import sys 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 mp_handpose import MPHandPose sys.path.append('../palm_detection_mediapipe') from mp_palmdet import MPPalmDet # 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='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_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='Filter out hands of confidence < conf_threshold.') parser.add_argument('--save', '-s', action='store_true', help='Specify to save results. This flag is invalid when using camera.') parser.add_argument('--vis', '-v', action='store_true', help='Specify 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) # used for gesture classification gc = GestureClassification() 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) gesture = gc.classify(landmarks_screen) # Print results if print_result: print('-----------hand {}-----------'.format(idx + 1)) print('conf: {:.2f}'.format(conf)) print('handedness: {}'.format(handedness_text)) print('gesture: {}'.format(gesture)) 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 gesture cv.putText(display_screen, '{}'.format(gesture), (bbox[0], bbox[1] + 30), 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 class GestureClassification: def _vector_2_angle(self, v1, v2): uv1 = v1 / np.linalg.norm(v1) uv2 = v2 / np.linalg.norm(v2) angle = np.degrees(np.arccos(np.dot(uv1, uv2))) return angle def _hand_angle(self, hand): angle_list = [] # thumb angle_ = self._vector_2_angle( np.array([hand[0][0] - hand[2][0], hand[0][1] - hand[2][1]]), np.array([hand[3][0] - hand[4][0], hand[3][1] - hand[4][1]]) ) angle_list.append(angle_) # index angle_ = self._vector_2_angle( np.array([hand[0][0] - hand[6][0], hand[0][1] - hand[6][1]]), np.array([hand[7][0] - hand[8][0], hand[7][1] - hand[8][1]]) ) angle_list.append(angle_) # middle angle_ = self._vector_2_angle( np.array([hand[0][0] - hand[10][0], hand[0][1] - hand[10][1]]), np.array([hand[11][0] - hand[12][0], hand[11][1] - hand[12][1]]) ) angle_list.append(angle_) # ring angle_ = self._vector_2_angle( np.array([hand[0][0] - hand[14][0], hand[0][1] - hand[14][1]]), np.array([hand[15][0] - hand[16][0], hand[15][1] - hand[16][1]]) ) angle_list.append(angle_) # pink angle_ = self._vector_2_angle( np.array([hand[0][0] - hand[18][0], hand[0][1] - hand[18][1]]), np.array([hand[19][0] - hand[20][0], hand[19][1] - hand[20][1]]) ) angle_list.append(angle_) return angle_list def _finger_status(self, lmList): fingerList = [] originx, originy = lmList[0] keypoint_list = [[5, 4], [6, 8], [10, 12], [14, 16], [18, 20]] for point in keypoint_list: x1, y1 = lmList[point[0]] x2, y2 = lmList[point[1]] if np.hypot(x2 - originx, y2 - originy) > np.hypot(x1 - originx, y1 - originy): fingerList.append(True) else: fingerList.append(False) return fingerList def _classify(self, hand): thr_angle = 65. thr_angle_thumb = 30. thr_angle_s = 49. gesture_str = "Undefined" angle_list = self._hand_angle(hand) thumbOpen, firstOpen, secondOpen, thirdOpen, fourthOpen = self._finger_status(hand) # Number if (angle_list[0] > thr_angle_thumb) and (angle_list[1] > thr_angle) and (angle_list[2] > thr_angle) and ( angle_list[3] > thr_angle) and (angle_list[4] > thr_angle) and \ not firstOpen and not secondOpen and not thirdOpen and not fourthOpen: gesture_str = "Zero" elif (angle_list[0] > thr_angle_thumb) and (angle_list[1] < thr_angle_s) and (angle_list[2] > thr_angle) and ( angle_list[3] > thr_angle) and (angle_list[4] > thr_angle) and \ firstOpen and not secondOpen and not thirdOpen and not fourthOpen: gesture_str = "One" elif (angle_list[0] > thr_angle_thumb) and (angle_list[1] < thr_angle_s) and (angle_list[2] < thr_angle_s) and ( angle_list[3] > thr_angle) and (angle_list[4] > thr_angle) and \ not thumbOpen and firstOpen and secondOpen and not thirdOpen and not fourthOpen: gesture_str = "Two" elif (angle_list[0] > thr_angle_thumb) and (angle_list[1] < thr_angle_s) and (angle_list[2] < thr_angle_s) and ( angle_list[3] < thr_angle_s) and (angle_list[4] > thr_angle) and \ not thumbOpen and firstOpen and secondOpen and thirdOpen and not fourthOpen: gesture_str = "Three" elif (angle_list[0] > thr_angle_thumb) and (angle_list[1] < thr_angle_s) and (angle_list[2] < thr_angle_s) and ( angle_list[3] < thr_angle_s) and (angle_list[4] < thr_angle) and \ firstOpen and secondOpen and thirdOpen and fourthOpen: gesture_str = "Four" elif (angle_list[0] < thr_angle_s) and (angle_list[1] < thr_angle_s) and (angle_list[2] < thr_angle_s) and ( angle_list[3] < thr_angle_s) and (angle_list[4] < thr_angle_s) and \ thumbOpen and firstOpen and secondOpen and thirdOpen and fourthOpen: gesture_str = "Five" elif (angle_list[0] < thr_angle_s) and (angle_list[1] > thr_angle) and (angle_list[2] > thr_angle) and ( angle_list[3] > thr_angle) and (angle_list[4] < thr_angle_s) and \ thumbOpen and not firstOpen and not secondOpen and not thirdOpen and fourthOpen: gesture_str = "Six" elif (angle_list[0] < thr_angle_s) and (angle_list[1] < thr_angle) and (angle_list[2] > thr_angle) and ( angle_list[3] > thr_angle) and (angle_list[4] > thr_angle_s) and \ thumbOpen and firstOpen and not secondOpen and not thirdOpen and not fourthOpen: gesture_str = "Seven" elif (angle_list[0] < thr_angle_s) and (angle_list[1] < thr_angle) and (angle_list[2] < thr_angle) and ( angle_list[3] > thr_angle) and (angle_list[4] > thr_angle_s) and \ thumbOpen and firstOpen and secondOpen and not thirdOpen and not fourthOpen: gesture_str = "Eight" elif (angle_list[0] < thr_angle_s) and (angle_list[1] < thr_angle) and (angle_list[2] < thr_angle) and ( angle_list[3] < thr_angle) and (angle_list[4] > thr_angle_s) and \ thumbOpen and firstOpen and secondOpen and thirdOpen and not fourthOpen: gesture_str = "Nine" return gesture_str def classify(self, landmarks): hand = landmarks[:21, :2] gesture = self._classify(hand) return gesture if __name__ == '__main__': backend_id = backend_target_pairs[args.backend_target][0] target_id = backend_target_pairs[args.backend_target][1] # palm detector palm_detector = MPPalmDet(modelPath='../palm_detection_mediapipe/palm_detection_mediapipe_2023feb.onnx', nmsThreshold=0.3, scoreThreshold=0.6, backendId=backend_id, targetId=target_id) # handpose detector handpose_detector = MPHandPose(modelPath=args.model, confThreshold=args.conf_threshold, backendId=backend_id, targetId=target_id) # 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()