ffc30d7377
- Fix swift_pose/swift_ocr Y-flip bugs (BUG-003~006) - Add heuristic_scene module + post-processing trigger (replaces Places365) - YOLOv5nu → YOLOv8s CoreML (+33% detections, +390% scene indicators) - Per-table SQL export (split 4.7GB single file → 478MB max per table) - Version/build check in deploy.sh (compare /health vs file_info.json) - Add file_uuid column to identities table + backfill - Identity pre-clean step in deploy (avoids UNIQUE conflicts on re-deploy) - Stranger_xxx naming fix with UUID context - Add DETECTOR_REGISTRY.md (25 detectors), DETECTOR_SELECTION_SOP.md - Update SPATIAL_COORDINATE_REGISTRY.md (P layer, 6-layer architecture) - New IDENTITY_LIFECYCLE.md - M4 response docs for deploy_script_fix and 111614 test report
514 lines
20 KiB
Python
514 lines
20 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Multi-Stage Identity Clustering Runner
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Stage 1: High-confidence face-level matching
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- Compare ALL face embeddings in each trace against identity references
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- Bind trace to identity if >90% of faces match with >0.90 similarity
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- These become "anchors" for Stage 2
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Stage 2: Trace centroid clustering of remaining unbounded traces
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- Use centroid of unbound traces, cluster with adaptive threshold
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- Merge clusters with speaker overlap verification
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Stage 3 (optional): TMDb matching
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"""
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import sys, os, json, argparse, time, numpy as np
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from datetime import datetime
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from collections import defaultdict
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from typing import Dict, List, Tuple, Optional
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import psycopg2
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DB_URL = os.environ.get("DATABASE_URL", "postgresql://accusys@localhost:5432/momentry")
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SCHEMA = "dev"
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EXPERIMENT_DIR = os.path.dirname(os.path.abspath(__file__))
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def get_conn(): return psycopg2.connect(DB_URL)
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def cosine_similarity(a, b):
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a, b = np.array(a), np.array(b)
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return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-10)
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def parse_pg_array(val):
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"""Parse PostgreSQL real[] array — returns numpy float64 array or None"""
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if val is None: return None
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if isinstance(val, np.ndarray): return val.astype(np.float64)
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if isinstance(val, list): return np.array(val, dtype=np.float64)
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if isinstance(val, str):
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s = val.strip('[]{}')
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if not s: return None
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return np.fromstring(s, sep=',').astype(np.float64)
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return None
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def fetch_trace_with_faces(cur, file_uuid: str, min_frames: int) -> List[dict]:
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"""Fetch traces with ALL their individual face embeddings"""
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# Get trace summaries
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cur.execute(
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f"""
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SELECT trace_id, COUNT(*) as fc, MIN(frame_number), MAX(frame_number),
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AVG(x::float), AVG(y::float), AVG(width::float), AVG(height::float)
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FROM {SCHEMA}.face_detections
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WHERE file_uuid=%s AND trace_id IS NOT NULL AND embedding IS NOT NULL
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GROUP BY trace_id HAVING COUNT(*)>=%s ORDER BY trace_id
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""", (file_uuid, min_frames))
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traces = []
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for row in cur.fetchall():
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tid = row[0]
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cur.execute(
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f"SELECT embedding FROM {SCHEMA}.face_detections WHERE file_uuid=%s AND trace_id=%s AND embedding IS NOT NULL ORDER BY confidence DESC",
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(file_uuid, tid))
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faces = []
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for r in cur.fetchall():
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emb = parse_pg_array(r[0])
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if emb is not None:
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faces.append({"embedding": emb.astype(np.float64)})
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traces.append({
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"trace_id": tid, "frame_count": row[1],
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"start_frame": row[2], "end_frame": row[3],
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"avg_bbox": {"x": row[4], "y": row[5], "w": row[6], "h": row[7]},
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"faces": faces,
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"centroid": np.mean([f["embedding"] for f in faces], axis=0).tolist() if faces else None,
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})
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return traces
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def fetch_speaker_overlaps(cur, file_uuid: str) -> dict:
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cur.execute(f"""
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SELECT REPLACE(n.external_id,'trace_','')::int, n2.external_id,
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(e.properties->>'overlap_ratio')::float
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FROM {SCHEMA}.tkg_edges e
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JOIN {SCHEMA}.tkg_nodes n ON e.source_node_id=n.id
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JOIN {SCHEMA}.tkg_nodes n2 ON e.target_node_id=n2.id
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WHERE e.edge_type='SPEAKS_AS' AND n.node_type='face_trace' AND n2.node_type='speaker' AND e.file_uuid=%s
