def main():
# ----------------------------------------
# Preparation
# ----------------------------------------
model_name = 'usrnet' # 'usrgan' | 'usrnet' | 'usrgan_tiny' | 'usrnet_tiny'
testset_name = 'set5' # test set, 'set5' | 'srbsd68'
need_degradation = True # default: True
sf = 4 # scale factor, only from {2, 3, 4}
show_img = False # default: False
save_L = True # save LR image
save_E = True # save estimated image
# load approximated bicubic kernels
#kernels = hdf5storage.loadmat(os.path.join('kernels', 'kernels_bicubicx234.mat'))['kernels']
kernels = loadmat(os.path.join('kernels',
'kernels_bicubicx234.mat'))['kernels']
kernel = kernels[0, sf - 2].astype(np.float64)
kernel = util.single2tensor4(kernel[..., np.newaxis])
task_current = 'sr' # fixed, 'sr' for super-resolution
n_channels = 3 # fixed, 3 for color image
model_pool = 'model_zoo' # fixed
testsets = 'testsets' # fixed
results = 'results' # fixed
noise_level_img = 0 # fixed: 0, noise level for LR image
noise_level_model = noise_level_img # fixed, noise level of model, default 0
result_name = testset_name + '_' + model_name + '_bicubic'
border = sf if task_current == 'sr' else 0 # shave boader to calculate PSNR and SSIM
model_path = os.path.join(model_pool, model_name + '.pth')
# ----------------------------------------
# L_path, E_path, H_path
# ----------------------------------------
L_path = os.path.join(
testsets, testset_name) # L_path, fixed, for Low-quality images
H_path = L_path # H_path, 'None' | L_path, for High-quality images
E_path = os.path.join(results,
result_name) # E_path, fixed, for Estimated images
util.mkdir(E_path)
if H_path == L_path:
need_degradation = True
logger_name = result_name
utils_logger.logger_info(logger_name,
log_path=os.path.join(E_path,
logger_name + '.log'))
logger = logging.getLogger(logger_name)
need_H = True if H_path is not None else False
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# ----------------------------------------
# load model
# ----------------------------------------
from models.network_usrnet import USRNet as net # for pytorch version <= 1.7.1
# from models.network_usrnet_v1 import USRNet as net # for pytorch version >=1.8.1
if 'tiny' in model_name:
model = net(n_iter=6,
h_nc=32,
in_nc=4,
out_nc=3,
nc=[16, 32, 64, 64],
nb=2,
act_mode="R",
downsample_mode='strideconv',
upsample_mode="convtranspose")
else:
model = net(n_iter=8,
h_nc=64,
in_nc=4,
out_nc=3,
nc=[64, 128, 256, 512],
nb=2,
act_mode="R",
downsample_mode='strideconv',
upsample_mode="convtranspose")
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for key, v in model.named_parameters():
v.requires_grad = False
number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
logger.info('Params number: {}'.format(number_parameters))
model = model.to(device)
logger.info('Model path: {:s}'.format(model_path))
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
test_results['psnr_y'] = []
test_results['ssim_y'] = []
logger.info('model_name:{}, image sigma:{}'.format(model_name,
noise_level_img))
logger.info(L_path)
L_paths = util.get_image_paths(L_path)
H_paths = util.get_image_paths(H_path) if need_H else None
for idx, img in enumerate(L_paths):
# ------------------------------------
# (1) img_L
# ------------------------------------
img_name, ext = os.path.splitext(os.path.basename(img))
logger.info('{:->4d}--> {:>10s}'.format(idx + 1, img_name + ext))
img_L = util.imread_uint(img, n_channels=n_channels)
img_L = util.uint2single(img_L)
# degradation process, bicubic downsampling
if need_degradation:
