63 lines
2.1 KiB
Python
63 lines
2.1 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.vertical_splits = self.opt['vertical_splits']
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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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if self.vertical_splits > 0:
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actual_index = int(index / self.vertical_splits)
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else:
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actual_index = index
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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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if self.vertical_splits > 0:
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w, h = img_LQ.size
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split_index = (index % self.vertical_splits)
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w_per_split = int(w / self.vertical_splits)
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left = w_per_split * split_index
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img_LQ = F.crop(img_LQ, 0, left, h, w_per_split)
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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 % self.vertical_splits))
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return {'LQ': img_LQ, 'LQ_path': LQ_path}
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def __len__(self):
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if self.vertical_splits > 0:
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return len(self.paths_LQ) * self.vertical_splits
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else:
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return len(self.paths_LQ)
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