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ref: Update some refactoring files for testing
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yakhyo
@@ -1,141 +1,92 @@
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"""Video Face Detection Script"""
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# Face detection on video files
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# Usage: python run_video_detection.py --input video.mp4 --output output.mp4
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import cv2
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import argparse
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from pathlib import Path
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import cv2
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from tqdm import tqdm
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from uniface import RetinaFace, SCRFD
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from uniface import SCRFD, RetinaFace
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from uniface.visualization import draw_detections
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def process_video(detector, input_path: str, output_path: str, vis_threshold: float = 0.6,
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fps: int = None, show_preview: bool = False):
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# Open input video
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def process_video(detector, input_path: str, output_path: str, threshold: float = 0.6, show_preview: bool = False):
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cap = cv2.VideoCapture(input_path)
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if not cap.isOpened():
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print(f"Error: Cannot open video file '{input_path}'")
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return
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# Get video properties
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# get video properties
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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source_fps = cap.get(cv2.CAP_PROP_FPS)
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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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output_fps = fps if fps is not None else source_fps
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print(f"Input: {input_path} ({width}x{height}, {fps:.1f} fps, {total_frames} frames)")
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print(f"Output: {output_path}")
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print(f"📹 Input: {input_path}")
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print(f" Resolution: {width}x{height}")
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print(f" FPS: {source_fps:.2f}")
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print(f" Total frames: {total_frames}")
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print(f"\n📹 Output: {output_path}")
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print(f" FPS: {output_fps:.2f}\n")
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# Initialize video writer
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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out = cv2.VideoWriter(output_path, fourcc, output_fps, (width, height))
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fourcc = cv2.VideoWriter_fourcc(*"mp4v") # codec for .mp4
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out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
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if not out.isOpened():
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print(f"Error: Cannot create output video '{output_path}'")
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cap.release()
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return
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# Process frames
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frame_count = 0
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total_faces = 0
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try:
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with tqdm(total=total_frames, desc="Processing", unit="frames") as pbar:
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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 _ in tqdm(range(total_frames), desc="Processing", unit="frames"):
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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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frame_count += 1
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faces = detector.detect(frame)
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total_faces += len(faces)
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# Detect faces
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faces = detector.detect(frame)
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total_faces += len(faces)
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bboxes = [f["bbox"] for f in faces]
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scores = [f["confidence"] for f in faces]
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landmarks = [f["landmarks"] for f in faces]
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draw_detections(frame, bboxes, scores, landmarks, vis_threshold=threshold)
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# Draw detections
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bboxes = [f['bbox'] for f in faces]
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scores = [f['confidence'] for f in faces]
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landmarks = [f['landmarks'] for f in faces]
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draw_detections(frame, bboxes, scores, landmarks, vis_threshold=vis_threshold)
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cv2.putText(frame, f"Faces: {len(faces)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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out.write(frame)
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# Add frame info
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cv2.putText(frame, f"Faces: {len(faces)}", (10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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# Write frame
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out.write(frame)
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# Show preview if requested
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if show_preview:
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cv2.imshow("Processing Video - Press 'q' to cancel", frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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print("\nProcessing cancelled by user")
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break
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pbar.update(1)
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except KeyboardInterrupt:
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print("\nProcessing interrupted")
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finally:
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cap.release()
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out.release()
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if show_preview:
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cv2.destroyAllWindows()
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cv2.imshow("Processing - Press 'q' to cancel", frame)
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if cv2.waitKey(1) & 0xFF == ord("q"):
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print("\nCancelled by user")
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break
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# Summary
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print(f"\nProcessing complete!")
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print(f" Processed: {frame_count} frames")
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print(f" Total faces detected: {total_faces}")
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print(f" Average faces per frame: {total_faces/frame_count:.2f}" if frame_count > 0 else "")
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print(f" Output saved: {output_path}")
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cap.release()
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out.release()
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if show_preview:
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cv2.destroyAllWindows()
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avg_faces = total_faces / frame_count if frame_count > 0 else 0
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print(f"\nDone! {frame_count} frames, {total_faces} faces ({avg_faces:.1f} avg/frame)")
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print(f"Saved: {output_path}")
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def main():
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parser = argparse.ArgumentParser(description="Process video with face detection")
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parser.add_argument("--input", type=str, required=True, help="Path to input video")
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parser.add_argument("--output", type=str, required=True, help="Path to output video")
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parser.add_argument("--detector", type=str, default="retinaface",
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choices=['retinaface', 'scrfd'], help="Face detector to use")
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parser.add_argument("--threshold", type=float, default=0.6,
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help="Confidence threshold for visualization")
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parser.add_argument("--fps", type=int, default=None,
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help="Output FPS (default: same as input)")
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parser.add_argument("--preview", action="store_true",
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help="Show live preview during processing")
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parser.add_argument("--verbose", action="store_true", help="Enable verbose logging")
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parser.add_argument("--input", type=str, required=True, help="Input video path")
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parser.add_argument("--output", type=str, required=True, help="Output video path")
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parser.add_argument("--detector", type=str, default="retinaface", choices=["retinaface", "scrfd"])
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parser.add_argument("--threshold", type=float, default=0.6, help="Visualization threshold")
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parser.add_argument("--preview", action="store_true", help="Show live preview")
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args = parser.parse_args()
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# Check input exists
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if not Path(args.input).exists():
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print(f"Error: Input file '{args.input}' does not exist")
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return
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# Create output directory if needed
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output_dir = Path(args.output).parent
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if output_dir != Path('.'):
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output_dir.mkdir(parents=True, exist_ok=True)
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Path(args.output).parent.mkdir(parents=True, exist_ok=True)
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if args.verbose:
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from uniface import enable_logging
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enable_logging()
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# Initialize detector
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print(f"Initializing detector: {args.detector}")
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if args.detector == 'retinaface':
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detector = RetinaFace()
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else:
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detector = SCRFD()
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print("Detector initialized\n")
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# Process video
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process_video(detector, args.input, args.output, args.threshold, args.fps, args.preview)
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detector = RetinaFace() if args.detector == "retinaface" else SCRFD()
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process_video(detector, args.input, args.output, args.threshold, args.preview)
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if __name__ == "__main__":
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