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| from os import path as osp | |
| from torch.utils import data as data | |
| from torchvision.transforms.functional import normalize | |
| from basicsr.data.data_util import paths_from_lmdb | |
| from basicsr.utils import FileClient, imfrombytes, img2tensor, scandir | |
| from basicsr.utils.matlab_functions import rgb2ycbcr | |
| from basicsr.utils.registry import DATASET_REGISTRY | |
| class SingleImageDataset(data.Dataset): | |
| """Read only lq images in the test phase. | |
| Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc). | |
| There are two modes: | |
| 1. 'meta_info_file': Use meta information file to generate paths. | |
| 2. 'folder': Scan folders to generate paths. | |
| Args: | |
| opt (dict): Config for train datasets. It contains the following keys: | |
| dataroot_lq (str): Data root path for lq. | |
| meta_info_file (str): Path for meta information file. | |
| io_backend (dict): IO backend type and other kwarg. | |
| """ | |
| def __init__(self, opt): | |
| super(SingleImageDataset, self).__init__() | |
| self.opt = opt | |
| # file client (io backend) | |
| self.file_client = None | |
| self.io_backend_opt = opt['io_backend'] | |
| self.mean = opt['mean'] if 'mean' in opt else None | |
| self.std = opt['std'] if 'std' in opt else None | |
| self.lq_folder = opt['dataroot_lq'] | |
| if self.io_backend_opt['type'] == 'lmdb': | |
| self.io_backend_opt['db_paths'] = [self.lq_folder] | |
| self.io_backend_opt['client_keys'] = ['lq'] | |
| self.paths = paths_from_lmdb(self.lq_folder) | |
| elif 'meta_info_file' in self.opt: | |
| with open(self.opt['meta_info_file'], 'r') as fin: | |
| self.paths = [osp.join(self.lq_folder, line.rstrip().split(' ')[0]) for line in fin] | |
| else: | |
| self.paths = sorted(list(scandir(self.lq_folder, full_path=True))) | |
| def __getitem__(self, index): | |
| if self.file_client is None: | |
| self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) | |
| # load lq image | |
| lq_path = self.paths[index] | |
| img_bytes = self.file_client.get(lq_path, 'lq') | |
| img_lq = imfrombytes(img_bytes, float32=True) | |
| # color space transform | |
| if 'color' in self.opt and self.opt['color'] == 'y': | |
| img_lq = rgb2ycbcr(img_lq, y_only=True)[..., None] | |
| # BGR to RGB, HWC to CHW, numpy to tensor | |
| img_lq = img2tensor(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) | |
| return {'lq': img_lq, 'lq_path': lq_path} | |
| def __len__(self): | |
| return len(self.paths) | |