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demo_test_dpsr.py
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demo_test_dpsr.py
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import os.path
import glob
import cv2
import logging
import numpy as np
from datetime import datetime
from collections import OrderedDict
from scipy.io import loadmat
import torch
from utils import utils_deblur
from utils import utils_logger
from utils import utils_image as util
from models.network_srresnet import SRResNet
'''
Spyder (Python 3.6)
PyTorch 0.4.1
Windows 10
Testing code of DPSR [x2,x3,x4] (and DPSRGAN [x4]) on
BSD68 with SRResNet+ [x2,x3,x4] (and SRGAN+ [x4]).
Three types of blur kernels, i.e.,
(g) Gaussian blur kernels,
(m) motion blur kernels, and
(d) disk blur kernels,
are considered.
-- + testsets
+ -- + BSD68
+ -- + GT # ground truth images
+ -- + x2_d # low-resolution images of scale factor 2 with disk blur kernels
+ -- + x3_d
+ -- + x4_d
+ -- + x2_g
+ -- + x3_g # low-resolution images of scale factor 3 with Gaussian blur kernels
+ -- + x4_g
+ -- + x2_m
+ -- + x3_m
+ -- + x4_m # low-resolution images of scale factor 4 with motion blur kernels
You can generate x2_d, ..., x4_m by generate_blurry_LR_images.m with Matlab or
you can download x2_d, ..., x4_m from:
https://drive.google.com/file/d/1IThQ0kZGL71pfIry5qzCoW0DftqLleOC/view?usp=sharing
For more information, please refer to the following paper.
@inproceedings{zhang2019deep,
title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={},
year={2019}
}
% If you have any question, please feel free to contact with me.
% Kai Zhang (e-mail: cskaizhang@gmail.com; github: https://github.com/cszn)
by Kai Zhang (03/03/2019)
'''
def main():
# --------------------------------
# let's start!
# --------------------------------
utils_logger.logger_info('test_dpsr', log_path='test_dpsr.log')
logger = logging.getLogger('test_dpsr')
# basic setting
# ================================================
sf = 4 # scale factor
noise_level_img = 0/255.0 # noise level of low quality image, default 0
noise_level_model = noise_level_img # noise level of model, default 0
show_img = True
use_srganplus = True # 'True' for SRGAN+ (x4) and 'False' for SRResNet+ (x2,x3,x4)
testsets = 'testsets'
testset_current = 'BSD68'
if use_srganplus and sf == 4:
model_prefix = 'DPSRGAN'
save_suffix = 'dpsrgan'
else:
model_prefix = 'DPSR'
save_suffix = 'dpsr'
model_path = os.path.join('DPSR_models', model_prefix+'x%01d.pth' % (sf))
iter_num = 15 # number of iterations, fixed
n_channels = 3 # only color images, fixed
border = sf**2 # shave boader to calculate PSNR, fixed
# k_type = ('d', 'm', 'g')
k_type = ('m') # motion blur kernel
# ================================================
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# --------------------------------
# load model
# --------------------------------
model = SRResNet(in_nc=4, out_nc=3, nc=96, nb=16, upscale=sf, act_mode='R', upsample_mode='pixelshuffle')
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
logger.info('Model path {:s}. Testing...'.format(model_path))
# --------------------------------
# read image (img) and kernel (k)
# --------------------------------
test_results = OrderedDict()
for k_type_n in range(len(k_type)):
# --1--> L_folder, folder of Low-quality images
testsubset_current = 'x%01d_%01s' % (sf, k_type[k_type_n])
L_folder = os.path.join(testsets, testset_current, testsubset_current)
# --2--> E_folder, folder of Estimated images
E_folder = os.path.join(testsets, testset_current, testsubset_current+'_'+save_suffix)
util.mkdir(E_folder)
# --3--> H_folder, folder of High-quality images
H_folder = os.path.join(testsets, testset_current, 'GT')
test_results['psnr_'+k_type[k_type_n]] = []
logger.info(L_folder)
idx = 0
for im in os.listdir(os.path.join(L_folder)):
if im.endswith('.jpg') or im.endswith('.bmp') or im.endswith('.png'):
# --------------------------------
# (1) img_L
# --------------------------------
idx += 1
img_name, ext = os.path.splitext(im)
img_L = util.imread_uint(os.path.join(L_folder, im), n_channels=n_channels)
util.imshow(img_L) if show_img else None
np.random.seed(seed=0) # for reproducibility
img_L = util.uint2single(img_L) + np.random.normal(0, noise_level_img, img_L.shape)
# --------------------------------
# (2) kernel
# --------------------------------
k = loadmat(os.path.join(L_folder, img_name+'.mat'))['kernel']
k = k.astype(np.float32)
k /= np.sum(k)
# --------------------------------
# (3) get upperleft, denominator
# --------------------------------
upperleft, denominator = utils_deblur.get_uperleft_denominator(img_L, k)
# --------------------------------
# (4) get rhos and sigmas
# --------------------------------
rhos, sigmas = utils_deblur.get_rho_sigma(sigma=max(0.255/255., noise_level_model), iter_num=iter_num)
# --------------------------------
# (5) main iteration
# --------------------------------
z = img_L
rhos = np.float32(rhos)
sigmas = np.float32(sigmas)
for i in range(iter_num):
# --------------------------------
# step 1, Eq. (9) // FFT
# --------------------------------
rho = rhos[i]
if i != 0:
z = util.imresize_np(z, 1/sf, True)
z = np.real(np.fft.ifft2((upperleft + rho*np.fft.fft2(z, axes=(0, 1)))/(denominator + rho), axes=(0, 1)))
# imsave('LR_deblurred_%02d.png'%i, np.clip(z, 0, 1))
# --------------------------------
# step 2, Eq. (12) // super-resolver
# --------------------------------
sigma = torch.from_numpy(np.array(sigmas[i]))
img_L = util.single2tensor4(z)
noise_level_map = torch.ones((1, 1, img_L.size(2), img_L.size(3)), dtype=torch.float).mul_(sigma)
img_L = torch.cat((img_L, noise_level_map), dim=1)
img_L = img_L.to(device)
# with torch.no_grad():
z = model(img_L)
z = util.tensor2single(z)
# --------------------------------
# (6) img_E
# --------------------------------
img_E = util.single2uint(z) # np.uint8((z * 255.0).round())
# --------------------------------
# (7) img_H
# --------------------------------
img_H = util.imread_uint(os.path.join(H_folder, img_name[:7]+'.png'), n_channels=n_channels)
util.imshow(np.concatenate([img_E, img_H], axis=1), title='Recovered / Ground-truth') if show_img else None
psnr = util.calculate_psnr(img_E, img_H, border=border)
logger.info('{:->4d}--> {:>10s}, {:.2f}dB'.format(idx, im, psnr))
test_results['psnr_'+k_type[k_type_n]].append(psnr)
util.imsave(img_E, os.path.join(E_folder, img_name+ext))
ave_psnr = sum(test_results['psnr_'+k_type[k_type_n]]) / len(test_results['psnr_'+k_type[k_type_n]])
logger.info('------> Average PSNR(RGB) of ({} - {}) is : {:.2f} dB'.format(testset_current, testsubset_current, ave_psnr))
if __name__ == '__main__':
main()