def __getitem__(self, idx):
# 将二进制文件转为imageio
lr, hr, filename = self._load_file(idx)
pair = self.get_patch(lr, hr)
pair = common.set_channel(*pair, n_channels=self.args.n_colors)
# 生成kernel
pair_t = common.np2Tensor(*pair, rgb_range=self.args.rgb_range)
k = utils_sisr.gen_kernel(scale_factor=np.array(
[self.scale, self.scale])) # Gaussian blur
r_value = np.random.randint(0, 8)
if r_value > 3:
k = utils_deblur.blurkernel_synthesis(h=25) # motion blur
else:
sf_k = random.choice(self.scale)
k = utils_sisr.gen_kernel(scale_factor=np.array(
[sf_k, sf_k])) # Gaussian blur
mode_k = np.random.randint(0, 8)
k = util.augment_img(k, mode=mode_k)
if np.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
return pair_t[0], pair_t[1], filename
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
}