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demo_test_srresnetplus.py
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demo_test_srresnetplus.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 SRResNet+ [x2,x3,x4] and SRGAN+ [x4] on Set5.
-- + testsets
+ -- + Set5
+ -- + GT # ground truth images
+ -- + x2 # low resolution images of scale factor 2
+ -- + x3 # low resolution images of scale factor 3
+ -- + x4 # low resolution images of scale factor 4
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: [email protected]; github: https://github.com/cszn)
by Kai Zhang (03/03/2019)
'''
def main():
# --------------------------------
# let's start!
# --------------------------------
utils_logger.logger_info('test_srresnetplus', log_path='test_srresnetplus.log')
logger = logging.getLogger('test_srresnetplus')
# basic setting
# ================================================
sf = 4 # scale factor
noise_level_img = 0/255.0 # noise level of L image
noise_level_model = noise_level_img
show_img = True
use_srganplus = True # 'True' for SRGAN+ (x4) and 'False' for SRResNet+ (x2,x3,x4)
testsets = 'testsets'
testset_current = 'Set5'
n_channels = 3 # only color images, fixed
border = sf # shave boader to calculate PSNR and SSIM
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))
# --------------------------------
# L_folder, E_folder, H_folder
# --------------------------------
# --1--> L_folder, folder of Low-quality images
testsubset_current = 'x%01d' % (sf)
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')
need_H = True if os.path.exists(H_folder) else False
# ================================================
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}. \nTesting...'.format(model_path))
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
test_results['psnr_y'] = []
test_results['ssim_y'] = []
idx = 0
logger.info(L_folder)
for im in os.listdir(os.path.join(L_folder)):
if im.endswith('.jpg') or im.endswith('.bmp') or im.endswith('.png'):
logger.info('{:->4d}--> {:>10s}'.format(idx, im)) if not need_H else None
# --------------------------------
# (1) img_L
# --------------------------------
idx += 1
img_name, ext = os.path.splitext(im)
img = util.imread_uint(os.path.join(L_folder, im), n_channels=n_channels)
np.random.seed(seed=0) # for reproducibility
img = util.uint2single(img) + np.random.normal(0, noise_level_img, img.shape)
util.imshow(img, title='Low-resolution image') if show_img else None
img_L = util.single2tensor4(img)
noise_level_map = torch.ones((1, 1, img_L.size(2), img_L.size(3)), dtype=torch.float).mul_(noise_level_model)
img_L = torch.cat((img_L, noise_level_map), dim=1)
img_L = img_L.to(device)
# --------------------------------
# (2) img_E
# --------------------------------
img_E = model(img_L)
img_E = util.tensor2single(img_E)
img_E = util.single2uint(img_E) # np.uint8((z * 255.0).round())
if need_H:
# --------------------------------
# (3) img_H
# --------------------------------
img_H = util.imread_uint(os.path.join(H_folder, im), n_channels=n_channels)
img_H = util.modcrop(img_H, scale=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)
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)
logger.info('{:->20s} - PSNR: {:.2f} dB; SSIM: {:.4f}; PSNR_Y: {:.2f} dB; SSIM_Y: {:.4f}.'.format(im, psnr, ssim, psnr_y, ssim_y))
else:
logger.info('{:20s} - PSNR: {:.2f} dB; SSIM: {:.4f}.'.format(im, psnr, ssim))
# --------------------------------
# save results
# --------------------------------
util.imshow(np.concatenate([img_E, img_H], axis=1), title='Recovered / Ground-truth') if show_img else None
util.imsave(img_E, os.path.join(E_folder, img_name+'_x{}'.format(sf)+ext))
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('PSNR/SSIM(RGB) - {} - x{} -- PSNR: {:.2f} dB; SSIM: {:.4f}'.format(testset_current, 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('PSNR/SSIM( Y ) - {} - x{} -- PSNR: {:.2f} dB; SSIM: {:.4f}'.format(testset_current, sf, ave_psnr_y, ave_ssim_y))
if __name__ == '__main__':
main()