diff --git a/CHANGELOG.md b/CHANGELOG.md index ead21d9..cf78059 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -120,6 +120,14 @@ this project uses [semantic versioning](https://semver.org/). runs, and none of 136 controls. The sensitive rule did not (14% of the other runs). It is a function, not wired into the filter. [ADR-0022](docs/adr/0022-multipath-signature.md) is accepted. `navkit sweep multipath` runs the study. +- `navkit euroc fetch --images`, `navkit.vo_frontend`, `navkit euroc vo` and [`docs/real_front_end.md`](docs/real_front_end.md): + a NumPy two-view feature-tracking front end, measured against the ground truth on 30 s of real EuRoC frames. Its errors + are small (0.08° and 8 mm per one-frame pair, with the true scale) and correlated for 0.2 to 0.25 s. A Gaussian + surrogate with that size and correlation time keeps the stochastic clone inside its gate. Replaying the measured + errors does not (NEES 27, 55% coverage), and assuming four times the measured noise brings it back (NEES 4.3, 91%). + One sequence and one window. [ADR-0023](docs/adr/0023-clone-against-a-measured-front-end.md) is accepted: the clone + stays opt-in, with the inflation. + ### Changed - The implementation plan carries the same point-in-time banner as the other baseline records. P5-09 and P5-12 are diff --git a/CONSTRAINTS.md b/CONSTRAINTS.md index d0dd408..df8eb96 100644 --- a/CONSTRAINTS.md +++ b/CONSTRAINTS.md @@ -4,9 +4,9 @@ Invariants this project holds itself to. Every entry is either currently true or is a stated blocker; nothing here is aspirational. Ratchets are measured, not estimated, and the command to re-measure each one is given. -Last measured: 1259 tests collected, all of which pass except the deliberate -xfails and the skips that `pytest -rs` lists, and 94.2% -line coverage (9039/9598 executable lines) on **CPython 3.13**, measured +Last measured: 1302 tests collected, all of which pass except the deliberate +xfails and the skips that `pytest -rs` lists, and 94.3% +line coverage (9819/10408 executable lines) on **CPython 3.13**, measured with `python scripts/coverage_report.py`. The figures come from `docs/data/metrics.json`, which `scripts/metrics.py` checks in CI. Coverage is quoted from that script alone: it is the project's dependency-free tracer (S1 forbids adding `pytest-cov`), and a number taken from a different tool is not comparable with the module figures @@ -83,9 +83,9 @@ These are floors, not goals. Each must not regress; raising one is welcome. | Ratchet | Floor | Current (CPython 3.13) | Re-measure with | |---|---:|---:|---| -| Tests collected | 405 | 1259 | `pytest`, `scripts/metrics.py --check` | +| Tests collected | 405 | 1302 | `pytest`, `scripts/metrics.py --check` | | Tests passing | 300 | all collected, except the deliberate xfails and the listed skips | `pytest -rs` | -| Line coverage | 75% | 94.2% | `scripts/coverage_report.py`, `scripts/metrics.py --check-coverage` | +| Line coverage | 75% | 94.3% | `scripts/coverage_report.py`, `scripts/metrics.py --check-coverage` | | `io/trajectory.py` coverage | 85% | 95.0% | as above | | `analysis/findings.py` coverage | 80% | 99.7% | as above | | `config.py` coverage | 80% | 98.4% | as above | @@ -104,7 +104,7 @@ These are floors, not goals. Each must not regress; raising one is welcome. | `outage_sweep.py` coverage | 80% | 98.4% | as above | | `fdir/nis_monitor.py` coverage | 80% | 98.8% | as above | | `degrade/` coverage | 50% | 78.0% (`config.py`) / 84.2% (`inject.py`) | as above | -| Docs | README + architecture + calibration + every ADR | 22 ADRs, all in the index | `scripts/metrics.py --check` | +| Docs | README + architecture + calibration + every ADR | 23 ADRs, all in the index | `scripts/metrics.py --check` | | Documented tables match the generated benchmark | exact | yes | `scripts/check_doc_tables.py` | | Open blockers documented | all | see ROADMAP | manual | diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index b9121d4..011c265 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -70,7 +70,7 @@ mypy src --ignore-missing-imports `ruff format src tests scripts` applies formatting; the `--check` form is the gate. -The suite collects 1259 tests and all of them pass on a fresh clone, except the deliberate +The suite collects 1302 tests and all of them pass on a fresh clone, except the deliberate xfails and the skips. `pytest -rs` lists the skips: two need TUM VI reference data that is not vendored (see below), and the doc-table check runs once `python scripts/run_benchmark.py` has written `results/benchmark.json`. The count and the other numbers the docs quote are in diff --git a/ROADMAP.md b/ROADMAP.md index 56cac2d..bbad97d 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -4,7 +4,7 @@ Sequenced by dependency, not by ambition. Each item names its exit test. Status markers: `[x]` done, `[~]` in progress, `[ ]` not started, `[!]` blocked. -Measured baseline: 1259 tests collected and 94.2% line coverage on CPython +Measured baseline: 1302 tests collected and 94.3% line coverage on CPython 3.13 (`docs/data/metrics.json`, from `scripts/metrics.py` and `scripts/coverage_report.py`). Every number below re-measures against that, not against a remembered value. @@ -259,8 +259,10 @@ this removes it. needed there. - [~] A visual front end with correlated errors, scale ambiguity and outliers. Outliers and scale drift are survived. Correlated errors are not, unless the - assumed visual noise is inflated by a factor found by trial. A real front end - is still untested. + assumed visual noise is inflated by a factor found by trial. A plain two-view front end was + then measured on real frames ([real front end](docs/real_front_end.md), ADR-0023): its errors are + correlated for about 0.25 s, and when they are replayed the clone stays inside the gate only with the assumed + noise inflated by four. One sequence and one window, so the default does not change. **Exit test:** NEES below 10 with vision enabled and GNSS denied, coverage above 90%, `vision_enabled = True` as the shipped default. Current value 286.2 for the diff --git a/docs/adr/0023-clone-against-a-measured-front-end.md b/docs/adr/0023-clone-against-a-measured-front-end.md new file mode 100644 index 0000000..b549798 --- /dev/null +++ b/docs/adr/0023-clone-against-a-measured-front-end.md @@ -0,0 +1,32 @@ +# ADR-0023: The clone against a measured front end + +- Status: accepted +- Date: 2026-10 +- Related: [ADR-0017](0017-stochastic-clone-for-the-visual-update.md), [ADR-0020](0020-second-source-against-a-ramp.md), [real front end](../real_front_end.md) + +## Context + +ADR-0017 left the clone opt-in because it is calibrated for independent visual errors, and no data said which a real +front end has. A plain NumPy two-view front end was run on 30 s of real EuRoC frames and its errors were measured. They +are small and correlated over a fraction of a second. A Gaussian surrogate with that size and correlation time keeps +the clone inside its gate. Replaying the measured errors themselves does not: NEES 27 and 55% coverage. Assuming four +times the measured noise brings the clone back inside, with NEES 4.3 and 91% coverage. + +## Options + +1. **Leave the clone opt-in and record the finding.** `vision_enabled` stays False. The opt-in clone is used with the + assumed visual noise inflated by four. +2. **Make the clone the default with the inflation.** One sequence and one error series do not support that. +3. **Measure more first.** More sequences, other windows, and a keyframe-based front end. + +## Decision + +Options 1 and 3. `vision_enabled` stays False and the clone stays opt-in. For a feature-tracking front end like the +one measured, the opt-in clone should assume four times the measured visual noise. B1 stays open for the shipped +configuration. The next step is more sequences and windows, so that the replay result has a spread. + +## Consequences + +The finding is limited to this front end, this window and a known translation scale. The factor of four is a trial +value that held in one replay, not a derived constant. A front end with another error structure may need another +factor. diff --git a/docs/adr/index.md b/docs/adr/index.md index 9eda882..70ef55d 100644 --- a/docs/adr/index.md +++ b/docs/adr/index.md @@ -27,3 +27,4 @@ what it cost, and what was rejected. | [ADR-0020](0020-second-source-against-a-ramp.md) | A second source against a slow-ramp spoof | accepted | | [ADR-0021](0021-fault-classification-from-innovations.md) | Fault classification from the GNSS innovations | accepted | | [ADR-0022](0022-multipath-signature.md) | A multipath signature on the GNSS innovations | accepted | +| [ADR-0023](0023-clone-against-a-measured-front-end.md) | The clone against a measured front end | accepted | diff --git a/docs/data/metrics.json b/docs/data/metrics.json index 53c5bd9..40f9535 100644 --- a/docs/data/metrics.json +++ b/docs/data/metrics.json @@ -1,8 +1,8 @@ { - "adr_count": 22, - "coverage_lines_hit": 9039, - "coverage_lines_total": 9598, - "coverage_percent": 94.18, + "adr_count": 23, + 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+MH_01_easy,4,89.355000064,0.20000000000000284,0.1451794731808802,0.03790283585497774,0.09874530312232953,0.018860188215085196,-0.0009057872380831608,-0.001208590151532687,-0.003536010544875069,1.5176502157877017,321,310 +MH_01_easy,6,89.355000064,0.30000000000001137,0.21800988395447343,-0.0004045458403160898,0.05615366468062781,-0.03163907052243877,0.0017389991004677608,-0.0035757452269657816,0.0055830510094666335,1.80167719095217,307,289 +MH_01_easy,10,89.355000064,0.5,0.36812207890761756,-0.14944123092756612,0.010009253348496725,0.04811382384709103,0.0065303318225726804,-0.001548110652866587,0.02742430440778855,4.398688686013245,281,124 +MH_01_easy,1,89.405000192,0.04999987200000078,0.03665916031114152,0.013789940647912072,0.04750278415820602,-0.025964837372883246,-0.0013583308951095946,-0.0004416500137303216,-0.004618763786063719,7.578185482154157,322,318 +MH_01_easy,2,89.405000192,0.09999999999999432,0.07237348047076873,0.027410254215880975,0.1259322720536274,-0.05021060706594963,-0.0032277106046401122,-0.001722797201769002,-0.011763021587763907,9.80018227474972,320,305 +MH_01_easy,4,89.405000192,0.20000000000000284,0.14479697267559064,-0.019092304982163067,0.12232393962745007,-0.08691336269330924,-0.0008338721472750442,-0.003162336685477265,-0.0036455745496901944,1.9383146249821435,309,293 +MH_01_easy,6,89.405000192,0.29999999999999716,0.2188144287616453,-0.030395090222356723,0.04056509792222891,-0.02878909340855409,0.0026874156178434646,-0.003223438206561433,0.010258457704170245,2.903473524312933,296,273 +MH_01_easy,10,89.405000192,0.5,0.36987520569730487,-0.22194209662358425,0.25988784885551147,0.08122957353800242,0.003620554178550881,-0.0069164459804224475,0.018449162194906717,3.1047302302609627,275,96 +MH_01_easy,1,89.455000064,0.05000012799999354,0.035731376361725666,0.024299096871360393,0.040806282303111154,-0.02097638082320247,-0.0010755511906704533,0.0005017608302708621,-0.0037842921500648385,6.372727527524593,337,332 +MH_01_easy,2,89.455000064,0.09999999999999432,0.07197524096235587,0.027016434362463745,0.1296568881339601,-0.04170315656479038,-0.002825204583292511,-0.0009846146931149857,-0.010643861642270018,8.836383521475351,332,316 +MH_01_easy,4,89.455000064,0.20000000000000284,0.1451017264856409,0.014042752801803969,0.0525852404705407,-0.0030386276130113265,0.001275059115075953,-0.0004976408638652103,0.004962238267773768,2.0330189773622767,315,297 +MH_01_easy,6,89.455000064,0.29999999999999716,0.2201184326315328,-0.0648673016944728,0.007017990960759919,0.047163597874829724,0.004232942319646599,-0.0029150438723867292,0.017878172386484762,4.847863715185413,308,264 +MH_01_easy,10,89.455000064,0.5,0.3717120939672199,0.0964135588080214,0.08576460300639373,0.14828075766363616,0.0014251474633532467,0.005647967595638136,0.005105610141615841,1.1940298331025214,271,220 diff --git a/docs/real_front_end.md b/docs/real_front_end.md new file mode 100644 index 0000000..d2f197a --- /dev/null +++ b/docs/real_front_end.md @@ -0,0 +1,91 @@ +# A real front end's errors, and the stochastic clone + +[ADR-0017](adr/0017-stochastic-clone-for-the-visual-update.md) made the stochastic clone calibrated for independent +visual errors and found it fails when they are correlated over about 0.5 to 2 s. It left open which a real front end +has. [ADR-0020](adr/0020-second-source-against-a-ramp.md) made the answer the next step. This page measures the errors +of a small two-view front end on real camera frames, and runs the clone against them. + +## Method + +The front end is [`navkit.vo_frontend`](api/index.md), in NumPy only: Shi-Tomasi corners, pyramidal Lucas-Kanade +tracking with a forward-backward check, an eight-point essential matrix inside a RANSAC loop, and a cheirality test. +It is a plain feature tracker, not a visual odometry system, and it does not claim to match a production one. Frames +are tracked step by step, so a pair `g` frames apart is tracked through every frame between. + +The data is 600 left-camera frames (30 s, 20 Hz) of EuRoC MH_01_easy, from 60 s to 90 s into the recording, where +the drone moves at about 0.5 m/s. Each pair is compared with the ground truth, through the camera-to-body extrinsic in +the dataset's calibration. A monocular pair has no scale, so the translation error is the error of the direction times +the true baseline. That is the error a front end with perfect scale would hand the filter. A real monocular front end +would be worse. Pairs where the camera did not move are skipped. + +Then the clone is checked two ways, in a 30 s window with GNSS healthy for the first 10 s and lost for the last 20 s: + +- **Surrogate**: the simulated front end of the earlier studies, given the measured error size and correlation time. +- **Replay**: the measured errors of the one-frame pairs, added to the true relative motion and fed to the filter as + the visual stream. It carries the real errors, with their outliers, instead of a Gaussian stand-in. + +## The errors + + + +| Gap, frames | Pairs | Median baseline m | Rotation RMS deg | Lateral RMS m | Median direction error deg | Outlier share | Rotation lag-1 autocorrelation | Lateral lag-1 autocorrelation | Rotation correlation time s | Lateral correlation time s | +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| 1 | 590 | 0.028 | 0.08 | 0.008 | 11.7 | 2% | 0.54 | 0.57 | 0.20 | 0.25 | +| 2 | 590 | 0.055 | 0.09 | 0.011 | 5.6 | 7% | 0.58 | 0.68 | 0.15 | 0.25 | +| 4 | 590 | 0.110 | 0.14 | 0.018 | 3.1 | 8% | 0.53 | 0.66 | 0.10 | 0.20 | +| 6 | 590 | 0.165 | 0.19 | 0.026 | 2.3 | 9% | 0.28 | 0.51 | 0.05 | 0.20 | +| 10 | 590 | 0.273 | 0.38 | 0.042 | 1.8 | 13% | 0.20 | 0.36 | 0.05 | 0.05 | + + + +*Lag-1 autocorrelation* is the correlation between a pair and the next pair. Pairs that overlap share frames, so the +figure for longer gaps includes that overlap. *Correlation time* is the lag at which the autocorrelation first falls +below 1/e. *Outlier share* is the share of pairs whose rotation error exceeds the median by more than five robust +standard deviations. + +## The clone against them + + + +| Variant | Runs | Median mean NEES | Median 2-sigma coverage | Median final error m | Median claimed sigma m | Meets the gate (NEES < 10, coverage > 90%) | +|---|---:|---:|---:|---:|---:|---| +| IMU only | 5 | 1.5 | 100% | 64.33 | 56.15 | yes | +| anchor, measured errors | 5 | 8030.7 | 6% | 50.86 | 0.27 | no | +| clone, independent errors | 5 | 2.0 | 100% | 0.30 | 0.27 | yes | +| clone, measured correlation | 5 | 5.6 | 93% | 0.51 | 0.27 | yes | +| clone, measured correlation, noise assumed x4 | 5 | 1.4 | 100% | 0.49 | 0.67 | yes | +| replay, anchor | 5 | 23497.9 | 5% | 93.64 | 0.27 | no | +| replay, clone | 5 | 27.0 | 55% | 0.25 | 0.27 | no | +| replay, clone, noise assumed x4 | 5 | 4.3 | 91% | 0.75 | 0.67 | yes | + + + +## What it shows + +1. **The front end's errors are smaller than the surrogate's.** At one frame the rotation error is 0.08° RMS and the + lateral error 8 mm, against the surrogate's defaults of 0.35° and 5 cm. The translation has the true scale. +2. **They are correlated for a fraction of a second.** The correlation time is 0.2 s for rotation and 0.25 s for + translation at one frame, and 0.05 s at ten frames. That is below the 0.5 to 2 s where ADR-0017 found the clone + failing. +3. **A longer baseline gives a better direction and a larger error in metres.** The direction error falls from 12° at + a 3 cm baseline to 2° at 27 cm, and the lateral error grows from 8 mm to 42 mm because it is the direction error + times the baseline. +4. **Outliers grow with the gap.** 2% of one-frame pairs, 13% of ten-frame pairs. +5. **A Gaussian surrogate with the measured size and correlation time passes the gate.** NEES 5.6 and 93% coverage. +6. **The real errors do not.** Replayed at the measured size, the clone has NEES 27 and 55% coverage and fails the + gate. The surrogate misses what the real error series has beyond a size and a correlation time. +7. **Assuming four times the measured noise brings it back, narrowly.** NEES 4.3 and 91% coverage. That is the factor + ADR-0017 found by trial. The margin is thin. +8. **The single anchor fails badly on the same errors.** NEES in the tens of thousands and 5% coverage, as in the + earlier studies. +9. **Vision is worth having.** Dead reckoning on the IMU alone ends the window 64 m off. The clone ends it within + a metre. + +## Limits + +One sequence, one 30 s window, and one error series replayed: the five seeds change the simulated GNSS and not the +visual errors, so the replay result is one realisation and the 91% coverage has no spread behind it. The front end is +mine and plain. The translation has the true scale, which no monocular front end has. The ground truth is itself an +estimate. The conclusion that the clone needs about four times the measured noise holds for this front end on this +window, and it is not shown for another front end or sequence. `vision_enabled` stays False. +See [ADR-0023](adr/0023-clone-against-a-measured-front-end.md). diff --git a/mkdocs.yml b/mkdocs.yml index 66779d4..bf6a1b2 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -108,6 +108,7 @@ nav: - A second source against a ramp: vision_ramp.md - Telling the causes apart: fault_classification.md - A multipath signature: multipath.md + - A real front end and the clone: real_front_end.md - EuRoC, recorded IMU: euroc.md - TUM VI, recorded IMU: tumvi.md - Performance: PERFORMANCE.md @@ -147,6 +148,7 @@ nav: - "0020 Second source against a ramp": adr/0020-second-source-against-a-ramp.md - "0021 Fault classification": adr/0021-fault-classification-from-innovations.md - "0022 Multipath signature": adr/0022-multipath-signature.md + - "0023 Clone against a measured front end": adr/0023-clone-against-a-measured-front-end.md - Project: - Scope and responsible use: scope.md - Status and limits: status.md diff --git a/src/navkit/euroc_cli.py b/src/navkit/euroc_cli.py index e38d331..ddd32ce 100644 --- a/src/navkit/euroc_cli.py +++ b/src/navkit/euroc_cli.py @@ -12,6 +12,7 @@ from .euroc_eval import run_command, selftest_command from .io.euroc_fetch import main as fetch_main from .timing_check import main as timing_main +from .vo_error import main as vo_main def main(argv: list[str] | None = None) -> int: @@ -23,14 +24,16 @@ def main(argv: list[str] | None = None) -> int: "compare": compare_main, "timing": timing_main, "selftest": selftest_command, + "vo": vo_main, } if not args or args[0] in ("-h", "--help") or args[0] not in commands: - usage = "usage: navkit euroc {fetch,run,compare,timing,selftest} [options]" + usage = "usage: navkit euroc {fetch,run,compare,timing,vo,selftest} [options]" kinds = ( " fetch download the IMU and ground-truth files of a sequence (needs network)", " run run the filter on a fetched sequence, GNSS simulated from its ground truth", " compare run several filter settings over the fetched sequences and count lost runs", " timing timestamp regularity and the ground-truth-to-IMU time offset of fetched sequences", + " vo measure the errors of the two-view visual front end on fetched camera frames", " selftest run the whole pipeline on a synthetic sequence in the EuRoC layout", ) print("\n".join((usage, *kinds)), file=sys.stderr) diff --git a/src/navkit/euroc_eval.py b/src/navkit/euroc_eval.py index 8bc546e..3d9b177 100644 --- a/src/navkit/euroc_eval.py +++ b/src/navkit/euroc_eval.py @@ -61,7 +61,7 @@ from .recorded import GRAVITY_Z_UP, EurocSequence, SequenceError, sha256_of from .sensors.models import GnssConfig, VisionConfig, gnss_fixes, visual_updates from .synthetic import SyntheticConfig, analytic_kinematics, analytic_pose -from .types import GRAVITY, ImuSample, Trajectory, interpolate_trajectory +from .types import GRAVITY, ImuSample, Trajectory, VisionUpdate, interpolate_trajectory DEFAULT_ROOT = "data/raw/euroc" @@ -225,6 +225,9 @@ class RunOptions: vision: str | None = None vision_corr_s: float = 0.0 vision_noise_scale: float = 1.0 + #: (rotation sigma in degrees, translation sigma in metres) of the simulated front end's errors, in place of the + #: surrogate's defaults. The filter assumes these times ``vision_noise_scale``. + vision_sigmas: tuple[float, float] | None = None def as_dict(self) -> dict[str, Any]: extra: dict[str, Any] = {"spoof": list(self.spoof)} if self.spoof is not None else {} @@ -235,6 +238,7 @@ def as_dict(self) -> dict[str, Any]: "model": self.vision, "corr_s": self.vision_corr_s, "noise_scale": self.vision_noise_scale, + **({"sigmas": list(self.vision_sigmas)} if self.vision_sigmas is not None else {}), } return { "gnss_rate_hz": self.gnss_rate_hz, @@ -450,13 +454,15 @@ def run_sequence( innovations_out: list[tuple[float, np.ndarray, np.ndarray]] | None = None, events_out: list[dict[str, Any]] | None = None, bias_out: list[tuple[float, np.ndarray, np.ndarray]] | None = None, + vision_override: VisionUpdate | None = None, ) -> dict[str, Any]: """Run the ESKF on one sequence and return a result record shaped like the benchmark's. ``innovations_out``, when given, receives ``(t, residual, S)`` for every GNSS fix before gating. The times are on the filter's clock, which starts at the first IMU sample of the window. ``events_out`` receives the FDIR event log of the run, ``bias_out`` the ``(t, gyro bias, accel bias)`` estimate at - every GNSS fix. + every GNSS fix. ``vision_override`` replaces the generated visual stream (the options still say how the filter + treats it), for replaying errors measured on a real front end. """ opts = opts or RunOptions() mode = opts.init_bias or ("truth" if seq.bias_known else "static") @@ -493,7 +499,9 @@ def run_sequence( if opts.vision not in ("anchor", "clone"): raise SequenceError(f"unknown vision model {opts.vision!r}; known: anchor, clone") vcfg = VisionConfig(seed=opts.seed, noise_corr_s=opts.vision_corr_s) - vision = visual_updates(reference, vcfg) + if opts.vision_sigmas is not None: + vcfg.rot_sigma_deg, vcfg.trans_sigma_m = opts.vision_sigmas + vision = vision_override if vision_override is not None else visual_updates(reference, vcfg) vision_kwargs = { "vision_enabled": True, "vision_model": opts.vision, diff --git a/src/navkit/io/euroc_fetch.py b/src/navkit/io/euroc_fetch.py index 1827a53..30d7a56 100644 --- a/src/navkit/io/euroc_fetch.py +++ b/src/navkit/io/euroc_fetch.py @@ -464,6 +464,168 @@ def extract_sequence( return result +# ---------------------------------------------------------------------- images + +CAM0_DIR = "mav0/cam0" +CAM0_FILES = ("mav0/cam0/data.csv", "mav0/cam0/sensor.yaml") + + +def select_frames(csv_text: str, t0_ns: int, start_s: float, stop_s: float, every: int) -> list[tuple[int, str]]: + """The ``(timestamp ns, file name)`` rows of ``cam0/data.csv`` inside the window, every ``every``-th.""" + if every < 1 or stop_s <= start_s: + raise FetchError("need --image-every >= 1 and an image window with stop > start") + rows: list[tuple[int, str]] = [] + for line in csv_text.splitlines(): + if not line.strip() or line.startswith("#"): + continue + ts, name = (c.strip() for c in line.split(",")[:2]) + t = (int(ts) - t0_ns) / 1e9 + if start_s <= t <= stop_s: + rows.append((int(ts), name)) + return rows[::every] + + +def _first_imu_ns(dest: Path, seq: str) -> int: + path = dest / seq / IMU_CSV + if not path.is_file(): + raise FetchError( + f"{seq}: fetch the sequence first ({path} is missing); the image window is counted from its IMU" + ) + with path.open(encoding="utf-8") as fh: + for line in fh: + if line.strip() and not line.startswith("#"): + return int(line.split(",")[0]) + raise FetchError(f"{path} has no samples") + + +def extract_images( + zf: zipfile.ZipFile, + fobj: IO[bytes], + seq: str, + dest: Path, + *, + start_s: float, + stop_s: float, + every: int, + work_dir: Path, + max_nested_bytes: int = 4 << 30, +) -> list[Extracted]: + """Extract the left-camera frames of one window, with the camera's calibration and timestamps.""" + t0_ns = _first_imu_ns(dest, seq) + members = _index(zf) + nested = [info for name, info in members.items() if name.lower().split("/")[-1] == f"{seq.lower()}.zip"] + local: Path | None = None + opened: IO[bytes] | None = None # a file this function opened, which must be closed before it is deleted + if nested: + info = nested[0] + if info.compress_type == zipfile.ZIP_STORED: + window = cast(IO[bytes], SliceFile(fobj, stored_data_offset(fobj, info), info.file_size)) + else: + if info.file_size > max_nested_bytes: + raise FetchError( + f"{seq}: the nested ZIP is compressed and {info.file_size:,} bytes; raise --max-nested-gb" + ) + work_dir.mkdir(parents=True, exist_ok=True) + local = work_dir / f"{seq}.zip" + _log(f"{seq}: copying the nested ZIP to {local} first ({info.file_size:,} bytes)") + _copy_member(zf, info, local) + window = opened = local.open("rb") + inner_zip = zipfile.ZipFile(window) + prefix = "" + else: + inner_zip, prefix = zf, f"{seq}/" + try: + inner = _index(inner_zip) + meta = {rel: _pick(inner, f"{prefix}{rel}") for rel in CAM0_FILES} + missing = [rel for rel, info in meta.items() if info is None] + if missing: + raise LayoutError(f"{seq}: the archive has no {missing[0]}") + csv_info = meta[CAM0_FILES[0]] + assert csv_info is not None + frames = select_frames(inner_zip.read(csv_info).decode("utf-8"), t0_ns, start_s, stop_s, every) + if not frames: + raise FetchError(f"{seq}: no camera frame between {start_s} s and {stop_s} s of the IMU clock") + out: list[Extracted] = [] + for rel, meta_info in meta.items(): + assert meta_info is not None + item = _copy_member(inner_zip, meta_info, dest / seq / rel) + item.path = f"{seq}/{rel}" + out.append(item) + for _ts, name in frames: + frame_info = _pick(inner, f"{prefix}{CAM0_DIR}/data/{name}") + if frame_info is None: + raise LayoutError(f"{seq}: frame {name} is listed in cam0/data.csv but not in the archive") + item = _copy_member(inner_zip, frame_info, dest / seq / CAM0_DIR / "data" / Path(name).name) + item.path = f"{seq}/{CAM0_DIR}/data/{Path(name).name}" + out.append(item) + _log(f" {len(frames)} frames, {sum(i.bytes for i in out):,} bytes") + return out + finally: + if inner_zip is not zf: + inner_zip.close() + if opened is not None: + opened.close() # Windows cannot delete a file that is still open + if local is not None: + local.unlink(missing_ok=True) + + +def fetch_images( + sequence: str, + dest: Path, + *, + start_s: float, + stop_s: float, + every: int = 1, + base_url: str = BASE_URL, + full_download: bool = False, + attempts: int = 6, + max_nested_bytes: int = 4 << 30, + sleep: Callable[[float], None] = time.sleep, +) -> list[Extracted]: + """Fetch the left-camera frames of one window of one sequence and add them to the manifest. + + The IMU and ground truth of the sequence must be fetched first: the window is counted from its first IMU sample. + A compressed nested archive has to be copied whole to disk first (over a gigabyte), then deleted. With + ``full_download`` the whole group archive is read from ``/.downloads/.zip``, which is downloaded + first if it is absent. A browser download placed there works as well, which is the way round a server that + ignores byte ranges. + """ + if sequence not in SEQUENCES: + raise FetchError(f"unknown sequence {sequence!r}; known: {', '.join(SEQUENCES)}") + group = SEQUENCES[sequence] + url = archive_url(group, base_url) + if full_download: + archive = dest / ".downloads" / f"{group}.zip" + if not archive.exists(): + download_whole(url, archive, attempts=attempts, sleep=sleep) + _log(f"{group}: reading {archive}") + fobj: IO[bytes] = archive.open("rb") + else: + fobj = cast(IO[bytes], RangeFile(url, attempts=attempts, sleep=sleep)) + try: + with zipfile.ZipFile(fobj) as zf: + files = extract_images( + zf, + fobj, + sequence, + dest, + start_s=start_s, + stop_s=stop_s, + every=every, + work_dir=dest / ".work", + max_nested_bytes=max_nested_bytes, + ) + finally: + fobj.close() + manifest = _manifest_path(dest) + data = json.loads(manifest.read_text(encoding="utf-8")) if manifest.is_file() else {} + entry = data.setdefault("sequences", {}).setdefault(sequence, {"archive": group, "url": url, "files": []}) + known = {f["path"] for f in entry["files"]} + entry["files"].extend(f.as_dict() for f in files if f.path not in known) + manifest.write_text(json.dumps(data, indent=2, sort_keys=True), encoding="utf-8") + return files + + # ---------------------------------------------------------------------- driver @@ -594,6 +756,19 @@ def build_parser() -> argparse.ArgumentParser: parser.add_argument( "--max-nested-gb", type=float, default=4.0, help="largest compressed nested ZIP to copy (default: %(default)s)" ) + parser.add_argument( + "--images", + action="store_true", + help="fetch left-camera frames instead of the IMU files (needs the sequence fetched first; a compressed " + "nested archive of over a gigabyte is copied whole to disk, then deleted)", + ) + parser.add_argument( + "--image-window", + default="60,90", + metavar="START,STOP", + help="seconds after the first IMU sample (default: %(default)s)", + ) + parser.add_argument("--image-every", type=int, default=1, help="keep every n-th frame (default: %(default)s)") return parser @@ -607,6 +782,29 @@ def main(argv: list[str] | None = None) -> int: if not args.sequence: print("give at least one --sequence NAME (see --list)", file=sys.stderr) return 2 + if args.images: + try: + start, stop = (float(x) for x in args.image_window.split(",")) + files = [ + f + for seq in args.sequence + for f in fetch_images( + seq, + Path(args.dest), + start_s=start, + stop_s=stop, + every=args.image_every, + base_url=args.base_url, + full_download=args.full_download, + attempts=args.attempts, + max_nested_bytes=int(args.max_nested_gb * (1 << 30)), + ) + ] + except (FetchError, ValueError, zipfile.BadZipFile) as exc: + print(f"error: {exc}", file=sys.stderr) + return 1 + print(f"wrote {len(files)} files under {args.dest}; manifest: {_manifest_path(Path(args.dest))}") + return 0 try: written = fetch( args.sequence, diff --git a/src/navkit/vo_error.py b/src/navkit/vo_error.py new file mode 100644 index 0000000..ebf8d5d --- /dev/null +++ b/src/navkit/vo_error.py @@ -0,0 +1,489 @@ +"""How wrong, and how correlated, are the errors of a real two-view front end? + +ADR-0017 made the stochastic clone calibrated for independent visual errors and found it fails when they are +correlated over about 0.5 to 2 s. ADR-0020 made the question of which a real front end has the next step. This runs +the small front end of :mod:`navkit.vo_frontend` on real camera frames and compares each pair with the ground truth. + +A monocular pair has no scale, so the translation error is the error of the direction, expressed as a lateral distance +by multiplying with the true baseline. That is what a front end with a perfect scale would hand the filter. A real +monocular front end would be worse. The ground truth is itself an estimate (millimetres for the Machine Hall). + +Frames are paired ``gap`` frames apart. A pair of consecutive 20 Hz frames has a baseline of centimetres, too short for +a translation direction, so larger gaps are measured too. +""" + +from __future__ import annotations + +import argparse +import csv +import sys +from pathlib import Path +from typing import Any + +import numpy as np + +from .euroc_compare import CONFIGS +from .euroc_eval import RunOptions, load_sequence, run_sequence +from .geometry.rigid import rot_exp, rot_log +from .types import Trajectory, VisionUpdate, interpolate_trajectory +from .vo_frontend import Camera, camera_from_yaml, load_gray, motion_from_pair, relative_poses_along + +GAPS = (1, 2, 4, 6, 10) +FRAME_STEP_S = 0.05 # the camera is 20 Hz +#: Below this true baseline the direction of the motion is undefined and the pair is skipped. +MIN_TRUE_BASELINE_M = 1e-3 +MAX_LAG = 40 +CSV_COLUMNS = ( + "sequence", + "gap_frames", + "t_s", + "dt_s", + "baseline_m", + "rot_x_deg", + "rot_y_deg", + "rot_z_deg", + "lat_x_m", + "lat_y_m", + "lat_z_m", + "dir_err_deg", + "tracked", + "inliers", +) + + +def frame_list(seq_dir: Path) -> list[tuple[float, Path]]: + """``(time on the IMU clock in s, path)`` for every fetched frame, in time order.""" + first_ns = None + with (seq_dir / "mav0/imu0/data.csv").open(encoding="utf-8") as fh: + for line in fh: + if line.strip() and not line.startswith("#"): + first_ns = int(line.split(",")[0]) + break + if first_ns is None: + raise ValueError(f"{seq_dir}: the IMU file has no samples") + out = [] + with (seq_dir / "mav0/cam0/data.csv").open(encoding="utf-8") as fh: + for line in fh: + if not line.strip() or line.startswith("#"): + continue + ts, name = (c.strip() for c in line.split(",")[:2]) + path = seq_dir / "mav0/cam0/data" / Path(name).name + if path.is_file(): + out.append(((int(ts) - first_ns) / 1e9, path)) + return sorted(out) + + +def true_motion(truth: Trajectory, cam: Camera, t0: float, t1: float) -> tuple[np.ndarray, np.ndarray]: + """The camera's motion from time ``t0`` to ``t1`` in the camera 0 frame: ``(R_c0_c1, t_c0_c1)``.""" + poses = interpolate_trajectory(truth, np.array([t0, t1])).poses + T0 = poses[0] @ cam.T_BS + T1 = poses[1] @ cam.T_BS + rel = np.linalg.inv(T0) @ T1 + return rel[:3, :3], rel[:3, 3] + + +def pair_row( + name: str, gap: int, t0: float, t1: float, estimate: Any, truth: Trajectory, cam: Camera +) -> dict[str, Any] | None: + """The error of one pair against the ground truth, as a CSV row. None when the camera did not move.""" + R_true, t_true = true_motion(truth, cam, t0, t1) + R_est, u_est = motion_from_pair(estimate) + baseline = float(np.linalg.norm(t_true)) + if baseline < MIN_TRUE_BASELINE_M: + return None + u_true = t_true / baseline + rot = np.degrees(rot_log(R_true.T @ R_est)) + diff = u_est - u_true + lateral = baseline * (diff - (diff @ u_true) * u_true) + angle = float(np.degrees(np.arccos(np.clip(float(u_est @ u_true), -1.0, 1.0)))) + return { + "sequence": name, + "gap_frames": gap, + "t_s": t0, + "dt_s": t1 - t0, + "baseline_m": baseline, + "rot_x_deg": rot[0], + "rot_y_deg": rot[1], + "rot_z_deg": rot[2], + "lat_x_m": lateral[0], + "lat_y_m": lateral[1], + "lat_z_m": lateral[2], + "dir_err_deg": angle, + "tracked": estimate.tracked, + "inliers": estimate.inliers, + } + + +def measure(seq_dir: Path, gaps: tuple[int, ...] = GAPS, min_baseline_m: float = 0.0) -> list[dict[str, Any]]: + """Run the front end on every pair ``gap`` frames apart and return one row per pair that produced an estimate.""" + seq = load_sequence(seq_dir.parent, seq_dir.name) + cam = camera_from_yaml(seq_dir / "mav0/cam0/sensor.yaml") + frames = frame_list(seq_dir) + if len(frames) < max(gaps) + 2: + raise ValueError(f"{seq_dir.name}: {len(frames)} frames are not enough for gaps up to {max(gaps)}") + images: dict[int, np.ndarray] = {} + + def image(i: int) -> np.ndarray: + if i not in images: + images[i] = load_gray(frames[i][1]) + images.pop(i - 2 * max(gaps) - 4, None) # keep memory bounded + return images[i] + + rows: list[dict[str, Any]] = [] + top = max(gaps) + for i in range(0, len(frames) - top): + window = [image(i + k) for k in range(top + 1)] + found = relative_poses_along(window, gaps, cam, seed=i) + for gap in gaps: + est = found[gap] + (t0, _), (t1, _) = frames[i], frames[i + gap] + if est is None or not (seq.truth.t[0] <= t0 and t1 <= seq.truth.t[-1]): + continue + row = pair_row(seq_dir.name, gap, t0, t1, est, seq.truth, cam) + if row is not None and row["baseline_m"] >= min_baseline_m: + rows.append(row) + return rows + + +# ------------------------------------------------------------------ statistics + + +def autocorrelation(x: np.ndarray, max_lag: int = MAX_LAG) -> np.ndarray: + """Normalised autocorrelation at lags 0 to ``max_lag`` of a series (mean removed).""" + x = np.asarray(x, dtype=float) + x = x - x.mean() + var = float(x @ x) + if var <= 0.0 or len(x) <= max_lag: + return np.full(max_lag + 1, np.nan) + return np.array([float(x[: len(x) - k] @ x[k:]) / var for k in range(max_lag + 1)]) + + +def _mean_acf(series: list[np.ndarray]) -> np.ndarray: + """The mean autocorrelation of the components, or all NaN when no component is long enough to have one.""" + acfs = np.array([autocorrelation(x) for x in series]) + if np.isnan(acfs).all(): + return np.full(acfs.shape[1], np.nan) + return np.asarray(np.nanmean(acfs, axis=0)) + + +def correlation_time(acf: np.ndarray, step_s: float) -> float: + """Seconds until the autocorrelation first falls below 1/e. Capped at the longest lag if it never does. + + NaN when the series was too short to have an autocorrelation. + """ + if np.isnan(acf).all(): + return float("nan") + below = np.flatnonzero(acf < np.exp(-1.0)) + return float((below[0] if len(below) else len(acf)) * step_s) + + +def summary(rows: list[dict[str, Any]], gap: int, step_s: float) -> dict[str, float]: + """Sizes, outliers and the correlation time of one gap's errors. ``step_s`` is the spacing between pairs.""" + sel = sorted((r for r in rows if int(r["gap_frames"]) == gap), key=lambda r: float(r["t_s"])) + rot = np.array([[float(r[k]) for k in ("rot_x_deg", "rot_y_deg", "rot_z_deg")] for r in sel]) + lat = np.array([[float(r[k]) for k in ("lat_x_m", "lat_y_m", "lat_z_m")] for r in sel]) + ang = np.array([float(r["dir_err_deg"]) for r in sel]) + base = np.array([float(r["baseline_m"]) for r in sel]) + rot_mag = np.linalg.norm(rot, axis=1) + lat_mag = np.linalg.norm(lat, axis=1) + med = float(np.median(rot_mag)) + mad = float(np.median(np.abs(rot_mag - med))) * 1.4826 + acf_rot = _mean_acf([rot[:, k] for k in range(3)]) + acf_lat = _mean_acf([lat[:, k] for k in range(3)]) + return { + "pairs": float(len(sel)), + "baseline_m": float(np.median(base)), + "rot_rms_deg": float(np.sqrt((rot**2).mean())), + "rot_median_deg": med, + "lat_rms_m": float(np.sqrt((lat**2).sum(axis=1).mean() / 2.0)), + "dir_median_deg": float(np.median(ang)), + "outlier_share": float(np.mean(rot_mag > med + 5.0 * max(mad, 1e-9))), + "rot_acf_lag1": float(acf_rot[1]), + "lat_acf_lag1": float(acf_lat[1]), + "rot_corr_time_s": correlation_time(acf_rot, step_s), + "lat_corr_time_s": correlation_time(acf_lat, step_s), + "lat_median_m": float(np.median(lat_mag)), + } + + +CLONE_COLUMNS = ("variant", "seed", "nees_mean", "coverage_2sigma", "final_error_m", "max_error_m", "claimed_sigma_m") +#: Variant name -> (vision model or None, correlation time as a multiple of the measured one, assumed-noise scale). +CLONE_VARIANTS: dict[str, tuple[str | None, float, float]] = { + "IMU only": (None, 0.0, 1.0), + "anchor, measured errors": ("anchor", 1.0, 1.0), + "clone, independent errors": ("clone", 0.0, 1.0), + "clone, measured correlation": ("clone", 1.0, 1.0), + "clone, measured correlation, noise assumed x4": ("clone", 1.0, 4.0), +} + + +def replay_stream( + rows: list[dict[str, Any]], truth: Trajectory, cam: Camera, t_from: float, t_to: float +) -> VisionUpdate: + """The measured errors of the one-frame pairs, applied to the true relative motion, as a visual stream. + + Each pair's rotation and lateral errors (measured in the camera frame) are added to the true relative camera pose; + the result is moved to the body frame with the camera-to-body extrinsic, which is the frame the filter's visual + update works in. The stream carries the real errors, with their correlation and their outliers, in place of + a Gaussian surrogate. The translation has the true scale (a monocular front end has none). + """ + sel = sorted( + ( + r + for r in rows + if int(r["gap_frames"]) == 1 and t_from <= float(r["t_s"]) and float(r["t_s"]) + float(r["dt_s"]) <= t_to + ), + key=lambda r: float(r["t_s"]), + ) + if not sel: + raise ValueError("no one-frame pairs inside the window") + T_bs = cam.T_BS + times, R_rel, t_rel = [], [], [] + for r in sel: + t0, t1 = float(r["t_s"]), float(r["t_s"]) + float(r["dt_s"]) + R_c, t_c = true_motion(truth, cam, t0, t1) + e_rot = np.radians([float(r["rot_x_deg"]), float(r["rot_y_deg"]), float(r["rot_z_deg"])]) + lateral = np.array([float(r["lat_x_m"]), float(r["lat_y_m"]), float(r["lat_z_m"])]) + T_c = np.eye(4) + T_c[:3, :3] = R_c @ rot_exp(e_rot) + T_c[:3, 3] = t_c + lateral + T_b = T_bs @ T_c @ np.linalg.inv(T_bs) + times.append(t1) + R_rel.append(T_b[:3, :3]) + t_rel.append(T_b[:3, 3]) + return VisionUpdate(t=np.array(times), R_rel=np.array(R_rel), t_rel=np.array(t_rel), name="replayed_front_end") + + +REPLAY_VARIANTS: dict[str, tuple[str, float]] = { + "replay, anchor": ("anchor", 1.0), + "replay, clone": ("clone", 1.0), + "replay, clone, noise assumed x4": ("clone", 4.0), +} + + +def replay_rows( + root: str | Path, + sequence: str, + vo_rows: list[dict[str, Any]], + rot_sigma_deg: float, + trans_sigma_m: float, + seeds: int = 5, +) -> list[dict[str, Any]]: + """The clone fed the measured errors themselves. The filter assumes the measured sigmas times the scale.""" + seq = load_sequence(root, sequence) + cam = camera_from_yaml(Path(root) / sequence / "mav0/cam0/sensor.yaml") + skip, duration, out_at = CLONE_WINDOW + t_from = skip + float(seq.imu.t[0]) + stream = replay_stream(vo_rows, seq.truth, cam, t_from, t_from + duration) + rows: list[dict[str, Any]] = [] + for name, (model, scale) in REPLAY_VARIANTS.items(): + for seed in range(seeds): + opts = RunOptions( + seed=seed, + skip_s=skip, + duration_s=duration, + outages=((out_at, duration - out_at),), + vision=model, + vision_noise_scale=scale, + vision_sigmas=(rot_sigma_deg, trans_sigma_m), + **dict(CONFIGS["walk10"]), + ) + rec = run_sequence(seq, opts, vision_override=stream) + head = rec["headline"] + err = np.asarray(rec["error_time_series"]["position_error_m"], dtype=float) + rows.append( + { + "variant": name, + "seed": seed, + "nees_mean": head["nees_mean"], + "coverage_2sigma": head["coverage"]["2sigma"], + "final_error_m": float(err[-1]), + "max_error_m": float(err.max()), + "claimed_sigma_m": head["claimed_sigma_p95_m"], + } + ) + return rows + + +CLONE_WINDOW = (60.0, 30.0, 10.0) # skip, duration, and the start of the GNSS outage that runs to the end + + +def clone_rows( + root: str | Path, sequence: str, rot_sigma_deg: float, trans_sigma_m: float, corr_s: float, seeds: int = 5 +) -> list[dict[str, Any]]: + """The stochastic clone against a simulated front end that has the measured error size and correlation. + + GNSS is healthy for the first 10 s of a 30 s window and lost for the last 20 s, so that the filter's biases have + settled before the outage. NEES and coverage are over the whole window, which includes the 10 aided seconds. + """ + seq = load_sequence(root, sequence) + skip, duration, out_at = CLONE_WINDOW + base = dict(CONFIGS["walk10"]) + rows: list[dict[str, Any]] = [] + for name, (model, corr_mult, scale) in CLONE_VARIANTS.items(): + for seed in range(seeds): + opts = RunOptions( + seed=seed, + skip_s=skip, + duration_s=duration, + outages=((out_at, duration - out_at),), + vision=model, + vision_corr_s=corr_s * corr_mult, + vision_noise_scale=scale, + vision_sigmas=(rot_sigma_deg, trans_sigma_m) if model else None, + **base, + ) + rec = run_sequence(seq, opts) + head = rec["headline"] + err = np.asarray(rec["error_time_series"]["position_error_m"], dtype=float) + rows.append( + { + "variant": name, + "seed": seed, + "nees_mean": head["nees_mean"], + "coverage_2sigma": head["coverage"]["2sigma"], + "final_error_m": float(err[-1]), + "max_error_m": float(err.max()), + "claimed_sigma_m": head["claimed_sigma_p95_m"], + } + ) + return rows + + +def clone_markdown(rows: list[dict[str, Any]]) -> str: + lines = [ + "| Variant | Runs | Median mean NEES | Median 2-sigma coverage | Median final error m | Median claimed sigma m " + "| Meets the gate (NEES < 10, coverage > 90%) |", + "|---|---:|---:|---:|---:|---:|---|", + ] + for name in [*CLONE_VARIANTS, *REPLAY_VARIANTS]: + sel = [r for r in rows if r["variant"] == name] + if not sel: + continue + nees = float(np.median([float(r["nees_mean"]) for r in sel])) + cov = float(np.median([float(r["coverage_2sigma"]) for r in sel])) + ok = "yes" if nees < 10.0 and cov > 0.9 else "no" + lines.append( + f"| {name} | {len(sel)} | {nees:.1f} | {100 * cov:.0f}% | " + f"{np.median([float(r['final_error_m']) for r in sel]):.2f} | " + f"{np.median([float(r['claimed_sigma_m']) for r in sel]):.2f} | {ok} |" + ) + return "\n".join(lines) + + +def write_clone_csv(path: str | Path, rows: list[dict[str, Any]]) -> None: + target = Path(path) + target.parent.mkdir(parents=True, exist_ok=True) + with target.open("w", newline="", encoding="utf-8") as fh: + writer = csv.DictWriter(fh, fieldnames=CLONE_COLUMNS) + writer.writeheader() + writer.writerows(rows) + + +def write_csv(path: str | Path, rows: list[dict[str, Any]]) -> None: + target = Path(path) + target.parent.mkdir(parents=True, exist_ok=True) + with target.open("w", newline="", encoding="utf-8") as fh: + writer = csv.DictWriter(fh, fieldnames=CSV_COLUMNS) + writer.writeheader() + writer.writerows(rows) + + +def read_csv(path: str | Path) -> list[dict[str, Any]]: + with Path(path).open(newline="", encoding="utf-8") as fh: + return list(csv.DictReader(fh)) + + +def summary_markdown(rows: list[dict[str, Any]], frame_step_s: float = 0.05) -> str: + lines = [ + "| Gap, frames | Pairs | Median baseline m | Rotation RMS deg | Lateral RMS m | Median direction error deg " + "| Outlier share | Rotation lag-1 autocorrelation | Lateral lag-1 autocorrelation " + "| Rotation correlation time s | Lateral correlation time s |", + "|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|", + ] + for