| |
| from pathlib import Path |
|
|
| import cv2 |
| import numpy as np |
| import onnxruntime as ort |
|
|
| from .onnxdet import inference_detector |
| from .onnxpose import inference_pose |
|
|
| ModelDataPathPrefix = Path("./pretrained_weights") |
|
|
|
|
| class Wholebody: |
| def __init__(self, device="cuda:0"): |
| providers = ( |
| ["CPUExecutionProvider"] if device == "cpu" else ["CUDAExecutionProvider"] |
| ) |
| onnx_det = ModelDataPathPrefix.joinpath("DWPose/yolox_l.onnx") |
| onnx_pose = ModelDataPathPrefix.joinpath("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 |
|
|
