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| # This file is part of OpenCV Zoo project. | |
| # It is subject to the license terms in the LICENSE file found in the same directory. | |
| # | |
| # Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved. | |
| # Third party copyrights are property of their respective owners. | |
| import argparse | |
| import numpy as np | |
| import cv2 as cv | |
| # Check OpenCV version | |
| opencv_python_version = lambda str_version: tuple(map(int, (str_version.split(".")))) | |
| assert opencv_python_version(cv.__version__) >= opencv_python_version("4.10.0"), \ | |
| "Please install latest opencv-python for benchmark: python3 -m pip install --upgrade opencv-python" | |
| from yunet import YuNet | |
| # Valid combinations of backends and targets | |
| backend_target_pairs = [ | |
| [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU], | |
| [cv.dnn.DNN_BACKEND_CUDA, cv.dnn.DNN_TARGET_CUDA], | |
| [cv.dnn.DNN_BACKEND_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16], | |
| [cv.dnn.DNN_BACKEND_TIMVX, cv.dnn.DNN_TARGET_NPU], | |
| [cv.dnn.DNN_BACKEND_CANN, cv.dnn.DNN_TARGET_NPU] | |
| ] | |
| parser = argparse.ArgumentParser(description='YuNet: A Fast and Accurate CNN-based Face Detector (https://github.com/ShiqiYu/libfacedetection).') | |
| parser.add_argument('--input', '-i', type=str, | |
| help='Usage: Set input to a certain image, omit if using camera.') | |
| parser.add_argument('--model', '-m', type=str, default='face_detection_yunet_2023mar.onnx', | |
| help="Usage: Set model type, defaults to 'face_detection_yunet_2023mar.onnx'.") | |
| parser.add_argument('--backend_target', '-bt', type=int, default=0, | |
| help='''Choose one of the backend-target pair to run this demo: | |
| {:d}: (default) OpenCV implementation + CPU, | |
| {:d}: CUDA + GPU (CUDA), | |
| {:d}: CUDA + GPU (CUDA FP16), | |
| {:d}: TIM-VX + NPU, | |
| {:d}: CANN + NPU | |
| '''.format(*[x for x in range(len(backend_target_pairs))])) | |
| parser.add_argument('--conf_threshold', type=float, default=0.9, | |
| help='Usage: Set the minimum needed confidence for the model to identify a face, defauts to 0.9. Smaller values may result in faster detection, but will limit accuracy. Filter out faces of confidence < conf_threshold.') | |
| parser.add_argument('--nms_threshold', type=float, default=0.3, | |
| help='Usage: Suppress bounding boxes of iou >= nms_threshold. Default = 0.3.') | |
| parser.add_argument('--top_k', type=int, default=5000, | |
| help='Usage: Keep top_k bounding boxes before NMS.') | |
| parser.add_argument('--save', '-s', action='store_true', | |
| help='Usage: Specify to save file with results (i.e. bounding box, confidence level). Invalid in case of camera input.') | |
| parser.add_argument('--vis', '-v', action='store_true', | |
| help='Usage: Specify to open a new window to show results. Invalid in case of camera input.') | |
| args = parser.parse_args() | |
| def visualize(image, results, box_color=(0, 255, 0), text_color=(0, 0, 255), fps=None): | |
| output = image.copy() | |
| landmark_color = [ | |
| (255, 0, 0), # right eye | |
| ( 0, 0, 255), # left eye | |
| ( 0, 255, 0), # nose tip | |
| (255, 0, 255), # right mouth corner | |
| ( 0, 255, 255) # left mouth corner | |
| ] | |
| if fps is not None: | |
| cv.putText(output, 'FPS: {:.2f}'.format(fps), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, text_color) | |
| for det in results: | |
| bbox = det[0:4].astype(np.int32) | |
| cv.rectangle(output, (bbox[0], bbox[1]), (bbox[0]+bbox[2], bbox[1]+bbox[3]), box_color, 2) | |
| conf = det[-1] | |
| cv.putText(output, '{:.4f}'.format(conf), (bbox[0], bbox[1]+12), cv.FONT_HERSHEY_DUPLEX, 0.5, text_color) | |
| landmarks = det[4:14].astype(np.int32).reshape((5,2)) | |
| for idx, landmark in enumerate(landmarks): | |
| cv.circle(output, landmark, 2, landmark_color[idx], 2) | |
| return output | |
| if __name__ == '__main__': | |
| backend_id = backend_target_pairs[args.backend_target][0] | |
| target_id = backend_target_pairs[args.backend_target][1] | |
| # Instantiate YuNet | |
| model = YuNet(modelPath=args.model, | |
| inputSize=[320, 320], | |
| confThreshold=args.conf_threshold, | |
| nmsThreshold=args.nms_threshold, | |
| topK=args.top_k, | |
| backendId=backend_id, | |
| targetId=target_id) | |
| # If input is an image | |
| if args.input is not None: | |
| image = cv.imread(args.input) | |
| h, w, _ = image.shape | |
| # Inference | |
| model.setInputSize([w, h]) | |
| results = model.infer(image) | |
| # Print results | |
| print('{} faces detected.'.format(results.shape[0])) | |
| for idx, det in enumerate(results): | |
| print('{}: {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f}'.format( | |
| idx, *det[:-1]) | |
| ) | |
| # Draw results on the input image | |
| image = visualize(image, results) | |
| # Save results if save is true | |
| if args.save: | |
| print('Resutls saved to result.jpg\n') | |
| cv.imwrite('result.jpg', image) | |
| # Visualize results in a new window | |
| if args.vis: | |
| cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE) | |
| cv.imshow(args.input, image) | |
| cv.waitKey(0) | |
| else: # Omit input to call default camera | |
| deviceId = 0 | |
| cap = cv.VideoCapture(deviceId) | |
| w = int(cap.get(cv.CAP_PROP_FRAME_WIDTH)) | |
| h = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT)) | |
| model.setInputSize([w, h]) | |
| tm = cv.TickMeter() | |
| while cv.waitKey(1) < 0: | |
| hasFrame, frame = cap.read() | |
| if not hasFrame: | |
| print('No frames grabbed!') | |
| break | |
| # Inference | |
| tm.start() | |
| results = model.infer(frame) # results is a tuple | |
| tm.stop() | |
| # Draw results on the input image | |
| frame = visualize(frame, results, fps=tm.getFPS()) | |
| # Visualize results in a new Window | |
| cv.imshow('YuNet Demo', frame) | |
| tm.reset() | |
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