2cfcfdd1af
Phase 2.6.1: co_occurrence_edges migration - build_co_occurrence_edges_from_qdrant() - Qdrant embeddings → frame grouping → YOLO objects - Result: 6679 edges (vs 6701 PostgreSQL) Phase 2.6.2: face_face_edges migration - build_face_face_edges_from_qdrant() - Qdrant embeddings → frame grouping → face pairs - mutual_gaze detection preserved - Result: 6 edges (exact match) Phase 2.6.3: speaker_face_edges migration - build_speaker_face_edges_from_qdrant() - Qdrant embeddings → trace_id frame ranges - SPEAKS_AS edge creation Architecture: - All edges use Qdrant payload (no face_detections queries) - PostgreSQL fallback for empty Qdrant - Estimated 3.6x performance improvement Testing: - Playground (3003): ✓ All Phase 2.6 logs verified - Edge counts: ✓ Close match with PostgreSQL - Fallback: ✓ Working Docs: - docs_v1.0/DESIGN/TKG_PHASE2_6_EDGES_MIGRATION.md - docs_v1.0/M4_workspace/2026-06-21_phase2_6_test.md
103 lines
3.0 KiB
Python
103 lines
3.0 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Backfill missing Age & Gender for persons.
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"""
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import os
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import cv2
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import psycopg2
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import insightface
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DB_CONFIG = {"host": "localhost", "user": "accusys", "dbname": "momentry"}
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BASE_VIDEO_DIR = "output"
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def main():
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print("=== Starting Missing Demographics Backfill ===")
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conn = psycopg2.connect(**DB_CONFIG)
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cur = conn.cursor()
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# Load Model
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print("Loading InsightFace model...")
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try:
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app = insightface.app.FaceAnalysis(
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name="buffalo_l", providers=["CPUExecutionProvider"]
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)
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app.prepare(ctx_id=0, det_size=(320, 320))
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print("Model loaded.")
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except Exception as e:
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print(f"Error loading model: {e}")
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return
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# Query persons missing data
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# Join with appearances to find a valid timestamp
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cur.execute("""
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SELECT DISTINCT ON (pi.person_id) pi.person_id, pa.video_uuid, pa.start_time
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FROM person_identities pi
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JOIN person_appearances pa ON pi.person_id = pa.person_id
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WHERE pi.age IS NULL OR pi.gender IS NULL
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ORDER BY pi.person_id, pa.start_time
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""")
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rows = cur.fetchall()
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print(f"Found {len(rows)} entries to process.")
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for i, (person_id, video_uuid, start_time) in enumerate(rows):
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# Skip if time is null
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if start_time is None:
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continue
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print(f"[{i + 1}/{len(rows)}] Processing: {person_id} @ {start_time:.1f}s")
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video_path = f"{BASE_VIDEO_DIR}/{video_uuid}/{video_uuid}.mp4"
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if not os.path.exists(video_path):
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print(f" -> Video not found at {video_path}")
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continue
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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print(" -> Could not open video.")
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continue
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# Seek
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cap.set(cv2.CAP_PROP_POS_MSEC, start_time * 1000)
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ret, frame = cap.read()
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cap.release()
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if not ret or frame is None:
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print(" -> Failed to read frame.")
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continue
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faces = app.get(frame)
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if faces:
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face = faces[0]
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age = int(face.age) if hasattr(face, "age") else None
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gender_val = face.gender if hasattr(face, "gender") else None
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gender = (
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"female" if gender_val == 0 else ("male" if gender_val == 1 else None)
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)
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if age is not None and gender is not None:
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cur.execute(
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"""
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UPDATE person_identities
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SET age = %s, gender = %s
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WHERE person_id = %s
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""",
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(age, gender, person_id),
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)
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conn.commit()
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print(f" -> Updated: Age {age}, Gender {gender}")
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else:
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print(f" -> Detection incomplete (Age:{age}, Gender:{gender})")
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else:
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print(" -> No face found in frame.")
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print("=== Done ===")
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conn.close()
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if __name__ == "__main__":
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main()
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