fix: face group name read consistency, sync_file_status fix, cleanup ghost records, identity_agent replaced with face_dedup
- get_face_groups_handler: COALESCE(tp.name, tn.label) for name consistency - sync_file_status: compare JSON vs pre_chunks (not chunk table) - face consistency: compare frames.len() not total_faces - cleanup 2 ghost records with NULL file_name/file_path - replace identity_agent with face_dedup in pipeline stages - remove identity_agent_api.rs and all references - update required_processors to match actual processors - update AGENTS.md with team responsibilities - add Studio pipeline changes documentation
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Accusys
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#!/usr/bin/env python3
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"""
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Backfill trace profiles from Qdrant _faces collection.
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For each (file_uuid, trace_id) group in Qdrant:
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1. Compute frame_count, start_frame, end_frame, avg_confidence
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2. Pick representative frame (highest confidence)
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3. Extract key_frame.jpg from video via ffmpeg
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4. Crop key_face.jpg from key_frame using representative bbox
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5. Write output/{file_uuid}/trace_{N}/trace_profile.json
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Usage:
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python3 backfill_trace_profiles.py [--file-uuid UUID] [--dry-run]
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"""
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import argparse
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import json
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import os
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import subprocess
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import sys
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import urllib.request
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import urllib.error
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from collections import defaultdict
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OUTPUT_DIR = os.environ.get("MOMENTRY_OUTPUT_DIR", "/Users/accusys/momentry/output")
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QDRANT_URL = os.environ.get("QDRANT_URL", "http://localhost:6333")
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QDRANT_API_KEY = os.environ.get("QDRANT_API_KEY", "Test3200Test3200Test3200")
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FACES_COLLECTION = "_faces"
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BATCH_SIZE = 1000
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def qdrant_scroll(filter_dict, limit=BATCH_SIZE, offset=None, with_payload=None):
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"""Scroll Qdrant collection with filter."""
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body = {"limit": limit, "filter": filter_dict, "with_vector": False}
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if offset:
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body["offset"] = offset
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if with_payload:
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body["with_payload"] = with_payload
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url = f"{QDRANT_URL}/collections/{FACES_COLLECTION}/points/scroll"
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data = json.dumps(body).encode()
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req = urllib.request.Request(url, data=data, method="POST")
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req.add_header("Content-Type", "application/json")
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req.add_header("Api-Key", QDRANT_API_KEY)
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with urllib.request.urlopen(req) as resp:
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return json.loads(resp.read())
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def scroll_all(filter_dict, with_payload=None):
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"""Scroll all matching points."""
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all_points = []
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offset = None
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while True:
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result = qdrant_scroll(filter_dict, offset=offset, with_payload=with_payload)
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points = result.get("result", {}).get("points", [])
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if not points:
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break
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all_points.extend(points)
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offset = result.get("result", {}).get("next_page_offset")
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if not offset or len(points) < BATCH_SIZE:
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break
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return all_points
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def get_video_path(file_uuid):
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"""Get video file path from database."""
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psql = "/opt/homebrew/Cellar/libpq/18.4/bin/psql"
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result = subprocess.run(
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[psql, "-U", "accusys", "-d", "momentry", "-t", "-A", "-c",
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f"SELECT file_path FROM videos WHERE file_uuid = '{file_uuid}'"],
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capture_output=True, text=True
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)
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if result.returncode == 0 and result.stdout.strip():
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return result.stdout.strip()
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return None
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def extract_key_frame(video_path, frame_num, fps, output_path):
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"""Extract a specific frame from video using ffmpeg."""
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if fps <= 0:
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return False
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timestamp = frame_num / fps
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try:
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result = subprocess.run(
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["ffmpeg", "-y", "-ss", f"{timestamp:.3f}",
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"-i", video_path,
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"-vframes", "1", "-vf", "scale=640:-1", "-q:v", "5",
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output_path],
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capture_output=True, timeout=30
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)
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return result.returncode == 0 and os.path.exists(output_path)
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except Exception as e:
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print(f" key_frame extraction failed: {e}", file=sys.stderr)
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return False
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def crop_key_face(key_frame_path, bbox, output_path):
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"""Crop key_face from key_frame using bbox."""
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x, y, w, h = bbox["x"], bbox["y"], bbox["width"], bbox["height"]
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if w <= 0 or h <= 0:
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return False
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try:
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result = subprocess.run(
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["ffmpeg", "-y", "-i", key_frame_path,
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"-vf", f"crop={w}:{h}:{x}:{y}",
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"-q:v", "2", output_path],
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capture_output=True, timeout=10
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)
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return result.returncode == 0 and os.path.exists(output_path)
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except Exception as e:
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print(f" key_face crop failed: {e}", file=sys.stderr)
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return False
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def build_trace_profiles(file_uuid=None, dry_run=False):
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"""Build trace profiles from Qdrant _faces data."""
