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import random
import numpy as np
import torch
import torch.utils.data as data
import utils.utils_image as util
from utils import utils_sisr
import hdf5storage
import os
class DatasetSRMD(data.Dataset):
'''
# -----------------------------------------
# Get L/H/M for noisy image SR with Gaussian kernels.
# Only "paths_H" is needed, sythesize bicubicly downsampled L on-the-fly.
# -----------------------------------------
# e.g., SRMD, H = f(L, kernel, sigma), sigma is noise level
# -----------------------------------------
'''
def __init__(self, opt):
super(DatasetSRMD, self).__init__()
self.opt = opt
self.n_channels = opt['n_channels'] if opt['n_channels'] else 3
self.sf = opt['scale'] if opt['scale'] else 4
self.patch_size = self.opt['H_size'] if self.opt['H_size'] else 96
self.L_size = self.patch_size // self.sf
self.sigma = opt['sigma'] if opt['sigma'] else [0, 50]
self.sigma_min, self.sigma_max = self.sigma[0], self.sigma[1]
self.sigma_test = opt['sigma_test'] if opt['sigma_test'] else 0
# -------------------------------------
# PCA projection matrix
# -------------------------------------
self.p = hdf5storage.loadmat(os.path.join('kernels', 'srmd_pca_pytorch.mat'))['p']
self.ksize = int(np.sqrt(self.p.shape[-1])) # kernel size
# ------------------------------------
# get paths of L/H
# ------------------------------------
self.paths_H = util.get_image_paths(opt['dataroot_H'])
self.paths_L = util.get_image_paths(opt['dataroot_L'])
def __getitem__(self, index):
# ------------------------------------
# get H image
# ------------------------------------
H_path = self.paths_H[index]
img_H = util.imread_uint(H_path, self.n_channels)
img_H = util.uint2single(img_H)
# ------------------------------------
# modcrop for SR
# ------------------------------------
img_H = util.modcrop(img_H, self.sf)
# ------------------------------------
# kernel
# ------------------------------------
if self.opt['phase'] == 'train':
l_max = 10
theta = np.pi*random.random()
l1 = 0.1+l_max*random.random()
l2 = 0.1+(l1-0.1)*random.random()
kernel = utils_sisr.anisotropic_Gaussian(ksize=self.ksize, theta=theta, l1=l1, l2=l2)
else:
kernel = utils_sisr.anisotropic_Gaussian(ksize=self.ksize, theta=np.pi, l1=0.1, l2=0.1)
k = np.reshape(kernel, (-1), order="F")
k_reduced = np.dot(self.p, k)
k_reduced = torch.from_numpy(k_reduced).float()
# ------------------------------------
# sythesize L image via specified degradation model
# ------------------------------------
H, W, _ = img_H.shape
img_L = utils_sisr.srmd_degradation(img_H, kernel, self.sf)
img_L = np.float32(img_L)
if self.opt['phase'] == 'train':
"""
# --------------------------------
# get L/H patch pairs
# --------------------------------
"""
H, W, C = img_L.shape
# --------------------------------
# randomly crop L patch
# --------------------------------
rnd_h = random.randint(0, max(0, H - self.L_size))
rnd_w = random.randint(0, max(0, W - self.L_size))
img_L = img_L[rnd_h:rnd_h + self.L_size, rnd_w:rnd_w + self.L_size, :]
# --------------------------------
# crop corresponding H patch
# --------------------------------
rnd_h_H, rnd_w_H = int(rnd_h * self.sf), int(rnd_w * self.sf)
img_H = img_H[rnd_h_H:rnd_h_H + self.patch_size, rnd_w_H:rnd_w_H + self.patch_size, :]
# --------------------------------
# augmentation - flip and/or rotate
# --------------------------------
mode = random.randint(0, 7)
img_L, img_H = util.augment_img(img_L, mode=mode), util.augment_img(img_H, mode=mode)
# --------------------------------
# get patch pairs
# --------------------------------
img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(img_L)
# --------------------------------
# select noise level and get Gaussian noise
# --------------------------------
if random.random() < 0.1:
noise_level = torch.zeros(1).float()
else:
noise_level = torch.FloatTensor([np.random.uniform(self.sigma_min, self.sigma_max)])/255.0
# noise_level = torch.rand(1)*50/255.0
# noise_level = torch.min(torch.from_numpy(np.float32([7*np.random.chisquare(2.5)/255.0])),torch.Tensor([50./255.]))
else:
img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(img_L)
noise_level = noise_level = torch.FloatTensor([self.sigma_test])
# ------------------------------------
# add noise
# ------------------------------------
noise = torch.randn(img_L.size()).mul_(noise_level).float()
img_L.add_(noise)
# ------------------------------------
# get degradation map M
# ------------------------------------
M_vector = torch.cat((k_reduced, noise_level), 0).unsqueeze(1).unsqueeze(1)
M = M_vector.repeat(1, img_L.size()[-2], img_L.size()[-1])
"""
# -------------------------------------
# concat L and noise level map M
# -------------------------------------
"""
img_L = torch.cat((img_L, M), 0)
L_path = H_path
return {'L': img_L, 'H': img_H, 'L_path': L_path, 'H_path': H_path}
def __len__(self):
return len(self.paths_H)
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