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_2023feb.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=0.9, 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): display_screen = image.copy() display_3d = np.zeros((400, 400, 3), np.uint8) cv.line(display_3d, (200, 0), (200, 400), (255, 255, 255), 2) cv.line(display_3d, (0, 200), (400, 200), (255, 255, 255), 2) cv.putText(display_3d, 'Main View', (0, 12), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255)) cv.putText(display_3d, 'Top View', (200, 12), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255)) cv.putText(display_3d, 'Left View', (0, 212), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255)) cv.putText(display_3d, 'Right View', (200, 212), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255)) is_draw = False # ensure only one hand is drawn def draw_lines(image, landmarks, is_draw_point=True, thickness=2): cv.line(image, landmarks[0], landmarks[1], (255, 255, 255), thickness) cv.line(image, landmarks[1], landmarks[2], (255, 255, 255), thickness) cv.line(image, landmarks[2], landmarks[3], (255, 255, 255), thickness) cv.line(image, landmarks[3], landmarks[4], (255, 255, 255), thickness) cv.line(image, landmarks[0], landmarks[5], (255, 255, 255), thickness) cv.line(image, landmarks[5], landmarks[6], (255, 255, 255), thickness) cv.line(image, landmarks[6], landmarks[7], (255, 255, 255), thickness) cv.line(image, landmarks[7], landmarks[8], (255, 255, 255), thickness) cv.line(image, landmarks[0], landmarks[9], (255, 255, 255), thickness) cv.line(image, landmarks[9], landmarks[10], (255, 255, 255), thickness) cv.line(image, landmarks[10], landmarks[11], (255, 255, 255), thickness) cv.line(image, landmarks[11], landmarks[12], (255, 255, 255), thickness) cv.line(image, landmarks[0], landmarks[13], (255, 255, 255), thickness) cv.line(image, landmarks[13], landmarks[14], (255, 255, 255), thickness) cv.line(image, landmarks[14], landmarks[15], (255, 255, 255), thickness) cv.line(image, landmarks[15], landmarks[16], (255, 255, 255), thickness) cv.line(image, landmarks[0], landmarks[17], (255, 255, 255), thickness) cv.line(image, landmarks[17], landmarks[18], (255, 255, 255), thickness) cv.line(image, landmarks[18], landmarks[19], (255, 255, 255), thickness) cv.line(image, landmarks[19], landmarks[20], (255, 255, 255), thickness) if is_draw_point: for p in landmarks: cv.circle(image, p, thickness, (0, 0, 255), -1) for idx, handpose in enumerate(hands): conf = handpose[-1] bbox = handpose[0:4].astype(np.int32) handedness = handpose[-2] if handedness <= 0.5: handedness_text = 'Left' else: handedness_text = 'Right' landmarks_screen = handpose[4:67].reshape(21, 3).astype(np.int32) landmarks_word = handpose[67:130].reshape(21, 3) # Print results if print_result: print('-----------hand {}-----------'.format(idx + 1)) print('conf: {:.2f}'.format(conf)) print('handedness: {}'.format(handedness_text)) print('hand box: {}'.format(bbox)) print('hand landmarks: ') for l in landmarks_screen: print('\t{}'.format(l)) print('hand world landmarks: ') for l in landmarks_word: print('\t{}'.format(l)) # draw box cv.rectangle(display_screen, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2) # draw handedness cv.putText(display_screen, '{}'.format(handedness_text), (bbox[0], bbox[1] + 12), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255)) # Draw line between each key points landmarks_xy = landmarks_screen[:, 0:2] draw_lines(display_screen, landmarks_xy, is_draw_point=False) # z value is relative to WRIST for p in landmarks_screen: r = max(5 - p[2] // 5, 0) r = min(r, 14) cv.circle(display_screen, np.array([p[0], p[1]]), r, (0, 0, 255), -1) if is_draw is False: is_draw = True # Main view landmarks_xy = landmarks_word[:, [0, 1]] landmarks_xy = (landmarks_xy * 1000 + 100).astype(np.int32) draw_lines(display_3d, landmarks_xy, thickness=5) # Top view landmarks_xz = landmarks_word[:, [0, 2]] landmarks_xz[:, 1] = -landmarks_xz[:, 1] landmarks_xz = (landmarks_xz * 1000 + np.array([300, 100])).astype(np.int32) draw_lines(display_3d, landmarks_xz, thickness=5) # Left view landmarks_yz = landmarks_word[:, [2, 1]] landmarks_yz[:, 0] = -landmarks_yz[:, 0] landmarks_yz = (landmarks_yz * 1000 + np.array([100, 300])).astype(np.int32) draw_lines(display_3d, landmarks_yz, thickness=5) # Right view landmarks_zy = landmarks_word[:, [2, 1]] landmarks_zy = (landmarks_zy * 1000 + np.array([300, 300])).astype(np.int32) draw_lines(display_3d, landmarks_zy, thickness=5) return display_screen, display_3d if __name__ == '__main__': # palm detector palm_detector = MPPalmDet(modelPath='../palm_detection_mediapipe/palm_detection_mediapipe_2023feb.onnx', nmsThreshold=0.3, scoreThreshold=0.6, 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, 132)) # 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, view_3d = visualize(image, hands, True) if len(palms) == 0: print('No palm detected!') else: print('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.imshow('3D HandPose Demo', view_3d) 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, 132)) 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, view_3d = visualize(frame, hands) if len(palms) == 0: print('No palm detected!') else: print('Palm detected!') 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) cv.imshow('3D HandPose Demo', view_3d) tm.reset()