import numpy as np import onnxruntime from .detector import inference_detector from .pose import inference_pose class Wholebody: """detect human pose by dwpose""" def __init__(self, model_det, model_pose, device="cpu"): device = str(device) if device == "cpu": providers = ["CPUExecutionProvider"] provider_options = None else: providers = ["CUDAExecutionProvider"] if ":" in device: gpu_id = int(device.split(":")[1]) provider_options = [{"device_id": gpu_id}] else: provider_options = [{"device_id": 0}] self.session_det = onnxruntime.InferenceSession( path_or_bytes=model_det, providers=providers, provider_options=provider_options ) self.session_pose = onnxruntime.InferenceSession( path_or_bytes=model_pose, providers=providers, provider_options=provider_options ) def __call__(self, oriImg): """call to process dwpose-detect Args: oriImg (np.ndarray): detected image """ det_result = inference_detector(self.session_det, oriImg) keypoints, scores = inference_pose(self.session_pose, det_result, oriImg) keypoints_info = np.concatenate((keypoints, scores[..., None]), axis=-1) # compute neck joint neck = np.mean(keypoints_info[:, [5, 6]], axis=1) # neck score when visualizing pred neck[:, 2:4] = np.logical_and(keypoints_info[:, 5, 2:4] > 0.3, keypoints_info[:, 6, 2:4] > 0.3).astype(int) new_keypoints_info = np.insert(keypoints_info, 17, neck, axis=1) mmpose_idx = [17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3] openpose_idx = [1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17] new_keypoints_info[:, openpose_idx] = new_keypoints_info[:, mmpose_idx] keypoints_info = new_keypoints_info keypoints, scores = keypoints_info[..., :2], keypoints_info[..., 2] return keypoints, scores