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_pose import MPPose sys.path.append('../person_detection_mediapipe') from mp_persondet import MPPersonDet # 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='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='./pose_estimation_mediapipe_2023mar.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=0.8, 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, poses): 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 person is drawn def _draw_lines(image, landmarks, keep_landmarks, is_draw_point=True, thickness=2): def _draw_by_presence(idx1, idx2): if keep_landmarks[idx1] and keep_landmarks[idx2]: cv.line(image, landmarks[idx1], landmarks[idx2], (255, 255, 255), thickness) _draw_by_presence(0, 1) _draw_by_presence(1, 2) _draw_by_presence(2, 3) _draw_by_presence(3, 7) _draw_by_presence(0, 4) _draw_by_presence(4, 5) _draw_by_presence(5, 6) _draw_by_presence(6, 8) _draw_by_presence(9, 10) _draw_by_presence(12, 14) _draw_by_presence(14, 16) _draw_by_presence(16, 22) _draw_by_presence(16, 18) _draw_by_presence(16, 20) _draw_by_presence(18, 20) _draw_by_presence(11, 13) _draw_by_presence(13, 15) _draw_by_presence(15, 21) _draw_by_presence(15, 19) _draw_by_presence(15, 17) _draw_by_presence(17, 19) _draw_by_presence(11, 12) _draw_by_presence(11, 23) _draw_by_presence(23, 24) _draw_by_presence(24, 12) _draw_by_presence(24, 26) _draw_by_presence(26, 28) _draw_by_presence(28, 30) _draw_by_presence(28, 32) _draw_by_presence(30, 32) _draw_by_presence(23, 25) _draw_by_presence(25, 27) _draw_by_presence(27, 31) _draw_by_presence(27, 29) _draw_by_presence(29, 31) if is_draw_point: for i, p in enumerate(landmarks): if keep_landmarks[i]: cv.circle(image, p, thickness, (0, 0, 255), -1) for idx, pose in enumerate(poses): bbox, landmarks_screen, landmarks_word, mask, heatmap, conf = pose edges = cv.Canny(mask, 100, 200) kernel = np.ones((2, 2), np.uint8) # expansion edge to 2 pixels edges = cv.dilate(edges, kernel, iterations=1) edges_bgr = cv.cvtColor(edges, cv.COLOR_GRAY2BGR) edges_bgr[edges == 255] = [0, 255, 0] display_screen = cv.add(edges_bgr, display_screen) # draw box bbox = bbox.astype(np.int32) cv.rectangle(display_screen, bbox[0], bbox[1], (0, 255, 0), 2) cv.putText(display_screen, '{:.4f}'.format(conf), (bbox[0][0], bbox[0][1] + 12), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255)) # Draw line between each key points landmarks_screen = landmarks_screen[:-6, :] landmarks_word = landmarks_word[:-6, :] keep_landmarks = landmarks_screen[:, 4] > 0.8 # only show visible keypoints which presence bigger than 0.8 landmarks_screen = landmarks_screen landmarks_word = landmarks_word landmarks_xy = landmarks_screen[:, 0: 2].astype(np.int32) _draw_lines(display_screen, landmarks_xy, keep_landmarks, is_draw_point=False) # z value is relative to HIP, but we use constant to instead for i, p in enumerate(landmarks_screen[:, 0: 3].astype(np.int32)): if keep_landmarks[i]: cv.circle(display_screen, np.array([p[0], p[1]]), 2, (0, 0, 255), -1) if is_draw is False: is_draw = True # Main view landmarks_xy = landmarks_word[:, [0, 1]] landmarks_xy = (landmarks_xy * 100 + 100).astype(np.int32) _draw_lines(display_3d, landmarks_xy, keep_landmarks, thickness=2) # Top view landmarks_xz = landmarks_word[:, [0, 2]] landmarks_xz[:, 1] = -landmarks_xz[:, 1] landmarks_xz = (landmarks_xz * 100 + np.array([300, 100])).astype(np.int32) _draw_lines(display_3d, landmarks_xz,keep_landmarks, thickness=2) # Left view landmarks_yz = landmarks_word[:, [2, 1]] landmarks_yz[:, 0] = -landmarks_yz[:, 0] landmarks_yz = (landmarks_yz * 100 + np.array([100, 300])).astype(np.int32) _draw_lines(display_3d, landmarks_yz, keep_landmarks, thickness=2) # Right view landmarks_zy = landmarks_word[:, [2, 1]] landmarks_zy = (landmarks_zy * 100 + np.array([300, 300])).astype(np.int32) _draw_lines(display_3d, landmarks_zy, keep_landmarks, thickness=2) return display_screen, display_3d if __name__ == '__main__': backend_id = backend_target_pairs[args.backend_target][0] target_id = backend_target_pairs[args.backend_target][1] # person detector person_detector = MPPersonDet(modelPath='../person_detection_mediapipe/person_detection_mediapipe_2023mar.onnx', nmsThreshold=0.3, scoreThreshold=0.5, topK=5000, # usually only one person has good performance backendId=backend_id, targetId=target_id) # pose estimator pose_estimator = MPPose(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) # person detector inference persons = person_detector.infer(image) poses = [] # Estimate the pose of each person for person in persons: # pose estimator inference pose = pose_estimator.infer(image, person) if pose is not None: poses.append(pose) # Draw results on the input image image, view_3d = visualize(image, poses) if len(persons) == 0: print('No person detected!') else: print('Person 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 Pose 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 # person detector inference persons = person_detector.infer(frame) poses = [] tm.start() # Estimate the pose of each person for person in persons: # pose detector inference pose = pose_estimator.infer(frame, person) if pose is not None: poses.append(pose) tm.stop() # Draw results on the input image frame, view_3d = visualize(frame, poses) if len(persons) == 0: print('No person detected!') else: print('Person detected!') cv.putText(frame, 'FPS: {:.2f}'.format(tm.getFPS()), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255)) cv.imshow('MediaPipe Pose Detection Demo', frame) cv.imshow('3D Pose Demo', view_3d) tm.reset()