def __getitem__(self, index):
if self.opt['phase'] == 'train':
patch_L, patch_H = self.L_data[index], self.H_data[index]
# --------------------------------
# augmentation - flip and/or rotate
# --------------------------------
mode = random.randint(0, 7)
patch_L = util.augment_img(patch_L, mode=mode)
patch_H = util.augment_img(patch_H, mode=mode)
patch_L, patch_H = util.uint2tensor3(patch_L), util.uint2tensor3(
patch_H)
else:
L_path, H_path = self.paths_L[index], self.paths_H[index]
patch_L = util.imread_uint(L_path, self.n_channels)
patch_H = util.imread_uint(H_path, self.n_channels)
patch_L, patch_H = util.uint2tensor3(patch_L), util.uint2tensor3(
patch_H)
return {'L': patch_L, 'H': patch_H}
def __getitem__(self, index):
# ------------------------------------
# get H image
# ------------------------------------
H_path = self.paths_H[index]
img_H = util.imread_uint(H_path, self.n_channels)
# ------------------------------------
# get L image
# ------------------------------------
L_path = self.paths_L[index]
img_L = util.imread_uint(L_path, self.n_channels)
# ------------------------------------
# if train, get L/H patch pair
# ------------------------------------
if self.opt['phase'] == 'train':
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_L = img_L[rnd_h:rnd_h + self.patch_size,
rnd_w:rnd_w + self.patch_size, :]
patch_H = img_H[rnd_h:rnd_h + self.patch_size,
rnd_w:rnd_w + self.patch_size, :]
# --------------------------------
# augmentation - flip and/or rotate
# --------------------------------
mode = np.random.randint(0, 8)
patch_L, patch_H = util.augment_img(
patch_L, mode=mode), util.augment_img(patch_H, mode=mode)
# --------------------------------
# HWC to CHW, numpy(uint) to tensor
# --------------------------------
img_L, img_H = util.uint2tensor3(patch_L), util.uint2tensor3(
patch_H)
else:
# --------------------------------
# HWC to CHW, numpy(uint) to tensor
# --------------------------------
img_L, img_H = util.uint2tensor3(img_L), util.uint2tensor3(img_H)
return {'L': img_L, 'H': img_H, 'L_path': L_path, 'H_path': H_path}
def __getitem__(self, index: int) -> Dict[str, Union[str, torch.Tensor]]:
# get H image
img_path = self.img_paths[index]
img_H = util.imread_uint(img_path, self.n_channels)
H, W = img_H.shape[:2]
if self.opt['phase'] == 'train':
self.count += 1
# crop
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
patch_H = util.augment_img(patch_H, mode=np.random.randint(0, 8))
# HWC to CHW, numpy(uint) to tensor
img_H = util.uint2tensor3(patch_H)
img_L: torch.Tensor = img_H.clone()
# get noise level
noise_level: torch.FloatTensor = torch.FloatTensor(
[np.random.uniform(self.sigma[0], self.sigma[1])]) / 255.0
# add noise
noise = torch.randn(img_L.size()).mul_(noise_level).float()
img_L.add_(noise)
else:
img_H = util.uint2single(img_H)
img_L = np.copy(img_H)
# add noise
np.random.seed(seed=0)
img_L += np.random.normal(0, self.sigma / 255.0, img_L.shape)
noise_level = torch.FloatTensor([self.sigma / 255.0])
img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(
img_L)
return {
'y': img_L,
'y_gt': img_H,
'sigma': noise_level.unsqueeze(1).unsqueeze(1),
'path': img_path
}
def __getitem__(self, index):
H_path = 'toy.png'
if self.opt['phase'] == 'train':
patch_H = self.H_data[index]
# --------------------------------
# augmentation - flip and/or rotate
# --------------------------------
mode = np.random.randint(0, 8)
patch_H = util.augment_img(patch_H, mode=mode)
patch_H = util.uint2tensor3(patch_H)
patch_L = patch_H.clone()
# ------------------------------------
# add noise
# ------------------------------------
noise = torch.randn(patch_L.size()).mul_(self.sigma / 255.0)
patch_L.add_(noise)
else:
H_path = self.paths_H[index]
img_H = util.imread_uint(H_path, self.n_channels)
img_H = util.uint2single(img_H)
img_L = np.copy(img_H)
# ------------------------------------
# add noise
# ------------------------------------
np.random.seed(seed=0)
img_L += np.random.normal(0, self.sigma_test / 255.0, img_L.shape)
patch_L, patch_H = util.single2tensor3(img_L), util.single2tensor3(
img_H)
L_path = H_path
return {'L': patch_L, 'H': patch_H, 'L_path': L_path, 'H_path': H_path}
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 = np.random.randint(0, 8)
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 __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 patch pairs
# --------------------------------
"""
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)
# --------------------------------
# HWC to CHW, numpy(uint) to tensor
# --------------------------------
img_H = util.uint2tensor3(patch_H)
img_L = img_H.clone()
# --------------------------------
# add noise
# --------------------------------
noise = torch.randn(img_L.size()).mul_(self.sigma / 255.0)
