feat: update Python processors and add utility scripts
- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
This commit is contained in:
Warren
@@ -1,114 +1,159 @@
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#!/opt/homebrew/bin/python3.11
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"""
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Pose Processor - Pose Estimation
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Pose Processor - Pose Estimation with Resume Support
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Uses YOLOv8 Pose via ultralytics (local model)
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Resume Feature:
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- Auto-detect existing results and resume from last frame
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- Auto-save at configurable intervals (default: 30 seconds)
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- Ctrl+C gracefully saves and exits
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Note: YOLOv8 Pose uses stream mode which is optimized for video processing.
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For resume support, we need to process frames manually with OpenCV.
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"""
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import sys
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import json
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import argparse
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import os
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import signal
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import time
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from datetime import datetime
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from redis_publisher import RedisPublisher
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from resume_framework import ResumeFramework, format_time, print_progress
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def signal_handler(signum, frame):
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print(f"POSE: Received signal {signum}, exiting...")
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sys.exit(1)
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KEYPOINT_NAMES = [
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"nose",
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"left_eye",
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"right_eye",
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"left_ear",
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"right_ear",
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"left_shoulder",
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"right_shoulder",
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"left_elbow",
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"right_elbow",
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"left_wrist",
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"right_wrist",
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"left_hip",
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"right_hip",
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"left_knee",
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"right_knee",
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"left_ankle",
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"right_ankle",
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]
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def process_pose(video_path: str, output_path: str, uuid: str = ""):
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"""Process video for pose estimation using YOLOv8 Pose"""
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def process_pose(
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video_path: str,
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output_path: str,
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uuid: str = "",
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auto_save_interval: int = 30,
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auto_save_frames: int = 300,
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force_restart: bool = False,
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):
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"""Process video for pose estimation using YOLOv8 Pose with resume support"""
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# Set up signal handlers
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signal.signal(signal.SIGTERM, signal_handler)
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signal.signal(signal.SIGINT, signal_handler)
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framework = ResumeFramework(
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output_path=output_path,
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processor_name="pose",
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uuid=uuid,
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auto_save_interval=auto_save_interval,
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auto_save_frames=auto_save_frames,
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force_restart=force_restart,
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)
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publisher = RedisPublisher(uuid) if uuid else None
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if publisher:
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publisher.info("pose", "POSE_START")
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framework.publish_info("POSE_START")
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try:
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from ultralytics import YOLO # pyright: ignore
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from ultralytics import YOLO
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except ImportError:
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if publisher:
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publisher.error("pose", "ultralytics not installed")
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result = {"frame_count": 0, "fps": 0.0, "frames": []}
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if publisher:
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publisher.complete("pose", "0 frames")
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framework.publish_error("ultralytics not installed")
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result = {
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"metadata": {"status": "error", "error": "ultralytics not installed"},
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"frames": {},
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}
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with open(output_path, "w") as f:
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json.dump(result, f, indent=2)
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return result
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if publisher:
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publisher.info("pose", "POSE_LOADING_MODEL")
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framework.publish_info("POSE_LOADING_MODEL")
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# Load YOLOv8 Pose model
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# yolov8n-pose.pt = nano (fastest)
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# yolov8s-pose.pt = small
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# yolov8m-pose.pt = medium
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model = YOLO("yolov8n-pose.pt")
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# Get video info
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import cv2
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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print(f"Error: Cannot open video: {video_path}")
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return {"metadata": {"status": "error"}, "frames": {}}
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fps = cap.get(cv2.CAP_PROP_FPS)
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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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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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total_duration = total_frames / fps if fps > 0 else 0
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cap.release()
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if publisher:
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publisher.info("pose", f"fps={fps}, frames={total_frames}")
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publisher.progress("pose", 0, total_frames, "Starting")
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framework.publish_info(f"fps={fps}, frames={total_frames}")
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# Process video with YOLO Pose
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results = model(
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video_path,
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conf=0.5, # confidence threshold
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save=False,
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stream=True,
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verbose=False,
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pose=True, # Enable pose estimation
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)
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existing_data, last_checkpoint = framework.load_existing_data()
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resume_mode = existing_data is not None and last_checkpoint > 0 and not force_restart
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# COCO keypoint names
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KEYPOINT_NAMES = [
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"nose",
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"left_eye",
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"right_eye",
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"left_ear",
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"right_ear",
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"left_shoulder",
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"right_shoulder",
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"left_elbow",
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"right_elbow",
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"left_wrist",
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"right_wrist",
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"left_hip",
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"right_hip",
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"left_knee",
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"right_knee",
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"left_ankle",
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"right_ankle",
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]
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if resume_mode:
