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| import random | |
| import numpy as np | |
| import torch.utils.data as data | |
| import utils.utils_image as util | |
| class DatasetPlain(data.Dataset): | |
| ''' | |
| # ----------------------------------------- | |
| # Get L/H for image-to-image mapping. | |
| # Both "paths_L" and "paths_H" are needed. | |
| # ----------------------------------------- | |
| # e.g., train denoiser with L and H | |
| # ----------------------------------------- | |
| ''' | |
| def __init__(self, opt): | |
| super(DatasetPlain, self).__init__() | |
| print('Get L/H for image-to-image mapping. Both "paths_L" and "paths_H" are needed.') | |
| self.opt = opt | |
| self.n_channels = opt['n_channels'] if opt['n_channels'] else 3 | |
| self.patch_size = self.opt['H_size'] if self.opt['H_size'] else 64 | |
| # ------------------------------------ | |
| # get the path of L/H | |
| # ------------------------------------ | |
| self.paths_H = util.get_image_paths(opt['dataroot_H']) | |
| self.paths_L = util.get_image_paths(opt['dataroot_L']) | |
| assert self.paths_H, 'Error: H path is empty.' | |
| assert self.paths_L, 'Error: L path is empty. Plain dataset assumes both L and H are given!' | |
| if self.paths_L and self.paths_H: | |
| assert len(self.paths_L) == len(self.paths_H), 'L/H mismatch - {}, {}.'.format(len(self.paths_L), len(self.paths_H)) | |
| def __getitem__(self, index): | |
| # ------------------------------------ | |
| # get H image | |
| # ------------------------------------ | |
| H_path = self.paths_H[index] | |
| img_H = util.imread_uint(H_path, self.n_channels) | |
| # ------------------------------------ | |
| # get L image | |
| # ------------------------------------ | |
| L_path = self.paths_L[index] | |
| img_L = util.imread_uint(L_path, self.n_channels) | |
| # ------------------------------------ | |
| # if train, get L/H patch pair | |
| # ------------------------------------ | |
| if self.opt['phase'] == 'train': | |
| H, W, _ = img_H.shape | |
| # -------------------------------- | |
| # randomly crop the patch | |
| # -------------------------------- | |
| rnd_h = random.randint(0, max(0, H - self.patch_size)) | |
| rnd_w = random.randint(0, max(0, W - self.patch_size)) | |
| patch_L = img_L[rnd_h:rnd_h + self.patch_size, rnd_w:rnd_w + self.patch_size, :] | |
| patch_H = img_H[rnd_h:rnd_h + self.patch_size, rnd_w:rnd_w + self.patch_size, :] | |
| # -------------------------------- | |
| # augmentation - flip and/or rotate | |
| # -------------------------------- | |
| mode = random.randint(0, 7) | |
| patch_L, patch_H = util.augment_img(patch_L, mode=mode), util.augment_img(patch_H, mode=mode) | |
| # -------------------------------- | |
| # HWC to CHW, numpy(uint) to tensor | |
| # -------------------------------- | |
| img_L, img_H = util.uint2tensor3(patch_L), util.uint2tensor3(patch_H) | |
| else: | |
| # -------------------------------- | |
| # HWC to CHW, numpy(uint) to tensor | |
| # -------------------------------- | |
| img_L, img_H = util.uint2tensor3(img_L), util.uint2tensor3(img_H) | |
| return {'L': img_L, 'H': img_H, 'L_path': L_path, 'H_path': H_path} | |
| def __len__(self): | |
| return len(self.paths_H) | |