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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_deblur | |
| from utils import utils_sisr | |
| import os | |
| from scipy import ndimage | |
| from scipy.io import loadmat | |
| # import hdf5storage | |
| class DatasetUSRNet(data.Dataset): | |
| ''' | |
| # ----------------------------------------- | |
| # Get L/k/sf/sigma for USRNet. | |
| # Only "paths_H" and kernel is needed, synthesize L on-the-fly. | |
| # ----------------------------------------- | |
| ''' | |
| def __init__(self, opt): | |
| super(DatasetUSRNet, 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 self.opt['H_size'] else 96 | |
| self.sigma_max = self.opt['sigma_max'] if self.opt['sigma_max'] is not None else 25 | |
| self.scales = opt['scales'] if opt['scales'] is not None else [1,2,3,4] | |
| self.sf_validation = opt['sf_validation'] if opt['sf_validation'] is not None else 3 | |
| #self.kernels = hdf5storage.loadmat(os.path.join('kernels', 'kernels_12.mat'))['kernels'] | |
| self.kernels = loadmat(os.path.join('kernels', 'kernels_12.mat'))['kernels'] # for validation | |
| # ------------------- | |
| # get the path of H | |
| # ------------------- | |
| self.paths_H = util.get_image_paths(opt['dataroot_H']) # return None if input is None | |
| self.count = 0 | |
| 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': | |
| # --------------------------- | |
| # 1) scale factor, ensure each batch only involves one scale factor | |
| # --------------------------- | |
| if self.count % self.opt['dataloader_batch_size'] == 0: | |
| # sf = random.choice([1,2,3,4]) | |
| self.sf = random.choice(self.scales) | |
| # self.count = 0 # optional | |
| self.count += 1 | |
| 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 = np.random.randint(0, 8) | |
| patch_H = util.augment_img(patch_H, mode=mode) | |
| # --------------------------- | |
| # 2) kernel | |
| # --------------------------- | |
| r_value = random.randint(0, 7) | |
| if r_value>3: | |
| k = utils_deblur.blurkernel_synthesis(h=25) # motion blur | |
| else: | |
| sf_k = random.choice(self.scales) | |
| k = utils_sisr.gen_kernel(scale_factor=np.array([sf_k, sf_k])) # Gaussian blur | |
| mode_k = random.randint(0, 7) | |
| k = util.augment_img(k, mode=mode_k) | |
| # --------------------------- | |
| # 3) noise level | |
| # --------------------------- | |
| if random.randint(0, 8) == 1: | |
| noise_level = 0/255.0 | |
| else: | |
| noise_level = np.random.randint(0, self.sigma_max)/255.0 | |
| # --------------------------- | |
| # Low-quality image | |
| # --------------------------- | |
| img_L = ndimage.filters.convolve(patch_H, np.expand_dims(k, axis=2), mode='wrap') | |
| img_L = img_L[0::self.sf, 0::self.sf, ...] | |
| # add Gaussian noise | |
| img_L = util.uint2single(img_L) + np.random.normal(0, noise_level, img_L.shape) | |
| img_H = patch_H | |
| else: | |
| k = self.kernels[0, 0].astype(np.float64) # validation kernel | |
| k /= np.sum(k) | |
| noise_level = 0./255.0 # validation noise level | |
| # ------------------------------------ | |
| # modcrop | |
| # ------------------------------------ | |
| img_H = util.modcrop(img_H, self.sf_validation) | |
| img_L = ndimage.filters.convolve(img_H, np.expand_dims(k, axis=2), mode='wrap') # blur | |
| img_L = img_L[0::self.sf_validation, 0::self.sf_validation, ...] # downsampling | |
| img_L = util.uint2single(img_L) + np.random.normal(0, noise_level, img_L.shape) | |
| self.sf = self.sf_validation | |
| k = util.single2tensor3(np.expand_dims(np.float32(k), axis=2)) | |
| img_H, img_L = util.uint2tensor3(img_H), util.single2tensor3(img_L) | |
| noise_level = torch.FloatTensor([noise_level]).view([1,1,1]) | |
| return {'L': img_L, 'H': img_H, 'k': k, 'sigma': noise_level, 'sf': self.sf, 'L_path': L_path, 'H_path': H_path} | |
| def __len__(self): | |
| return len(self.paths_H) | |