gap in sorted({int(r["gap_frames"]) for r in rows}): + s = summary(rows, gap, frame_step_s) + lines.append( + f"| {gap} | {s['pairs']:.0f} | {s['baseline_m']:.3f} | {s['rot_rms_deg']:.2f} | {s['lat_rms_m']:.3f} | " + f"{s['dir_median_deg']:.1f} | {100 * s['outlier_share']:.0f}% | {s['rot_acf_lag1']:.2f} | " + f"{s['lat_acf_lag1']:.2f} | {s['rot_corr_time_s']:.2f} | {s['lat_corr_time_s']:.2f} |" + ) + return "\n".join(lines) + + +def build_parser() -> argparse.ArgumentParser: + p = argparse.ArgumentParser( + prog="navkit euroc vo", description="Measure the errors of the two-view front end on fetched frames." + ) + p.add_argument("--sequence", default="MH_01_easy") + p.add_argument("--root", default="data/raw/euroc") + p.add_argument("--gaps", default=",".join(str(g) for g in GAPS)) + p.add_argument("--csv", default=None, help="write one row per pair") + p.add_argument("--from-csv", default=None, help="print the summary of a saved --csv file; no images needed") + p.add_argument( + "--clone", action="store_true", help="also run the clone against the measured errors (a few minutes)" + ) + p.add_argument("--clone-csv", default=None, help="write (with --clone) or read the clone check's runs") + p.add_argument("--page", default=None, help="fill the vo-errors and vo-clone blocks of this Markdown page") + p.add_argument("--markdown", action="store_true", help="print the tables") + return p + + +def read_clone_csv(path: str | Path) -> list[dict[str, Any]]: + return read_csv(path) + + +def write_block(path: str | Path, name: str, block: str) -> None: + page = Path(path) + text = page.read_text(encoding="utf-8") + start, end = f"", f"" + if start not in text or end not in text or text.index(start) > text.index(end): + raise ValueError(f"{page} needs the lines {start} and {end}, in that order") + head, rest = text.split(start, 1) + _, tail = rest.split(end, 1) + page.write_text(f"{head}{start}\n\n{block}\n\n{end}{tail}", encoding="utf-8") + + +def main(argv: list[str] | None = None) -> int: + args = build_parser().parse_args(argv) + if args.from_csv: + rows = read_csv(args.from_csv) + else: + gaps = tuple(int(g) for g in args.gaps.split(",")) + try: + rows = measure(Path(args.root) / args.sequence, gaps) + except (OSError, ValueError) as exc: + print(f"error: {exc}", file=sys.stderr) + return 1 + if args.csv: + write_csv(args.csv, rows) + print(f"wrote {args.csv}", file=sys.stderr) + clone = None + if args.clone: + one = summary(rows, 1, FRAME_STEP_S) + sigmas = (round(one["rot_rms_deg"], 3), round(one["lat_rms_m"], 4)) + corr = round(one["lat_corr_time_s"], 2) + root, seq = args.root, args.sequence + clone = clone_rows(root, seq, *sigmas, corr_s=corr) + replay_rows(root, seq, rows, *sigmas) + if args.clone_csv: + write_clone_csv(args.clone_csv, clone) + print(f"wrote {args.clone_csv}", file=sys.stderr) + elif args.clone_csv and Path(args.clone_csv).is_file(): + clone = read_clone_csv(args.clone_csv) + if args.page: + try: + write_block(args.page, "vo-errors", summary_markdown(rows)) + if clone is not None: + write_block(args.page, "vo-clone", clone_markdown(clone)) + except ValueError as exc: + print(f"error: {exc}", file=sys.stderr) + return 1 + if args.markdown or not (args.csv or args.page): + print(summary_markdown(rows)) + if clone is not None: + print("\n" + clone_markdown(clone)) + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/navkit/vo_frontend.py b/src/navkit/vo_frontend.py new file mode 100644 index 0000000..b3853b3 --- /dev/null +++ b/src/navkit/vo_frontend.py @@ -0,0 +1,405 @@ +"""A small two-view visual front end, in NumPy only. + +Its purpose is to measure what the errors of a real visual front end look like (size, outliers, and above all how +long they stay correlated), which decides whether the stochastic clone's independence assumption holds +(ADR-0017, ADR-0020). +It is not a visual odometry system, and it makes no claim to match a production one. Its steps are plain and each is +tested on synthetic data: Shi-Tomasi corners, pyramidal Lucas-Kanade tracking with a forward-backward check, a +radial-tangential undistortion, an eight-point essential matrix inside a RANSAC loop, and a cheirality test to pick the +relative pose. The result is a rotation and a translation direction. A monocular pair has no scale. + +Convention: ``X1 = R X0 + t`` for a point seen in camera 0 and camera 1, so ``(R, t)`` is the pose of camera 0 in camera +1. The motion of the camera from frame 0 to frame 1 is the inverse, ``T_c0_c1 = [R', -R' t]``. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from itertools import pairwise +from pathlib import Path + +import numpy as np +import yaml + + +@dataclass(frozen=True) +class Camera: + """Pinhole intrinsics, radial-tangential distortion, and the camera pose in the body frame.""" + + fx: float + fy: float + cx: float + cy: float + dist: tuple[float, float, float, float] # k1, k2, p1, p2 + T_BS: np.ndarray # (4, 4) camera (sensor) frame in the body frame + width: int + height: int + + +def camera_from_yaml(path: str | Path) -> Camera: + """Read an EuRoC ``sensor.yaml`` of a camera.""" + cfg = yaml.safe_load(Path(path).read_text(encoding="utf-8")) + fx, fy, cx, cy = (float(v) for v in cfg["intrinsics"]) + k1, k2, p1, p2 = (float(v) for v in cfg["distortion_coefficients"]) + t = cfg["T_BS"] + T = np.array(t["data"], dtype=float).reshape(int(t["rows"]), int(t["cols"])) + w, h = (int(v) for v in cfg["resolution"]) + return Camera(fx, fy, cx, cy, (k1, k2, p1, p2), T, w, h) + + +def load_gray(path: str | Path) -> np.ndarray: + """A PNG as a float32 image in [0, 1], one channel. Matplotlib reads it; no other image library is used.""" + import matplotlib.image as mpimg + + img = np.asarray(mpimg.imread(str(path)), dtype=np.float32) + if img.ndim == 3: + img = img[..., :3].mean(axis=2) + return img + + +# --------------------------------------------------------------------- geometry + + +def undistort(px: np.ndarray, cam: Camera, iterations: int = 8) -> np.ndarray: + """Pixels ``(N, 2)`` to undistorted normalised image coordinates ``(N, 2)``, by fixed-point iteration.""" + k1, k2, p1, p2 = cam.dist + xd = np.stack([(px[:, 0] - cam.cx) / cam.fx, (px[:, 1] - cam.cy) / cam.fy], axis=1) + x = xd.copy() + for _ in range(iterations): + r2 = (x**2).sum(axis=1) + radial = 1.0 + k1 * r2 + k2 * r2**2 + dx = 2 * p1 * x[:, 0] * x[:, 1] + p2 * (r2 + 2 * x[:, 0] ** 2) + dy = p1 * (r2 + 2 * x[:, 1] ** 2) + 2 * p2 * x[:, 0] * x[:, 1] + x = np.stack([(xd[:, 0] - dx) / radial, (xd[:, 1] - dy) / radial], axis=1) + return x + + +def distort(x: np.ndarray, cam: Camera) -> np.ndarray: + """Normalised coordinates to pixels. The inverse of :func:`undistort`, used by the tests.""" + k1, k2, p1, p2 = cam.dist + r2 = (x**2).sum(axis=1) + radial = 1.0 + k1 * r2 + k2 * r2**2 + xd = x[:, 0] * radial + 2 * p1 * x[:, 0] * x[:, 1] + p2 * (r2 + 2 * x[:, 0] ** 2) + yd = x[:, 1] * radial + p1 * (r2 + 2 * x[:, 1] ** 2) + 2 * p2 * x[:, 0] * x[:, 1] + return np.stack([xd * cam.fx + cam.cx, yd * cam.fy + cam.cy], axis=1) + + +def rot_log_small(R: np.ndarray) -> np.ndarray: + """The rotation vector of ``R`` in radians (angle-axis), by the standard logarithm.""" + cos = np.clip((np.trace(R) - 1.0) / 2.0, -1.0, 1.0) + angle = float(np.arccos(cos)) + if angle < 1e-9: + return np.zeros(3) + axis = np.array([R[2, 1] - R[1, 2], R[0, 2] - R[2, 0], R[1, 0] - R[0, 1]]) / (2.0 * np.sin(angle)) + return axis * angle + + +def _essential_from_nullvec(e: np.ndarray) -> np.ndarray: + """Project a 3x3 matrix onto the essential matrices: singular values (1, 1, 0).""" + u, _, vt = np.linalg.svd(e.reshape(3, 3)) + return u @ np.diag([1.0, 1.0, 0.0]) @ vt + + +def eight_point(x0: np.ndarray, x1: np.ndarray) -> np.ndarray: + """Essential matrix from at least eight normalised correspondences, ``x1' E x0 = 0``.""" + a = np.stack( + [ + x1[:, 0] * x0[:, 0], + x1[:, 0] * x0[:, 1], + x1[:, 0], + x1[:, 1] * x0[:, 0], + x1[:, 1] * x0[:, 1], + x1[:, 1], + x0[:, 0], + x0[:, 1], + np.ones(len(x0)), + ], + axis=1, + ) + _, _, vt = np.linalg.svd(a) + return _essential_from_nullvec(vt[-1]) + + +def sampson(E: np.ndarray, x0: np.ndarray, x1: np.ndarray) -> np.ndarray: + """Squared Sampson distance of each correspondence to ``E`` (normalised units).""" + h0 = np.concatenate([x0, np.ones((len(x0), 1))], axis=1) + h1 = np.concatenate([x1, np.ones((len(x1), 1))], axis=1) + ex0 = h0 @ E.T + etx1 = h1 @ E + num = (h1 * ex0).sum(axis=1) ** 2 + den = ex0[:, 0] ** 2 + ex0[:, 1] ** 2 + etx1[:, 0] ** 2 + etx1[:, 1] ** 2 + return num / np.maximum(den, 1e-18) + + +def essential_ransac( + x0: np.ndarray, x1: np.ndarray, threshold: float, iterations: int = 200, seed: int = 0 +) -> tuple[np.ndarray, np.ndarray]: + """Essential matrix and inlier mask by RANSAC over eight-point models. ``threshold`` is in normalised units.""" + n = len(x0) + if n < 8: + raise ValueError("need at least eight correspondences") + rng = np.random.default_rng(seed) + best_e, best_in, best_count = None, None, -1 + for _ in range(iterations): + idx = rng.choice(n, size=8, replace=False) + try: + e = eight_point(x0[idx], x1[idx]) + except np.linalg.LinAlgError: # pragma: no cover - a degenerate sample + continue + inl = sampson(e, x0, x1) < threshold**2 + if inl.sum() > best_count: + best_e, best_in, best_count = e, inl, int(inl.sum()) + if best_e is None or best_in is None or best_count < 8: + raise ValueError("RANSAC found no model with eight inliers") + # Local optimisation with a shrinking threshold: refit on everything inside 4x, 3x, 2x and then 1x the threshold. A + # model from eight noisy points is poor, and fitting on a wide band first is what pulls it onto the true geometry. + model, inliers = best_e, best_in + for mult in (4.0, 3.0, 2.0, 1.5, 1.0, 1.0): + band = sampson(model, x0, x1) < (mult * threshold) ** 2 + if band.sum() < 8: + break + model = eight_point(x0[band], x1[band]) + inliers = sampson(model, x0, x1) < threshold**2 + if inliers.sum() < 8: + return best_e, best_in + return model, inliers + + +def decompose(E: np.ndarray, x0: np.ndarray, x1: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """``(R, t)`` with ``X1 = R X0 + t`` and ``|t| = 1``, chosen by the number of points in front of both cameras.""" + u, _, vt = np.linalg.svd(E) + if np.linalg.det(u) < 0: + u[:, -1] *= -1 + if np.linalg.det(vt) < 0: + vt[-1] *= -1 + w = np.array([[0.0, -1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 1.0]]) + best = (-1, np.eye(3), np.array([1.0, 0.0, 0.0])) + for r in (u @ w @ vt, u @ w.T @ vt): + for t in (u[:, 2], -u[:, 2]): + count = _in_front(r, t, x0, x1) + if count > best[0]: + best = (count, r, t) + return best[1], best[2] + + +def _in_front(R: np.ndarray, t: np.ndarray, x0: np.ndarray, x1: np.ndarray) -> int: + """Points with positive depth in both cameras, by linear triangulation of each correspondence.""" + h0 = np.concatenate([x0, np.ones((len(x0), 1))], axis=1) + h1 = np.concatenate([x1, np.ones((len(x1), 1))], axis=1) + # depth d0 along h0 and d1 along h1: d1 h1 = R (d0 h0) + t -> least squares for (d0, d1) + rh0 = h0 @ R.T + a = np.stack([rh0, -h1], axis=2) # (N, 3, 2) + ata = np.einsum("nij,nik->njk", a, a) + atb = np.einsum("nij,i->nj", a, -t) + d = np.linalg.solve(ata + 1e-12 * np.eye(2), atb[..., None])[..., 0] + return int(((d[:, 0] > 0) & (d[:, 1] > 0)).sum()) + + +# --------------------------------------------------------------------- images + + +def pyramid(img: np.ndarray, levels: int) -> list[np.ndarray]: + """A Gaussian-like pyramid by 2x2 averaging.""" + out = [img] + for _ in range(levels - 1): + a = out[-1] + h, w = (a.shape[0] // 2) * 2, (a.shape[1] // 2) * 2 + out.append(0.25 * (a[0:h:2, 0:w:2] + a[1:h:2, 0:w:2] + a[0:h:2, 1:w:2] + a[1:h:2, 1:w:2])) + return out + + +def _box(a: np.ndarray, r: int) -> np.ndarray: + """Sum over a (2r+1)^2 window with zero padding, by two cumulative sums.""" + k = 2 * r + 1 + p = np.pad(a, r + 1, mode="constant") + c = p.cumsum(axis=0).cumsum(axis=1) + return c[k:, k:] - c[:-k, k:] - c[k:, :-k] + c[:-k, :-k] + + +def _gradients(img: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + gx = np.zeros_like(img) + gy = np.zeros_like(img) + gx[:, 1:-1] = 0.5 * (img[:, 2:] - img[:, :-2]) + gy[1:-1, :] = 0.5 * (img[2:, :] - img[:-2, :]) + return gx, gy + + +def shi_tomasi( + img: np.ndarray, max_corners: int = 400, cell: int = 24, border: int = 20, min_quality: float = 0.01 +) -> np.ndarray: + """Corners ``(N, 2)`` as ``(x, y)`` pixels: the best structure-tensor minimum eigenvalue in each grid cell.""" + gx, gy = _gradients(img) + sxx, syy, sxy = _box(gx * gx, 2), _box(gy * gy, 2), _box(gx * gy, 2) + lam = 0.5 * (sxx + syy - np.sqrt((sxx - syy) ** 2 + 4 * sxy**2)) + lam[:border, :] = 0 + lam[-border:, :] = 0 + lam[:, :border] = 0 + lam[:, -border:] = 0 + floor = min_quality * float(lam.max()) + pts: list[tuple[float, float, float]] = [] + h, w = lam.shape + for y0 in range(0, h, cell): + for x0 in range(0, w, cell): + block = lam[y0 : y0 + cell, x0 : x0 + cell] + k = int(np.argmax(block)) + v = float(block.flat[k]) + if v > floor: + yy, xx = divmod(k, block.shape[1]) + pts.append((float(x0 + xx), float(y0 + yy), v)) + pts.sort(key=lambda p: -p[2]) + return np.array([(p[0], p[1]) for p in pts[:max_corners]], dtype=float).reshape(-1, 2) + + +def bilinear(img: np.ndarray, x: np.ndarray, y: np.ndarray) -> np.ndarray: + """Sample ``img`` at float coordinates (any shape). Coordinates outside the image are clamped to its edge.""" + h, w = img.shape + x = np.clip(x, 0.0, w - 1.001) + y = np.clip(y, 0.0, h - 1.001) + x0, y0 = np.floor(x).astype(int), np.floor(y).astype(int) + fx, fy = x - x0, y - y0 + return ( + img[y0, x0] * (1 - fx) * (1 - fy) + + img[y0, x0 + 1] * fx * (1 - fy) + + img[y0 + 1, x0] * (1 - fx) * fy + + img[y0 + 1, x0 + 1] * fx * fy + ) + + +def lucas_kanade( + pyr0: list[np.ndarray], + pyr1: list[np.ndarray], + pts: np.ndarray, + win: int = 7, + iterations: int = 15, + min_det: float = 1e-6, +) -> tuple[np.ndarray, np.ndarray]: + """Pyramidal Lucas-Kanade from ``pyr0`` to ``pyr1``. Returns the tracked points and a mask of usable tracks.""" + n = len(pts) + r = win // 2 + off = np.arange(-r, r + 1, dtype=float) + ox, oy = np.meshgrid(off, off) + flow = np.zeros((n, 2)) + ok = np.ones(n, dtype=bool) + for lvl in range(len(pyr0) - 1, -1, -1): + a0, a1 = pyr0[lvl], pyr1[lvl] + gx, gy = _gradients(a0) + scale = 2.0**lvl + p = pts / scale + px = p[:, 0, None, None] + ox[None] + py = p[:, 1, None, None] + oy[None] + t0 = bilinear(a0, px, py) + ix, iy = bilinear(gx, px, py), bilinear(gy, px, py) + gxx, gyy, gxy = (ix * ix).sum((1, 2)), (iy * iy).sum((1, 2)), (ix * iy).sum((1, 2)) + det = gxx * gyy - gxy**2 + good = det > min_det + ok &= good + det = np.where(good, det, 1.0) + if lvl < len(pyr0) - 1: + flow = flow * 2.0 + for _ in range(iterations): + t1 = bilinear(a1, px + flow[:, 0, None, None], py + flow[:, 1, None, None]) + err = t0 - t1 + bx, by = (ix * err).sum((1, 2)), (iy * err).sum((1, 2)) + dx = (gyy * bx - gxy * by) / det + dy = (gxx * by - gxy * bx) / det + flow += np.stack([dx, dy], axis=1) + if float(np.max(np.abs(np.stack([dx, dy])))) < 0.01: + break + return pts + flow, ok + + +def track( + img0: np.ndarray, img1: np.ndarray, pts: np.ndarray, levels: int = 3, fb_tolerance: float = 0.5 +) -> tuple[np.ndarray, np.ndarray]: + """Track ``pts`` from ``img0`` to ``img1`` with a forward-backward check. Returns the points and a usable mask.""" + p0, p1 = pyramid(img0, levels), pyramid(img1, levels) + fwd, ok_f = lucas_kanade(p0, p1, pts) + back, ok_b = lucas_kanade(p1, p0, fwd) + h, w = img0.shape + inside = (fwd[:, 0] > 2) & (fwd[:, 0] < w - 3) & (fwd[:, 1] > 2) & (fwd[:, 1] < h - 3) + consistent = np.linalg.norm(back - pts, axis=1) < fb_tolerance + return fwd, ok_f & ok_b & inside & consistent + + +# ------------------------------------------------------------------ the front end + + +@dataclass(frozen=True) +class PairResult: + """The estimate for one pair of frames.""" + + R: np.ndarray # camera 0 in camera 1: X1 = R X0 + t + t_dir: np.ndarray # unit vector, same convention + tracked: int + inliers: int + + +def relative_pose( + img0: np.ndarray, img1: np.ndarray, cam: Camera, seed: int = 0, ransac_px: float = 1.0 +) -> PairResult | None: + """Rotation and translation direction between two frames, or None if too few points survive.""" + corners = shi_tomasi(img0) + if len(corners) < 20: + return None + nxt, ok = track(img0, img1, corners) + if int(ok.sum()) < 20: + return None + x0, x1 = undistort(corners[ok], cam), undistort(nxt[ok], cam) + try: + E, inl = essential_ransac(x0, x1, ransac_px / cam.fx, seed=seed) + except ValueError: + return None + R, t = decompose(E, x0[inl], x1[inl]) + return PairResult(R=R, t_dir=t / np.linalg.norm(t), tracked=int(ok.sum()), inliers=int(inl.sum())) + + +def motion_from_pair(res: PairResult) -> tuple[np.ndarray, np.ndarray]: + """The camera's own motion ``T_c0_c1``: its rotation and the direction of its translation, in camera 0.""" + return res.R.T, -res.R.T @ res.t_dir + + +def track_chain( + images: list[np.ndarray], pts: np.ndarray, levels: int = 3, fb_tolerance: float = 0.5 +) -> list[tuple[np.ndarray, np.ndarray]]: + """Track ``pts`` of ``images[0]`` through every later frame, one step at a time. + + Returns, for each later frame, the positions and the mask of points that survived every step so far. Tracking + step by step is how a feature tracker copes with the large image motion of a long baseline. + """ + alive = np.ones(len(pts), dtype=bool) + cur = pts.copy() + out: list[tuple[np.ndarray, np.ndarray]] = [] + for prev, nxt in pairwise(images): + moved, ok = track(prev, nxt, cur, levels=levels, fb_tolerance=fb_tolerance) + alive &= ok + cur = np.where(alive[:, None], moved, cur) + out.append((cur.copy(), alive.copy())) + return out + + +def relative_poses_along( + images: list[np.ndarray], gaps: tuple[int, ...], cam: Camera, seed: int = 0, ransac_px: float = 1.0 +) -> dict[int, PairResult | None]: + """The relative pose from ``images[0]`` to ``images[g]`` for each gap ``g``, from one chain of tracks. + + The corners are found once in ``images[0]``; a gap's pose uses the points still tracked at that frame. + """ + if max(gaps) >= len(images): + raise ValueError("need a frame for every gap") + corners = shi_tomasi(images[0]) + result: dict[int, PairResult | None] = dict.fromkeys(gaps) + if len(corners) < 20: + return result + chain = track_chain(images[: max(gaps) + 1], corners) + for g in gaps: + pos, alive = chain[g - 1] + if int(alive.sum()) < 20: + continue + x0, x1 = undistort(corners[alive], cam), undistort(pos[alive], cam) + try: + E, inl = essential_ransac(x0, x1, ransac_px / cam.fx, seed=seed) + except ValueError: + continue + R, t = decompose(E, x0[inl], x1[inl]) + result[g] = PairResult(R=R, t_dir=t / np.linalg.norm(t), tracked=int(alive.sum()), inliers=int(inl.sum())) + return result diff --git a/tests/test_euroc_fetch.py b/tests/test_euroc_fetch.py index 7d9a56b..afda8b1 100644 --- a/tests/test_euroc_fetch.py +++ b/tests/test_euroc_fetch.py @@ -382,3 +382,119 @@ def test_no_sequence_is_a_usage_error(capsys): def test_every_sequence_maps_to_a_known_archive(): assert set(ef.SEQUENCES.values()) <= set(ef.BITSTREAMS) assert len(ef.SEQUENCES) == 11 + + +# ------------------------------------------------------------------- camera frames + +T0_NS = 1403636579763555584 + + +def _cam_members(prefix: str) -> dict[str, bytes]: + stamps = [T0_NS + 50_000_000 * k for k in range(40)] # 20 Hz, 2 s + csv_text = "#timestamp [ns],filename\n" + "".join(f"{t},{t}.png\n" for t in stamps) + out = { + f"{prefix}mav0/cam0/data.csv": csv_text.encode(), + f"{prefix}mav0/cam0/sensor.yaml": b"sensor_type: camera\nrate_hz: 20\n", + } + out.update({f"{prefix}mav0/cam0/data/{t}.png": b"PNG" + t.to_bytes(8, "big") * 8 for t in stamps}) + return out + + +def _flat_with_frames() -> bytes: + return _zip_bytes({**_members(f"{SEQ}/"), **_cam_members(f"{SEQ}/")}, zipfile.ZIP_STORED) + + +def _nested_with_frames(inner_method: int) -> bytes: + inner = _zip_bytes({**_members(""), **_cam_members("")}, inner_method) + outer = zipfile.ZIP_STORED if inner_method == zipfile.ZIP_STORED else zipfile.ZIP_DEFLATED + return _zip_bytes({f"machine_hall/{SEQ}/{SEQ}.zip": inner}, outer) + + +def _with_imu(server, dest, blob): + server.serve(GROUP, blob) + _fetch(server, dest) + + +def test_select_frames_keeps_the_window_and_every_nth_frame(): + text = _cam_members("")["mav0/cam0/data.csv"].decode() + rows = ef.select_frames(text, T0_NS, 0.5, 1.5, 2) + assert [t for t, _ in rows] == [T0_NS + 50_000_000 * k for k in range(10, 31, 2)] + with pytest.raises(ef.FetchError): + ef.select_frames(text, T0_NS, 1.0, 1.0, 1) + with pytest.raises(ef.FetchError): + ef.select_frames(text, T0_NS, 0.0, 1.0, 0) + + +def test_frames_of_a_window_are_fetched_after_the_sequence(server, tmp_path): + _with_imu(server, tmp_path, _flat_with_frames()) + files = ef.fetch_images(SEQ, tmp_path, start_s=0.5, stop_s=1.5, every=2, base_url=server.base_url) + frames = [f for f in files if f.path.endswith(".png")] + assert len(frames) == 11 + assert (tmp_path / SEQ / "mav0/cam0/sensor.yaml").is_file() and (tmp_path / SEQ / "mav0/cam0/data.csv").is_file() + first = T0_NS + 50_000_000 * 10 + assert (tmp_path / SEQ / f"mav0/cam0/data/{first}.png").read_bytes() == b"PNG" + first.to_bytes(8, "big") * 8 + manifest = json.loads((tmp_path / "MANIFEST.json").read_text())["sequences"][SEQ]["files"] + paths = {f["path"] for f in manifest} + assert f"{SEQ}/mav0/imu0/data.csv" in paths and f"{SEQ}/mav0/cam0/data/{first}.png" in paths + + +def test_frames_from_a_compressed_nested_archive_leave_no_copy_behind(server, tmp_path): + _with_imu(server, tmp_path, _nested_with_frames(zipfile.ZIP_DEFLATED)) + files = ef.fetch_images(SEQ, tmp_path, start_s=0.0, stop_s=0.4, base_url=server.base_url) + assert sum(f.path.endswith(".png") for f in files) == 9 + assert not list((tmp_path / ".work").glob("*.zip")) + + +def test_frames_from_a_stored_nested_archive_are_read_in_place(server, tmp_path): + _with_imu(server, tmp_path, _nested_with_frames(zipfile.ZIP_STORED)) + files = ef.fetch_images(SEQ, tmp_path, start_s=0.0, stop_s=0.2, base_url=server.base_url) + assert sum(f.path.endswith(".png") for f in files) == 5 + + +def test_frames_need_the_sequence_first_and_a_window_with_frames(server, tmp_path): + server.serve(GROUP, _flat_with_frames()) + with pytest.raises(ef.FetchError, match="fetch the sequence first"): + ef.fetch_images(SEQ, tmp_path, start_s=0.0, stop_s=1.0, base_url=server.base_url) + _fetch(server, tmp_path) + with pytest.raises(ef.FetchError, match="no camera frame"): + ef.fetch_images(SEQ, tmp_path, start_s=50.0, stop_s=60.0, base_url=server.base_url) + with pytest.raises(ef.FetchError, match="unknown sequence"): + ef.fetch_images("NOPE", tmp_path, start_s=0.0, stop_s=1.0, base_url=server.base_url) + + +def test_an_archive_without_camera_files_is_a_layout_error(server, tmp_path): + _with_imu(server, tmp_path, flat_archive()) + with pytest.raises(ef.LayoutError): + ef.fetch_images(SEQ, tmp_path, start_s=0.0, stop_s=1.0, base_url=server.base_url) + + +def test_the_images_flag_fetches_frames(server, tmp_path, capsys): + _with_imu(server, tmp_path, _flat_with_frames()) + code = ef.main( + [ + "--images", + "--sequence", + SEQ, + "--dest", + str(tmp_path), + "--base-url", + server.base_url, + "--image-window", + "0,0.2", + ] + ) + assert code == 0 and "files under" in capsys.readouterr().out + bad = ef.main( + ["--images", "--sequence", SEQ, "--dest", str(tmp_path), "--base-url", server.base_url, "--image-window", "x"] + ) + assert bad == 1 + + +def test_frames_can_be_read_from_a_whole_archive_placed_on_disk(server, tmp_path): + _with_imu(server, tmp_path, _nested_with_frames(zipfile.ZIP_DEFLATED)) + local = tmp_path / ".downloads" + local.mkdir() + (local / f"{GROUP}.zip").write_bytes(_nested_with_frames(zipfile.ZIP_DEFLATED)) + server.blobs.clear() # nothing may be fetched: the archive on disk is used + files = ef.fetch_images(SEQ, tmp_path, start_s=0.0, stop_s=0.2, full_download=True, base_url=server.base_url) + assert sum(f.path.endswith(".png") for f in files) == 5 diff --git a/tests/test_vo_error.py b/tests/test_vo_error.py new file mode 100644 index 0000000..38f5e71 --- /dev/null +++ b/tests/test_vo_error.py @@ -0,0 +1,211 @@ +"""The error study of the two-view front end, tested with the front end replaced by one that has known errors.""