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import cv2 |
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import math |
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import numpy as np |
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import os |
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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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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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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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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)): |
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"""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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""" |
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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): |
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"""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. |
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""" |
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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. |
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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. |
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""" |
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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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""" |
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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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