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
+25
-26
@@ -35,7 +35,8 @@ from redis_publisher import RedisPublisher
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from qdrant_faces import push_face_embeddings_batch
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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SWIFT_BIN = os.path.join(SCRIPT_DIR, "swift_processors", ".build", "release", "swift_face_pose")
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SWIFT_BIN = os.path.join(SCRIPT_DIR, "swift_processors", ".build", "release", "swift_face")
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SWIFT_BIN_DEBUG = os.path.join(SCRIPT_DIR, "swift_processors", ".build", "debug", "swift_face")
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FACENET_PATH = os.path.join(SCRIPT_DIR, "..", "models", "facenet512.mlpackage")
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# Pose angle classification from roll/yaw
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@@ -113,33 +114,33 @@ class FaceProcessorVision:
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return None
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def process_with_swift(self) -> Dict:
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"""Step 1: Run swift_face_pose to get bbox + pose (generates face.json + pose.json)"""
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print(f"[FACE_V2] Step 1: Vision detection (face + pose)...")
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"""Step 1: Run swift_face to get bbox (generates face_detect.json only)
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Note: swift_face only does face detection.
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Pose and appearance expansion happen later via separate processors:
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- swift_pose_expansion reads face_traced.json (with trace_id)
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- swift_appearance_expansion reads pose.json
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"""
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print(f"[FACE_V2] Step 1: Vision detection (face only)...")
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# Build swift_face_pose if needed
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if not os.path.exists(SWIFT_BIN):
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# Build swift_face if needed
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if not os.path.exists(SWIFT_BIN) and not os.path.exists(SWIFT_BIN_DEBUG):
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build_dir = os.path.join(SCRIPT_DIR, "swift_processors")
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print(f"[FACE_V2] Building swift_face_pose in {build_dir}...")
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print(f"[FACE_V2] Building swift_face in {build_dir}...")
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subprocess.run(
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["swift", "build", "-c", "debug", "--product", "swift_face_pose"],
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["swift", "build", "-c", "release", "--product", "swift_face"],
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cwd=build_dir, check=True
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)
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# Determine which binary to use
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swift_bin = SWIFT_BIN if os.path.exists(SWIFT_BIN) else SWIFT_BIN_DEBUG
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swift_face_out = self.output_path.replace(".json", "_detect.json")
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# Pose output: same directory, but replace "face" with "pose" in filename
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output_dir = os.path.dirname(self.output_path)
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output_basename = os.path.basename(self.output_path)
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pose_basename = output_basename.replace("face", "pose")
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swift_pose_out = os.path.join(output_dir, pose_basename)
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# Appearance output: same directory, but replace "face" with "appearance" in filename
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appearance_basename = output_basename.replace("face", "appearance")
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swift_appearance_out = os.path.join(output_dir, appearance_basename)
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cmd = [
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SWIFT_BIN,
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swift_bin,
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self.video_path,
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swift_face_out,
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swift_pose_out,
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swift_appearance_out,
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"--sample-interval", str(self.sample_interval),
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]
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if self.uuid:
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@@ -169,10 +170,10 @@ class FaceProcessorVision:
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pass
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log_f.close()
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if proc.returncode != 0:
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stderr_out = proc.stderr.read()
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stderr_out = proc.stderr.read() if proc.stderr else ""
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if stderr_out:
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print(stderr_out.strip(), file=sys.stderr)
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raise RuntimeError(f"swift_face_pose exited with code {proc.returncode}")
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raise RuntimeError(f"swift_face exited with code {proc.returncode}")
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elapsed = time.time() - t0
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print(f"[FACE_V2] Detection done in {elapsed:.1f}s")
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@@ -180,10 +181,6 @@ class FaceProcessorVision:
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with open(swift_face_out) as f:
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face_data = json.load(f)
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# Also check if pose.json was generated (for reference)
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if os.path.exists(swift_pose_out):
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print(f"[FACE_V2] Pose file generated: {swift_pose_out}")
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return face_data
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def embed_and_save(self, detection_data: Dict):
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@@ -215,7 +212,7 @@ class FaceProcessorVision:
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for frame_info in frames:
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frame_num = frame_info["frame"]
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faces = []
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for face in frame_info.get("faces", []):
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for face_idx, face in enumerate(frame_info.get("faces", [])):
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bb = face["bbox"]
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x, y, w, h = bb["x"], bb["y"], bb["width"], bb["height"]
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@@ -242,9 +239,10 @@ class FaceProcessorVision:
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if emb is not None:
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embed_count += 1
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# Collect for batch Qdrant push
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# Use face_idx to distinguish multiple faces in same frame
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all_embeddings.append({
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"frame": frame_num,
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"trace_id": 0, # Initial, updated by face_tracker
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"trace_id": face_idx, # Use face_idx as unique identifier within frame
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"bbox": {"x": x, "y": y, "width": w, "height": h},
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"confidence": face.get("confidence", 0.5),
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"embedding": emb,
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@@ -345,6 +343,7 @@ def main():
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parser.add_argument("--uuid", "-u", default="")
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parser.add_argument("--sample-interval", type=int, default=3)
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parser.add_argument("--force", action="store_true")
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parser.add_argument("--frames", type=str, default=None, help=argparse.SUPPRESS)
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args = parser.parse_args()
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publisher = RedisPublisher(args.uuid) if args.uuid else None
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