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