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| import numpy as np | |
| import onnxruntime as ort | |
| from .onnxdet import inference_detector | |
| from .onnxpose import inference_pose | |
| class Wholebody: | |
| """detect human pose by dwpose | |
| """ | |
| def __init__(self, model_det, model_pose, device="cpu"): | |
| providers = ['CPUExecutionProvider'] if device == 'cpu' else ['CUDAExecutionProvider'] | |
| provider_options = None if device == 'cpu' else [{'device_id': 0}] | |
| self.session_det = ort.InferenceSession( | |
| path_or_bytes=model_det, providers=providers, provider_options=provider_options | |
| ) | |
| self.session_pose = ort.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 | |