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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#!/opt/homebrew/bin/python3.11
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"""
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MediaPipe Pose Processor - Using MediaPipe Pose Landmarker
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Detects human pose with 33 keypoints, including face landmarks (nose, eyes).
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Coordinates are normalized (0-1), converted to pixel coordinates.
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Usage:
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python3 scripts/mediapipe_pose_processor.py --video /path/to/video.mp4 --file-uuid <uuid> --output-dir /path/to/output
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Output:
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{uuid}.pose.mediapipe.json
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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 sys
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import time
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from pathlib import Path
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try:
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import cv2
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import mediapipe as mp
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import numpy as np
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from mediapipe.tasks.python.vision import PoseLandmarker, PoseLandmarkerOptions
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from mediapipe.tasks.python.core.base_options import BaseOptions
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except ImportError as e:
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print(f"Missing dependency: {e}", file=sys.stderr)
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sys.exit(1)
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# MediaPipe Pose landmark names (33 keypoints)
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LANDMARK_NAMES = [
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"nose", "left_eye_inner", "left_eye", "left_eye_outer",
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"right_eye_inner", "right_eye", "right_eye_outer",
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"left_ear", "right_ear", "mouth_left", "mouth_right",
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"left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
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"left_wrist", "right_wrist", "left_pinky", "right_pinky",
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"left_index", "right_index", "left_thumb", "right_thumb",
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"left_hip", "right_hip", "left_knee", "right_knee",
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"left_ankle", "right_ankle", "left_heel", "right_heel",
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"left_foot_index", "right_foot_index",
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]
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def process_video(
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video_path: str,
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output_path: str,
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file_uuid: str,
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sample_interval: int = 3, # Match Apple Vision default
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) -> dict:
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"""
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Process video with MediaPipe Pose.
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Args:
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video_path: Path to video file
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output_path: Output JSON path
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file_uuid: File UUID
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sample_interval: Process every N frames
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Returns:
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Dict with pose data
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"""
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# Download model if not exists
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model_path = os.path.expanduser("~/.mediapipe/models/pose_landmarker_heavy.task")
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if not os.path.exists(model_path):
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os.makedirs(os.path.dirname(model_path), exist_ok=True)
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print(f"[mediapipe_pose] Downloading model...")
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import urllib.request
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url = "https://storage.googleapis.com/mediapipe-models/pose_landmarker/pose_landmarker_heavy/float16/1/pose_landmarker_heavy.task"
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urllib.request.urlretrieve(url, model_path)
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print(f"[mediapipe_pose] Model downloaded to {model_path}")
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# Initialize Pose Landmarker
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options = PoseLandmarkerOptions(
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base_options=BaseOptions(model_asset_path=model_path),
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running_mode=mp.tasks.vision.RunningMode.VIDEO,
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)
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detector = PoseLandmarker.create_from_options(options)
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# Open video
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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print(f"[mediapipe_pose] Cannot open video: {video_path}", file=sys.stderr)
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return {"error": "Cannot open video"}
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fps = cap.get(cv2.CAP_PROP_FPS)
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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print(f"[mediapipe_pose] Video: {total_frames} frames, {fps:.2f} fps, {width}x{height}")
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print(f"[mediapipe_pose] Processing every {sample_interval} frames...")
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frames_data = []
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frame_num = 0
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processed_count = 0
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start_time = time.time()
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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# Process every N frames
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if frame_num % sample_interval == 0:
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# Convert BGR to RGB
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rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# Create MediaPipe Image
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mp_image = mp.Image(mp.ImageFormat.SRGB, rgb_frame)
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# Detect pose
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results = detector.detect_for_video(mp_image, int(frame_num * 1000 / fps))
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if results.pose_landmarks:
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for pose_landmarks in results.pose_landmarks:
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keypoints = []
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face_keypoints = []
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for idx, landmark in enumerate(pose_landmarks):
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name = LANDMARK_NAMES[idx] if idx < len(LANDMARK_NAMES) else f"landmark_{idx}"
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kp = {
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"name": name,
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"x": landmark.x * width,
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"y": landmark.y * height,
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"z": landmark.z if hasattr(landmark, 'z') else 0,
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"confidence": landmark.visibility if hasattr(landmark, 'visibility') else 1.0,
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}
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keypoints.append(kp)
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if name in ["nose", "left_eye", "right_eye"]:
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face_keypoints.append(kp)
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# Calculate bbox from all keypoints
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valid_kps = [kp for kp in keypoints if kp["confidence"] > 0.3]
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if valid_kps:
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x_coords = [kp["x"] for kp in valid_kps]
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y_coords = [kp["y"] for kp in valid_kps]
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bbox = {
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"x": min(x_coords),
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"y": min(y_coords),
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"width": max(x_coords) - min(x_coords),
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"height": max(y_coords) - min(y_coords),
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}
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else:
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bbox = {"x": 0, "y": 0, "width": 0, "height": 0}
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frames_data.append({
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"frame": frame_num,
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"timestamp": frame_num / fps,
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"persons": [{
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"keypoints": keypoints,
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"bbox": bbox,
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"face_keypoints": face_keypoints,
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}],
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})
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processed_count += 1
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if processed_count % 100 == 0:
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elapsed = time.time() - start_time
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print(f"[mediapipe_pose] Processed {processed_count} poses ({elapsed:.1f}s)")
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frame_num += 1
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cap.release()
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detector.close()
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elapsed = time.time() - start_time
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# Build output
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output = {
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"file_uuid": file_uuid,
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"processor": "mediapipe_pose",
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"fps": fps,
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"frame_count": total_frames,
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"sample_interval": sample_interval,
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"total_poses": len(frames_data),
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"elapsed_seconds": round(elapsed, 2),
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"frames": frames_data,
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}
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# Save
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with open(output_path, "w") as f:
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json.dump(output, f)
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print(f"[mediapipe_pose] Saved: {output_path}")
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print(f"[mediapipe_pose] Total poses: {len(frames_data)}")
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print(f"[mediapipe_pose] Elapsed: {elapsed:.1f}s")
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return output
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def main():
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parser = argparse.ArgumentParser(description="MediaPipe Pose Processor")
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parser.add_argument("--video", "-v", required=True, help="Video file path")
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parser.add_argument("--file-uuid", "-u", required=True, help="File UUID")
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parser.add_argument("--output-dir", "-o", default="/Users/accusys/momentry/output", help="Output directory")
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parser.add_argument("--sample-interval", "-s", type=int, default=3, help="Process every N frames")
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args = parser.parse_args()
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output_path = Path(args.output_dir) / f"{args.file_uuid}.pose.mediapipe.json"
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result = process_video(
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args.video,
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str(output_path),
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args.file_uuid,
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args.sample_interval,
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)
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if "error" not in result:
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print(f"\n[mediapipe_pose] Done. Output: {output_path}")
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if __name__ == "__main__":
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main()
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