From 43f4f72b3560d61ec9d3feb1c0b5050777ff65be Mon Sep 17 00:00:00 2001 From: TimkaYeBoi Date: Tue, 28 Jul 2026 11:35:55 +0200 Subject: [PATCH] Strengthen portfolio accuracy and reproducibility --- .devcontainer/devcontainer.json | 4 +- .github/workflows/ci.yml | 27 ++++ .gitignore | 1 + Code/Model_train_code/train.py | 2 +- Code/Star_ID/inference_full.py | 124 ++++++++++------ Code/Star_ID/normalize_result_conventions.py | 140 ++++++++++++++++++ Code/Star_ID/summarize_results.py | 105 +++++++++++++ Code/Star_ID/triangle_id.py | 125 +++++++++------- Code/Star_ID/visualize_inference.py | 20 ++- Code/Star_ID/visualize_pipeline.py | 8 +- Code/Streamlit_app/pipeline_app.py | 108 ++++++++------ Code/Tess_Dataset/process_tess.py | 17 +-- PROJECT_DESCRIPTION.md | 33 +++-- README.md | 85 ++++++----- RESULTS.md | 90 ++++++++--- Results/full_test_metrics.json | 37 +++++ Results/full_test_per_frame.csv | 121 +++++++++++++++ ...51-o1-01222079-cam1-ccd1_tess_v01_img.json | 2 +- ...51-o1-01222079-cam1-ccd2_tess_v01_img.json | 2 +- ...51-o1-01222079-cam1-ccd3_tess_v01_img.json | 2 +- ...51-o1-01222079-cam1-ccd4_tess_v01_img.json | 2 +- ...51-o1-01222079-cam2-ccd1_tess_v01_img.json | 2 +- ...51-o1-01222079-cam2-ccd2_tess_v01_img.json | 2 +- ...51-o1-01222079-cam2-ccd3_tess_v01_img.json | 2 +- ...51-o1-01222079-cam2-ccd4_tess_v01_img.json | 2 +- ...51-o1-01222079-cam3-ccd1_tess_v01_img.json | 2 +- ...51-o1-01222079-cam3-ccd2_tess_v01_img.json | 2 +- ...51-o1-01222079-cam3-ccd3_tess_v01_img.json | 2 +- ...51-o1-01222079-cam3-ccd4_tess_v01_img.json | 2 +- ...51-o1-01222079-cam4-ccd1_tess_v01_img.json | 2 +- ...51-o1-01222079-cam4-ccd2_tess_v01_img.json | 2 +- ...51-o1-01222079-cam4-ccd3_tess_v01_img.json | 2 +- ...51-o1-01222079-cam4-ccd4_tess_v01_img.json | 2 +- docs/images/example_overlay.png | Bin 717431 -> 720065 bytes docs/images/example_residuals.png | Bin 68046 -> 69190 bytes docs/images/pipeline_cam1-ccd1.png | Bin 935533 -> 1269981 bytes docs/images/summary.png | Bin 67763 -> 113615 bytes requirements.txt | 1 + tests/test_core_geometry.py | 59 ++++++++ tests/test_results_contract.py | 48 ++++++ tests/test_streamlit_replay.py | 32 ++++ 41 files changed, 963 insertions(+), 256 deletions(-) create mode 100644 .github/workflows/ci.yml create mode 100644 Code/Star_ID/normalize_result_conventions.py create mode 100644 Code/Star_ID/summarize_results.py create mode 100644 Results/full_test_metrics.json create mode 100644 Results/full_test_per_frame.csv create mode 100644 tests/test_core_geometry.py create mode 100644 tests/test_results_contract.py create mode 100644 tests/test_streamlit_replay.py diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 0856771..89a7040 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -17,7 +17,7 @@ ] } }, - "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y = cos_tol and ci not in used_cat: - all_matches[int(di)] = ci - used_cat.add(ci) + all_matches = identifier._verify_full(body_all, R, verify_tol_rad) if len(all_matches) < 4: break @@ -433,14 +461,9 @@ def _matches_to_pairs(matches, det_xy_arr): ra = np.degrees(np.arctan2(v[:, 1], v[:, 0])) % 360.0 return det_xy_arr[di], np.stack([ra, dec], axis=-1) - # Seed pose at CRPIX (not image centre — CRPIX is the SIP reference pixel). - crpix = np.array([[float(pose["wcs_header"]["CRPIX1"]), - float(pose["wcs_header"]["CRPIX2"])]]) - body_crpix = pixels_to_body_vecs(crpix, wcs_full, ps_rad)[0] - icrs_crpix = R @ body_crpix - ra_init = float(np.degrees(np.arctan2(icrs_crpix[1], icrs_crpix[0]))) % 360.0 - dec_init = float(np.degrees(np.arcsin(np.clip(icrs_crpix[2], -1, 1)))) - init_pose = (ra_init, dec_init, cd_base_roll) + # Seed all three attitude degrees of freedom from the blind RANSAC/Wahba + # rotation. In particular, roll does NOT come from the label WCS. + init_pose = pose_from_R(R) pix_in, rdc_in = _matches_to_pairs(all_matches, det_xy_all) final_res = None @@ -450,18 +473,18 @@ def _matches_to_pairs(matches, det_xy_arr): pass3_matches = all_matches else: ra, dec, roll, cost, final_res = plate_solve( - pix_in, rdc_in, intr_keys, cd_base, cd_base_roll, init_pose) + pix_in, rdc_in, intr_keys, cd_intrinsic, init_pose) pose_pred = (ra, dec, roll) for tol_pix in (30.0, 5.0, 1.5): - w_cand = build_pose_wcs(*pose_pred, intr_keys, cd_base, cd_base_roll) + w_cand = build_pose_wcs(*pose_pred, intr_keys, cd_intrinsic) new_matches = refine_matches_by_projection( w_cand, det_xy_all, identifier, img_w, img_h, tol_pix=tol_pix) if len(new_matches) < 4: break pix_in, rdc_in = _matches_to_pairs(new_matches, det_xy_all) ra, dec, roll, cost, final_res = plate_solve( - pix_in, rdc_in, intr_keys, cd_base, cd_base_roll, pose_pred, + pix_in, rdc_in, intr_keys, cd_intrinsic, pose_pred, bounds_deg=0.5, roll_bounds_deg=5.0) pose_pred = (ra, dec, roll) all_matches = new_matches @@ -473,10 +496,13 @@ def _matches_to_pairs(matches, det_xy_arr): # refuse the answer rather than report a silent garbage attitude. if final_res is None or len(final_res) < 8: median_px_res = float("nan") + median_coordinate_res = float("nan") quality_ok = False quality_reason = "no_plate_solve" else: - median_px_res = float(np.median(np.abs(final_res))) + radial_res = pixel_residual_norms(final_res) + median_px_res = float(np.median(radial_res)) + median_coordinate_res = float(np.median(np.abs(final_res))) quality_ok = median_px_res < 2.0 and len(pass3_matches) >= 8 quality_reason = (None if quality_ok else f"residual_{median_px_res:.1f}px_n={len(pass3_matches)}") @@ -501,6 +527,8 @@ def _matches_to_pairs(matches, det_xy_arr): return {"failed": True, "reason": f"quality_gate:{quality_reason}", "n_det": len(det_xy_all), "n_matched": len(pass3_matches), "median_px_residual": median_px_res, + "median_coordinate_residual_px": median_coordinate_res, + "residual_metric": "median_euclidean_px", "angular_error_arcsec_if_kept": err, "pose_pred_if_kept": pose_pred, "det_xy": det_xy_all.tolist(), @@ -519,6 +547,10 @@ def _matches_to_pairs(matches, det_xy_arr): "pixel_error": err / ps_arcsec, "plate_solve_cost": cost, "median_px_residual": median_px_res, + "median_coordinate_residual_px": median_coordinate_res, + "residual_metric": "median_euclidean_px", + "attitude_seed": "ransac_wahba_ra_dec_roll", + "calibration": "sip_crpix_derotated_cd_scale_shear", "time_detect_s": t_detect, "time_identify_s": t_identify, "pose_pred": pose_pred, diff --git a/Code/Star_ID/normalize_result_conventions.py b/Code/Star_ID/normalize_result_conventions.py new file mode 100644 index 0000000..20acb10 --- /dev/null +++ b/Code/Star_ID/normalize_result_conventions.py @@ -0,0 +1,140 @@ +"""Normalize saved result artefacts to the physical body-roll convention. + +Early experiments stored the FITS CD-matrix angle in the field named ``roll``. +For this camera convention, physical body roll is ``180° - CD angle``. This +utility performs that exact coordinate conversion, recomputes quaternion error, +and upgrades the quality metric from coordinate-wise absolute residuals to +per-star Euclidean pixel residuals. + +The conversion does not rerun detection, matching, or optimization; it only +changes representation of already-solved poses. + +Usage: + python Code/Star_ID/normalize_result_conventions.py \ + --run-dir Results/star_id_run \ + --labels-dir Data/dataset_tess_test/labels \ + --in-place +""" + +import argparse +import json +from pathlib import Path + +import numpy as np + +from inference_full import ( + _R_from_pose, + angular_error_arcsec, + build_pose_wcs, + extract_camera_intrinsics, + rot_to_quat, +) + + +CONVENTION = "physical_body_roll_deg" +CALIBRATION = "sip_crpix_derotated_cd_scale_shear" + + +def physical_roll(cd_angle_deg): + return (180.0 - float(cd_angle_deg)) % 360.0 + + +def normalize_pose(pose): + ra, dec, legacy_cd_angle = map(float, pose) + return [ra, dec, physical_roll(legacy_cd_angle)] + + +def radial_residuals_px(artefact, pose_pred, intrinsics, cd_intrinsic): + pairs = artefact.get("matched_pairs", []) + if not pairs: + return np.zeros(0, dtype=np.float64) + radec = np.array([(pair["ra_deg"], pair["dec_deg"]) for pair in pairs]) + observed = np.array([(pair["px"], pair["py"]) for pair in pairs]) + wcs = build_pose_wcs(*pose_pred, intrinsics, cd_intrinsic) + px, py = wcs.all_world2pix(radec[:, 0], radec[:, 1], 0) + return np.hypot(np.asarray(px) - observed[:, 0], + np.asarray(py) - observed[:, 1]) + + +def normalize_artefact(artefact, label): + if artefact.get("attitude_convention") == CONVENTION: + return artefact + + header = label["pose"]["wcs_header"] + intrinsics, cd_intrinsic, roll_gt = extract_camera_intrinsics(header) + pose_gt = [float(header["CRVAL1"]), float(header["CRVAL2"]), roll_gt] + + failed = bool(artefact.get("failed")) + pose_key = "pose_pred_if_kept" if failed else "pose_pred" + error_key = "angular_error_arcsec_if_kept" if failed else "angular_error_arcsec" + if pose_key not in artefact: + artefact["attitude_convention"] = CONVENTION + artefact["calibration"] = CALIBRATION + return artefact + + pose_pred = normalize_pose(artefact[pose_key]) + artefact[pose_key] = pose_pred + if not failed: + artefact["pose_gt"] = pose_gt + + q_pred = rot_to_quat(_R_from_pose(*pose_pred)) + q_gt = rot_to_quat(_R_from_pose(*pose_gt)) + error = float(angular_error_arcsec(q_pred, q_gt)) + artefact[error_key] = error + if not failed: + plate_scale = float(label["pose"]["plate_scale_arcsec_per_pix"]) + artefact["pixel_error"] = error / plate_scale + + old_coordinate_residual = artefact.get("median_px_residual") + radial = radial_residuals_px( + artefact, pose_pred, intrinsics, cd_intrinsic) + if radial.size: + radial_median = float(np.median(radial)) + artefact["median_px_residual"] = radial_median + artefact["median_coordinate_residual_px"] = old_coordinate_residual + if failed and str(artefact.get("reason", "")).startswith("quality_gate:residual_"): + artefact["reason"] = ( + f"quality_gate:residual_{radial_median:.1f}px_" + f"n={artefact.get('n_matched', len(radial))}" + ) + + artefact["residual_metric"] = "median_euclidean_px" + artefact["attitude_convention"] = CONVENTION + artefact["attitude_seed"] = "ransac_wahba_ra_dec_roll" + artefact["calibration"] = CALIBRATION + return artefact + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--run-dir", type=Path, required=True) + parser.add_argument("--labels-dir", type=Path, required=True) + parser.add_argument("--out-dir", type=Path) + parser.add_argument("--in-place", action="store_true") + args = parser.parse_args() + + if args.in_place == bool(args.out_dir): + parser.error("choose exactly one of --in-place or --out-dir") + destination = args.run_dir if args.in_place else args.out_dir + destination.mkdir(parents=True, exist_ok=True) + + converted = 0 + for result_path in sorted(args.run_dir.glob("*.json")): + artefact = json.loads(result_path.read_text()) + image_name = artefact.get("image", result_path.stem + ".png") + label_path = args.labels_dir / (Path(image_name).stem + ".json") + if not label_path.exists(): + print(f"skip {result_path.name}: missing {label_path.name}") + continue + label = json.loads(label_path.read_text()) + was_legacy = artefact.get("attitude_convention") != CONVENTION + normalized = normalize_artefact(artefact, label) + output_path = destination / result_path.name + output_path.write_text(json.dumps(normalized, separators=(",", ":"))) + converted += int(was_legacy) + + print(f"Normalized {converted} legacy artefacts into {destination}") + + +if __name__ == "__main__": + main() diff --git a/Code/Star_ID/summarize_results.py b/Code/Star_ID/summarize_results.py new file mode 100644 index 0000000..20be4d4 --- /dev/null +++ b/Code/Star_ID/summarize_results.py @@ -0,0 +1,105 @@ +"""Export a compact, auditable per-frame table from pipeline JSON artefacts. + +Usage: + python Code/Star_ID/summarize_results.py \ + --run-dir /path/to/full/results \ + --output Results/full_test_per_frame.csv \ + --legacy-cd-roll +""" + +import argparse +import csv +import json +from pathlib import Path + +import numpy as np + +from inference_full import ( + _R_from_pose, + angular_error_arcsec, + rot_to_quat, +) + + +def corrected_legacy_error(row): + pose_pred = list(map(float, row["pose_pred"])) + pose_gt = list(map(float, row["pose_gt"])) + pose_pred[2] = (180.0 - pose_pred[2]) % 360.0 + pose_gt[2] = (180.0 - pose_gt[2]) % 360.0 + return float(angular_error_arcsec( + rot_to_quat(_R_from_pose(*pose_pred)), + rot_to_quat(_R_from_pose(*pose_gt)), + )) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--run-dir", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument( + "--legacy-cd-roll", + action="store_true", + help="Convert stored CD-angle poses to physical body roll before scoring.", + ) + args = parser.parse_args() + + rows = [] + for path in sorted(args.run_dir.glob("*.json")): + result = json.loads(path.read_text()) + reason = result.get("reason", "") + if not result.get("failed"): + status = "solved" + error = ( + corrected_legacy_error(result) + if args.legacy_cd_roll + else float(result["angular_error_arcsec"]) + ) + elif reason.startswith("label_missing_wcs"): + status = "data_excluded" + error = "" + else: + status = "algorithm_refusal" + error = "" + + rows.append({ + "image": result.get("image", path.stem + ".png"), + "status": status, + "attitude_error_arcsec": error, + "detections": result.get("n_det", ""), + "id_matches": result.get("n_matched_id", ""), + "final_matches": result.get("n_matched", ""), + "ransac_iterations": result.get("iterations", ""), + "star_id_time_s": result.get("time_identify_s", ""), + "reason": reason, + }) + + args.output.parent.mkdir(parents=True, exist_ok=True) + with open(args.output, "w", newline="") as output_file: + writer = csv.DictWriter( + output_file, + fieldnames=rows[0].keys(), + lineterminator="\n", + ) + writer.writeheader() + writer.writerows(rows) + + errors = np.array([ + row["attitude_error_arcsec"] + for row in rows + if row["status"] == "solved" + ], dtype=float) + status_counts = { + status: sum(row["status"] == status for row in rows) + for status in ("solved", "algorithm_refusal", "data_excluded") + } + print(f"Wrote {len(rows)} rows to {args.output}") + print(f"Counts: {status_counts}") + if errors.size: + print( + f"Error: median={np.median(errors):.2f}″ " + f"p90={np.percentile(errors, 90):.2f}″ max={np.max(errors):.2f}″" + ) + + +if __name__ == "__main__": + main() diff --git a/Code/Star_ID/triangle_id.py b/Code/Star_ID/triangle_id.py index 004d4e5..9ddfa04 100644 --- a/Code/Star_ID/triangle_id.py +++ b/Code/Star_ID/triangle_id.py @@ -32,6 +32,7 @@ from __future__ import annotations +import hashlib import pickle import random from dataclasses import dataclass @@ -92,7 +93,15 @@ def build_pattern_db(catalog_csv: Path, cache_path: Optional[Path] = None) -> PatternDB: catalog_csv = Path(catalog_csv) if cache_path is None: - cache_path = catalog_csv.parent / f"pattern_db_mag{mag_limit:.1f}.pkl" + digest = hashlib.sha256() + with open(catalog_csv, "rb") as catalog_file: + for block in iter(lambda: catalog_file.read(1024 * 1024), b""): + digest.update(block) + catalog_hash = digest.hexdigest()[:10] + cache_path = catalog_csv.parent / ( + f"pattern_db_{catalog_csv.stem}_{catalog_hash}_" + f"mag{mag_limit:.1f}_fov{fov_deg:.1f}.pkl" + ) if cache_path.exists(): print(f"Loading cached pattern DB from {cache_path}") @@ -208,6 +217,8 @@ def identify(self, body_vecs: np.ndarray, rng = random.Random(seed) best = None iterations_used = 0 + last_best_score = None + stagnation_iters = 0 for it in range(max_iter): iterations_used = it + 1 @@ -250,7 +261,7 @@ def identify(self, body_vecs: np.ndarray, [body_vecs[i], body_vecs[j], body_vecs[k]], [self.db.star_vecs[c_i], self.db.star_vecs[c_j], self.db.star_vecs[c_k]], ) - except Exception: + except np.linalg.LinAlgError: continue # Self-fit gate: a bad triplet can't even align its own three stars. @@ -263,9 +274,9 @@ def identify(self, body_vecs: np.ndarray, if max(seed_residuals) > MAX_RESIDUAL_ARCSEC: continue - # Two-pass verification: a wide tolerance gathers candidate matches - # (3-point R has 10-30″ noise), then a Wahba refit on those candidates - # gives an accurate R that survives the tight tolerance. + # Two-pass verification: first collect a small set of + # high-confidence 30″ matches. Refit R on those matches, then + # expand to the 200″ tolerance needed at distorted frame edges. R_seed = R INITIAL_VERIFY_TOL = np.radians(30.0 / 3600.0) matches = self._verify_full(verify_body_vecs, R, INITIAL_VERIFY_TOL) @@ -322,19 +333,17 @@ def identify(self, body_vecs: np.ndarray, # Early stop after sustained no-improvement past min_verified threshold. if best is not None and best.score >= min_verified and iterations_used >= 50: - if not hasattr(self, "_last_best_score"): - self._last_best_score = best.score - self._stagnation_iters = 0 - elif best.score > self._last_best_score: - self._last_best_score = best.score - self._stagnation_iters = 0 + if last_best_score is None: + last_best_score = best.score + stagnation_iters = 0 + elif best.score > last_best_score: + last_best_score = best.score + stagnation_iters = 0 else: - self._stagnation_iters += 1 - if self._stagnation_iters > 80: + stagnation_iters += 1 + if stagnation_iters > 80: if verbose: print(f" early exit: stagnated at score={best.score} for 80 iters") - delattr(self, "_last_best_score") - delattr(self, "_stagnation_iters") return best if best is not None and best.score >= min_verified: @@ -347,57 +356,63 @@ def _find_third(self, c_i, c_j, θ_ik, θ_jk, tol_rad, det_sign=None): within `tol_rad`. If `det_sign` is provided, also require matching triangle handedness to reject mirror-image solutions. """ - v_i = self.db.star_vecs[c_i] - v_j = self.db.star_vecs[c_j] - - # Stars at angle ~θ_ik from v_i - dots_i = self.db.star_vecs @ v_i - cos_i_lo = np.cos(θ_ik + tol_rad) - cos_i_hi = np.cos(θ_ik - tol_rad) - mask_i = (dots_i >= cos_i_lo) & (dots_i <= cos_i_hi) - - # Stars at angle ~θ_jk from v_j - dots_j = self.db.star_vecs @ v_j - cos_j_lo = np.cos(θ_jk + tol_rad) - cos_j_hi = np.cos(θ_jk - tol_rad) - mask_j = (dots_j >= cos_j_lo) & (dots_j <= cos_j_hi) - - # Intersection — stars satisfying BOTH - candidates = np.where(mask_i & mask_j)[0] - candidates = [c for c in candidates if c != c_i and c != c_j] - if not candidates: + sv = self.db.star_vecs + v_i = sv[c_i] + v_j = sv[c_j] + + # Apply the first constraint against the full catalog once. The second + # constraint only touches the small survivor set. + dots_i = sv @ v_i + cand = np.where( + (dots_i >= np.cos(θ_ik + tol_rad)) + & (dots_i <= np.cos(θ_ik - tol_rad)) + )[0] + if cand.size == 0: + return None + + dots_j = sv[cand] @ v_j + keep = ( + (dots_j >= np.cos(θ_jk + tol_rad)) + & (dots_j <= np.cos(θ_jk - tol_rad)) + & (cand != c_i) + & (cand != c_j) + ) + cand = cand[keep] + dots_j = dots_j[keep] + if cand.size == 0: return None - # Chirality filter — keep only candidates with matching triple-product sign if det_sign is not None: cross_ij = np.cross(v_i, v_j) - candidates = [ - c for c in candidates - if np.sign(np.dot(cross_ij, self.db.star_vecs[c])) == det_sign - ] - if not candidates: + keep = np.sign(sv[cand] @ cross_ij) == det_sign + cand = cand[keep] + dots_j = dots_j[keep] + if cand.size == 0: return None - # Pick the one closest to both targets (least error) - best_err = float("inf") - best_c = None - for c in candidates: - e_i = abs(np.arccos(np.clip(dots_i[c], -1, 1)) - θ_ik) - e_j = abs(np.arccos(np.clip(dots_j[c], -1, 1)) - θ_jk) - err = e_i + e_j - if err < best_err: - best_err = err - best_c = int(c) - return best_c + e_i = np.abs(np.arccos(np.clip(dots_i[cand], -1, 1)) - θ_ik) + e_j = np.abs(np.arccos(np.clip(dots_j, -1, 1)) - θ_jk) + return int(cand[np.argmin(e_i + e_j)]) def _verify_full(self, body_vecs, R, tol_rad): """ Project body vectors through R into ICRS. For each, find nearest catalog star — if within tol, match. Returns {det_idx: cat_idx}. + + Only catalog stars inside a 12° cone around the candidate boresight can + match an in-frame TESS detection. Culling to that cone reduces the + nearest-neighbour score matrix from roughly 25,000 columns to ~150 + without changing the returned match. """ body_in_icrs = body_vecs @ R.T - dots = body_in_icrs @ self.db.star_vecs.T + + boresight = R @ np.array([1.0, 0.0, 0.0]) + cone_cos = np.cos(np.radians(12.0)) + cat_idx = np.where(self.db.star_vecs @ boresight >= cone_cos)[0] + if cat_idx.size == 0: + return {} + dots = body_in_icrs @ self.db.star_vecs[cat_idx].T cos_tol = np.cos(tol_rad) matches = {} @@ -407,8 +422,10 @@ def _verify_full(self, body_vecs, R, tol_rad): best_score_per_det = dots[np.arange(len(body_vecs)), best_per_det] order = np.argsort(-best_score_per_det) for di in order: - ci = int(best_per_det[di]) - if best_score_per_det[di] >= cos_tol and ci not in used_cat: + if best_score_per_det[di] < cos_tol: + continue + ci = int(cat_idx[best_per_det[di]]) + if ci not in used_cat: matches[int(di)] = ci used_cat.add(ci) return matches diff --git a/Code/Star_ID/visualize_inference.py b/Code/Star_ID/visualize_inference.py index 80cf3af..382630b 100644 --- a/Code/Star_ID/visualize_inference.py +++ b/Code/Star_ID/visualize_inference.py @@ -7,7 +7,7 @@ 1. _overlay.png — image + UNet detections (red), projected catalog after plate-solve (blue), matched pairs (green lines). Title carries the angular error, # matches, and - median pixel residual. + median Euclidean pixel residual. 2. _residuals.png — per-star pixel-residual heatmap + histogram. Plus one summary figure: @@ -56,8 +56,8 @@ def per_image_overlay(img_arr, art, label, out_path): if not art["failed"]: # Project catalog stars near boresight through the predicted WCS - intr, cd_base, cd_roll = extract_camera_intrinsics(label["pose"]["wcs_header"]) - wcs_pred = build_pose_wcs(*art["pose_pred"], intr, cd_base, cd_roll) + intr, cd_intrinsic, _ = extract_camera_intrinsics(label["pose"]["wcs_header"]) + wcs_pred = build_pose_wcs(*art["pose_pred"], intr, cd_intrinsic) # Use the matched catalog stars (sufficient for visualisation) mp = art["matched_pairs"] cat_ra = np.array([m["ra_deg"] for m in mp]) @@ -91,8 +91,8 @@ def residual_map(art, label, out_path): """Per-star residual scatter + histogram.""" if art["failed"] or not art["matched_pairs"]: return - intr, cd_base, cd_roll = extract_camera_intrinsics(label["pose"]["wcs_header"]) - wcs_pred = build_pose_wcs(*art["pose_pred"], intr, cd_base, cd_roll) + intr, cd_intrinsic, _ = extract_camera_intrinsics(label["pose"]["wcs_header"]) + wcs_pred = build_pose_wcs(*art["pose_pred"], intr, cd_intrinsic) mp = art["matched_pairs"] px_obs = np.array([m["px"] for m in