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main_dpir_demosaick.py
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main_dpir_demosaick.py
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import os.path
import cv2
import logging
import numpy as np
from collections import OrderedDict
import torch
from utils import utils_model
from utils import utils_mosaic
from utils import utils_logger
from utils import utils_pnp as pnp
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_gray # model_name, for color images
|--drunet_color
|--testset # testsets
|--results # results
# --------------------------------------------
How to run:
step 1: download [drunet_color.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_model', 'iter_num'.
step 3: 'python main_dpir_demosaick.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 # set noise level of model, default: 0
model_name = 'ircnn_color' # set denoiser, 'drunet_color' | 'ircnn_color'
testset_name = 'Set18' # set testing set, 'set18' | 'set24'
x8 = True # set PGSE to boost performance, default: True
iter_num = 40 # set number of iterations, default: 40 for demosaicing
modelSigma1 = 49 # set sigma_1, default: 49
modelSigma2 = max(0.6, noise_level_model*255.) # set sigma_2, default
matlab_init = True
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
border = 10 # default 10 for demosaicing
task_current = 'dm' # 'dm' for demosaicing
n_channels = 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)
test_results = OrderedDict()
test_results['psnr'] = []
for idx, img in enumerate(L_paths):
# --------------------------------
# (1) get img_H and img_L
# --------------------------------
idx += 1
img_name, ext = os.path.splitext(os.path.basename(img))
img_H = util.imread_uint(img, n_channels=n_channels)
CFA, CFA4, mosaic, mask = utils_mosaic.mosaic_CFA_Bayer(img_H)
# --------------------------------
# (2) initialize x
# --------------------------------
if matlab_init: # matlab demosaicing for initialization
CFA4 = util.uint2tensor4(CFA4).to(device)
x = utils_mosaic.dm_matlab(CFA4)
else:
x = cv2.cvtColor(CFA, cv2.COLOR_BAYER_BG2RGB_EA)
x = util.uint2tensor4(x).to(device)
img_L = util.tensor2uint(x)
y = util.uint2tensor4(mosaic).to(device)
util.imshow(img_L) if show_img else None
mask = util.single2tensor4(mask.astype(np.float32)).to(device)
# --------------------------------
# (3) get rhos and sigmas
# --------------------------------
rhos, sigmas = pnp.get_rho_sigma(sigma=max(0.255/255., noise_level_img), iter_num=iter_num, modelSigma1=modelSigma1, modelSigma2=modelSigma2, w=1.0)
rhos, sigmas = torch.tensor(rhos).to(device), torch.tensor(sigmas).to(device)
# --------------------------------
# (4) main iterations
# --------------------------------
for i in range(iter_num):
# --------------------------------
# step 1, closed-form solution
# --------------------------------
x = (y+rhos[i].float()*x).div(mask+rhos[i])
# --------------------------------
# step 2, denoiser
# --------------------------------
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
x = torch.clamp(x, 0, 1)
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)
# x = model(x)
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)
x[mask.to(torch.bool)] = y[mask.to(torch.bool)]
# --------------------------------
# (4) img_E
# --------------------------------
img_E = util.tensor2uint(x)
psnr = util.calculate_psnr(img_E, img_H, border=border)
test_results['psnr'].append(psnr)
logger.info('{:->4d}--> {:>10s} -- PSNR: {:.2f}dB'.format(idx, img_name+ext, psnr))
if save_E:
util.imsave(img_E, os.path.join(E_path, img_name+'_'+model_name+'.png'))
if save_L:
util.imsave(img_L, os.path.join(E_path, img_name+'_L.png'))
if save_LEH:
util.imsave(np.concatenate([img_L, img_E, img_H], axis=1), os.path.join(E_path, img_name+model_name+'_LEH.png'))
ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
logger.info('------> Average PSNR(RGB) of ({}) is : {:.2f} dB'.format(testset_name, ave_psnr))
if __name__ == '__main__':
main()