import os from torch.utils import data as data from torchvision.transforms.functional import normalize 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 @DATASET_REGISTRY.register(suffix='basicsr') 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_file**: Use meta information file to generate paths. \ If opt['io_backend'] != lmdb and opt['meta_info_file'] 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) if 'num_pic' in self.opt: self.paths = self.paths[:self.opt['num_pic']] if 'phase' not in self.opt: self.opt['phase'] = 'test' if 'scale' not in self.opt: self.opt['scale'] = 1 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)