def get_patch(self, lr, hr):
scale = self.scale[self.idx_scale]
# print("scale:",scale)
if self.train:
lr, hr = common.get_patch(lr,
hr,
patch_size=self.args.patch_size,
scale=scale,
multi=(len(self.scale) > 1),
input_large=self.input_large)
if not self.args.no_augment: lr, hr = common.augment(lr, hr)
elif not self.benchmark:
lr, hr = common.get_patch(lr,
hr,
patch_size=self.args.patch_size,
scale=scale,
multi=(len(self.scale) > 1),
input_large=self.input_large)
if not self.args.no_augment: lr, hr = common.augment(lr, hr)
else:
try:
ih, iw = lr.shape[:2]
except:
ih, iw = lr[0]['image'].shape[:2]
try:
hr = hr[0:ih * scale, 0:iw * scale]
except:
hr = hr[0]['image'][0:ih * scale, 0:iw * scale]
return lr, hr
def get_patch(self, lr, hr, lrr, hq):
scale = self.scale[self.idx_scale]
multi_scale = len(self.scale) > 1
if self.train:
lr, hr = common.get_patch(lr,
hr,
patch_size=self.args.patch_size,
scale=scale,
multi_scale=multi_scale)
lrr, _ = common.get_patch(lrr,
lrr,
patch_size=self.args.patch_size,
scale=scale,
multi_scale=multi_scale)
hq, _ = common.get_patch(hq,
hq,
patch_size=self.args.patch_size,
scale=scale,
multi_scale=multi_scale)
if not self.args.no_augment:
lr, hr, lrr, hq = common.augment(lr, hr, lrr, hq)
else:
ih, iw = lr.shape[:2]
hr = hr[0:ih, 0:iw]
lrr = lrr[0:ih, 0:iw]
hq = hq[0:ih, 0:iw]
return lr, hr, lrr, hq
def get_patch(self, lr, hr):
scale = self.scale[self.idx_scale]
if self.train:
lr, hr = common.get_patch(lr,
hr,
patch_size=self.args.patch_size,
scale=scale,
multi=(len(self.scale) > 1),
input_large=self.input_large)
#print(lr.shape)
if not self.args.no_augment: lr, hr = common.augment(lr, hr)
# print(lr.shape)
else:
# lr, hr = common.get_patch(
# lr, hr,
# patch_size=self.args.patch_size,
# scale=scale,
# multi=(len(self.scale) > 1),
# input_large=self.input_large
# )
# if not self.args.no_augment: lr, hr = common.augment(lr, hr)
ih, iw = lr.shape[:2]
hr = hr[0:ih * scale, 0:iw * scale]
return lr, hr
def _get_patch_bk(self, lr, hr):
LR_size = self.opt['LR_size']
lr, hr = common.get_patch(lr, hr, LR_size, self.scale)
lr, hr = common.augment([lr, hr])
return lr, hr
def _get_patch(self, lr, hr):
LR_size = self.opt['LR_size']
# random crop and augment
lr, hr = common.get_patch(lr, hr, LR_size, self.scale)
lr, hr = common.augment([lr, hr])
lr = common.add_noise(lr, self.opt['noise'])
return lr, hr
def get_patch(self, lr, hr, ix, iy):
"""
Returns patches for multiple scales
"""
scale = self.scale
if self.train:
patch_size = self.args.patch_size - (self.args.patch_size % 4)
lr, hr = common.get_patch(
lr,
hr,
patch_size=patch_size,
scale=scale,
ix=ix,
iy=iy
)
if not self.args.no_augment:
lr, hr = common.augment(lr, hr)
else:
ih, iw = lr.shape[:2]
ih -= ih % 4
iw -= iw % 4
lr = lr[:ih, :iw]
hr = hr[:ih * scale, :iw * scale]
return lr, hr
def get_patch(self, lr, labels):
data = []
data.append(lr)
for label in labels:
data.append(label)
# LR, HR, labels
scale = self.scale[self.idx_scale]
if self.n_colors == 4:
scale = scale * 2
#print(scale)
#print(self.args.test_patch_size)
if self.train:
data = common.get_patch(*data,
patch_size=self.args.patch_size,
scale=scale,
multi=(len(self.scale) > 1),
input_large=self.input_large)
else:
data = common.get_center_patch(
*data,
patch_size=self.args.test_patch_size,
scale=scale,
multi=(len(self.scale) > 1),
input_large=self.input_large)
if self.train and not self.args.no_augment:
data = common.augment(*data)
#print(data[0].shape)
#print(data[1].shape)
return data
def _get_patch(self, lr, hr,lr2=None,hr2=None):
patch_size = self.args.patch_size
scale = self.scale[self.idx_scale]
multi_scale = len(self.scale) > 1
if self.train:
if (self.args.nmodels == 1):
lr, hr = common.get_patch(
lr, hr, patch_size, scale, multi_scale=multi_scale
)
lr, hr = common.augment([lr, hr])
lr = common.add_noise(lr, self.args.noise)
else:
lr, hr, lr2, hr2 = common.get_patch_2model(
