function [featureVector] = makeScaledFeatureTexture(img, type, scale, numberOfStatistics)
% 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 columnWidth nGridCol nGridRow rowWidth
global scale2Factor
global nDim nLaw nHarris

if nargin < 4
    numberOfStatistics = 1;
end

if nargin < 3
	scale = 1;
end
if nargin < 2
	type = 'laws';
end

nGCol = nGridCol;
nGRow = nGridRow;

numoverlaps = 0;

% If you want to work with
% reduced image size, just
% scale the image appropriately
% below.

numoverlaps = scale - 1;
edgefactor = (2*numoverlaps + 1);

% Assume that the image is correctly oriented.
imheight = size(img, 1);
imwidth  = size(img, 2);
% Step sizes in x and y
stepwidth  = imwidth/nGridCol;
stepheight = imheight/nGridRow;

%===========calculating filter banks
[H2, H4, H1] = calculateFilterBanks(img, type);

nDim = size(H2, 3) + 0;
%forming spatial masks

%=========================================
%featureVectorTmp = zeros( nGRow, nGCol, 2*nLaw+3*nHarris);
featureVectorTmp = zeros( nGRow, nGCol, nDim);

for g = 1:nGRow
    for c = 1:nGCol
	gridLeft  = round( (g - 1 - numoverlaps)*stepheight + 1);
	gridRight = round( (g + numoverlaps)*stepheight );
	normfactor = 1.0;
	if gridLeft < 1
		gridLeft = 1;
		normfactor = normfactor * edgefactor / (numoverlaps + g);
	elseif gridRight > (nGRow * stepheight)
		gridRight = round(nGRow*stepheight);
		normfactor = normfactor * edgefactor ...
			/ (numoverlaps + nGRow - g + 1);
	end
	gridTop = round( (c - 1 - numoverlaps)*stepwidth + 1);
	gridBot = round( (c + numoverlaps)*stepwidth );
	if gridTop < 1
		gridTop = 1;
		normfactor = normfactor * edgefactor / (numoverlaps + c);
	elseif gridBot > (nGCol * stepwidth)
		gridBot = round(nGCol*stepwidth);
		normfactor = normfactor * edgefactor ...
			/ (numoverlaps + nGCol - c + 1);
	end

        %LAWS-----------
        if nLaw
            featureVectorTmp(g,c,1:nLaw) = sum( sum( H2( gridLeft:gridRight, gridTop:gridBot, :), 2), 1);
%            featureVectorTmp(g,c,nLaw+(1:nLaw)) = sum( sum( H4( gridLeft:gridRight, gridTop:gridBot, 1:nLaw), 2), 1);
        end
        if nHarris
            %Can be optimized
            for row = gridLeft:gridRight
                for col = gridTop:gridBot;
                    featureVectorTmp(g,c,2*nLaw+angleImage(row,col) ) = featureVectorTmp(g,c,2*nLaw+angleImage(row,col) ) + ...
                                            gradientMagImage(row,col);
                    %featureVectorTmp(g,c,2*nLaw+nHarris+angleImage(row,col) ) = featureVectorTmp(g,c,2*nLaw+nHarris+angleImage(row,col) ) + ...
                                            gradientMagImage(row,col) .* (2*col<(gridTop+gridBot));
                    %featureVectorTmp(g,c,2*nLaw+2*nHarris+angleImage(row,col) ) = featureVectorTmp(g,c,2*nLaw+2*nHarris+angleImage(row,col) ) + ...
                                            gradientMagImage(row,col) .* (2*col>=(gridTop+gridBot));
                end
            end
            featureVectorTmp(g,c,2*nLaw+(1:nHarris) ) = featureVectorTmp(g,c,2*nLaw+(1:nHarris) ) ./ ...
                (max( featureVectorTmp(g,c,2*nLaw+(1:nHarris) ) ) + upsilon);
            %featureVectorTmp(g,c,2*nLaw+nHarris+(1:nHarris) ) = featureVectorTmp(g,c,2*nLaw+nHarris+(1:nHarris) ) ./ ...
            %    (max( featureVectorTmp(g,c,2*nLaw+nHarris+(1:nHarris) ) ) + upsilon);
            %featureVectorTmp(g,c,2*nLaw+2*nHarris+(1:nHarris) ) = featureVectorTmp(g,c,2*nLaw+2*nHarris+(1:nHarris) ) ./ ...
            %    (max( featureVectorTmp(g,c,2*nLaw+2*nHarris+(1:nHarris) ) ) + upsilon);
        end

        % normalize
	featureVectorTmp(g,c,1:nLaw) = normfactor*featureVectorTmp(g,c,1:nLaw);
    end
end
 
featureVector = featureVectorTmp;
%permute( featureVectorTmp, [3 1 2]);
%featureVector = reshape( featureVector3, [size(featureVector2,1)*size(featureVector3,2) nGridCol] );
%    if displayFlag
%        figure,
%        subplot(2,2,1), imshow(img);
%        subplot(2,2,2), imagesc(ycbcr_img(:,:,1));
%        subplot(2,2,4), imagesc(ycbcr_img(:,:,2));
%        subplot(2,2,3); plot( squeeze(featureVector(frame,:,:)) );
%        axis tight;
%    end
return;