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from pathlib import Path |
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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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from .onnxdet import inference_detector |
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from .onnxpose import inference_pose |
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ModelDataPathPrefix = Path("./pretrained_weights") |
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class Wholebody: |
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def __init__(self, device="cuda:0"): |
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providers = ( |
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["CPUExecutionProvider"] if device == "cpu" else ["CUDAExecutionProvider"] |
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) |
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onnx_det = ModelDataPathPrefix.joinpath("DWPose/yolox_l.onnx") |
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onnx_pose = ModelDataPathPrefix.joinpath("DWPose/dw-ll_ucoco_384.onnx") |
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self.session_det = ort.InferenceSession( |
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path_or_bytes=onnx_det, providers=providers |
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) |
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self.session_pose = ort.InferenceSession( |
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path_or_bytes=onnx_pose, providers=providers |
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) |
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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((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, keypoints_info[:, 6, 2:4] > 0.3 |
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).astype(int) |
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new_keypoints_info = np.insert(keypoints_info, 17, neck, axis=1) |
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mmpose_idx = [17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3] |
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openpose_idx = [1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17] |
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new_keypoints_info[:, openpose_idx] = new_keypoints_info[:, mmpose_idx] |
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keypoints_info = new_keypoints_info |
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keypoints, scores = keypoints_info[..., :2], keypoints_info[..., 2] |
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return keypoints, scores |
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