| | import cv2 |
| | import numpy as np |
| |
|
| | import onnxruntime as ort |
| | from huggingface_hub import hf_hub_download |
| | from .onnxdet import inference_detector |
| | from .onnxpose import inference_pose |
| |
|
| |
|
| | class Wholebody: |
| | def __init__(self, device="cuda:0"): |
| | providers = ['CPUExecutionProvider'] if device == 'cpu' else ['CUDAExecutionProvider'] |
| | onnx_det = hf_hub_download("yzd-v/DWPose", "yolox_l.onnx") |
| | onnx_pose = hf_hub_download("yzd-v/DWPose", "dw-ll_ucoco_384.onnx") |
| |
|
| | self.session_det = ort.InferenceSession(path_or_bytes=onnx_det, providers=providers) |
| | self.session_pose = ort.InferenceSession(path_or_bytes=onnx_pose, providers=providers) |
| |
|
| | def __call__(self, oriImg): |
| | 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) |
| | |
| | neck = np.mean(keypoints_info[:, [5, 6]], axis=1) |
| | |
| | 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 |
| |
|
| |
|
| |
|
