234 lines
8.8 KiB
Python
234 lines
8.8 KiB
Python
import os.path
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import logging
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import re
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import numpy as np
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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_deblur
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from utils import utils_sisr as sr
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from utils import utils_logger
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from utils import utils_image as util
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from utils import utils_model
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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/SRMD
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@inproceedings{zhang2018learning,
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title={Learning a single convolutional super-resolution network for multiple degradations},
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author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
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booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
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pages={3262--3271},
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year={2018}
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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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|--srmdnf_x2 # model_name, for noise-free LR image SR
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|--srmdnf_x3
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|--srmdnf_x4
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|--srmd_x2 # model_name, for noisy LR image
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|--srmd_x3
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|--srmd_x4
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|--testset # testsets
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|--set5 # testset_name
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|--srbsd68
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|--results # results
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|--set5_srmdnf_x2 # result_name = testset_name + '_' + model_name
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|--set5_srmdnf_x3
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|--set5_srmdnf_x4
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|--set5_srmd_x2
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|--srbsd68_srmd_x2
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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 = 0 # default: 0, noise level for LR image
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noise_level_model = noise_level_img # noise level for model
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model_name = 'srmdnf_x4' # 'srmd_x2' | 'srmd_x3' | 'srmd_x4' | 'srmdnf_x2' | 'srmdnf_x3' | 'srmdnf_x4'
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testset_name = 'set5' # test set, 'set5' | 'srbsd68'
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sf = [int(s) for s in re.findall(r'\d+', model_name)][0] # scale factor
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x8 = False # default: False, x8 to boost performance
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need_degradation = True # default: True, use degradation model to generate LR image
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show_img = False # default: False
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srmd_pca_path = os.path.join('kernels', 'srmd_pca_matlab.mat')
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task_current = 'sr' # 'dn' for denoising | 'sr' for super-resolution
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n_channels = 3 # fixed
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in_nc = 18 if 'nf' in model_name else 19
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nc = 128 # fixed, number of channels
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nb = 12 # fixed, number of conv layers
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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
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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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from models.network_srmd import SRMD as net
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model = net(in_nc=in_nc, out_nc=n_channels, nc=nc, nb=nb, upscale=sf, act_mode='R', upsample_mode='pixelshuffle')
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model.load_state_dict(torch.load(model_path), strict=False)
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model.eval()
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for k, 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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test_results['psnr_y'] = []
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test_results['ssim_y'] = []
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logger.info('model_name:{}, model sigma:{}, image sigma:{}'.format(model_name, noise_level_img, noise_level_model))
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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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# ----------------------------------------
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# kernel and PCA reduced feature
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# ----------------------------------------
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# kernel = sr.anisotropic_Gaussian(ksize=15, theta=np.pi, l1=4, l2=4)
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kernel = utils_deblur.fspecial('gaussian', 15, 0.01) # Gaussian kernel, delta kernel 0.01
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P = loadmat(srmd_pca_path)['P']
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degradation_vector = np.dot(P, np.reshape(kernel, (-1), order="F"))
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if 'nf' not in model_name: # noise-free SR
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degradation_vector = np.append(degradation_vector, noise_level_model/255.)
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degradation_vector = torch.from_numpy(degradation_vector).view(1, -1, 1, 1).float()
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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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# degradation process, blur + bicubic downsampling + Gaussian noise
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if need_degradation:
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img_L = util.modcrop(img_L, sf)
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img_L = sr.srmd_degradation(img_L, kernel, sf) # equivalent to bicubic degradation if kernel is a delta kernel
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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='LR 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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degradation_map = degradation_vector.repeat(1, 1, img_L.size(-2), img_L.size(-1))
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img_L = torch.cat((img_L, degradation_map), dim=1)
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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, sf=sf)
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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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img_H = util.modcrop(img_H, sf)
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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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if np.ndim(img_H) == 3: # RGB image
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img_E_y = util.rgb2ycbcr(img_E, only_y=True)
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img_H_y = util.rgb2ycbcr(img_H, only_y=True)
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psnr_y = util.calculate_psnr(img_E_y, img_H_y, border=border)
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ssim_y = util.calculate_ssim(img_E_y, img_H_y, border=border)
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test_results['psnr_y'].append(psnr_y)
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test_results['ssim_y'].append(ssim_y)
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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+'.png'))
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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) - {} - x{} --PSNR: {:.2f} dB; SSIM: {:.4f}'.format(result_name, sf, ave_psnr, ave_ssim))
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if np.ndim(img_H) == 3:
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ave_psnr_y = sum(test_results['psnr_y']) / len(test_results['psnr_y'])
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ave_ssim_y = sum(test_results['ssim_y']) / len(test_results['ssim_y'])
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logger.info('Average PSNR/SSIM( Y ) - {} - x{} - PSNR: {:.2f} dB; SSIM: {:.4f}'.format(result_name, sf, ave_psnr_y, ave_ssim_y))
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if __name__ == '__main__':
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main()
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