img_L = util.modcrop(img_L, sf)
img_L = util.imresize_np(img_L, 1 / sf)
# img_L = util.uint2single(util.single2uint(img_L))
# np.random.seed(seed=0) # for reproducibility
# img_L += np.random.normal(0, noise_level_img/255., img_L.shape)
w, h = img_L.shape[:2]
if save_L:
util.imsave(
util.single2uint(img_L),
os.path.join(E_path, img_name + '_LR_x' + str(sf) + '.png'))
img = cv2.resize(img_L, (sf * h, sf * w),
interpolation=cv2.INTER_NEAREST)
img = utils_deblur.wrap_boundary_liu(img, [
int(np.ceil(sf * w / 8 + 2) * 8),
int(np.ceil(sf * h / 8 + 2) * 8)
])
img_wrap = sr.downsample_np(img, sf, center=False)
img_wrap[:w, :h, :] = img_L
img_L = img_wrap
util.imshow(util.single2uint(img_L),
title='LR image with noise level {}'.format(
noise_level_img)) if show_img else None
img_L = util.single2tensor4(img_L)
img_L = img_L.to(device)
# ------------------------------------
# (2) img_E
# ------------------------------------
sigma = torch.tensor(noise_level_model).float().view([1, 1, 1, 1])
[img_L, kernel,
sigma] = [el.to(device) for el in [img_L, kernel, sigma]]
img_E = model(img_L, kernel, sf, sigma)
img_E = util.tensor2uint(img_E)
img_E = img_E[:sf * w, :sf * h, :]
if need_H:
# --------------------------------
# (3) img_H
# --------------------------------
img_H = util.imread_uint(H_paths[idx], n_channels=n_channels)
img_H = img_H.squeeze()
img_H = util.modcrop(img_H, sf)
# --------------------------------
# PSNR and SSIM
# --------------------------------
psnr = util.calculate_psnr(img_E, img_H, border=border)
ssim = util.calculate_ssim(img_E, img_H, border=border)
test_results['psnr'].append(psnr)
test_results['ssim'].append(ssim)
logger.info('{:s} - PSNR: {:.2f} dB; SSIM: {:.4f}.'.format(
img_name + ext, psnr, ssim))
util.imshow(np.concatenate([img_E, img_H], axis=1),
title='Recovered / Ground-truth') if show_img else None
if np.ndim(img_H) == 3: # RGB image
img_E_y = util.rgb2ycbcr(img_E, only_y=True)
img_H_y = util.rgb2ycbcr(img_H, only_y=True)
psnr_y = util.calculate_psnr(img_E_y, img_H_y, border=border)
ssim_y = util.calculate_ssim(img_E_y, img_H_y, border=border)
test_results['psnr_y'].append(psnr_y)
test_results['ssim_y'].append(ssim_y)
# ------------------------------------
# save results
# ------------------------------------
if save_E:
util.imsave(
img_E,
os.path.join(
E_path,
img_name + '_x' + str(sf) + '_' + model_name + '.png'))
if need_H:
ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
ave_ssim = sum(test_results['ssim']) / len(test_results['ssim'])
logger.info(
'Average PSNR/SSIM(RGB) - {} - x{} --PSNR: {:.2f} dB; SSIM: {:.4f}'
.format(result_name, sf, ave_psnr, ave_ssim))
if np.ndim(img_H) == 3:
ave_psnr_y = sum(test_results['psnr_y']) / len(
test_results['psnr_y'])
ave_ssim_y = sum(test_results['ssim_y']) / len(
test_results['ssim_y'])
logger.info(
'Average PSNR/SSIM( Y ) - {} - x{} - PSNR: {:.2f} dB; SSIM: {:.4f}'
.format(result_name, sf, ave_psnr_y, ave_ssim_y))
def main():
# ----------------------------------------
# Preparation
# ----------------------------------------
model_name = 'usrnet' # 'usrgan' | 'usrnet' | 'usrgan_tiny' | 'usrnet_tiny'
testset_name = 'set5' # test set, 'set5' | 'srbsd68'
test_sf = [4] if 'gan' in model_name else [
2, 3, 4
] # scale factor, from {1,2,3,4}
show_img = False # default: False
save_L = True # save LR image
save_E = True # save estimated image
save_LEH = False # save zoomed LR, E and H images
# ----------------------------------------
# load testing kernels
# ----------------------------------------
# kernels = hdf5storage.loadmat(os.path.join('kernels', 'kernels.mat'))['kernels']
kernels = loadmat(os.path.join('kernels', 'kernels_12.mat'))['kernels']
