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import random
import numpy as np
import torch
import torch.utils.data as data
import utils.utils_image as util
class DatasetDPSR(data.Dataset):
'''
# -----------------------------------------
# Get L/H/M for noisy image SR.
# Only "paths_H" is needed, sythesize bicubicly downsampled L on-the-fly.
# -----------------------------------------
# e.g., SRResNet super-resolver prior for DPSR
# -----------------------------------------
'''
def __init__(self, opt):
super(DatasetDPSR, 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
# ------------------------------------
# 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'])
assert self.paths_H, 'Error: H path is empty.'
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)
# ------------------------------------
# sythesize L image via matlab's bicubic
# ------------------------------------
H, W, _ = img_H.shape
img_L = util.imresize_np(img_H, 1 / self.sf, True)
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 = torch.FloatTensor([self.sigma_test])
# ------------------------------------
# add noise
# ------------------------------------
noise = torch.randn(img_L.size()).mul_(noise_level).float()
img_L.add_(noise)
# ------------------------------------
# get noise level map M
# ------------------------------------
M_vector = noise_level.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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