model = sft.SFT_Net()
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
model = model.cuda()
print('sftgan testing...')
idx = 0
for path in glob.glob(test_img_folder + '/*'):
idx += 1
basename = os.path.basename(path)
base = os.path.splitext(basename)[0]
print(idx, base)
# read image
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
img = modcrop(img, 8)
img = img * 1.0 / 255
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]],
(2, 0, 1))).float()
# matlab imresize
img_LR = imresize(img, 1 / 4, antialiasing=True)
img_LR = img_LR.unsqueeze(0)
img_LR = img_LR.cuda()
# read seg
seg = torch.load(os.path.join(test_prob_path, base + '_bic.pth'))
seg = seg.unsqueeze(0)
# change probability
# seg.fill_(0)
def __getitem__(self, index):
HR_path, LR_path = None, None
data_type = self.opt.get('data_type', 'img')
scale = self.opt['scale']
HR_size = self.opt['HR_size']
# get HR image
if self.opt['phase'] == 'train' and \
random.choice(list(range(self.ratio))) == 0: # read background images
bg_index = random.randint(0, len(self.paths_HR_bg) - 1)
HR_path = self.paths_HR_bg[bg_index]
img_HR = util.read_img(env=data_type,
path=HR_path,
lmdb_env=self.HR_env_bg)
seg = torch.FloatTensor(8, img_HR.shape[0],
img_HR.shape[1]).fill_(0)
seg[0, :, :] = 1 # background
else:
HR_path = self.paths_HR[index]
img_HR = util.read_img(env=data_type,
path=HR_path,
lmdb_env=self.HR_env)
seg = torch.load(
HR_path.replace('/img/', '/bicseg/').replace('.png', '.pth'))
# read segmentation files, you should change it to your settings.
# modcrop in the validation / test phase
if self.opt['phase'] != 'train':
img_HR = util.modcrop(img_HR, 8)
seg = np.transpose(seg.numpy(), (1, 2, 0))
# get LR image
if self.paths_LR:
LR_path = self.paths_LR[index]
img_LR = util.read_img(env=data_type,
path=LR_path,
lmdb_env=self.LR_env)
else: # down-sampling on-the-fly
# randomly scale during training
if self.opt['phase'] == 'train':
random_scale = random.choice(self.random_scale_list)
H_s, W_s, _ = seg.shape
def _mod(n, random_scale, scale, thres):
rlt = int(n * random_scale)
rlt = (rlt // scale) * scale
return thres if rlt < thres else rlt
H_s = _mod(H_s, random_scale, scale, HR_size)
W_s = _mod(W_s, random_scale, scale, HR_size)
img_HR = cv2.resize(np.copy(img_HR), (W_s, H_s),
interpolation=cv2.INTER_LINEAR)
seg = cv2.resize(np.copy(seg), (W_s, H_s),
interpolation=cv2.INTER_NEAREST)
H, W, _ = img_HR.shape
# using matlab imresize
img_LR = imresize(img_HR, 1 / scale, antialiasing=True)
if img_LR.ndim == 2:
img_LR = np.expand_dims(img_LR, axis=2)
H, W, C = img_LR.shape
if self.opt['phase'] == 'train':
LR_size = HR_size // scale
# randomly crop
rnd_h = random.randint(0, max(0, H - LR_size))
rnd_w = random.randint(0, max(0, W - LR_size))
img_LR = img_LR[rnd_h:rnd_h + LR_size, rnd_w:rnd_w + LR_size, :]
rnd_h_HR, rnd_w_HR = int(rnd_h * scale), int(rnd_w * scale)
img_HR = img_HR[rnd_h_HR:rnd_h_HR + HR_size,
rnd_w_HR:rnd_w_HR + HR_size, :]
seg = seg[rnd_h_HR:rnd_h_HR + HR_size,
rnd_w_HR:rnd_w_HR + HR_size, :]
# augmentation - flip, rotate
img_LR, img_HR, seg = util.augment([img_LR, img_HR, seg],
self.opt['use_flip'],
self.opt['use_rot'])
# category
if 'building' in HR_path:
category = 1
elif 'plant' in HR_path:
category = 2
elif 'mountain' in HR_path:
category = 3
elif 'water' in HR_path:
category = 4
elif 'sky' in HR_path:
category = 5
elif 'grass' in HR_path:
category = 6
elif 'animal' in HR_path:
category = 7
else:
category = 0 # background
else:
category = -1 # during val, useless
# BGR to RGB, HWC to CHW, numpy to tensor
img_HR = util.np2tensor(img_HR, normalize=self.znorm, add_batch=False)
img_LR = util.np2tensor(img_LR, normalize=self.znorm, add_batch=False)
seg = util.np2tensor(seg, normalize=self.znorm, add_batch=False)
if LR_path is None:
LR_path = HR_path
return {
'LR': img_LR,
'HR': img_HR,
'seg': seg,
'category': category,
'LR_path': LR_path,
'HR_path': HR_path
}
def __getitem__(self, index):
HR_path, LR_path = None, None
scale = self.opt.get('scale', 4)
HR_size = self.opt.get('HR_size', 128)
if HR_size:
LR_size = HR_size // scale
# Default case: tensor will result in the [0,1] range
# Alternative: tensor will be z-normalized to the [-1,1] range
znorm = self.opt.get('znorm', False)
######## Read the images ########
#TODO: check cases where default of 3 channels will be troublesome
image_channels = self.opt.get('image_channels', 3)
# Check if LR Path is provided
if self.paths_LR:
#If LR is provided, check if 'rand_flip_LR_HR' is enabled
if self.opt.get('rand_flip_LR_HR',
None) and self.opt['phase'] == 'train':
LRHRchance = random.uniform(0, 1)
flip_chance = self.opt.get('flip_chance', 0.05)
#print("Random Flip Enabled")
# Normal case, no flipping:
else:
LRHRchance = 0.
flip_chance = 0.
