134 lines
4.7 KiB
Python
134 lines
4.7 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 DatasetDnPatch(data.Dataset):
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"""
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# -----------------------------------------
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# Get L/H for denosing on AWGN with fixed sigma.
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# ****Get all H patches first****
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# Only dataroot_H is needed.
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# -----------------------------------------
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# e.g., DnCNN with BSD400
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# -----------------------------------------
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"""
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def __init__(self, opt):
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super(DatasetDnPatch, self).__init__()
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print('Get L/H for denosing on AWGN with fixed sigma. Only dataroot_H is 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 = opt['H_size'] if opt['H_size'] else 64
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self.sigma = opt['sigma'] if opt['sigma'] else 25
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self.sigma_test = opt['sigma_test'] if opt['sigma_test'] else self.sigma
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self.num_patches_per_image = opt['num_patches_per_image'] if opt['num_patches_per_image'] else 40
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self.num_sampled = opt['num_sampled'] if opt['num_sampled'] else 3000
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# ------------------------------------
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# get paths of H
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# ------------------------------------
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self.paths_H = util.get_image_paths(opt['dataroot_H'])
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assert self.paths_H, 'Error: H path is empty.'
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# ------------------------------------
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# number of sampled H images
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# ------------------------------------
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self.num_sampled = min(self.num_sampled, len(self.paths_H))
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# ------------------------------------
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# reserve space with zeros
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# ------------------------------------
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self.total_patches = self.num_sampled * self.num_patches_per_image
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self.H_data = np.zeros([self.total_patches, self.patch_size, self.patch_size, self.n_channels], dtype=np.uint8)
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# ------------------------------------
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# update H patches
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# ------------------------------------
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self.update_data()
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def update_data(self):
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"""
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# ------------------------------------
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# update whole H patches
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# ------------------------------------
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"""
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self.index_sampled = random.sample(range(0, len(self.paths_H), 1), self.num_sampled)
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n_count = 0
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for i in range(len(self.index_sampled)):
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H_patches = self.get_patches(self.index_sampled[i])
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for H_patch in H_patches:
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self.H_data[n_count,:,:,:] = H_patch
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n_count += 1
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print('Training data updated! Total number of patches is: %5.2f X %5.2f = %5.2f\n' % (len(self.H_data)//128, 128, len(self.H_data)))
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def get_patches(self, index):
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"""
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# ------------------------------------
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# get H patches from an H image
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# ------------------------------------
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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) # uint format
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H, W = img_H.shape[:2]
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H_patches = []
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num = self.num_patches_per_image
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for _ in range(num):
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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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H_patch = img_H[rnd_h:rnd_h + self.patch_size, rnd_w:rnd_w + self.patch_size, :]
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H_patches.append(H_patch)
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return H_patches
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def __getitem__(self, index):
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H_path = 'toy.png'
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if self.opt['phase'] == 'train':
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patch_H = self.H_data[index]
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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_H = util.augment_img(patch_H, mode=mode)
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patch_H = util.uint2tensor3(patch_H)
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patch_L = patch_H.clone()
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# ------------------------------------
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# add noise
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# ------------------------------------
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noise = torch.randn(patch_L.size()).mul_(self.sigma/255.0)
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patch_L.add_(noise)
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else:
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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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img_L = np.copy(img_H)
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# ------------------------------------
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# add noise
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# ------------------------------------
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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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patch_L, patch_H = util.single2tensor3(img_L), util.single2tensor3(img_H)
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L_path = H_path
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return {'L': patch_L, 'H': patch_H, 'L_path': L_path, 'H_path': H_path}
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def __len__(self):
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return len(self.H_data)
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