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| import os.path | |
| import logging | |
| import argparse | |
| import numpy as np | |
| from datetime import datetime | |
| from collections import OrderedDict | |
| # from scipy.io import loadmat | |
| import torch | |
| from utils import utils_logger | |
| from utils import utils_model | |
| from utils import utils_image as util | |
| ''' | |
| Spyder (Python 3.6) | |
| PyTorch 1.1.0 | |
| Windows 10 or Linux | |
| Kai Zhang (cskaizhang@gmail.com) | |
| github: https://github.com/cszn/KAIR | |
| https://github.com/cszn/DnCNN | |
| @article{zhang2017beyond, | |
| title={Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising}, | |
| author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei}, | |
| journal={IEEE Transactions on Image Processing}, | |
| volume={26}, | |
| number={7}, | |
| pages={3142--3155}, | |
| year={2017}, | |
| publisher={IEEE} | |
| } | |
| % 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 (12/Dec./2019) | |
| ''' | |
| """ | |
| # -------------------------------------------- | |
| |--model_zoo # model_zoo | |
| |--dncnn_15 # model_name | |
| |--dncnn_25 | |
| |--dncnn_50 | |
| |--dncnn_gray_blind | |
| |--dncnn_color_blind | |
| |--dncnn3 | |
| |--testset # testsets | |
| |--set12 # testset_name | |
| |--bsd68 | |
| |--cbsd68 | |
| |--results # results | |
| |--set12_dncnn_15 # result_name = testset_name + '_' + model_name | |
| |--set12_dncnn_25 | |
| |--bsd68_dncnn_15 | |
| # -------------------------------------------- | |
| """ | |
| def main(): | |
| # ---------------------------------------- | |
| # Preparation | |
| # ---------------------------------------- | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--model_name', type=str, default='dncnn_25', help='dncnn_15, dncnn_25, dncnn_50, dncnn_gray_blind, dncnn_color_blind, dncnn3') | |
| parser.add_argument('--testset_name', type=str, default='set12', help='test set, bsd68 | set12') | |
| parser.add_argument('--noise_level_img', type=int, default=15, help='noise level: 15, 25, 50') | |
| parser.add_argument('--x8', type=bool, default=False, help='x8 to boost performance') | |
| parser.add_argument('--show_img', type=bool, default=False, help='show the image') | |
| parser.add_argument('--model_pool', type=str, default='model_zoo', help='path of model_zoo') | |
| parser.add_argument('--testsets', type=str, default='testsets', help='path of testing folder') | |
| parser.add_argument('--results', type=str, default='results', help='path of results') | |
| parser.add_argument('--need_degradation', type=bool, default=True, help='add noise or not') | |
| parser.add_argument('--task_current', type=str, default='dn', help='dn for denoising, fixed!') | |
| parser.add_argument('--sf', type=int, default=1, help='unused for denoising') | |
| args = parser.parse_args() | |
| if 'color' in args.model_name: | |
| n_channels = 3 # fixed, 1 for grayscale image, 3 for color image | |
| else: | |
| n_channels = 1 # fixed for grayscale image | |
| if args.model_name in ['dncnn_gray_blind', 'dncnn_color_blind', 'dncnn3']: | |
| nb = 20 # fixed | |
| else: | |
| nb = 17 # fixed | |
| result_name = args.testset_name + '_' + args.model_name # fixed | |
| border = args.sf if args.task_current == 'sr' else 0 # shave boader to calculate PSNR and SSIM | |
| model_path = os.path.join(args.model_pool, args.model_name+'.pth') | |
| # ---------------------------------------- | |
| # L_path, E_path, H_path | |
| # ---------------------------------------- | |
| L_path = os.path.join(args.testsets, args.testset_name) # L_path, for Low-quality images | |
| H_path = L_path # H_path, for High-quality images | |
| E_path = os.path.join(args.results, result_name) # E_path, for Estimated images | |
| util.mkdir(E_path) | |
| if H_path == L_path: | |
| args.need_degradation = True | |
| 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) | |
| need_H = True if H_path is not None else False | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # ---------------------------------------- | |
| # load model | |
| # ---------------------------------------- | |
| from models.network_dncnn import DnCNN as net | |
| model = net(in_nc=n_channels, out_nc=n_channels, nc=64, nb=nb, act_mode='R') | |
| # model = net(in_nc=n_channels, out_nc=n_channels, nc=64, nb=nb, act_mode='BR') # use this if BN is not merged by utils_bnorm.merge_bn(model) | |
| 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}'.format(model_path)) | |
| number_parameters = sum(map(lambda x: x.numel(), model.parameters())) | |
| logger.info('Params number: {}'.format(number_parameters)) | |
| test_results = OrderedDict() | |
| test_results['psnr'] = [] | |
| test_results['ssim'] = [] | |
| logger.info('model_name:{}, image sigma:{}'.format(args.model_name, args.noise_level_img)) | |
| logger.info(L_path) | |
| L_paths = util.get_image_paths(L_path) | |
| H_paths = util.get_image_paths(H_path) if need_H else None | |
| for idx, img in enumerate(L_paths): | |
| # ------------------------------------ | |
| # (1) img_L | |
| # ------------------------------------ | |
| img_name, ext = os.path.splitext(os.path.basename(img)) | |
| # logger.info('{:->4d}--> {:>10s}'.format(idx+1, img_name+ext)) | |
| img_L = util.imread_uint(img, n_channels=n_channels) | |
| img_L = util.uint2single(img_L) | |
| if args.need_degradation: # degradation process | |
| np.random.seed(seed=0) # for reproducibility | |
| img_L += np.random.normal(0, args.noise_level_img/255., img_L.shape) | |
| util.imshow(util.single2uint(img_L), title='Noisy image with noise level {}'.format(args.noise_level_img)) if args.show_img else None | |
| img_L = util.single2tensor4(img_L) | |
| img_L = img_L.to(device) | |
| # ------------------------------------ | |
| # (2) img_E | |
| # ------------------------------------ | |
| if not args.x8: | |
| img_E = model(img_L) | |
| else: | |
| img_E = utils_model.test_mode(model, img_L, mode=3) | |
| img_E = util.tensor2uint(img_E) | |
| if need_H: | |
| # -------------------------------- | |
| # (3) img_H | |
| # -------------------------------- | |
| img_H = util.imread_uint(H_paths[idx], n_channels=n_channels) | |
| img_H = img_H.squeeze() | |
| # -------------------------------- | |
| # 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) | |
| logger.info('{:s} - PSNR: {:.2f} dB; SSIM: {:.4f}.'.format(img_name+ext, psnr, ssim)) | |
| util.imshow(np.concatenate([img_E, img_H], axis=1), title='Recovered / Ground-truth') if args.show_img else None | |
| # ------------------------------------ | |
| # save results | |
| # ------------------------------------ | |
| util.imsave(img_E, os.path.join(E_path, img_name+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('Average PSNR/SSIM(RGB) - {} - PSNR: {:.2f} dB; SSIM: {:.4f}'.format(result_name, ave_psnr, ave_ssim)) | |
| if __name__ == '__main__': | |
| main() | |