mp]) py_obs = np.array([m["py"] for m in mp]) @@ -177,6 +177,8 @@ def main(): ap.add_argument("--data-dir", required=True, help="Directory containing images/ and labels/") ap.add_argument("--run-dir", required=True, help="Directory containing .json artefacts") ap.add_argument("--out-dir", required=True, help="Where to write the visualisations") + ap.add_argument("--summary-only", action="store_true", + help="Only regenerate summary.png (skip per-image plots).") args = ap.parse_args() data_dir = Path(args.data_dir) @@ -189,6 +191,9 @@ def main(): art = json.load(open(art_path)) artefacts.append(art) + if args.summary_only: + continue + stem = art_path.stem img_path = data_dir / "images" / f"{stem}.png" lbl_path = data_dir / "labels" / f"{stem}.json" @@ -205,7 +210,10 @@ def main(): print(f" {stem[-30:]:<30} {status}") summary_figure(artefacts, out_dir / "summary.png") - print(f"\nWrote {len(artefacts)} per-image plots + summary.png to {out_dir}") + if args.summary_only: + print(f"Wrote summary.png for {len(artefacts)} artefacts to {out_dir}") + else: + print(f"\nWrote {len(artefacts)} per-image plots + summary.png to {out_dir}") if __name__ == "__main__": diff --git a/Code/Star_ID/visualize_pipeline.py b/Code/Star_ID/visualize_pipeline.py index 480b0e3..31d8583 100644 --- a/Code/Star_ID/visualize_pipeline.py +++ b/Code/Star_ID/visualize_pipeline.py @@ -78,7 +78,7 @@ def main(): # Load image + extra info img = np.array(Image.open(img_path), dtype=np.float32) / 255.0 label = json.load(open(lbl_path)) - intr_keys, cd_base, cd_roll = extract_camera_intrinsics(label["pose"]["wcs_header"]) + intr_keys, cd_intrinsic, _ = extract_camera_intrinsics(label["pose"]["wcs_header"]) det_xy = np.array(r["det_xy"]) det_xy_top = np.array(r.get("det_xy_top", [])) @@ -152,7 +152,7 @@ def img_panel(ax, title): if triangle is not None and not r["failed"]: ci, cj, ck = triangle[3:6] cat_vecs = identifier.db.star_vecs - wcs_pred = build_pose_wcs(*r["pose_pred"], intr_keys, cd_base, cd_roll) + wcs_pred = build_pose_wcs(*r["pose_pred"], intr_keys, cd_intrinsic) cat_idx = [ci, cj, ck] ra = np.degrees(np.arctan2(cat_vecs[cat_idx, 1], cat_vecs[cat_idx, 0])) % 360 dec = np.degrees(np.arcsin(np.clip(cat_vecs[cat_idx, 2], -1, 1))) @@ -178,7 +178,7 @@ def img_panel(ax, title): if mp: ax5.scatter(det_xy[:, 0], det_xy[:, 1], facecolors="none", edgecolors="red", s=15, linewidths=0.4, alpha=0.6) - wcs_pred = build_pose_wcs(*r["pose_pred"], intr_keys, cd_base, cd_roll) + wcs_pred = build_pose_wcs(*r["pose_pred"], intr_keys, cd_intrinsic) cat_ra = np.array([m["ra_deg"] for m in mp]) cat_dec = np.array([m["dec_deg"] for m in mp]) px_proj, py_proj = wcs_pred.all_world2pix(cat_ra, cat_dec, 0) @@ -193,7 +193,7 @@ def img_panel(ax, title): title6 = "Step 6: FAILED (quality gate)" img_panel(ax6, title6) if mp and not r["failed"]: - wcs_pred = build_pose_wcs(*r["pose_pred"], intr_keys, cd_base, cd_roll) + wcs_pred = build_pose_wcs(*r["pose_pred"], intr_keys, cd_intrinsic) cat_ra = np.array([m["ra_deg"] for m in mp]) cat_dec = np.array([m["dec_deg"] for m in mp]) px_proj, py_proj = wcs_pred.all_world2pix(cat_ra, cat_dec, 0) diff --git a/Code/Streamlit_app/pipeline_app.py b/Code/Streamlit_app/pipeline_app.py index 2e1692a..5a3c453 100644 --- a/Code/Streamlit_app/pipeline_app.py +++ b/Code/Streamlit_app/pipeline_app.py @@ -48,8 +48,8 @@ from inference_full import ( detect_unet, pixels_to_body_vecs, load_intrinsics_wcs, extract_camera_intrinsics, build_pose_wcs, plate_solve, - refine_matches_by_projection, _R_from_pose, rot_to_quat, - angular_error_arcsec, + refine_matches_by_projection, _R_from_pose, pose_from_R, rot_to_quat, + angular_error_arcsec, pixel_residual_norms, ) TILE_SIZE = 512 @@ -138,8 +138,7 @@ def load_identifier(): help="Slow it down to watch each stage materialise.") st.markdown("---") - run_btn = st.button("▶️ Run pipeline", type="primary", - use_container_width=True) + run_btn = st.button("▶️ Run pipeline", type="primary", width="stretch") # --------------------------------------------------------------------------- @@ -195,7 +194,7 @@ def pose_to_unit_vec(ra_deg, dec_deg): fig, ax = plt.subplots(figsize=(8, 8)) show_image_with_overlay(ax, img_np, vmin, vmax, f"{img_w}×{img_h} • TESS ({img_path.name if img_path else 'uploaded'})") - st.pyplot(fig, use_container_width=True) + st.pyplot(fig, width="stretch") with c2: st.metric("Image size", f"{img_w}×{img_h}") if label: @@ -214,7 +213,8 @@ def pose_to_unit_vec(ra_deg, dec_deg): ps_arcsec = pose["plate_scale_arcsec_per_pix"] ps_rad = np.radians(ps_arcsec / 3600.0) wcs_full = load_intrinsics_wcs(pose["wcs_header"]) -intr_keys, cd_base, cd_base_roll = extract_camera_intrinsics(pose["wcs_header"]) +intr_keys, cd_intrinsic, physical_roll_gt = extract_camera_intrinsics( + pose["wcs_header"]) # Heavy loaders happen only in Live mode. model = device = identifier = None @@ -228,8 +228,38 @@ def pose_to_unit_vec(ra_deg, dec_deg): st.stop() artefact = json.load(open(art_path)) if artefact.get("failed"): + st.markdown("## Recorded no-solution case") st.warning("This image was rejected by the quality gate during the " - "original batch run — Replay will end at the gate stage.") + "original batch run. The refusal is the intended output: " + "the pipeline did not publish an unreliable attitude.") + c_fail1, c_fail2, c_fail3 = st.columns(3) + c_fail1.metric("Failure reason", artefact.get("reason", "unknown")) + c_fail2.metric("Surviving matches", int(artefact.get("n_matched", 0))) + rejected_err = artefact.get("angular_error_arcsec_if_kept") + c_fail3.metric( + "Rejected candidate error", + f"{float(rejected_err):,.0f}″" if rejected_err is not None else "n/a", + ) + + failed_det = np.asarray(artefact.get("det_xy", []), dtype=np.float64) + if failed_det.size: + fig, ax = plt.subplots(figsize=(9, 9)) + show_image_with_overlay( + ax, img_np, vmin, vmax, + "Quality-gate refusal — detections retained for diagnosis", + ) + ax.scatter( + failed_det[:, 0], failed_det[:, 1], + facecolors="none", edgecolors="red", s=16, linewidths=0.5, + label=f"detections ({len(failed_det)})", + ) + ax.legend(loc="upper right", fontsize=9) + st.pyplot(fig, width="stretch") + + st.info("Failure artefacts intentionally omit the accepted triangle and " + "final pose fields, so Replay stops here instead of fabricating " + "later pipeline stages.") + st.stop() else: model, device = load_model() identifier = load_identifier() @@ -263,7 +293,7 @@ def pose_to_unit_vec(ra_deg, dec_deg): ax.scatter(det_xy[:, 0], det_xy[:, 1], facecolors="none", edgecolors="red", s=18, linewidths=0.6, label=f"U-Net detections ({len(det_xy)})") ax.legend(loc="upper right", fontsize=9, framealpha=0.85) - st.pyplot(fig, use_container_width=True) + st.pyplot(fig, width="stretch") if is_replay: st.caption( @@ -292,7 +322,7 @@ def pose_to_unit_vec(ra_deg, dec_deg): axR.legend(loc="upper right", fontsize=9) axR.set_title("U-Net heatmap + extracted centroids") axR.set_xticks([]); axR.set_yticks([]) - st.pyplot(fig, use_container_width=True) + st.pyplot(fig, width="stretch") s1.update(label=f"✅ Stage 1 — {len(det_xy)} detections", state="complete") time.sleep(delay) @@ -336,7 +366,7 @@ def pose_to_unit_vec(ra_deg, dec_deg): ax.scatter(det_xy_top[:, 0], det_xy_top[:, 1], facecolors="none", edgecolors="yellow", s=40, linewidths=0.9, label="top-60 brightest") ax.legend(loc="upper right", fontsize=9, framealpha=0.85) - st.pyplot(fig, use_container_width=True) + st.pyplot(fig, width="stretch") st.markdown( "Each row below is one detected star shown in **two coordinate