feat: Add MiniFasNet for Face Anti Spoofing (#41)

scripts/run_anonymization.py

@@ -205,3 +205,4 @@ Examples:
 
 if __name__ == '__main__':
     main()
+

scripts/run_spoofing.py

@@ -0,0 +1,201 @@
+# Face Anti-Spoofing Detection
+# Usage:
+#   Image: python run_spoofing.py --image path/to/image.jpg
+#   Video: python run_spoofing.py --video path/to/video.mp4
+#   Webcam: python run_spoofing.py --source 0
+
+import argparse
+import os
+from pathlib import Path
+
+import cv2
+import numpy as np
+
+from uniface import RetinaFace
+from uniface.constants import MiniFASNetWeights
+from uniface.spoofing import create_spoofer
+
+
+def draw_spoofing_result(
+    image: np.ndarray,
+    bbox: list,
+    label_idx: int,
+    score: float,
+    thickness: int = 2,
+) -> None:
+    """Draw bounding box with anti-spoofing result.
+
+    Args:
+        image: Input image to draw on.
+        bbox: Bounding box in [x1, y1, x2, y2] format.
+        label_idx: Prediction label index (0 = Fake, 1 = Real).
+        score: Confidence score (0.0 to 1.0).
+        thickness: Line thickness for bounding box.
+    """
+    x1, y1, x2, y2 = map(int, bbox[:4])
+
+    # Color based on result (green for real, red for fake)
+    is_real = label_idx == 1
+    color = (0, 255, 0) if is_real else (0, 0, 255)
+
+    # Draw bounding box
+    cv2.rectangle(image, (x1, y1), (x2, y2), color, thickness)
+
+    # Prepare label
+    label = 'Real' if is_real else 'Fake'
+    text = f'{label}: {score:.1%}'
+
+    # Draw label background
+    (tw, th), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)
+    cv2.rectangle(image, (x1, y1 - th - 10), (x1 + tw + 10, y1), color, -1)
+
+    # Draw label text
+    cv2.putText(image, text, (x1 + 5, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
+
+
+def process_image(detector, spoofer, image_path: str, save_dir: str = 'outputs') -> None:
+    """Process a single image for face anti-spoofing detection."""
+    image = cv2.imread(image_path)
+    if image is None:
+        print(f"Error: Failed to load image from '{image_path}'")
+        return
+
+    # Detect faces
+    faces = detector.detect(image)
+    print(f'Detected {len(faces)} face(s)')
+
+    if not faces:
+        print('No faces detected in the image.')
+        return
+
+    # Run anti-spoofing on each face
+    for i, face in enumerate(faces, 1):
+        label_idx, score = spoofer.predict(image, face['bbox'])
+        # label_idx: 0 = Fake, 1 = Real
+        label = 'Real' if label_idx == 1 else 'Fake'
+        print(f'  Face {i}: {label} ({score:.1%})')
+
+        # Draw result on image
+        draw_spoofing_result(image, face['bbox'], label_idx, score)
+
+    # Save output
+    os.makedirs(save_dir, exist_ok=True)
+    output_path = os.path.join(save_dir, f'{Path(image_path).stem}_spoofing.jpg')
+    cv2.imwrite(output_path, image)
+    print(f'Output saved: {output_path}')
+
+
+def process_video(detector, spoofer, source, save_dir: str = 'outputs') -> None:
+    """Process video or webcam stream for face anti-spoofing detection."""
+    # Handle webcam or video file
+    if isinstance(source, int) or source.isdigit():
+        cap = cv2.VideoCapture(int(source))
+        is_webcam = True
+        output_name = 'webcam_spoofing.mp4'
+    else:
+        cap = cv2.VideoCapture(source)
+        is_webcam = False
+        output_name = f'{Path(source).stem}_spoofing.mp4'
+
+    if not cap.isOpened():
+        print(f'Error: Failed to open video source: {source}')
+        return
+
+    # Get video properties
+    fps = int(cap.get(cv2.CAP_PROP_FPS)) if not is_webcam else 30
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    # Setup video writer
+    os.makedirs(save_dir, exist_ok=True)
+    output_path = os.path.join(save_dir, output_name)
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    writer = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+    print("Processing video... Press 'q' to quit")
+    frame_count = 0
+
+    try:
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            frame_count += 1
+
+            # Detect faces
+            faces = detector.detect(frame)
+
+            # Run anti-spoofing on each face
+            for face in faces:
+                label_idx, score = spoofer.predict(frame, face['bbox'])
+                draw_spoofing_result(frame, face['bbox'], label_idx, score)
+
+            # Write frame
+            writer.write(frame)
+
+            # Display frame
+            cv2.imshow('Face Anti-Spoofing', frame)
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                print('Stopped by user.')
+                break
+
+    finally:
+        cap.release()
+        writer.release()
+        cv2.destroyAllWindows()
+
+    print(f'Processed {frame_count} frames')
+    if not is_webcam:
+        print(f'Output saved: {output_path}')
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Face Anti-Spoofing Detection')
+    parser.add_argument('--image', type=str, help='Path to input image')
+    parser.add_argument('--video', type=str, help='Path to input video')
+    parser.add_argument('--source', type=str, help='Video source (0 for webcam)')
+    parser.add_argument(
+        '--model',
+        type=str,
+        default='v2',
+        choices=['v1se', 'v2'],
+        help='Model variant: v1se or v2 (default: v2)',
+    )
+    parser.add_argument('--scale', type=float, default=None, help='Custom crop scale (default: auto)')
+    parser.add_argument('--save_dir', type=str, default='outputs', help='Output directory')
+    args = parser.parse_args()
+
+    # Check that at least one input source is provided
+    if not any([args.image, args.video, args.source]):
+        parser.print_help()
+        print('\nError: Please provide --image, --video, or --source')
+        return
+
+    # Select model variant
+    model_name = MiniFASNetWeights.V1SE if args.model == 'v1se' else MiniFASNetWeights.V2
+
+    # Initialize models
+    print(f'Initializing models (MiniFASNet {args.model.upper()})...')
+    detector = RetinaFace()
+    spoofer = create_spoofer(model_name=model_name, scale=args.scale)
+
+    # Process input
+    if args.image:
+        if not os.path.exists(args.image):
+            print(f'Error: Image not found: {args.image}')
+            return
+        process_image(detector, spoofer, args.image, args.save_dir)
+
+    elif args.video:
+        if not os.path.exists(args.video):
+            print(f'Error: Video not found: {args.video}')
+            return
+        process_video(detector, spoofer, args.video, args.save_dir)
+
+    elif args.source:
+        process_video(detector, spoofer, args.source, args.save_dir)
+
+
+if __name__ == '__main__':
+    main()