import os.path import logging import numpy as np 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 https://github.com/cszn/FFDNet @article{zhang2018ffdnet, title={FFDNet: Toward a fast and flexible solution for CNN-based image denoising}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, journal={IEEE Transactions on Image Processing}, volume={27}, number={9}, pages={4608--4622}, year={2018}, 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 |--fdncnn_color # model_name, for color images |--fdncnn_gray |--fdncnn_color_clip # for clipped uint8 color images |--fdncnn_gray_clip |--testset # testsets |--set12 # testset_name |--bsd68 |--cbsd68 |--results # results |--set12_fdncnn_color # result_name = testset_name + '_' + model_name |--set12_fdncnn_gray |--cbsd68_fdncnn_color_clip # -------------------------------------------- """ def main(): # ---------------------------------------- # Preparation # ---------------------------------------- noise_level_img = 15 # noise level for noisy image noise_level_model = noise_level_img # noise level for model model_name = 'fdncnn_gray' # 'fdncnn_gray' | 'fdncnn_color' | 'fdncnn_color_clip' | 'fdncnn_gray_clip' testset_name = 'bsd68' # test set, 'bsd68' | 'cbsd68' | 'set12' need_degradation = True # default: True x8 = False # default: False, x8 to boost performance show_img = False # default: Falsedefault: False task_current = 'dn' # 'dn' for denoising | 'sr' for super-resolution sf = 1 # unused for denoising if 'color' in model_name: n_channels = 3 # 3 for color image else: n_channels = 1 # 1 for grayscale image if 'clip' in model_name: use_clip = True # clip the intensities into range of [0, 1] else: use_clip = False model_pool = 'model_zoo' # fixed testsets = 'testsets' # fixed results = 'results' # fixed result_name = testset_name + '_' + model_name border = sf if task_current == 'sr' else 0 # shave boader to calculate PSNR and SSIM model_path = os.path.join(model_pool, model_name+'.pth') # ---------------------------------------- # L_path, E_path, H_path # ---------------------------------------- L_path = os.path.join(testsets, testset_name) # L_path, for Low-quality images H_path = L_path # H_path, for High-quality images E_path = os.path.join(results, result_name) # E_path, for Estimated images util.mkdir(E_path) if H_path == L_path: 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 FDnCNN as net model = net(in_nc=n_channels+1, out_nc=n_channels, nc=64, nb=20, act_mode='R') 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:{}, model sigma:{}, image sigma:{}'.format(model_name, noise_level_img, noise_level_model)) 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 need_degradation: # degradation process np.random.seed(seed=0) # for reproducibility img_L += np.random.normal(0, noise_level_img/255., img_L.shape) if use_clip: img_L = util.uint2single(util.single2uint(img_L)) util.imshow(util.single2uint(img_L), title='Noisy image with noise level {}'.format(noise_level_img)) if show_img else None img_L = util.single2tensor4(img_L) noise_level_map = torch.ones((1, 1, img_L.size(2), img_L.size(3)), dtype=torch.float).mul_(noise_level_model/255.) img_L = torch.cat((img_L, noise_level_map), dim=1) img_L = img_L.to(device) # ------------------------------------ # (2) img_E # ------------------------------------ if not 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 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()