156 lines
5.9 KiB
Python
156 lines
5.9 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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from utils import utils_sisr
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import hdf5storage
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import os
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class DatasetSRMD(data.Dataset):
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'''
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# -----------------------------------------
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# Get L/H/M for noisy image SR with Gaussian kernels.
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# Only "paths_H" is needed, sythesize bicubicly downsampled L on-the-fly.
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# -----------------------------------------
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# e.g., SRMD, H = f(L, kernel, sigma), sigma is noise level
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# -----------------------------------------
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'''
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def __init__(self, opt):
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super(DatasetSRMD, 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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self.sigma = opt['sigma'] if opt['sigma'] else [0, 50]
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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 0
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# -------------------------------------
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# PCA projection matrix
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# -------------------------------------
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self.p = hdf5storage.loadmat(os.path.join('kernels', 'srmd_pca_pytorch.mat'))['p']
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self.ksize = int(np.sqrt(self.p.shape[-1])) # kernel size
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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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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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img_H = util.uint2single(img_H)
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# ------------------------------------
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# modcrop for SR
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# ------------------------------------
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img_H = util.modcrop(img_H, self.sf)
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# ------------------------------------
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# kernel
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# ------------------------------------
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if self.opt['phase'] == 'train':
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l_max = 10
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theta = np.pi*random.random()
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l1 = 0.1+l_max*random.random()
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l2 = 0.1+(l1-0.1)*random.random()
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kernel = utils_sisr.anisotropic_Gaussian(ksize=self.ksize, theta=theta, l1=l1, l2=l2)
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else:
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kernel = utils_sisr.anisotropic_Gaussian(ksize=self.ksize, theta=np.pi, l1=0.1, l2=0.1)
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k = np.reshape(kernel, (-1), order="F")
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k_reduced = np.dot(self.p, k)
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k_reduced = torch.from_numpy(k_reduced).float()
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# ------------------------------------
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# sythesize L image via specified degradation model
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# ------------------------------------
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H, W, _ = img_H.shape
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img_L = utils_sisr.srmd_degradation(img_H, kernel, self.sf)
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img_L = np.float32(img_L)
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if self.opt['phase'] == 'train':
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"""
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# --------------------------------
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# get L/H patch pairs
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# --------------------------------
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"""
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H, W, C = img_L.shape
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# --------------------------------
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# randomly crop 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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# get patch 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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# select noise level and get Gaussian noise
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# --------------------------------
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if random.random() < 0.1:
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noise_level = torch.zeros(1).float()
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else:
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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 = torch.rand(1)*50/255.0
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# noise_level = torch.min(torch.from_numpy(np.float32([7*np.random.chisquare(2.5)/255.0])),torch.Tensor([50./255.]))
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else:
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img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(img_L)
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noise_level = noise_level = torch.FloatTensor([self.sigma_test])
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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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# ------------------------------------
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# get degradation map M
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# ------------------------------------
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M_vector = torch.cat((k_reduced, noise_level), 0).unsqueeze(1).unsqueeze(1)
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M = M_vector.repeat(1, img_L.size()[-2], img_L.size()[-1])
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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, M), 0)
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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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