n_channels = 1 if 'gray' in model_name else 3 # 3 for color image, 1 for grayscale image
model_pool = 'model_zoo' # fixed
testsets = 'testsets' # fixed
results = 'results' # fixed
noise_level_img = 0 # fixed: 0, noise level for LR image
noise_level_model = noise_level_img # fixed, noise level of model, default 0
result_name = testset_name + '_' + model_name
model_path = os.path.join(model_pool, model_name + '.pth')
# ----------------------------------------
# L_path = H_path, E_path, logger
# ----------------------------------------
L_path = os.path.join(
testsets,
testset_name) # L_path and H_path, fixed, for Low-quality images
E_path = os.path.join(results,
result_name) # E_path, fixed, for Estimated images
util.mkdir(E_path)
logger_name = result_name
utils_logger.logger_info(logger_name,
log_path=os.path.join(E_path,
logger_name + '.log'))
logger = logging.getLogger(logger_name)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# ----------------------------------------
# load model
# ----------------------------------------
if 'tiny' in model_name:
model = net(n_iter=6,
h_nc=32,
in_nc=4,
out_nc=3,
nc=[16, 32, 64, 64],
nb=2,
act_mode="R",
downsample_mode='strideconv',
upsample_mode="convtranspose")
else:
model = net(n_iter=8,
h_nc=64,
in_nc=4,
out_nc=3,
nc=[64, 128, 256, 512],
nb=2,
act_mode="R",
downsample_mode='strideconv',
upsample_mode="convtranspose")
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for key, v in model.named_parameters():
v.requires_grad = False
number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
model = model.to(device)
logger.info('Model path: {:s}'.format(model_path))
logger.info('Params number: {}'.format(number_parameters))
logger.info('Model_name:{}, image sigma:{}'.format(model_name,
noise_level_img))
logger.info(L_path)
L_paths = util.get_image_paths(L_path)
# --------------------------------
# read images
# --------------------------------
test_results_ave = OrderedDict()
test_results_ave['psnr_sf_k'] = []
for sf in test_sf:
for k_index in range(kernels.shape[1]):
test_results = OrderedDict()
test_results['psnr'] = []
kernel = kernels[0, k_index].astype(np.float64)
## other kernels
# kernel = utils_deblur.blurkernel_synthesis(h=25) # motion kernel
# kernel = utils_deblur.fspecial('gaussian', 25, 1.6) # Gaussian kernel
# kernel = sr.shift_pixel(kernel, sf) # pixel shift; optional
# kernel /= np.sum(kernel)
util.surf(kernel) if show_img else None
idx = 0
for img in L_paths:
# --------------------------------
# (1) classical degradation, img_L
# --------------------------------
idx += 1
img_name, ext = os.path.splitext(os.path.basename(img))
img_H = util.imread_uint(
img, n_channels=n_channels) # HR image, int8
img_H = util.modcrop(img_H, np.lcm(sf, 8)) # modcrop
# generate degraded LR image
img_L = ndimage.filters.convolve(img_H,
kernel[..., np.newaxis],
mode='wrap') # blur
img_L = sr.downsample_np(
img_L, sf,
center=False) # downsample, standard s-fold downsampler
img_L = util.uint2single(img_L) # uint2single
np.random.seed(seed=0) # for reproducibility
img_L += np.random.normal(0, noise_level_img,
img_L.shape) # add AWGN
util.imshow(util.single2uint(img_L)) if show_img else None
x = util.single2tensor4(img_L)
k = util.single2tensor4(kernel[..., np.newaxis])
sigma = torch.tensor(noise_level_model).float().view(
[1, 1, 1, 1])
[x, k, sigma] = [el.to(device) for el in [x, k, sigma]]
# --------------------------------
# (2) inference
# --------------------------------
x = model(x, k, sf, sigma)