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print(f"\nFound existing data: {output_path}")
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print(f"Last processed frame: {last_checkpoint}")
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print(f"Will resume from frame {last_checkpoint + 1}")
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frames = []
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frame_count = 0
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if resume_mode and existing_data:
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pose_data = existing_data
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frame_count = last_checkpoint
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processed_frames = set(int(k) for k in existing_data.get("frames", {}).keys())
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cap = cv2.VideoCapture(video_path)
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cap.set(cv2.CAP_PROP_POS_FRAMES, frame_count)
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else:
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pose_data = {
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"metadata": framework.init_metadata(
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video_path=video_path,
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fps=fps,
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width=width,
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height=height,
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total_frames=total_frames,
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total_duration=total_duration,
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extra={"model": "yolov8n-pose"},
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),
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"frames": {},
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}
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frame_count = 0
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processed_frames = set()
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cap = cv2.VideoCapture(video_path)
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framework.set_data(pose_data)
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start_time = time.time()
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framework.last_save_time = start_time
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print(f"\nProcessing video: {total_frames} frames @ {fps:.2f} fps")
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print(f"Auto-save every {auto_save_interval}s or {auto_save_frames} frames")
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print(f"Resume from frame {frame_count + 1 if resume_mode else 1}")
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print()
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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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for result in results:
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frame_count += 1
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current_time = (frame_count - 1) / fps if fps > 0 else 0
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# Get frame number and timestamp
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frame_idx = (
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result.orig_frame_idx
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if hasattr(result, "orig_frame_idx")
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else frame_count - 1
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)
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timestamp = frame_idx / fps if fps > 0 else 0
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if frame_count in processed_frames:
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continue
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results = model(frame, conf=0.5, verbose=False, pose=True)
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result = results[0]
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# Get pose keypoints
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persons = []
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if result.keypoints is not None:
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@@ -128,7 +173,6 @@ def process_pose(video_path: str, output_path: str, uuid: str = ""):
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}
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)
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# Get bounding box from keypoints if available
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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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xs = [kp["x"] for kp in valid_kps]
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@@ -144,35 +188,70 @@ def process_pose(video_path: str, output_path: str, uuid: str = ""):
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persons.append({"keypoints": keypoints, "bbox": bbox})
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# Only add frames with poses or sample periodically
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if persons or frame_count % 30 == 0:
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frames.append(
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{
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"frame": frame_idx,
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"timestamp": round(timestamp, 3),
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"persons": persons,
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}
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)
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pose_data["frames"][str(frame_count)] = {
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"frame_number": frame_count,
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"time_seconds": round(current_time, 3),
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"time_formatted": format_time(current_time),
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"persons": persons,
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}
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processed_frames.add(frame_count)
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if publisher:
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publisher.progress("pose", frame_count, total_frames, f"Frame {frame_idx}")
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if frame_count % 500 == 0:
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elapsed = time.time() - start_time
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print_progress(frame_count, total_frames, elapsed, f"{len(persons)} persons")
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framework.publish_progress(frame_count, total_frames, f"frame {frame_count}")
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result = {"frame_count": total_frames, "fps": fps, "frames": frames}
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if framework.should_auto_save(frame_count):
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framework.save_progress(frame_count, silent=True)
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if publisher:
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publisher.complete("pose", f"{len(frames)} frames with poses")
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cap.release()
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with open(output_path, "w") as f:
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json.dump(result, f, indent=2)
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total_processed = len(processed_frames)
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return result
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framework.finalize(
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total_processed=total_processed,
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extra_metadata={"model": "yolov8n-pose"},
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)
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print(f"\nPose estimation completed: {total_processed} frames processed")
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print(f"Frames with poses: {len([f for f in pose_data['frames'].values() if f['persons']])}")
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return pose_data
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Pose Estimation")
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parser = argparse.ArgumentParser(description="Pose Estimation with Resume Support")
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parser.add_argument("video_path", help="Path to video file")
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parser.add_argument("output_path", help="Output JSON path")
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parser.add_argument("--uuid", "-u", help="UUID for Redis progress", default="")
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parser.add_argument(
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"--auto-save-interval",
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"-a",
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help="Auto-save interval in seconds",
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type=int,
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default=30,
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)
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parser.add_argument(
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"--auto-save-frames",
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"-f",
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help="Auto-save interval in frames",
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type=int,
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default=300,
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)
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parser.add_argument(
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"--force-restart",
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"-r",
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help="Force restart (ignore existing data)",
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action="store_true",
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)
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args = parser.parse_args()
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process_pose(args.video_path, args.output_path, args.uuid)
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process_pose(
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args.video_path,
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args.output_path,
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args.uuid,
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args.auto_save_interval,
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args.auto_save_frames,
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args.force_restart,
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)
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