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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 DatasetDnPatch(data.Dataset): | |
| """ | |
| # ----------------------------------------- | |
| # Get L/H for denosing on AWGN with fixed sigma. | |
| # ****Get all H patches first**** | |
| # Only dataroot_H is needed. | |
| # ----------------------------------------- | |
| # e.g., DnCNN with BSD400 | |
| # ----------------------------------------- | |
| """ | |
| def __init__(self, opt): | |
| super(DatasetDnPatch, self).__init__() | |
| print('Get L/H for 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 | |
| self.num_patches_per_image = opt['num_patches_per_image'] if opt['num_patches_per_image'] else 40 | |
| self.num_sampled = opt['num_sampled'] if opt['num_sampled'] else 3000 | |
| # ------------------------------------ | |
| # get paths of H | |
| # ------------------------------------ | |
| self.paths_H = util.get_image_paths(opt['dataroot_H']) | |
| assert self.paths_H, 'Error: H path is empty.' | |
| # ------------------------------------ | |
| # number of sampled H images | |
| # ------------------------------------ | |
| self.num_sampled = min(self.num_sampled, len(self.paths_H)) | |
| # ------------------------------------ | |
| # reserve space with zeros | |
| # ------------------------------------ | |
| self.total_patches = self.num_sampled * self.num_patches_per_image | |
| self.H_data = np.zeros([self.total_patches, self.patch_size, self.patch_size, self.n_channels], dtype=np.uint8) | |
| # ------------------------------------ | |
| # update H patches | |
| # ------------------------------------ | |
| self.update_data() | |
| def update_data(self): | |
| """ | |
| # ------------------------------------ | |
| # update whole H patches | |
| # ------------------------------------ | |
| """ | |
| self.index_sampled = random.sample(range(0, len(self.paths_H), 1), self.num_sampled) | |
| n_count = 0 | |
| for i in range(len(self.index_sampled)): | |
| H_patches = self.get_patches(self.index_sampled[i]) | |
| for H_patch in H_patches: | |
| self.H_data[n_count,:,:,:] = H_patch | |
| n_count += 1 | |
| print('Training data updated! Total number of patches is: %5.2f X %5.2f = %5.2f\n' % (len(self.H_data)//128, 128, len(self.H_data))) | |
| def get_patches(self, index): | |
| """ | |
| # ------------------------------------ | |
| # get H patches from an H image | |
| # ------------------------------------ | |
| """ | |
| H_path = self.paths_H[index] | |
| img_H = util.imread_uint(H_path, self.n_channels) # uint format | |
| H, W = img_H.shape[:2] | |
| H_patches = [] | |
| num = self.num_patches_per_image | |
| for _ in range(num): | |
| rnd_h = random.randint(0, max(0, H - self.patch_size)) | |
| rnd_w = random.randint(0, max(0, W - self.patch_size)) | |
| H_patch = img_H[rnd_h:rnd_h + self.patch_size, rnd_w:rnd_w + self.patch_size, :] | |
| H_patches.append(H_patch) | |
| return H_patches | |
| def __getitem__(self, index): | |
| H_path = 'toy.png' | |
| if self.opt['phase'] == 'train': | |
| patch_H = self.H_data[index] | |
| # -------------------------------- | |
| # augmentation - flip and/or rotate | |
| # -------------------------------- | |
| mode = random.randint(0, 7) | |
| patch_H = util.augment_img(patch_H, mode=mode) | |
| patch_H = util.uint2tensor3(patch_H) | |
| patch_L = patch_H.clone() | |
| # ------------------------------------ | |
| # add noise | |
| # ------------------------------------ | |
| noise = torch.randn(patch_L.size()).mul_(self.sigma/255.0) | |
| patch_L.add_(noise) | |
| else: | |
| H_path = self.paths_H[index] | |
| img_H = util.imread_uint(H_path, self.n_channels) | |
| 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) | |
| patch_L, patch_H = util.single2tensor3(img_L), util.single2tensor3(img_H) | |
| L_path = H_path | |
| return {'L': patch_L, 'H': patch_H, 'L_path': L_path, 'H_path': H_path} | |
| def __len__(self): | |
| return len(self.H_data) | |