67139602f5
Allows bifurcating large images put into the test pipeline This code is fixed and not dynamic. Needs some fixes.
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
import numpy as np
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import lmdb
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import torch
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import torch.utils.data as data
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import data.util as util
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import torchvision.transforms.functional as F
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from PIL import Image
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import os.path as osp
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class LQDataset(data.Dataset):
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'''Read LQ images only in the test phase.'''
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def __init__(self, opt):
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super(LQDataset, self).__init__()
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self.opt = opt
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self.data_type = self.opt['data_type']
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if 'start_at' in self.opt.keys():
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self.start_at = self.opt['start_at']
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else:
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self.start_at = 0
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self.paths_LQ, self.paths_GT = None, None
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self.LQ_env = None # environment for lmdb
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self.paths_LQ, self.sizes_LQ = util.get_image_paths(self.data_type, opt['dataroot_LQ'])
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self.paths_LQ = self.paths_LQ[self.start_at:]
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assert self.paths_LQ, 'Error: LQ paths are empty.'
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def _init_lmdb(self):
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self.LQ_env = lmdb.open(self.opt['dataroot_LQ'], readonly=True, lock=False, readahead=False,
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meminit=False)
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def __getitem__(self, index):
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if self.data_type == 'lmdb' and self.LQ_env is None:
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self._init_lmdb()
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actual_index = int(index / 2)
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is_left = (index % 2) == 0
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# get LQ image
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LQ_path = self.paths_LQ[actual_index]
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img_LQ = Image.open(LQ_path)
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left = 0 if is_left else 2000
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img_LQ = F.crop(img_LQ, 74, left + 74, 1900, 1900)
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img_LQ = F.to_tensor(img_LQ)
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img_name = osp.splitext(osp.basename(LQ_path))[0]
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LQ_path = LQ_path.replace(img_name, img_name + "_%i" % (index % 2))
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return {'LQ': img_LQ, 'LQ_path': LQ_path}
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def __len__(self):
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return len(self.paths_LQ) * 2
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