => {
     });
   });
 
+  it('builds the visual guide from coordinate medians', () => {
+    const makeGuide = (offset) => ({
+      aspectRatio: 4 / 3,
+      box: { x: 0.2 + offset, y: 0.1, width: 0.5, height: 0.7 },
+      landmarks: Array.from({ length: 68 }, (_, index) => ({
+        x: index / 100 + offset,
+        y: index / 200,
+      })),
+    });
+    const guide = summarizeGuides([makeGuide(0), makeGuide(0.2), makeGuide(0.1)]);
+    expect(guide.box.x).toBeCloseTo(0.3);
+    expect(guide.landmarks).toHaveLength(68);
+    expect(guide.landmarks[10].x).toBeCloseTo(0.2);
+  });
+
   it('excludes pose and light changes but accepts comparable frames', () => {
     const baseline = {
       rollDegrees: 0,
@@ -46,11 +62,22 @@ describe('realtime temporal measurement helpers', () => {
     expect(compareConditions(baseline, { ...baseline, yawOffset: 0.03 }).ok).toBe(true);
     expect(compareConditions(baseline, { ...baseline, rollDegrees: 7 })).toMatchObject({
       ok: false,
-      reason: 'Return to the baseline pose and framing.',
+      reason: 'Head angle differs from the baseline pose (roll Δ7.0°; limit 4°).',
     });
     expect(compareConditions(baseline, { ...baseline, luma: 0.7 })).toMatchObject({
       ok: false,
-      reason: 'Lighting differs too much from the baseline.',
+      reason: 'Room lighting differs from the baseline (luma Δ0.20; limit 0.10).',
+    });
+    expect(compareConditions(baseline, {
+      ...baseline,
+      boxWidthRatio: 0.6,
+      centerXRatio: 0.7,
+    })).toMatchObject({
+      ok: false,
+      reasons: [
+        'Face size differs from the baseline framing. Move slightly farther from the camera.',
+        'Face position differs from the baseline framing. Re-centre it in the frame.',
+      ],
     });
   });
 
@@ -71,17 +98,23 @@ describe('realtime temporal measurement helpers', () => {
       centerYRatio: 0.5,
       luma: 0.5,
     };
+    const guide = {
+      aspectRatio: 4 / 3,
+      box: { x: 0.2, y: 0.1, width: 0.5, height: 0.7 },
+      landmarks: Array.from({ length: 68 }, () => ({ x: 0.5, y: 0.5 })),
+    };
     collector.start();
-    expect(collector.offer({ ok: true, reason: '', metrics }, [0, 0]).status).toBe('collecting');
+    expect(collector.offer({ ok: true, reason: '', metrics, guide }, [0, 0]).status).toBe('collecting');
     vi.advanceTimersByTime(500);
-    expect(collector.offer({ ok: true, reason: '', metrics }, [0.01, 0]).status).toBe('collecting');
+    expect(collector.offer({ ok: true, reason: '', metrics, guide }, [0.01, 0]).status).toBe('collecting');
     vi.advanceTimersByTime(500);
-    const result = collector.offer({ ok: true, reason: '', metrics }, [0.02, 0]);
+    const result = collector.offer({ ok: true, reason: '', metrics, guide }, [0.02, 0]);
     expect(result.status).toBe('done');
     expect(result.baseline.descriptor).toEqual([0.01, 0]);
     for (const [key, value] of Object.entries(metrics)) {
       expect(result.baseline.conditions[key]).toBeCloseTo(value);
     }
+    expect(result.baseline.guide).toEqual(guide);
   });
 
   it('keeps post-baseline appearance changes measurable', () => {
@@ -116,7 +149,8 @@ describe('realtime temporal measurement helpers', () => {
 
       expect(gate.evaluate(detection)).toMatchObject({
         ok: false,
-        reason: 'Detector confidence is low (0.40). Hold a frontal, steady pose and check glare or shadows.',
+        code: 'confidence',
+        reason: 'Face detected, but confidence is low (0.40; target 0.85). Hold still and check glare or shadows.',
       });
       expect(gate.evaluate(detection, { allowAppearanceChanges: true })).toMatchObject({
         ok: true,
@@ -130,4 +164,69 @@ describe('realtime temporal measurement helpers', () => {
       getContext.mockImplementation(previous);
     }
   });
+
+  it('reports every failed quality check while keeping a primary reason', () => {
+    const getContext = HTMLCanvasElement.prototype.getContext;
+    const previous = getContext.getMockImplementation();
+    const pixels = new Uint8ClampedArray(96 * 96 * 4).fill(255);
+    getContext.mockImplementation(() => ({
+      drawImage: vi.fn(),
+      getImageData: vi.fn(() => ({ data: pixels })),
+    }));
+
+    try {
+      const video = { videoWidth: 640, videoHeight: 480 };
+      const positions = Array.from({ length: 68 }, () => ({ x: 320, y: 220 }));
+      positions[36] = { x: 256, y: 180 };
+      positions[45] = { x: 384, y: 220 };
+      positions[30] = { x: 380, y: 230 };
+      const gate = createQualityGate(video);
+      const verdict = gate.evaluate({
+        detection: { score: 0.4, box: { x: 0, y: 0, width: 120, height: 120 } },
+        landmarks: { positions },
+      });
+
+      expect(verdict.ok).toBe(false);
+      expect(verdict.code).toBe('confidence');
+      expect(verdict.failures.map(({ code }) => code)).toEqual([
+        'confidence',
+        'scale',
+        'framing',
+        'roll',
+        'yaw',
+        'overexposed',
+      ]);
+      expect(verdict.reasons).toHaveLength(6);
+    } finally {
+      getContext.mockImplementation(previous);
+    }
+  });
+
+  it('uses media time and ignores a repeated local-video frame', () => {
+    vi.useFakeTimers();
+    vi.setSystemTime(new Date('2026-10-12T10:00:00Z'));
+    const collector = createBaselineCollector({
+      targetSamples: 2,
+      minDurationMs: 1000,
+      timeoutMs: 5000,
+      maxSpread: 0.2,
+    });
+    const verdict = { ok: true, reason: '', metrics: {} };
+
+    collector.start();
+    expect(collector.offer(verdict, [0, 0], { sourceTimeMs: 2000, id: '2.000' })).toMatchObject({
+      status: 'collecting',
+      accepted: 1,
+    });
+    vi.advanceTimersByTime(1500);
+    expect(collector.offer(verdict, [0, 0], { sourceTimeMs: 2000, id: '2.000' })).toMatchObject({
+      status: 'waiting',
+      accepted: 1,
+      duplicates: 1,
+    });
+    expect(collector.offer(verdict, [0.01, 0], { sourceTimeMs: 3000, id: '3.000' })).toMatchObject({
+      status: 'done',
+      accepted: 2,
+    });
+  });
 });
diff --git a/tests/unit/realtime-gallery.test.js b/tests/unit/realtime-gallery.test.js
new file mode 100644
index 0000000..5d8ee7b
--- /dev/null
+++ b/tests/unit/realtime-gallery.test.js
@@ -0,0 +1,16 @@
+import { describe, expect, it } from 'vitest';
+import { formatCaptureMeasurement } from '../../lab-js/realtime/realtime-gallery.js';
+
+describe('realtime capture labels', () => {
+  it('reports a distance at or below the threshold as a continuing match', () => {
+    expect(formatCaptureMeasurement({ distance: 0.156, threshold: 0.6 }))
+      .toBe('distance 0.156/0.60 · still match');
+    expect(formatCaptureMeasurement({ distance: 0.6, threshold: 0.6 }))
+      .toBe('distance 0.600/0.60 · still match');
+  });
+
+  it('reports a distance above the threshold as a different person', () => {
+    expect(formatCaptureMeasurement({ distance: 0.601, threshold: 0.6 }))
+      .toBe('distance 0.601/0.60 · different person');
+  });
+});
diff --git a/tests/unit/realtime-graph.test.js b/tests/unit/realtime-graph.test.js
index 4011936..1623c2f 100644
--- a/tests/unit/realtime-graph.test.js
+++ b/tests/unit/realtime-graph.test.js
@@ -62,12 +62,26 @@ describe('realtime timeline phases', () => {
 
     graph.pushSample(1000, 0.1, 'applying');
     graph.addEvent(2000, 'measuring', 'step one');
-    graph.pushSample(3000, 0.2, 'measuring');
+    graph.pushSample(3000, 0.2, 'measuring', 'outside-guide');
+    graph.pushSample(3500, null, 'measuring', 'no-face');
     graph.beginGap(4000, 'pose/light excluded');
     graph.beginGap(4500, 'pose/light excluded');
     graph.endGap(5000);
 
-    expect(graph.stats()).toEqual({ samples: 2, gaps: 1, events: 1, following: true });
+    const beforeFilter = graph.stats();
+    expect(beforeFilter).toMatchObject({
+      samples: 3,
+      visibleSamples: 3,
+      gaps: 1,
+      events: 1,
+      following: true,
+      quality: { valid: 1, 'outside-guide': 1, lighting: 0, 'no-face': 1 },
+    });
+    graph.setQualityVisible('outside-guide', false);
+    const afterFilter = graph.stats();
+    expect(afterFilter.qualityVisible['outside-guide']).toBe(false);
+    expect(afterFilter.visibleSamples).toBe(2);
+    expect(afterFilter.virtualHeight).toBeLessThan(beforeFilter.virtualHeight);
     const labels = ctx.fillText.mock.calls.map(([label]) => label);
     expect(labels).toContain('step one');
     expect(labels.some((label) => String(label).startsWith('step one ·'))).toBe(false);
diff --git a/tests/unit/realtime-guide.test.js b/tests/unit/realtime-guide.test.js
new file mode 100644
index 0000000..45edade
--- /dev/null
+++ b/tests/unit/realtime-guide.test.js
@@ -0,0 +1,21 @@
+import { describe, expect, it } from 'vitest';
+
+import { canUseGuideShortcut } from '../../lab-js/realtime/realtime-guide.js';
+
+describe('realtime baseline guide shortcuts', () => {
+  it('leaves Space to video and interactive controls', () => {
+    for (const tag of ['video', 'button', 'input', 'summary']) {
+      const element = document.createElement(tag);
+      expect(canUseGuideShortcut(element)).toBe(false);
+    }
+    const editable = document.createElement('div');
+    editable.contentEditable = 'true';
+    editable.setAttribute('contenteditable', 'true');
+    expect(canUseGuideShortcut(editable)).toBe(false);
+  });
+
+  it('allows the page shortcut from non-interactive content', () => {
+    expect(canUseGuideShortcut(document.body)).toBe(true);
+    expect(canUseGuideShortcut(document.createElement('canvas'))).toBe(true);
+  });
+});
diff --git a/tests/unit/realtime-radial.test.js b/tests/unit/realtime-radial.test.js
index fdf6a22..c31e3c0 100644
--- a/tests/unit/realtime-radial.test.js
+++ b/tests/unit/realtime-radial.test.js
@@ -1,5 +1,9 @@
 import { afterEach, describe, expect, it, vi } from 'vitest';
-import { createRadialGraph, radialPoints } from '../../lab-js/realtime/realtime-radial.js';
+import {
+  createRadialGraph,
+  radialPoints,
+  rankedDeltaBands,
+} from '../../lab-js/realtime/realtime-radial.js';
 
 describe('realtime radial fingerprint', () => {
   afterEach(() => vi.unstubAllGlobals());
@@ -27,7 +31,16 @@ describe('realtime radial fingerprint', () => {
     expect(radialPoints([])).toEqual([]);
   });
 
-  it('draws live and measured 128-coordinate fingerprints and releases its observer', () => {
+  it('sorts 128 absolute deltas into 16 RMS distribution bands', () => {
+    const values = Array.from({ length: 128 }, (_, index) => index % 2 ? -index : index);
+    const bands = rankedDeltaBands(values);
+    expect(bands).toHaveLength(16);
+    expect(bands[0]).toBeCloseTo(Math.sqrt((0 ** 2 + 1 ** 2 + 2 ** 2 + 3 ** 2 + 4 ** 2 + 5 ** 2 + 6 ** 2 + 7 ** 2) / 8));
+    expect(bands[15]).toBeGreaterThan(bands[14]);
+    expect(rankedDeltaBands([])).toEqual([]);
+  });
+
+  it('draws live and measured ranked distributions and releases its observer', () => {
     const disconnect = vi.fn();
     const observe = vi.fn();
     vi.stubGlobal('ResizeObserver', class ResizeObserver {
diff --git a/tests/unit/realtime-settings.test.js b/tests/unit/realtime-settings.test.js
index a91af5b..768a1e2 100644
--- a/tests/unit/realtime-settings.test.js
+++ b/tests/unit/realtime-settings.test.js
@@ -15,6 +15,11 @@ const conditions = {
   centerYRatio: 0.5,
   luma: 0.6,
 };
+const guide = {
+  aspectRatio: 4 / 3,
+  box: { x: 0.2, y: 0.1, width: 0.5, height: 0.7 },
+  landmarks: Array.from({ length: 68 }, (_, index) => ({ x: index / 100, y: index / 200 })),
+};
 
 describe('realtime baseline persistence', () => {
   beforeEach(() => localStorage.clear());
@@ -26,6 +31,7 @@ describe('realtime baseline persistence', () => {
       noiseFloor: 0.01,
       capturedAt: 1234,
       sourceKind: 'camera',
+      guide,
     };
 
     saveBaseline(baseline);
@@ -41,12 +47,16 @@ describe('realtime baseline persistence', () => {
       noiseFloor: 0.01,
       capturedAt: 1234,
       sourceKind: 'camera',
-      format: 2,
+      format: 3,
+      guide,
     };
 
-    localStorage.setItem(key, JSON.stringify({ ...valid, format: 1 }));
+    localStorage.setItem(key, JSON.stringify({ ...valid, format: 2 }));
     expect(loadBaseline()).toBeNull();
 
+    localStorage.setItem(key, JSON.stringify(valid));
+    expect(loadBaseline()).toMatchObject({ guide });
+
     localStorage.setItem(key, JSON.stringify({ ...valid, sourceKind: 'video' }));
     expect(loadBaseline()).toBeNull();
 
@@ -61,7 +71,7 @@ describe('realtime baseline persistence', () => {
   });
 