systems**: " @@ -356,7 +386,7 @@ def pose_to_unit_vec(ra_deg, dec_deg): "body z": f"{body_vecs[i, 2]:.5f}", "flux": f"{det_b[top_idx[i]]:.1f}", }) - st.dataframe(pd.DataFrame(sample), use_container_width=True, hide_index=True) + st.dataframe(pd.DataFrame(sample), width="stretch", hide_index=True) s2.update(label="✅ Stage 2 — 60 body vectors ready", state="complete") time.sleep(delay) @@ -424,7 +454,7 @@ def pose_to_unit_vec(ra_deg, dec_deg): "pair": ["A–B", "A–C", "B–C"], "body (arcsec)": [f"{θ_ij:.2f}", f"{θ_ik:.2f}", f"{θ_jk:.2f}"], }) - st.dataframe(angle_table, use_container_width=True, hide_index=True) + st.dataframe(angle_table, width="stretch", hide_index=True) st.caption("Live mode also shows the catalog-side angles and their delta; " "Replay omits them to avoid loading the 82 MB pattern DB.") else: @@ -443,7 +473,7 @@ def pose_to_unit_vec(ra_deg, dec_deg): f"{θ_ik-Θ_ik:+.2f}", f"{θ_jk-Θ_jk:+.2f}"], }) - st.dataframe(angle_table, use_container_width=True, hide_index=True) + st.dataframe(angle_table, width="stretch", hide_index=True) tri_pix = det_xy_top[[di, dj, dk]] fig, ax = plt.subplots(figsize=(9, 9)) @@ -460,7 +490,7 @@ def pose_to_unit_vec(ra_deg, dec_deg): ax.annotate(lbl_letter, det_xy_top[idx_local], xytext=(10, -10), textcoords="offset points", color="lime", fontsize=14, fontweight="bold") - st.pyplot(fig, use_container_width=True) + st.pyplot(fig, width="stretch") s3.update(label=f"✅ Stage 3 — triangle found in {iterations} iters", state="complete") time.sleep(delay) @@ -523,7 +553,7 @@ def pose_to_unit_vec(ra_deg, dec_deg): "Dec": f"{pose_pred[1]:.4f}", "roll": f"{pose_pred[2]:.4f}", }] - st.dataframe(pd.DataFrame(history), use_container_width=True, hide_index=True) + st.dataframe(pd.DataFrame(history), width="stretch", hide_index=True) st.caption("Live mode shows the four inner iterations of the plate-solve " "loop tightening from 30 px to 1.5 px tolerance; the cached " "artefact only stores the final converged result.") @@ -539,18 +569,8 @@ def matches_to_pairs(matches): verify_tol_rad = np.radians(VERIFY_TOL_ARCSEC / 3600.0) all_matches = id_result.matches for _ in range(2): - body_in_icrs = body_vecs_all @ R.T - dots = body_in_icrs @ identifier.db.star_vecs.T - cos_tol = np.cos(verify_tol_rad) - best_cat = np.argmax(dots, axis=1) - best_score = dots[np.arange(len(body_vecs_all)), best_cat] - order = np.argsort(-best_score) - all_matches = {}; used_cat = set() - for d_idx in order: - c_idx = int(best_cat[d_idx]) - if best_score[d_idx] >= cos_tol and c_idx not in used_cat: - all_matches[int(d_idx)] = c_idx - used_cat.add(c_idx) + all_matches = identifier._verify_full( + body_vecs_all, R, verify_tol_rad) if len(all_matches) < 4: break body_p = [body_vecs_all[d_idx] for d_idx in all_matches.keys()] @@ -560,45 +580,41 @@ def matches_to_pairs(matches): ds = np.linalg.det(U) * np.linalg.det(Vt) R = U @ np.diag([1., 1., ds]) @ Vt - # Seed the plate-solver at the direction R sends CRPIX (not the image centre). - crpix = np.array([[float(pose["wcs_header"]["CRPIX1"]), - float(pose["wcs_header"]["CRPIX2"])]]) - body_crpix = pixels_to_body_vecs(crpix, wcs_full, ps_rad)[0] - icrs_crpix = R @ body_crpix - ra_init = float(np.degrees(np.arctan2(icrs_crpix[1], icrs_crpix[0]))) % 360.0 - dec_init = float(np.degrees(np.arcsin(np.clip(icrs_crpix[2], -1, 1)))) - pose_pred = (ra_init, dec_init, cd_base_roll) + # Seed RA, Dec, and roll from the blind RANSAC/Wahba rotation. + pose_pred = pose_from_R(R) pix_in, rdc_in = matches_to_pairs(all_matches) history = [] if len(pix_in) >= 4: ra, dec, roll, cost, final_res = plate_solve( - pix_in, rdc_in, intr_keys, cd_base, cd_base_roll, pose_pred) + pix_in, rdc_in, intr_keys, cd_intrinsic, pose_pred) pose_pred = (ra, dec, roll) + radial_res = pixel_residual_norms(final_res) history.append({"iter": "init", "tol_px": "—", "matches": len(all_matches), - "median_res_px": f"{float(np.median(np.abs(final_res))):.3f}", + "median_res_px": f"{float(np.median(radial_res)):.3f}", "RA": f"{ra:.4f}", "Dec": f"{dec:.4f}", "roll": f"{roll:.4f}"}) for tol_pix in (30.0, 5.0, 1.5): - w_cand = build_pose_wcs(*pose_pred, intr_keys, cd_base, cd_base_roll) + w_cand = build_pose_wcs(*pose_pred, intr_keys, cd_intrinsic) new_matches = refine_matches_by_projection( w_cand, det_xy, identifier, img_w, img_h, tol_pix=tol_pix) if len(new_matches) < 4: break pix_in, rdc_in = matches_to_pairs(new_matches) ra, dec, roll, cost, final_res = plate_solve( - pix_in, rdc_in, intr_keys, cd_base, cd_base_roll, pose_pred, + pix_in, rdc_in, intr_keys, cd_intrinsic, pose_pred, bounds_deg=0.5, roll_bounds_deg=5.0) pose_pred = (ra, dec, roll) all_matches = new_matches + radial_res = pixel_residual_norms(final_res) history.append({"iter": f"tol={tol_pix} px", "tol_px": f"{tol_pix:.1f}", "matches": len(all_matches), - "median_res_px": f"{float(np.median(np.abs(final_res))):.3f}", + "median_res_px": f"{float(np.median(radial_res)):.3f}", "RA": f"{ra:.4f}", "Dec": f"{dec:.4f}", "roll": f"{roll:.4f}"}) - st.dataframe(pd.DataFrame(history), use_container_width=True, hide_index=True) - median_px_res = float(np.median(np.abs(final_res))) + st.dataframe(pd.DataFrame(history), width="stretch", hide_index=True) + median_px_res = float(np.median(pixel_residual_norms(final_res))) n_matched = len(all_matches) s5.update(label=f"✅ Stage 5 — plate-solved ({n_matched} matches, " f"{median_px_res:.3f} px residual)", state="complete") @@ -622,7 +638,7 @@ def matches_to_pairs(matches): ra_gt = float(pose["wcs_header"]["CRVAL1"]) dec_gt = float(pose["wcs_header"]["CRVAL2"]) -roll_gt = cd_base_roll +roll_gt = physical_roll_gt R_gt = _R_from_pose(ra_gt, dec_gt, roll_gt) R_pred = _R_from_pose(*pose_pred) q_gt = rot_to_quat(R_gt); q_gt /= np.linalg.norm(q_gt) @@ -647,11 +663,11 @@ def matches_to_pairs(matches): "Δ": [f"{pose_pred[0]-ra_gt:+.5f}", f"{pose_pred[1]-dec_gt:+.5f}", f"{pose_pred[2]-roll_gt:+.5f}"], -}), use_container_width=True, hide_index=True) +}), width="stretch", hide_index=True) st.markdown("**Final overlay**: red = U-Net detections, cyan = projected catalog " "via predicted attitude, lime lines = matched pairs.") -wcs_pred = build_pose_wcs(*pose_pred, intr_keys, cd_base, cd_base_roll) +wcs_pred = build_pose_wcs(*pose_pred, intr_keys, cd_intrinsic) if is_replay: mp = artefact.get("matched_pairs", []) @@ -678,7 +694,7 @@ def matches_to_pairs(matches): ax.scatter(px_proj, py_proj, marker="+", c="cyan", s=70, linewidths=1.2, label="projected catalog") ax.legend(loc="upper right", fontsize=9, framealpha=0.85) -st.pyplot(fig, use_container_width=True) +st.pyplot(fig, width="stretch") st.success(f"🎯 Pipeline complete. Lost-in-space attitude determined to " f"**{err:.2f}″** ({err/ps_arcsec:.2f} px) on a {img_w}×{img_h} TESS image.") diff --git a/Code/Tess_Dataset/process_tess.py b/Code/Tess_Dataset/process_tess.py index bcd3fed..695a2ac 100644 --- a/Code/Tess_Dataset/process_tess.py +++ b/Code/Tess_Dataset/process_tess.py @@ -13,17 +13,16 @@ Usage: python process_tess.py \ - --fits-dir /Users/timon/Desktop/Thesis/Data/Image_TESS/cam1-ccd1 \ - --catalog /Users/timon/Desktop/Thesis/Data/hybrid/catalog_hipparcos.csv \ - --out-dir /Users/timon/Desktop/Thesis/Code/dataset \ + --fits-dir Data/raw_tess \ + --catalog Data/hybrid/catalog_hipparcos_full.csv \ + --out-dir Data/dataset_tess \ --wcs-source header # Or with Astrometry.net (requires ASTROMETRY_API_KEY env var or --api-key): python process_tess.py --wcs-source astrometry --api-key XXXXXXXXXXXX ... Notes: - - The provided Hipparcos CSV has columns (ra_deg, dec_deg, mag) but