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""", (file_uuid,))
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overlaps = defaultdict(lambda: defaultdict(float))
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for tid, spk, ratio in cur.fetchall():
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if tid and spk: overlaps[int(tid)][spk] = float(ratio or 0)
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return dict(overlaps)
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def fetch_identity_references(cur) -> List[dict]:
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"""Get registered identities with face embeddings as references"""
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cur.execute(f"SELECT id, name, face_embedding FROM {SCHEMA}.identities WHERE face_embedding IS NOT NULL")
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results = []
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for r in cur.fetchall():
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emb = parse_pg_array(r[2])
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if emb is None: continue
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results.append({"id": r[0], "name": r[1], "embedding": emb.astype(np.float64)})
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return results
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# ===== STAGE 1: High-confidence face-level matching =====
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def stage1_high_confidence_binding(
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traces: List[dict], identities: List[dict],
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face_match_threshold: float = 0.92,
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trace_bind_ratio: float = 0.85,
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) -> Tuple[List[dict], List[dict]]:
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"""
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For each trace, compare EVERY face against EVERY identity.
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Bind trace to identity if >trace_bind_ratio% of faces match with >face_match_threshold.
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Returns (bound_traces, unbound_traces)
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"""
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bound = []
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unbound = []
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for trace in traces:
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faces = trace.get("faces", [])
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if not faces:
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unbound.append(trace)
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continue
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best_identity = None
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best_match_count = 0
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for ident in identities:
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match_count = 0
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for face in faces:
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sim = cosine_similarity(face["embedding"], ident["embedding"])
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if sim >= face_match_threshold:
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match_count += 1
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ratio = match_count / len(faces)
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if ratio >= trace_bind_ratio and match_count > best_match_count:
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best_match_count = match_count
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best_identity = {
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"id": ident["id"],
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"name": ident["name"],
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"match_ratio": round(ratio, 3),
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"matched_faces": match_count,
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"total_faces": len(faces),
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}
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if best_identity:
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trace["binding"] = best_identity
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trace["binding_stage"] = "stage1_face_level"
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bound.append(trace)
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else:
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unbound.append(trace)
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return bound, unbound
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# ===== STAGE 2: Centroid clustering of unbound traces =====
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def stage2_cluster_unbound(
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traces: List[dict], threshold: float, adaptive: bool = False
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) -> List[dict]:
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"""Cluster unbound traces by centroid similarity + speaker verify"""
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clusters = []
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assigned = set()
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for i, t1 in enumerate(traces):
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if t1["trace_id"] in assigned: continue
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cluster = [t1]; assigned.add(t1["trace_id"])
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for j, t2 in enumerate(traces):
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if t2["trace_id"] in assigned or i == j: continue
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if t1["centroid"] is None or t2["centroid"] is None: continue
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sim = cosine_similarity(t1["centroid"], t2["centroid"])
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th = threshold
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if adaptive and (t1["frame_count"] < 10 or t2["frame_count"] < 10):
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th -= 0.05
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if sim >= th:
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cluster.append(t2); assigned.add(t2["trace_id"])
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clusters.append(cluster)
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return clusters
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def apply_speaker_verification(clusters: List[dict], speaker_overlaps: dict) -> List[dict]:
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"""Label clusters with speaker + merge same-speaker clusters"""
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labels = []
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for i, cluster in enumerate(clusters):
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trace_ids = [t["trace_id"] for t in cluster]
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votes = defaultdict(float)
