66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
import numpy as np
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import comfy.model_management as mm
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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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from .jit_det import inference_detector as inference_jit_yolox
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from .jit_pose import inference_pose as inference_jit_pose
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class Wholebody:
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"""detect human pose by dwpose
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"""
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def __init__(self, model_det, model_pose):
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#providers = ['CPUExecutionProvider'] if device == 'cpu' else ['CUDAExecutionProvider']
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#provider_options = None if device == 'cpu' else [{'device_id': 0}]
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# self.session_det = ort.InferenceSession(
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# path_or_bytes=model_det, providers=providers, provider_options=provider_options
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# )
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# self.session_pose = ort.InferenceSession(
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# path_or_bytes=model_pose, providers=providers, provider_options=provider_options
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# )
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self.det = model_det
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self.pose = model_pose
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def __call__(self, oriImg):
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"""call to process dwpose-detect
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Args:
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oriImg (np.ndarray): detected image
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"""
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det_result = inference_jit_yolox(self.det, oriImg, detect_classes=[0])
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keypoints, scores = inference_jit_pose(self.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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# compute neck joint
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neck = np.mean(keypoints_info[:, [5, 6]], axis=1)
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# neck score when visualizing pred
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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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