106 lines
3.8 KiB
Python
106 lines
3.8 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 DatasetSR(data.Dataset):
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'''
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# -----------------------------------------
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# Get L/H for SISR.
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# If only "paths_H" is provided, sythesize bicubicly downsampled L on-the-fly.
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# -----------------------------------------
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# e.g., SRResNet
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# -----------------------------------------
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'''
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def __init__(self, opt):
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super(DatasetSR, self).__init__()
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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.sf = opt['scale'] if opt['scale'] else 4
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self.patch_size = self.opt['H_size'] if self.opt['H_size'] else 96
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self.L_size = self.patch_size // self.sf
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# ------------------------------------
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# get paths 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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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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L_path = None
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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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img_H = util.uint2single(img_H)
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# ------------------------------------
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# modcrop
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# ------------------------------------
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img_H = util.modcrop(img_H, self.sf)
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# ------------------------------------
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# get L image
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# ------------------------------------
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if self.paths_L:
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# --------------------------------
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# directly load 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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img_L = util.uint2single(img_L)
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else:
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# --------------------------------
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# sythesize L image via matlab's bicubic
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# --------------------------------
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H, W = img_H.shape[:2]
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img_L = util.imresize_np(img_H, 1 / self.sf, True)
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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, C = img_L.shape
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# --------------------------------
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# randomly crop the L patch
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# --------------------------------
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rnd_h = random.randint(0, max(0, H - self.L_size))
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rnd_w = random.randint(0, max(0, W - self.L_size))
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img_L = img_L[rnd_h:rnd_h + self.L_size, rnd_w:rnd_w + self.L_size, :]
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# --------------------------------
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# crop corresponding H patch
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# --------------------------------
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rnd_h_H, rnd_w_H = int(rnd_h * self.sf), int(rnd_w * self.sf)
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img_H = img_H[rnd_h_H:rnd_h_H + self.patch_size, rnd_w_H:rnd_w_H + 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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img_L, img_H = util.augment_img(img_L, mode=mode), util.augment_img(img_H, mode=mode)
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# ------------------------------------
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# L/H pairs, HWC to CHW, numpy to tensor
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# ------------------------------------
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img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(img_L)
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if L_path is None:
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L_path = H_path
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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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