def __getitem__(self, index ):
# 从文件中读出HR图片，返回HR、LR图片对
HR_path , LR_path = None, None
HR_path = self.HR_paths[index]
img_HR = util.read_img_(HR_path, self.n_channels) # return: Numpy float32, HWC, BGR, [0,1]
scale = self.scale
patch_size = self.patch_size
if self.opt.phase == 'train':
H_s, W_s, _ = img_HR.shape
img_HR = cv2.resize(np.copy(img_HR), (W_s, H_s), interpolation=cv2.INTER_LINEAR)
# force to 3 channels
if img_HR.ndim == 2:
img_HR = cv2.cvtColor(img_HR, cv2.COLOR_GRAY2BGR)
# using matlab imresize
img_LR = util.imresize_np(img_HR, 1 / scale, True)
H, W, C = img_LR.shape
if img_LR.ndim == 2:
img_LR = np.expand_dims(img_LR, axis=2)
if self.opt.phase == 'train':
patch_H, patch_L = util.paired_random_crop(img_HR, img_LR, patch_size, scale, gt_path)
# augmentation - flip, rotate
img_HR , img_LR = util.augment([patch_H, patch_L], self.opt.use_flip,self.opt.use_rot)
img_HR = util.img2tensor(img_HR)
img_LR = util.img2tensor(img_LR)
if LR_path is None:
LR_path = HR_path
return {'LR': img_LR, 'HR': img_HR, 'LR_path': LR_path, 'HR_path': HR_path}
def __getitem__(self, index):
# 从文件中读出HR图片，返回HR、LR图片对
HR_path, LR_path = None, None
HR_path = self.HR_paths[index]
scale = self.scale[self.idx_scale]
patch_size = self.patch_size
img_HR = util.read_img_(HR_path, self.n_colors)
# 归一化
img_HR = util.unit2single(img_HR)
img_HR = util.modcrop(img_HR, self.scale)
if self.opt['phase'] == 'train':
l_max = 50
theta = np.pi * np.random.rand(1)
l1 = 0.1 + l_max * np.random.rand(1)
l2 = 0.1 + (l1 - 0.1) * np.random.rand(1)
kernel = util.anisotropic_Gaussian(ksize=self.ksize,
theta=theta[0],
l1=l1[0],
l2=l2[0])
else:
kernel = util.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()
H, W, _ = img_HR.shape
img_LR = util.srmd_degradation(img_HR, kernel, self.scale)
if not self.opt.test_only:
patch_H, patch_L = util.paired_random_crop(img_HR, img_LR,
patch_size, scale)
# augmentation - flip, rotate
img_HR, img_LR = util.augment([patch_H, patch_L],
self.opt.use_flip, self.opt.use_rot)
if random.random < 0.1:
noise_level = np.zeros(1).float()
else:
noise_level = np.random.uniform(0, 15)
else:
noise_level = np.zeros(self.sigma_test).float
img_LR = img_LR + np.random.normal(0, noise_level / 255., img_LR.shape)
img_HR = util.img_single2tensor(img_HR)
img_LR = util.img_single2tensor(img_LR)
M_vector = torch.cat((k_reduced, noise_level),
0).unsqueeze(1).unsqueeze(1)
M = M_vector.repeat(1, img_LR.size()[-2], img_LR.size()[-1])
img_LR = torch.cat((img_LR, M), 0)
if LR_path is None:
LR_path = HR_path
return img_LR, img_HR, LR_path, HR_path
def __getitem__(self, index):
# 从文件中读出HR图片，返回HR、LR图片对
HR_path = self.HR_paths[index]
LR_path = self.LR_paths[index]
img_HR = util.read_img_(HR_path, self.n_colors)
img_LR = util.read_img_(LR_path, self.n_colors)
if not self.opt.test_only == 'train':
patch_H, patch_L = util.paired_random_crop(img_HR, img_LR,
self.patch_size,
self.scale)
img_HR, img_LR = util.augment([patch_H, patch_L],
self.opt.use_flip, self.opt.use_rot)
img_HR = util.img2tensor(img_HR)
img_LR = util.img2tensor(img_LR)
return {'LR': img_LR, 'HR': img_HR}
def __getitem__(self, index):
# 从文件中读出HR图片，返回HR、LR图片对
HR_path, LR_path = None, None
HR_path = self.HR_paths[index]
img_HR = util.read_img_(HR_path, self.n_colors)
# 归一化
img_HR = util.unit2single(img_HR)
img_HR = util.modcrop(img_HR, self.scale)
kernel = fspecial('gaussian', 15, 2.6)
H, W, _ = img_HR.shape
img_LR = util.srmd_degradation(img_HR, kernel, self.scale)
if self.opt.phase == 'train':
patch_H, patch_L = util.paired_random_crop(img_HR, img_LR,
self.patch_size,
self.scale)
# augmentation - flip, rotate
img_HR, img_LR = util.augment([patch_H, patch_L],
self.opt.use_flip, self.opt.use_rot)
img_HR = util.img_single2tensor(img_HR)
img_LR = util.img_single2tensor(img_LR)
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
else:
img_HR = util.img_single2tensor(img_HR)
