120 lines
4.3 KiB
Python
120 lines
4.3 KiB
Python
<<<<<<< HEAD
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import os
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import cv2
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import numpy as np
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import onnxruntime as ort
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from ..dwpose.onnxdet import inference_detector
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from ..dwpose.onnxpose import inference_pose
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class Wholebody:
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def __init__(self):
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device = 'cuda' # 'cpu' #
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providers = ['CPUExecutionProvider'
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] if device == 'cpu' else ['CUDAExecutionProvider']
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current_directory = os.path.dirname(os.path.abspath(__file__))
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print("This file is located at "+os.path.dirname(os.path.abspath(__file__)))
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parent_directory = os.path.dirname(current_directory)
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onnx_det = os.path.join(parent_directory, 'checkpoints/yolox_l.onnx')
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onnx_pose = os.path.join(parent_directory, 'checkpoints/dw-ll_ucoco_384.onnx')
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# onnx_det = "../../checkpoints/yolox_l.onnx"
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# onnx_pose = "../../checkpoints/dw-ll_ucoco_384.onnx"
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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(
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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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=======
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import os
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import cv2
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import numpy as np
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import onnxruntime as ort
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from ..dwpose.onnxdet import inference_detector
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from ..dwpose.onnxpose import inference_pose
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class Wholebody:
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def __init__(self):
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device = 'cuda' # 'cpu' #
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providers = ['CPUExecutionProvider'
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] if device == 'cpu' else ['CUDAExecutionProvider']
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current_directory = os.path.dirname(os.path.abspath(__file__))
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print("This file is located at "+os.path.dirname(os.path.abspath(__file__)))
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parent_directory = os.path.dirname(current_directory)
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onnx_det = os.path.join(parent_directory, 'checkpoints/yolox_l.onnx')
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onnx_pose = os.path.join(parent_directory, 'checkpoints/dw-ll_ucoco_384.onnx')
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# onnx_det = "../../checkpoints/yolox_l.onnx"
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# onnx_pose = "../../checkpoints/dw-ll_ucoco_384.onnx"
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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(
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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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>>>>>>> 626e7afc02230297b6f553675ea1c32c29971314
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