#print("No Random Flip")
# get HR and LR images
# If enabled, random chance that LR and HR images are flipped
# Normal case, no flipping
# If img_LR (LR_path) doesn't exist, use img_HR (HR_path)
if LRHRchance < (1 - flip_chance):
HR_path = self.paths_HR[index]
LR_path = self.paths_LR[index]
if LR_path is None:
LR_path = HR_path
#print("HR kept")
# Flipped case:
# If img_HR (LR_path) doesn't exist, use img_HR (LR_path)
else:
HR_path = self.paths_LR[index]
LR_path = self.paths_HR[index]
if HR_path is None:
HR_path = LR_path
#print("HR flipped")
# Read the LR and HR images from the provided paths
img_LR = util.read_img(self.LR_env, LR_path, out_nc=image_channels)
img_HR = util.read_img(self.HR_env, HR_path, out_nc=image_channels)
# Even if LR dataset is provided, force to generate aug_downscale % of downscales OTF from HR
# The code will later make sure img_LR has the correct size
if self.opt.get('aug_downscale', None):
#aug_downscale = self.opt['aug_downscale']
if np.random.rand() < self.opt['aug_downscale']:
img_LR = img_HR
# If LR is not provided, use HR and modify on the fly
else:
HR_path = self.paths_HR[index]
img_HR = util.read_img(self.HR_env, HR_path, out_nc=image_channels)
img_LR = img_HR
LR_path = HR_path
# tmp_vis(img_HR, False)
# tmp_vis(img_LR, False)
######## Modify the images ########
# HR modcrop in the validation / test phase
if self.opt['phase'] != 'train':
img_HR = util.modcrop(img_HR, scale)
# change color space if necessary
# Note: Changing the LR colorspace here could make it so some colors are introduced when
# doing the augmentations later (ie: with Gaussian or Speckle noise), may be good if the
# model can learn to remove color noise in grayscale images, otherwise move to before
# converting to tensors
# self.opt['color'] For both LR and HR as in the the original code, kept for compatibility
# self.opt['color_HR'] and self.opt['color_LR'] for independent control
if self.opt.get('color', None): # Change both
img_HR = util.channel_convert(img_HR.shape[2], self.opt['color'],
[img_HR])[0]
img_LR = util.channel_convert(img_LR.shape[2], self.opt['color'],
[img_LR])[0]
if self.opt.get('color_HR', None): # Only change HR
img_HR = util.channel_convert(img_HR.shape[2],
self.opt['color_HR'], [img_HR])[0]
if self.opt.get('color_LR', None): # Only change LR
img_LR = util.channel_convert(img_LR.shape[2],
self.opt['color_LR'], [img_LR])[0]
######## Augmentations ########
#Augmentations during training
if self.opt['phase'] == 'train':
# Note: this is NOT recommended, HR should not be exposed to degradation, as it will
# degrade the model's results, only added because it exist as an option in downstream forks
# HR downscale
if self.opt.get('hr_downscale', None):
ds_algo = self.opt.get('hr_downscale_types', 777)
hr_downscale_amt = self.opt.get('hr_downscale_amt', 2)
if isinstance(hr_downscale_amt, list):
hr_downscale_amt = random.choice(hr_downscale_amt)
# will ignore if 1 or if result is smaller than hr size
if hr_downscale_amt > 1 and img_HR.shape[
0] // hr_downscale_amt >= HR_size and img_HR.shape[
1] // hr_downscale_amt >= HR_size:
#img_HR, hr_scale_interpol_algo = augmentations.scale_img(img_HR, hr_downscale_amt, algo=ds_algo)
img_HR, _ = Scale(img=img_HR,
scale=hr_downscale_amt,
algo=ds_algo)
# Downscales LR to match new size of HR if scale does not match after
if img_LR is not None and (
img_HR.shape[0] // scale != img_LR.shape[0]
or img_HR.shape[1] // scale != img_LR.shape[1]):
#img_LR, lr_scale_interpol_algo = augmentations.scale_img(img_LR, hr_downscale_amt, algo=ds_algo)
img_LR, _ = Scale(img=img_LR,
scale=hr_downscale_amt,
algo=ds_algo)
# Validate there's an img_LR, if not, use img_HR
if img_LR is None:
img_LR = img_HR
print("Image LR: ", LR_path, (
"was not loaded correctly, using HR pair to downscale on the fly."
))
# Check that HR and LR have the same dimensions ratio, else use an option to process
#TODO: add options variable
shape_change = self.opt.get('shape_change',
'reshape_hr') #"reshape_lr"
if img_HR.shape[0] // img_LR.shape[0] != img_HR.shape[
1] // img_LR.shape[1]:
if shape_change == "reshape_lr":
img_LR = transforms.Resize(
(int(img_HR.shape[0] / scale),
int(img_HR.shape[1] / scale)),
interpolation="BILINEAR")(np.copy(img_LR))
elif shape_change == "reshape_hr":
# print("pre: ", img_HR.shape[0], img_HR.shape[1])
nh = img_HR.shape[0] * (2 * img_LR.shape[1]) // (
img_LR.shape[0] + img_LR.shape[1])
nw = img_HR.shape[1] * (2 * img_LR.shape[0]) // (
img_LR.shape[0] + img_LR.shape[1])
#TODO: temporary change to test contextual loss with unaligned LR-HR pairs, forcing them to have the correct scale
#img_HR, _ = augmentations.resize_img(np.copy(img_HR), newdim=(img_LR.shape[1]*scale,img_LR.shape[0]*scale), algo=cv2.INTER_LINEAR)
#img_HR = transforms.Resize((img_LR.shape[0]*scale,img_LR.shape[1]*scale), interpolation="BILINEAR")(np.copy(img_HR))
img_HR = transforms.Resize(
(nh, nw), interpolation="BILINEAR")(np.copy(img_HR))
# print("post: ", img_HR.shape[0], img_HR.shape[1])
img_LR = transforms.Resize(
(int(img_HR.shape[0] / scale),
int(img_HR.shape[1] / scale)),
interpolation="BILINEAR")(np.copy(img_LR))
#TODO: check, this may not make sense
# elif shape_change == "pad_lr":
# LR_pad, _ = get_pad(img_LR, HR_size//scale, fill=0, padding_mode='constant')
# img_LR = LR_pad(np.copy(img_LR))
else: # generate new LR from HR
#TODO: disabled to test cx loss
#print("Warning: img_LR dimensions ratio does not match img_HR dimensions ratio for: ", HR_path)
img_LR = img_HR
# centercrop image
if self.opt['center_crop'] == True:
