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main_dpir_sisr.py
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main_dpir_sisr.py
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import os.path
import glob
import cv2
import logging
import time
import numpy as np
from datetime import datetime
from collections import OrderedDict
import hdf5storage
import torch
from utils import utils_deblur
from utils import utils_logger
from utils import utils_model
from utils import utils_pnp as pnp
from utils import utils_sisr as sr
from utils import utils_image as util
"""
Spyder (Python 3.7)
PyTorch 1.6.0
Windows 10 or Linux
Kai Zhang ([email protected])
github: https://github.com/cszn/DPIR
https://github.com/cszn/IRCNN
https://github.com/cszn/KAIR
@article{zhang2020plug,
title={Plug-and-Play Image Restoration with Deep Denoiser Prior},
author={Zhang, Kai and Li, Yawei and Zuo, Wangmeng and Zhang, Lei and Van Gool, Luc and Timofte, Radu},
journal={arXiv preprint},
year={2020}
}
% If you have any question, please feel free to contact with me.
% Kai Zhang (e-mail: [email protected]; homepage: https://cszn.github.io/)
by Kai Zhang (01/August/2020)
# --------------------------------------------
|--model_zoo # model_zoo
|--drunet_color # model_name, for color images
|--drunet_gray
|--testset # testsets
|--results # results
# --------------------------------------------
How to run:
step 1: download [drunet_gray.pth, drunet_color.pth, ircnn_gray.pth, ircnn_color.pth] from https://drive.google.com/drive/folders/13kfr3qny7S2xwG9h7v95F5mkWs0OmU0D
step 2: set your own testset 'testset_name' and parameter setting such as 'noise_level_img', 'iter_num'.
step 3: 'python main_dpir_sisr.py'
"""
def main():
# ----------------------------------------
# Preparation
# ----------------------------------------
noise_level_img = 0/255.0 # set AWGN noise level for LR image, default: 0,
noise_level_model = noise_level_img # setnoise level of model, default 0
model_name = 'drunet_color' # set denoiser, | 'drunet_color' | 'ircnn_gray' | 'drunet_gray' | 'ircnn_color'
testset_name = 'srbsd68' # set test set, 'set5' | 'srbsd68'
x8 = True # default: False, x8 to boost performance
test_sf = [2] # set scale factor, default: [2, 3, 4], [2], [3], [4]
iter_num = 24 # set number of iterations, default: 24 for SISR
modelSigma1 = 49 # set sigma_1, default: 49
classical_degradation = True # set classical degradation or bicubic degradation
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
task_current = 'sr' # 'sr' for super-resolution
n_channels = 1 if 'gray' in model_name else 3 # fixed
model_zoo = 'model_zoo' # fixed
testsets = 'testsets' # fixed
results = 'results' # fixed
result_name = testset_name + '_' + task_current + '_' + model_name
model_path = os.path.join(model_zoo, model_name+'.pth')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.cuda.empty_cache()
# ----------------------------------------
# L_path, E_path, H_path
# ----------------------------------------
L_path = os.path.join(testsets, testset_name) # L_path, for Low-quality images
E_path = os.path.join(results, result_name) # E_path, 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)
# ----------------------------------------
# load model
# ----------------------------------------
if 'drunet' in model_name:
from models.network_unet import UNetRes as net
model = net(in_nc=n_channels+1, out_nc=n_channels, nc=[64, 128, 256, 512], nb=4, act_mode='R', downsample_mode="strideconv", upsample_mode="convtranspose")
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for _, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
elif 'ircnn' in model_name:
from models.network_dncnn import IRCNN as net
model = net(in_nc=n_channels, out_nc=n_channels, nc=64)
model25 = torch.load(model_path)
former_idx = 0
logger.info('model_name:{}, image sigma:{:.3f}, model sigma:{:.3f}'.format(model_name, noise_level_img, noise_level_model))
logger.info('Model path: {:s}'.format(model_path))
logger.info(L_path)
L_paths = util.get_image_paths(L_path)
# --------------------------------
# load kernel
# --------------------------------
# kernels = hdf5storage.loadmat(os.path.join('kernels', 'Levin09.mat'))['kernels']
if classical_degradation:
kernels = hdf5storage.loadmat(os.path.join('kernels', 'kernels_12.mat'))['kernels']
else:
kernels = hdf5storage.loadmat(os.path.join('kernels', 'kernel_bicubicx234.mat'))['kernels']
test_results_ave = OrderedDict()
test_results_ave['psnr_sf_k'] = []
test_results_ave['psnr_y_sf_k'] = []
for sf in test_sf:
border = sf
modelSigma2 = max(sf, noise_level_model*255.)
