import numpy as np
import lmdb
import torch
import torch.utils.data as data
import data.util as util
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']
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'])
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()
LQ_path = None
# get LQ image
LQ_path = self.paths_LQ[index]
resolution = [int(s) for s in self.sizes_LQ[index].split('_')
] if self.data_type == 'lmdb' else None
img_LQ = util.read_img(self.LQ_env, LQ_path, resolution)
H, W, C = img_LQ.shape
if self.opt['color']: # change color space if necessary
img_LQ = util.channel_convert(C, self.opt['color'], [img_LQ])[0]
# BGR to RGB, HWC to CHW, numpy to tensor
if img_LQ.shape[2] == 3:
img_LQ = img_LQ[:, :, [2, 1, 0]]
img_LQ = torch.from_numpy(np.ascontiguousarray(np.transpose(img_LQ, (2, 0, 1)))).float()
return {'LQ': img_LQ, 'LQ_path': LQ_path}
def __len__(self):
return len(self.paths_LQ)
