代码备份：处理 SUN397 的代码，将其分为 80% 训练数据 以及 20% 的测试数据

 

处理SUN397 的代码，将其分为80% 训练数据以及20% 的测试数据

2016-07-27

 1 %% Code for Process SUN397 Scene Classification 
 2 %     Just the a part : 24 kinds and 6169 images total 
 3 %     used for train a initial classifier and predict the additional dataset.
 4 clc; 
 5 impath = '/home/wangxiao/Downloads/SUN397/SUN397/a/';
 6 files = dir(impath); 
 7 label = -1 ; 
 8 
 9 train_fid = fopen('/home/wangxiao/Downloads/SUN397/selected_sun/train_list.txt', 'a');
10 test_fid   = fopen('/home/wangxiao/Downloads/SUN397/selected_sun/test_list.txt', 'a');
11 
12 train_im_savePath = '/home/wangxiao/Downloads/SUN397/selected_sun/train_images/' ; 
13 test_im_savePath = '/home/wangxiao/Downloads/SUN397/selected_sun/test_images/' ; 
14 
15 for i = 3:size(files, 1)
16 %     disp( [' ==> disp current ', num2str(i-2), '/', num2str(size(files, 1) - 2) , ' waiting . . . ' ]) ; 
17     label = label + 1; 
18     category = files(i).name ; 
19     newPath = [impath, category, '/'] ; 
20     images = dir([newPath, '*.jpg']) ;
21     
22     for j = 1:size(images, 1)
23         disp( [' ==> deal with Class: ', num2str(i-2), '        ==> disp image:  ', num2str(j), '/', num2str(size(images, 1) - 2) , ' waiting . . . ' ]) ; 
24         num_per_kind = size(images, 1) - 2 ; 
25         random_num = randperm(size(images, 1)) ;
26         
27         num_train = round( num_per_kind * 0.8 ) ;     %% number of train data 
28         num_test   = round ( num_per_kind * 0.2 )  ;   %% number of test data 
29          
30         %% train data 
31        
32         if j <=  num_train
33              
34             idx = random_num(j) ; 
35             trainImage_name =  images(idx).name ;  
36             im = imread([newPath, trainImage_name]);
37             im = imresize(im, [256, 256]) ; 
38             imwrite( im, [train_im_savePath, trainImage_name]) ; 
39             fprintf(train_fid, '%s ' ,  num2str(trainImage_name) ) ; 
40             fprintf(train_fid, '%s ', ' ') ; 
41             fprintf(train_fid, '%s \n', num2str(label)) ; 
42         else
43             if j <  num_per_kind
44                 idx2 = random_num(j) ; 
45                 testImage_name =  images(idx2).name ;  
46                 im2 = imread([newPath, testImage_name]);
47                 im2 = imresize(im2, [227, 227]) ; 
48                 imwrite( im2, [test_im_savePath, testImage_name]) ; 
49                 fprintf(test_fid, '%s ' ,  num2str(testImage_name) ) ; 
50                 fprintf(test_fid, '%s ', ' ') ; 
51                 fprintf(test_fid, '%s \n', num2str(label)) ; 
52             else
53                 break; 
54             end
55         end 
56         
57         
58  
59         
60   
61         
62     end
63     
64 end
 

　　

 

 

 

path = '/home/wangxiao/Downloads/SUN397/Sun-100/';
file1 = importdata([path, 'Sun_100_Labeled_Train_0.5_.txt' ]);
file2 = importdata([path, 'Sun_100_UnLabel_Train_0.5_.txt' ]); 
file3 = importdata([path, 'Sun_100_Test_0.5_.txt' ]);

%% return the index of searched vector.  
[C, ia, ic] = unique(file1.data) ; 
labelMatrix = zeros(size(file1.data)) ;     
for i = 1:size(ia, 1)
    count = i-1; 
    index_1 = ia(i, 1) ; % start index 
    index_2 = ia(i+1, 1) ; % end index
    labelMatrix(index_1:index_2, 1) = count ;
end
% select 80 classes.
    select_labelMatrix = labelMatrix(1:9060) ;



%% return the index of searched vector.  
[C, ia, ic] = unique(file2.data) ; 
labelMatrix = zeros(size(file2.data)) ;     
for i = 1:size(ia, 1)
    count = i-1; 
    index_1 = ia(i, 1) ; % start index 
    index_2 = ia(i+1, 1) ; % end index
    labelMatrix(index_1:index_2, 1) = count ;
end
% select 80 classes.
     select_labelMatrix_2 = labelMatrix(1:9180) ;



%% return the index of searched vector.  
[C, ia, ic] = unique(file3.data) ; 
labelMatrix = zeros(size(file3.data)) ;     
for i = 1:size(ia, 1)
    count = i-1; 
    index_1 = ia(i, 1) ; % start index 
    index_2 = ia(i+1, 1) ; % end index
    labelMatrix(index_1:index_2, 1) = count ;
end
% select 80 classes.
    select_labelMatrix_3 = labelMatrix(1:4560) ;

%% save the selected 80 classes into txt files. 
savePath = '/home/wangxiao/Downloads/SUN397/Sun-100/';
fid1 = fopen([savePath, 'Sun_80_50%_Labeled_data.txt'], 'a');
fid2 = fopen([savePath, 'Sun_80_50%_Unlabeled_data.txt'], 'a');
fid3 = fopen([savePath, 'Sun_80_50%_test_data.txt'], 'a');

for i = 1:size(select_labelMatrix, 1)
    imageName = file1.textdata{i, 1} ; 
    imageLabel = select_labelMatrix(i, 1) ; 
    fprintf(fid1, '%s ', num2str(imageName)) ; 
    fprintf(fid1, '%s\n ', num2str(imageLabel)) ; 
end

for i = 1:size(select_labelMatrix_2, 1)
    imageName = file2.textdata{i, 1} ; 
    imageLabel = select_labelMatrix_2(i, 1) ; 
    fprintf(fid2, '%s ', num2str(imageName)) ; 
    fprintf(fid2, '%s\n ', num2str(imageLabel)) ; 
end

for i = 1:size(select_labelMatrix_3, 1)
    imageName = file3.textdata{i, 1} ; 
    imageLabel = select_labelMatrix_3(i, 1) ; 
    fprintf(fid3, '%s ', num2str(imageName)) ; 
    fprintf(fid3, '%s\n ', num2str(imageLabel)) ; 
end