function createTrainingSet(featureType, vecFileImages)
% This function makes the feature vector and saves it in the .mat directory


% Version 0.1, Feb 2006.
% This code is copyrighted by Ashutosh Saxena, Stanford University
% It is available for non-commercial use only. For commericial use, please
% contact the authors.
% Any publication or report resulting from Use of this code, should cite:
% Learning Depth from Single Monocular Images, Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng, NIPS 2005.
% 3-D Depth Reconstruction from a Single Still Image, Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng, To appear in IJCV 2007.
% For more info and dataset, visit: http://ai.stanford.edu/~asaxena/learningdepth/
% For a better version of the algorithm, also visit: 
% http://ai.stanford.edu/~asaxena/reconstruction3d/




global imageDirectory depthMapDirectory featureDirectory
global scale2Factor
global nGridCol nGridRow
global nDim nLaw

scales = [1 2 5 -1 -2];
%resizing_factor = scale*2-1

if nargin < 1
    featureType = 'laws';
end


%======initializing===========
configurationFile;

dirList = dir([imageDirectory 'img-*.jpg']);
imageFilename = dirList(1).name;

inputImage = imread([imageDirectory imageFilename]);
inputImage = permute(inputImage, [2 1 3]);
inputImage = inputImage(:,end:-1:1,:);
load( [depthMapDirectory 'depth' imageFilename(4:(end-4)) '.mat']);
configurationFile(inputImage, depthMap);

if nargin < 2
    vecFileImages = 1:length(dirList);
    length(dirList);
end

allFeatureVector = zeros(nGridRow,nGridCol,nDim*(length(scales)-1), length(vecFileImages));
imageFeatureVector = zeros(1,1,nDim, length(vecFileImages) );
%appenedFeatureVector = zeros(nGridRow, nGridCol, nDim);

dataNumber = 0;
for n=vecFileImages %1:length(dirList)
    imageFilename = dirList(n).name;
    inputImage = imread([imageDirectory imageFilename]);
    inputImage = permute(inputImage(end:-1:1,:,:), [2 1 3]);
    %inputImage = inputImage(:,end:-1:1,:);
        
    %imshow(inputImage);
    %featureVector = makeFeatureVector(inputImage, featureType);
    
    %=========using multiscale feature vector ===========
    featureVector = makeHybridFeatureVector(inputImage, featureType, 2, 30, 50, scales);
    
    dataNumber = dataNumber + 1;
    disp(dataNumber);
    
    %=======appending the feature vector with its 4-neighbors=======
    %for r = 1:nGridRow
    %    r_minus = max(1,r-1);
    %    r_plus  = min(nGridRow,r+1);
    %    
    %    for c = 2:(nGridCol-1)
    %        appendedFeatureVector(r,c,:) = cat(3, featureVector(r,c,:), featureVector(r_minus,c,:), featureVector(r_plus,c,:), ...
    %            featureVector(r,c-1,:), featureVector(r,c+1,:) );
    %    end
    %    appendedFeatureVector(r,1,:) = cat(3, featureVector(r,1,:), featureVector(r_minus,1,:), featureVector(r_plus,1,:), ...
    %            featureVector(r,1,:), featureVector(r,1+1,:) );
    %    appendedFeatureVector(r,nGridCol,:) = cat(3, featureVector(r,nGridCol,:), featureVector(r_minus,nGridCol,:), featureVector(r_plus,nGridCol,:), ...
    %            featureVector(r,nGridCol-1,:), featureVector(r,nGridCol,:) );
    %end
    
    %===============================================================
    %size(appendedFeatureVector);
    %allFeatureVector(:,:,:,dataNumber) = appendedFeatureVector;
    allFeatureVector(:,:,:,dataNumber) = featureVector(:,:,1:(nDim*(length(scales)-1))  );
    imageFeatureVector(1,1,:,dataNumber) = featureVector(1,1,(1:nDim) + nDim*(length(scales)-1));
    clear featureVector;
    %clear appendedFeatureVector;
    
    load( [depthMapDirectory 'depth' imageFilename(4:(end-4)) '.mat']);
    if size(depthMap,1) ~= nGridRow
        depthMap = imresize(depthMap, [nGridRow, nGridCol], 'nearest');
    end
    allDepthMaps(:,:,dataNumber) = log(depthMap);
    
    %imshow(inputImage); axis square;
    %figure, imagesc(depthMap); axis square;
    
    %size(allFeatureVector)
    %size(imageFeatureVector)
end

allFeatureVector = allFeatureVector(:,:,:,1:dataNumber);
allDepthmaps = allDepthMaps(:,:,1:dataNumber);
save( [featureDirectory 'allTrainingDepthData' num2str(vecFileImages(1)) '.mat'], 'allDepthMaps');
save( [featureDirectory 'allTrainingFeatureData_image' num2str(vecFileImages(1)) '.mat'], 'imageFeatureVector');
for r=1:nGridRow
    rowFeatureVector = allFeatureVector(r,:,:,:);
    save( [featureDirectory 'allTrainingFeatureData_row' num2str(r) '_' num2str(vecFileImages(1)) '.mat'], 'rowFeatureVector');
end

return;