import argparse import numpy as np import cv2 as cv def str2bool(v): if v.lower() in ['on', 'yes', 'true', 'y', 't']: return True elif v.lower() in ['off', 'no', 'false', 'n', 'f']: return False else: raise NotImplementedError parser = argparse.ArgumentParser() parser.add_argument('--image1', '-i1', type=str, help='Path to the input image1. Omit for detecting on default camera.') parser.add_argument('--image2', '-i2', type=str, help='Path to the input image2. When image1 and image2 parameters given then the program try to find a face on both images and runs face recognition algorithm.') parser.add_argument('--video', '-v', type=str, help='Path to the input video.') parser.add_argument('--scale', '-sc', type=float, default=1.0, help='Scale factor used to resize input video frames.') parser.add_argument('--face_detection_model', '-fd', type=str, default='face_detection_yunet_2021dec.onnx', help='Path to the face detection model. Download the model at https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet') parser.add_argument('--face_recognition_model', '-fr', type=str, default='face_recognition_sface_2021dec.onnx', help='Path to the face recognition model. Download the model at https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface') parser.add_argument('--score_threshold', type=float, default=0.9, help='Filtering out faces of score < score_threshold.') parser.add_argument('--nms_threshold', type=float, default=0.3, help='Suppress bounding boxes of iou >= nms_threshold.') parser.add_argument('--top_k', type=int, default=5000, help='Keep top_k bounding boxes before NMS.') parser.add_argument('--save', '-s', type=str2bool, default=False, help='Set true to save results. This flag is invalid when using camera.') args = parser.parse_args() def visualize(input, faces, fps, thickness=2): if faces[1] is not None: for idx, face in enumerate(faces[1]): print('Face {}, top-left coordinates: ({:.0f}, {:.0f}), box width: {:.0f}, box height {:.0f}, score: {:.2f}'.format(idx, face[0], face[1], face[2], face[3], face[-1])) coords = face[:-1].astype(np.int32) cv.rectangle(input, (coords[0], coords[1]), (coords[0]+coords[2], coords[1]+coords[3]), (0, 255, 0), thickness) cv.circle(input, (coords[4], coords[5]), 2, (255, 0, 0), thickness) cv.circle(input, (coords[6], coords[7]), 2, (0, 0, 255), thickness) cv.circle(input, (coords[8], coords[9]), 2, (0, 255, 0), thickness) cv.circle(input, (coords[10], coords[11]), 2, (255, 0, 255), thickness) cv.circle(input, (coords[12], coords[13]), 2, (0, 255, 255), thickness) cv.putText(input, 'FPS: {:.2f}'.format(fps), (1, 16), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) if __name__ == '__main__': ## [initialize_FaceDetectorYN] detector = cv.FaceDetectorYN.create( args.face_detection_model, "", (320, 320), args.score_threshold, args.nms_threshold, args.top_k ) ## [initialize_FaceDetectorYN] tm = cv.TickMeter() # If input is an image if args.image1 is not None: img1 = cv.imread(cv.samples.findFile(args.image1)) img1Width = int(img1.shape[1]*args.scale) img1Height = int(img1.shape[0]*args.scale) img1 = cv.resize(img1, (img1Width, img1Height)) tm.start() ## [inference] # Set input size before inference detector.setInputSize((img1Width, img1Height)) faces1 = detector.detect(img1) ## [inference] tm.stop() assert faces1[1] is not None, 'Cannot find a face in {}'.format(args.image1) # Draw results on the input image visualize(img1, faces1, tm.getFPS()) # Save results if save is true if args.save: print('Results saved to result.jpg\n') cv.imwrite('result.jpg', img1) # Visualize results in a new window cv.imshow("image1", img1) if args.image2 is not None: img2 = cv.imread(cv.samples.findFile(args.image2)) tm.reset() tm.start() detector.setInputSize((img2.shape[1], img2.shape[0])) faces2 = detector.detect(img2) tm.stop() assert faces2[1] is not None, 'Cannot find a face in {}'.format(args.image2) visualize(img2, faces2, tm.getFPS()) cv.imshow("image2", img2) ## [initialize_FaceRecognizerSF] recognizer = cv.FaceRecognizerSF.create( args.face_recognition_model,"") ## [initialize_FaceRecognizerSF] ## [facerecognizer] # Align faces face1_align = recognizer.alignCrop(img1, faces1[1][0]) face2_align = recognizer.alignCrop(img2, faces2[1][0]) # Extract features face1_feature = recognizer.feature(face1_align) face2_feature = recognizer.feature(face2_align) ## [facerecognizer] cosine_similarity_threshold = 0.363 l2_similarity_threshold = 1.128 ## [match] cosine_score = recognizer.match(face1_feature, face2_feature, cv.FaceRecognizerSF_FR_COSINE) l2_score = recognizer.match(face1_feature, face2_feature, cv.FaceRecognizerSF_FR_NORM_L2) ## [match] msg = 'different identities' if cosine_score >= cosine_similarity_threshold: msg = 'the same identity' print('They have {}. Cosine Similarity: {}, threshold: {} (higher value means higher similarity, max 1.0).'.format(msg, cosine_score, cosine_similarity_threshold)) msg = 'different identities' if l2_score <= l2_similarity_threshold: msg = 'the same identity' print('They have {}. NormL2 Distance: {}, threshold: {} (lower value means higher similarity, min 0.0).'.format(msg, l2_score, l2_similarity_threshold)) cv.waitKey(0) else: # Omit input to call default camera if args.video is not None: deviceId = args.video else: deviceId = 0 cap = cv.VideoCapture(deviceId) frameWidth = int(cap.get(cv.CAP_PROP_FRAME_WIDTH)*args.scale) frameHeight = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT)*args.scale) detector.setInputSize([frameWidth, frameHeight]) while cv.waitKey(1) < 0: hasFrame, frame = cap.read() if not hasFrame: print('No frames grabbed!') break frame = cv.resize(frame, (frameWidth, frameHeight)) # Inference tm.start() faces = detector.detect(frame) # faces is a tuple tm.stop() # Draw results on the input image visualize(frame, faces, tm.getFPS()) # Visualize results cv.imshow('Live', frame) cv.destroyAllWindows()