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| import random | |
| import numpy as np | |
| import torch | |
| import torch.utils.data as data | |
| import utils.utils_image as util | |
| class DatasetFFDNet(data.Dataset): | |
| """ | |
| # ----------------------------------------- | |
| # Get L/H/M for denosing on AWGN with a range of sigma. | |
| # Only dataroot_H is needed. | |
| # ----------------------------------------- | |
| # e.g., FFDNet, H = f(L, sigma), sigma is noise level | |
| # ----------------------------------------- | |
| """ | |
| def __init__(self, opt): | |
| super(DatasetFFDNet, self).__init__() | |
| self.opt = opt | |
| self.n_channels = opt['n_channels'] if opt['n_channels'] else 3 | |
| self.patch_size = self.opt['H_size'] if opt['H_size'] else 64 | |
| self.sigma = opt['sigma'] if opt['sigma'] else [0, 75] | |
| self.sigma_min, self.sigma_max = self.sigma[0], self.sigma[1] | |
| self.sigma_test = opt['sigma_test'] if opt['sigma_test'] else 25 | |
| # ------------------------------------- | |
| # get the 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/M patch pairs | |
| # -------------------------------- | |
| """ | |
| H, W = img_H.shape[:2] | |
| # --------------------------------- | |
| # 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() | |
| # --------------------------------- | |
| # get noise level | |
| # --------------------------------- | |
| # noise_level = torch.FloatTensor([np.random.randint(self.sigma_min, self.sigma_max)])/255.0 | |
| noise_level = torch.FloatTensor([np.random.uniform(self.sigma_min, self.sigma_max)])/255.0 | |
| # --------------------------------- | |
| # add noise | |
| # --------------------------------- | |
| noise = torch.randn(img_L.size()).mul_(noise_level).float() | |
| img_L.add_(noise) | |
| else: | |
| """ | |
| # -------------------------------- | |
| # get L/H/sigma image pairs | |
| # -------------------------------- | |
| """ | |
| img_H = util.uint2single(img_H) | |
| img_L = np.copy(img_H) | |
| np.random.seed(seed=0) | |
| img_L += np.random.normal(0, self.sigma_test/255.0, img_L.shape) | |
| noise_level = torch.FloatTensor([self.sigma_test/255.0]) | |
| # --------------------------------- | |
| # L/H image pairs | |
| # --------------------------------- | |
| img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(img_L) | |
| noise_level = noise_level.unsqueeze(1).unsqueeze(1) | |
| return {'L': img_L, 'H': img_H, 'C': noise_level, 'L_path': L_path, 'H_path': H_path} | |
| def __len__(self): | |
| return len(self.paths_H) | |