184 lines
6.5 KiB
Python
184 lines
6.5 KiB
Python
import os.path
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import logging
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import numpy as np
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from datetime import datetime
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from collections import OrderedDict
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from scipy.io import loadmat
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import torch
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from utils import utils_logger
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from utils import utils_model
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from utils import utils_image as util
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'''
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Spyder (Python 3.6)
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PyTorch 1.1.0
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Windows 10 or Linux
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Kai Zhang (cskaizhang@gmail.com)
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github: https://github.com/cszn/KAIR
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https://github.com/cszn/IRCNN
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@inproceedings{zhang2017learning,
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title={Learning deep CNN denoiser prior for image restoration},
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author={Zhang, Kai and Zuo, Wangmeng and Gu, Shuhang and Zhang, Lei},
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booktitle={IEEE conference on computer vision and pattern recognition},
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pages={3929--3938},
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year={2017}
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}
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% If you have any question, please feel free to contact with me.
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% Kai Zhang (e-mail: cskaizhang@gmail.com; github: https://github.com/cszn)
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by Kai Zhang (12/Dec./2019)
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'''
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"""
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# --------------------------------------------
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|--model_zoo # model_zoo
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|--ircnn_gray # model_name
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|--ircnn_color
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|--testset # testsets
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|--set12 # testset_name
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|--bsd68
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|--cbsd68
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|--results # results
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|--set12_ircnn_gray # result_name = testset_name + '_' + model_name
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|--cbsd68_ircnn_color
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# --------------------------------------------
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"""
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def main():
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# ----------------------------------------
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# Preparation
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# ----------------------------------------
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noise_level_img = 50 # noise level for noisy image
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model_name = 'ircnn_gray' # 'ircnn_gray' | 'ircnn_color'
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testset_name = 'set12' # test set, 'bsd68' | 'set12'
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need_degradation = True # default: True
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x8 = False # default: False, x8 to boost performance
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show_img = False # default: False
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current_idx = min(24, np.int(np.ceil(noise_level_img/2)-1)) # current_idx+1 th denoiser
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task_current = 'dn' # fixed, 'dn' for denoising | 'sr' for super-resolution
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sf = 1 # unused for denoising
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if 'color' in model_name:
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n_channels = 3 # fixed, 1 for grayscale image, 3 for color image
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else:
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n_channels = 1 # fixed for grayscale image
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model_pool = 'model_zoo' # fixed
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testsets = 'testsets' # fixed
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results = 'results' # fixed
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result_name = testset_name + '_' + model_name # fixed
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border = sf if task_current == 'sr' else 0 # shave boader to calculate PSNR and SSIM
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model_path = os.path.join(model_pool, model_name+'.pth')
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# ----------------------------------------
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# L_path, E_path, H_path
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# ----------------------------------------
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L_path = os.path.join(testsets, testset_name) # L_path, for Low-quality images
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H_path = L_path # H_path, for High-quality images
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E_path = os.path.join(results, result_name) # E_path, for Estimated images
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util.mkdir(E_path)
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if H_path == L_path:
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need_degradation = True
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logger_name = result_name
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utils_logger.logger_info(logger_name, log_path=os.path.join(E_path, logger_name+'.log'))
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logger = logging.getLogger(logger_name)
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need_H = True if H_path is not None else False
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# ----------------------------------------
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# load model
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# ----------------------------------------
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model25 = torch.load(model_path)
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from models.network_dncnn import IRCNN as net
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model = net(in_nc=n_channels, out_nc=n_channels, nc=64)
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model.load_state_dict(model25[str(current_idx)], strict=True)
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model.eval()
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for _, v in model.named_parameters():
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v.requires_grad = False
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model = model.to(device)
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logger.info('Model path: {:s}'.format(model_path))
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number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
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logger.info('Params number: {}'.format(number_parameters))
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test_results = OrderedDict()
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test_results['psnr'] = []
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test_results['ssim'] = []
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logger.info('model_name:{}, image sigma:{}'.format(model_name, noise_level_img))
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logger.info(L_path)
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L_paths = util.get_image_paths(L_path)
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H_paths = util.get_image_paths(H_path) if need_H else None
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for idx, img in enumerate(L_paths):
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# ------------------------------------
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# (1) img_L
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# ------------------------------------
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img_name, ext = os.path.splitext(os.path.basename(img))
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# logger.info('{:->4d}--> {:>10s}'.format(idx+1, img_name+ext))
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img_L = util.imread_uint(img, n_channels=n_channels)
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img_L = util.uint2single(img_L)
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if need_degradation: # degradation process
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np.random.seed(seed=0) # for reproducibility
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img_L += np.random.normal(0, noise_level_img/255., img_L.shape)
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util.imshow(util.single2uint(img_L), title='Noisy image with noise level {}'.format(noise_level_img)) if show_img else None
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img_L = util.single2tensor4(img_L)
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img_L = img_L.to(device)
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# ------------------------------------
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# (2) img_E
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# ------------------------------------
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if not x8:
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img_E = model(img_L)
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else:
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img_E = utils_model.test_mode(model, img_L, mode=3)
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img_E = util.tensor2uint(img_E)
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if need_H:
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# --------------------------------
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# (3) img_H
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# --------------------------------
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img_H = util.imread_uint(H_paths[idx], n_channels=n_channels)
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img_H = img_H.squeeze()
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# --------------------------------
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# PSNR and SSIM
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# --------------------------------
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psnr = util.calculate_psnr(img_E, img_H, border=border)
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ssim = util.calculate_ssim(img_E, img_H, border=border)
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test_results['psnr'].append(psnr)
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test_results['ssim'].append(ssim)
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logger.info('{:s} - PSNR: {:.2f} dB; SSIM: {:.4f}.'.format(img_name+ext, psnr, ssim))
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util.imshow(np.concatenate([img_E, img_H], axis=1), title='Recovered / Ground-truth') if show_img else None
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# ------------------------------------
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# save results
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# ------------------------------------
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util.imsave(img_E, os.path.join(E_path, img_name+ext))
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if need_H:
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ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
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ave_ssim = sum(test_results['ssim']) / len(test_results['ssim'])
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logger.info('Average PSNR/SSIM(RGB) - {} - PSNR: {:.2f} dB; SSIM: {:.4f}'.format(result_name, ave_psnr, ave_ssim))
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if __name__ == '__main__':
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main()
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