feat: add Qdrant _faces collection embedding push
- Add qdrant_faces.py utility module for _faces collection operations - Modify face_processor.py to push embeddings to Qdrant (CoreML extraction re-enabled) - Modify store_traced_faces.py to update trace_id in Qdrant after face tracking - Collection schema: 512D vectors, Cosine distance, fixed name '_faces' - Payload: file_uuid, frame, trace_id, bbox, confidence, identity_id/uuid, stranger_id - Batch size: 100 (default), configurable via QDRANT_BATCH_SIZE env var - Error handling: face_processor.py exits with error if Qdrant push fails
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Accusys
308
scripts/utils/qdrant_faces.py
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308
scripts/utils/qdrant_faces.py
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#!/opt/homebrew/bin/python3.11
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"""
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Qdrant _faces Collection Operations
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Functions:
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- ensure_faces_collection(): Create _faces collection if not exists
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- generate_point_id(): Generate consistent point ID
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- push_face_embeddings_batch(): Batch push embeddings to Qdrant
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- update_trace_ids(): Update trace_id after face tracking
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Collection Schema:
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- Name: _faces (fixed, no schema prefix)
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- Vector: 512D, Cosine distance
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- Payload: {file_uuid, frame, trace_id, bbox, confidence, identity_id, identity_uuid, stranger_id}
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"""
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import os
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import json
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import hashlib
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import urllib.request
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import urllib.error
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from typing import Optional
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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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VECTOR_DIM = 512
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BATCH_SIZE = int(os.environ.get("QDRANT_BATCH_SIZE", "100"))
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def qdrant_request(method: str, path: str, body: dict = None) -> dict:
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"""Make HTTP request to Qdrant"""
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url = f"{QDRANT_URL}{path}"
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data = json.dumps(body).encode() if body else None
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req = urllib.request.Request(url, data=data, method=method)
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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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try:
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with urllib.request.urlopen(req) as resp:
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return json.loads(resp.read())
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except urllib.error.HTTPError as e:
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error_body = e.read().decode()
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raise RuntimeError(f"Qdrant HTTP {e.code}: {error_body}")
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def ensure_faces_collection() -> bool:
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"""Create _faces collection if not exists"""
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url = f"{QDRANT_URL}/collections/{FACES_COLLECTION}"
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req = urllib.request.Request(url, method="GET")
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req.add_header("Api-Key", QDRANT_API_KEY)
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try:
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urllib.request.urlopen(req)
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return True # Collection exists
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except urllib.error.HTTPError as e:
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if e.code != 404:
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raise RuntimeError(f"Qdrant check failed: {e.read().decode()}")
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# Create collection
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body = {
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"vectors": {
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"size": VECTOR_DIM,
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"distance": "Cosine"
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}
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}
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create_url = f"{QDRANT_URL}/collections/{FACES_COLLECTION}"
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data = json.dumps(body).encode()
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req = urllib.request.Request(create_url, data=data, method="PUT")
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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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try:
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urllib.request.urlopen(req)
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print(f"[QDRANT] Created collection: {FACES_COLLECTION}")
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return True
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except urllib.error.HTTPError as e:
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raise RuntimeError(f"Qdrant create collection failed: {e.read().decode()}")
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def generate_point_id(file_uuid: str, frame: int, trace_id: int = 0) -> int:
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"""Generate consistent point ID from file_uuid + frame + trace_id"""
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key = f"{file_uuid}_{frame}_{trace_id}"
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return int(hashlib.md5(key.encode()).hexdigest()[:16], 16)
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def push_face_embeddings_batch(
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file_uuid: str,
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faces: list,
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publisher=None
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) -> int:
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"""Batch push face embeddings to _faces collection
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Args:
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file_uuid: Video file UUID
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faces: List of {frame, trace_id, bbox, confidence, embedding}
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publisher: RedisPublisher for progress reporting (optional)
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Returns:
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Number of successfully pushed embeddings
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Raises:
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RuntimeError: If Qdrant push fails
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"""
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if not faces:
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return 0
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ensure_faces_collection()
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total = len(faces)
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pushed = 0
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for i in range(0, total, BATCH_SIZE):
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batch = faces[i:i + BATCH_SIZE]
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points = []
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for face in batch:
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point_id = generate_point_id(
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file_uuid,
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face["frame"],
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face.get("trace_id", 0)
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)
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points.append({
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"id": point_id,
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"vector": face["embedding"],
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"payload": {
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"file_uuid": file_uuid,
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"frame": face["frame"],
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"trace_id": face.get("trace_id", 0),
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"bbox": face["bbox"],
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"confidence": face.get("confidence", 0.5),
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"identity_id": None,
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"identity_uuid": None,
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"stranger_id": None,
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}
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})
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body = {"points": points}
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url = f"{QDRANT_URL}/collections/{FACES_COLLECTION}/points?wait=true"
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data = json.dumps(body).encode()
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req = urllib.request.Request(url, data=data, method="PUT")
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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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try:
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urllib.request.urlopen(req)
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pushed += len(batch)
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except urllib.error.HTTPError as e:
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error_body = e.read().decode()
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raise RuntimeError(
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f"Qdrant push failed (batch {i//BATCH_SIZE}): HTTP {e.code} - {error_body}"
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)
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if publisher:
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pct = int((i + len(batch)) * 100 / total)
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publisher.progress("face", i + len(batch), total, f"Qdrant push {pct}%")
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print(f"[QDRANT] Pushed {pushed} embeddings to {FACES_COLLECTION}")
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return pushed
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def update_trace_ids(file_uuid: str, trace_mapping: dict) -> int:
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"""Update trace_id for all face points in a file
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Called by store_traced_faces.py after face tracking.