   it('deletes the stored biometric template', () => {
-    saveBaseline({ descriptor, conditions, noiseFloor: 0.01, sourceKind: 'camera' });
+    saveBaseline({ descriptor, conditions, guide, noiseFloor: 0.01, sourceKind: 'camera' });
     clearBaseline();
     expect(loadBaseline()).toBeNull();
   });
diff --git a/tutorials/realtime-fr-method.md b/tutorials/realtime-fr-method.md
deleted file mode 100644
index fb4ebea..0000000
--- a/tutorials/realtime-fr-method.md
+++ /dev/null
@@ -1,1081 +0,0 @@
-# Ghostmaxxing — Realtime Visualizer: Methodology, Sources and Limits
-
-**What this document is.** A full accounting of what the realtime latent-space
-visualizer measures, how it measures it, where every number came from, and what
-the surrounding research does and does not support. It is written so that
-someone who has never opened the code can judge whether a reading from this tool
-means anything.
-
-**Scope.** Covers `realtime.html`, `styles/realtime.css`,
-`lab-js/realtime/realtime.js`, and its `realtime-*.js` modules. The 2D/3D effect
-pipelines in `lab.html` use different models
-(MediaPipe FaceLandmarker, MobileNetV3 ImageEmbedder) and are out of scope here.
-
-**Status of claims.** Every threshold in this document is tagged with its
-provenance:
-
-| Tag | Meaning |
-|---|---|
-| **CITED** | Traceable to a published source, linked inline |
-| **MEASURED** | Computed from the operator's own session, on their own hardware |
-| **CHOSEN** | A design decision with stated reasoning but no external authority |
-| **UNTUNED** | Chosen, and known to need empirical calibration |
-
-If a number is not tagged CITED, it is not evidence of anything about face
-recognition in general. It is a setting.
-
----
-
-## Table of contents
-
-1. [The honest summary](#1-the-honest-summary)
-2. [The pipeline](#2-the-pipeline)
-3. [What a 128-dimensional face descriptor is](#3-what-a-128-dimensional-face-descriptor-is)
-4. [Where 0.6 comes from](#4-where-06-comes-from)
-5. [What LFW does and does not tell you](#5-what-lfw-does-and-does-not-tell-you)
-6. [Demographic differentials](#6-demographic-differentials)
-7. [The metrics vocabulary](#7-the-metrics-vocabulary)
-8. [The table that is not in this tool, and why](#8-the-table-that-is-not-in-this-tool-and-why)
-9. [Algorithms implemented here](#9-algorithms-implemented-here)
-10. [Every threshold, and where it came from](#10-every-threshold-and-where-it-came-from)
-11. [Adversarial makeup: the research](#11-adversarial-makeup-the-research)
-12. [Transferability — the central caveat](#12-transferability--the-central-caveat)
-13. [What this tool can and cannot tell you](#13-what-this-tool-can-and-cannot-tell-you)
-14. [Privacy and data handling](#14-privacy-and-data-handling)
-15. [Regulatory context](#15-regulatory-context)
-16. [Vendored assets and provenance](#16-vendored-assets-and-provenance)
-17. [Reading list](#17-reading-list)
-18. [Glossary](#18-glossary)
-
----
-
-## 1. The honest summary
-
-This tool measures **one number**: the Euclidean distance between a 128-dimensional
-descriptor of your face captured before a makeup session, and a descriptor of
-your face right now, both produced by dlib's face recognition ResNet as shipped
-in face-api.js.
-
-That number tells you how far one specific, publicly available 2017 model has
-moved you in its own embedding space. It does **not** tell you:
-
-- whether you would be recognised by any deployed surveillance system
-- whether the makeup would work on a different model
-- whether the makeup would work under different lighting or from a different angle
-- whether your face is "safe"
-
-What it is genuinely good for is **iteration**. If you are developing a makeup
-technique, you need a fast local signal to compare variant A against variant B.
-This gives you that, cheaply, offline, on your own device, without sending a
-face anywhere. It can take either a live camera or a local video, mark the
-operator-declared application intervals, and measure each finished step over a
-short temporal window. Treat it as a rehearsal instrument, not a verdict.
-
-The rest of this document explains why that framing is the correct one.
-
----
-
-## 2. The pipeline
-
-The input is either the live camera or a video file selected from the device.
-The local file is read through a temporary object URL; it is not uploaded or
-persisted. Both inputs go through the same 2D face-api pipeline below.
-
-Three separate models run in sequence on each frame. They are frequently
-conflated in public discussion, and the distinction matters because
-countermeasures that defeat one do not necessarily touch the others.
-
-### 2.1 Detection — TinyFaceDetector
-
-Finds face bounding boxes. A compact CNN shipped with
-[face-api.js](https://github.com/vladmandic/face-api), based on Tiny YOLO v2.
-Configured in `config.js`:
-
-```js
-export const DETECTOR_OPTIONS = new faceapi.TinyFaceDetectorOptions({
-   inputSize: 416,      // square input dimension
-   scoreThreshold: 0.5  // minimum reported confidence
-});
-```
-
-**This is the stage that classical anti-surveillance makeup attacked.** See
-[§11.1](#111-cv-dazzle-2010-and-its-own-obsolescence).
-
-### 2.2 Alignment — 68-point facial landmarks
-
-`faceLandmark68Net` locates 68 points: jaw outline (0–16), eyebrows (17–26),
-nose (27–35), eyes (36–47), mouth (48–67). The recognition network needs a
-canonically-aligned crop, so landmarks are used to rotate and scale the face
-before embedding.
-
-This tool also reads landmarks directly for pose estimation — see
-[§9.2](#92-the-quality-gate).
-
-### 2.3 Recognition — 128-d descriptor
-
-`faceRecognitionNet`, a port of
-[dlib's `dlib_face_recognition_resnet_model_v1`](https://github.com/davisking/dlib-models).
-Takes a 150×150×3 aligned face crop, outputs 128 floating-point numbers.
-
-Architecture and training, from
-[Davis King's announcement post](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html):
-
-- A ResNet with **29 convolutional layers** — a reduced
-  [ResNet-34](https://arxiv.org/abs/1512.03385) (He, Zhang, Ren & Sun, 2015)
-  with layers removed and filters per layer halved
-- Trained from scratch on **~3 million face images** across **7,485 identities**
-- Sources: [FaceScrub](http://vintage.winklerbros.net/facescrub.html),
-  the [VGG Face dataset](https://www.robots.ox.ac.uk/~vgg/data/vgg_face/),
-  and images scraped from the web by the author
-- LFW identities were deliberately excluded so the LFW evaluation stayed valid
-- The model file is public domain
-
-**Age note.** This model was published in February 2017 and has not been
-retrained. It is a reasonable stand-in for "an open-source face recogniser," and
-a poor stand-in for the state of the art. See
-[§12](#12-transferability--the-central-caveat).
-
----
-
-## 3. What a 128-dimensional face descriptor is
-
-### 3.1 Deep metric learning
-
-Classical face recognition extracted hand-designed features
-([Eigenfaces](https://www.face-rec.org/algorithms/PCA/jcn.pdf), Turk & Pentland
-1991; Fisherfaces; LBP histograms). Modern systems instead learn a **mapping**
-from face image to a vector space where geometric distance corresponds to
-identity difference.
-
-The canonical statement of this idea is
-[**FaceNet**](https://arxiv.org/abs/1503.03832) (Schroff, Kalenichenko & Philbin,
-CVPR 2015), which trained with a *triplet loss*: pull an anchor face toward
-another image of the same person, push it away from a different person, by at
-least a margin. Everything downstream — including dlib's model — is a variation
-on this.
-
-The practical consequence: **recognition becomes arithmetic.** Once faces are
-vectors, identifying someone is a nearest-neighbour lookup. There is no
-"template" of your face in a human-legible sense; there is a point in R¹²⁸.
-
-### 3.2 Distance metrics
-
-For descriptors *a* and *b*, this tool uses **Euclidean (L2) distance**:
-
-```
-d(a, b) = √( Σᵢ (aᵢ − bᵢ)² )
-```
-
-Implemented in `realtime-capture.js` as `euclidean()`. Deliberately not
-`faceapi.euclideanDistance` — keeping it local means the module can be unit
-tested without loading the vendor bundle.
-
-**Do not substitute cosine similarity.** Davis King has answered this directly
-[in the comments of the announcement post](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html):
-a user reported high cosine similarity between images of different people, and
-the answer was that the model is calibrated for Euclidean distance. Cosine
-similarity discards vector magnitude; for this model, that magnitude carries
-information.
-
-Other systems differ.
-[ArcFace](https://arxiv.org/abs/1801.07698) (Deng et al., CVPR 2019) optimises
-angular margin and *is* naturally read with cosine similarity. **Thresholds and
-metrics are not portable between models.** A distance of 0.6 means something in
-dlib's space and nothing at all in ArcFace's.
-
-### 3.3 Vector norm — a subtlety that affects the baseline
-
-dlib's descriptors are not explicitly L2-normalised to unit length, though in
-practice their norms cluster near 1. This matters for one reason: **averaging
-several descriptors produces a vector shorter than its inputs**, because
-imperfectly-aligned vectors partially cancel. A mean-of-N baseline therefore
-sits slightly inside the manifold, and every subsequent distance measured
-against it carries a small systematic offset.
-
-This tool avoids the problem entirely by using the **medoid** rather than the
-mean — see [§9.3](#93-medoid-baseline).
-
----
-
-## 4. Where 0.6 comes from
-
-This is the single most-cited and least-understood number in open-source face
-recognition. It has two independent justifications, and most write-ups mention
-only the second.
-
-### 4.1 It is baked into the training objective
-
-From [Davis King's post](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html):
-the network was trained with a structured metric loss that attempts to project
-each identity into a **non-overlapping ball of radius 0.6**. It is a pairwise
-hinge loss over all pairs in a mini-batch, with hard-negative mining.
-
-This is the important part. **0.6 is not a threshold discovered by tuning after
-training — it is the geometry the network was explicitly optimised to produce.**
-The model was told to make same-identity clusters fit inside spheres of that
-radius. The threshold is the training target, read back out.
-
-That makes 0.6 far more principled than a tuned hyperparameter, *and* far more
-model-specific. It is a fact about this network's loss function. It transfers to
-no other model.
-
-### 4.2 It is the operating point of the reported benchmark
-
-From [dlib's `face_recognition.py` example](https://github.com/davisking/dlib/blob/master/python_examples/face_recognition.py):
-at a distance threshold of 0.6, the model reaches **99.38% accuracy on LFW**.
-The docs state the plain-language rule — descriptors closer than 0.6 are
-generally the same person, further apart generally are not.
-
-`face-api.js` inherits this as the default `distanceThreshold` in its
-[`FaceMatcher`](https://github.com/vladmandic/face-api).
-
-### 4.3 What "99.38%" actually measures
-
-It is a **verification** figure on a specific protocol: given a pair of images,
-correctly say same/different, on LFW's balanced 6,000-pair test. It is not:
-
-- an identification (1:N search) figure — those degrade with gallery size
-- a figure for any demographic subgroup
-- a figure for non-frontal, low-resolution, or surveillance-quality imagery
-- a figure that has been re-validated on any recent dataset
-
-Accuracy on 1:1 verification and accuracy on 1:N identification against a
-12-million-person gallery are different problems. See
-[§7](#7-the-metrics-vocabulary).
-
----
-
-## 5. What LFW does and does not tell you
-
-[Labeled Faces in the Wild](https://vis-www.cs.umass.edu/lfw/) (Huang, Ramesh,
-Berg & Learned-Miller, UMass Amherst TR 07-49, 2007) is ~13,000 images of ~5,700
-individuals, scraped from news photographs. It became the field's default
-yardstick.
-
-### 5.1 It is demographically skewed
-
-The widely-cited estimate is that LFW is approximately **83.5% white** and
-**77.5% male**. This figure originates from Han & Jain's
-[demographic analysis of unconstrained face images](https://www.researchgate.net/publication/263697688_Age_Gender_and_Race_Estimation_from_Unconstrained_Face_Images)
-and is reproduced across the fairness literature — for example in
-[Gender Classification and Bias Mitigation in Facial Images](https://arxiv.org/abs/2007.06141)
-and the
-[Harvard JOLT digest on racial bias in facial recognition](https://jolt.law.harvard.edu/digest/why-racial-bias-is-prevalent-in-facial-recognition-technology).
-The [Algorithmic Fairness Datasets survey](https://arxiv.org/abs/2202.01711)
-(Fabris et al.) documents LFW's skew toward white, male and under-60 subjects.
-
-Because the images are news photographs of public figures, this skew reflects
-whose faces circulate in Anglophone news media — which is a fact about media,
-now baked into a benchmark.
-
-### 5.2 The model's author says so himself
-
-In the comments of the dlib announcement post, Davis King notes that LFW is
-heavily biased toward white adult American public figures, that the training
-data carries a similar bias, and that this creates an obvious problem for people
-outside that distribution — who would **likely require a different threshold**.
-
-There is empirical support for this. A study of the model on Asian faces
-([Neural Network Facial Authentication for Public Electric Vehicle Charging Station](https://arxiv.org/abs/2106.10432))
-reports accuracy dropping from the 99.38% LFW figure to **98.18%**, citing a
-GitHub issue raised against dlib.
-
-### 5.3 It is saturated
-
-A benchmark where everything scores 99%+ has stopped discriminating between
-methods. The field moved on to IJB-A/B/C, MegaFace, and NIST's ongoing FRVT
-precisely because LFW no longer separates good systems from great ones.
-
-**Consequence for this tool.** The 0.60 line is labelled in the UI as dlib's LFW
-default, explicitly as a benchmark's number rather than a property of your face.
-If you are not a white adult American public figure photographed by press
-photographers, its calibration is less well established for you — and there is no
-correction factor to apply, because nobody has published one.
-
----
-
-## 6. Demographic differentials
-
-This is the best-evidenced finding in the whole field and the most relevant to
-anyone building anti-surveillance tooling.
-
-### 6.1 NIST FRVT Part 3
-
-[**NISTIR 8280: Face Recognition Vendor Test Part 3 — Demographic Effects**](https://doi.org/10.6028/NIST.IR.8280)
-(Grother, Ngan & Hanaoka, December 2019) —
-[full PDF](https://pages.nist.gov/frvt/reports/demographics/nistir_8280.pdf),
-[NIST summary](https://www.nist.gov/news-events/news/2019/12/nist-study-evaluates-effects-race-age-sex-face-recognition-software).
-
-189 algorithms from 99 developers. Key findings:
-
-- Demographic differentials exist in the **majority** of algorithms studied
-- Among US-developed algorithms, elevated false-positive rates in one-to-one
-  matching for Asian, African American and Native American groups, with the
-  American Indian demographic showing the highest false positive rates
-- Many algorithms were 10–100× more likely to misidentify a Black or East Asian
-  face than a white one
-- Most algorithms were substantially less likely to correctly identify a Black
-  woman than any other demographic group
-
-Grother's own framing in the NIST release is careful: the report quantifies the
-differentials without claiming to explain their causes.
-
-### 6.2 The follow-up: false positives are the harder problem
-
-[**NISTIR 8429: Face Recognition Vendor Test Part 8 — Summarizing Demographic Differentials**](https://pages.nist.gov/frvt/reports/demographics/nistir_8429.pdf)
-
-The finding that matters:
-
-- **False positive differentials are widespread and occur even in pristine
-  images.** Within-group false positive rates were found to vary by a factor of
-  several thousand.
-- False *negative* inequities are substantially attributable to poor photography
-  of certain groups — notably under-exposure of dark-skinned individuals — and
-  are fixable with better cameras and capture conditions.
-- The much larger false *positive* variations are not fixable that way. They must
-  be addressed in the algorithm.
-
-This asymmetry is important. "Better lighting" is a real mitigation for one class
-of error and no mitigation at all for the other.
-
-### 6.3 The academic literature
-
-- [**Gender Shades**](https://proceedings.mlr.press/v81/buolamwini18a.html)
-  (Buolamwini & Gebru, FAccT 2018) — the study that put this on the public
-  agenda. **Note carefully:** it audits gender *classification*, not face
-  verification. It is frequently miscited as a face-recognition-accuracy study.
-  Its finding — error rates far higher for darker-skinned women than
-  lighter-skinned men — is about a different task on the same faces.
-- [Characterizing the Variability in Face Recognition Accuracy Relative to Race](https://arxiv.org/abs/1904.07325)
-  (Krishnapriya et al.) — impostor distributions for African-American cohorts
-  shift toward higher similarity scores relative to Caucasian cohorts across
-  multiple matchers, raising false match rates at any fixed threshold.
-- [Analysis of Gender Inequality In Face Recognition Accuracy](https://arxiv.org/abs/2002.00065)
-  (Albiero et al.) — female impostor distributions shift toward higher
-  similarity, female genuine distributions toward lower; d′ separation is
-  consistently worse for women. The paper's subset analysis (neutral expression,
-  head pose, visible forehead, **no makeup**) is directly relevant to this
-  project.
-- [Exploring Causes of Demographic Variations In Face Recognition Accuracy](https://arxiv.org/abs/2304.07175)
-- [Impact of Blur and Resolution on Demographic Disparities in 1-to-Many Facial Identification](https://arxiv.org/abs/2309.04447)
-
-**Consequence for this tool.** A single global threshold produces systematically
-different error rates for different people. Since this tool measures *your*
-distance from *your own* baseline, it sidesteps the cross-identity false-match
-problem — but the underlying embedding still carries whatever demographic
