import sys import argparse import numpy as np import cv2 as cv from mp_handpose import MPHandPose sys.path.append('../palm_detection_mediapipe') from mp_palmdet import MPPalmDet 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 backends = [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_CUDA] targets = [cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16] help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA" help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16" try: backends += [cv.dnn.DNN_BACKEND_TIMVX] targets += [cv.dnn.DNN_TARGET_NPU] help_msg_backends += "; {:d}: TIMVX" help_msg_targets += "; {:d}: NPU" except: print('This version of OpenCV does not support TIM-VX and NPU. Visit https://github.com/opencv/opencv/wiki/TIM-VX-Backend-For-Running-OpenCV-On-NPU for more information.') parser = argparse.ArgumentParser(description='Hand Pose Estimation from MediaPipe') parser.add_argument('--input', '-i', type=str, help='Path to the input image. Omit for using default camera.') parser.add_argument('--model', '-m', type=str, default='./handpose_estimation_mediapipe_2022may.onnx', help='Path to the model.') parser.add_argument('--backend', '-b', type=int, default=backends[0], help=help_msg_backends.format(*backends)) parser.add_argument('--target', '-t', type=int, default=targets[0], help=help_msg_targets.format(*targets)) parser.add_argument('--conf_threshold', type=float, default=0.8, help='Filter out hands of confidence < conf_threshold.') parser.add_argument('--save', '-s', type=str, default=False, help='Set true to save results. This flag is invalid when using camera.') parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Set true to open a window for result visualization. This flag is invalid when using camera.') args = parser.parse_args() def visualize(image, hands, print_result=False): output = image.copy() for idx, handpose in enumerate(hands): conf = handpose[-1] bbox = handpose[0:4].astype(np.int32) landmarks = handpose[4:-1].reshape(21, 2).astype(np.int32) # Print results if print_result: print('-----------hand {}-----------'.format(idx + 1)) print('conf: {:.2f}'.format(conf)) print('hand box: {}'.format(bbox)) print('hand landmarks: ') for l in landmarks: print('\t{}'.format(l)) # Draw line between each key points cv.line(output, landmarks[0], landmarks[1], (255, 255, 255), 2) cv.line(output, landmarks[1], landmarks[2], (255, 255, 255), 2) cv.line(output, landmarks[2], landmarks[3], (255, 255, 255), 2) cv.line(output, landmarks[3], landmarks[4], (255, 255, 255), 2) cv.line(output, landmarks[0], landmarks[5], (255, 255, 255), 2) cv.line(output, landmarks[5], landmarks[6], (255, 255, 255), 2) cv.line(output, landmarks[6], landmarks[7], (255, 255, 255), 2) cv.line(output, landmarks[7], landmarks[8], (255, 255, 255), 2) cv.line(output, landmarks[0], landmarks[9], (255, 255, 255), 2) cv.line(output, landmarks[9], landmarks[10], (255, 255, 255), 2) cv.line(output, landmarks[10], landmarks[11], (255, 255, 255), 2) cv.line(output, landmarks[11], landmarks[12], (255, 255, 255), 2) cv.line(output, landmarks[0], landmarks[13], (255, 255, 255), 2) cv.line(output, landmarks[13], landmarks[14], (255, 255, 255), 2) cv.line(output, landmarks[14], landmarks[15], (255, 255, 255), 2) cv.line(output, landmarks[15], landmarks[16], (255, 255, 255), 2) cv.line(output, landmarks[0], landmarks[17], (255, 255, 255), 2) cv.line(output, landmarks[17], landmarks[18], (255, 255, 255), 2) cv.line(output, landmarks[18], landmarks[19], (255, 255, 255), 2) cv.line(output, landmarks[19], landmarks[20], (255, 255, 255), 2) for p in landmarks: cv.circle(output, p, 2, (0, 0, 255), 2) return output if __name__ == '__main__': # palm detector palm_detector = MPPalmDet(modelPath='../palm_detection_mediapipe/palm_detection_mediapipe_2023feb.onnx', nmsThreshold=0.3, scoreThreshold=0.8, backendId=args.backend, targetId=args.target) # handpose detector handpose_detector = MPHandPose(modelPath=args.model, confThreshold=args.conf_threshold, backendId=args.backend, targetId=args.target) # If input is an image if args.input is not None: image = cv.imread(args.input) # Palm detector inference palms = palm_detector.infer(image) hands = np.empty(shape=(0, 47)) # Estimate the pose of each hand for palm in palms: # Handpose detector inference handpose = handpose_detector.infer(image, palm) if handpose is not None: hands = np.vstack((hands, handpose)) # Draw results on the input image image = visualize(image, hands, True) if len(palms) == 0: print('No palm detected!') # Save results if args.save: cv.imwrite('result.jpg', image) print('Results saved to result.jpg\n') # 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) tm = cv.TickMeter() while cv.waitKey(1) < 0: hasFrame, frame = cap.read() if not hasFrame: print('No frames grabbed!') break # Palm detector inference palms = palm_detector.infer(frame) hands = np.empty(shape=(0, 47)) tm.start() # Estimate the pose of each hand for palm in palms: # Handpose detector inference handpose = handpose_detector.infer(frame, palm) if handpose is not None: hands = np.vstack((hands, handpose)) tm.stop() # Draw results on the input image frame = visualize(frame, hands) if len(palms) == 0: print('No palm detected!') else: cv.putText(frame, 'FPS: {:.2f}'.format(tm.getFPS()), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255)) cv.imshow('MediaPipe Handpose Detection Demo', frame) tm.reset()