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| import random | |
| import numpy as np | |
| import torch.utils.data as data | |
| import utils.utils_image as util | |
| import os | |
| from utils import utils_blindsr as blindsr | |
| class DatasetBlindSR(data.Dataset): | |
| ''' | |
| # ----------------------------------------- | |
| # dataset for BSRGAN | |
| # ----------------------------------------- | |
| ''' | |
| def __init__(self, opt): | |
| super(DatasetBlindSR, 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.shuffle_prob = opt['shuffle_prob'] if opt['shuffle_prob'] else 0.1 | |
| self.use_sharp = opt['use_sharp'] if opt['use_sharp'] else False | |
| self.degradation_type = opt['degradation_type'] if opt['degradation_type'] else 'bsrgan' | |
| self.lq_patchsize = self.opt['lq_patchsize'] if self.opt['lq_patchsize'] else 64 | |
| self.patch_size = self.opt['H_size'] if self.opt['H_size'] else self.lq_patchsize*self.sf | |
| self.paths_H = util.get_image_paths(opt['dataroot_H']) | |
| print(len(self.paths_H)) | |
| # for n, v in enumerate(self.paths_H): | |
| # if 'face' in v: | |
| # del self.paths_H[n] | |
| # time.sleep(1) | |
| assert self.paths_H, 'Error: H path is empty.' | |
| def __getitem__(self, index): | |
| L_path = None | |
| # ------------------------------------ | |
| # get H image | |
| # ------------------------------------ | |
| H_path = self.paths_H[index] | |
| img_H = util.imread_uint(H_path, self.n_channels) | |
| img_name, ext = os.path.splitext(os.path.basename(H_path)) | |
| H, W, C = img_H.shape | |
| if H < self.patch_size or W < self.patch_size: | |
| img_H = np.tile(np.random.randint(0, 256, size=[1, 1, self.n_channels], dtype=np.uint8), (self.patch_size, self.patch_size, 1)) | |
| # ------------------------------------ | |
| # if train, get L/H patch pair | |
| # ------------------------------------ | |
| if self.opt['phase'] == 'train': | |
| H, W, C = img_H.shape | |
| rnd_h_H = random.randint(0, max(0, H - self.patch_size)) | |
| rnd_w_H = random.randint(0, max(0, W - self.patch_size)) | |
| img_H = img_H[rnd_h_H:rnd_h_H + self.patch_size, rnd_w_H:rnd_w_H + self.patch_size, :] | |
| if 'face' in img_name: | |
| mode = random.choice([0, 4]) | |
| img_H = util.augment_img(img_H, mode=mode) | |
| else: | |
| mode = random.randint(0, 7) | |
| img_H = util.augment_img(img_H, mode=mode) | |
| img_H = util.uint2single(img_H) | |
| if self.degradation_type == 'bsrgan': | |
| img_L, img_H = blindsr.degradation_bsrgan(img_H, self.sf, lq_patchsize=self.lq_patchsize, isp_model=None) | |
| elif self.degradation_type == 'bsrgan_plus': | |
| img_L, img_H = blindsr.degradation_bsrgan_plus(img_H, self.sf, shuffle_prob=self.shuffle_prob, use_sharp=self.use_sharp, lq_patchsize=self.lq_patchsize) | |
| else: | |
| img_H = util.uint2single(img_H) | |
| if self.degradation_type == 'bsrgan': | |
| img_L, img_H = blindsr.degradation_bsrgan(img_H, self.sf, lq_patchsize=self.lq_patchsize, isp_model=None) | |
| elif self.degradation_type == 'bsrgan_plus': | |
| img_L, img_H = blindsr.degradation_bsrgan_plus(img_H, self.sf, shuffle_prob=self.shuffle_prob, use_sharp=self.use_sharp, lq_patchsize=self.lq_patchsize) | |
| # ------------------------------------ | |
| # L/H pairs, HWC to CHW, numpy to tensor | |
| # ------------------------------------ | |
| img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(img_L) | |
| if L_path is None: | |
| 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) | |