MATCONVNET/cnn_setup_data.m at master · YunpengZhai/MATCONVNET · GitHub

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function imdb =cnn_setup_data(datadir)

inputSize =[64,64];
subdir=dir(datadir);
imdb.images.data=[];
imdb.images.labels=[];
imdb.images.set = [] ;
imdb.meta.sets = {'train', 'val', 'test'} ;
image_counter=0;
trainratio=0.8;
for i=3:length(subdir)
    imdb.meta.classes(i-2) = {subdir(i).name};
	imgfiles=dir(fullfile(datadir,subdir(i).name));
	imgpercategory_count=length(imgfiles)-2;
	disp([i-2 imgpercategory_count]);
	image_counter=image_counter+imgpercategory_count;
	for j=3:length(imgfiles)
        img=imread(fullfile(datadir,subdir(i).name,imgfiles(j).name));
        img=imresize(img, inputSize(1:2));
        img=single(img);
        imdb.images.data(:,:,:,end+1)=single(img);
        imdb.images.labels(end+1)= i-2;
        if j-2<imgpercategory_count*trainratio
            imdb.images.set(end+1)=1;
        else
            imdb.images.set(end+1)=3;
        end
	end
end

dataMean=mean(imdb.images.data,4);
imdb.images.data = single(bsxfun(@minus,imdb.images.data, dataMean)) ;
imdb.images.data_mean = single(dataMean);%!!!!!!!!!!!
end