86 lines
3.3 KiB
Python
86 lines
3.3 KiB
Python
import random
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import numpy as np
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import torch.utils.data as data
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import utils.utils_image as util
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class DatasetPlain(data.Dataset):
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'''
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# -----------------------------------------
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# Get L/H for image-to-image mapping.
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# Both "paths_L" and "paths_H" are needed.
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# -----------------------------------------
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# e.g., train denoiser with L and H
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# -----------------------------------------
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'''
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def __init__(self, opt):
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super(DatasetPlain, self).__init__()
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print('Get L/H for image-to-image mapping. Both "paths_L" and "paths_H" are needed.')
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self.opt = opt
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self.n_channels = opt['n_channels'] if opt['n_channels'] else 3
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self.patch_size = self.opt['H_size'] if self.opt['H_size'] else 64
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# ------------------------------------
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# get the path of L/H
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# ------------------------------------
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self.paths_H = util.get_image_paths(opt['dataroot_H'])
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self.paths_L = util.get_image_paths(opt['dataroot_L'])
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assert self.paths_H, 'Error: H path is empty.'
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assert self.paths_L, 'Error: L path is empty. Plain dataset assumes both L and H are given!'
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if self.paths_L and self.paths_H:
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assert len(self.paths_L) == len(self.paths_H), 'L/H mismatch - {}, {}.'.format(len(self.paths_L), len(self.paths_H))
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def __getitem__(self, index):
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# ------------------------------------
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# get H image
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# ------------------------------------
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H_path = self.paths_H[index]
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img_H = util.imread_uint(H_path, self.n_channels)
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# ------------------------------------
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# get L image
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# ------------------------------------
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L_path = self.paths_L[index]
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img_L = util.imread_uint(L_path, self.n_channels)
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# ------------------------------------
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# if train, get L/H patch pair
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# ------------------------------------
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if self.opt['phase'] == 'train':
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H, W, _ = img_H.shape
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# --------------------------------
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# randomly crop the patch
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# --------------------------------
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rnd_h = random.randint(0, max(0, H - self.patch_size))
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rnd_w = random.randint(0, max(0, W - self.patch_size))
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patch_L = img_L[rnd_h:rnd_h + self.patch_size, rnd_w:rnd_w + self.patch_size, :]
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patch_H = img_H[rnd_h:rnd_h + self.patch_size, rnd_w:rnd_w + self.patch_size, :]
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# --------------------------------
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# augmentation - flip and/or rotate
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# --------------------------------
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mode = random.randint(0, 7)
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patch_L, patch_H = util.augment_img(patch_L, mode=mode), util.augment_img(patch_H, mode=mode)
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# --------------------------------
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# HWC to CHW, numpy(uint) to tensor
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# --------------------------------
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img_L, img_H = util.uint2tensor3(patch_L), util.uint2tensor3(patch_H)
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else:
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# --------------------------------
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# HWC to CHW, numpy(uint) to tensor
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# --------------------------------
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img_L, img_H = util.uint2tensor3(img_L), util.uint2tensor3(img_H)
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return {'L': img_L, 'H': img_H, 'L_path': L_path, 'H_path': H_path}
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def __len__(self):
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return len(self.paths_H)
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