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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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