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import pytest + +from navkit import euroc_eval as ee +from navkit import vo_error as ve +from navkit import vo_frontend as vf +from navkit.euroc_cli import main as euroc_main +from navkit.geometry.rigid import rot_exp + + +def _write_cam(seq_dir: Path, t_start: float, t_stop: float, rate: float = 20.0) -> None: + import matplotlib.image as mpimg + + first_ns = None + for line in (seq_dir / "mav0/imu0/data.csv").read_text().splitlines(): + if line.strip() and not line.startswith("#"): + first_ns = int(line.split(",")[0]) + break + cam = seq_dir / "mav0/cam0" + (cam / "data").mkdir(parents=True) + (cam / "sensor.yaml").write_text( + "intrinsics: [450.0, 450.0, 32.0, 24.0]\n" + "distortion_coefficients: [0.0, 0.0, 0.0, 0.0]\n" + "resolution: [64, 48]\n" + "T_BS:\n cols: 4\n rows: 4\n data: [1,0,0,0.05, 0,1,0,0, 0,0,1,0, 0,0,0,1]\n" + ) + rows = ["#timestamp [ns],filename"] + for k in range(int((t_stop - t_start) * rate)): + ts = first_ns + int((t_start + k / rate) * 1e9) + rows.append(f"{ts},{ts}.png") + img = np.zeros((48, 64, 3), dtype=np.uint8) # RGB: saved as it is, with no colour map to quantise it + img[0, 0, :], img[0, 1, :] = k // 256, k % 256 # the frame index, for a replacement front end to read back + mpimg.imsave(str(cam / "data" / f"{ts}.png"), img) + (cam / "data.csv").write_text("\n".join(rows) + "\n") + + +@pytest.fixture +def seq_dir(tmp_path): + ee.write_fixture(tmp_path, "MH_01_easy", duration_s=40.0, seed=2) + d = tmp_path / "MH_01_easy" + _write_cam(d, 5.0, 15.0) + return d + + +def _index(img): + return int(round(float(img[0, 0]) * 255)) * 256 + int(round(float(img[0, 1]) * 255)) + + +def _exact(monkeypatch, seq_dir, noise_deg=0.0, seed=0): + """Replace the front end by one that returns the true relative pose plus a random rotation error.""" + seq = ee.load_sequence(seq_dir.parent, seq_dir.name) + cam = vf.camera_from_yaml(seq_dir / "mav0/cam0/sensor.yaml") + times = [t for t, _ in ve.frame_list(seq_dir)] + rng = np.random.default_rng(seed) + + def fake(images, gaps, camera, seed=0): + out = {} + for g in gaps: + t0, t1 = times[_index(images[0])], times[_index(images[g])] + R, t = ve.true_motion(seq.truth, cam, t0, t1) + Rm = R @ rot_exp(np.radians(noise_deg) * rng.standard_normal(3)) + norm = np.linalg.norm(t) + u = t / norm if norm > 1e-9 else np.array([1.0, 0.0, 0.0]) + out[g] = vf.PairResult(R=Rm.T, t_dir=-Rm.T @ u, tracked=100, inliers=90) + return out + + monkeypatch.setattr(ve, "relative_poses_along", fake) + + +def test_frame_list_is_sorted_on_the_imu_clock_and_skips_missing_files(seq_dir): + frames = ve.frame_list(seq_dir) + assert len(frames) == 200 + assert frames[0][0] == pytest.approx(5.0) and frames[1][0] - frames[0][0] == pytest.approx(0.05) + next(iter((seq_dir / "mav0/cam0/data").iterdir())).unlink() + assert len(ve.frame_list(seq_dir)) == 199 + + +def test_true_motion_follows_the_extrinsic(seq_dir): + seq = ee.load_sequence(seq_dir.parent, seq_dir.name) + cam = vf.camera_from_yaml(seq_dir / "mav0/cam0/sensor.yaml") + R, t = ve.true_motion(seq.truth, cam, 10.0, 10.5) + assert np.allclose(R @ R.T, np.eye(3), atol=1e-9) and np.linalg.norm(t) > 0.0 + # with no extrinsic offset the translation is the body's own, rotated into the start frame + cam0 = vf.Camera(**{**cam.__dict__, "T_BS": np.eye(4)}) + R0, t0 = ve.true_motion(seq.truth, cam0, 10.0, 10.5) + assert np.allclose(R, R0, atol=1e-9) and not np.allclose(t, t0) + + +def test_a_perfect_pair_has_no_error(): + R = rot_exp(np.array([0.01, -0.02, 0.03])) + u = np.array([0.6, 0.0, 0.8]) + + class Truth: + pass + + est = vf.PairResult(R=R.T, t_dir=-R.T @ u, tracked=50, inliers=40) + seq_cam = vf.Camera(450, 450, 32, 24, (0, 0, 0, 0), np.eye(4), 64, 48) + traj = _line_trajectory(R, u * 0.4) + row = ve.pair_row("s", 1, 0.0, 1.0, est, traj, seq_cam) + assert row is not None + assert abs(row["rot_x_deg"]) + abs(row["rot_y_deg"]) + abs(row["rot_z_deg"]) < 1e-6 + assert abs(row["lat_x_m"]) + abs(row["lat_y_m"]) + abs(row["lat_z_m"]) < 1e-9 and row["dir_err_deg"] < 1e-6 + assert row["baseline_m"] == pytest.approx(0.4) + + +def _line_trajectory(R, t): + from navkit.types import Trajectory + + poses = np.stack([np.eye(4), np.eye(4)]) + poses[1, :3, :3] = R + poses[1, :3, 3] = t + return Trajectory(t=np.array([0.0, 1.0]), poses=poses) + + +def test_a_rotation_error_and_a_direction_error_are_measured(): + R = np.eye(3) + u = np.array([1.0, 0.0, 0.0]) + off = rot_exp(np.radians([1.0, 0.0, 0.0])) + tilted = np.array([np.cos(np.radians(10)), np.sin(np.radians(10)), 0.0]) + est = vf.PairResult(R=(R @ off).T, t_dir=-(R @ off).T @ tilted, tracked=10, inliers=9) + cam = vf.Camera(450, 450, 32, 24, (0, 0, 0, 0), np.eye(4), 64, 48) + row = ve.pair_row("s", 1, 0.0, 1.0, est, _line_trajectory(R, u * 0.5), cam) + assert row is not None + assert abs(row["rot_x_deg"]) == pytest.approx(1.0, abs=1e-6) + assert row["dir_err_deg"] == pytest.approx(10.0, abs=1e-6) + assert abs(row["lat_y_m"]) == pytest.approx(0.5 * np.sin(np.radians(10)), abs=1e-6) + + +def test_autocorrelation_and_correlation_time_of_known_series(): + rng = np.random.default_rng(0) + white = rng.standard_normal(4000) + ar = np.zeros(4000) + for i in range(1, 4000): + ar[i] = 0.9 * ar[i - 1] + rng.standard_normal() + a_white, a_ar = ve.autocorrelation(white), ve.autocorrelation(ar) + assert a_white[0] == pytest.approx(1.0) and abs(a_white[1]) < 0.06 + assert a_ar[1] == pytest.approx(0.9, abs=0.04) + assert ve.correlation_time(a_white, 0.05) == pytest.approx(0.05) + assert 0.35 < ve.correlation_time(a_ar, 0.05) < 0.65 # -dt / ln(0.9) is 0.47 s + assert np.isnan(ve.autocorrelation(np.ones(10))).all() and np.isnan(ve.autocorrelation(white[:10])).all() + assert ve.correlation_time(np.ones(5), 0.1) == pytest.approx(0.5) + + +def test_measure_runs_the_pairs_and_summary_reads_them(seq_dir, monkeypatch): + _exact(monkeypatch, seq_dir, noise_deg=0.0) + rows = ve.measure(seq_dir, gaps=(1, 4)) + assert {int(r["gap_frames"]) for r in rows} == {1, 4} + assert max(abs(float(r["rot_x_deg"])) for r in rows) < 1e-6 + s = ve.summary(rows, 1, 0.05) + assert s["pairs"] > 100 and s["rot_rms_deg"] < 1e-6 + assert "Gap, frames" in ve.summary_markdown(rows) + + +def test_measure_refuses_too_few_frames(seq_dir): + with pytest.raises(ValueError, match="not enough"): + ve.measure(seq_dir, gaps=(500,)) + + +def test_measure_skips_pairs_the_front_end_could_not_solve(seq_dir, monkeypatch): + monkeypatch.setattr(ve, "relative_poses_along", lambda images, gaps, cam, seed=0: dict.fromkeys(gaps)) + assert ve.measure(seq_dir, gaps=(1,)) == [] + + +def test_csv_round_trip_cli_and_the_error_path(seq_dir, monkeypatch, tmp_path, capsys): + _exact(monkeypatch, seq_dir, noise_deg=0.5, seed=1) + rows = ve.measure(seq_dir, gaps=(1,)) + out = tmp_path / "vo.csv" + ve.write_csv(out, rows) + assert len(ve.read_csv(out)) == len(rows) + assert euroc_main(["vo", "--from-csv", str(out), "--markdown"]) == 0 + assert "Rotation correlation time" in capsys.readouterr().out + assert ve.main(["--sequence", "MH_01_easy", "--root", str(tmp_path / "nowhere")]) == 1 + + +def test_a_pair_where_the_camera_did_not_move_is_skipped(): + still = _line_trajectory(np.eye(3), np.zeros(3)) + cam = vf.Camera(450, 450, 32, 24, (0, 0, 0, 0), np.eye(4), 64, 48) + est = vf.PairResult(R=np.eye(3), t_dir=np.array([1.0, 0.0, 0.0]), tracked=10, inliers=9) + assert ve.pair_row("s", 1, 0.0, 1.0, est, still, cam) is None + + +def test_a_short_series_has_no_autocorrelation_and_no_warning(): + rows = [ + { + "sequence": "s", + "gap_frames": 1, + "t_s": 0.05 * k, + "dt_s": 0.05, + "baseline_m": 0.03, + "rot_x_deg": 0.1 * k % 0.7, + "rot_y_deg": 0.0, + "rot_z_deg": 0.0, + "lat_x_m": 0.0, + "lat_y_m": 0.001 * k, + "lat_z_m": 0.0, + "dir_err_deg": 3.0, + "tracked": 50, + "inliers": 40, + } + for k in range(10) + ] + s = ve.summary(rows, 1, 0.05) + assert np.isnan(s["rot_acf_lag1"]) and np.isnan(s["lat_corr_time_s"]) and s["pairs"] == 10 + assert "nan" in ve.summary_markdown(rows).lower() or "-" in ve.summary_markdown(rows) + assert np.isnan(ve.correlation_time(np.full(5, np.nan), 0.05)) diff --git a/tests/test_vo_frontend.py b/tests/test_vo_frontend.py new file mode 100644 index 0000000..a330bed --- /dev/null +++ b/tests/test_vo_frontend.py @@ -0,0 +1,185 @@ +"""The two-view front end, tested piece by piece on synthetic data. The real-image check is the study that uses it.""" + +from __future__ import annotations + +import numpy as np +import pytest + +from navkit import vo_frontend as vo +from navkit.geometry.rigid import rot_exp + +CAM = vo.Camera( + fx=450.0, fy=450.0, cx=370.0, cy=240.0, dist=(-0.28, 0.07, 0.0002, 0.00002), T_BS=np.eye(4), width=752, height=480 +) + + +def _scene(n=200, seed=0, motion=(0.05, 0.04, 0.02), rot=(0.01, -0.02, 0.015)): + rng = np.random.default_rng(seed) + X0 = np.column_stack([rng.uniform(-3, 3, n), rng.uniform(-2, 2, n), rng.uniform(3, 12, n)]) + R = rot_exp(np.array(rot)) + t = np.array(motion) + X1 = X0 @ R.T + t + x0 = X0[:, :2] / X0[:, 2:] + x1 = X1[:, :2] / X1[:, 2:] + return x0, x1, R, t / np.linalg.norm(t) + + +def _angle(a, b): + return float(np.degrees(np.arccos(np.clip(abs(float(a @ b)), 0.0, 1.0)))) + + +def test_distort_and_undistort_are_inverses(): + rng = np.random.default_rng(1) + x = rng.uniform(-0.6, 0.6, (50, 2)) + assert np.allclose(vo.undistort(vo.distort(x, CAM), CAM), x, atol=1e-7) + + +def test_eight_point_and_decompose_recover_a_known_motion(): + x0, x1, R, t = _scene() + E = vo.eight_point(x0, x1) + assert np.all(vo.sampson(E, x0, x1) < 1e-16) + Rh, th = vo.decompose(E, x0, x1) + assert np.linalg.norm(Rh - R) < 1e-6 and _angle(th, t) < 1e-4 + assert th @ t > 0 + + +def test_ransac_survives_outliers_and_noise(): + # A baseline of about 0.6 m against depths of 3 to 12 m: the translation direction needs parallax. + x0, x1, R, t = _scene(n=300, seed=3, motion=(0.5, 0.4, 0.2)) + rng = np.random.default_rng(5) + x1 = x1 + rng.normal(0, 0.5 / CAM.fx, x1.shape) + bad = rng.choice(300, 90, replace=False) + x1[bad] = rng.uniform(-0.5, 0.5, (90, 2)) + E, inl = vo.essential_ransac(x0, x1, 1.0 / CAM.fx, seed=1) + Rh, th = vo.decompose(E, x0[inl], x1[inl]) + assert 150 <= inl.sum() <= 215 # 210 true correspondences, each noisy; no outlier should be kept in bulk + assert np.degrees(np.linalg.norm(vo.rot_log_small(Rh @ R.T))) < 0.3 + assert _angle(th, t) < 3.0 + + +def test_ransac_needs_eight_points(): + with pytest.raises(ValueError): + vo.essential_ransac(np.zeros((5, 