feat: update Python processors and add utility scripts

- Update ASR, face, OCR, pose processors
- Add release pre-flight check script
- Add synonym generation, chunk processing scripts
- Add face recognition, stamp search utilities

scripts/update_all_demographics.py

@@ -0,0 +1,132 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Comprehensive Age & Gender Updater.
+Scans all persons in DB, finds a representative frame, and updates demographics using InsightFace.
+"""
+
+import os
+import cv2
+import psycopg2
+import insightface
+import numpy as np
+
+# Configuration
+DB_CONFIG = {"host": "localhost", "user": "accusys", "dbname": "momentry"}
+BASE_VIDEO_DIR = "output"
+
+
+def get_face_app():
+    print("Loading InsightFace model (buffalo_l)...")
+    app = insightface.app.FaceAnalysis(
+        name="buffalo_l", providers=["CPUExecutionProvider"]
+    )
+    app.prepare(ctx_id=0, det_size=(640, 640))
+    return app
+
+
+def get_video_path(video_uuid):
+    """Locate video file."""
+    path = f"{BASE_VIDEO_DIR}/{video_uuid}/{video_uuid}.mp4"
+    if os.path.exists(path):
+        return path
+    return None
+
+
+def update_db(conn, person_id, age, gender):
+    """Update demographics in DB."""
+    cur = conn.cursor()
+    cur.execute(
+        """
+        UPDATE person_identities 
+        SET age = %s, gender = %s 
+        WHERE person_id = %s
+    """,
+        (age, gender, person_id),
+    )
+    conn.commit()
+
+
+def process_person(app, conn, person_id, video_uuid, timestamp):
+    """Extract frame and analyze face."""
+    video_path = get_video_path(video_uuid)
+    if not video_path:
+        return
+
+    # OpenCV seek (approximate)
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        return
+
+    # Try seeking by msec
+    cap.set(cv2.CAP_PROP_POS_MSEC, timestamp * 1000)
+    ret, frame = cap.read()
+
+    # If seeking failed or frame is bad, try frame number estimation (assuming 30fps as fallback, though inaccurate)
+    # But for this script, we just try a few times around the timestamp
+    attempts = 0
+    while not ret and attempts < 3:
+        ret, frame = cap.read()
+        attempts += 1
+
+    cap.release()
+
+    if not ret or frame is None:
+        print(f"  - Failed to get frame for {person_id}")
+        return
+
+    # Analyze
+    faces = app.get(frame)
+    if faces:
+        # Take the first (usually largest/clearest) face
+        face = faces[0]
+        age = int(face.age) if hasattr(face, "age") else None
+        gender_val = face.gender if hasattr(face, "gender") else None
+
+        # gender is often 0 or 1 in insightface, map it
+        gender = "female" if gender_val == 0 else ("male" if gender_val == 1 else None)
+
+        if age and gender:
+            print(f"  -> Detected: Age {age}, Gender {gender}")
+            update_db(conn, person_id, age, gender)
+        else:
+            print(f"  -> Face found but attributes missing.")
+    else:
+        print(f"  -> No face detected in frame.")
+
+
+def main():
+    print("=== Starting Full Demographics Scan ===")
+
+    conn = psycopg2.connect(**DB_CONFIG)
+    cur = conn.cursor()
+
+    # Get all persons missing age/gender
+    # We group by person_id and video_uuid to handle multiple videos if necessary
+    cur.execute("""
+        SELECT person_id, video_uuid, MIN(first_appearance_time) as min_time
+        FROM person_identities 
+        WHERE age IS NULL OR gender IS NULL
+        GROUP BY person_id, video_uuid
+    """)
+    rows = cur.fetchall()
+
+    if not rows:
+        print("All persons already have demographics data!")
+        return
+
+    print(f"Found {len(rows)} persons to process.")
+
+    app = get_face_app()
+
+    for i, (person_id, video_uuid, min_time) in enumerate(rows):
+        print(
+            f"[{i + 1}/{len(rows)}] Processing: {person_id} (Video: {video_uuid}, Time: {min_time:.1f}s)"
+        )
+        process_person(app, conn, person_id, video_uuid, min_time)
+
+    print("=== Done ===")
+    conn.close()
+
+
+if __name__ == "__main__":
+    main()