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https://github.com/invoke-ai/InvokeAI
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45 lines
1.8 KiB
Python
45 lines
1.8 KiB
Python
# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
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# Modified pathing to suit Invoke
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from pathlib import Path
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import numpy as np
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import onnxruntime as ort
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from invokeai.app.services.config.config_default import get_config
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from invokeai.backend.image_util.dw_openpose.onnxdet import inference_detector
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from invokeai.backend.image_util.dw_openpose.onnxpose import inference_pose
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from invokeai.backend.util.devices import TorchDevice
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config = get_config()
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class Wholebody:
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def __init__(self, onnx_det: Path, onnx_pose: Path):
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device = TorchDevice.choose_torch_device()
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providers = ["CUDAExecutionProvider"] if device.type == "cuda" else ["CPUExecutionProvider"]
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self.session_det = ort.InferenceSession(path_or_bytes=onnx_det, providers=providers)
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self.session_pose = ort.InferenceSession(path_or_bytes=onnx_pose, providers=providers)
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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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# 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(keypoints_info[:, 5, 2:4] > 0.3, keypoints_info[:, 6, 2:4] > 0.3).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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