k_num = 8 if classical_degradation else 1
for k_index in range(k_num):
logger.info('--------- sf:{:>1d} --k:{:>2d} ---------'.format(sf, k_index))
test_results = OrderedDict()
test_results['psnr'] = []
test_results['psnr_y'] = []
if not classical_degradation: # for bicubic degradation
k_index = sf-2
k = kernels[0, k_index].astype(np.float64)
util.surf(k) if show_img else None
for idx, img in enumerate(L_paths):
# --------------------------------
# (1) get img_L
# --------------------------------
img_name, ext = os.path.splitext(os.path.basename(img))
img_H = util.imread_uint(img, n_channels=n_channels)
img_H = util.modcrop(img_H, sf) # modcrop
if classical_degradation:
img_L = sr.classical_degradation(img_H, k, sf)
util.imshow(img_L) if show_img else None
img_L = util.uint2single(img_L)
else:
img_L = util.imresize_np(util.uint2single(img_H), 1/sf)
np.random.seed(seed=0) # for reproducibility
img_L += np.random.normal(0, noise_level_img, img_L.shape) # add AWGN
# --------------------------------
# (2) get rhos and sigmas
# --------------------------------
rhos, sigmas = pnp.get_rho_sigma(sigma=max(0.255/255., noise_level_model), iter_num=iter_num, modelSigma1=modelSigma1, modelSigma2=modelSigma2, w=1)
rhos, sigmas = torch.tensor(rhos).to(device), torch.tensor(sigmas).to(device)
# --------------------------------
# (3) initialize x, and pre-calculation
# --------------------------------
x = cv2.resize(img_L, (img_L.shape[1]*sf, img_L.shape[0]*sf), interpolation=cv2.INTER_CUBIC)
if np.ndim(x)==2:
x = x[..., None]
if classical_degradation:
x = sr.shift_pixel(x, sf)
x = util.single2tensor4(x).to(device)
img_L_tensor, k_tensor = util.single2tensor4(img_L), util.single2tensor4(np.expand_dims(k, 2))
[k_tensor, img_L_tensor] = util.todevice([k_tensor, img_L_tensor], device)
FB, FBC, F2B, FBFy = sr.pre_calculate(img_L_tensor, k_tensor, sf)
# --------------------------------
# (4) main iterations
# --------------------------------
for i in range(iter_num):
# --------------------------------
# step 1, FFT
# --------------------------------
tau = rhos[i].float().repeat(1, 1, 1, 1)
x = sr.data_solution(x.float(), FB, FBC, F2B, FBFy, tau, sf)
if 'ircnn' in model_name:
current_idx = np.int(np.ceil(sigmas[i].cpu().numpy()*255./2.)-1)
if current_idx != former_idx:
model.load_state_dict(model25[str(current_idx)], strict=True)
model.eval()
for _, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
former_idx = current_idx
# --------------------------------
# step 2, denoiser
# --------------------------------
if x8:
x = util.augment_img_tensor4(x, i % 8)
if 'drunet' in model_name:
x = torch.cat((x, sigmas[i].float().repeat(1, 1, x.shape[2], x.shape[3])), dim=1)
x = utils_model.test_mode(model, x, mode=2, refield=32, min_size=256, modulo=16)
elif 'ircnn' in model_name:
x = model(x)
if x8:
if i % 8 == 3 or i % 8 == 5:
x = util.augment_img_tensor4(x, 8 - i % 8)
else:
x = util.augment_img_tensor4(x, i % 8)
# --------------------------------
# (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)+'_'+model_name+'.png'))
if n_channels == 1:
img_H = img_H.squeeze()
# --------------------------------
# (4) img_LEH
# --------------------------------
img_L = util.single2uint(img_L).squeeze()
if save_LEH:
k_v = k/np.max(k)*1.0
if n_channels==1:
k_v = util.single2uint(k_v)
else:
k_v = util.single2uint(np.tile(k_v[..., np.newaxis], [1, 1, n_channels]))
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)+'_LEH.png'))
if save_L:
util.imsave(img_L, os.path.join(E_path, img_name+'_x'+str(sf)+'_k'+str(k_index)+'_LR.png'))
psnr = util.calculate_psnr(img_E, img_H, border=border)
test_results['psnr'].append(psnr)
logger.info('{:->4d}--> {:>10s} -- sf:{:>1d} --k:{:>2d} PSNR: {:.2f}dB'.format(idx+1, img_name+ext, sf, k_index, psnr))
if n_channels == 3:
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)
test_results['psnr_y'].append(psnr_y)
# --------------------------------
# Average PSNR for all kernels
# --------------------------------
ave_psnr_k = sum(test_results['psnr']) / len(test_results['psnr'])
logger.info('------> Average PSNR(RGB) of ({}) scale factor: ({}), kernel: ({}) sigma: ({:.2f}): {:.2f} dB'.format(testset_name, sf, k_index, noise_level_model, ave_psnr_k))
test_results_ave['psnr_sf_k'].append(ave_psnr_k)
if n_channels == 3: # RGB image
ave_psnr_y_k = sum(test_results['psnr_y']) / len(test_results['psnr_y'])
logger.info('------> Average PSNR(Y) of ({}) scale factor: ({}), kernel: ({}) sigma: ({:.2f}): {:.2f} dB'.format(testset_name, sf, k_index, noise_level_model, ave_psnr_y_k))
test_results_ave['psnr_y_sf_k'].append(ave_psnr_y_k)
# ---------------------------------------
# Average PSNR for all sf and kernels
# ---------------------------------------
ave_psnr_sf_k = sum(test_results_ave['psnr_sf_k']) / len(test_results_ave['psnr_sf_k'])
logger.info('------> Average PSNR of ({}) {:.2f} dB'.format(testset_name, ave_psnr_sf_k))
if n_channels == 3:
ave_psnr_y_sf_k = sum(test_results_ave['psnr_y_sf_k']) / len(test_results_ave['psnr_y_sf_k'])
logger.info('------> Average PSNR of ({}) {:.2f} dB'.format(testset_name, ave_psnr_y_sf_k))
if __name__ == '__main__':
main()