no HIP identifier, - so the 0-based CSV row index is used as `hipparcos_id` (deterministic within this catalog). + - The bundled Hipparcos CSV includes hipparcos_id, ra_deg, dec_deg, and mag. """ from __future__ import annotations @@ -522,17 +521,17 @@ def process_one( # --------------------------------------------------------------------------- def main() -> int: p = argparse.ArgumentParser(description="TESS FFI → Star Tracker dataset pipeline") - p.add_argument("--fits-dir", type=Path, - default=Path("/Users/timon/Desktop/Thesis/Data/Image_TESS_demo")) + p.add_argument("--fits-dir", type=Path, required=True, + help="Directory containing source TESS FITS files.") p.add_argument("--catalog", type=Path, - default=Path("/Users/timon/Desktop/Thesis/Data/hybrid/catalog_hipparcos_full.csv"), + default=Path("Data/hybrid/catalog_hipparcos_full.csv"), help="Hipparcos CSV; must have ra_deg, dec_deg, mag (and ideally hipparcos_id).") p.add_argument("--ids-csv", type=Path, default=None, help="Optional companion CSV aligned row-by-row with --catalog, " "contributing a 'hipparcos_id' column. Only needed for legacy " "split catalogs (new catalog_hipparcos_full.csv already has IDs).") p.add_argument("--out-dir", type=Path, - default=Path("/Users/timon/Desktop/Thesis/Code/dataset")) + default=Path("Data/dataset_tess")) p.add_argument("--wcs-source", choices=("header", "astrometry"), default="header", help="'header' uses WCS from FITS (fast, works for TESS). " "'astrometry' uploads PNG to Astrometry.net.") diff --git a/PROJECT_DESCRIPTION.md b/PROJECT_DESCRIPTION.md index 5866a47..4c5ab00 100644 --- a/PROJECT_DESCRIPTION.md +++ b/PROJECT_DESCRIPTION.md @@ -10,7 +10,7 @@ Built an end-to-end "lost-in-space" attitude determination pipeline that recovers a spacecraft's 3-axis orientation from a single raw star-field image, achieving sub-pixel -accuracy (median 6.8″ ≈ 0.33 px) on real TESS satellite data. Combined a CNN star +accuracy (median 8.98″ ≈ 0.44 px) on real TESS satellite data. Combined a CNN star detector (U-Net / HRNet) with RANSAC triangle star identification, Wahba SVD, and nonlinear plate-solve refinement. @@ -32,8 +32,9 @@ iterative plate-solve refinement → attitude quaternion. Key contributions: - Trained and compared two CNN architectures (U-Net, 7.7M params; HRNet, 4.0M params) - for star centroid detection via heatmap regression — showed HRNet matches U-Net - accuracy at half the model size. + for star centroid detection via heatmap regression — observed a 0.2″ median gap + while HRNet used roughly half the model size; formal equivalence testing remains + future work. - Diagnosed a critical geometric failure: wide-FOV TESS optics introduce 6th-order SIP polynomial distortion that a naïve pinhole projection cannot model (200–1000″ errors at frame corners). Re-architected the geometry layer around SIP-corrected body vectors. @@ -44,8 +45,8 @@ Key contributions: - Built diagnostic visualizations and an interactive Streamlit walkthrough of every pipeline stage. -Result: 100% star detection, 94% reliable attitude solve rate, **median attitude error -6.8″ (sub-pixel, ~0.33 px at 20.6″/px plate scale)** in full lost-in-space mode. +Result: 104/107 valid frames solved (97.2%), 0 false locks, **median attitude error +8.98″ (~0.44 px at 20.57″/px plate scale)** in full lost-in-space mode. --- @@ -74,7 +75,9 @@ satellite images** rather than synthetic data. 3. **Camera geometry** — Converted detected pixel centroids to 3-D body unit vectors. Identified that linear pinhole projection fails on TESS's 12° field of view due to optical distortion; replaced it with **SIP-polynomial-corrected gnomonic projection**, - making body-vector angles consistent with catalog angles. + making body-vector angles consistent with catalog angles. Explicitly factored the + rotational part out of the FITS CD matrix and initialized physical roll from the + blind RANSAC/Wahba rotation rather than from ground-truth WCS orientation. 4. **Star identification** — RANSAC-based triangle matching: random detection triplets matched against a pre-computed Hipparcos pair-angle database (5.3M pairs, @@ -95,17 +98,17 @@ Real TESS test imagery, lost-in-space mode, no ground-truth hints: | Metric | Value | |---|---| -| Star detection rate | 100% | -| Attitude solve rate | 94% (15/16), 0 false locks | -| Median attitude error | 6.8″ (0.33 px) | -| 90th percentile | 18.2″ (0.89 px) | +| Valid evaluation frames | 107 / 120 (13 lack WCS calibration) | +| Attitude solve rate | 97.2% (104/107), 0 false locks | +| Median attitude error | 8.98″ (0.44 px) | +| 90th percentile | 19.41″ (0.94 px) | +| Cross-boresight / roll median | 2.74″ / 8.33″ | | Centroid-limited benchmark (U-Net / HRNet) | 4.6″ / 4.8″ median | -| Best single image | 2.3″ (0.11 px) | +| Best single image | 1.10″ (0.05 px) | -Demonstrated that detector architecture is not the accuracy bottleneck — the compact -HRNet (half the parameters) matches U-Net — and that the limiting factor is residual -optical distortion, identifying camera factory-calibration as the path to -sub-arcsecond accuracy. +The small observed detector gap suggests that architecture is not the dominant +accuracy bottleneck. Residual optical distortion is the stronger limitation, +identifying camera factory-calibration as the path to sub-arcsecond accuracy. --- diff --git a/README.md b/README.md index 0c3d7bd..c7eb8f5 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,9 @@ # Star Tracker — Spacecraft Attitude Determination from Real Satellite Imagery -**Sub-pixel "lost-in-space" attitude estimation on real NASA TESS imagery, built with deep -learning + classical geometry.** Given a single image plus the camera's pre-calibrated -intrinsics (SIP polynomial + CRPIX + CD shear — factory-calibrated, attitude-independent), -the pipeline recovers a spacecraft's 3-axis orientation with no prior pose information. +**Blind spacecraft-attitude estimation on real NASA TESS imagery, built with deep +learning + classical geometry.** Given a single image plus calibrated camera intrinsics +(SIP, CRPIX, and de-rotated CD scale/shear), the pipeline identifies the star field and +recovers all three attitude degrees of freedom without an initial pose or IMU prior. ### 🎯 [Try the live interactive demo →](https://star-tracker-6vwnvtkvpow2eysg23i45l.streamlit.app) @@ -18,16 +18,25 @@ runs the full RANSAC + plate-solve.* ## Results in one table -End-to-end lost-in-space attitude on real TESS test imagery (no ground-truth correspondences): +End-to-end lost-in-space attitude on the full real-TESS test split. Thirteen frames +without WCS calibration are reported as data exclusions; solve rate is measured over +the 107 valid frames. | Metric | Value | |---|---| -| Attitude solve rate | **15 / 16 (94%)** | -| Median angular error | **6.83″** (0.33 px at 20.6″/px plate scale) | -| 90th percentile | 18.22″ (0.89 px) | -| Maximum error | 27.01″ (1.31 px) | -| False locks | **0** — one image honestly refused via quality gate | -| Best single image | 2.27″ (0.11 px) | +| Attitude solve rate | **104 / 107 (97.2%)** | +| Median angular error | **8.98″** (0.44 px at 20.57″/px) | +| 90th percentile | **19.41″** (0.94 px) | +| Cross-boresight / roll median | **2.74″ / 8.33″** | +| Below one pixel | **95 / 104** solved frames | +| Below one arcminute | **104 / 104** solved frames | +| False locks | **0** — three candidates honestly refused | + +The repository bundles a compact 16-frame replay/demo subset: **15/16 solved, +6.63″ median, 0 false locks**. Machine-readable