if img_HR.shape[0] > img_HR.shape[1]:
img_HR = transforms.CenterCrop(
(img_HR.shape[1], img_HR.shape[1]))(np.copy(img_HR))
img_LR = transforms.CenterCrop(
(img_HR.shape[1], img_HR.shape[1]))(np.copy(img_LR))
else:
img_HR = transforms.CenterCrop(
(img_HR.shape[0], img_HR.shape[0]))(np.copy(img_HR))
img_LR = transforms.CenterCrop(
(img_HR.shape[0], img_HR.shape[0]))(np.copy(img_LR))
# resize lr and hr image to given hr dimension
if self.opt['resize_HR_dimension'] == True:
img_HR = transforms.Resize(
(self.opt['HR_size'], self.opt['HR_size']),
interpolation="BILINEAR")(np.copy(img_HR))
img_LR = transforms.Resize(
(self.opt['HR_size'], self.opt['HR_size']),
interpolation="BILINEAR")(np.copy(img_LR))
# Random Crop (reduce computing cost and adjust images to correct size first)
if img_HR.shape[0] > HR_size or img_HR.shape[1] > HR_size:
#Here the scale should be in respect to the images, not to the training scale (in case they are being scaled on the fly)
scaleor = img_HR.shape[0] // img_LR.shape[0]
img_HR, img_LR = augmentations.random_crop_pairs(
img_HR, img_LR, HR_size, scaleor)
#TODO: test to be removed
# img_HR = transforms.Resize((HR_size-100,img_HR.shape[1]), interpolation="BILINEAR")(np.copy(img_HR))
# img_LR = transforms.Resize((LR_size-(100//scale),img_LR.shape[1]), interpolation="BILINEAR")(np.copy(img_LR))
# Or if the HR images are too small, Resize to the HR_size size and fit LR pair to LR_size too
#TODO: add options variable
dim_change = self.opt.get('dim_change', 'pad')
if img_HR.shape[0] < HR_size or img_HR.shape[1] < HR_size:
if dim_change == "resize":
#TODO: temp disabled to test
#print("Warning: Image: ", HR_path, " size does not match HR size: (", HR_size,"). The image size is: ", img_HR.shape)
# rescale HR image to the HR_size
# img_HR, _ = augmentations.resize_img(np.copy(img_HR), newdim=(HR_size,HR_size), algo=cv2.INTER_LINEAR)
img_HR = transforms.Resize(
(HR_size, HR_size),
interpolation="BILINEAR")(np.copy(img_HR))
# rescale LR image to the LR_size (The original code discarded the img_LR and generated a new one on the fly from img_HR)
#img_LR, _ = augmentations.resize_img(np.copy(img_LR), newdim=(LR_size,LR_size), algo=cv2.INTER_LINEAR)
img_LR = transforms.Resize(
(LR_size, LR_size),
interpolation="BILINEAR")(np.copy(img_LR))
elif dim_change == "pad":
# if img_LR is img_HR, padding will be wrong, downscaling LR before padding
if img_LR.shape[
0] != img_HR.shape[0] // scale or img_LR.shape[
1] != img_HR.shape[1] // scale:
ds_algo = 777 # default to matlab-like bicubic downscale
if self.opt.get(
'lr_downscale', None
): # if manually set and scale algorithms are provided, then:
ds_algo = self.opt.get('lr_downscale_types', 777)
if self.opt.get('lr_downscale', None) and self.opt.get(
'dataroot_kernels', None
) and 999 in self.opt["lr_downscale_types"]:
ds_kernel = self.ds_kernels
else:
ds_kernel = None
img_LR, _ = Scale(img=img_LR,
scale=scale,
algo=ds_algo,
ds_kernel=ds_kernel)
HR_pad, fill = get_pad(img_HR,
HR_size,
fill='random',
padding_mode=self.opt.get(
'pad_mode', 'constant'))
img_HR = HR_pad(np.copy(img_HR))
LR_pad, _ = get_pad(img_LR,
HR_size // scale,
fill=fill,
padding_mode=self.opt.get(
'pad_mode', 'constant'))
img_LR = LR_pad(np.copy(img_LR))
# (Randomly) scale LR (from HR) during training if :
# - LR dataset is not provided
# - LR dataset is not in the correct scale
# - Also to check if LR is not at the correct scale already (if img_LR was changed to img_HR)
if img_LR.shape[0] != LR_size or img_LR.shape[1] != LR_size:
ds_algo = 777 # default to matlab-like bicubic downscale
if self.opt.get(
'lr_downscale', None
): # if manually set and scale algorithms are provided, then:
ds_algo = self.opt.get('lr_downscale_types', 777)
else: # else, if for some reason img_LR is too large, default to matlab-like bicubic downscale
#if not self.opt['aug_downscale']: #only print the warning if not being forced to use HR images instead of LR dataset (which is a known case)
print(
"LR image is too large, auto generating new LR for: ",
LR_path)
if self.opt.get('lr_downscale', None) and self.opt.get(
'dataroot_kernels',
None) and 999 in self.opt["lr_downscale_types"]:
ds_kernel = self.ds_kernels #KernelDownscale(scale, self.kernel_paths, self.num_kernel)
else:
ds_kernel = None
#img_LR, scale_interpol_algo = augmentations.scale_img(img_LR, scale, algo=ds_algo)
#print("algo")
#print(ds_algo)
img_LR, _ = Scale(img=img_LR,
scale=scale,
algo=ds_algo,
ds_kernel=ds_kernel)
# resize = get_resize(size=LR_size, scale=scale, ds_algo=ds_algo)
# img_LR = resize(np.copy(img_LR))
#"""
# Rotations. 'use_flip' = 180 or 270 degrees (mirror), 'use_rot' = 90 degrees, 'HR_rrot' = random rotations +-45 degrees
if (self.opt['use_flip'] or self.opt['use_rot']) and self.opt.get(
'hr_rrot', None):
if np.random.rand() > 0.5:
img_LR, img_HR = util.augment([img_LR, img_HR], self.opt['use_flip'], \
self.opt['use_rot'])
else:
if np.random.rand(
) > 0.5: # randomize the random rotations, so half the images are the original
img_HR, img_LR = augmentations.random_rotate_pairs(
img_HR, img_LR, HR_size, scale)
elif (self.opt['use_flip'] or
self.opt['use_rot']) and not self.opt.get('hr_rrot', None):
# augmentation - flip, rotate
img_LR, img_HR = util.augment([img_LR, img_HR], self.opt['use_flip'], \
self.opt['use_rot'])
elif self.opt.get('hr_rrot', None):
if np.random.rand(
) > 0.5: # randomize the random rotations, so half the images are the original
img_HR, img_LR = augmentations.random_rotate_pairs(
img_HR, img_LR, HR_size, scale)