lr, hr, lr2, hr2, patch_size, scale, multi_scale=multi_scale
)
else:
if (self.args.nmodels == 1):
ih, iw = lr.shape[0:2]
hr = hr[0:ih * scale, 0:iw * scale]
else:
ih, iw = lr.shape[0:2]
hr = hr[0:ih * scale, 0:iw * scale]
hr2 = hr2[0:ih * scale, 0:iw * scale]
return lr, hr, lr2, hr2
def _get_patch(self, ): # lr, hr):
patch_size = self.args.patch_size
scale = self.scale[0]
multi_scale = len(self.scale) > 1
batch_size = 1 #min(self.args.batch_size, len(self.remain_idx))
# scale = self.scale[self.idx_scale]
# multi_scale = len(self.scale) > 1
idx = self.remain_idx[:batch_size]
self.remain_idx = self.remain_idx[batch_size:]
lr, hr = self.raw_lr[idx], self.raw_hr[idx]
lr = lr.astype(np.float32)[0]
hr = hr.astype(np.float32)[0]
lr = lr[:, :, ::-1]
hr = hr[:, :, ::-1]
# lr = lr.transpose(2,0,1)
# hr = hr.transpose(2,0,1)
if self.train:
lr, hr = common.get_patch(lr,
hr,
patch_size,
scale,
multi_scale=multi_scale)
lr, hr = common.augment([lr, hr])
# lr = common.add_noise(lr, self.args.noise)
else:
ih, iw = lr.shape[0:2]
hr = hr[0:ih * scale, 0:iw * scale]
return lr, hr
def get_patch(self, lr, hr, nr):
lr, hr, nr = common.get_patch(lr,
hr,
nr,
patch_size=self.opt.patch_size,
n_channels=1)
lr, hr, nr = common.augment(lr, hr, nr)
return lr, hr, nr
def _get_patch(self, lr1, lr2, lr3, hr):
LR_size = self.opt['LR_size']
# random crop and augment
lr1, lr2, lr3, hr = common.get_patch(lr1, lr2, lr3, hr, LR_size, self.scale)
lr1, lr2, lr3, hr = common.augment([lr1, lr2, lr3, hr])
return lr1, lr2, lr3, hr
def _get_patch(self, ir, vis):
Label_Size = self.opt['Label_Size']
if self.train:
ir, vis = common.get_patch(ir, vis, Label_Size, self.scale)
ir, vis = common.augment([ir, vis])
ir = common.add_noise(ir, self.opt['noise'])
return ir, vis
def _get_patch(self, lr, hr, scale, patch_size, phase_str):
if phase_str == 'train':
lr, hr = common.get_patch(
lr, hr, patch_size, scale)
lr, hr = common.augment([lr, hr], self.opt['use_flip'], self.opt['use_rot'])
lr = common.add_noise(lr, self.opt['noise'])
else:
hr = common.modcrop(hr, scale)
return lr, hr
def _get_patch(self, lr, hr, lrpan, pan, msx2=False):
LR_size = self.opt['LR_size']
# random crop and augment
lr, hr, lrpan, pan = common.get_patch(lr, hr, pan,
LR_size, self.scale, lrpan=lrpan, msx2=msx2)
lr, hr, lrpan, pan = common.augment([lr, hr, lrpan, pan])
lr = common.add_noise(lr, self.opt['noise'])
return lr, hr, lrpan, pan
def _get_patch(self, lr, hr):
LR_size = self.opt['LR_size']
# random crop and augment
lr, hr = common.get_patch(lr, hr, LR_size, self.scale)
# print('shapes again here', lr.shape, hr.shape)
lr, hr = common.augment([lr, hr])
# lr = common.add_noise(lr, self.opt['noise'])
# print('shapes again', lr.shape, hr.shape)
return lr, hr
def _get_patch(self, img, tar_shape, scale, seed):
# random crop and augment
patch = common.get_patch(img, tar_shape, scale, seed)
# print('shapes again here', lr.shape, hr.shape)
# lr, hr = common.augment([lr, hr])
# lr = common.add_noise(lr, self.opt['noise'])
# print('shapes again', lr.shape, hr.shape)
return patch
def _get_patch(self, hr, filter, filename, scale_factor, quality_factor, sigma0, sigma1, blur_flag):
patch_size = self.args.patch_size
# scale = self.scale[self.idx_scale]
# multi_scale = len(self.scale) > 1
if self.train:
sigma, lr, hr = common.get_patch(
hr, patch_size, filename, filter, scale_factor, quality_factor, sigma0, sigma1, blur_flag
)
# sigma, lr, hr = common.augment([sigma, lr, hr])
return sigma, lr, hr
def _get_patch(self, lr, hr):
LR_size = self.opt['LR_size']
if self.train:
lr, hr = common.get_patch(
lr, hr, LR_size, self.scale)
lr, hr = common.augment([lr, hr])
lr = common.add_noise(lr, self.opt['noise'])
else:
hr = common.modcrop(hr, self.scale)
return lr, hr
def _get_patch(self, img_in, img_tar):
patch_size = self.opt.patch_size
scale = self.scale
if self.train:
img_in, img_tar = common.get_patch(