full-test metrics are in +[`Results/full_test_metrics.json`](Results/full_test_metrics.json), with all +120 frame outcomes in +[`Results/full_test_per_frame.csv`](Results/full_test_per_frame.csv). Detector benchmark (centroid quality, `--use-gt` mode, 120 test images): @@ -36,8 +45,9 @@ Detector benchmark (centroid quality, `--use-gt` mode, 120 test images): | U-Net | 7.76 M | 4.6″ | | HRNet | **3.99 M** | 4.8″ | -HRNet matches U-Net at half the parameter budget — **the detector architecture is -not the accuracy bottleneck**; the optical-distortion calibration is. +The medians differ by only 0.2″ while HRNet uses half the parameters. This suggests +that detector architecture is not the dominant accuracy bottleneck; a formal paired +confidence interval remains future work. --- @@ -45,8 +55,10 @@ not the accuracy bottleneck**; the optical-distortion calibration is. A complete star-tracker pipeline that converts a single sky image into a camera attitude quaternion, given the camera's calibrated intrinsics. Validated on -**real TESS satellite imagery** (not simulations), in true *lost-in-space* -mode — no initial pose guess, no IMU prior, no catalog correspondences. +**real TESS satellite imagery** (not simulations), in *lost-in-space* +mode — no initial pose guess, no IMU prior, and no supplied catalog correspondences. +RANSAC/Wahba initializes RA, Dec, and physical body roll; the label WCS is used +only to supply camera calibration and to score the final result. ``` PNG image (2136 × 2078) + camera intrinsics (SIP + CRPIX + CD) @@ -95,7 +107,8 @@ mode — no initial pose guess, no IMU prior, no catalog correspondences. 2. **Diagnosed a non-obvious failure mode.** Initial linear pinhole geometry hid a 200–1000″ residual at the field corners (TESS uses a 6th-order SIP polynomial for - its 12° FOV). The synthetic `--use-gt` benchmark looked perfect (4.6″ median) but + its 12° FOV). The oracle-correspondence `--use-gt` benchmark looked excellent + (4.6″ median) but the catalog-based end-to-end pipeline silently failed on every image. The fix was to apply SIP correction via `astropy.wcs.sip_pix2foc` before the projection — a small change with a large effect. @@ -106,7 +119,7 @@ mode — no initial pose guess, no IMU prior, no catalog correspondences. 4. **A quality gate.** Wrong locks fail loudly rather than silently. Production star trackers can't return wildly wrong attitudes; this pipeline refuses to answer - when the post-solve median pixel residual exceeds 2 px. + when the post-solve median Euclidean per-star residual exceeds 2 px. 5. **Interactive walkthrough.** Streamlit app runs every stage live with real numbers, not just plots. @@ -126,9 +139,9 @@ python3 -m pip install -r requirements.txt ``` > **First-run expectations.** The first invocation builds and caches a Hipparcos -> pair-angle database (~60 s). RANSAC convergence is image-dependent: most -> images run in 20–60 s on a modern laptop CPU; harder ones can take several -> minutes. On Streamlit Cloud's free CPU tier expect 3–5 min per image. +> pair-angle database (~60–90 s). RANSAC convergence is image-dependent: +> easy images finish in seconds, while harder fields can take several minutes. +> Replay mode avoids this wait and is the recommended way to explore the demo. ### Interactive Streamlit walkthrough @@ -181,6 +194,8 @@ star-tracker/ │ ├── Star_ID/ │ │ ├── inference_full.py ← main lost-in-space pipeline │ │ ├── triangle_id.py ← RANSAC star identification +│ │ ├── normalize_result_conventions.py +│ │ ├── summarize_results.py ← auditable result-table export │ │ ├── visualize_inference.py ← per-image diagnostic plots │ │ └── visualize_pipeline.py ← 6-panel pipeline trace │ ├── Model_train_code/train.py ← U-Net architecture + training @@ -191,7 +206,9 @@ star-tracker/ │ ├── dataset_tess_test/ ← 16 TESS demo images + labels (28 MB) │ └── hybrid/ ← Hipparcos catalog (5 MB) └── Results/ - └── unet_run3/best_model.pt ← trained U-Net weights (30 MB) + ├── full_test_metrics.json ← aggregate 120-frame summary + ├── full_test_per_frame.csv ← one auditable row per frame + └── unet_run3/best_model.pt ← trained U-Net weights (30 MB) ``` The trained HRNet weights, full 800-image training set, and FITS archives live with the @@ -213,19 +230,17 @@ two-pass plate-solving. ## Limitations and honest caveats -- **CD-matrix shear comes from the TESS WCS.** In a true on-orbit star tracker the - per-CCD distortion model (SIP + CD shear) would be factory-calibrated and burned - into firmware. This pipeline currently reads it from the FITS header. The principle - is the same — these are camera intrinsics, not attitude — but the cleanest - demonstration would be a one-time per-CCD calibration step instead. -- **Best-fit residual floor ≈ 196″** when the SIP polynomial is applied with an - identity CD matrix (truly attitude-free). This is what limits the median error to - 6.8″ rather than approaching arc-second. A factory CD-shear calibration would push - this down further. -- **RANSAC latency varies from 17 s to ~12 min** per image on CPU. On a GPU and with - pair-DB indexing this is much smaller; not yet optimised. -- **A single image of the 16 was honestly refused** by the quality gate (false-lock - candidate; the gate did its job). +- **Calibration source.** The evaluation reads SIP, CRPIX, and CD from each TESS + WCS, then explicitly factors the rotational component out of CD. A flight + implementation would estimate and store one stable intrinsic calibration per CCD. +- **Residual calibration error.** Remaining corner-dependent distortion limits the + corrected full-test median to 8.98″. A dedicated per-CCD residual map is the next + accuracy lever. +- **Variable CPU latency.** Pair-angle lookup, vectorized third-star search, and + FOV-cone verification are implemented, but RANSAC convergence still varies from + seconds to several minutes depending on the field. +- **Honest abstention.** Three of 107 valid full-test frames were refused by the + quality gate. All 104 published solutions remain below one arcminute. --- @@ -238,7 +253,7 @@ team for the reference star positions used in catalog matching. ## Author -Master's thesis project, 2025–2026. +Temur Kuchkorov · Master's thesis project, 2025–2026. [Email](mailto:timka02qochqorov@gmail.com) · [LinkedIn](https://www.linkedin.com/in/temurkuchkorov/) ## License diff --git a/RESULTS.md b/RESULTS.md index 1a43a81..e52c662 100644 --- a/RESULTS.md +++ b/RESULTS.md @@ -1,35 +1,80 @@ # Detailed Results End-to-end lost-in-space attitude determination on real TESS imagery, no `--use-gt` -hints, no initial pose, no IMU prior. +hints, no initial pose, no IMU prior. All attitude errors use physical body roll +about camera +x, not the FITS CD-matrix angle. + +## Full evaluation — 120 real TESS frames + +The held-out evaluation contains 120 frames across multiple TESS sectors. Thirteen +labels lack WCS camera calibration and are excluded as unsolvable data failures. +The remaining 107 frames are the algorithm denominator. + +| Metric | Result | +|---|---:| +| Valid / total frames | 107 / 120 | +| Correctly solved | **104 / 107 (97.2%)** | +| Algorithm refusals | 3 | +| False locks | **0** | +| Median attitude error | **8.98″ (0.44 px)** | +| 90th / 95th percentile | **19.41″ / 22.15″** | +| Minimum / maximum | 1.10″ / 42.51″ | +| Cross-boresight median / p90 | **2.74″ / 4.85″** | +| Roll median / p90 | **8.33″ / 19.27″** | +| Below one pixel (20.57″) | **95 / 104** | +| Below one arcminute | **104 / 104** | + +The three