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| import cv2 | |
| import numpy as np | |
| import time | |
| import os | |
| from retinaface import RetinaFace | |
| def blur_faces(image, blur_amount=30, verbose=False): | |
| # Start timer | |
| start_time = time.time() | |
| # Convert image from BGR to RGB (if using OpenCV's imread) | |
| # image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| # Detect faces using RetinaFace | |
| detection_start = time.time() | |
| faces = RetinaFace.detect_faces(image) | |
| detection_time = time.time() - detection_start | |
| # Create a copy of the image | |
| output_image = image.copy() | |
| face_count = 0 | |
| blurring_start = time.time() | |
| # import pdb; pdb.set_trace() | |
| # Process each face | |
| if faces and isinstance(faces, dict): | |
| for face_key in faces: | |
| face_count += 1 | |
| face_data = faces[face_key] | |
| # Get bounding box coordinates | |
| x1, y1, x2, y2 = face_data['facial_area'] | |
| # Ensure the coordinates are within the image boundaries | |
| ih, iw = image.shape[:2] | |
| x1, y1 = max(0, x1), max(0, y1) | |
| x2, y2 = min(iw, x2), min(ih, y2) | |
| # Extract face region | |
| face_region = output_image[y1:y2, x1:x2] | |
| # Apply blur | |
| blurred_face = cv2.GaussianBlur(face_region, (blur_amount, blur_amount), 0) | |
| # Replace face region with blurred version | |
| output_image[y1:y2, x1:x2] = blurred_face | |
| blurring_time = time.time() - blurring_start | |
| total_time = time.time() - start_time | |
| if verbose: | |
| # Print timing information | |
| print(f"Face blurring performance metrics:") | |
| print(f"Total faces detected: {face_count}") | |
| print(f"Face detection time: {detection_time:.4f} seconds") | |
| print(f"Face blurring time: {blurring_time:.4f} seconds") | |
| print(f"Total processing time: {total_time:.4f} seconds") | |
| print(f"Average time per face: {(total_time/max(1, face_count)):.4f} seconds") | |
| return output_image, { | |
| "face_count": face_count, | |
| "detection_time": detection_time, | |
| "blurring_time": blurring_time, | |
| "total_time": total_time, | |
| "avg_time_per_face": total_time/max(1, face_count) | |
| } | |
| # Example usage | |
| if __name__ == "__main__": | |
| # Read an image from file | |
| image_path = "multiple_people2.jpeg" # Replace with your image path | |
| output_path = "blurred_output.jpg" # Output path for the blurred image | |
| image = cv2.imread(image_path) | |
| if image is None: | |
| print(f"Could not read image from {image_path}") | |
| else: | |
| # Process the image with timing | |
| result_image, timing_info = blur_faces(image, blur_amount=31) | |
| # Save the result | |
| cv2.imwrite(output_path, result_image) | |
| print(f"Blurred image saved to {output_path}") | |
| ## requirements and cv2 | |
| # pip install retina-face | |
| # pip install opencv-python |