# Final sizes checks
# if the resulting HR image size so far is too large or too small, resize HR to the correct size and downscale to generate a new LR on the fly
# if the resulting LR so far does not have the correct dimensions, also generate a new HR-LR image pair on the fly
if img_HR.shape[0] != HR_size or img_HR.shape[
1] != HR_size or img_LR.shape[
0] != LR_size or img_LR.shape[1] != LR_size:
#if img_HR.shape[0] != HR_size or img_HR.shape[1] != HR_size:
#TODO: temp disabled to test
#print("Image: ", HR_path, " size does not match HR size: (", HR_size,"). The image size is: ", img_HR.shape)
#if img_LR.shape[0] != LR_size or img_LR.shape[0] != LR_size:
#TODO: temp disabled to test
#print("Image: ", LR_path, " size does not match LR size: (", HR_size//scale,"). The image size is: ", img_LR.shape)
# rescale HR image to the HR_size (should not be needed in LR case, but something went wrong before, just for sanity)
#img_HR, _ = augmentations.resize_img(np.copy(img_HR), newdim=(HR_size,HR_size), algo=cv2.INTER_LINEAR)
img_HR = transforms.Resize(
(HR_size, HR_size),
interpolation="BILINEAR")(np.copy(img_HR))
# if manually provided and scale algorithms are provided, then use it, else use matlab imresize to generate LR pair
ds_algo = self.opt.get('lr_downscale_types', 777)
#img_LR, _ = augmentations.scale_img(img_HR, scale, algo=ds_algo)
img_LR, _ = Scale(img_HR, scale, algo=ds_algo)
# Below are the On The Fly augmentations
# Apply "auto levels" to images
#rand_levels = (1 - self.opt.get('rand_auto_levels', 0)) # Randomize for augmentation
rand_levels = self.opt.get('rand_auto_levels', 0)
#rand_percent = self.opt.get('rand_auto_per', 10)
#if self.opt.get('auto_levels', None) and np.random.rand() > rand_levels:
if self.opt.get('auto_levels', None) == 'HR' or self.opt.get(
'auto_levels', None) == 'Both':
#img_HR = augmentations.simplest_cb(img_HR, znorm=znorm) #TODO: now images are processed in the [0,255] range
#img_HR = augmentations.simplest_cb(img_HR)
img_HR = transforms.FilterColorBalance(
p=rand_levels, percent=10, random_params=True)(img_HR)
if self.opt.get('auto_levels', None) == 'LR' or self.opt.get(
'auto_levels', None) == 'Both':
#img_LR = augmentations.simplest_cb(img_LR, znorm=znorm) #TODO: now images are processed in the [0,255] range
#img_LR = augmentations.simplest_cb(img_LR)
img_LR = transforms.FilterColorBalance(
p=rand_levels, percent=10, random_params=True)(img_LR)
# Apply unsharpening mask to LR images
'''
rand_unsharp = (1 - self.opt.get('lr_rand_unsharp', 0)) # Randomize for augmentation
if self.opt.get('lr_unsharp_mask', None) and np.random.rand() > rand_unsharp:
#img_LR = augmentations.unsharp_mask(img_LR, znorm=znorm) #TODO: now images are processed in the [0,255] range
img_LR = augmentations.unsharp_mask(img_LR)
'''
if self.opt.get('lr_unsharp_mask', None):
lr_rand_unsharp = self.opt.get('lr_rand_unsharp', 0)
img_LR = transforms.FilterUnsharp(p=lr_rand_unsharp)(img_LR)
# Apply unsharpening mask to HR images
'''
rand_unsharp = (1 - self.opt.get('hr_rand_unsharp', 0)) # Randomize for augmentation
if self.opt.get('hr_unsharp_mask', None) and np.random.rand() > rand_unsharp:
#img_HR = augmentations.unsharp_mask(img_HR, znorm=znorm) #TODO: now images are processed in the [0,255] range
img_HR = augmentations.unsharp_mask(img_HR)
'''
if self.opt.get('hr_unsharp_mask', None):
hr_rand_unsharp = self.opt.get('hr_rand_unsharp', 0)
img_HR = transforms.FilterUnsharp(p=hr_rand_unsharp)(img_HR)
# Add noise to HR if enabled AND noise types are provided (for noise2noise and similar)
if self.opt.get('hr_noise', None):
noise_option = get_noise(self.opt.get('hr_noise_types', None))
if noise_option:
#img_HR, hr_noise_algo = augmentations.noise_img(img_HR, noise_types=self.opt['hr_noise_types'])
img_HR = noise_option(img_HR)
#TODO: check if necessary
# else:
# print("Noise types 'hr_noise_types' not defined. Skipping OTF noise for HR.")
# Create color fringes
# Caution: Can easily destabilize a model
# Only applied to a small % of the images. Around 20% and 50% appears to be stable.
if self.opt.get('lr_fringes', None):
lr_fringes_chance = self.opt['lr_fringes_chance'] if self.opt[
'lr_fringes_chance'] else 0.4
if np.random.rand() > (1. - lr_fringes_chance):
img_LR = augmentations.translate_chan(img_LR)
#"""
#v LR blur AND blur types are provided, else will skip
if self.opt.get('lr_blur', None):
blur_option = get_blur(self.opt.get('lr_blur_types', None))
if blur_option:
#img_LR, blur_algo, blur_kernel_size = augmentations.blur_img(img_LR, blur_algos=blur_algos)
img_LR = blur_option(img_LR)
#TODO: check if necessary
# else:
# print("Blur types 'lr_blur_types' not defined. Skipping OTF blur.")
#"""
#"""
#v LR primary noise: Add noise to LR if enabled AND noise types are provided, else will skip
if self.opt.get('lr_noise', None):
noise_option = get_noise(self.opt.get('lr_noise_types', None),
self.noise_patches)
if noise_option:
#img_LR, noise_algo = augmentations.noise_img(img_LR, noise_types=self.opt['lr_noise_types'])
img_LR = noise_option(img_LR)
#TODO: check if necessary
# else:
# print("Noise types 'lr_noise_types' not defined. Skipping OTF noise.")
#v LR secondary noise: Add additional noise to LR if enabled AND noise types are provided, else will skip
if self.opt.get('lr_noise2', None):
noise_option = get_noise(self.opt.get('lr_noise_types2', None),
self.noise_patches)
if noise_option:
#img_LR, noise_algo2 = augmentations.noise_img(img_LR, noise_types=self.opt['lr_noise_types2'])
img_LR = noise_option(img_LR)