-structure the training data gave it, and there is no way to measure that from
-one face.
-
----
-
-## 7. The metrics vocabulary
-
-Necessary to read any of the linked literature without being misled.
-
-| Term | Meaning |
-|---|---|
-| **Verification (1:1)** | Are these two images the same person? What LFW measures. |
-| **Identification (1:N)** | Which of N enrolled people is this? Harder; error grows with N. |
-| **Genuine / mated pair** | Two images of the same person. |
-| **Impostor / non-mated pair** | Two images of different people. |
-| **FMR** (False Match Rate) | Fraction of impostor pairs accepted. A false accept. |
-| **FNMR** (False Non-Match Rate) | Fraction of genuine pairs rejected. A false reject. |
-| **FPIR / FNIR** | The 1:N identification analogues of FMR/FNMR. |
-| **ROC** | FNMR plotted against FMR as the threshold sweeps. The whole curve, not one point. |
-| **d′ (d-prime)** | Separation between genuine and impostor distributions: `|μ_G − μ_I| / √((σ²_G + σ²_I)/2)`. Assumes roughly Gaussian distributions. Higher is better. |
-
-**The threshold is a dial, not a fact.** Moving it trades FMR against FNMR. Any
-single accuracy number is one point on a curve, and quoting it without the
-operating point is meaningless. This is why the visualizer does *not* let you
-move the threshold: 0.60 is the one operating point with a documented
-provenance, and a moved line would have none.
-
----
-
-## 8. The table that is not in this tool, and why
-
-During design, a banded interpretation table was proposed:
-
-| Distance | Proposed meaning |
-|---|---|
-| 0.00–0.25 | Same face, session noise |
-| 0.25–0.45 | Same face, natural variation |
-| 0.45–0.60 | Approaching the boundary |
-| 0.60+ | Reads as a different person |
-
-**It was rejected, and it is documented here so nobody reintroduces it.**
-
-Only the 0.60 boundary is sourceable. The 0.25 and 0.45 edges were a plausible
-synthesis, not a published finding. A search of the literature turned up no
-source: the genuine/impostor distribution work that exists
-([Krishnapriya et al.](https://arxiv.org/abs/1904.07325),
-[Albiero et al.](https://arxiv.org/abs/2002.00065)) covers other matchers and
-reports similarity scores rather than dlib Euclidean distances, so it does not
-transfer.
-
-Rendering unsourced numbers as thin labelled lines on a plot would read as
-*calibration*. Users would reasonably assume someone measured them. In a project
-whose subject is facial recognition accountability, shipping a fabricated axis is
-the wrong failure to have.
-
-**What replaced it.** The tool draws exactly two reference lines, and they carry
-different kinds of claim:
-
-| Line | Provenance | Meaning |
-|---|---|---|
-| Noise floor | **MEASURED** — this session, this camera, this face | Below it, nothing changed |
-| 0.60 | **CITED** — dlib training objective + LFW | A benchmark's number |
-
-The gridlines at 0.25 / 0.50 / 0.75 / 1.00 / 1.25 are **axis ticks only**, drawn
-at hairline weight so they cannot be mistaken for claims. They let you read a
-value off the trace. They assert nothing.
-
-The space between the noise floor and 0.60 is deliberately left uncalibrated,
-and the UI says so.
-
----
-
-## 9. Algorithms implemented here
-
-### 9.1 Sampling and smoothing
-
-Detection runs on `requestAnimationFrame` with a busy-flag guard, so the
-detector is never re-entered while a pass is in flight. Readings accumulate in a
-buffer; a separate 3 Hz timer averages the buffer into one timeline sample.
-
-Rationale: raw per-frame distance is noisy, and the graph's job is trend
-legibility, not maximum temporal resolution. The numeric readout is unsmoothed
-and updates per frame, so instantaneous values remain visible.
-
-Baseline and declared-step measurements use a different aggregation path. Each
-collects eight accepted descriptors distributed over at least three seconds,
-then selects their medoid. A step is therefore represented by a temporal window,
-not by the frame that happened to be visible when the button was pressed.
-
-### 9.2 The quality gate
-
-`realtime-capture.js → createQualityGate()`. Baseline frames use the strict gate
-below:
-
-**Confidence.** Detector score ≥ 0.85, above the 0.5 reporting floor in
-`DETECTOR_OPTIONS`. A frame that barely registered as a face produces a
-correspondingly unreliable descriptor.
-
-**Scale.** Face box width ≥ 25% of video width. The recognition network resamples
-to 150×150; a small box means feeding it upscaled mush.
-
-**Framing.** Box fully inside the frame with ≥ 2% margin on every edge. A clipped
-face yields a garbage descriptor that looks perfectly valid downstream — one of
-the more insidious failure modes, because nothing errors.
-
-**Roll**, from landmarks 36 and 45 (outer eye corners):
-
-```
-roll = |atan2(y₄₅ − y₃₆, x₄₅ − x₃₆)|   →   gate at 8°
-```
-
-**Yaw proxy**, from the nose tip (30) against the eye-corner midpoint,
-normalised by interocular distance:
-
-```
-yaw ≈ |x₃₀ − (x₃₆ + x₄₅)/2| / ‖p₄₅ − p₃₆‖   →   gate at 0.12
-```
-
-This is a cheap monocular approximation, not a calibrated pose estimate. For
-real head-pose work, see
-[OpenFace](https://github.com/TadasBaltrusaitis/OpenFace) (Baltrušaitis et al.).
-
-**Sharpness.** Gradient energy over a 96×96 greyscale crop:
-
-```
-E = (1/N) Σ (∂I/∂x)² + (∂I/∂y)²
-```
-
-A discrete approximation of the Tenengrad focus measure, related to the standard
-variance-of-Laplacian blur detector. **Used only for ranking candidates against
-each other, never against a fixed floor** — an absolute sharpness threshold would
-vary with every camera, lens and lighting setup, and would be one more invented
-constant.
-
-**Exposure.** Mean crop luminance in [0.18, 0.86] (Rec. 601 weighting), and fewer
-than 2% of pixels above 0.98. Rejects both under- and over-exposure.
-
-The exposure gate connects directly to
-[NISTIR 8429's finding](https://pages.nist.gov/frvt/reports/demographics/nistir_8429.pdf)
-that false-negative inequities are substantially caused by under-exposure of
-dark-skinned subjects. Refusing to build a baseline from an under-exposed frame
-is a small, concrete mitigation.
-
-Intervention frames deliberately use a different mode. Detector confidence and
-face-crop exposure are not rejection criteria, because makeup may change exactly
-those signals. Scale, framing, roll and yaw must still pass. The frame is then
-compared with the baseline's averaged 2D conditions: roll, yaw proxy, face-box
-scale and centre, plus ambient luminance sampled **outside** the face box. A
-frame beyond those relative tolerances is omitted from both the timeline and a
-declared-step measurement.
-
-This is exclusion, not correction. The software does not rotate, relight or
-otherwise normalise a changed frame back to the baseline. The checks reduce
-obvious pose, framing and room-light confounds, but they cannot prove that the
-remaining distance came only from makeup. Pitch is not estimated, and ambient
-luminance is a deliberately simple 2D proxy.
-
-### 9.3 Medoid baseline
-
-Given N accepted descriptors, compute the pairwise distance matrix and select the
-member minimising summed distance to all others:
-
-```
-medoid = argmin_i  Σ_j d(xᵢ, xⱼ)
-spread = (1/(N−1)) Σ_j d(x_medoid, xⱼ)
-```
-
-**Why medoid rather than mean:**
-
-1. It stays a **real point** in the embedding, not a synthetic average.
-2. It sidesteps the norm-shrinkage problem in [§3.3](#33-vector-norm--a-subtlety-that-affects-the-baseline)
-   — no systematic offset gets baked into the reference.
-3. It is naturally robust to one marginal frame slipping the gate. The mean is not.
-
-Cost is O(N²) in distance computations. At N = 8 that is 28 comparisons of
-128-element vectors, computed once for the baseline and once per declared step.
-
-### 9.4 The noise floor
-
-`spread` above **is** the noise floor: how far the descriptor moves when nothing
-about the face has changed. It is the tool's only empirical reference line, and
-the most useful number it produces.
-
-Without it you cannot tell whether a small movement in the descriptor exceeds
-the variability of the baseline window. With it, a reading of 0.09 against a
-measured floor of 0.06 is weak evidence, while a reading of 0.55 against the
-same floor is a much larger model-local effect. The relative-condition gate in
-§9.2 removes obvious head and light changes; neither mechanism can guarantee
-that all nuisance variation has been removed.
-
-If spread exceeds 0.20 (**UNTUNED**) the batch is rejected outright — the subject
-moved or the lighting flickered, and accepting it would set a floor so high that
-real results vanish beneath it.
-
-### 9.5 Sustained spike detection
-
-Capture fires on a reading that **holds**, not an instantaneous value. Two
-triggers:
-
-- **Personal best** — a new session maximum by ≥ 0.05, held for ≥ 1.5s
-- **Threshold crossing** — the first time the run clears 0.60, held for ≥ 1.5s
-
-Plus a manual shutter, because no heuristic catches everything.
-
-Automatic spike capture is active only while the session is in its ready state.
-During an operator-declared application interval and its measurement window,
-the explicit step snapshot is the record of interest.
-
-The frame written to disk is the **sharpest** observed across the sustain window,
-not whichever frame tripped the trigger. Rationale: a single frame at 0.50 is as
-likely to be motion blur as a result, and "the makeup is working" should not rest
-on a photograph of someone moving. A run that dips back below its starting value
-aborts.
-
-### 9.6 What the equalizer shows
-
-The right panel shows **per-dimension absolute delta** — `|baseline[i] − current[i]|`
-for each of the 128 components, scaled against `MAX_DELTA = 0.15`.
-
-**This is a debugging aid, not a measurement.** Individual dimensions of a
-learned embedding have no assigned semantic meaning. Dimension 47 is not "nose
-width." The panel is useful for seeing *whether* change is concentrated or
-diffuse across the embedding, and for nothing else. Work such as
-[Feature Representation in Deep Metric Embeddings](https://arxiv.org/abs/2102.03176)
-(which uses this exact dlib model) examines what structure embeddings do carry;
-per-dimension interpretation is not part of it.
-
-### 9.7 Declared makeup steps and the radial fingerprint
-
-After setting a baseline, the operator controls the experiment semantics:
-
-1. **Start makeup step** marks the beginning of an application interval.
-2. **Measure this step** begins an eight-sample, minimum-three-second window.
-3. The tool stores the window medoid as the step snapshot, reports its distance
-   and temporal spread, and keeps the sharpest accepted thumbnail.
-
-The timeline changes background during application and measurement and draws a
-labelled event line for each transition. With a local video, events also include
-the media time, so pause, seek and replay can be used as an experiment fixture.
-The controls resume a paused local video before beginning a marked interval.
-
-The radial plot uses all 128 absolute component deltas. The live reading is an
-outline; the most recent measured step is a filled shape. It can make two
-descriptor-change patterns easier to compare than 128 equalizer rows, but it
-does not add new information and it is not a map of facial regions. Spoke order
-is merely descriptor index order, and rotation or local peaks have no semantic
-interpretation.
-
----
-
-## 10. Every threshold, and where it came from
-
-### Cited
-
-| Constant | Value | Source |
-|---|---|---|
-| `MATCH_THRESHOLD` | 0.60 | dlib's [structured metric loss target radius](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html) and the [documented LFW operating point](https://github.com/davisking/dlib/blob/master/python_examples/face_recognition.py) |
-
-That is the complete list. One number.
-
-### Measured
-
-| Quantity | Source |
-|---|---|
-| Baseline noise floor | Medoid spread over 8 gated frames, this session |
-| Step temporal spread | Medoid spread over 8 comparable frames, this step |
-
-### Chosen
-
-| Constant | Value | Reasoning |
-|---|---|---|
-| `GRAPH_MAX_DISTANCE` | 1.25 | Headroom above 0.60. Effective adversarial makeup exceeds 1.0; a ceiling at 1.0 would saturate exactly when the experiment starts working. |
-| `GRAPH_WINDOW_SECONDS` | 60 | Long enough for a makeup pass, short enough to read. |
-| `SAMPLES_PER_SECOND` | 3 | Smoothing vs. responsiveness. |
-| `NO_FACE_DEBOUNCE_MS` | 1500 | Longer than a blink or head turn. |
-| `GAP_BLOCK_PX` | 30 | Fixed height for a collapsed absence. |
-| `BASELINE_TARGET_SAMPLES` | 8 | A compact temporal medoid rather than one frame. |
-| `BASELINE_MIN_MS` | 3000 | Distributes accepted frames across the interval. |
-| `BASELINE_TIMEOUT_MS` | 30000 | Leaves time to respond to a diagnostic rejection. |
-| `MEASUREMENT_TARGET_SAMPLES` | 8 | Uses the same temporal aggregation for every declared step. |
-| `MEASUREMENT_MIN_MS` | 3000 | Distributes accepted comparison frames across the interval. |
-| `MEASUREMENT_TIMEOUT_MS` | 30000 | Fails explicitly when comparable frames cannot be collected. |
-| `SPIKE_SUSTAIN_MS` | 1500 | ~4–5 samples at 3 Hz. |
-| `SPIKE_BEST_MARGIN` | 0.05 | Avoids capturing noise as a personal best. |
-| `SPIKE_COOLDOWN_MS` | 8000 | Keeps the gallery to keepers. |
-| `minScore` | 0.85 | Detector floor is 0.5; used only for strict baseline capture. |
-| `minBoxWidthRatio` | 0.25 | Recognition net input is 150×150. |
-| `maxRollDegrees` | 8 | Tight enough to matter, loose enough to achieve. |
-| `maxYawOffset` | 0.12 | As above; proxy metric, see §9.2. |
-| `maxRollDelta` | 4° | Relative 2D comparison tolerance; frames beyond it are omitted. |
-| `maxYawDelta` | 0.055 | Relative yaw-proxy tolerance. |
-| `maxScaleDelta` | 0.08 | Relative face-box width tolerance. |
-| `maxCenterDelta` | 0.065 | Relative horizontal or vertical centre tolerance. |
-| `maxLumaDelta` | 0.10 | Relative ambient-luminance tolerance, measured outside the face box. |
-| `MAX_DELTA` | 0.15 | Equalizer and radial scaling only. Purely visual. |
-
-### Untuned — needs empirical calibration
-
-| Constant | Value | Why it needs work |
-|---|---|---|
-| `BASELINE_MAX_SPREAD` / `MEASUREMENT_MAX_SPREAD` | 0.20 | Should be derived from observed spread distributions across cameras and subjects. Currently a guess. |
-| `minLuma` / `maxLuma` | 0.18 / 0.86 | Should be validated across skin tones specifically, given §6.2. A gate tuned on light skin would systematically reject dark-skinned subjects and is exactly the failure NIST documents. **This is the highest-priority calibration task in the codebase.** |
-| Relative condition tolerances | values above | Need repeated-camera and repeated-subject studies. They reduce confounds but are not calibrated guarantees of pose or illumination invariance. |
-
-### Inherited from `config.js`
-
-| Constant | Value |
-|---|---|
-| `inputSize` | 416 |
-| `scoreThreshold` | 0.5 |
-
----
-
-## 11. Adversarial makeup: the research
-
-### 11.1 CV Dazzle (2010) — and its own obsolescence
-
-[**CV Dazzle**](https://adam.harvey.studio/cvdazzle), Adam Harvey. Named after
-[dazzle camouflage](https://en.wikipedia.org/wiki/Dazzle_camouflage) — WWI ship
-patterns that confused rangefinders rather than concealing the ship. Uses makeup,
-hair and styling to break the light/dark relationships a face detector expects.
-The first documented camouflage technique to successfully attack a computer
-vision algorithm.
-
-**Read Harvey's own current statement on the project before using any of it.**
-The original 2010–2013 looks targeted the
-[Viola-Jones](https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-cvpr-01.pdf)
-Haar cascade detector. Harvey's site states plainly that Viola-Jones was
-deprecated in security use around 2013–2016, and that **the original patterns are
-no longer active looks.** He developed new looks in 2020 aimed at CNN-based
-recognition.
-
-This is the most intellectually honest thing in the field, and the lesson
-generalises: **anti-surveillance techniques have expiry dates.** A look that
-worked against one detector generation says nothing about the next.
-
-Note also the *category* shift. CV Dazzle originally attacked **detection** —
-being found at all. This tool measures **recognition** — being identified once
-found. Defeating a detector and moving an embedding are different problems with
-different countermeasures. If the detector loses your face entirely, this tool
-records a gap, not a low distance.
-
-Further reading:
-[Harvey's DIS Magazine writeup](https://dismagazine.com/dystopia/evolved-lifestyles/8115/anti-surveillance-how-to-hide-from-machines/),
-[2011 Artblog interview](https://www.theartblog.org/2020/01/adam-harvey-and-the-anti-face/),
-[Wikipedia overview](https://en.wikipedia.org/wiki/Computer_vision_dazzle).
-
-### 11.2 Accessorize to a Crime (2016)
-
-[**Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition**](https://dl.acm.org/doi/10.1145/2976749.2978392)
-— Sharif, Bhagavatula, Bauer & Reiter, ACM CCS 2016.
-[PDF](https://www.cs.cmu.edu/~sbhagava/papers/face-rec-ccs16.pdf) ·
-[author page](https://mahmoods01.github.io/publication/ccs16-adv-ml/)
-
-The paper that established physically-realisable adversarial attacks on face
-recognition. Printed eyeglass frames that let the wearer evade recognition **or
-impersonate a specific different person**. Primarily white-box, with black-box
-and detection-evasion demonstrations.
-
-The impersonation result is the striking one: the attack is not just "make me
-unrecognisable" but "make me register as this specific other individual."
-
-Extended in [A General Framework for Adversarial Examples with Objectives](https://dl.acm.org/doi/10.1145/3317611)
-(ACM TOPS, 2019).
-
-### 11.3 The physical attack lineage
-
-- [**AdvHat**](https://arxiv.org/abs/1908.08705) (Komkov & Petiushko, ICPR 2020)
-  — printed sticker on a hat, attacking ArcFace.
-- [**Adversarial Mask**](https://arxiv.org/abs/2111.10759) — universal patterned
-  face mask.
-- [**Towards Effective Adversarial Textured 3D Meshes**](https://arxiv.org/abs/2303.15818)
-  — 3D-aware physical attacks.
-- [**Imperceptible Physical Attack via LED Illumination Modulation**](https://arxiv.org/abs/2307.13294)
-  — attacks the *illumination* rather than the face. Evaluated against dlib,
