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| import os.path | |
| import random | |
| import numpy as np | |
| import torch | |
| import torch.utils.data as data | |
| import utils.utils_image as util | |
| class DatasetDnCNN(data.Dataset): | |
| """ | |
| # ----------------------------------------- | |
| # Get L/H for denosing on AWGN with fixed sigma. | |
| # Only dataroot_H is needed. | |
| # ----------------------------------------- | |
| # e.g., DnCNN | |
| # ----------------------------------------- | |
| """ | |
| def __init__(self, opt): | |
| super(DatasetDnCNN, self).__init__() | |
| print('Dataset: Denosing on AWGN with fixed sigma. Only dataroot_H is needed.') | |
| self.opt = opt | |
| self.n_channels = opt['n_channels'] if opt['n_channels'] else 3 | |
| self.patch_size = opt['H_size'] if opt['H_size'] else 64 | |
| self.sigma = opt['sigma'] if opt['sigma'] else 25 | |
| self.sigma_test = opt['sigma_test'] if opt['sigma_test'] else self.sigma | |
| # ------------------------------------ | |
| # get path of H | |
| # return None if input is None | |
| # ------------------------------------ | |
| self.paths_H = util.get_image_paths(opt['dataroot_H']) | |
| def __getitem__(self, index): | |
| # ------------------------------------ | |
| # get H image | |
| # ------------------------------------ | |
| H_path = self.paths_H[index] | |
| img_H = util.imread_uint(H_path, self.n_channels) | |
| L_path = H_path | |
| if self.opt['phase'] == 'train': | |
| """ | |
| # -------------------------------- | |
| # get L/H patch pairs | |
| # -------------------------------- | |
| """ | |
| 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_H = img_H[rnd_h:rnd_h + self.patch_size, rnd_w:rnd_w + self.patch_size, :] | |
| # -------------------------------- | |
| # augmentation - flip, rotate | |
| # -------------------------------- | |
| mode = random.randint(0, 7) | |
| patch_H = util.augment_img(patch_H, mode=mode) | |
| # -------------------------------- | |
| # HWC to CHW, numpy(uint) to tensor | |
| # -------------------------------- | |
| img_H = util.uint2tensor3(patch_H) | |
| img_L = img_H.clone() | |
| # -------------------------------- | |
| # add noise | |
| # -------------------------------- | |
| noise = torch.randn(img_L.size()).mul_(self.sigma/255.0) | |
| img_L.add_(noise) | |
| else: | |
| """ | |
| # -------------------------------- | |
| # get L/H image pairs | |
| # -------------------------------- | |
| """ | |
| img_H = util.uint2single(img_H) | |
| img_L = np.copy(img_H) | |
| # -------------------------------- | |
| # add noise | |
| # -------------------------------- | |
| np.random.seed(seed=0) | |
| img_L += np.random.normal(0, self.sigma_test/255.0, img_L.shape) | |
| # -------------------------------- | |
| # HWC to CHW, numpy to tensor | |
| # -------------------------------- | |
| img_L = util.single2tensor3(img_L) | |
| img_H = util.single2tensor3(img_H) | |
| return {'L': img_L, 'H': img_H, 'H_path': H_path, 'L_path': L_path} | |
| def __len__(self): | |
| return len(self.paths_H) | |