127 lines
5.0 KiB
Python
127 lines
5.0 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_deblur
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from utils import utils_sisr
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import os
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from scipy import ndimage
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from scipy.io import loadmat
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# import hdf5storage
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class DatasetUSRNet(data.Dataset):
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'''
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# -----------------------------------------
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# Get L/k/sf/sigma for USRNet.
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# Only "paths_H" and kernel is needed, synthesize L on-the-fly.
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# -----------------------------------------
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'''
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def __init__(self, opt):
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super(DatasetUSRNet, 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 self.opt['H_size'] else 96
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self.sigma_max = self.opt['sigma_max'] if self.opt['sigma_max'] is not None else 25
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self.scales = opt['scales'] if opt['scales'] is not None else [1,2,3,4]
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self.sf_validation = opt['sf_validation'] if opt['sf_validation'] is not None else 3
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#self.kernels = hdf5storage.loadmat(os.path.join('kernels', 'kernels_12.mat'))['kernels']
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self.kernels = loadmat(os.path.join('kernels', 'kernels_12.mat'))['kernels'] # for validation
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# -------------------
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# get the path of H
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# -------------------
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self.paths_H = util.get_image_paths(opt['dataroot_H']) # return None if input is None
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self.count = 0
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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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# 1) scale factor, ensure each batch only involves one scale factor
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# ---------------------------
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if self.count % self.opt['dataloader_batch_size'] == 0:
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# sf = random.choice([1,2,3,4])
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self.sf = random.choice(self.scales)
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# self.count = 0 # optional
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self.count += 1
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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_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 = np.random.randint(0, 8)
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patch_H = util.augment_img(patch_H, mode=mode)
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# ---------------------------
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# 2) kernel
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# ---------------------------
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r_value = random.randint(0, 7)
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if r_value>3:
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k = utils_deblur.blurkernel_synthesis(h=25) # motion blur
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else:
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sf_k = random.choice(self.scales)
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k = utils_sisr.gen_kernel(scale_factor=np.array([sf_k, sf_k])) # Gaussian blur
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mode_k = random.randint(0, 7)
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k = util.augment_img(k, mode=mode_k)
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# ---------------------------
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# 3) noise level
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# ---------------------------
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if random.randint(0, 8) == 1:
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noise_level = 0/255.0
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else:
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noise_level = np.random.randint(0, self.sigma_max)/255.0
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# ---------------------------
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# Low-quality image
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# ---------------------------
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img_L = ndimage.filters.convolve(patch_H, np.expand_dims(k, axis=2), mode='wrap')
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img_L = img_L[0::self.sf, 0::self.sf, ...]
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# add Gaussian noise
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img_L = util.uint2single(img_L) + np.random.normal(0, noise_level, img_L.shape)
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img_H = patch_H
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else:
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k = self.kernels[0, 0].astype(np.float64) # validation kernel
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k /= np.sum(k)
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noise_level = 0./255.0 # validation noise level
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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_validation)
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img_L = ndimage.filters.convolve(img_H, np.expand_dims(k, axis=2), mode='wrap') # blur
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img_L = img_L[0::self.sf_validation, 0::self.sf_validation, ...] # downsampling
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img_L = util.uint2single(img_L) + np.random.normal(0, noise_level, img_L.shape)
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self.sf = self.sf_validation
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k = util.single2tensor3(np.expand_dims(np.float32(k), axis=2))
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img_H, img_L = util.uint2tensor3(img_H), util.single2tensor3(img_L)
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noise_level = torch.FloatTensor([noise_level]).view([1,1,1])
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return {'L': img_L, 'H': img_H, 'k': k, 'sigma': noise_level, 'sf': self.sf, '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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