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for tid in trace_ids:
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if tid in speaker_overlaps:
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for spk, r in speaker_overlaps[tid].items():
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votes[spk] += r
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best_spk = max(votes, key=votes.get) if votes else None
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labels.append({
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"cluster_id": i, "trace_count": len(cluster),
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"trace_ids": trace_ids,
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"dominant_speaker": best_spk,
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"speaker_score": round(votes.get(best_spk, 0), 3) if best_spk else 0,
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"binding": cluster[0].get("binding"),
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"binding_stage": cluster[0].get("binding_stage"),
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})
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return labels
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# ===== Main Experiment =====
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def run_experiment(config: dict) -> dict:
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exp_id = config["id"]; file_uuid = config.get("file_uuid", "")
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conn = get_conn(); cur = conn.cursor()
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t0 = time.time()
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out = lambda *a: None # noqa
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# Load data
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traces = fetch_trace_with_faces(cur, file_uuid, config.get("min_frames", 3))
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identities = fetch_identity_references(cur) if config.get("enable_identity_match", True) else []
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speaker_overlaps = fetch_speaker_overlaps(cur, file_uuid)
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print(f"Traces: {len(traces)}, Identities: {len(identities)}, Speaker edges: {len(speaker_overlaps)}")
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# Stage 1: TMDb-based first-pass binding (relaxed threshold)
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bound, unbound = [], traces
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if identities:
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bound, unbound = stage1_high_confidence_binding(
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traces, identities,
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config.get("stage1_face_threshold", 0.55),
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config.get("stage1_bind_ratio", 0.60),
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)
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print(f"Stage 1 (TMDb): {len(bound)} traces bound, {len(unbound)} unbound")
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# Stage 1b+2: Iterative enrichment — each bound trace adds 3 best faces as references
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if bound and identities and unbound:
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# Build initial reference sets from Stage 1 bound traces
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# For each identity, collect top-3 confidence faces from each bound trace
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identity_refs = {} # identity_id -> list of reference embeddings
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for t in bound:
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b = t.get("binding", {})
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iid = b.get("id") if isinstance(b, dict) else None
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if not iid or not t.get("faces"): continue
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if iid not in identity_refs:
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identity_refs[iid] = []
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# Sample 3 best faces from this trace (top confidence = best quality)
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faces = t["faces"]
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n_sample = min(3, len(faces))
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for f in faces[:n_sample]:
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identity_refs[iid].append(f["embedding"])
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# Build identity lookup
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id_to_name = {ident["id"]: ident["name"] for ident in identities}
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for iid, refs in identity_refs.items():
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print(f" {id_to_name.get(iid, '?'):<20} {len(refs)} reference faces (multi-angle sampling)")
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# Speaker segment counts for weighting
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speaker_counts = defaultdict(float)
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for tid, spks in speaker_overlaps.items():
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speaker_counts[tid] = sum(spks.values())
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# Iterative matching with growing reference set
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round_num = 0
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while True:
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round_num += 1
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bound_this_round = []
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for t in unbound:
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best_score = 0
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best_iid = None
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best_sim = 0
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best_match_count = 0
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for iid, refs in identity_refs.items():
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faces = t.get("faces", [])
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if not faces: continue
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# Compare each face against ALL references, take max per face
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face_sims = []
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for face in faces:
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max_sim = max(
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cosine_similarity(face["embedding"], ref) for ref in refs
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)
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face_sims.append(max_sim)
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avg_sim = np.mean(face_sims) if face_sims else 0
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match_ratio = sum(1 for s in face_sims if s >= config.get("stage1_face_threshold", 0.55)) / len(face_sims)
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# Absolute minimum: if avg similarity is too low, never bind