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Args:
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file_uuid: Video file UUID
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trace_mapping: {frame: {bbox_key: trace_id}}
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bbox_key = f"{x}_{y}_{width}_{height}"
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Returns:
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Number of updated points
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"""
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all_points = []
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offset = None
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while True:
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body = {
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"limit": BATCH_SIZE,
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"with_payload": True,
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"with_vector": True,
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"filter": {
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"must": [
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{"key": "file_uuid", "match": {"value": file_uuid}}
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]
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}
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}
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if offset:
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body["offset"] = offset
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result = qdrant_request("POST", f"/collections/{FACES_COLLECTION}/points/scroll", body)
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batch = result.get("result", {}).get("points", [])
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if not batch:
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break
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all_points.extend(batch)
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offset = result.get("result", {}).get("next_page_offset")
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if not offset:
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break
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updates = []
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for point in all_points:
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point_id = point["id"]
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payload = point.get("payload", {})
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vector = point.get("vector", [])
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frame = payload.get("frame")
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bbox = payload.get("bbox", {})
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bbox_key = f"{bbox.get('x')}_{bbox.get('y')}_{bbox.get('width')}_{bbox.get('height')}"
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trace_id = trace_mapping.get(frame, {}).get(bbox_key)
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if trace_id is None:
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continue
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payload["trace_id"] = trace_id
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updates.append({
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"id": point_id,
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"vector": vector,
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"payload": payload,
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})
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if not updates:
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return 0
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for i in range(0, len(updates), BATCH_SIZE):
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batch = updates[i:i + BATCH_SIZE]
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body = {"points": batch}
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qdrant_request("PUT", f"/collections/{FACES_COLLECTION}/points?wait=true", body)
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print(f"[QDRANT] Updated {len(updates)} trace_ids in {FACES_COLLECTION}")
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return len(updates)
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def delete_file_faces(file_uuid: str) -> int:
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"""Delete all face points for a file
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Args:
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file_uuid: Video file UUID
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Returns:
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Number of deleted points
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"""
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body = {
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"filter": {
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"must": [
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{"key": "file_uuid", "match": {"value": file_uuid}}
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]
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}
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}
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result = qdrant_request("POST", f"/collections/{FACES_COLLECTION}/points/delete", body)
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deleted = result.get("result", {}).get("operation_id", 0)
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print(f"[QDRANT] Deleted faces for file_uuid={file_uuid}")
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return deleted
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def get_file_faces(file_uuid: str) -> list:
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"""Get all face points for a file
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Args:
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file_uuid: Video file UUID
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Returns:
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List of points with payload and vector
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"""
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all_points = []
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offset = None
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while True:
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body = {
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"limit": BATCH_SIZE,
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"with_payload": True,
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"with_vector": True,
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"filter": {
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"must": [
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{"key": "file_uuid", "match": {"value": file_uuid}}
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]
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}
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}
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if offset:
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body["offset"] = offset
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result = qdrant_request("POST", f"/collections/{FACES_COLLECTION}/points/scroll", body)
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batch = result.get("result", {}).get("points", [])
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if not batch:
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break
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all_points.extend(batch)
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offset = result.get("result", {}).get("next_page_offset")
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if not offset:
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break
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return all_points
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def count_file_faces(file_uuid: str) -> int:
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"""Count face points for a file
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Args:
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file_uuid: Video file UUID
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Returns:
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Number of face points
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"""
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body = {
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"filter": {
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"must": [
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{"key": "file_uuid", "match": {"value": file_uuid}}
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]
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}
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}
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result = qdrant_request("POST", f"/collections/{FACES_COLLECTION}/points/count", body)
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return result.get("result", {}).get("count", 0)
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