| |
| 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 |
|
|