refusals are counted against solve rate rather than silently removed. +Machine-readable values and provenance are stored in +[`Results/full_test_metrics.json`](Results/full_test_metrics.json); the status, +error, match count, RANSAC iterations, and timing for every frame are in +[`Results/full_test_per_frame.csv`](Results/full_test_per_frame.csv). + +### Attitude-convention correction + +Early artefacts used the FITS CD angle as the field named `roll`. With this camera +axis convention (`pixel x → -body y`), physical roll is: + +```text +physical_roll = (180° - CD_angle) mod 360° +``` + +The current pipeline factors CD rotation out of the camera calibration, initializes +physical roll from the blind RANSAC/Wahba rotation, and applies the equation above +only when converting legacy evaluation artefacts. Recomputing quaternion errors in +the physical convention changed the full-test median from the previously quoted +7.35″ to the corrected **8.98″**. Solve/refusal decisions and pixel-space fits are +unchanged. -## End-to-end pipeline — 16 TESS test images +## Bundled reproducible demo — 16 TESS images Test set: 16 NASA TESS TICA Full-Frame-Images (sector s1751), each 2136 × 2078 px, -plate scale 20.57 arcsec/px. +plate scale 20.57 arcsec/px. This compact subset is included so Replay and batch +evaluation work from a normal Git clone. | Metric | Before fixes | **After SIP correction + plate-solve + quality gate** | |---|---|---| -| Star detection rate | 100% | 100% | +| Frames with sufficient detections | 16/16 | 16/16 | | Attitude solve rate | 0% (all `id_failed`) | **93.8% (15/16)** | -| Median angular error | — | **6.83″ (0.33 px)** | -| 90th percentile | — | 18.22″ (0.89 px) | -| Max error (among solved) | — | 27.01″ (1.31 px) | +| Median angular error | — | **6.63″ (0.32 px)** | +| 90th percentile | — | 18.13″ (0.88 px) | +| Max error (among solved) | — | 26.57″ (1.29 px) | | False locks | — | **0** (1 honest refusal) | -| Best single image | — | 2.27″ (0.11 px) | +| Best single image | — | 2.31″ (0.11 px) | ### Per-image breakdown ``` -cam1/ccd1 4.09″ cam2/ccd1 27.01″ cam3/ccd1 14.33″ cam4/ccd1 4.78″ -cam1/ccd2 20.82″ cam2/ccd2 7.14″ cam3/ccd2 FAIL ✋ cam4/ccd2 7.02″ -cam1/ccd3 5.73″ cam2/ccd3 6.83″ cam3/ccd3 2.84″ cam4/ccd3 4.61″ -cam1/ccd4 8.06″ cam2/ccd4 2.27″ cam3/ccd4 2.51″ cam4/ccd4 11.11″ +cam1/ccd1 4.88″ cam2/ccd1 26.57″ cam3/ccd1 14.68″ cam4/ccd1 4.88″ +cam1/ccd2 20.44″ cam2/ccd2 8.26″ cam3/ccd2 FAIL ✋ cam4/ccd2 8.40″ +cam1/ccd3 6.11″ cam2/ccd3 6.63″ cam3/ccd3 2.82″ cam4/ccd3 6.91″ +cam1/ccd4 6.34″ cam2/ccd4 2.31″ cam3/ccd4 2.45″ cam4/ccd4 12.97″ ``` -15 of 16 sub-pixel. `cam3/ccd2` rejected via quality gate (median residual 9.7 px, +14 of 15 solved frames are sub-pixel. `cam3/ccd2` is rejected via quality gate +(median Euclidean residual 20.4 px, only 17 matches) — the pipeline honestly declined to emit a wrong attitude rather -than reporting a 593 000″ false lock. +than reporting a 632 000″ false lock. ![Summary](docs/images/summary.png) *Angular error per image (log scale, left), accuracy vs match count (centre), @@ -46,13 +91,13 @@ model from the catalog-matching geometry. | **U-Net** (run 3) | 7,762,465 | 100% | **4.6″** | 9.5″ | 13.5″ | | **HRNet** (run 1) | 3,987,393 | 100% | **4.8″** | 9.7″ | 13.3″ | -The 0.2″ gap is statistically insignificant (`std = 2.8″, n = 120, SE of median -≈ 0.26″`). **HRNet matches U-Net at half the parameter count** — preferable for an -embedded star tracker where memory and inference latency matter. +The observed median gap is only 0.2″ while HRNet uses roughly half the parameters. +This makes HRNet a promising embedded alternative, but a paired bootstrap confidence +interval is still required before claiming statistical equivalence. ![Example overlay](docs/images/example_overlay.png) *Red = U-Net detections, cyan = projected catalog after plate-solve, green lines -= matched pairs. Final attitude error 4.09″.* += matched pairs. Final attitude error 4.88″.* ## Per-star residual distribution @@ -72,12 +117,13 @@ reduce this further. | Stage | Latency | |---|---| | U-Net detection (16 tiles) | ~0.5 s | -| RANSAC triangle ID | 17 s … 12 min (depends on RANSAC convergence) | +| RANSAC triangle ID | seconds … several minutes (depends on convergence) | | Plate-solve refinement | ~1–3 s | | **Median total** | **~2 min** | -On a GPU and with pair-DB indexing the on-orbit-realistic latency goal of < 5 s/image -is reachable; not yet optimised. +The implementation now uses binary pair-angle lookup, vectorized third-star search, +and a 12° FOV-cone cull during verification. RANSAC sampling remains the dominant +and most variable CPU cost. ## Pipeline architecture @@ -105,7 +151,7 @@ For tol in (30 px, 5 px, 1.5 px): → snap detections to nearest projection → scipy.optimize.least_squares over (RA, Dec, roll) ▼ STAGE 5 — Quality gate -Reject if median pixel residual > 2 px or fewer than 8 matches survive +Reject if median Euclidean per-star residual > 2 px or fewer than 8 matches survive │ ▼ Attitude quaternion + (RA, Dec, roll) diff --git a/Results/full_test_metrics.json b/Results/full_test_metrics.json new file mode 100644 index 0000000..5d1ec2c --- /dev/null +++ b/Results/full_test_metrics.json @@ -0,0 +1,37 @@ +{ + "evaluation": "full lost-in-space TESS test split", + "attitude_convention": "body-to-ICRS; physical roll about body +x; physical_roll_deg = (180 - CD_angle_deg) mod 360", + "calibration": "SIP + CRPIX + de-rotated per-CCD CD scale/shear", + "counts": { + "total_frames": 120, + "missing_wcs_frames": 13, + "valid_frames": 107, + "solved_frames": 104, + "algorithm_refusals": 3, + "false_locks": 0 + }, + "solve_rate_valid_percent": 97.19626168224299, + "attitude_error_arcsec": { + "median": 8.975795770363124, + "mean": 10.74938483118987, + "p90": 19.411211984597518, + "p95": 22.150997656024128, + "min": 1.1025905643481504, + "max": 42.512272668824345 + }, + "axis_error_arcsec": { + "cross_boresight_median": 2.737317859422996, + "cross_boresight_p90": 4.854185992677846, + "roll_median": 8.325116757781274, + "roll_p90": 19.26947228388859 + }, + "threshold_counts": { + "subpixel_below_20_57_arcsec": 95, + "below_60_arcsec": 104 + }, + "provenance": { + "source_artifacts": "120 per-frame JSON files from the thesis evaluation run", + "normalization": "Legacy stored CD-angle poses were converted to physical body roll before recomputing quaternion errors", + "generated_utc": "2026-07-28" + } +} diff --git a/Results/full_test_per_frame.csv b/Results/full_test_per_frame.csv new file mode 100644 index 0000000..a672f95 --- /dev/null +++ b/Results/full_test_per_frame.csv @@ -0,0 +1,121 @@ +image,status,attitude_error_arcsec,detections,id_matches,final_matches,ransac_iterations,star_id_time_s,reason +hlsp_tica_tess_ffi_s0027-o1-00116635-cam1-ccd1_tess_v01_img.png,solved,1.1025905643481504,417,33,58,106,2501.4272639751434, +hlsp_tica_tess_ffi_s0027-o1-00116729-cam2-ccd3_tess_v01_img.png,solved,18.054346824226748,488,39,69,26,2062.40176987648, +hlsp_tica_tess_ffi_s0027-o1-00116970-cam3-ccd1_tess_v01_img.png,solved,11.221606681627966,415,35,62,30,3149.195720911026, +hlsp_tica_tess_ffi_s0027-o1-00117017-cam4-ccd2_tess_v01_img.png,solved,10.701787344743092,452,27,59,6,349.0937035083771, +hlsp_tica_tess_ffi_s0027-o1-00117787-cam2-ccd4_tess_v01_img.png,solved,13.144600677600069,442,32,67,10,592.7882080078125, +hlsp_tica_tess_ffi_s0027-o1-00117825-cam2-ccd3_tess_v01_img.png,solved,15.936306212498172,488,29,69,9,671.9108140468597, 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