Upload scripts/detect_video.py with huggingface_hub

scripts/detect_video.py

@@ -0,0 +1,110 @@
+#!/usr/bin/env python3
+"""
+Video face detection with tracking and temporal smoothing.
+
+Usage:
+    # Process video file
+    python scripts/detect_video.py \\
+        --model scrfd_34g \\
+        --checkpoint checkpoints/scrfd_34g_best.pth \\
+        --input video.mp4 \\
+        --output output.mp4
+
+    # Webcam (real-time)
+    python scripts/detect_video.py \\
+        --model scrfd_2.5g \\
+        --checkpoint checkpoints/scrfd_2.5g_best.pth \\
+        --input 0 \\
+        --show
+
+    # RTSP stream
+    python scripts/detect_video.py \\
+        --model scrfd_2.5g \\
+        --checkpoint checkpoints/scrfd_2.5g_best.pth \\
+        --input rtsp://192.168.1.100/stream \\
+        --output output.mp4
+"""
+
+import os
+import sys
+import argparse
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+from models.detector import build_detector
+from engine.video_detector import VideoFaceDetector
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Video Face Detection')
+    parser.add_argument('--model', type=str, default='scrfd_34g')
+    parser.add_argument('--checkpoint', type=str, required=True)
+    parser.add_argument('--input', type=str, required=True,
+                        help='Video file, webcam index (0), or RTSP URL')
+    parser.add_argument('--output', type=str, default=None,
+                        help='Output video path')
+    parser.add_argument('--input-size', type=int, default=640)
+    parser.add_argument('--score-thresh', type=float, default=0.4)
+    parser.add_argument('--nms-thresh', type=float, default=0.4)
+    parser.add_argument('--device', type=str, default='cuda')
+    parser.add_argument('--no-tracking', action='store_true')
+    parser.add_argument('--no-smoothing', action='store_true')
+    parser.add_argument('--keyframe-interval', type=int, default=0,
+                        help='Run detector every N frames (0=every frame)')
+    parser.add_argument('--max-frames', type=int, default=-1)
+    parser.add_argument('--show', action='store_true',
+                        help='Display video in window')
+    parser.add_argument('--onnx', type=str, default=None,
+                        help='Use ONNX model instead of PyTorch')
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+
+    # Build model
+    if args.onnx:
+        model = None
+        model_path = args.onnx
+    else:
+        model = build_detector(
+            args.model,
+            score_threshold=args.score_thresh,
+            nms_threshold=args.nms_thresh,
+        )
+        model_path = args.checkpoint
+
+    # Build video detector
+    detector = VideoFaceDetector(
+        model=model,
+        model_path=model_path,
+        model_name=args.model,
+        device=args.device,
+        score_threshold=args.score_thresh,
+        nms_threshold=args.nms_thresh,
+        input_size=args.input_size,
+        use_tracking=not args.no_tracking,
+        use_smoothing=not args.no_smoothing,
+        keyframe_interval=args.keyframe_interval,
+    )
+
+    # Process video
+    source = int(args.input) if args.input.isdigit() else args.input
+    stats = detector.process_video(
+        source=source,
+        max_frames=args.max_frames,
+        output_path=args.output,
+        show=args.show,
+    )
+
+    print(f"\nProcessing complete:")
+    print(f"  Frames: {stats['total_frames']}")
+    print(f"  Average FPS: {stats['avg_fps']:.1f}")
+    print(f"  Average faces/frame: {stats['avg_faces_per_frame']:.1f}")
+    if args.output:
+        print(f"  Output saved to: {args.output}")
+
+
+if __name__ == '__main__':
+    main()