117 lines
4.1 KiB
Python
117 lines
4.1 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Face landmark QC: verify eyes/nose are within face bounding box.
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Flags faces in DB where landmarks don't match the bbox.
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Usage: python3 face_landmark_qc.py <file_uuid> [--threshold 0.5] [--apply]
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"""
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import sys, json, psycopg2, argparse, os
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parser = argparse.ArgumentParser()
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parser.add_argument("uuid")
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parser.add_argument("--threshold", "-t", type=float, default=0.5,
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help="Fraction of landmark points that must be inside bbox (default: 0.5)")
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parser.add_argument("--apply", action="store_true",
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help="Write qc_ok to face_detections.metadata in DB")
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parser.add_argument("--schema", default="dev",
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help="DB schema (default: dev)")
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args = parser.parse_args()
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UUID = args.uuid
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THRESHOLD = args.threshold
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SCHEMA = args.schema
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OUTPUT_DIR = os.environ.get("MOMENTRY_OUTPUT_DIR", f"/Users/accusys/momentry/output_dev")
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FACE_PATH = f"{OUTPUT_DIR}/{UUID}.face.json"
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print(f"=== Face Landmark QC ===")
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print(f"UUID: {UUID}")
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print(f"Schema: {SCHEMA}")
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print(f"Face file: {FACE_PATH}")
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print(f"Threshold: {THRESHOLD * 100:.0f}% points must be inside bbox")
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# Load face.json
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with open(FACE_PATH) as f:
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data = json.load(f)
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total_faces = 0
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faces_with_lm = 0
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good_faces = 0
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bad_faces = 0
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qc_results = [] # list of (frame, face_idx, qc_ok, x, y, w, h)
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# Build frame lookup for fast access
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frame_map = {}
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for frm in data['frames']:
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frame_map[frm['frame']] = frm
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for frame_num, frm in frame_map.items():
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for fi, face in enumerate(frm.get('faces', [])):
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total_faces += 1
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lm = face.get('landmarks')
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if not lm:
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bbox = face.get('bbox', {})
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qc_results.append((frame_num, fi, False, bbox.get('x'), bbox.get('y'),
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bbox.get('width'), bbox.get('height')))
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bad_faces += 1
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continue
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faces_with_lm += 1
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bbox = face.get('bbox', {})
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x, y, w, h = bbox.get('x'), bbox.get('y'), bbox.get('width'), bbox.get('height')
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if None in (x, y, w, h):
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qc_results.append((frame_num, fi, False, x, y, w, h))
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bad_faces += 1
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continue
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inside_pts = 0
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total_pts = 0
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eye_nose_inside = 0
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for lm_type in ['left_eye', 'right_eye', 'nose']:
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points = lm.get(lm_type, [])
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total_pts += len(points)
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any_inside = False
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for pt in points:
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px, py = pt[0], pt[1]
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if (x <= px <= x + w) and (y <= py <= y + h):
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inside_pts += 1
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any_inside = True
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if any_inside:
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eye_nose_inside += 1
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ratio = inside_pts / max(1, total_pts)
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qc_ok = (ratio >= THRESHOLD and eye_nose_inside >= 2)
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qc_results.append((frame_num, fi, qc_ok, x, y, w, h))
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if qc_ok:
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good_faces += 1
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else:
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bad_faces += 1
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print(f"\nTotal faces: {total_faces:,}")
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print(f"Faces with landmarks: {faces_with_lm:,}")
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print(f"✅ Good (≥{THRESHOLD*100:.0f}% inside + ≥2 features): {good_faces:,}")
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print(f"❌ Bad (no eyes or insufficient landmarks): {bad_faces:,}")
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print(f"Quality pass rate: {100 * good_faces / max(1, faces_with_lm):.1f}%")
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# Apply mode: write qc_ok to face_detections.metadata
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if args.apply:
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print(f"\n=== Applying QC results to {SCHEMA}.face_detections ===")
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db_url = os.environ.get("DATABASE_URL", "postgres://accusys@localhost:5432/momentry")
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conn = psycopg2.connect(db_url)
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cur = conn.cursor()
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updated = 0
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for frame_num, fi, qc_ok, x, y, w, h in qc_results:
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qc_str = "true" if qc_ok else "false"
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cur.execute(
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f"UPDATE {SCHEMA}.face_detections "
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f"SET metadata = jsonb_set(COALESCE(metadata, '{{}}'::jsonb), '{{qc_ok}}', '\"{qc_str}\"'::jsonb) "
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f"WHERE file_uuid = %s AND frame_number = %s AND x = %s AND y = %s AND width = %s AND height = %s",
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(UUID, frame_num, x, y, w, h)
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)
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if cur.rowcount > 0:
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updated += 1
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conn.commit()
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cur.close()
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conn.close()
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print(f"Updated {updated} rows in {SCHEMA}.face_detections")
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print(f"Skipped {len(qc_results) - updated} rows (no matching face_detections row)")
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