-  FaceNet and ArcFace, and useful for seeing how the three compare.
-
-### 11.4 Makeup-specific attacks — the directly relevant work
-
-- [**Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition**](https://arxiv.org/abs/2105.03162)
-  (Yin et al., IJCAI 2021) —
-  [proceedings](https://www.ijcai.org/proceedings/2021/173) ·
-  [code](https://github.com/TencentYoutuResearch/Adv-Makeup).
-  Synthesises adversarial eye shadow over the orbital region. Explicitly targets
-  **imperceptibility** (looks like makeup, not like an attack) and
-  **transferability** (works black-box), using meta-learning across multiple
-  surrogate models. Includes a physical-world case study against two commercial
-  platforms. **The closest published analogue to what this project is doing.**
-- [**AMT-GAN: Protecting Facial Privacy via Style-robust Makeup Transfer**](https://arxiv.org/abs/2203.03121)
-  (Hu et al., CVPR 2022) — adversarial identity masks through makeup transfer.
-- [Generating Adversarial Examples by Makeup Attacks on Face Recognition](https://ieeexplore.ieee.org/document/8803269)
-  (Zhu, Lu & Chiang, ICIP 2019) — earlier work in the same direction.
-- [Adv-Attribute: Inconspicuous and Transferable Adversarial Attack](https://arxiv.org/abs/2210.06871)
-  — attribute-space rather than pixel-space perturbation.
-
-**Note the methodological gap.** These papers optimise perturbations with
-gradient access to a model. Physical makeup applied by hand is a much coarser
-instrument, and none of these results should be read as predicting what a human
-with brushes can achieve. That gap is precisely the space this tool is meant to
-explore empirically.
-
-### 11.5 Digital cloaking — a different threat model
-
-- [**Fawkes: Protecting Privacy against Unauthorized Deep Learning Models**](https://www.usenix.org/conference/usenixsecurity20/presentation/shan)
-  (Shan et al., USENIX Security 2020) —
-  [PDF](https://people.cs.uchicago.edu/~ravenben/publications/pdf/fawkes-usenix20.pdf) ·
-  [code](https://github.com/Shawn-Shan/fawkes) ·
-  [project site](https://sandlab.cs.uchicago.edu/fawkes/).
-  Perturbs images *before upload* so that a scraper training on them builds a
-  model that misidentifies the real you. Reported 95%+ protection, 80%+ even when
-  clean images leak.
-- [**LowKey**](https://arxiv.org/abs/2101.07922) (Cherepanova et al., ICLR 2021)
-  — similar goal, ensemble-based for better black-box transfer, with a perceptual
-  metric in the loss.
-
-**Critically, this is poisoning, not evasion.** Fawkes protects a *training set*.
-It does nothing for you walking past a camera. Independent work has also
-questioned its practical robustness — see
-[Oriole](https://arxiv.org/abs/2102.11502), which reports the perturbations
-becoming plainly visible on high-resolution images.
-
-### 11.6 The survey to read first
-
-[**SoK: Anti-Facial Recognition Technology**](https://arxiv.org/abs/2112.04558)
-— Wenger, Shan, Zheng & Zhao. Systematises the whole space: evasion vs.
-poisoning, digital vs. physical, detection vs. recognition. If you read one
-paper from this list, read this one.
-
-Also useful:
-[Privacy-Enhancing Face Biometrics: A Comprehensive Survey](https://ieeexplore.ieee.org/document/9481149)
-(Meden et al., IEEE TIFS 2021).
-
----
-
-## 12. Transferability — the central caveat
-
-**A low score in this tool does not mean you are unrecognisable.**
-
-The entire adversarial ML literature converges on this: attacks optimised against
-one model **transfer poorly** to others. Transferability is treated as the core
-open challenge in
-[Adv-Makeup](https://arxiv.org/abs/2105.03162), which needed a dedicated
-meta-learning strategy across multiple surrogate models just to get black-box
-success — and that with full gradient access during optimisation.
-
-The model in this tool is:
-
-- **From 2017**, never retrained
-- **Trained with a different loss** (structured metric loss) than modern systems
-  (ArcFace's angular margin, AdaFace's quality-adaptive margin)
-- **Read with a different metric** (Euclidean, not cosine)
-- **Small** — 29 conv layers, halved filters
-- **Not** what any commercial system uses
-
-Deployed systems — Clearview, Idemia, NEC, Azure Face, Amazon Rekognition — use
-proprietary models, typically larger, trained on far more data, and often
-ensembled. NIST's FRVT programme
-([ongoing results](https://pages.nist.gov/frvt/html/frvt11.html)) evaluates
-hundreds of them; the open-source models in this class sit well below the
-commercial frontier. NISTIR 8280 itself notes that some open-source algorithms
-included in prior comparative work are inaccurate enough not to represent
-commercial deployment.
-
-So: **moving 0.9 in dlib's space is evidence that you moved something in dlib's
-space.** It is weak evidence about ArcFace, and near-zero evidence about a
-production surveillance stack.
-
-### What would strengthen a claim
-
-If the goal is a defensible finding rather than a local signal, the standard
-approach is an **ensemble** of independent recognisers, reporting the distance in
-each. A technique that moves all of them is meaningfully more likely to
-generalise than one that moves a single 2017 model. Candidates:
-
-- [ArcFace / InsightFace](https://github.com/deepinsight/insightface)
-- [FaceNet (facenet-pytorch)](https://github.com/timesler/facenet-pytorch)
-- [AdaFace](https://github.com/mk-minchul/AdaFace)
-- [DeepFace](https://github.com/serengilx/deepface) as a wrapper over several
-
-This is a substantial architectural change — different preprocessing, different
-metrics, different thresholds per model — and it is the natural next step for the
-project, not something the current page does.
-
----
-
-## 13. What this tool can and cannot tell you
-
-### It can
-
-- Tell you whether a makeup pass moved dlib's embedding **at all**, above your
-  own measured noise floor
-- Let you **compare two makeup variants** on the same face, same session, same
-  camera — the comparison it is actually built for
-- Mark operator-declared application intervals and aggregate each finished step
-  over time instead of reporting only one clicked frame
-- Show whether component changes are diffuse or concentrated, with an equalizer
-  and a radial fingerprint that preserve all 128 absolute deltas
-- Give you a **timestamped, watermarked record** with the measurement burned in;
-  local-video events also retain their media time
-- Do all of this **offline, on-device**, with no face leaving the machine
-
-### It cannot
-
-- Tell you whether any deployed system would recognise you
-- Tell you whether the technique transfers to another model
-- Prove invariance to pose or lighting. It omits frames with sufficiently large
-  2D pose, framing or ambient-light differences; it does not correct them and
-  does not estimate pitch
-- Map a descriptor dimension or radial spoke to a facial feature or makeup line
-- Tell you anything about **detection** evasion — if the detector loses you, the
-  tool records a gap, not a success
-- Establish that a technique works for anyone other than you
-- Correct for demographic differentials in the underlying embedding
-
-### Methodological hygiene, if you want the results to mean something
-
-1. **Re-baseline whenever source, lighting or camera setup changes.** A baseline
-   captured under different conditions is a different experiment.
-2. **Report the noise floor with every result.** A distance without its noise
-   floor is uninterpretable.
-3. **Use the declared-step controls.** Mark the start of application, then
-   measure only after returning to the baseline pose and lighting.
-4. **Compare within a session.** Cross-session comparison confounds makeup with
-   lighting, camera position and time of day.
-5. **Repeat the step.** One accepted temporal window is stronger than a peak,
-   but it is still one small sample. Report distance, baseline noise floor and
-   step temporal spread together.
-6. **Test pose separately.** Deliberately vary yaw, roll and pitch in a separate
-   protocol rather than treating excluded frames as proof of invariance.
-
----
-
-## 14. Privacy and data handling
-
-The tool keeps the following local records. None is transmitted.
-
-| Data | Store | Deletion |
-|---|---|---|
-| Camera baseline descriptor + noise floor + averaged conditions + timestamp | `localStorage` (`gstmxx.realtime.baseline`, format 2) | "Delete baseline" — removes the key |
-| Local-video baseline | Memory for the current page session | Change source, delete baseline, or close the page |
-| Spike, manual and declared-step captures (watermarked JPEGs) | IndexedDB (`gstmxx-realtime` / `captures`) | "Delete all", or per-capture in the viewer |
-| View settings | `localStorage` (`gstmxx.realtime.settings`) | Browser storage clear |
-
-The selected video is exposed to the page through a temporary browser object
-URL. The file and that URL are not stored in IndexedDB or `localStorage`.
-
-**Why IndexedDB for captures.** `localStorage` caps near 5MB per origin, is
-shared with everything else the site stores, and inflates binary by ~33% through
-base64. Photographs belong in a store that takes Blobs natively.
-
-**A 128-d descriptor is biometric data.** Under
-[GDPR Article 9](https://gdpr-info.eu/art-9-gdpr/), biometric data processed for
-unique identification is a special category. Nothing here is transmitted, so no
-processor relationship arises — but the classification is why deletion is a real
-removal and not a flag.
-
-**Deleting the baseline does not clear the gallery.** Deliberate: the gallery is
-expected to outlive a re-baseline. Stated in the UI rather than left to be
-inferred.
-
-No third-party requests. All models are vendored under `/lab-js/vendor`; fonts
-are self-hosted. The page functions with the network unplugged.
-
----
-
-## 15. Regulatory context
-
-Relevant because it explains what this tooling is a response to.
-
-### EU AI Act
-
-[Regulation (EU) 2024/1689](https://eur-lex.europa.eu/eli/reg/2024/1689/oj) —
-[Article 5, Prohibited AI Practices](https://artificialintelligenceact.eu/article/5/).
-Prohibitions applicable from **2 February 2025**, with the penalty regime from
-2 August 2025. Penalties for prohibited practices reach €35M or 7% of global
-annual turnover.
-
-Provisions bearing on facial recognition:
-
-- **Art. 5(1)(e)** — bans creating or expanding facial recognition databases
-  through **untargeted scraping** of internet images or CCTV footage. "Untargeted"
-  is the operative word. See the Future of Privacy Forum's analysis:
-  [Red Lines under the EU AI Act](https://fpf.org/blog/red-lines-under-the-eu-ai-act-understanding-the-ban-of-the-untargeted-scraping-of-facial-images-and-facial-recognition-databases/).
-- **Art. 5(1)(g)** — bans biometric categorisation inferring race, political
-  opinions, trade union membership, religious belief, sex life or sexual
-  orientation.
-- **Art. 5(1)(h)** — bans **real-time** remote biometric identification in
-  publicly accessible spaces for law enforcement, with narrow exceptions
-  requiring prior judicial or independent administrative authorisation.
-
-**The gap worth knowing.** The ban targets *real-time* RBI. **Post-remote**
-biometric identification — running recognition over footage after the fact — is
-classified high-risk rather than prohibited. A demonstration can be filmed and
-every face run against a database days later, subject to authorisation and
-documentation requirements. Restrictive, but not a ban.
-
-*These provisions are recent and interpretation is still developing; verify
-against the current consolidated text and any Commission guidelines before
-relying on this summary.*
-
-### GDPR
-
-[Article 9](https://gdpr-info.eu/art-9-gdpr/) — biometric data for unique
-identification is a special category requiring explicit consent or another
-Art. 9 condition. Between 2022 and 2024 several EU data protection authorities
-fined Clearview AI over facial recognition practices; those decisions are the
-enforcement backdrop to Art. 5(1)(e).
-
-### Standards
-
-- [ISO/IEC 19795](https://www.iso.org/standard/73515.html) — biometric performance
-  testing and reporting. Part 10 addresses quantifying performance variation
-  across demographic groups.
-- [NIST FRVT](https://pages.nist.gov/frvt/html/frvt11.html) — the ongoing
-  independent evaluation programme.
-
----
-
-## 16. Vendored assets and provenance
-
-Full inventory in `lab-js/vendor/README.md`; refresh via
-`lab-js/vendor/fetch-sources.sh`.
-
-| Asset | Upstream | Version |
-|---|---|---|
-| `face-api.js` | [@vladmandic/face-api](https://github.com/vladmandic/face-api) | floating — no pinned version in the source URL |
-| Model shards | [justadudewhohacks/face-api.js-models](https://github.com/justadudewhohacks/face-api.js-models) | `@master` |
-| Original model weights | [davisking/dlib-models](https://github.com/davisking/dlib-models) | `dlib_face_recognition_resnet_model_v1` |
-
-**Two provenance weaknesses worth fixing.** The face-api bundle URL carries no
-version, and the model shards track `@master`. Both mean a re-run of
-`fetch-sources.sh` could silently change the model — and therefore every
-measurement — with no record of what changed. For research output, pin both to
-commit SHAs and record the SHA alongside results.
-
----
-
-## 17. Reading list
-
-### Start here
-
-1. [SoK: Anti-Facial Recognition Technology](https://arxiv.org/abs/2112.04558) — the map of the territory
-2. [High Quality Face Recognition with Deep Metric Learning](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html) — the model in this tool, from its author
-3. [NISTIR 8280: FRVT Part 3 — Demographic Effects](https://doi.org/10.6028/NIST.IR.8280) — the evidence base for bias
-4. [CV Dazzle](https://adam.harvey.studio/cvdazzle) — including its own obsolescence notice
-
-### Foundations
-
-- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://arxiv.org/abs/1503.03832) — Schroff et al., CVPR 2015
-- [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) — He et al., 2015
-- [ArcFace: Additive Angular Margin Loss](https://arxiv.org/abs/1801.07698) — Deng et al., CVPR 2019
-- [Rapid Object Detection using a Boosted Cascade of Simple Features](https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-cvpr-01.pdf) — Viola & Jones, 2001
-- [Deep Face Recognition: A Survey](https://arxiv.org/abs/1804.06655) — Wang & Deng
-
-### Benchmarks and their critique
-
-- [Labeled Faces in the Wild](https://vis-www.cs.umass.edu/lfw/) — the original
-- [Algorithmic Fairness Datasets: the Story so Far](https://arxiv.org/abs/2202.01711) — Fabris et al.
-- [FairFace](https://github.com/joojs/fairface) — a demographically balanced alternative
-- [Racial Faces in the Wild](https://github.com/whdeng/RFW) — Wang et al.
-
-### Bias and fairness
-
-- [Gender Shades](https://proceedings.mlr.press/v81/buolamwini18a.html) — Buolamwini & Gebru (gender *classification*, not verification)
-- [NISTIR 8429: FRVT Part 8](https://pages.nist.gov/frvt/reports/demographics/nistir_8429.pdf)
-- [Characterizing the Variability in Face Recognition Accuracy Relative to Race](https://arxiv.org/abs/1904.07325)
-- [Analysis of Gender Inequality In Face Recognition Accuracy](https://arxiv.org/abs/2002.00065)
-- [Exploring Causes of Demographic Variations In Face Recognition Accuracy](https://arxiv.org/abs/2304.07175)
-
-### Attacks
-
-- [Accessorize to a Crime](https://www.cs.cmu.edu/~sbhagava/papers/face-rec-ccs16.pdf) — Sharif et al., CCS 2016
-- [Adv-Makeup](https://arxiv.org/abs/2105.03162) — Yin et al., IJCAI 2021
-- [AMT-GAN](https://arxiv.org/abs/2203.03121) — Hu et al., CVPR 2022
-- [AdvHat](https://arxiv.org/abs/1908.08705) — Komkov & Petiushko
-- [Fawkes](https://www.usenix.org/conference/usenixsecurity20/presentation/shan) — Shan et al., USENIX Sec 2020
-- [LowKey](https://arxiv.org/abs/2101.07922) — Cherepanova et al., ICLR 2021
-- [Intriguing Properties of Neural Networks](https://arxiv.org/abs/1312.6199) — Szegedy et al., where adversarial examples begin
-
-### Policy
-
-- [EU AI Act, Article 5](https://artificialintelligenceact.eu/article/5/)
-- [FPF: Red Lines under the EU AI Act](https://fpf.org/blog/red-lines-under-the-eu-ai-act-understanding-the-ban-of-the-untargeted-scraping-of-facial-images-and-facial-recognition-databases/)
-- [GDPR Article 9](https://gdpr-info.eu/art-9-gdpr/)
-
----
-
-## 18. Glossary
-
-**Alignment** — Warping a detected face to a canonical pose before embedding.
-
-**Descriptor / embedding / template** — The vector representing a face. Here, 128 floats.
-
-**Detection vs. recognition** — Finding that a face exists vs. determining whose it is. Different models, different attacks.
-
-**Euclidean distance** — Straight-line distance in vector space. The metric this model is calibrated for.
-
-**Genuine / impostor distribution** — Distance distributions for same-person and different-person pairs. Their overlap determines error rates.
-
-**Medoid** — The member of a set with smallest summed distance to the others. Unlike the mean, always a real member.
-
-**Noise floor** — Here: descriptor movement when nothing about the face changed. Measured per session.
-
-**Threshold** — The distance below which two descriptors are declared a match. A dial trading false accepts against false rejects, not a fact.
-
-**Transferability** — Whether an attack developed against one model works against another. Generally poor. The reason this tool's readings are local signal rather than evidence.
-
-**White-box / black-box** — Whether the attacker has access to model internals and gradients. This tool is white-box against dlib and tells you nothing about black-box performance.
-
----
-
-*Sources current as of October 2026. The EU AI Act sections describe recent and
-evolving provisions — verify against the consolidated text before relying on
-them. If you re-run `fetch-sources.sh`, record the resulting commit SHAs: the
-upstream model references are unpinned, and an unnoticed model change invalidates
-every prior measurement.*
diff --git a/tutorials/realtime.md b/tutorials/realtime.md
index e5bf571..ef01f58 100644
--- a/tutorials/realtime.md
+++ b/tutorials/realtime.md
@@ -1,112 +1,1274 @@
-# Latent Space Visualizer
+# Realtime latent space visualizer: how it works and why
 
