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