# --------------------------------
# (3) img_E
# --------------------------------
img_E = util.tensor2uint(x)
if save_E:
util.imsave(
img_E,
os.path.join(
E_path, img_name + '_x' + str(sf) + '_k' +
str(k_index + 1) + '_' + model_name + '.png'))
# --------------------------------
# (4) img_LEH
# --------------------------------
img_L = util.single2uint(img_L)
if save_LEH:
k_v = kernel / np.max(kernel) * 1.2
k_v = util.single2uint(
np.tile(k_v[..., np.newaxis], [1, 1, 3]))
k_v = cv2.resize(k_v, (3 * k_v.shape[1], 3 * k_v.shape[0]),
interpolation=cv2.INTER_NEAREST)
img_I = cv2.resize(
img_L, (sf * img_L.shape[1], sf * img_L.shape[0]),
interpolation=cv2.INTER_NEAREST)
img_I[:k_v.shape[0], -k_v.shape[1]:, :] = k_v
img_I[:img_L.shape[0], :img_L.shape[1], :] = img_L
util.imshow(np.concatenate([img_I, img_E, img_H], axis=1),
title='LR / Recovered / Ground-truth'
) if show_img else None
util.imsave(
np.concatenate([img_I, img_E, img_H], axis=1),
os.path.join(
E_path, img_name + '_x' + str(sf) + '_k' +
str(k_index + 1) + '_LEH.png'))
if save_L:
util.imsave(
img_L,
os.path.join(
E_path, img_name + '_x' + str(sf) + '_k' +
str(k_index + 1) + '_LR.png'))
psnr = util.calculate_psnr(
img_E, img_H, border=sf**2) # change with your own border
test_results['psnr'].append(psnr)
logger.info(
'{:->4d}--> {:>10s} -- x{:>2d} --k{:>2d} PSNR: {:.2f}dB'.
format(idx, img_name + ext, sf, k_index, psnr))
ave_psnr_k = sum(test_results['psnr']) / len(test_results['psnr'])
logger.info(
'------> Average PSNR(RGB) of ({}) scale factor: ({}), kernel: ({}) sigma: ({}): {:.2f} dB'
.format(testset_name, sf, k_index + 1, noise_level_model,
ave_psnr_k))
test_results_ave['psnr_sf_k'].append(ave_psnr_k)
logger.info(test_results_ave['psnr_sf_k'])
def main():
# ----------------------------------------
# Preparation
# ----------------------------------------
model_name = 'usrnet' # 'usrgan' | 'usrnet' | 'usrgan_tiny' | 'usrnet_tiny'
testset_name = 'set_real' # test set, 'set_real'
test_image = 'chip.png' # 'chip.png', 'comic.png'
#test_image = 'comic.png'
sf = 4 # scale factor, only from {1, 2, 3, 4}
show_img = False # default: False
save_E = True # save estimated image
save_LE = True # save zoomed LR, Estimated images
# ----------------------------------------
# set noise level and kernel
# ----------------------------------------
if 'chip' in test_image:
noise_level_img = 15 # noise level for LR image, 15 for chip
kernel_width_default_x1234 = [0.6, 0.9, 1.7, 2.2] # Gaussian kernel widths for x1, x2, x3, x4
else:
noise_level_img = 2 # noise level for LR image, 0.5~3 for clean images
kernel_width_default_x1234 = [0.4, 0.7, 1.5, 2.0] # default Gaussian kernel widths of clean/sharp images for x1, x2, x3, x4
noise_level_model = noise_level_img/255. # noise level of model
kernel_width = kernel_width_default_x1234[sf-1]
# set your own kernel width
# kernel_width = 2.2
k = utils_deblur.fspecial('gaussian', 25, kernel_width)
k = sr.shift_pixel(k, sf) # shift the kernel
k /= np.sum(k)
util.surf(k) if show_img else None
# scio.savemat('kernel_realapplication.mat', {'kernel':k})
# load approximated bicubic kernels
#kernels = hdf5storage.loadmat(os.path.join('kernels', 'kernel_bicubicx234.mat'))['kernels']
# kernels = loadmat(os.path.join('kernels', 'kernel_bicubicx234.mat'))['kernels']
# kernel = kernels[0, sf-2].astype(np.float64)
kernel = util.single2tensor4(k[..., np.newaxis])
n_channels = 1 if 'gray' in model_name else 3 # 3 for color image, 1 for grayscale image
model_pool = 'model_zoo' # fixed
testsets = 'testsets' # fixed