 **Project:** Ghostmaxxing
 
-**Audience:** people iterating on physical makeup and researchers inspecting
-face-descriptor drift
+**Audience:** people iterating on physical makeup, researchers inspecting
+face-descriptor drift, and contributors who need to audit the method
 
 **Status:** experimental
 
-The Latent Space Visualizer (`realtime.html`) is a local, real-time instrument
-for comparing a face-api/dlib descriptor with a baseline. It is designed around
-a makeup session: capture the untreated state, tell the tool when application
-starts, then ask it to measure each finished step over a short window.
+The Realtime Latent Space Visualizer (`realtime.html`) is a local browser
+instrument for testing how a physical makeup step changes one face descriptor
+relative to a session baseline. It uses the 2D face-api/dlib pipeline. It does
+not use MediaPipe or the experimental 3D path.
 
-It reports what this one browser model did under the tested conditions. It does
-not establish protection from another recognition system.
+The tool reports what one publicly available 2017 model did under the tested
+conditions. It does not make a face invisible, establish protection from face
+recognition, or predict the output of a deployed system. A face match is a
+system output, not a truth. This page makes that output inspectable and gives
+each consequential number a provenance.
 
-## 1. Choose a source
+Use the first part as an operating guide. The sections that follow account for
+the pipeline, algorithms, thresholds, research context, privacy model, and
+limits. Someone who has never opened the code should be able to run a session
+and judge what its readings do and do not support.
 
-The default source is the camera. Open **Source → Open local video** to use a
-recording instead. The file remains on the device and can be paused, sought and
-replayed with the browser's video controls.
+**Code scope:** `realtime.html`, `styles/realtime.css`,
+`lab-js/realtime/realtime.js`, and its `realtime-*.js` modules. The 2D and 3D
+effect pipelines in `lab.html` use different models (MediaPipe FaceLandmarker
+and MobileNetV3 ImageEmbedder) and are outside this document's scope.
+
+## Run an experiment
+
+### Choose a source
+
+The camera is the default source. Open **Source → Open local video** to use a
+recording instead. The file remains on the device and can be paused, sought,
+and replayed with the browser's video controls.
 
 Changing source invalidates the current baseline. Camera baselines can be
-restored after reload; local-video baselines last only for the current page
+restored after reload. Local-video baselines last only for the current page
 session because the temporary file URL does not survive a reload.
 
-## 2. Capture a baseline
+**Source → Brightness** changes only how the video looks on screen. The model
+continues to read the original pixels, so this control cannot improve lighting
+or change a measurement.
+
+### Capture a baseline
 
 1. Return to a neutral, frontal pose and keep the room light steady.
 2. Press **Set baseline**.
-3. Hold the pose while the ring collects eight accepted samples across at least
-   three seconds.
+3. Keep your face still and look at the centre target while the tool collects
+   five accepted samples across at least two seconds. The target pulses when a
+   clean sample is accepted. The diagnostic line counts excluded frames
+   separately.
 
-The tool rejects baseline frames when detection confidence is low, the face is
-too small or clipped, the head roll/yaw is too large, or the face crop is badly
-under- or overexposed. The message in the pill names the current rejection; it
-is not a generic lighting warning.
+The strict baseline gate rejects a frame when detection confidence is low, the
+face is too small or clipped, head roll or yaw is too large, or the face crop is
+badly underexposed or overexposed. A detected face can therefore be excluded
+from measurement. Descriptor extraction succeeded, but the frame was not
+comparable enough to use. The capture message reports every failed check.
 
-The resulting medoid becomes the baseline. Its average spread is shown as the
-session noise floor.
+For a local video, sample spacing follows `video.currentTime`. A paused or
+ended video cannot contribute the same decoded frame repeatedly.
 
-## 3. Measure a makeup step
+The resulting medoid becomes the baseline. Its average spread is the session
+noise floor. The accepted frames also produce a separate median 2D alignment
+guide: jaw, upper detector arc, eye line, and nose centre. It appears briefly
+when the baseline is set. Press **Show guide** on touch or mobile, or press
+**Space** while no video or form control has focus, to show it for one second.
+It then fades over 600 ms. The guide helps a person return to approximately the
+same framing. It never enters descriptor-distance calculations.
+
+### Measure a makeup step
 
 1. Press **Start makeup step** before applying one change. The timeline marks
    the event and shades the application interval.
-2. Apply the change. You may leave the frame; a sustained absence becomes one
-   collapsed gap rather than a long run of false zeroes.
-3. Return to the baseline pose and light, then press **Measure this step**.
-4. Hold still while eight comparable samples are collected across at least
+2. Apply the change. You may leave the frame. The timeline continues with a
+   **No face** background and does not invent a distance line.
+3. Return to the baseline pose, framing, and light, then press **Measure this
+   step**.
+4. Hold still while the tool collects eight comparable samples across at least
    three seconds.
 
-The result is the medoid of that window, not a single clicked frame. The tool
-shows its distance from the original baseline and its temporal spread, updates
-the measured radial shape, and saves the sharpest accepted frame as a step
-thumbnail. Repeat with **Start next step** for cumulative changes.
+The result is the medoid of that window, not the single frame visible when the
+button was pressed. The tool shows its distance from the original baseline and
+its temporal spread, updates the measured radial shape, and saves the sharpest
+accepted frame as a step thumbnail. Repeat with **Start next step** for
+cumulative changes.
 
-With a local video, phase markers include the media time. Starting or measuring
-a step resumes a paused video, so the collection window can advance instead of
+With a local video, phase markers include media time. Starting or measuring a
+step resumes a paused video so the collection window advances instead of
 silently sampling one frozen frame.
 
-## 4. What gets excluded
+### Understand exclusions
 
 After baseline capture, the software permits appearance changes that may lower
 detector confidence or darken the face crop. It still excludes frames whose 2D
-pose, face-box size/position, or ambient luminance differs too much from the
+pose, face-box size or position, or ambient luminance differs too much from the
 baseline. Ambient light is estimated outside the face box so dark makeup is
 less likely to be mistaken for a room-light change.
 
 This is a nuisance gate, not full pose or lighting normalisation. It does not
-measure pitch, cannot know that makeup caused the remaining change, and cannot
-separate every shadow from every intervention.
+measure pitch. It cannot know that makeup caused the remaining change, and it
+cannot separate every shadow from every intervention.
+
+Excluded descriptor readings do not stop the timeline. Their raw distance is
+drawn over **Outside baseline guide** or **Lighting changed** backgrounds, but
+it is not used for the noise floor, declared-step snapshots, automatic spike
+captures, equalizer, or radial plot. Click a quality item in the timeline
+legend to hide or restore that state without deleting it. Hiding a state also
+collapses its vertical duration, so valid rows before and after it become
+adjacent.
 
-## 5. Read the three panels
+The distance header states **Valid distance** when the reading is admitted to
+measurement. It states **No distance** with the cause when the visible number
+is raw context only. The one-line status pill carries the same colour family as
+the timeline state and reports the diagnostic values for pose, framing, or
+light. Each state remains visible for at least 1.5 seconds so rapid changes do
+not make the instruction unreadable. Hovering the pill exposes the complete
+diagnostic when the visible line is truncated.
 
-- **Distance.** The live Euclidean distance and a scrolling timeline. The noise
-  floor is measured from this baseline; `0.60` is dlib's documented LFW
+### Read the three panels
+
+- **Distance:** the live Euclidean distance and a scrolling timeline. The noise
+  floor is measured from this baseline. `0.60` is dlib's documented LFW
   operating point. The space between them is deliberately uncalibrated.
-- **Source and experiment.** Camera or local video, baseline progress, phase
-  controls, status, capture controls and local gallery actions. **Display** only
-  changes CSS brightness; it does not change pixels seen by the model.
-- **Descriptor Delta.** The equalizer and radial fingerprint show the absolute
-  change in all 128 descriptor coordinates. The live radial outline can be
-  compared with the filled shape from the latest measured step.
+- **Source and experiment:** camera or local video, baseline progress, phase
+  controls, status, capture controls, and local gallery actions. Brightness is
+  a display setting only.
+- **Descriptor delta:** the equalizer shows each of the 128 absolute coordinate
+  deltas. The radial plot sorts those magnitudes and reduces them into 16
+  equal-sized RMS bands. Its live valid outline can be compared with the filled
+  shape from the latest measured step. On an excluded frame it freezes and
+  says **Last valid frame** instead of displaying an uncontrolled reading.
 
-Descriptor dimensions are not facial regions. A radial spoke cannot identify
-which cheek, eyebrow or makeup line caused a change. These views can show
-whether two 128-value patterns differ; they cannot explain the face semantically.
+Descriptor dimensions and ranked bands are not facial regions. A radial spoke
+cannot identify which cheek, eyebrow, or makeup line caused a change. These
+views show whether two 128-value patterns differ. They do not explain the face
+semantically.
 
-## 6. Captures and privacy
+### Manage captures and local data
 
-Manual, sustained-spike and declared-step captures are saved as watermarked
-JPEGs in browser IndexedDB. Camera baselines and view settings use localStorage.
-The selected video itself is neither uploaded nor persisted by the page.
+Manual, sustained-spike, and declared-step captures are saved as watermarked
+JPEGs in browser IndexedDB. Their footer carries the Ghostmaxxing mark plus the
+captured distance, match threshold, and resulting verdict. The capture's
+timestamp remains in stored metadata and exported filenames. Camera baselines
+and view settings use `localStorage`. The selected video is neither uploaded
+nor persisted by the page.
 
-**Delete baseline** removes the persisted camera descriptor but deliberately
-does not erase the gallery. Use **Delete all** or delete individual captures in
-the viewer when those records are no longer needed.
+**Source → Reset baseline** removes the persisted camera descriptor but does
+not erase the gallery. Use **Delete all** or delete individual captures in the
+viewer when those records are no longer needed.
 
 A 128-value face descriptor is biometric data. Treat exported images and local
 browser storage accordingly, even though this page does not transmit them.
 