2)), np.zeros((5, 2)), 0.01) + + +def _texture(h=120, w=160, seed=0): + rng = np.random.default_rng(seed) + img = rng.random((h, w)).astype(np.float32) + for _ in range(3): + img = (img + np.roll(img, 1, 0) + np.roll(img, -1, 0) + np.roll(img, 1, 1) + np.roll(img, -1, 1)) / 5.0 + return (img - img.min()) / (img.max() - img.min()) + + +def test_corners_are_found_on_texture_and_not_on_a_flat_image(): + assert len(vo.shi_tomasi(_texture(), cell=16, border=8)) > 20 + assert len(vo.shi_tomasi(np.full((120, 160), 0.5, dtype=np.float32), cell=16, border=8)) == 0 + + +def test_corners_are_found_on_a_checkerboard(): + y, x = np.mgrid[0:120, 0:160] + board = (((x // 20) + (y // 20)) % 2).astype(np.float32) + pts = vo.shi_tomasi(board, cell=16, border=8) + assert len(pts) >= 10 + near = [(abs(p[0] / 20 - round(p[0] / 20)) * 20, abs(p[1] / 20 - round(p[1] / 20)) * 20) for p in pts] + assert np.median([max(a, b) for a, b in near]) < 3.0 + + +def test_lucas_kanade_recovers_a_subpixel_shift(): + img0 = _texture(seed=2) + h, w = img0.shape + yy, xx = np.mgrid[0:h, 0:w].astype(float) + shift = np.array([3.2, -1.7]) + img1 = vo.bilinear(img0, xx - shift[0], yy - shift[1]).astype(np.float32) # content moves by +shift + pts = vo.shi_tomasi(img0, cell=16, border=16) + out, ok = vo.track(img0, img1, pts) + assert ok.sum() > 0.5 * len(pts) + err = np.linalg.norm((out[ok] - pts[ok]) - shift, axis=1) + assert np.median(err) < 0.1 + + +def test_tracking_rejects_points_that_leave_the_image(): + img = _texture(seed=4) + h, w = img.shape + pts = np.array([[w - 4.0, 50.0], [80.0, 60.0]]) + yy, xx = np.mgrid[0:h, 0:w].astype(float) + moved = vo.bilinear(img, xx - 10.0, yy).astype(np.float32) + _, ok = vo.track(img, moved, pts) + assert not ok[0] + + +def test_relative_pose_from_exact_tracks(monkeypatch): + x0, x1, R, t = _scene(n=150, seed=7) + px0, px1 = vo.distort(x0, CAM), vo.distort(x1, CAM) + monkeypatch.setattr(vo, "shi_tomasi", lambda img: px0) + monkeypatch.setattr(vo, "track", lambda a, b, p: (px1, np.ones(len(p), dtype=bool))) + res = vo.relative_pose(np.zeros((480, 752), dtype=np.float32), np.zeros((480, 752), dtype=np.float32), CAM) + assert res is not None and res.inliers == 150 + Rm, tm = vo.motion_from_pair(res) + assert np.linalg.norm(Rm - R.T) < 1e-3 + assert _angle(tm, -R.T @ t) < 0.1 + + +def test_relative_pose_gives_none_when_nothing_is_tracked(monkeypatch): + monkeypatch.setattr(vo, "shi_tomasi", lambda img: np.zeros((5, 2))) + assert vo.relative_pose(np.zeros((48, 75), dtype=np.float32), np.zeros((48, 75), dtype=np.float32), CAM) is None + monkeypatch.setattr(vo, "shi_tomasi", lambda img: np.zeros((50, 2))) + monkeypatch.setattr(vo, "track", lambda a, b, p: (p, np.zeros(len(p), dtype=bool))) + assert vo.relative_pose(np.zeros((48, 75), dtype=np.float32), np.zeros((48, 75), dtype=np.float32), CAM) is None + + +def test_camera_is_read_from_a_euroc_sensor_yaml(tmp_path): + yaml_text = ( + "intrinsics: [458.654, 457.296, 367.215, 248.375]\n" + "distortion_coefficients: [-0.28340811, 0.07395907, 0.00019359, 1.76187114e-05]\n" + "resolution: [752, 480]\n" + "T_BS:\n cols: 4\n rows: 4\n data: [0.0148655429818, -0.999880929698, 0.00414029679422, -0.0216401454975,\n" + " 0.999557249008, 0.0149672133247, 0.025715529948, -0.064676986768,\n" + " -0.0257744366974, 0.00375618835797, 0.999660727178, 0.00981073058949,\n" + " 0.0, 0.0, 0.0, 1.0]\n" + ) + path = tmp_path / "sensor.yaml" + path.write_text(yaml_text) + cam = vo.camera_from_yaml(path) + assert cam.fx == pytest.approx(458.654) and cam.width == 752 and cam.T_BS.shape == (4, 4) + assert cam.T_BS[3].tolist() == [0.0, 0.0, 0.0, 1.0] + + +def test_a_png_is_read_as_a_single_float_channel(tmp_path): + import matplotlib.image as mpimg + + path = tmp_path / "a.png" + mpimg.imsave(str(path), (_texture(24, 32) * 255).astype(np.uint8), cmap="gray") + img = vo.load_gray(path) + assert img.shape == (24, 32) and img.min() >= 0.0 and img.max() <= 1.0 + + +def test_a_chain_of_tracks_survives_a_motion_too_large_for_one_step(): + img0 = _texture(seed=6) + h, w = img0.shape + yy, xx = np.mgrid[0:h, 0:w].astype(float) + frames = [vo.bilinear(img0, xx - 8.0 * k, yy).astype(np.float32) for k in range(4)] # 8 px per frame + pts = vo.shi_tomasi(img0, cell=16, border=40) + chain = vo.track_chain(frames, pts) + pos, alive = chain[-1] + assert alive.sum() > 0.4 * len(pts) + assert np.median(np.abs((pos[alive, 0] - pts[alive, 0]) - 24.0)) < 0.3 + + +def test_relative_poses_along_a_chain_use_the_points_still_tracked(monkeypatch): + x0, x1, _R, _t = _scene(n=150, seed=8) + px0, px1 = vo.distort(x0, CAM), vo.distort(x1, CAM) + monkeypatch.setattr(vo, "shi_tomasi", lambda img: px0) + keep = np.arange(150) < 100 # only 100 points survive the second step + + def fake_chain(images, pts, levels=3, fb_tolerance=0.5): + return [(px1, np.ones(150, dtype=bool)), (px1, keep)] + + monkeypatch.setattr(vo, "track_chain", fake_chain) + blank = np.zeros((480, 752), dtype=np.float32) + out = vo.relative_poses_along([blank, blank, blank], (1, 2), CAM) + assert out[1] is not None and out[2] is not None + assert out[1].tracked == 150 and out[2].tracked == 100 + with pytest.raises(ValueError): + vo.relative_poses_along([blank, blank], (1, 5), CAM) + monkeypatch.setattr(vo, "shi_tomasi", lambda img: np.zeros((3, 2))) + assert vo.relative_poses_along([blank, blank], (1,), CAM) == {1: None} diff --git a/tests/test_vo_page.py b/tests/test_vo_page.py new file mode 100644 index 0000000..942fed8 --- /dev/null +++ b/tests/test_vo_page.py @@ -0,0 +1,160 @@ +"""docs/real_front_end.md against the committed CSVs, and the replay and clone checks on a synthetic sequence.""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import pytest + +from navkit import euroc_eval as ee +from navkit import vo_error as ve +from navkit import vo_frontend as vf + +ROOT = Path(__file__).resolve().parent.parent +PAGE = ROOT / "docs" / "real_front_end.md" + + +@pytest.fixture(scope="module") +def rows(): + return ve.read_csv(ROOT / "docs" / "data" / "vo_errors.csv") + + +@pytest.fixture(scope="module") +def clone(): + return ve.read_csv(ROOT / "docs" / "data" / "vo_clone_check.csv") + + +def _variant(clone, name): + sel = [r for r in clone if r["variant"] == name] + return ( + float(np.median([float(r["nees_mean"]) for r in sel])), + float(np.median([float(r["coverage_2sigma"]) for r in sel])), + ) + + +def test_blocks_match_the_csvs(rows, clone): + text = PAGE.read_text(encoding="utf-8") + assert ve.summary_markdown(rows) in text + assert ve.clone_markdown(clone) in text + + +def test_the_one_frame_errors_are_small_and_shorter_lived_than_the_clone_needs(rows): + s = ve.summary(rows, 1, ve.FRAME_STEP_S) + assert 0.05 < s["rot_rms_deg"] < 0.12 and 0.005 < s["lat_rms_m"] < 0.012 + assert s["rot_rms_deg"] < 0.35 and s["lat_rms_m"] < 0.05 # the surrogate's defaults + assert 0.1 <= s["rot_corr_time_s"] <= 0.3 and 0.15 <= s["lat_corr_time_s"] <= 0.3 + assert max(s["rot_corr_time_s"], s["lat_corr_time_s"]) < 0.5 + + +def test_a_longer_baseline_gives_a_better_direction_and_more_outliers(rows): + by = {g: ve.summary(rows, g, ve.FRAME_STEP_S) for g in (1, 2, 4, 6, 10)} + direction = [by[g]["dir_median_deg"] for g in (1, 2, 4, 6, 10)] + outliers = [by[g]["outlier_share"] for g in (1, 2, 4, 6, 10)] + lateral = [by[g]["lat_rms_m"] for g in (1, 2, 4, 6, 10)] + assert direction == sorted(direction, reverse=True) and direction[0] > 10 > 2 > direction[-1] - 0.0 + assert outliers[0] < 0.03 and 0.1 < outliers[-1] < 0.16 and outliers == sorted(outliers) + assert lateral == sorted(lateral) and 0.03 < lateral[-1] < 0.05 + + +def test_the_surrogate_passes_and_the_replay_at_the_measured_size_fails(clone): + nees, cov = _variant(clone, "clone, measured correlation") + assert nees < 10 and cov > 0.9 + nees, cov = _variant(clone, "replay, clone") + assert nees > 10 and cov < 0.9 + assert 20 < nees < 40 and 0.5 < cov < 0.6 + + +def test_four_times_the_noise_brings_the_replay_back_narrowly(clone): + nees, cov = _variant(clone, "replay, clone, noise assumed x4") + assert nees < 10 and 0.9 < cov < 0.95 + + +def test_the_anchor_and_the_imu_alone(clone): + nees, cov = _variant(clone, "replay, anchor") + assert nees > 1000 and cov < 0.1 + sel = {r["variant"]: float(r["final_error_m"]) for r in clone if r["seed"] == "0"} + assert sel["IMU only"] > 30.0 and sel["replay, clone"] < 1.0 + + +def test_the_replay_carries_one_error_series_whatever_the_seed(clone): + # the seeds change the simulated GNSS only; the visual errors are the same, which the page says + seeds = {r["seed"] for r in clone if r["variant"] == "replay, clone"} + assert seeds == {"0", "1", "2", "3", "4"} + + +# ---------------------------------------------------------------- synthetic checks of the code + + +def _synthetic(tmp_path): + from test_vo_error import _write_cam + + ee.write_fixture(tmp_path, "MH_01_easy", duration_s=45.0, seed=2) + d = tmp_path / "MH_01_easy" + _write_cam(d, 5.0, 40.0) + return d + + +def _exact_frontend(monkeypatch, seq_dir, noise_deg=0.0): + from test_vo_error import _exact + + _exact(monkeypatch, seq_dir, noise_deg=noise_deg) + + +def test_a_replay_stream_with_no_errors_is_the_true_body_motion(tmp_path, monkeypatch): + d = _synthetic(tmp_path) + _exact_frontend(monkeypatch, d) + rows = ve.measure(d, gaps=(1,)) + seq = ee.load_sequence(tmp_path, "MH_01_easy") + cam = vf.camera_from_yaml(d / "mav0/cam0/sensor.yaml") + stream = ve.replay_stream(rows, seq.truth, cam, 5.0, 40.0) + assert len(stream) == len(rows) and np.all(np.diff(stream.t) > 0) + from navkit.types import interpolate_trajectory + + k = len(stream) // 2 + t1 = float(stream.t[k]) + p = interpolate_trajectory(seq.truth, np.array([t1 - 0.05, t1])).poses + assert np.allclose(stream.R_rel[k], p[0][:3, :3].T @ p[1][:3, :3], atol=1e-6) + assert np.allclose(stream.t_rel[k], p[0][:3, :3].T @ (p[1][:3, 3] - p[0][:3, 3]), atol=1e-6) + with pytest.raises(ValueError): + ve.replay_stream(rows, seq.truth, cam, 100.0, 110.0) + + +def test_clone_and_replay_runs_on_a_synthetic_sequence(tmp_path, monkeypatch): + d = _synthetic(tmp_path) + _exact_frontend(monkeypatch, d, noise_deg=0.05) + rows = ve.measure(d, gaps=(1,)) + monkeypatch.setattr(ve, "CLONE_WINDOW", (5.0, 30.0, 10.0)) + monkeypatch.setattr( + ve, "CLONE_VARIANTS", {k: ve.CLONE_VARIANTS[k] for k in ("IMU only", "clone, independent errors")} + ) + monkeypatch.setattr(ve, "REPLAY_VARIANTS", {"replay, clone": ve.REPLAY_VARIANTS["replay, clone"]}) + sim = ve.clone_rows(tmp_path, "MH_01_easy", 0.08, 0.008, 0.25, seeds=1) + rep = ve.replay_rows(tmp_path, "MH_01_easy", rows, 0.08, 0.008, seeds=1) + assert {r["variant"] for r in sim} == set(ve.CLONE_VARIANTS) + assert {r["variant"] for r in rep} == set(ve.REPLAY_VARIANTS) + imu = next(r for r in sim if r["variant"] == "IMU only") + clone = next(r for r in rep if r["variant"] == "replay, clone") + assert float(clone["final_error_m"]) < float(imu["final_error_m"]) + 1.0 + out = tmp_path / "c.csv" + ve.write_clone_csv(out, sim + rep) + assert len(ve.read_clone_csv(out)) == len(sim) + len(rep) + assert "replay, clone" in ve.clone_markdown(sim + rep) + + +def test_cli_fills_a_page_and_refuses_one_without_markers(rows, clone, tmp_path): + page = tmp_path / "p.md" + page.write_text( + "\n\n\n\n" + ) + args = [ + "--from-csv", + str(ROOT / "docs/data/vo_errors.csv"), + "--clone-csv", + str(ROOT / "docs/data/vo_clone_check.csv"), + ] + assert ve.main([*args, "--page", str(page)]) == 0 + assert "Rotation RMS" in page.read_text() and "replay, clone" in page.read_text() + bare = tmp_path / "b.md" + bare.write_text("none\n") + assert ve.main([*args, "--page", str(bare)]) == 1