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| # -*- coding: utf-8 -*- | |
| # Copyright (c) Alibaba, Inc. and its affiliates. | |
| import cv2 | |
| import numpy as np | |
| import onnxruntime as ort | |
| from .onnxdet import inference_detector | |
| from .onnxpose import inference_pose | |
| def HWC3(x): | |
| assert x.dtype == np.uint8 | |
| if x.ndim == 2: | |
| x = x[:, :, None] | |
| assert x.ndim == 3 | |
| H, W, C = x.shape | |
| assert C == 1 or C == 3 or C == 4 | |
| if C == 3: | |
| return x | |
| if C == 1: | |
| return np.concatenate([x, x, x], axis=2) | |
| if C == 4: | |
| color = x[:, :, 0:3].astype(np.float32) | |
| alpha = x[:, :, 3:4].astype(np.float32) / 255.0 | |
| y = color * alpha + 255.0 * (1.0 - alpha) | |
| y = y.clip(0, 255).astype(np.uint8) | |
| return y | |
| def resize_image(input_image, resolution): | |
| H, W, C = input_image.shape | |
| H = float(H) | |
| W = float(W) | |
| k = float(resolution) / min(H, W) | |
| H *= k | |
| W *= k | |
| H = int(np.round(H / 64.0)) * 64 | |
| W = int(np.round(W / 64.0)) * 64 | |
| img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA) | |
| return img | |
| class Wholebody: | |
| def __init__(self, onnx_det, onnx_pose, device = 'cuda:0'): | |
| providers = ['CPUExecutionProvider' | |
| ] if device == 'cpu' else ['CUDAExecutionProvider'] | |
| # onnx_det = 'annotator/ckpts/yolox_l.onnx' | |
| # onnx_pose = 'annotator/ckpts/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, ori_img): | |
| det_result = inference_detector(self.session_det, ori_img) | |
| keypoints, scores = inference_pose(self.session_pose, det_result, ori_img) | |
| 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, det_result | |