-## 7. Interpreting a result
+### Interpret and report a result
 
 Report the measured step distance together with the baseline noise floor and
-the step's temporal spread. Compare steps within one session, re-baseline after
-changing the source or setup, and repeat promising measurements. A high value is
-evidence of movement in this specific 2017 dlib embedding under these conditions;
-it is not evidence of evasion against a deployed system.
+the step's temporal spread. Compare steps within one session, capture a new
+baseline after changing the source or setup, and repeat promising measurements.
+A high value is evidence of movement in this specific dlib embedding under
+these conditions. It is not evidence of evasion against a deployed system.
+
+## Method, evidence, and limits
+
+Every threshold in the sections below is tagged with its provenance:
+
+| Tag | Meaning |
+|---|---|
+| **CITED** | Traceable to a published source, linked inline |
+| **MEASURED** | Computed from the operator's own session, on their own hardware |
+| **CHOSEN** | A design decision with stated reasoning but no external authority |
+| **UNTUNED** | Chosen, and known to need empirical calibration |
+
+If a number is not tagged CITED, it is not evidence of anything about face
+recognition in general. It is a setting.
+
+---
+
+## Table of contents
+
+- [Run an experiment](#run-an-experiment)
+- [Method, evidence, and limits](#method-evidence-and-limits)
+
+1. [The honest summary](#1-the-honest-summary)
+2. [The pipeline](#2-the-pipeline)
+3. [What a 128-dimensional face descriptor is](#3-what-a-128-dimensional-face-descriptor-is)
+4. [Where 0.6 comes from](#4-where-06-comes-from)
+5. [What LFW does and does not tell you](#5-what-lfw-does-and-does-not-tell-you)
+6. [Demographic differentials](#6-demographic-differentials)
+7. [The metrics vocabulary](#7-the-metrics-vocabulary)
+8. [The table that is not in this tool, and why](#8-the-table-that-is-not-in-this-tool-and-why)
+9. [Algorithms implemented here](#9-algorithms-implemented-here)
+10. [Every threshold, and where it came from](#10-every-threshold-and-where-it-came-from)
+11. [Adversarial makeup: the research](#11-adversarial-makeup-the-research)
+12. [Transferability: the central caveat](#12-transferability-the-central-caveat)
+13. [What this tool can and cannot tell you](#13-what-this-tool-can-and-cannot-tell-you)
+14. [Privacy and data handling](#14-privacy-and-data-handling)
+15. [Regulatory context](#15-regulatory-context)
+16. [Vendored assets and provenance](#16-vendored-assets-and-provenance)
+17. [Reading list](#17-reading-list)
+18. [Glossary](#18-glossary)
+
+---
+
+## 1. The honest summary
+
+This tool measures **one number**: the Euclidean distance between a 128-dimensional
+descriptor of your face captured before a makeup session, and a descriptor of
+your face right now, both produced by dlib's face recognition ResNet as shipped
+in face-api.js.
+
+That number tells you how far one specific, publicly available 2017 model has
+moved you in its own embedding space. It does **not** tell you:
+
+- whether you would be recognised by any deployed surveillance system
+- whether the makeup would work on a different model
+- whether the makeup would work under different lighting or from a different angle
+- whether your face is "safe"
+
+What it is genuinely good for is **iteration**. If you are developing a makeup
+technique, you need a fast local signal to compare variant A against variant B.
+This gives you that, cheaply, offline, on your own device, without sending a
+face anywhere. It can take either a live camera or a local video, mark the
+operator-declared application intervals, and measure each finished step over a
+short temporal window. Treat it as a rehearsal instrument, not a verdict.
+
+The rest of this document explains why that framing is the correct one.
+
+---
+
+## 2. The pipeline
+
+The input is either the live camera or a video file selected from the device.
+The local file is read through a temporary object URL; it is not uploaded or
+persisted. Both inputs go through the same 2D face-api pipeline below.
+
+Three separate models run in sequence on each frame. They are frequently
+conflated in public discussion, and the distinction matters because an
+intervention can affect one stage without affecting the others.
+
+### 2.1 Detection: TinyFaceDetector
+
+Finds face bounding boxes. A compact CNN shipped with
+[face-api.js](https://github.com/vladmandic/face-api), based on Tiny YOLO v2.
+Configured in `config.js`:
+
+```js
+export const DETECTOR_OPTIONS = new faceapi.TinyFaceDetectorOptions({
+   inputSize: 416,      // square input dimension
+   scoreThreshold: 0.5  // minimum reported confidence
+});
+```
+
+**This is the stage that classical anti-surveillance makeup targeted.** See
+[§11.1](#111-cv-dazzle-2010-and-its-own-obsolescence).
+
+### 2.2 Alignment: 68-point facial landmarks
+
+`faceLandmark68Net` locates 68 points: jaw outline (0–16), eyebrows (17–26),
+nose (27–35), eyes (36–47), mouth (48–67). The recognition network needs a
+canonically-aligned crop, so landmarks are used to rotate and scale the face
+before embedding.
+
+This tool also reads landmarks directly for pose estimation: see
+[§9.2](#92-the-quality-gate).
+
+### 2.3 Recognition: 128-d descriptor
+
+`faceRecognitionNet`, a port of
+[dlib's `dlib_face_recognition_resnet_model_v1`](https://github.com/davisking/dlib-models).
+Takes a 150×150×3 aligned face crop, outputs 128 floating-point numbers.
+
+Architecture and training, from
+[Davis King's announcement post](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html):
+
+- A ResNet with **29 convolutional layers**: a reduced
+  [ResNet-34](https://arxiv.org/abs/1512.03385) (He, Zhang, Ren & Sun, 2015)
+  with layers removed and filters per layer halved
+- Trained from scratch on **~3 million face images** across **7,485 identities**
+- Sources: [FaceScrub](http://vintage.winklerbros.net/facescrub.html),
+  the [VGG Face dataset](https://www.robots.ox.ac.uk/~vgg/data/vgg_face/),
+  and images scraped from the web by the author
+- LFW identities were deliberately excluded so the LFW evaluation stayed valid
+- The model file is public domain
+
+**Age note.** This model was published in February 2017 and has not been
+retrained. It is a reasonable stand-in for "an open-source face recogniser," and
+a poor stand-in for the state of the art. See
+[§12](#12-transferability-the-central-caveat).
+
+---
+
+## 3. What a 128-dimensional face descriptor is
+
+### 3.1 Deep metric learning
+
+Classical face recognition extracted hand-designed features
+([Eigenfaces](https://www.face-rec.org/algorithms/PCA/jcn.pdf), Turk & Pentland
+1991; Fisherfaces; LBP histograms). Modern systems instead learn a **mapping**
+from face image to a vector space where geometric distance corresponds to
+identity difference.
+
+The canonical statement of this idea is
+[**FaceNet**](https://arxiv.org/abs/1503.03832) (Schroff, Kalenichenko & Philbin,
+CVPR 2015), which trained with a *triplet loss*: pull an anchor face toward
+another image of the same person, push it away from a different person, by at
+least a margin. Everything downstream, including dlib's model, is a variation
+on this.
+
+The practical consequence: **recognition becomes arithmetic.** Once faces are
+vectors, identifying someone is a nearest-neighbour lookup. There is no
+"template" of your face in a human-legible sense; there is a point in R¹²⁸.
+
+### 3.2 Distance metrics
+
+For descriptors *a* and *b*, this tool uses **Euclidean (L2) distance**:
+
+```
+d(a, b) = √( Σᵢ (aᵢ − bᵢ)² )
+```
+
+Implemented in `realtime-capture.js` as `euclidean()`. Deliberately not
+`faceapi.euclideanDistance`: keeping it local means the module can be unit
+tested without loading the vendor bundle.
+
+**Do not substitute cosine similarity.** Davis King has answered this directly
+[in the comments of the announcement post](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html):
+a user reported high cosine similarity between images of different people, and
+the answer was that the model is calibrated for Euclidean distance. Cosine
+similarity discards vector magnitude; for this model, that magnitude carries
+information.
+
+Other systems differ.
+[ArcFace](https://arxiv.org/abs/1801.07698) (Deng et al., CVPR 2019) optimises
+angular margin and *is* naturally read with cosine similarity. **Thresholds and
+metrics are not portable between models.** A distance of 0.6 means something in
+dlib's space and nothing at all in ArcFace's.
+
+### 3.3 Vector norm: a subtlety that affects the baseline
+
+dlib's descriptors are not explicitly L2-normalised to unit length, though in
+practice their norms cluster near 1. This matters for one reason: **averaging
+several descriptors produces a vector shorter than its inputs**, because
+imperfectly-aligned vectors partially cancel. A mean-of-N baseline therefore
+sits slightly inside the manifold, and every subsequent distance measured
+against it carries a small systematic offset.
+
+This tool avoids the problem entirely by using the **medoid** rather than the
+mean: see [§9.3](#93-medoid-baseline).
+
+---
+
+## 4. Where 0.6 comes from
+
+This is the single most-cited and least-understood number in open-source face
+recognition. It has two independent justifications, and most write-ups mention
+only the second.
+
+### 4.1 It is baked into the training objective
+
+From [Davis King's post](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html):
+the network was trained with a structured metric loss that attempts to project
+each identity into a **non-overlapping ball of radius 0.6**. It is a pairwise
+hinge loss over all pairs in a mini-batch, with hard-negative mining.
+
+This is the important part. **0.6 is not a threshold discovered by tuning after
+training: it is the geometry the network was explicitly optimised to produce.**
+The model was told to make same-identity clusters fit inside spheres of that
+radius. The threshold is the training target, read back out.
+
+That makes 0.6 far more principled than a tuned hyperparameter, *and* far more
+model-specific. It is a fact about this network's loss function. It transfers to
+no other model.
+
+### 4.2 It is the operating point of the reported benchmark
+
+From [dlib's `face_recognition.py` example](https://github.com/davisking/dlib/blob/master/python_examples/face_recognition.py):
+at a distance threshold of 0.6, the model reaches **99.38% accuracy on LFW**.
+The docs state the plain-language rule: descriptors closer than 0.6 are
+generally the same person, further apart generally are not.
+
+`face-api.js` inherits this as the default `distanceThreshold` in its
+[`FaceMatcher`](https://github.com/vladmandic/face-api).
+
+### 4.3 What "99.38%" actually measures
+
+It is a **verification** figure on a specific protocol: given a pair of images,
+correctly say same/different, on LFW's balanced 6,000-pair test. It is not:
+
+- an identification (1:N search) figure: those degrade with gallery size
+- a figure for any demographic subgroup
+- a figure for non-frontal, low-resolution, or surveillance-quality imagery
+- a figure that has been re-validated on any recent dataset
+
+Accuracy on 1:1 verification and accuracy on 1:N identification against a
+12-million-person gallery are different problems. See
+[§7](#7-the-metrics-vocabulary).
+
+---
+
+## 5. What LFW does and does not tell you
+
+[Labeled Faces in the Wild](https://vis-www.cs.umass.edu/lfw/) (Huang, Ramesh,
+Berg & Learned-Miller, UMass Amherst TR 07-49, 2007) is ~13,000 images of ~5,700
+individuals, scraped from news photographs. It became the field's default
+yardstick.
+
+### 5.1 It is demographically skewed
+
+The widely-cited estimate is that LFW is approximately **83.5% white** and
+**77.5% male**. This figure originates from Han & Jain's
+[demographic analysis of unconstrained face images](https://www.researchgate.net/publication/263697688_Age_Gender_and_Race_Estimation_from_Unconstrained_Face_Images)
+and is reproduced across the fairness literature: for example in
+[Gender Classification and Bias Mitigation in Facial Images](https://arxiv.org/abs/2007.06141)
+and the
+[Harvard JOLT digest on racial bias in facial recognition](https://jolt.law.harvard.edu/digest/why-racial-bias-is-prevalent-in-facial-recognition-technology).
+The [Algorithmic Fairness Datasets survey](https://arxiv.org/abs/2202.01711)
+(Fabris et al.) documents LFW's skew toward white, male and under-60 subjects.
+
+Because the images are news photographs of public figures, this skew reflects
+whose faces circulate in Anglophone news media. That media pattern is now
+baked into a benchmark.
+
+### 5.2 The model's author says so himself
+
+In the comments of the dlib announcement post, Davis King notes that LFW is
+heavily biased toward white adult American public figures, that the training
+data carries a similar bias, and that this creates an obvious problem for people
+outside that distribution, who would **likely require a different threshold**.
+
+There is empirical support for this. A study of the model on Asian faces
+([Neural Network Facial Authentication for Public Electric Vehicle Charging Station](https://arxiv.org/abs/2106.10432))
+reports accuracy dropping from the 99.38% LFW figure to **98.18%**, citing a
+GitHub issue raised against dlib.
+
+### 5.3 It is saturated
+
+A benchmark where everything scores 99%+ has stopped discriminating between
+methods. The field moved on to IJB-A/B/C, MegaFace, and NIST's ongoing FRVT
+precisely because LFW no longer separates good systems from great ones.
+
+**Consequence for this tool.** The 0.60 line is labelled in the UI as dlib's LFW
+default, explicitly as a benchmark's number rather than a property of your face.
+If you are not a white adult American public figure photographed by press
+photographers, its calibration is less well established for you. There is no
+correction factor to apply, because nobody has published one.
+
+---
+
+## 6. Demographic differentials
+
+NIST evaluations and the academic literature document demographic performance
+differences across many face-recognition systems. These differences are central
+to any public-interest investigation of the technology.
+
+### 6.1 NIST FRVT Part 3
+
+[**NISTIR 8280: Face Recognition Vendor Test Part 3: Demographic Effects**](https://doi.org/10.6028/NIST.IR.8280)
+(Grother, Ngan & Hanaoka, December 2019). See the
+[full PDF](https://pages.nist.gov/frvt/reports/demographics/nistir_8280.pdf),
+[NIST summary](https://www.nist.gov/news-events/news/2019/12/nist-study-evaluates-effects-race-age-sex-face-recognition-software).
+
+189 algorithms from 99 developers. Key findings:
+
+- Demographic differentials exist in the **majority** of algorithms studied
+- Among US-developed algorithms, elevated false-positive rates in one-to-one
+  matching for Asian, African American and Native American groups, with the
+  American Indian demographic showing the highest false positive rates
+- Many algorithms were 10–100× more likely to misidentify a Black or East Asian
+  face than a white one
+- Most algorithms were substantially less likely to correctly identify a Black
+  woman than any other demographic group
+
+Grother's own framing in the NIST release is careful: the report quantifies the
+differentials without claiming to explain their causes.
+
+### 6.2 The follow-up: false positives are the harder problem
+
+[**NISTIR 8429: Face Recognition Vendor Test Part 8: Summarizing Demographic Differentials**](https://pages.nist.gov/frvt/reports/demographics/nistir_8429.pdf)
+
+The finding that matters:
+
+- **False positive differentials are widespread and occur even in pristine
+  images.** Within-group false positive rates were found to vary by a factor of
+  several thousand.
+- False *negative* inequities are substantially attributable to poor photography
+  of certain groups, notably under-exposure of dark-skinned individuals, and
+  are fixable with better cameras and capture conditions.
+- The much larger false *positive* variations are not fixable that way. They must
+  be addressed in the algorithm.
+
+This asymmetry is important. "Better lighting" is a real mitigation for one class
+of error and no mitigation at all for the other.
+
+### 6.3 The academic literature
+
+- [**Gender Shades**](https://proceedings.mlr.press/v81/buolamwini18a.html)
+  (Buolamwini & Gebru, FAccT 2018): the study that put this on the public
+  agenda. **Note carefully:** it audits gender *classification*, not face
+  verification. It is frequently miscited as a face-recognition-accuracy study.
+  Its finding of far higher error rates for darker-skinned women than
+  lighter-skinned men concerns a different task on the same faces.
+- [Characterizing the Variability in Face Recognition Accuracy Relative to Race](https://arxiv.org/abs/1904.07325)
+  (Krishnapriya et al.): impostor distributions for African-American cohorts
+  shift toward higher similarity scores relative to Caucasian cohorts across
+  multiple matchers, raising false match rates at any fixed threshold.
+- [Analysis of Gender Inequality In Face Recognition Accuracy](https://arxiv.org/abs/2002.00065)
+  (Albiero et al.): female impostor distributions shift toward higher
+  similarity, female genuine distributions toward lower; d′ separation is
+  consistently worse for women. The paper's subset analysis (neutral expression,
+  head pose, visible forehead, **no makeup**) is directly relevant to this
+  project.
+- [Exploring Causes of Demographic Variations In Face Recognition Accuracy](https://arxiv.org/abs/2304.07175)
+- [Impact of Blur and Resolution on Demographic Disparities in 1-to-Many Facial Identification](https://arxiv.org/abs/2309.04447)
+
+**Consequence for this tool.** A single global threshold produces systematically
+different error rates for different people. Since this tool measures *your*
+distance from *your own* baseline, it sidesteps the cross-identity false-match
+problem, but the underlying embedding still carries whatever demographic
+structure the training data gave it, and there is no way to measure that from
+one face.
+
+---
+
+## 7. The metrics vocabulary
+
+Necessary to read any of the linked literature without being misled.
+
+| Term | Meaning |
+|---|---|
+| **Verification (1:1)** | Are these two images the same person? What LFW measures. |
+| **Identification (1:N)** | Which of N enrolled people is this? Harder; error grows with N. |
+| **Genuine / mated pair** | Two images of the same person. |
+| **Impostor / non-mated pair** | Two images of different people. |
+| **FMR** (False Match Rate) | Fraction of impostor pairs accepted. A false accept. |
+| **FNMR** (False Non-Match Rate) | Fraction of genuine pairs rejected. A false reject. |
+| **FPIR / FNIR** | The 1:N identification analogues of FMR/FNMR. |
+| **ROC** | FNMR plotted against FMR as the threshold sweeps. The whole curve, not one point. |
+| **d′ (d-prime)** | Separation between genuine and impostor distributions: `|μ_G − μ_I| / √((σ²_G + σ²_I)/2)`. Assumes roughly Gaussian distributions. Higher is better. |
+
+**The threshold is a dial, not a fact.** Moving it trades FMR against FNMR. Any
+single accuracy number is one point on a curve, and quoting it without the
+operating point is meaningless. This is why the visualizer does *not* let you
+move the threshold: 0.60 is the one operating point with a documented
+provenance, and a moved line would have none.
