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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 DatasetFFDNet(data.Dataset):
"""
# -----------------------------------------
# Get L/H/M for denosing on AWGN with a range of sigma.
# Only dataroot_H is needed.
# -----------------------------------------
# e.g., FFDNet, H = f(L, sigma), sigma is noise level
# -----------------------------------------
"""
def __init__(self, opt):
super(DatasetFFDNet, 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 opt['H_size'] else 64
self.sigma = opt['sigma'] if opt['sigma'] else [0, 75]
self.sigma_min, self.sigma_max = self.sigma[0], self.sigma[1]
self.sigma_test = opt['sigma_test'] if opt['sigma_test'] else 25
# -------------------------------------
# get the path of H, return None if input is None
# -------------------------------------
self.paths_H = util.get_image_paths(opt['dataroot_H'])
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':
"""
# --------------------------------
# get L/H/M patch pairs
# --------------------------------
"""
H, W = img_H.shape[:2]
# ---------------------------------
# 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 = random.randint(0, 7)
patch_H = util.augment_img(patch_H, mode=mode)
# ---------------------------------
# HWC to CHW, numpy(uint) to tensor
# ---------------------------------
img_H = util.uint2tensor3(patch_H)
img_L = img_H.clone()
# ---------------------------------
# get noise level
# ---------------------------------
# noise_level = torch.FloatTensor([np.random.randint(self.sigma_min, self.sigma_max)])/255.0
noise_level = torch.FloatTensor([np.random.uniform(self.sigma_min, self.sigma_max)])/255.0
# ---------------------------------
# add noise
# ---------------------------------
noise = torch.randn(img_L.size()).mul_(noise_level).float()
img_L.add_(noise)
else:
"""
# --------------------------------
# get L/H/sigma image pairs
# --------------------------------
"""
img_H = util.uint2single(img_H)
img_L = np.copy(img_H)
np.random.seed(seed=0)
img_L += np.random.normal(0, self.sigma_test/255.0, img_L.shape)
noise_level = torch.FloatTensor([self.sigma_test/255.0])
# ---------------------------------
# L/H image pairs
# ---------------------------------
img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(img_L)
noise_level = noise_level.unsqueeze(1).unsqueeze(1)
return {'L': img_L, 'H': img_H, 'C': noise_level, 'L_path': L_path, 'H_path': H_path}
def __len__(self):
return len(self.paths_H)
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