#TODO: check if necessary
# else:
# print("Noise types 'lr_noise_types2' not defined. Skipping OTF secondary noise.")
#"""
#"""
#v LR cutout / LR random erasing (for inpainting/classification tests)
if self.opt.get(
'lr_cutout',
None) and (self.opt.get('lr_erasing', None) != True):
# img_LR = augmentations.cutout(img_LR, img_LR.shape[0] // 2)
img_LR = transforms.Cutout(p=1, mask_size=img_LR.shape[0] //
2)(img_LR)
elif self.opt.get(
'lr_erasing',
None) and (self.opt.get('lr_cutout', None) != True):
# img_LR = augmentations.random_erasing(img_LR)
img_LR = transforms.RandomErasing(p=1)(img_LR, mode=[3])
elif self.opt.get('lr_cutout', None) and self.opt.get(
'lr_erasing', None):
if np.random.rand(
) > 0.5: #only do cutout or erasing, not both at the same time
#img_LR = augmentations.cutout(img_LR, img_LR.shape[0] // 2, p=0.5)
img_LR = transforms.Cutout(p=1,
mask_size=img_LR.shape[0] //
2)(img_LR)
else:
#img_LR = augmentations.random_erasing(img_LR, p=0.5, modes=[3])
img_LR = transforms.RandomErasing(p=1)(img_LR, mode=[3])
#"""
# For testing and validation
if self.opt['phase'] != 'train':
# Randomly downscale LR if enabled
if self.opt['lr_downscale']:
# if self.opt['lr_downscale_types']:
# img_LR, scale_interpol_algo = augmentations.scale_img(img_LR, scale, algo=self.opt['lr_downscale_types'])
# else: # Default to matlab-like bicubic downscale
# img_LR, scale_interpol_algo = augmentations.scale_img(img_LR, scale, algo=777)
img_LR, _ = Scale(img_LR,
scale,
algo=self.opt.get('lr_downscale_types', 777))
# Alternative position for changing the colorspace of LR.
# if self.opt['color_LR']: # Only change LR
# img_LR = util.channel_convert(img_LR.shape[2], self.opt['color'], [img_LR])[0]
# Debug #TODO: use the debugging functions to visualize or save images instead
# Save img_LR and img_HR images to a directory to visualize what is the result of the on the fly augmentations
# DO NOT LEAVE ON DURING REAL TRAINING
# self.output_sample_imgs = True
if self.opt['phase'] == 'train':
if self.output_sample_imgs:
import os
# LR_dir, im_name = os.path.split(LR_path)
HR_dir, im_name = os.path.split(HR_path)
#baseHRdir, _ = os.path.split(HR_dir)
#debugpath = os.path.join(baseHRdir, os.sep, 'sampleOTFimgs')
# debugpath = os.path.join(os.path.split(LR_dir)[0], 'sampleOTFimgs')
debugpath = os.path.join('D:/tmp_test', 'sampleOTFimgs')
#print(debugpath)
if not os.path.exists(debugpath):
os.makedirs(debugpath)
import uuid
hex = uuid.uuid4().hex
cv2.imwrite(debugpath + "\\" + im_name + hex + '_LR.png',
img_LR) #random name to save
cv2.imwrite(debugpath + "\\" + im_name + hex + '_HR.png',
img_HR) #random name to save
# cv2.imwrite(debugpath+"\\"+im_name+hex+'_HR1.png',img_HRn1) #random name to save
######## Convert images to PyTorch Tensors ########
""" # for debugging
if (img_HR.min() < -1):
describe_numpy(img_HR, all=True)
print(HR_path)
if (img_HR.max() > 1):
describe_numpy(img_HR, all=True)
print(HR_path)
if (img_LR.min() < -1):
describe_numpy(img_LR, all=True)
print(LR_path)
if (img_LR.max() > 1):
describe_numpy(img_LR, all=True)
print(LR_path)
#"""
# check for grayscale images #TODO: should not be needed anymore
if len(img_HR.shape) < 3:
img_HR = img_HR[..., np.newaxis]
if len(img_LR.shape) < 3:
img_LR = img_LR[..., np.newaxis]
# tmp_vis(img_HR, False)
# tmp_vis(img_LR, False)
# edge-connect
if self.opt['training_with_canny'] == True:
img_HR_gray = cv2.cvtColor(img_HR, cv2.COLOR_BGR2GRAY)
img_HR_canny = cv2.Canny(img_HR_gray, 100, 150)
img_HR_gray = torch.from_numpy(img_HR_gray).unsqueeze(0)
img_HR_canny = torch.from_numpy(img_HR_canny).unsqueeze(0)
if self.opt['training_with_canny_SR'] == True:
img_LR_gray = cv2.cvtColor(img_LR, cv2.COLOR_BGR2GRAY)
img_LR_canny = cv2.Canny(img_LR_gray, 100, 150)
img_LR_canny = torch.from_numpy(img_LR_canny).unsqueeze(0)
if self.opt['noise_estimation'] == True:
ds_kernel = torch.from_numpy(getattr(
ds_kernel, 'used_kernel')).unsqueeze(0).float()
noise_est = noise_estimate(img_LR, 4)
sigma = torch.tensor(noise_est).float().view([1, 1, 1])
img_HR = util.np2tensor(img_HR, normalize=znorm,
add_batch=False) #.astype('uint8').clip(0,255)
img_LR = util.np2tensor(img_LR, normalize=znorm, add_batch=False)
if LR_path is None:
LR_path = HR_path
if self.opt['training_with_canny'] == True:
return {
'LR': img_LR,
'HR': img_HR,
'LR_path': LR_path,
'HR_path': HR_path,
'img_HR_gray': img_HR_gray,
'img_HR_canny': img_HR_canny
}
elif self.opt['training_with_canny_SR'] == True:
return {
'LR': img_LR,
'HR': img_HR,
'LR_path': LR_path,
'HR_path': HR_path,
'img_LR_canny': img_LR_canny
}
if self.opt['noise_estimation'] == True:
return {
'LR': img_LR,
'HR': img_HR,
'LR_path': LR_path,
'HR_path': HR_path,
'ds_kernel': ds_kernel,
'sigma': sigma
}
else:
return {
'LR': img_LR,
'HR': img_HR,
'LR_path': LR_path,
'HR_path': HR_path
}
def __getitem__(self, idx):
scale = self.opt.get('scale', 4)
HR_size = self.opt.get('HR_size', 128)
LR_size = HR_size // scale
idx_center = (self.num_frames - 1) // 2
ds_kernel = None
# Default case: tensor will result in the [0,1] range
# Alternative: tensor will be z-normalized to the [-1,1] range
znorm = self.opt.get('znorm', False)
if self.opt['phase'] == 'train':
if self.opt.get('lr_downscale', None) and self.opt.get('dataroot_kernels', None) and 999 in self.opt["lr_downscale_types"]:
ds_kernel = self.ds_kernels #KernelDownscale(scale, self.kernel_paths, self.num_kernel)
# get a random video directory
idx_video = random.randint(0, len(self.video_list)-1)
video_dir = self.video_list[idx_video]
# print(video_dir)
else:
# only one video and paths_LR/paths_HR is already the video dir
video_dir = ""
# list the frames in the directory
# hr_dir = self.trainset_dir + '/' + video_dir + '/hr'
paths_HR = util.get_image_paths(self.opt['data_type'], os.path.join(self.paths_HR, video_dir))
# print(paths_HR)
if self.opt['phase'] == 'train':
# random reverse augmentation
random_reverse = self.opt.get('random_reverse', False)
# skipping intermediate frames to learn from low FPS videos augmentation
# testing random frameskip up to 'max_frameskip' frames
max_frameskip = self.opt.get('max_frameskip', 0)
if max_frameskip > 0:
max_frameskip = min(max_frameskip, len(paths_HR)//(self.num_frames-1))
frameskip = random.randint(1, max_frameskip)
else:
frameskip = 1
# print("max_frameskip: ", max_frameskip)
assert ((self.num_frames-1)*frameskip) <= (len(paths_HR)-1), (
f'num_frame*frameskip must be smaller than the number of frames per video, check {video_dir}')
# if number of frames of training video is for example 31, "max index -num_frames" = 31-3=28
idx_frame = random.randint(0, (len(paths_HR)-1)-((self.num_frames-1)*frameskip))
# print('frameskip:', frameskip)
else:
frameskip = 1
idx_frame = idx
'''
List based frames loading
'''
if self.paths_LR:
paths_LR = util.get_image_paths(self.opt['data_type'], os.path.join(self.paths_LR, video_dir))
else:
paths_LR = paths_HR
ds_algo = 777 # default to matlab-like bicubic downscale
if self.opt.get('lr_downscale', None): # if manually set and scale algorithms are provided, then:
ds_algo = self.opt.get('lr_downscale_types', 777)
# get the video directory
HR_dir, _ = os.path.split(paths_HR[idx_frame])
LR_dir, _ = os.path.split(paths_HR[idx_frame])
# read HR & LR frames
HR_list = []
LR_list = []
resize_type = None
LR_bicubic = None
HR_center = None
# print('len(paths_HR)', len(paths_HR))
for i_frame in range(self.num_frames):
# print('frame path:', paths_HR[int(idx_frame)+(frameskip*i_frame)])
HR_img = util.read_img(None, paths_HR[int(idx_frame)+(frameskip*i_frame)], out_nc=self.image_channels)
HR_img = util.modcrop(HR_img, scale)
if self.opt['phase'] == 'train':
'''
If using individual image augmentations, get cropping parameters for reuse
'''
if self.otf_noise and i_frame == 0: #only need to calculate once, from the first frame
# reuse the cropping parameters for all LR and HR frames
hr_crop_params, lr_crop_params = get_crop_params(HR_img, LR_size, scale)
if self.opt.get('lr_noise', None):
# reuse the same noise type for all the frames
noise_option = get_noise(self.opt.get('lr_noise_types', None), self.noise_patches)
if self.opt.get('lr_blur', None):
# reuse the same blur type for all the frames
blur_option = get_blur(self.opt.get('lr_blur_types', None))
if self.paths_LR:
# LR images are provided at the correct scale
LR_img = util.read_img(None, paths_LR[int(idx_frame)+(frameskip*i_frame)], out_nc=self.image_channels)
if LR_img.shape == HR_img.shape:
LR_img, resize_type = Scale(img=HR_img, scale=scale, algo=ds_algo, ds_kernel=ds_kernel, resize_type=resize_type)
else:
# generate LR images on the fly
LR_img, resize_type = Scale(img=HR_img, scale=scale, algo=ds_algo, ds_kernel=ds_kernel, resize_type=resize_type)
# get the bicubic upscale of the center frame to concatenate for SR
if self.y_only and self.srcolors and i_frame == idx_center:
LR_bicubic, _ = Scale(img=LR_img, scale=1/scale, algo=777) # bicubic upscale
HR_center = HR_img
# tmp_vis(LR_bicubic, False)
# tmp_vis(HR_center, False)
if self.y_only:
# extract Y channel from frames
# normal path, only Y for both
HR_img = util.bgr2ycbcr(HR_img, only_y=True)
LR_img = util.bgr2ycbcr(LR_img, only_y=True)
# crop patches randomly if using otf noise
#TODO: make a BasicSR composable random_crop
#TODO: note the original crop should go here and crop after loading each image, but could also be much simpler
# to crop after concatenating. Check the speed difference.
if self.otf_noise and self.opt['phase'] == 'train':
HR_img, LR_img = apply_crop_params(HR_img, LR_img, hr_crop_params, lr_crop_params)
if self.y_only and self.srcolors and i_frame == idx_center:
LR_bicubic, _ = apply_crop_params(LR_bicubic, None, hr_crop_params, None)
HR_center, _ = apply_crop_params(HR_center, None, hr_crop_params, None)
# expand Y images to add the channel dimension
# normal path, only Y for both
if self.y_only:
HR_img = util.fix_img_channels(HR_img, 1)
LR_img = util.fix_img_channels(LR_img, 1)
if self.opt['phase'] == 'train':
# single frame augmentation (noise, blur, etc). Would only be efficient if patches are cropped in this loop
if self.opt.get('lr_blur', None):
if blur_option:
LR_img = blur_option(LR_img)
if self.opt.get('lr_noise', None):
if noise_option:
LR_img = noise_option(LR_img)
# expand LR images to add the channel dimension again if needed (blur removes the grayscale channel)
#TODO: add a if condition, can compare to the ndim before the augs, maybe move inside the aug condition
# if not fullimgchannels: #TODO: TMP, this should be when using srcolors for HR or when training with 3 channels tests, separatedly
if self.y_only:
LR_img = util.fix_img_channels(LR_img, 1)
