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| import random | |
| import numpy as np | |
| import torch | |
| import torch.utils.data as data | |
| import utils.utils_image as util | |
| from utils import utils_sisr | |
| import hdf5storage | |
| import os | |
| class DatasetSRMD(data.Dataset): | |
| ''' | |
| # ----------------------------------------- | |
| # Get L/H/M for noisy image SR with Gaussian kernels. | |
| # Only "paths_H" is needed, sythesize bicubicly downsampled L on-the-fly. | |
| # ----------------------------------------- | |
| # e.g., SRMD, H = f(L, kernel, sigma), sigma is noise level | |
| # ----------------------------------------- | |
| ''' | |
| def __init__(self, opt): | |
| super(DatasetSRMD, self).__init__() | |
| self.opt = opt | |
| self.n_channels = opt['n_channels'] if opt['n_channels'] else 3 | |
| self.sf = opt['scale'] if opt['scale'] else 4 | |
| self.patch_size = self.opt['H_size'] if self.opt['H_size'] else 96 | |
| self.L_size = self.patch_size // self.sf | |
| self.sigma = opt['sigma'] if opt['sigma'] else [0, 50] | |
| self.sigma_min, self.sigma_max = self.sigma[0], self.sigma[1] | |
| self.sigma_test = opt['sigma_test'] if opt['sigma_test'] else 0 | |
| # ------------------------------------- | |
| # PCA projection matrix | |
| # ------------------------------------- | |
| self.p = hdf5storage.loadmat(os.path.join('kernels', 'srmd_pca_pytorch.mat'))['p'] | |
| self.ksize = int(np.sqrt(self.p.shape[-1])) # kernel size | |
| # ------------------------------------ | |
| # get paths of L/H | |
| # ------------------------------------ | |
| self.paths_H = util.get_image_paths(opt['dataroot_H']) | |
| self.paths_L = util.get_image_paths(opt['dataroot_L']) | |
| def __getitem__(self, index): | |
| # ------------------------------------ | |
| # get H image | |
| # ------------------------------------ | |
| H_path = self.paths_H[index] | |
| img_H = util.imread_uint(H_path, self.n_channels) | |
| img_H = util.uint2single(img_H) | |
| # ------------------------------------ | |
| # modcrop for SR | |
| # ------------------------------------ | |
| img_H = util.modcrop(img_H, self.sf) | |
| # ------------------------------------ | |
| # kernel | |
| # ------------------------------------ | |
| if self.opt['phase'] == 'train': | |
| l_max = 10 | |
| theta = np.pi*random.random() | |
| l1 = 0.1+l_max*random.random() | |
| l2 = 0.1+(l1-0.1)*random.random() | |
| kernel = utils_sisr.anisotropic_Gaussian(ksize=self.ksize, theta=theta, l1=l1, l2=l2) | |
| else: | |
| kernel = utils_sisr.anisotropic_Gaussian(ksize=self.ksize, theta=np.pi, l1=0.1, l2=0.1) | |
| k = np.reshape(kernel, (-1), order="F") | |
| k_reduced = np.dot(self.p, k) | |
| k_reduced = torch.from_numpy(k_reduced).float() | |
| # ------------------------------------ | |
| # sythesize L image via specified degradation model | |
| # ------------------------------------ | |
| H, W, _ = img_H.shape | |
| img_L = utils_sisr.srmd_degradation(img_H, kernel, self.sf) | |
| img_L = np.float32(img_L) | |
| if self.opt['phase'] == 'train': | |
| """ | |
| # -------------------------------- | |
| # get L/H patch pairs | |
| # -------------------------------- | |
| """ | |
| H, W, C = img_L.shape | |
| # -------------------------------- | |
| # randomly crop L patch | |
| # -------------------------------- | |
| rnd_h = random.randint(0, max(0, H - self.L_size)) | |
| rnd_w = random.randint(0, max(0, W - self.L_size)) | |
| img_L = img_L[rnd_h:rnd_h + self.L_size, rnd_w:rnd_w + self.L_size, :] | |
| # -------------------------------- | |
| # crop corresponding H patch | |
| # -------------------------------- | |
| rnd_h_H, rnd_w_H = int(rnd_h * self.sf), int(rnd_w * self.sf) | |
| img_H = img_H[rnd_h_H:rnd_h_H + self.patch_size, rnd_w_H:rnd_w_H + self.patch_size, :] | |
| # -------------------------------- | |
| # augmentation - flip and/or rotate | |
| # -------------------------------- | |
| mode = random.randint(0, 7) | |
| img_L, img_H = util.augment_img(img_L, mode=mode), util.augment_img(img_H, mode=mode) | |
| # -------------------------------- | |
| # get patch pairs | |
| # -------------------------------- | |
| img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(img_L) | |
| # -------------------------------- | |
| # select noise level and get Gaussian noise | |
| # -------------------------------- | |
| if random.random() < 0.1: | |
| noise_level = torch.zeros(1).float() | |
| else: | |
| noise_level = torch.FloatTensor([np.random.uniform(self.sigma_min, self.sigma_max)])/255.0 | |
| # noise_level = torch.rand(1)*50/255.0 | |
| # noise_level = torch.min(torch.from_numpy(np.float32([7*np.random.chisquare(2.5)/255.0])),torch.Tensor([50./255.])) | |
| else: | |
| img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(img_L) | |
| noise_level = noise_level = torch.FloatTensor([self.sigma_test]) | |
| # ------------------------------------ | |
| # add noise | |
| # ------------------------------------ | |
| noise = torch.randn(img_L.size()).mul_(noise_level).float() | |
| img_L.add_(noise) | |
| # ------------------------------------ | |
| # get degradation map M | |
| # ------------------------------------ | |
| M_vector = torch.cat((k_reduced, noise_level), 0).unsqueeze(1).unsqueeze(1) | |
| M = M_vector.repeat(1, img_L.size()[-2], img_L.size()[-1]) | |
| """ | |
| # ------------------------------------- | |
| # concat L and noise level map M | |
| # ------------------------------------- | |
| """ | |
| img_L = torch.cat((img_L, M), 0) | |
| L_path = H_path | |
| return {'L': img_L, 'H': img_H, 'L_path': L_path, 'H_path': H_path} | |
| def __len__(self): | |
| return len(self.paths_H) | |