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# https://github.com/IDEA-Research/DWPose
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)
# 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
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