# print("HR_img.shape: ", HR_img.shape)
# print("LR_img.shape", LR_img.shape)
HR_list.append(HR_img) # h, w, c
LR_list.append(LR_img) # h, w, c
# print(len(HR_list))
# print(len(LR_list))
if self.opt['phase'] == 'train':
# random reverse sequence augmentation
if random_reverse and random.random() < 0.5:
HR_list.reverse()
LR_list.reverse()
if not self.y_only:
t = self.num_frames
HR = [np.asarray(GT) for GT in HR_list] # list -> numpy # input: list (contatin numpy: [H,W,C])
HR = np.asarray(HR) # numpy, [T,H,W,C]
h_HR, w_HR, c = HR_img.shape #HR_center.shape #TODO: check, may be risky
HR = HR.transpose(1,2,3,0).reshape(h_HR, w_HR, -1) # numpy, [H',W',CT]
LR = [np.asarray(LT) for LT in LR_list] # list -> numpy # input: list (contatin numpy: [H,W,C])
LR = np.asarray(LR) # numpy, [T,H,W,C]
LR = LR.transpose(1,2,3,0).reshape(h_HR//scale, w_HR//scale, -1) # numpy, [Hl',Wl',CT]
else:
HR = np.concatenate((HR_list), axis=2) # h, w, t
LR = np.concatenate((LR_list), axis=2) # h, w, t
if self.opt['phase'] == 'train':
'''
# If not using individual image augmentations, this cropping should be faster, only once
'''
# crop patches randomly. If not using otf noise, crop all concatenated images
if not self.otf_noise:
HR, LR, hr_crop_params, _ = random_crop_mod(HR, LR, LR_size, scale)
if self.y_only and self.srcolors:
LR_bicubic, _, _, _ = random_crop_mod(LR_bicubic, _, LR_size, scale, hr_crop_params)
HR_center, _, _, _ = random_crop_mod(HR_center, _, LR_size, scale, hr_crop_params)
# tmp_vis(LR_bicubic, False)
# tmp_vis(HR_center, False)
# data augmentation
#TODO: use BasicSR augmentations
#TODO: use variables from config
LR, HR, LR_bicubic, HR_center = augmentation()([LR, HR, LR_bicubic, HR_center])
# tmp_vis(HR, False)
# tmp_vis(LR, False)
# tmp_vis(LR_bicubic, False)
# tmp_vis(HR_center, False)
if self.y_only:
HR = util.np2tensor(HR, normalize=znorm, bgr2rgb=False, add_batch=False) # Tensor, [CT',H',W'] or [T, H, W]
LR = util.np2tensor(LR, normalize=znorm, bgr2rgb=False, add_batch=False) # Tensor, [CT',H',W'] or [T, H, W]
else:
HR = util.np2tensor(HR, normalize=znorm, bgr2rgb=True, add_batch=False) # Tensor, [CT',H',W'] or [T, H, W]
LR = util.np2tensor(LR, normalize=znorm, bgr2rgb=True, add_batch=False) # Tensor, [CT',H',W'] or [T, H, W]
#TODO: TMP to test generating 3 channel images for SR loss
# HR = util.np2tensor(HR, normalize=znorm, bgr2rgb=False, add_batch=True) # Tensor, [CT',H',W'] or [T, H, W]
# LR = util.np2tensor(LR, normalize=znorm, bgr2rgb=False, add_batch=True) # Tensor, [CT',H',W'] or [T, H, W]
# if self.srcolors:
# HR = HR.view(c,t,HR_size,HR_size) # Tensor, [C,T,H,W]
if not self.y_only:
HR = HR.view(c,t,HR_size,HR_size) # Tensor, [C,T,H,W]
LR = LR.view(c,t,LR_size,LR_size) # Tensor, [C,T,H,W]
if self.shape == 'TCHW':
HR = HR.transpose(0,1) # Tensor, [T,C,H,W]
LR = LR.transpose(0,1) # Tensor, [T,C,H,W]
# generate Cr, Cb channels using bicubic interpolation
#TODO: check, it might be easier to return the whole image and separate later when needed
if self.y_only and self.srcolors:
LR_bicubic = util.bgr2ycbcr(LR_bicubic, only_y=False)
# HR_center = util.bgr2ycbcr(HR_center, only_y=False) #not needed, can directly use rgb image
## LR_bicubic = util.ycbcr2rgb(LR_bicubic, only_y=False) #test, looks ok
## HR_center = util.ycbcr2rgb(HR_center, only_y=False) #test, looks ok
## _, SR_cb, SR_cr = util.bgr2ycbcr(LR_bicubic, only_y=False, separate=True)
LR_bicubic = util.np2tensor(LR_bicubic, normalize=znorm, bgr2rgb=False, add_batch=False)
# HR_center = util.np2tensor(HR_center, normalize=znorm, bgr2rgb=False, add_batch=False) # will test using rgb image instead
HR_center = util.np2tensor(HR_center, normalize=znorm, bgr2rgb=True, add_batch=False)
#TODO: TMP to test generating 3 channel images for SR loss
# LR_bicubic = util.np2tensor(LR_bicubic, normalize=znorm, bgr2rgb=False, add_batch=True)
# HR_center = util.np2tensor(HR_center, normalize=znorm, bgr2rgb=False, add_batch=True)
elif self.y_only and not self.srcolors:
LR_bicubic = []
HR_center = []
else:
HR_center = HR[:,idx_center,:,:] if self.shape == 'CTHW' else HR[idx_center,:,:,:]
LR_bicubic = []
# return toTensor(LR), toTensor(HR)
return {'LR': LR, 'HR': HR, 'LR_path': LR_dir, 'HR_path': HR_dir, 'LR_bicubic': LR_bicubic, 'HR_center': HR_center}
def __getitem__(self, idx):
scale = self.opt.get('scale', 4)
idx_center = (self.num_frames - 1) // 2
h_LR = None
w_LR = None
# Default case: tensor will result in the [0,1] range
# Alternative: tensor will be z-normalized to the [-1,1] range
znorm = self.opt.get('znorm', False)
# only one video and paths_LR/paths_HR is already the video dir
video_dir = ""
# list the frames in the directory
# hr_dir = self.trainset_dir + '/' + video_dir + '/hr'
'''
List based frames loading
'''
paths_LR = util.get_image_paths(self.opt['data_type'], os.path.join(self.paths_LR, video_dir))
assert self.num_frames <= len(paths_LR), (
f'num_frame must be smaller than the number of frames per video, check {video_dir}')
idx_frame = idx
LR_name = paths_LR[idx_frame + 1] # center frame
# print(LR_name)
# print(len(self.video_list))
# read LR frames
# HR_list = []
LR_list = []
resize_type = None
LR_bicubic = None
for i_frame in range(self.num_frames):
if idx_frame == len(self.video_list)-2 and self.num_frames == 3:
# print("second to last frame:", i_frame)
if i_frame == 0:
LR_img = util.read_img(None, paths_LR[int(idx_frame)], out_nc=self.image_channels)
else:
LR_img = util.read_img(None, paths_LR[int(idx_frame)+1], out_nc=self.image_channels)
elif idx_frame == len(self.video_list)-1 and self.num_frames == 3:
# print("last frame:", i_frame)
LR_img = util.read_img(None, paths_LR[int(idx_frame)], out_nc=self.image_channels)
# every other internal frame
else:
# print("normal frame:", idx_frame)
LR_img = util.read_img(None, paths_LR[int(idx_frame)+(i_frame)], out_nc=self.image_channels)
#TODO: check if this is necessary
LR_img = util.modcrop(LR_img, scale)
# get the bicubic upscale of the center frame to concatenate for SR
if not self.y_only and self.srcolors and i_frame == idx_center:
if self.opt.get('denoise_LRbic', False):
LR_bicubic = transforms.RandomAverageBlur(p=1, kernel_size=3)(LR_img)
# LR_bicubic = transforms.RandomBoxBlur(p=1, kernel_size=3)(LR_img)
else:
LR_bicubic = LR_img
LR_bicubic, _ = Scale(img=LR_bicubic, scale=1/scale, algo=777) # bicubic upscale
# HR_center = HR_img
# tmp_vis(LR_bicubic, False)
# tmp_vis(HR_center, False)
if self.y_only:
# extract Y channel from frames
# normal path, only Y for both
LR_img = util.bgr2ycbcr(LR_img, only_y=True)
# expand Y images to add the channel dimension
# normal path, only Y for both
LR_img = util.fix_img_channels(LR_img, 1)
# print("HR_img.shape: ", HR_img.shape)
# print("LR_img.shape", LR_img.shape)
LR_list.append(LR_img) # h, w, c
if not self.y_only and (not h_LR or not w_LR):
h_LR, w_LR, c = LR_img.shape
if not self.y_only:
t = self.num_frames
LR = [np.asarray(LT) for LT in LR_list] # list -> numpy # input: list (contatin numpy: [H,W,C])
LR = np.asarray(LR) # numpy, [T,H,W,C]
LR = LR.transpose(1,2,3,0).reshape(h_LR, w_LR, -1) # numpy, [Hl',Wl',CT]
else:
LR = np.concatenate((LR_list), axis=2) # h, w, t
if self.y_only:
LR = util.np2tensor(LR, normalize=znorm, bgr2rgb=False, add_batch=False) # Tensor, [CT',H',W'] or [T, H, W]
else:
LR = util.np2tensor(LR, normalize=znorm, bgr2rgb=True, add_batch=False) # Tensor, [CT',H',W'] or [T, H, W]
LR = LR.view(c,t,h_LR,w_LR) # Tensor, [C,T,H,W]
if self.shape == 'TCHW':
LR = LR.transpose(0,1) # Tensor, [T,C,H,W]
if self.y_only and self.srcolors:
# generate Cr, Cb channels using bicubic interpolation
LR_bicubic = util.bgr2ycbcr(LR_bicubic, only_y=False)
LR_bicubic = util.np2tensor(LR_bicubic, normalize=znorm, bgr2rgb=False, add_batch=False)
HR_center = []
else:
LR_bicubic = []
HR_center = []
# return toTensor(LR), toTensor(HR)
return {'LR': LR, 'LR_path': LR_name, 'LR_bicubic': LR_bicubic, 'HR_center': HR_center}
def __getitem__(self, index):
HR_path, LR_path = None, None
scale = self.opt['scale']
HR_size = self.opt['HR_size']
# get HR image
HR_path = self.paths_HR[index]
img_HR = util.read_img(self.HR_env, HR_path)
# modcrop in the validation / test phase
if self.opt['phase'] != 'train':
img_HR = util.modcrop(img_HR, scale)
# change color space if necessary
if self.opt['color']:
img_HR = util.channel_convert(img_HR.shape[2], self.opt['color'],
[img_HR])[0]
# get LR image
if self.paths_LR:
LR_path = self.paths_LR[index]
img_LR = util.read_img(self.LR_env, LR_path)
else: # down-sampling on-the-fly
# randomly scale during training
if self.opt['phase'] == 'train':
random_scale = random.choice(self.random_scale_list)
H_s, W_s, _ = img_HR.shape
def _mod(n, random_scale, scale, thres):
rlt = int(n * random_scale)
rlt = (rlt // scale) * scale
return thres if rlt < thres else rlt
H_s = _mod(H_s, random_scale, scale, HR_size)
W_s = _mod(W_s, random_scale, scale, HR_size)
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)
H, W, _ = img_HR.shape
# using matlab imresize
img_LR = util.imresize_np(img_HR, 1 / scale, True)
if img_LR.ndim == 2:
img_LR = np.expand_dims(img_LR, axis=2)
if self.opt['phase'] == 'train':
# if the image size is too small
H, W, _ = img_HR.shape
if H < HR_size or W < HR_size:
img_HR = cv2.resize(np.copy(img_HR), (HR_size, HR_size),
interpolation=cv2.INTER_LINEAR)
# using matlab imresize
img_LR = util.imresize_np(img_HR, 1 / scale, True)
if img_LR.ndim == 2:
img_LR = np.expand_dims(img_LR, axis=2)
H, W, C = img_LR.shape
LR_size = HR_size // scale
# randomly crop
rnd_h = random.randint(0, max(0, H - LR_size))
rnd_w = random.randint(0, max(0, W - LR_size))
img_LR = img_LR[rnd_h:rnd_h + LR_size, rnd_w:rnd_w + LR_size, :]
rnd_h_HR, rnd_w_HR = int(rnd_h * scale), int(rnd_w * scale)
img_HR = img_HR[rnd_h_HR:rnd_h_HR + HR_size,
rnd_w_HR:rnd_w_HR + HR_size, :]
# augmentation - flip, rotate
img_LR, img_HR = util.augment([img_LR, img_HR], self.opt['use_flip'], \
self.opt['use_rot'])
# change color space if necessary
if self.opt['color']:
img_LR = util.channel_convert(
C, self.opt['color'],
[img_LR])[0] # TODO during val no definetion
# BGR to RGB, HWC to CHW, numpy to tensor
if img_HR.shape[2] == 3:
img_HR = img_HR[:, :, [2, 1, 0]]
img_LR = img_LR[:, :, [2, 1, 0]]
elif img_LR.shape[2] == 4:
img_HR = img_HR[:, :, [2, 1, 0, 3]]
img_LR = img_LR[:, :, [2, 1, 0, 3]]
img_HR = torch.from_numpy(
np.ascontiguousarray(np.transpose(img_HR, (2, 0, 1)))).float()
img_LR = torch.from_numpy(
np.ascontiguousarray(np.transpose(img_LR, (2, 0, 1)))).float()
if LR_path is None:
LR_path = HR_path
return {
'LR': img_LR,
'HR': img_HR,
'LR_path': LR_path,
'HR_path': HR_path
}