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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import numpy as np | |
| import annotator.uniformer.mmcv as mmcv | |
| try: | |
| import torch | |
| except ImportError: | |
| torch = None | |
| def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True): | |
| """Convert tensor to 3-channel images. | |
| Args: | |
| tensor (torch.Tensor): Tensor that contains multiple images, shape ( | |
| N, C, H, W). | |
| mean (tuple[float], optional): Mean of images. Defaults to (0, 0, 0). | |
| std (tuple[float], optional): Standard deviation of images. | |
| Defaults to (1, 1, 1). | |
| to_rgb (bool, optional): Whether the tensor was converted to RGB | |
| format in the first place. If so, convert it back to BGR. | |
| Defaults to True. | |
| Returns: | |
| list[np.ndarray]: A list that contains multiple images. | |
| """ | |
| if torch is None: | |
| raise RuntimeError('pytorch is not installed') | |
| assert torch.is_tensor(tensor) and tensor.ndim == 4 | |
| assert len(mean) == 3 | |
| assert len(std) == 3 | |
| num_imgs = tensor.size(0) | |
| mean = np.array(mean, dtype=np.float32) | |
| std = np.array(std, dtype=np.float32) | |
| imgs = [] | |
| for img_id in range(num_imgs): | |
| img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0) | |
| img = mmcv.imdenormalize( | |
| img, mean, std, to_bgr=to_rgb).astype(np.uint8) | |
| imgs.append(np.ascontiguousarray(img)) | |
| return imgs | |