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import cv2 |
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import numpy as np |
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import onnxruntime as ort |
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import os |
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from .xxxdet import inference_detector |
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from .xxxpose import inference_pose |
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import os |
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class Wholebody: |
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def __init__(self): |
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device = 'cuda:0' |
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providers = ['CPUExecutionProvider' |
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] if device == 'cpu' else ['CUDAExecutionProvider'] |
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onnx_det = f'{os.environ.get("path", ".")}/dw_pose/checkpoints/yolox_l.xxx' |
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onnx_pose = f'{os.environ.get("path", ".")}/dw_pose/checkpoints/dw-ll_ucoco_384.xxx' |
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self.session_det = ort.InferenceSession(path_or_bytes=onnx_det, providers=providers) |
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self.session_pose = ort.InferenceSession(path_or_bytes=onnx_pose, providers=providers) |
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def __call__(self, oriImg): |
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det_result = inference_detector(self.session_det, oriImg) |
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keypoints, scores = inference_pose(self.session_pose, det_result, oriImg) |
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keypoints_info = np.concatenate( |
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(keypoints, scores[..., None]), axis=-1) |
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neck = np.mean(keypoints_info[:, [5, 6]], axis=1) |
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neck[:, 2:4] = np.logical_and( |
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keypoints_info[:, 5, 2:4] > 0.3, |
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keypoints_info[:, 6, 2:4] > 0.3).astype(int) |
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new_keypoints_info = np.insert( |
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keypoints_info, 17, neck, axis=1) |
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mmpose_idx = [ |
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17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3 |
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] |
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openpose_idx = [ |
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1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17 |
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] |
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new_keypoints_info[:, openpose_idx] = \ |
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new_keypoints_info[:, mmpose_idx] |
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keypoints_info = new_keypoints_info |
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keypoints, scores = keypoints_info[ |
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..., :2], keypoints_info[..., 2] |
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return keypoints, scores |
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