2019-08-23 13:42:47 +00:00
|
|
|
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
|
2019-09-01 14:20:10 +00:00
|
|
|
self.data_type = self.opt['data_type']
|
2019-08-23 13:42:47 +00:00
|
|
|
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
|
2019-09-01 14:20:10 +00:00
|
|
|
LQ_path = self.paths_LQ[index]
|
2019-08-23 13:42:47 +00:00
|
|
|
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)
|