+
+---
+
+## 8. The table that is not in this tool, and why
+
+During design, a banded interpretation table was proposed:
+
+| Distance | Proposed meaning |
+|---|---|
+| 0.00–0.25 | Same face, session noise |
+| 0.25–0.45 | Same face, natural variation |
+| 0.45–0.60 | Approaching the boundary |
+| 0.60+ | Reads as a different person |
+
+**It was rejected, and it is documented here so nobody reintroduces it.**
+
+Only the 0.60 boundary is sourceable. The 0.25 and 0.45 edges were a plausible
+synthesis, not a published finding. A search of the literature turned up no
+source: the genuine/impostor distribution work that exists
+([Krishnapriya et al.](https://arxiv.org/abs/1904.07325),
+[Albiero et al.](https://arxiv.org/abs/2002.00065)) covers other matchers and
+reports similarity scores rather than dlib Euclidean distances, so it does not
+transfer.
+
+Rendering unsourced numbers as thin labelled lines on a plot would read as
+*calibration*. Users would reasonably assume someone measured them. In a project
+whose subject is facial recognition accountability, shipping a fabricated axis is
+the wrong failure to have.
+
+**What replaced it.** The tool draws exactly two reference lines, and they carry
+different kinds of claim:
+
+| Line | Provenance | Meaning |
+|---|---|---|
+| Noise floor | **MEASURED**: this session, this camera, this face | At or below it, the reading is within the variation observed during baseline capture |
+| 0.60 | **CITED**: dlib training objective + LFW | A benchmark's number |
+
+The gridlines at 0.25 / 0.50 / 0.75 / 1.00 / 1.25 are **axis ticks only**, drawn
+at hairline weight so they cannot be mistaken for claims. They let you read a
+value off the trace. They assert nothing.
+
+The space between the noise floor and 0.60 is deliberately left uncalibrated,
+and the UI says so.
+
+---
+
+## 9. Algorithms implemented here
+
+### 9.1 Sampling and smoothing
+
+Detection runs on `requestAnimationFrame` with a busy-flag guard, so the
+detector is never re-entered while a pass is in flight. Readings accumulate in a
+buffer; a separate 3 Hz timer averages the buffer into one timeline sample.
+The buffer records a comparability state alongside each value. Valid values
+feed the experiment; pose/framing and lighting exclusions remain visible as raw
+context under distinct backgrounds. A no-face tick advances time with a null
+value, so the trace lifts its pen instead of drawing a false zero.
+
+The status pill is a deliberately slower projection of that stream. Its colour
+swatch identifies the corresponding timeline class, but each visible state has
+a 1.5-second minimum dwell so alternating valid/pose/light frames do not produce
+unreadable text flicker. This does not smooth, delay or discard timeline data.
+
+Rationale: raw per-frame distance is noisy, and the graph's job is trend
+legibility, not maximum temporal resolution. The numeric readout is unsmoothed
+and updates per frame, so instantaneous values remain visible.
+
+Baseline and declared-step measurements use a different aggregation path. The
+baseline collects five accepted descriptors distributed over at least two
+seconds; a declared step collects eight over at least three seconds. Each window
+then selects its medoid, so it is not represented by the frame that happened to
+be visible when the button was pressed. Local-video spacing uses media time and
+ignores repeated decoded frames while the video is paused or ended.
+
+### 9.2 The quality gate
+
+`realtime-capture.js → createQualityGate()`. Face detection and measurement
+admission are separate stages: a valid 128-value descriptor can still be
+excluded when pose, framing or light would make it a poor comparison with the
+baseline. Baseline frames use the strict gate below. The UI reports all failed
+checks, while retaining the first as the primary instruction:
+
+**Confidence.** Detector score ≥ 0.85, above the 0.5 reporting floor in
+`DETECTOR_OPTIONS`. A frame that barely registered as a face produces a
+correspondingly unreliable descriptor.
+
+**Scale.** Face box width ≥ 25% of video width. The recognition network resamples
+to 150×150; a small box means feeding it upscaled mush.
+
+**Framing.** Box fully inside the frame with ≥ 2% margin on every edge. A clipped
+face yields a garbage descriptor that looks perfectly valid downstream: one of
+the more insidious failure modes, because nothing errors.
+
+**Roll**, from landmarks 36 and 45 (outer eye corners):
+
+```
+roll = |atan2(y₄₅ − y₃₆, x₄₅ − x₃₆)|   →   gate at 8°
+```
+
+**Yaw proxy**, from the nose tip (30) against the eye-corner midpoint,
+normalised by interocular distance:
+
+```
+yaw ≈ |x₃₀ − (x₃₆ + x₄₅)/2| / ‖p₄₅ − p₃₆‖   →   gate at 0.12
+```
+
+This is a cheap monocular approximation, not a calibrated pose estimate. For
+real head-pose work, see
+[OpenFace](https://github.com/TadasBaltrusaitis/OpenFace) (Baltrušaitis et al.).
+
+**Sharpness.** Gradient energy over a 96×96 greyscale crop:
+
+```
+E = (1/N) Σ (∂I/∂x)² + (∂I/∂y)²
+```
+
+A discrete approximation of the Tenengrad focus measure, related to the standard
+variance-of-Laplacian blur detector. **Used only for ranking candidates against
+each other, never against a fixed floor**: an absolute sharpness threshold would
+vary with every camera, lens and lighting setup, and would be one more invented
+constant.
+
+**Exposure.** Mean crop luminance in [0.18, 0.86] (Rec. 601 weighting), and fewer
+than 2% of pixels above 0.98. Rejects both under- and over-exposure.
+
+The exposure gate connects directly to
+[NISTIR 8429's finding](https://pages.nist.gov/frvt/reports/demographics/nistir_8429.pdf)
+that false-negative inequities are substantially caused by under-exposure of
+dark-skinned subjects. Refusing to build a baseline from an under-exposed frame
+is a small, concrete mitigation.
+
+Intervention frames deliberately use a different mode. Detector confidence and
+face-crop exposure are not rejection criteria, because makeup may change exactly
+those signals. Scale, framing, roll and yaw must still pass. The frame is then
+compared with the baseline's averaged 2D conditions: roll, yaw proxy, face-box
+scale and centre, plus ambient luminance sampled **outside** the face box. A
+frame beyond those relative tolerances is omitted from every measurement-grade
+path: declared-step aggregation, spike capture, equalizer and radial plot. Its
+raw descriptor distance remains on the timeline under an exclusion background.
+The header labels it **No distance** and states the exclusion cause. The operator can hide or restore each exclusion class
+without deleting its samples. Hidden samples contribute zero virtual height, so
+the interval truly collapses instead of leaving an empty coloured block.
+
+This is exclusion, not correction. The software does not rotate, relight or
+otherwise normalise a changed frame back to the baseline. The checks reduce
+obvious pose, framing and room-light confounds, but they cannot prove that the
+remaining distance came only from makeup. Pitch is not estimated, and ambient
+luminance is a deliberately simple 2D proxy.
+
+### 9.3 Medoid baseline
+
+Given N accepted descriptors, compute the pairwise distance matrix and select the
+member minimising summed distance to all others:
+
+```
+medoid = argmin_i  Σ_j d(xᵢ, xⱼ)
+spread = (1/(N−1)) Σ_j d(x_medoid, xⱼ)
+```
+
+**Why medoid rather than mean:**
+
+1. It stays a **real point** in the embedding, not a synthetic average.
+2. It sidesteps the norm-shrinkage problem in [§3.3](#33-vector-norm-a-subtlety-that-affects-the-baseline),
+   so no systematic offset gets baked into the reference.
+3. It is naturally robust to one marginal frame slipping the gate. The mean is not.
+
+Cost is O(N²) in distance computations: 10 pairwise comparisons for the
+five-frame baseline and 28 for an eight-frame declared step, each over 128
+elements.
+
+The same accepted baseline frames produce a coordinate-wise median of the
+normalized detector box and 68 landmarks. This separate 2D record draws the
+temporary alignment guide; it is not a descriptor, is not selected as the
+medoid, and never enters distance arithmetic. Face-api provides no scalp
+landmarks, so the upper arc is explicitly derived from the detector box.
+
+### 9.4 The noise floor
+
+`spread` above **is** the noise floor: how far the descriptor moved during a
+baseline window intended to hold face, pose, and light steady. It is the tool's
+only empirical reference line, and the most useful number it produces.
+
+Without it you cannot tell whether a small movement in the descriptor exceeds
+the variability of the baseline window. With it, a reading of 0.09 against a
+measured floor of 0.06 is weak evidence, while a reading of 0.55 against the
+same floor is a much larger model-local effect. The relative-condition gate in
+§9.2 removes obvious head and light changes; neither mechanism can guarantee
+that all nuisance variation has been removed.
+
+If spread exceeds 0.20 (**UNTUNED**) the batch is rejected outright: the subject
+moved or the lighting flickered, and accepting it would set a floor so high that
+real results vanish beneath it.
+
+### 9.5 Sustained spike detection
+
+Capture fires on a reading that **holds**, not an instantaneous value. Two
+triggers:
+
+- **Personal best**: a new session maximum by ≥ 0.05, held for ≥ 1.5s
+- **Threshold crossing**: the first time the run clears 0.60, held for ≥ 1.5s
+
+Plus a manual shutter, because no heuristic catches everything.
+
+Automatic spike capture is active only while the session is in its ready state.
+During an operator-declared application interval and its measurement window,
+the explicit step snapshot is the record of interest.
+
+The frame written to disk is the **sharpest** observed across the sustain window,
+not whichever frame tripped the trigger. Rationale: a single frame at 0.50 is as
+likely to be motion blur as a result, and "the makeup is working" should not rest
+on a photograph of someone moving. A run that dips back below its starting value
+aborts.
+
+### 9.6 What the equalizer shows
+
+The right panel shows **per-dimension absolute delta**: `|baseline[i] − current[i]|`
+for each of the 128 components, scaled against `MAX_DELTA = 0.15`.
+
+**This is a debugging aid, not a measurement.** Individual dimensions of a
+learned embedding have no assigned semantic meaning. Dimension 47 is not "nose
+width." The panel is useful for seeing *whether* change is concentrated or
+diffuse across the embedding, and for nothing else. Work such as
+[Feature Representation in Deep Metric Embeddings](https://arxiv.org/abs/2102.03176)
+(which uses this exact dlib model) examines what structure embeddings do carry;
+per-dimension interpretation is not part of it.
+
+### 9.7 Declared makeup steps and the ranked radial distribution
+
+After setting a baseline, the operator controls the experiment semantics:
+
+1. **Start makeup step** marks the beginning of an application interval.
+2. **Measure this step** begins an eight-sample, minimum-three-second window.
+3. The tool stores the window medoid as the step snapshot, reports its distance
+   and temporal spread, and keeps the sharpest accepted thumbnail.
+
+The timeline changes background during application and measurement and draws a
+labelled event line for each transition. With a local video, events also include
+the media time, so pause, seek and replay can be used as an experiment fixture.
+The controls resume a paused local video before beginning a marked interval.
+Event rows mask the vertical threshold/noise references behind their labels;
+application boundaries are orange and measurement boundaries green, rather
+than borrowing the yellow reserved for the 0.60 threshold.
+
+The radial plot uses all 128 absolute component deltas, sorts them by magnitude,
+splits them into 16 consecutive groups of eight and plots the root-mean-square
+of each group. The live valid reading is an outline; the most recent measured
+step is a filled shape. This is easier to read than 128 spokes, but ranking
+deliberately discards coordinate identity: the plot describes the distribution
+of change, not where that change occurred. It freezes on the last valid frame
+whenever the current frame is excluded.
+
+---
+
+## 10. Every threshold, and where it came from
+
+### Cited
+
+| Constant | Value | Source |
+|---|---|---|
+| `MATCH_THRESHOLD` | 0.60 | dlib's [structured metric loss target radius](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html) and the [documented LFW operating point](https://github.com/davisking/dlib/blob/master/python_examples/face_recognition.py) |
+
+That is the complete list. One number.
+
+### Measured
+
+| Quantity | Source |
+|---|---|
+| Baseline noise floor | Medoid spread over 5 gated frames, this session |
+| Step temporal spread | Medoid spread over 8 comparable frames, this step |
+
+### Chosen
+
+| Constant | Value | Reasoning |
+|---|---|---|
+| `GRAPH_MAX_DISTANCE` | 1.25 | Headroom above 0.60. Large model-local distances can exceed 1.0; a ceiling at 1.0 would hide their magnitude. |
+| `GRAPH_WINDOW_SECONDS` | 60 | Long enough for a makeup pass, short enough to read. |
+| `SAMPLES_PER_SECOND` | 3 | Smoothing vs. responsiveness. |
+| `NO_FACE_DEBOUNCE_MS` | 1500 | Delays the paused UI state so a blink does not disable controls; timeline no-face marking is immediate. |
+| `STATUS_MIN_DWELL_MS` | 1500 | Keeps quality instructions readable while the raw timeline continues at full cadence. |
+| Timeline quality backgrounds | 3 states | Preserve raw pose/framing, lighting and no-face context without admitting it to measurements. |
+| `BASELINE_TARGET_SAMPLES` | 5 | A compact temporal medoid rather than one frame, with lower capture burden. |
+| `BASELINE_MIN_MS` | 2000 | Distributes accepted frames across the interval. |
+| `BASELINE_TIMEOUT_MS` | 30000 | Leaves time to respond to a diagnostic rejection. |
+| `MEASUREMENT_TARGET_SAMPLES` | 8 | Uses the same temporal aggregation for every declared step. |
+| `MEASUREMENT_MIN_MS` | 3000 | Distributes accepted comparison frames across the interval. |
+| `MEASUREMENT_TIMEOUT_MS` | 30000 | Fails explicitly when comparable frames cannot be collected. |
+| `SPIKE_SUSTAIN_MS` | 1500 | ~4–5 samples at 3 Hz. |
+| `SPIKE_BEST_MARGIN` | 0.05 | Avoids capturing noise as a personal best. |
+| `SPIKE_COOLDOWN_MS` | 8000 | Keeps the gallery to keepers. |
+| `minScore` | 0.85 | Detector floor is 0.5; used only for strict baseline capture. |
+| `minBoxWidthRatio` | 0.25 | Recognition net input is 150×150. |
+| `maxRollDegrees` | 8 | Tight enough to matter, loose enough to achieve. |
+| `maxYawOffset` | 0.12 | As above; proxy metric, see §9.2. |
+| `maxRollDelta` | 4° | Relative 2D comparison tolerance; frames beyond it are omitted. |
+| `maxYawDelta` | 0.055 | Relative yaw-proxy tolerance. |
+| `maxScaleDelta` | 0.08 | Relative face-box width tolerance. |
+| `maxCenterDelta` | 0.065 | Relative horizontal or vertical centre tolerance. |
+| `maxLumaDelta` | 0.10 | Relative ambient-luminance tolerance, measured outside the face box. |
+| `MAX_DELTA` | 0.15 | Equalizer and radial scaling only. Purely visual. |
+
+### Untuned: needs empirical calibration
+
+| Constant | Value | Why it needs work |
+|---|---|---|
+| `BASELINE_MAX_SPREAD` / `MEASUREMENT_MAX_SPREAD` | 0.20 | Should be derived from observed spread distributions across cameras and subjects. Currently a guess. |
+| `minLuma` / `maxLuma` | 0.18 / 0.86 | Should be validated across skin tones specifically, given §6.2. A gate tuned on light skin would systematically reject dark-skinned subjects and is exactly the failure NIST documents. **This is the highest-priority calibration task in the codebase.** |
+| Relative condition tolerances | values above | Need repeated-camera and repeated-subject studies. They reduce confounds but are not calibrated guarantees of pose or illumination invariance. |
+
+### Inherited from `config.js`
+
+| Constant | Value |
+|---|---|
+| `inputSize` | 416 |
+| `scoreThreshold` | 0.5 |
+
+---
+
+## 11. Adversarial makeup: the research
+
+### 11.1 CV Dazzle (2010) and its own obsolescence
+
+[**CV Dazzle**](https://adam.harvey.studio/cvdazzle), Adam Harvey. Named after
+[dazzle camouflage](https://en.wikipedia.org/wiki/Dazzle_camouflage): WWI ship
+patterns that confused rangefinders rather than concealing the ship. It uses
+makeup, hair, and styling to break the light/dark relationships expected by the
+detector it targeted. It is an early documented example of appearance-based
+camouflage tested against a computer-vision system.
+
+**Read Harvey's own current statement on the project before using any of it.**
+The original 2010–2013 looks targeted the
+[Viola-Jones](https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-cvpr-01.pdf)
+Haar cascade detector. Harvey's site states plainly that Viola-Jones was
+deprecated in security use around 2013–2016, and that **the original patterns are
+no longer active looks.** He developed new looks in 2020 aimed at CNN-based
+recognition.
+
+Harvey's revision makes the method's expiry date explicit. The lesson
+generalises: **anti-surveillance techniques have expiry dates.** A look that
+worked against one detector generation says nothing about the next.
+
+Note also the *category* shift. CV Dazzle originally targeted **detection**:
+being found at all. This tool measures **recognition**: being identified once
+found. Disrupting a detector and moving an embedding are different problems with
+different countermeasures. If the detector loses your face entirely, this tool
+records a no-face interval with no distance line, not a low distance.
+
+Further reading:
+[Harvey's DIS Magazine writeup](https://dismagazine.com/dystopia/evolved-lifestyles/8115/anti-surveillance-how-to-hide-from-machines/),
+[2011 Artblog interview](https://www.theartblog.org/2020/01/adam-harvey-and-the-anti-face/),
+[Wikipedia overview](https://en.wikipedia.org/wiki/Computer_vision_dazzle).
+
+### 11.2 Accessorize to a Crime (2016)
+
+[**Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition**](https://dl.acm.org/doi/10.1145/2976749.2978392),
+Sharif, Bhagavatula, Bauer & Reiter, ACM CCS 2016.
+[PDF](https://www.cs.cmu.edu/~sbhagava/papers/face-rec-ccs16.pdf) ·
+[author page](https://mahmoods01.github.io/publication/ccs16-adv-ml/)
+
+The paper that established physically-realisable adversarial attacks on face
+recognition. Printed eyeglass frames that let the wearer evade recognition **or
+impersonate a specific different person**. Primarily white-box, with black-box
+and detection-evasion demonstrations.
+
+The impersonation result is the striking one: the attack is not just "make me
+unrecognisable" but "make me register as this specific other individual."
+
+Extended in [A General Framework for Adversarial Examples with Objectives](https://dl.acm.org/doi/10.1145/3317611)
+(ACM TOPS, 2019).
+
+### 11.3 The physical attack lineage
+
+- [**AdvHat**](https://arxiv.org/abs/1908.08705) (Komkov & Petiushko, ICPR 2020)
+  describes a printed sticker on a hat targeting ArcFace.
+- [**Adversarial Mask**](https://arxiv.org/abs/2111.10759): universal patterned
+  face mask.
+- [**Towards Effective Adversarial Textured 3D Meshes**](https://arxiv.org/abs/2303.15818)
+  describes 3D-aware physical attacks.
+- [**Imperceptible Physical Attack via LED Illumination Modulation**](https://arxiv.org/abs/2307.13294)
+  targets the *illumination* rather than the face. It was evaluated against dlib,