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# Get all unique file_uuids with trace_id >= 0
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if file_uuid:
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file_uuids = [file_uuid]
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else:
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print("Scanning Qdrant for all file_uuids with trace_id >= 0...")
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points = scroll_all(
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{"must": [{"key": "trace_id", "range": {"gte": 0}}]},
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with_payload={"include": ["file_uuid"]}
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)
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file_uuids = sorted(set(p["payload"]["file_uuid"] for p in points))
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print(f"Found {len(file_uuids)} files with trace data")
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total_profiles = 0
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for fid in file_uuids:
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print(f"\n--- {fid} ---")
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# Scroll all points for this file with trace_id >= 0
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points = scroll_all(
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{
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"must": [
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{"key": "file_uuid", "match": {"value": fid}},
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{"key": "trace_id", "range": {"gte": 0}},
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]
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},
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with_payload={"include": ["frame", "trace_id", "bbox", "confidence"]}
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)
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if not points:
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print(" No points with trace_id >= 0")
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continue
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# Group by trace_id
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traces = defaultdict(list)
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for p in points:
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pl = p["payload"]
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tid = pl.get("trace_id", 0)
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traces[tid].append({
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"frame": pl["frame"],
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"bbox": pl.get("bbox", {}),
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"confidence": pl.get("confidence", 0.0),
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})
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print(f" {len(points)} points, {len(traces)} traces")
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# Get video path
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video_path = get_video_path(fid)
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if not video_path or not os.path.exists(video_path):
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print(f" Video not found, skipping key_frame extraction")
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video_path = None
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# Get FPS from DB
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fps = 30.0
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if video_path:
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psql = "/opt/homebrew/Cellar/libpq/18.4/bin/psql"
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result = subprocess.run(
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[psql, "-U", "accusys", "-d", "momentry", "-t", "-A", "-c",
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f"SELECT COALESCE(fps, 30.0) FROM videos WHERE file_uuid = '{fid}'"],
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capture_output=True, text=True
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)
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if result.returncode == 0 and result.stdout.strip():
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try:
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fps = float(result.stdout.strip())
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except ValueError:
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pass
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for tid, faces in sorted(traces.items()):
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if tid < 0:
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continue
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frames = [f["frame"] for f in faces]
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confidences = [f["confidence"] for f in faces]
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frame_count = len(faces)
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start_frame = min(frames)
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end_frame = max(frames)
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avg_confidence = sum(confidences) / frame_count if frame_count > 0 else 0.0
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# Representative frame: highest confidence
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best = max(faces, key=lambda f: f["confidence"])
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best_frame = best["frame"]
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best_bbox = best["bbox"]
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trace_dir = os.path.join(OUTPUT_DIR, fid, f"trace_{tid}")
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profile_path = os.path.join(trace_dir, "trace_profile.json")
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kf_path = os.path.join(trace_dir, "key_frame.jpg")
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face_path = os.path.join(trace_dir, "key_face.jpg")
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profile = {
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"version": "1.0",
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"file_uuid": fid,
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"trace_id": tid,
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"label": "",
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"frame_count": frame_count,
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"start_frame": start_frame,
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"end_frame": end_frame,
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"avg_confidence": round(avg_confidence, 6),
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"key_frame": "key_frame.jpg" if os.path.exists(kf_path) else None,
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"key_face": "key_face.jpg" if os.path.exists(face_path) else None,
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"status": "pending",
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}
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if dry_run:
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print(f" trace_{tid}: {frame_count} frames [{start_frame}-{end_frame}] "
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f"conf={avg_confidence:.3f} best_frame={best_frame}")
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continue
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os.makedirs(trace_dir, exist_ok=True)
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# Extract key_frame.jpg if not exists
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if not os.path.exists(kf_path) and video_path:
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extract_key_frame(video_path, best_frame, fps, kf_path)
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if os.path.exists(kf_path):
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profile["key_frame"] = "key_frame.jpg"
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# Crop key_face.jpg from key_frame if not exists
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if not os.path.exists(face_path) and os.path.exists(kf_path) and best_bbox:
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crop_key_face(kf_path, best_bbox, face_path)
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if os.path.exists(face_path):
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profile["key_face"] = "key_face.jpg"
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# Write trace_profile.json
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with open(profile_path, "w") as f:
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json.dump(profile, f, indent=2, ensure_ascii=False)
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total_profiles += 1
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if not dry_run:
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print(f" Created {len([t for t in traces if t >= 0])} trace profiles")
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print(f"\nDone: {total_profiles} trace profiles created")
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def main():
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parser = argparse.ArgumentParser(description="Backfill trace profiles from Qdrant")
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parser.add_argument("--file-uuid", help="Process only this file UUID")
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parser.add_argument("--dry-run", action="store_true", help="Show what would be created")
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args = parser.parse_args()
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build_trace_profiles(file_uuid=args.file_uuid, dry_run=args.dry_run)
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if __name__ == "__main__":
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main()
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