import numpy as np import lmdb import torch import torch.utils.data as data import data.util as util import torchvision.transforms.functional as F from PIL import Image import os.path as osp class LQDataset(data.Dataset): '''Read LQ images only in the test phase.''' def __init__(self, opt): super(LQDataset, self).__init__() self.opt = opt self.data_type = self.opt['data_type'] if 'start_at' in self.opt.keys(): self.start_at = self.opt['start_at'] else: self.start_at = 0 self.paths_LQ, self.paths_GT = None, None self.LQ_env = None # environment for lmdb self.paths_LQ, self.sizes_LQ = util.get_image_paths(self.data_type, opt['dataroot_LQ']) self.paths_LQ = self.paths_LQ[self.start_at:] assert self.paths_LQ, 'Error: LQ paths are empty.' def _init_lmdb(self): self.LQ_env = lmdb.open(self.opt['dataroot_LQ'], readonly=True, lock=False, readahead=False, meminit=False) def __getitem__(self, index): if self.data_type == 'lmdb' and self.LQ_env is None: self._init_lmdb() actual_index = index # int(index / 2) is_left = (index % 2) == 0 # get LQ image LQ_path = self.paths_LQ[actual_index] img_LQ = Image.open(LQ_path) left = 0 if is_left else 1920 # crop input if needed. #img_LQ = F.crop(img_LQ, 5, left + 5, 1900, 1900) img_LQ = F.to_tensor(img_LQ) img_name = osp.splitext(osp.basename(LQ_path))[0] LQ_path = LQ_path.replace(img_name, img_name + "_%i" % (index % 2)) return {'LQ': img_LQ, 'LQ_path': LQ_path} def __len__(self): return len(self.paths_LQ) # * 2