| | |
| | 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 |
| |
|
