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
213
scripts/face_processor_optimized.py
Executable file
213
scripts/face_processor_optimized.py
Executable file
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#!/opt/homebrew/bin/python3.11
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"""
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Face Processor - 優化版
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可調整採樣間隔，平衡速度與準確度
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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 subprocess
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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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def signal_handler(signum, frame):
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print(f"Face: Received signal {signum}, exiting...")
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sys.exit(1)
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def has_audio_stream(video_path):
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"""Check if video file has audio stream using ffprobe."""
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try:
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cmd = [
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"ffprobe",
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"-v",
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"error",
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"-select_streams",
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"a",
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"-show_entries",
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"stream=codec_type",
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"-of",
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"csv=p=0",
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video_path,
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]
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result = subprocess.run(cmd, capture_output=True, text=True, check=True)
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return bool(result.stdout.strip())
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except subprocess.CalledProcessError:
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return False
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except FileNotFoundError:
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print("WARNING: ffprobe not found, assuming audio exists")
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return True
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def process_face(
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video_path: str, output_path: str, uuid: str = "", sample_interval: int = 15
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):
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"""
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Process video for face detection
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Args:
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video_path: Path to video file
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output_path: Path to output JSON
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uuid: UUID for Redis progress
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sample_interval: Process every N frames (default: 15)
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"""
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signal.signal(signal.SIGTERM, signal_handler)
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signal.signal(signal.SIGINT, signal_handler)
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publisher = RedisPublisher(uuid) if uuid else None
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if publisher:
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publisher.info("face", "FACE_START")
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try:
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import cv2
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except ImportError:
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if publisher:
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publisher.error("face", "opencv-python 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("face", "0 frames")
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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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sys.exit(1)
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if publisher:
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publisher.info("face", "FACE_LOADING_CASCADE")
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# Load Haar Cascade
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face_cascade = cv2.CascadeClassifier(
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cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
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)
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if face_cascade.empty():
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if publisher:
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publisher.error("face", "Could not load Haar Cascade")
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result = {"frame_count": 0, "fps": 0.0, "frames": []}
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if publisher:
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publisher.complete("face", "0 frames")
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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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sys.exit(1)
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if publisher:
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publisher.info("face", "FACE_CASCADE_LOADED")
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# Get video info
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cap = cv2.VideoCapture(video_path)
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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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cap.release()
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if publisher:
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publisher.info(
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"face",
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f"fps={fps}, frames={total_frames}, sample_interval={sample_interval}",
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)
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publisher.progress("face", 0, total_frames, "Starting")
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frames = []
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frame_count = 0
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processed = 0
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cap = cv2.VideoCapture(video_path)
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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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frame_count += 1
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# Sample frames
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if frame_count % sample_interval != 0:
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continue
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processed += 1
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timestamp = (frame_count - 1) / fps if fps > 0 else 0
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# Convert to grayscale
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# Detect faces
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try:
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faces = face_cascade.detectMultiScale(
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gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)
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)
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except Exception as e:
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if publisher:
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publisher.error("face", f"Frame {frame_count}: {e}")
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faces = []
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face_list = []
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for x, y, w, h in faces:
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face_list.append(
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{
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"face_id": None,
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"x": int(x),
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"y": int(y),
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"width": int(w),
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"height": int(h),
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"confidence": 0.8,
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}
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)
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# Only add frames with faces
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if face_list:
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frames.append(
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{
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"frame": frame_count - 1,
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"timestamp": round(timestamp, 3),
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"faces": face_list,
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}
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)
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if publisher:
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publisher.progress(
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"face",
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processed,
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total_frames // sample_interval,
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f"Frame {frame_count}, {len(face_list)} faces",
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)
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cap.release()
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result = {
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"frame_count": total_frames,
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"fps": fps,
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"frames": frames,
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"sample_interval": sample_interval,
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"total_faces_detected": len(frames),
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}
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if publisher:
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publisher.complete("face", f"{len(frames)} frames with faces")
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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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sys.stderr.write(
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f"Face: Detection complete, {len(frames)} frames written to {output_path}\n"
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)
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sys.stderr.flush()
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sys.exit(0)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Face Detection (Optimized)")
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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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"--sample-interval",
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"-s",
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type=int,
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default=15,
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help="Process every N frames (default: 15)",
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)
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args = parser.parse_args()
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process_face(args.video_path, args.output_path, args.uuid, args.sample_interval)
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