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| import os | |
| from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb | |
| from basicsr.data.transforms import augment, paired_random_crop | |
| from basicsr.utils import FileClient, imfrombytes, img2tensor | |
| from basicsr.utils.registry import DATASET_REGISTRY | |
| from torch.utils import data as data | |
| from torchvision.transforms.functional import normalize | |
| class RealESRGANPairedDataset(data.Dataset): | |
| """Paired image dataset for image restoration. | |
| Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs. | |
| There are three modes: | |
| 1. 'lmdb': Use lmdb files. | |
| If opt['io_backend'] == lmdb. | |
| 2. 'meta_info': Use meta information file to generate paths. | |
| If opt['io_backend'] != lmdb and opt['meta_info'] is not None. | |
| 3. 'folder': Scan folders to generate paths. | |
| The rest. | |
| Args: | |
| opt (dict): Config for train datasets. It contains the following keys: | |
| dataroot_gt (str): Data root path for gt. | |
| dataroot_lq (str): Data root path for lq. | |
| meta_info (str): Path for meta information file. | |
| io_backend (dict): IO backend type and other kwarg. | |
| filename_tmpl (str): Template for each filename. Note that the template excludes the file extension. | |
| Default: '{}'. | |
| gt_size (int): Cropped patched size for gt patches. | |
| use_hflip (bool): Use horizontal flips. | |
| use_rot (bool): Use rotation (use vertical flip and transposing h | |
| and w for implementation). | |
| scale (bool): Scale, which will be added automatically. | |
| phase (str): 'train' or 'val'. | |
| """ | |
| def __init__(self, opt): | |
| super(RealESRGANPairedDataset, self).__init__() | |
| self.opt = opt | |
| self.file_client = None | |
| self.io_backend_opt = opt['io_backend'] | |
| # mean and std for normalizing the input images | |
| self.mean = opt['mean'] if 'mean' in opt else None | |
| self.std = opt['std'] if 'std' in opt else None | |
| self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq'] | |
| self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}' | |
| # file client (lmdb io backend) | |
| if self.io_backend_opt['type'] == 'lmdb': | |
| self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder] | |
| self.io_backend_opt['client_keys'] = ['lq', 'gt'] | |
| self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt']) | |
| elif 'meta_info' in self.opt and self.opt['meta_info'] is not None: | |
| # disk backend with meta_info | |
| # Each line in the meta_info describes the relative path to an image | |
| with open(self.opt['meta_info']) as fin: | |
| paths = [line.strip() for line in fin] | |
| self.paths = [] | |
| for path in paths: | |
| gt_path, lq_path = path.split(', ') | |
| gt_path = os.path.join(self.gt_folder, gt_path) | |
| lq_path = os.path.join(self.lq_folder, lq_path) | |
| self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)])) | |
| else: | |
| # disk backend | |
| # it will scan the whole folder to get meta info | |
| # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file | |
| self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl) | |
| def __getitem__(self, index): | |
| if self.file_client is None: | |
| self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) | |
| scale = self.opt['scale'] | |
| # Load gt and lq images. Dimension order: HWC; channel order: BGR; | |
| # image range: [0, 1], float32. | |
| gt_path = self.paths[index]['gt_path'] | |
| img_bytes = self.file_client.get(gt_path, 'gt') | |
| img_gt = imfrombytes(img_bytes, float32=True) | |
| lq_path = self.paths[index]['lq_path'] | |
| img_bytes = self.file_client.get(lq_path, 'lq') | |
| img_lq = imfrombytes(img_bytes, float32=True) | |
| # augmentation for training | |
| if self.opt['phase'] == 'train': | |
| gt_size = self.opt['gt_size'] | |
| # random crop | |
| img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path) | |
| # flip, rotation | |
| img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot']) | |
| # BGR to RGB, HWC to CHW, numpy to tensor | |
| img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True) | |
| # normalize | |
| if self.mean is not None or self.std is not None: | |
| normalize(img_lq, self.mean, self.std, inplace=True) | |
| normalize(img_gt, self.mean, self.std, inplace=True) | |
| return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path} | |
| def __len__(self): | |
| return len(self.paths) | |