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min_sim = config.get("stage1b_min_face_similarity", 0.30)
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if avg_sim < min_sim:
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continue
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# Composite score: similarity + match ratio + speaker weight
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spk_weight = 1.0 + 0.3 * speaker_counts.get(t["trace_id"], 0) / max(max(speaker_counts.values(), default=1), 1)
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composite = avg_sim * spk_weight * (0.4 + 0.6 * match_ratio)
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composite_threshold = config.get("stage1b_composite_threshold", 0.50)
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if composite > best_score and composite > composite_threshold:
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best_score = composite
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best_iid = iid
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best_sim = avg_sim
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best_match_count = sum(1 for s in face_sims if s >= 0.50)
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if best_iid is not None:
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t["binding"] = {
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"id": best_iid, "name": id_to_name.get(best_iid, "?"),
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"avg_similarity": round(best_sim, 3),
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"match_ratio": round(best_match_count / max(len(t.get("faces", [])), 1), 3),
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"composite_score": round(best_score, 3),
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"source": f"video_ref_r{round_num}",
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}
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t["binding_stage"] = f"stage1b_r{round_num}"
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bound_this_round.append(t)
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bound.append(t)
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if not bound_this_round:
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break
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# Enrich references: add 3 best faces from newly bound traces
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for t in bound_this_round:
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iid = t["binding"]["id"]
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faces = t.get("faces", [])
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n = min(3, len(faces))
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for f in faces[:n]:
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identity_refs[iid].append(f["embedding"])
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# Remove from unbound
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bound_ids = {t["trace_id"] for t in bound_this_round}
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unbound = [t for t in unbound if t["trace_id"] not in bound_ids]
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print(f" Round {round_num}: {len(bound_this_round)} traces bound, {len(unbound)} unbound")
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clusters = stage2_cluster_unbound(
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unbound,
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config.get("stage2_threshold", 0.85),
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config.get("stage2_adaptive", False),
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)
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print(f"Stage 2: {len(clusters)} clusters from {len(unbound)} unbound traces")
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# Speaker verification
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all_labels = apply_speaker_verification(clusters, speaker_overlaps)
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# --- Temporal Collision Check ---
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# Split traces that have overlapping frames within the same identity
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if config.get("enable_temporal_collision_check", True):
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# Build trace timing map: trace_id → (min_frame, max_frame)
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trace_timing = {}
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for t in traces:
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trace_timing[t["trace_id"]] = (t["start_frame"], t["end_frame"])
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collision_splits = 0
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for label in all_labels:
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if label.get("trace_count", 0) < 2:
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continue
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tids = label["trace_ids"]
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# Check all pairs in this label
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for i in range(len(tids)):
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for j in range(i+1, len(tids)):
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a, b = tids[i], tids[j]
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ta = trace_timing.get(a)
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tb = trace_timing.get(b)
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if not ta or not tb: continue
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# Overlap: max(start) < min(end)
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if max(ta[0], tb[0]) < min(ta[1], tb[1]):
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collision_splits += 1
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print(f" COLLISION: trace {a} & {b} overlap (frames {max(ta[0],tb[0])}-{min(ta[1],tb[1])}), splitting...")
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# Move the lower-confidence trace to a new label
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# Get avg confidence from face embeddings (we don't store per-face confidence in trace dict)
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# Use the existing confidence data from DB
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cur2 = conn.cursor()
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cur2.execute(f"SELECT AVG(confidence) FROM {SCHEMA}.face_detections WHERE file_uuid=%s AND trace_id=%s", (file_uuid, a))
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conf_a = cur2.fetchone()[0] or 0
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cur2.execute(f"SELECT AVG(confidence) FROM {SCHEMA}.face_detections WHERE file_uuid=%s AND trace_id=%s", (file_uuid, b))
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conf_b = cur2.fetchone()[0] or 0
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cur2.close()
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if conf_a < conf_b:
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loser_tid = a
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else:
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loser_tid = b
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# Remove loser from this label, create new label
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label["trace_ids"].remove(loser_tid)
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label["trace_count"] -= 1
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all_labels.append({
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"cluster_id": len(all_labels),
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"trace_count": 1,
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"trace_ids": [loser_tid],
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"binding": None,
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"binding_stage": "collision_split",
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})
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if collision_splits > 0:
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print(f" Temporal collision: {collision_splits} traces split")
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# Merge Stage 1 bound traces into labels
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for t in bound:
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all_labels.append({
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"cluster_id": len(all_labels),
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"trace_count": 1,
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"trace_ids": [t["trace_id"]],
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"binding": t.get("binding"),
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"binding_stage": "stage1_face_level",
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"dominant_speaker": next(iter(speaker_overlaps.get(t["trace_id"], {}).keys()), None) if t["trace_id"] in speaker_overlaps else None,
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})
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# --- Temp Identity: assign names to unbound clusters ---
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temp_count = 0
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for label in all_labels:
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if label.get("binding") is not None:
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continue # already has known identity
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tids = label.get("trace_ids", [])
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if len(tids) < 1:
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continue
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# Create temp identity for all unbound clusters (even singletons as "strangers")
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if len(tids) >= 1:
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temp_count += 1
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if len(tids) >= 2:
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temp_name = f"Person_{file_uuid[:8]}_{temp_count:03d}"
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else:
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temp_name = f"Stranger_{file_uuid[:8]}_{temp_count:03d}"
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label["binding"] = {
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"name": temp_name,
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"source": "auto_temp",
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"trace_count": len(tids),
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}
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label["binding_stage"] = "auto_temp"
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if temp_count > 0:
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print(f" Temp identities created: {temp_count}")
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# Metrics
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metrics = {
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"total_traces": len(traces),
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"stage1_bound": len(bound),
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"stage1_bound_traces": len(bound),
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"stage2_clusters": len(clusters),
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"stage2_unbound_clustered": sum(len(c) for c in clusters),
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"total_clusters": len(all_labels),
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"execution_time_s": time.time() - t0,
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"coverage": (len(bound) + sum(len(c) for c in clusters)) / max(len(traces), 1),
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}
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for k, v in metrics.items():
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print(f" {k}: {v}")
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cur.close(); conn.close()
|
|
|
|
# --- Write bindings to database ---
|
|
if config.get("write_db", False):
|
|
conn2 = get_conn(); cur2 = conn2.cursor()
|
|
total_written = 0
|
|
for label in all_labels:
|
|
binding = label.get("binding")
|
|
if not binding: continue
|
|
identity_name = binding.get("name", "")
|
|
if not identity_name: continue
|
|
|
|
# Get or create identity
|
|
cur2.execute(f"SELECT id FROM {SCHEMA}.identities WHERE name=%s", (identity_name,))
|
|
row = cur2.fetchone()
|
|
if row:
|
|
identity_id = row[0]
|
|
else:
|
|
source = binding.get("source", "auto")
|
|
cur2.execute(
|
|
f"INSERT INTO {SCHEMA}.identities (name, identity_type, source, status) VALUES (%s,'people',%s,'pending') RETURNING id",
|
|
(identity_name, source))
|
|
identity_id = cur2.fetchone()[0]
|
|
|
|
# Bind all faces in each trace to the identity
|
|
for tid in label["trace_ids"]:
|
|
cur2.execute(
|
|
f"UPDATE {SCHEMA}.face_detections SET identity_id=%s WHERE file_uuid=%s AND trace_id=%s AND identity_id IS NULL",
|
|
(identity_id, file_uuid, tid))
|
|
affected = cur2.rowcount
|
|
if affected > 0:
|
|
# Write to identity_bindings for traceability
|
|
confidence = float(binding.get("avg_similarity", 0.8))
|
|
cur2.execute(
|
|
f"INSERT INTO {SCHEMA}.identity_bindings (identity_id, identity_type, identity_value, confidence) VALUES (%s,'trace',%s,%s) ON CONFLICT DO NOTHING",
|
|
(identity_id, str(tid), confidence))
|
|
total_written += affected
|
|
|
|
conn2.commit()
|
|
cur2.close(); conn2.close()
|
|
print(f"\nDB write: {total_written} face_detections updated")
|
|
|
|
# Save
|
|
result_dir = os.path.join(EXPERIMENT_DIR, "results", f"exp_{exp_id}")
|
|
os.makedirs(result_dir, exist_ok=True)
|
|
for name, data in [("labels.json", all_labels), ("metrics.json", metrics), ("config.json", config)]:
|
|
with open(os.path.join(result_dir, name), "w") as f:
|
|
json.dump(data, f, indent=2, ensure_ascii=False, default=str)
|
|
|
|
print(f"\nSaved to {result_dir}")
|
|
return metrics
|
|
|
|
|
|
def main():
|
|
p = argparse.ArgumentParser()
|
|
p.add_argument("--config", required=True)
|
|
p.add_argument("--write-db", action="store_true", help="Write bindings to database")
|
|
args = p.parse_args()
|
|
with open(args.config) as f: config = json.load(f)
|
|
if args.write_db:
|
|
config["write_db"] = True
|
|
run_experiment(config)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|