img_in, img_tar, patch_size=patch_size, scale=scale)
img_in, img_tar = common.augment(img_in, img_tar)
else:
ih, iw = img_in.shape[:2]
img_tar = img_tar[0:ih * scale, 0:iw * scale, :]
return img_in, img_tar
def get_patch(self, lr, hr):
scale = self.scale[0]
if self.train:
lr, hr = common.get_patch(
lr, hr,
patch_size=self.args.patch_size,
scale=scale,
multi=(len(self.scale) > 1),
)
if not self.args.no_augment:
lr, hr = common.augment(lr, hr)
return lr, hr
def get_patch(self, lr, hr):
scale = self.scale[self.idx_scale]
if self.train:
hr, lr = common.get_patch(hr,
lr,
patch_size=self.args.patch_size,
scale=scale,
multi=(len(self.scale) > 1),
input_large=True)
if self.args.no_augment: lr, hr = common.augment(lr, hr)
return lr, hr
def _get_patch(self, img_input, img_tar):
patch_size = self.args.patch_size
scale = self.scale
if self.train:
img_input, img_tar = common.get_patch(img_input, img_tar, patch_size, scale)
img_input, img_tar = common.augment([img_input, img_tar])
img_input = common.add_noise(img_input, self.sigma)
else:
ih, iw = img_input.shape[0:2]
img_tar = img_tar[0:ih * scale, 0:iw * scale]
return img_input, img_tar
def _get_patch(self, img_in, img_tar):
scale = self.scale[self.idx_scale]
if self.train:
img_in, img_tar, pi = common.get_patch(
img_in, img_tar, self.args, scale)
img_in, img_tar, ai = common.augment(img_in, img_tar)
return img_in, img_tar, pi, ai
else:
ih, iw, c = img_in.shape
img_tar = img_tar[0:ih * scale, 0:iw * scale, :]
return img_in, img_tar, None, None
def get_patch(self, lr, hr):
scale = 1
if self.mode == 'train':
lr, hr = common.get_patch(lr,
hr,
patch_size=self.args.patch_size,
n_channels=self.n_channels)
if self.args.augment: lr, hr = common.augment(lr, hr)
else:
ih, iw = lr.shape[:2]
hr = hr[0:ih * scale, 0:iw * scale]
return lr, hr
def get_patch(self, lr, hr):
if self.train:
lr, hr = common.get_patch(lr,
hr,
patch_size=args.input_size,
scale=args.scale,
input_large=True)
lr, hr = common.augment(lr, hr) # 直接实现图像增强
else:
ih, iw = lr.shape[:2]
hr = hr[0:ih * args.scale, 0:iw * args.scale]
return lr, hr
def get_patch(self, lr, hr):
scale = self.scale[self.idx_scale]
# if self.args.feature_map:
# return lr, hr
if self.train:
lr, hr = common.get_patch(lr, hr, self.args.patch_size, scale,
(len(self.scale) > 1))
if not self.args.no_augment:
lr, hr = common.augment(lr, hr)
else:
ih, iw = lr.shape[:2]
hr = hr[0:ih * scale, 0:iw * scale]
return lr, hr
def get_patch(self, lr, hr):
if self.train:
lr, hr = common.get_patch(lr,
hr,
patch_size=self.args.patch_size,
scale=1,
multi=(len(self.scale) > 1),
input_large=self.input_large)
if not self.args.no_augment: lr, hr = common.augment(lr, hr)
else:
ih, iw = lr.shape[:2]
hr = hr[0:ih, 0:iw]
return lr, hr
def get_patch(self, lr, hr):
scale = self.scale[self.idx_scale]
scale2 = self.scale2[self.idx_scale]
if self.train:
if self.args.asymm:
lr, hr = common.get_patch(lr,
hr,
patch_size=self.args.patch_size,
scale=scale,
scale2=scale2)
else:
lr, hr = common.get_patch(lr,
hr,
patch_size=self.args.patch_size,
scale=scale,
scale2=scale)
if not self.args.no_augment:
lr, hr = common.augment(lr, hr)
else:
ih, iw = lr.shape[:2]
hr = hr[0:int(ih * scale), 0:int(iw * scale2)]
return lr, hr
def get_patch(self, blur, sharp):
if self.train:
blur, sharp = common.get_patch(
blur,
sharp,
patch_size=self.args.patch_size,
)
if not self.args.no_augment:
blur, sharp = common.augment(blur, sharp)
else:
ih, iw = blur.shape[:2]
blur = blur[:ih, :iw]
sharp = sharp[:ih, :iw]
return blur, sharp
def _get_patch(self, lr, hr):
patch_size = self.args.patch_size
scale = self.noise_g[self.idx_scale]
multi_scale = len(self.noise_g) > 1
if self.train:
lr, hr = common.get_patch(
lr, hr, patch_size, scale, multi_scale=multi_scale
)
lr, hr = common.augment([lr, hr])
lr = common.add_noise(lr, self.args.noise)
else:
ih, iw = lr.shape[0:2]
hr = hr[0:ih * scale, 0:iw * scale]
return lr, hr
