UMSN Files
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@@ -12,8 +12,13 @@ We propose a novel multi-stream architecture and training methodology that explo
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2. Python 2 or 3
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2. Python 2 or 3
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3. CPU or NVIDIA GPU + CUDA CuDNN (CUDA 8.0)
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3. CPU or NVIDIA GPU + CUDA CuDNN (CUDA 8.0)
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## To test UMRL:
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## To test UMRL:
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python test_face_deblur.py --dataroot ./facades/github/ --valDataroot <path_to_test_data> --netG ./pretrained_models/Deblur_epoch_Best.pth
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1. Download test datasets provided the authors of Ziyi et al.
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- https://sites.google.com/site/ziyishenmi/cvpr18_face_deblur
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2. run test_data_generation.m
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- It renames the files counting from example 000001.png
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3. python test_face_deblur.py --dataroot ./facades/github/ --valDataroot <path_to_test_data> --netG ./pretrained_models/Deblur_epoch_Best.pth
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## To train UMRL:
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## To train UMRL:
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python train_face_deblur.py --dataroot <path_to_train_data> --valDataroot ./facades/github/ --exp ./face_deblur --batchSize 10
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python train_face_deblur.py --dataroot <path_to_train_data> --valDataroot ./facades/github/ --exp ./face_deblur --batchSize 10
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@@ -0,0 +1,31 @@
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close all;
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clear all;
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clc;
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%Download Test Data provided by authors Ziyi et al. from,
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%https://sites.google.com/site/ziyishenmi/cvpr18_face_deblur
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listinfo = dir('./Test_data_Helen/final_Helen_gt');
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m = length(listinfo);
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count =1;
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for i = 3:m
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imgname = strcat('./Test_data_Helen/final_Helen_gt','/',listinfo(i).name);
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tmp = listinfo(i).name;
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tmp1 = split(tmp,'.');
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I = imread(imgname);
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im_name = tmp1{1};
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for j = 1:10
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for k = 13:2:27
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blrname = strcat('./Test_data_Helen/final_Helen_blur/',im_name,'_ker',num2str(j,'%02d'),'_blur_k',num2str(k),'.png');
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B = imread(blrname);
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blrname = strcat('./final_Helen_result/',im_name,'_ker',num2str(j,'%02d'),'_blur_k',num2str(k),'_random.png');
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S = imread(blrname);
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filename = strcat('./Testh_gt/',num2str((count),'%06d'),'.png');
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imwrite(I,filename);
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filename = strcat('./Testh/',num2str((count),'%06d'),'.png');
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imwrite(B,filename);
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filename = strcat('./Testh_st/',num2str((count),'%06d'),'.png');
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imwrite(S,filename);
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count = count+1;
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end
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end
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i
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end
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