img_LR = util.img_single2tensor(img_LR)
noise_level = torch.FloatTensor([self.sigma_test])
noise = torch.randn(img_LR.size()).mul_(noise_level).float()
img_LR.add_(noise)
kernel = util.single2tensor3(np.expand_dims(np.float32(kernel),
axis=2))
noise_level = torch.FloatTensor([noise_level]).view([1, 1, 1])
if LR_path is None:
LR_path = HR_path
return {
'LR': img_LR,
'HR': img_HR,
'k': kernel,
'sigma': noise_level,
'sf': self.scale,
'LR_path': LR_path,
'HR_path': HR_path
}
def _scan(self):
path = self.dir_hr
names_hr = []
for dirpath, _, fnames in sorted(os.walk(path)):
for fname in sorted(fnames):
img_path = dirpath + '/' + fname
names_hr.append(img_path)
# 读取HR图片，生成 LR/BlurMap
names_lr = [[] for _ in self.scale]
for f in names_hr:
img_HR = util.read_img_(f, self.n_colors)
# 归一化
img_HR = util.unit2single(img_HR)
img_HR = util.modcrop(img_HR, self.scale)
if self.opt['phase'] == 'train':
l_max = 50
theta = np.pi * np.random.rand(1)
l1 = 0.1 + l_max * np.random.rand(1)
l2 = 0.1 + (l1 - 0.1) * np.random.rand(1)
kernel = util.anisotropic_Gaussian(ksize=self.ksize,
theta=theta[0],
l1=l1[0],
l2=l2[0])
else:
kernel = util.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()
H, W, _ = img_HR.shape
img_LR = util.srmd_degradation(img_HR, kernel, self.scale)
if not self.opt.test_only:
patch_H, patch_L = util.paired_random_crop(
img_HR, img_LR, patch_size, scale)
# augmentation - flip, rotate
img_HR, img_LR = util.augment([patch_H, patch_L],
self.opt.use_flip,
self.opt.use_rot)
if random.random < 0.1:
noise_level = np.zeros(1).float()
else:
noise_level = np.random.uniform(0, 15)
else:
noise_level = np.zeros(self.sigma_test).float
img_LR = img_LR + np.random.normal(0, noise_level / 255.,
img_LR.shape)
img_HR = util.img_single2tensor(img_HR)
img_LR = util.img_single2tensor(img_LR)
M_vector = torch.cat((k_reduced, noise_level),
0).unsqueeze(1).unsqueeze(1)
M = M_vector.repeat(1, img_LR.size()[-2], img_LR.size()[-1])
img_LR = torch.cat((img_LR, M), 0)
filename, _ = os.path.splitext(os.path.basename(f))
for si, s in enumerate(self.scale):
# 1018测试的图片格式与训练图片格式不符，记得修改！
if self.name == 'DIV2K':
names_lr[si].append(self.dir_lr + '/' + 'X{}'.format(s) +
'/' + 'sig2.6_' + filename + '.jpg')
elif self.name == 'Set5':
names_lr[si].append(self.dir_lr + '/' + 'X{}'.format(s) +
'/' + 'sig2.6_' + filename + '.png')
return names_hr, names_lr
def __getitem__(self, index):
# 从文件中读出HR图片，返回HR、LR图片对
HR_path, LR_path = None, None
HR_path = self.HR_paths[index]
img_HR = util.read_img_(HR_path, self.n_colors)
# 归一化
img_HR = util.unit2single(img_HR)
img_HR = util.modcrop(img_HR, self.scale)
if self.opt.phase == 'train':
l_max = 50
theta = np.pi * np.random.rand(1)
l1 = 0.1 + l_max * np.random.rand(1)
l2 = 0.1 + (l1 - 0.1) * np.random.rand(1)
kernel = util.anisotropic_Gaussian(ksize=self.ksize,
theta=theta[0],
l1=l1[0],
l2=l2[0])
else:
kernel = util.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()
H, W, _ = img_HR.shape
img_LR = util.srmd_degradation(img_HR, kernel, self.scale)
if self.opt.phase == 'train':
patch_H, patch_L = util.paired_random_crop(img_HR, img_LR,
self.patch_size,
self.scale)
# augmentation - flip, rotate
img_HR, img_LR = util.augment([patch_H, patch_L],
self.opt.use_flip, self.opt.use_rot)
img_HR = util.img_single2tensor(img_HR)
img_LR = util.img_single2tensor(img_LR)
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
else:
img_HR = util.img_single2tensor(img_HR)
img_LR = util.img_single2tensor(img_LR)
noise_level = torch.FloatTensor([self.sigma_test])
noise = torch.randn(img_LR.size()).mul_(noise_level).float()
img_LR.add_(noise)
M_vector = torch.cat((k_reduced, noise_level),
0).unsqueeze(1).unsqueeze(1)
M = M_vector.repeat(1, img_LR.size()[-2], img_LR.size()[-1])
img_LR = torch.cat((img_LR, M), 0)
if LR_path is None:
LR_path = HR_path
return {
'LR': img_LR,
'HR': img_HR,
'LR_path': LR_path,
'HR_path': HR_path
}