+  FaceNet and ArcFace, and useful for seeing how the three compare.
+
+### 11.4 Makeup-specific attacks: the directly relevant work
+
+- [**Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition**](https://arxiv.org/abs/2105.03162)
+  (Yin et al., IJCAI 2021):
+  [proceedings](https://www.ijcai.org/proceedings/2021/173) ·
+  [code](https://github.com/TencentYoutuResearch/Adv-Makeup).
+  Synthesises adversarial eye shadow over the orbital region. Explicitly targets
+  **imperceptibility** (looks like makeup, not like an attack) and
+  **transferability** (works black-box), using meta-learning across multiple
+  surrogate models. Includes a physical-world case study against two commercial
+  platforms. **The closest published analogue to what this project is doing.**
+- [**AMT-GAN: Protecting Facial Privacy via Style-robust Makeup Transfer**](https://arxiv.org/abs/2203.03121)
+  (Hu et al., CVPR 2022): adversarial identity masks through makeup transfer.
+- [Generating Adversarial Examples by Makeup Attacks on Face Recognition](https://ieeexplore.ieee.org/document/8803269)
+  (Zhu, Lu & Chiang, ICIP 2019): earlier work in the same direction.
+- [Adv-Attribute: Inconspicuous and Transferable Adversarial Attack](https://arxiv.org/abs/2210.06871)
+  uses attribute-space rather than pixel-space perturbation.
+
+**Note the methodological gap.** These papers optimise perturbations with
+gradient access to a model. Physical makeup applied by hand is a much coarser
+instrument, and none of these results should be read as predicting what a human
+with brushes can achieve. That gap is precisely the space this tool is meant to
+explore empirically.
+
+### 11.5 Digital cloaking: a different threat model
+
+- [**Fawkes: Protecting Privacy against Unauthorized Deep Learning Models**](https://www.usenix.org/conference/usenixsecurity20/presentation/shan)
+  (Shan et al., USENIX Security 2020):
+  [PDF](https://people.cs.uchicago.edu/~ravenben/publications/pdf/fawkes-usenix20.pdf) ·
+  [code](https://github.com/Shawn-Shan/fawkes) ·
+  [project site](https://sandlab.cs.uchicago.edu/fawkes/).
+  Perturbs images *before upload* so that a scraper training on them builds a
+  model that misidentifies the real you. The authors reported 95%+ protection,
+  and 80%+ even when clean images leak, under the paper's test conditions.
+- [**LowKey**](https://arxiv.org/abs/2101.07922) (Cherepanova et al., ICLR 2021)
+  pursues a similar goal, using an ensemble for better black-box transfer and a perceptual
+  metric in the loss.
+
+**Critically, this is poisoning, not evasion.** Fawkes aims to alter what a
+scraped training set teaches a model. It does not address a person walking past
+a camera. Independent work has also
+questioned its practical robustness: see
+[Oriole](https://arxiv.org/abs/2102.11502), which reports the perturbations
+becoming plainly visible on high-resolution images.
+
+### 11.6 The survey to read first
+
+[**SoK: Anti-Facial Recognition Technology**](https://arxiv.org/abs/2112.04558),
+Wenger, Shan, Zheng & Zhao, systematises the whole space: evasion vs.
+poisoning, digital vs. physical, detection vs. recognition. If you read one
+paper from this list, read this one.
+
+Also useful:
+[Privacy-Enhancing Face Biometrics: A Comprehensive Survey](https://ieeexplore.ieee.org/document/9481149)
+(Meden et al., IEEE TIFS 2021).
+
+---
+
+## 12. Transferability: the central caveat
+
+**A high distance in this tool does not mean you are unrecognisable.**
+
+The entire adversarial ML literature converges on this: attacks optimised against
+one model **transfer poorly** to others. Transferability is treated as the core
+open challenge in
+[Adv-Makeup](https://arxiv.org/abs/2105.03162), which needed a dedicated
+meta-learning strategy across multiple surrogate models just to get black-box
+success, even with full gradient access during optimisation.
+
+The model in this tool is:
+
+- **From 2017**, never retrained
+- **Trained with a different loss** (structured metric loss) than modern systems
+  (ArcFace's angular margin, AdaFace's quality-adaptive margin)
+- **Read with a different metric** (Euclidean, not cosine)
+- **Small**: 29 conv layers, halved filters
+- **Not** what any commercial system uses
+
+Deployed systems from vendors such as Clearview, Idemia, NEC, Microsoft, and
+Amazon use proprietary models, typically larger, trained on far more data, and often
+ensembled. NIST's FRVT programme
+([ongoing results](https://pages.nist.gov/frvt/html/frvt11.html)) evaluates
+hundreds of them; the open-source models in this class sit well below the
+commercial frontier. NISTIR 8280 itself notes that some open-source algorithms
+included in prior comparative work are inaccurate enough not to represent
+commercial deployment.
+
+So: **moving 0.9 in dlib's space is evidence that you moved something in dlib's
+space.** It is weak evidence about ArcFace, and near-zero evidence about a
+production surveillance stack.
+
+### What would strengthen a claim
+
+If the goal is a defensible finding rather than a local signal, the standard
+approach is an **ensemble** of independent recognisers, reporting the distance in
+each. A technique that moves all of them is meaningfully more likely to
+generalise than one that moves a single 2017 model. Candidates:
+
+- [ArcFace / InsightFace](https://github.com/deepinsight/insightface)
+- [FaceNet (facenet-pytorch)](https://github.com/timesler/facenet-pytorch)
+- [AdaFace](https://github.com/mk-minchul/AdaFace)
+- [DeepFace](https://github.com/serengilx/deepface) as a wrapper over several
+
+This would be a substantial architectural change, with different preprocessing,
+metrics, and thresholds per model. It is a possible next step for the project,
+not something the current page does.
+
+---
+
+## 13. What this tool can and cannot tell you
+
+### It can
+
+- Tell you whether a makeup pass moved dlib's embedding **at all**, above your
+  own measured noise floor
+- Let you **compare two makeup variants** on the same face, same session, same
+  camera. This is the comparison it is built for
+- Mark operator-declared application intervals and aggregate each finished step
+  over time instead of reporting only one clicked frame
+- Show whether component changes are diffuse or concentrated, with a 128-row
+  equalizer and a 16-band ranked RMS distribution using all 128 deltas
+- Give you a **watermarked record** carrying the project mark, distance,
+  threshold and verdict; its timestamp remains in the stored metadata and
+  exported filename, while local-video events also retain their media time
+- Do all of this **offline, on-device**, with no face leaving the machine
+
+### It cannot
+
+- Tell you whether any deployed system would recognise you
+- Tell you whether the technique transfers to another model
+- Prove invariance to pose or lighting. It omits frames with sufficiently large
+  2D pose, framing or ambient-light differences; it does not correct them and
+  does not estimate pitch
+- Map a descriptor dimension or ranked radial band to a facial feature or makeup line
+- Tell you anything about **detection** evasion: if the detector loses you, the
+  tool records a no-face interval, not a success
+- Establish that a technique works for anyone other than you
+- Correct for demographic differentials in the underlying embedding
+
+### Methodological hygiene, if you want the results to mean something
+
+1. **Re-baseline whenever source, lighting or camera setup changes.** A baseline
+   captured under different conditions is a different experiment.
+2. **Report the noise floor with every result.** A distance without its noise
+   floor is uninterpretable.
+3. **Use the declared-step controls.** Mark the start of application, then
+   measure only after returning to the baseline pose and lighting.
+4. **Compare within a session.** Cross-session comparison confounds makeup with
+   lighting, camera position and time of day.
+5. **Repeat the step.** One accepted temporal window is stronger than a peak,
+   but it is still one small sample. Report distance, baseline noise floor and
+   step temporal spread together.
+6. **Test pose separately.** Deliberately vary yaw, roll and pitch in a separate
+   protocol rather than treating excluded frames as proof of invariance.
+
+---
+
+## 14. Privacy and data handling
+
+The tool keeps the following local records. None is transmitted.
+
+| Data | Store | Deletion |
+|---|---|---|
+| Camera baseline descriptor + noise floor + averaged conditions + median 2D guide + timestamp | `localStorage` (`gstmxx.realtime.baseline`, format 3) | "Source → Reset baseline": removes the key |
+| Local-video baseline | Memory for the current page session | Change source, delete baseline, or close the page |
+| Spike, manual and declared-step captures (watermarked JPEGs) | IndexedDB (`gstmxx-realtime` / `captures`) | "Delete all", or per-capture in the viewer |
+| View settings | `localStorage` (`gstmxx.realtime.settings`) | Browser storage clear |
+
+The selected video is exposed to the page through a temporary browser object
+URL. The file and that URL are not stored in IndexedDB or `localStorage`.
+
+**Why IndexedDB for captures.** `localStorage` caps near 5MB per origin, is
+shared with everything else the site stores, and inflates binary by ~33% through
+base64. Photographs belong in a store that takes Blobs natively.
+
+**A 128-d descriptor is biometric data.** Under
+[GDPR Article 9](https://eur-lex.europa.eu/eli/reg/2016/679/oj), biometric data
+processed for unique identification is a special category. The page does not
+send it to a remote processor. Local processing does not make the data
+insignificant, which is why deletion is a real removal and not a flag.
+
+**Deleting the baseline does not clear the gallery.** Deliberate: the gallery is
+expected to outlive a re-baseline. Stated in the UI rather than left to be
+inferred.
+
+No third-party requests. All models are vendored under `/lab-js/vendor`; fonts
+are self-hosted. The page functions with the network unplugged.
+
+---
+
+## 15. Regulatory context
+
+Relevant because it explains what this tooling is a response to.
+
+### EU AI Act
+
+[Regulation (EU) 2024/1689](https://eur-lex.europa.eu/eli/reg/2024/1689/oj)
+applies in stages. The prohibited practices have applied since **2 February
+2025**. The enforcement powers of the AI Office and national competent
+authorities apply from **2 August 2026**. Infringements involving prohibited
+practices can attract penalties of up to €35 million or 7% of worldwide annual
+turnover. The European Commission maintains an
+[application timeline](https://digital-strategy.ec.europa.eu/en/policies/enforcement-ai-act)
+and [guidance on biometric identification](https://digital-strategy.ec.europa.eu/en/faqs/navigating-ai-act).
+
+Provisions bearing on facial recognition:
+
+- **Art. 5(1)(e)**: bans creating or expanding facial recognition databases
+  through **untargeted scraping** of internet images or CCTV footage. "Untargeted"
+  is the operative word. See the Future of Privacy Forum's analysis:
+  [Red Lines under the EU AI Act](https://fpf.org/blog/red-lines-under-the-eu-ai-act-understanding-the-ban-of-the-untargeted-scraping-of-facial-images-and-facial-recognition-databases/).
+- **Art. 5(1)(g)**: bans biometric categorisation inferring race, political
+  opinions, trade union membership, religious belief, sex life or sexual
+  orientation.
+- **Art. 5(1)(h)**: bans **real-time** remote biometric identification in
+  publicly accessible spaces for law enforcement, with narrow exceptions
+  requiring prior judicial or independent administrative authorisation.
+
+**The gap worth knowing.** The prohibition targets *real-time* RBI.
+**Post-remote** biometric identification, which runs recognition over previously
+collected material, is not prohibited. The Commission states that its use for a
+person under investigation requires prior judicial or independent
+administrative authorisation, plus notification to the relevant authorities.
+It is regulated, but not banned.
+
+*These provisions are recent and interpretation is still developing; verify
+against the current consolidated text and any Commission guidelines before
+relying on this summary.*
+
+### GDPR
+
+[Article 9](https://eur-lex.europa.eu/eli/reg/2016/679/oj): biometric data for unique
+identification is a special category requiring explicit consent or another
+Art. 9 condition. Between 2022 and 2024 several EU data protection authorities
+fined Clearview AI over facial recognition practices; those decisions are the
+enforcement backdrop to Art. 5(1)(e).
+
+### Standards
+
+- [ISO/IEC 19795](https://www.iso.org/standard/73515.html): biometric performance
+  testing and reporting. Part 10 addresses quantifying performance variation
+  across demographic groups.
+- [NIST FRVT](https://pages.nist.gov/frvt/html/frvt11.html): the ongoing
+  independent evaluation programme.
+
+---
+
+## 16. Vendored assets and provenance
+
+Full inventory in `lab-js/vendor/README.md`; refresh via
+`lab-js/vendor/fetch-sources.sh`.
+
+| Asset | Upstream | Version |
+|---|---|---|
+| `face-api.js` | [@vladmandic/face-api](https://github.com/vladmandic/face-api) | floating: no pinned version in the source URL |
+| Model shards | [justadudewhohacks/face-api.js-models](https://github.com/justadudewhohacks/face-api.js-models) | `@master` |
+| Original model weights | [davisking/dlib-models](https://github.com/davisking/dlib-models) | `dlib_face_recognition_resnet_model_v1` |
+
+**Two provenance weaknesses worth fixing.** The face-api bundle URL carries no
+version, and the model shards track `@master`. Both mean a re-run of
+`fetch-sources.sh` could silently change the model, and therefore every
+measurement, with no record of what changed. For research output, pin both to
+commit SHAs and record the SHA alongside results.
+
+---
+
+## 17. Reading list
+
+### Start here
+
+1. [SoK: Anti-Facial Recognition Technology](https://arxiv.org/abs/2112.04558): the map of the territory
+2. [High Quality Face Recognition with Deep Metric Learning](https://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html): the model in this tool, from its author
+3. [NISTIR 8280: FRVT Part 3: Demographic Effects](https://doi.org/10.6028/NIST.IR.8280): the evidence base for bias
+4. [CV Dazzle](https://adam.harvey.studio/cvdazzle): including its own obsolescence notice
+
+### Foundations
+
+- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://arxiv.org/abs/1503.03832): Schroff et al., CVPR 2015
+- [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385): He et al., 2015
+- [ArcFace: Additive Angular Margin Loss](https://arxiv.org/abs/1801.07698): Deng et al., CVPR 2019
+- [Rapid Object Detection using a Boosted Cascade of Simple Features](https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-cvpr-01.pdf): Viola & Jones, 2001
+- [Deep Face Recognition: A Survey](https://arxiv.org/abs/1804.06655): Wang & Deng
+
+### Benchmarks and their critique
+
+- [Labeled Faces in the Wild](https://vis-www.cs.umass.edu/lfw/): the original
+- [Algorithmic Fairness Datasets: the Story so Far](https://arxiv.org/abs/2202.01711): Fabris et al.
+- [FairFace](https://github.com/joojs/fairface): a demographically balanced alternative
+- [Racial Faces in the Wild](https://github.com/whdeng/RFW): Wang et al.
+
+### Bias and fairness
+
+- [Gender Shades](https://proceedings.mlr.press/v81/buolamwini18a.html): Buolamwini & Gebru (gender *classification*, not verification)
+- [NISTIR 8429: FRVT Part 8](https://pages.nist.gov/frvt/reports/demographics/nistir_8429.pdf)
+- [Characterizing the Variability in Face Recognition Accuracy Relative to Race](https://arxiv.org/abs/1904.07325)
+- [Analysis of Gender Inequality In Face Recognition Accuracy](https://arxiv.org/abs/2002.00065)
+- [Exploring Causes of Demographic Variations In Face Recognition Accuracy](https://arxiv.org/abs/2304.07175)
+
+### Attacks
+
+- [Accessorize to a Crime](https://www.cs.cmu.edu/~sbhagava/papers/face-rec-ccs16.pdf): Sharif et al., CCS 2016
+- [Adv-Makeup](https://arxiv.org/abs/2105.03162): Yin et al., IJCAI 2021
+- [AMT-GAN](https://arxiv.org/abs/2203.03121): Hu et al., CVPR 2022
+- [AdvHat](https://arxiv.org/abs/1908.08705): Komkov & Petiushko
+- [Fawkes](https://www.usenix.org/conference/usenixsecurity20/presentation/shan): Shan et al., USENIX Sec 2020
+- [LowKey](https://arxiv.org/abs/2101.07922): Cherepanova et al., ICLR 2021
+- [Intriguing Properties of Neural Networks](https://arxiv.org/abs/1312.6199): Szegedy et al., where adversarial examples begin
+
+### Policy
+
+- [EU AI Act, official text](https://eur-lex.europa.eu/eli/reg/2024/1689/oj)
+- [European Commission: navigating the AI Act](https://digital-strategy.ec.europa.eu/en/faqs/navigating-ai-act)
+- [FPF: Red Lines under the EU AI Act](https://fpf.org/blog/red-lines-under-the-eu-ai-act-understanding-the-ban-of-the-untargeted-scraping-of-facial-images-and-facial-recognition-databases/)
+- [GDPR, official text](https://eur-lex.europa.eu/eli/reg/2016/679/oj)
+
+---
+
+## 18. Glossary
+
+**Alignment**: Warping a detected face to a canonical pose before embedding.
+
+**Descriptor / embedding / template**: The vector representing a face. Here, 128 floats.
+
+**Detection vs. recognition**: Finding that a face exists vs. determining whose it is. Different models, different attacks.
+
+**Euclidean distance**: Straight-line distance in vector space. The metric this model is calibrated for.
+
+**Genuine / impostor distribution**: Distance distributions for same-person and different-person pairs. Their overlap determines error rates.
+
+**Medoid**: The member of a set with smallest summed distance to the others. Unlike the mean, always a real member.
+
+**Noise floor**: Descriptor movement observed during a baseline window intended to hold face, pose, and light steady. Measured per session.
+
+**Threshold**: The distance below which two descriptors are declared a match. A dial trading false accepts against false rejects, not a fact.
+
+**Transferability**: Whether an attack developed against one model works against another. Generally poor. The reason this tool's readings are local signal rather than evidence.
+
+**White-box / black-box**: Whether the attacker has access to model internals and gradients. This tool is white-box against dlib and tells you nothing about black-box performance.
+
+---
 
-For thresholds, algorithms, research provenance and limitations, read
-[Realtime Visualizer: Methodology, Sources and Limits](/docs/jsdoc/tutorials/realtime-fr-method).
+*Sources current as of October 2026. The EU AI Act sections describe recent and
+evolving provisions: verify against the consolidated text before relying on
+them. If you re-run `fetch-sources.sh`, record the resulting commit SHAs: the
+upstream model references are unpinned, and an unnoticed model change invalidates
+every prior measurement.*
diff --git a/tutorials/tutorials.json b/tutorials/tutorials.json
index c0855bb..a216cb2 100644
--- a/tutorials/tutorials.json
+++ b/tutorials/tutorials.json
@@ -33,10 +33,7 @@
      "title": "(internal) Seekable Video Loader architecture"
   },
   "realtime": {
-     "title": "Latent Space Visualizer"
-  },
-  "realtime-fr-method": {
-     "title": "Realtime Visualizer: Methodology, Sources and Limits"
+     "title": "Realtime Visualizer: How It Works and Why"
   },
   "aigen-image-labeler": {
      "title": "(internal) AI Gen image labeler"