img_L.add_(noise)
else:
"""
# --------------------------------
# get L/H image pairs
# --------------------------------
"""
img_H = util.uint2single(img_H)
img_L = np.copy(img_H)
# --------------------------------
# add noise
# --------------------------------
np.random.seed(seed=0)
img_L += np.random.normal(0, self.sigma_test / 255.0, img_L.shape)
# --------------------------------
# HWC to CHW, numpy to tensor
# --------------------------------
img_L = util.single2tensor3(img_L)
img_H = util.single2tensor3(img_H)
return {'L': img_L, 'H': img_H, 'H_path': H_path, 'L_path': L_path}
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
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)
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 __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 = np.random.randint(0, 8)
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 __getitem__(self, index):
# ------------------------------------
# get H image
# ------------------------------------
H_path = self.paths_H[index]
H_resolution = [int(s) for s in self.sizes_H[index].split('_')]
img_H = _read_img_lmdb(self.h_env, H_path, H_resolution)
img_H = util.uint2single(img_H)
img_H = util.modcrop(img_H, self.sf)
# ----------------------------------------------
# get down4 sharp image(img_h0) to be target in train for deblur
# ----------------------------------------------
img_h0 = util.imresize_np(img_H, 1 / 4, True)
# ------------------------------------
# get L image
# ------------------------------------
L_path = self.paths_L[index]
L_resolution = [int(s) for s in self.sizes_L[index].split('_')]
img_L = _read_img_lmdb(self.l_env, L_path, L_resolution)
img_L = util.uint2single(img_L)
# # ----------------------------------------------
# # get blur image to be input in train for deblur
# # ----------------------------------------------
# img_l0 = img_L
# ------------------------------------
# if train, get L/H patch pair
# ------------------------------------
if self.opt['phase'] == 'train':
H, W, C = img_L.shape
# --------------------------------
# randomly crop the 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, :]
img_h0 = img_h0[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 = np.random.randint(0, 8)
img_L, img_H, img_h0 = util.augment_img(
img_L, mode=mode), util.augment_img(
img_H, mode=mode), util.augment_img(img_h0, mode=mode)
# ------------------------------------
# L/H pairs, HWC to CHW, numpy to tensor
# ------------------------------------
img_deblur = [img_L, img_h0]
img_deblur = util.generate_pyramid(*img_deblur, n_scales=3)
img_deblur = util.np2tensor(*img_deblur)
img_ls = img_deblur[0]
img_hs = img_deblur[1]
img_L = img_deblur[0]
img_H = util.single2tensor3(img_H)
img_L0 = img_L[0]
#print(img_L[0].shape,img_H.shape,img_ls[0].shape,img_hs[0].shape)
return {
'L0': img_L0,
'L': img_L,
'H': img_H,
'L_path': L_path,
'H_path': H_path,
'ls': img_ls,
'hs': img_hs
}
def __getitem__(self, index):
L_path = None
# ------------------------------------
# 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
# ------------------------------------
img_H = util.modcrop(img_H, self.sf)
# ------------------------------------
# get L image
# ------------------------------------
if self.paths_L:
# --------------------------------
# directly load L image
# --------------------------------
L_path = self.paths_L[index]
img_L = util.imread_uint(L_path, self.n_channels)
img_L = util.uint2single(img_L)
else:
# --------------------------------
# sythesize L image via matlab's bicubic
# --------------------------------
H, W = img_H.shape[:2]
img_L = util.imresize_np(img_H, 1 / self.sf, True)
# ------------------------------------
# if train, get L/H patch pair
# ------------------------------------
if self.opt['phase'] == 'train':
H, W, C = img_L.shape
# --------------------------------
# randomly crop the 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 = np.random.randint(0, 8)
img_L, img_H = util.augment_img(img_L, mode=mode), util.augment_img(img_H, mode=mode)
# ------------------------------------
# L/H pairs, HWC to CHW, numpy to tensor
# ------------------------------------
img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(img_L)
if L_path is None:
L_path = H_path
return {'L': img_L, 'H': img_H, 'L_path': L_path, 'H_path': H_path}
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*np.random.rand(1)
l1 = 0.1+l_max*np.random.rand(1)
l2 = 0.1+(l1-0.1)*np.random.rand(1)
kernel = utils_sisr.anisotropic_Gaussian(ksize=self.ksize, theta=theta[0], l1=l1[0], l2=l2[0])
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 = np.random.randint(0, 8)
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}
