| import cv2
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| import math
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| import numpy as np
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| import os
|
| import torch
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| from torchvision.utils import make_grid
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|
|
|
|
| def img2tensor(imgs, bgr2rgb=True, float32=True):
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| """Numpy array to tensor.
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|
|
| Args:
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| imgs (list[ndarray] | ndarray): Input images.
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| bgr2rgb (bool): Whether to change bgr to rgb.
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| float32 (bool): Whether to change to float32.
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|
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| Returns:
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| list[tensor] | tensor: Tensor images. If returned results only have
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| one element, just return tensor.
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| """
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|
|
| def _totensor(img, bgr2rgb, float32):
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| if img.shape[2] == 3 and bgr2rgb:
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| if img.dtype == 'float64':
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| img = img.astype('float32')
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| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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| img = torch.from_numpy(img.transpose(2, 0, 1))
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| if float32:
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| img = img.float()
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| return img
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|
|
| if isinstance(imgs, list):
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| return [_totensor(img, bgr2rgb, float32) for img in imgs]
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| else:
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| return _totensor(imgs, bgr2rgb, float32)
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|
|
|
|
| def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
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| """Convert torch Tensors into image numpy arrays.
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|
|
| After clamping to [min, max], values will be normalized to [0, 1].
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|
|
| Args:
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| tensor (Tensor or list[Tensor]): Accept shapes:
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| 1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
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| 2) 3D Tensor of shape (3/1 x H x W);
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| 3) 2D Tensor of shape (H x W).
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| Tensor channel should be in RGB order.
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| rgb2bgr (bool): Whether to change rgb to bgr.
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| out_type (numpy type): output types. If ``np.uint8``, transform outputs
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| to uint8 type with range [0, 255]; otherwise, float type with
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| range [0, 1]. Default: ``np.uint8``.
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| min_max (tuple[int]): min and max values for clamp.
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|
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| Returns:
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| (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
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| shape (H x W). The channel order is BGR.
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| """
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| if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
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| raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')
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|
|
| if torch.is_tensor(tensor):
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| tensor = [tensor]
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| result = []
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| for _tensor in tensor:
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| _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
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| _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])
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|
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| n_dim = _tensor.dim()
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| if n_dim == 4:
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| img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
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| img_np = img_np.transpose(1, 2, 0)
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| if rgb2bgr:
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| img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
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| elif n_dim == 3:
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| img_np = _tensor.numpy()
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| img_np = img_np.transpose(1, 2, 0)
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| if img_np.shape[2] == 1:
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| img_np = np.squeeze(img_np, axis=2)
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| else:
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| if rgb2bgr:
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| img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
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| elif n_dim == 2:
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| img_np = _tensor.numpy()
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| else:
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| raise TypeError(f'Only support 4D, 3D or 2D tensor. But received with dimension: {n_dim}')
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| if out_type == np.uint8:
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|
|
| img_np = (img_np * 255.0).round()
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| img_np = img_np.astype(out_type)
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| result.append(img_np)
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| if len(result) == 1:
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| result = result[0]
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| return result
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|
|
|
|
| def tensor2img_fast(tensor, rgb2bgr=True, min_max=(0, 1)):
|
| """This implementation is slightly faster than tensor2img.
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| It now only supports torch tensor with shape (1, c, h, w).
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|
|
| Args:
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| tensor (Tensor): Now only support torch tensor with (1, c, h, w).
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| rgb2bgr (bool): Whether to change rgb to bgr. Default: True.
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| min_max (tuple[int]): min and max values for clamp.
|
| """
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| output = tensor.squeeze(0).detach().clamp_(*min_max).permute(1, 2, 0)
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| output = (output - min_max[0]) / (min_max[1] - min_max[0]) * 255
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| output = output.type(torch.uint8).cpu().numpy()
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| if rgb2bgr:
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| output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
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| return output
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|
|
|
|
| def imfrombytes(content, flag='color', float32=False):
|
| """Read an image from bytes.
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|
|
| Args:
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| content (bytes): Image bytes got from files or other streams.
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| flag (str): Flags specifying the color type of a loaded image,
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| candidates are `color`, `grayscale` and `unchanged`.
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| float32 (bool): Whether to change to float32., If True, will also norm
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| to [0, 1]. Default: False.
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|
|
| Returns:
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| ndarray: Loaded image array.
|
| """
|
| img_np = np.frombuffer(content, np.uint8)
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| imread_flags = {'color': cv2.IMREAD_COLOR, 'grayscale': cv2.IMREAD_GRAYSCALE, 'unchanged': cv2.IMREAD_UNCHANGED}
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| img = cv2.imdecode(img_np, imread_flags[flag])
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| if float32:
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| img = img.astype(np.float32) / 255.
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| return img
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|
|
|
|
| def imwrite(img, file_path, params=None, auto_mkdir=True):
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| """Write image to file.
|
|
|
| Args:
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| img (ndarray): Image array to be written.
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| file_path (str): Image file path.
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| params (None or list): Same as opencv's :func:`imwrite` interface.
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| auto_mkdir (bool): If the parent folder of `file_path` does not exist,
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| whether to create it automatically.
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|
|
| Returns:
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| bool: Successful or not.
|
| """
|
| if auto_mkdir:
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| dir_name = os.path.abspath(os.path.dirname(file_path))
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| os.makedirs(dir_name, exist_ok=True)
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| ok = cv2.imwrite(file_path, img, params)
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| if not ok:
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| raise IOError('Failed in writing images.')
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|
|
|
|
| def crop_border(imgs, crop_border):
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| """Crop borders of images.
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|
|
| Args:
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| imgs (list[ndarray] | ndarray): Images with shape (h, w, c).
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| crop_border (int): Crop border for each end of height and weight.
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|
|
| Returns:
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| list[ndarray]: Cropped images.
|
| """
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| if crop_border == 0:
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| return imgs
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| else:
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| if isinstance(imgs, list):
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| return [v[crop_border:-crop_border, crop_border:-crop_border, ...] for v in imgs]
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| else:
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| return imgs[crop_border:-crop_border, crop_border:-crop_border, ...]
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|
|
