110 lines
4.3 KiB
Python
110 lines
4.3 KiB
Python
import random
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import numpy as np
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import torch
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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 DatasetFDnCNN(data.Dataset):
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"""
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# -----------------------------------------
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# Get L/H/M for denosing on AWGN with a range of sigma.
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# Only dataroot_H is needed.
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# -----------------------------------------
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# e.g., FDnCNN, H = f(cat(L, M)), M is noise level map
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# -----------------------------------------
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"""
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def __init__(self, opt):
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super(DatasetFDnCNN, 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.patch_size = self.opt['H_size'] if opt['H_size'] else 64
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self.sigma = opt['sigma'] if opt['sigma'] else [0, 75]
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self.sigma_min, self.sigma_max = self.sigma[0], self.sigma[1]
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self.sigma_test = opt['sigma_test'] if opt['sigma_test'] else 25
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# -------------------------------------
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# get the path of H, return None if input is None
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# -------------------------------------
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self.paths_H = util.get_image_paths(opt['dataroot_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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L_path = H_path
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if self.opt['phase'] == 'train':
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"""
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# --------------------------------
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# get L/H/M patch pairs
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# --------------------------------
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"""
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H, W = img_H.shape[:2]
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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_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, rotate
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# ---------------------------------
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mode = random.randint(0, 7)
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patch_H = 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_H = util.uint2tensor3(patch_H)
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img_L = img_H.clone()
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# ---------------------------------
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# get noise level
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# ---------------------------------
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# noise_level = torch.FloatTensor([np.random.randint(self.sigma_min, self.sigma_max)])/255.0
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noise_level = torch.FloatTensor([np.random.uniform(self.sigma_min, self.sigma_max)])/255.0
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noise_level_map = torch.ones((1, img_L.size(1), img_L.size(2))).mul_(noise_level).float() # torch.full((1, img_L.size(1), img_L.size(2)), noise_level)
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# ---------------------------------
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# add noise
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# ---------------------------------
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noise = torch.randn(img_L.size()).mul_(noise_level).float()
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img_L.add_(noise)
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else:
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"""
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# --------------------------------
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# get L/H/M image pairs
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# --------------------------------
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"""
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img_H = util.uint2single(img_H)
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img_L = np.copy(img_H)
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np.random.seed(seed=0)
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img_L += np.random.normal(0, self.sigma_test/255.0, img_L.shape)
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noise_level_map = torch.ones((1, img_L.shape[0], img_L.shape[1])).mul_(self.sigma_test/255.0).float() # torch.full((1, img_L.size(1), img_L.size(2)), noise_level)
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# ---------------------------------
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# L/H image pairs
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# ---------------------------------
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img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(img_L)
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"""
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# -------------------------------------
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# concat L and noise level map M
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# -------------------------------------
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"""
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img_L = torch.cat((img_L, noise_level_map), 0)
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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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