results = 'results' # fixed
result_name = testset_name + '_' + model_name
model_path = os.path.join(model_pool, model_name+'.pth')
# ----------------------------------------
# L_path, E_path
# ----------------------------------------
L_path = os.path.join(testsets, testset_name) # L_path, fixed, for Low-quality images
E_path = os.path.join(results, result_name) # E_path, fixed, for Estimated images
util.mkdir(E_path)
logger_name = result_name
utils_logger.logger_info(logger_name, log_path=os.path.join(E_path, logger_name+'.log'))
logger = logging.getLogger(logger_name)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# ----------------------------------------
# load model
# ----------------------------------------
if 'tiny' in model_name:
model = net(n_iter=6, h_nc=32, in_nc=4, out_nc=3, nc=[16, 32, 64, 64],
nb=2, act_mode="R", downsample_mode='strideconv', upsample_mode="convtranspose")
else:
model = net(n_iter=8, h_nc=64, in_nc=4, out_nc=3, nc=[64, 128, 256, 512],
nb=2, act_mode="R", downsample_mode='strideconv', upsample_mode="convtranspose")
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for key, v in model.named_parameters():
v.requires_grad = False
number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
logger.info('Params number: {}'.format(number_parameters))
model = model.to(device)
logger.info('Model path: {:s}'.format(model_path))
logger.info('model_name:{}, image sigma:{}'.format(model_name, noise_level_img))
logger.info(L_path)
img = os.path.join(L_path, test_image)
# ------------------------------------
# (1) img_L
# ------------------------------------
img_name, ext = os.path.splitext(os.path.basename(img))
img_L = util.imread_uint(img, n_channels=n_channels)
img_L = util.uint2single(img_L)
util.imshow(img_L) if show_img else None
w, h = img_L.shape[:2]
logger.info('{:>10s}--> ({:>4d}x{:<4d})'.format(img_name+ext, w, h))
# boundary handling
boarder = 8 # default setting for kernel size 25x25
img = cv2.resize(img_L, (sf*h, sf*w), interpolation=cv2.INTER_NEAREST)
img = utils_deblur.wrap_boundary_liu(img, [int(np.ceil(sf*w/boarder+2)*boarder), int(np.ceil(sf*h/boarder+2)*boarder)])
img_wrap = sr.downsample_np(img, sf, center=False)
img_wrap[:w, :h, :] = img_L
img_L = img_wrap
util.imshow(util.single2uint(img_L), title='LR image with noise level {}'.format(noise_level_img)) if show_img else None
img_L = util.single2tensor4(img_L)
img_L = img_L.to(device)
# ------------------------------------
# (2) img_E
# ------------------------------------
sigma = torch.tensor(noise_level_model).float().view([1, 1, 1, 1])
[img_L, kernel, sigma] = [el.to(device) for el in [img_L, kernel, sigma]]
img_E = model(img_L, kernel, sf, sigma)
img_E = util.tensor2uint(img_E)[:sf*w, :sf*h, ...]
if save_E:
util.imsave(img_E, os.path.join(E_path, img_name+'_x'+str(sf)+'_'+model_name+'.png'))
# --------------------------------
# (3) save img_LE
# --------------------------------
if save_LE:
k_v = k/np.max(k)*1.2
k_v = util.single2uint(np.tile(k_v[..., np.newaxis], [1, 1, 3]))
k_factor = 3
k_v = cv2.resize(k_v, (k_factor*k_v.shape[1], k_factor*k_v.shape[0]), interpolation=cv2.INTER_NEAREST)
img_L = util.tensor2uint(img_L)[:w, :h, ...]
img_I = cv2.resize(img_L, (sf*img_L.shape[1], sf*img_L.shape[0]), interpolation=cv2.INTER_NEAREST)
img_I[:k_v.shape[0], :k_v.shape[1], :] = k_v
util.imshow(np.concatenate([img_I, img_E], axis=1), title='LR / Recovered') if show_img else None
util.imsave(np.concatenate([img_I, img_E], axis=1), os.path.join(E_path, img_name+'_x'+str(sf)+'_'+model_name+'_LE.png'))
