I am using EsaRahtu algorithm for object detection which is available in MATLAB, but when I run it it is giving me error

This is code.......

function [salMat,salMatInd]=saliencyMeasure(dataCell)
% function [salMat,salMatInd]=saliencyMeasure(dataCell) computes saliency measure values for given image.
% 
% Inputs:
% dataCell = Cell array containing the input information channels. With current configuration four choices are supported
%            dataCell={L}, dataCell={L,absFlow}, dataCell={L,a,b}, and dataCell={L,a,b,absFlow}, where L,a,b correspond to
%            Lab-color channels and absFlow is an absolute value of optical flow. For other choices you must change configuration.
%            In addition 2D-distributions are also supported (need change in config e.g. numBins). Then input is e.g. dataCell={L,ab}, where 
%            ab=cat(3,a,b); 
%
% Outputs:
% salMat    =  Matrix containing saliency measure values for each pixel
% salMatInd =  Cell array containin saliency measure values per each individual information channel. 
%              (Computed automatically if two output arguments defined, otherwise not computed (faster))
%
% Example usage:
img=imread('C:\Users\hp\Documents\MATLAB\project images\sunflower6.jpg');
[L,a,b]=RGB2Lab(img); % You need some function RGB2Lab to perform the conversion
salMat=saliencyMeasure({L,a,b});
% or
% >>[salMat,salMatInd]=saliencyMeasure({L,a,b});
%
% Copyright 2010 Esa Rahtu
% Matlab implementation for illustrative purposes.

%%Default parameters
% General config
% Window parameters (relative to max(imRow,imCol))
config.windowRows=[0.25 0.3 0.5 0.7]; % Window row size relative to image size max(imRow,imCol))
config.windowCols=[0.1 0.3 0.5 0.4]; % Window column size relative to image size max(imRow,imCol))
config.sampleStep=[0.01 0.015 0.03 0.04]; % Window sampling step relative to image size max(imRow,imCol)) (one for each window size) 

% Saliency detector parameters (borders relative to max(imRow,imCol))
config.verticalBorder=0.1; % Vertical border size relative to image size
config.horizontalBorder=0.1; % Horizontal border size relative to image size
config.pH1=0.2; % pH1 parameter (pH0=1-pH1)

% Feature related config (assumes input {L,a,b,absFlow}}
% L channel
featConf(1).numBin=60; % Number of bins in histogram (if 2D histogram are used, numBin=[numBin1, numBin2];)
featConf(1).minVal=0;  % Minimum feature value
featConf(1).maxVal=255; % Maximum feature value
featConf(1).smoothFactor=5; % Smoothing factor
% a and b channels    
featConf(2).numBin=60;
featConf(2).minVal=-20;
featConf(2).maxVal=20;
featConf(2).smoothFactor=5;
featConf(3)=featConf(2);
% Motion flow channel
featConf(4).numBin=15;
featConf(4).minVal=0;
featConf(4).maxVal=15;
featConf(4).smoothFactor=1;

% Other
config.verbose=2; %0->show nothing, 1->display processing msgs, 2->display also result images
config.flipBox=0; % Turn window according to image aspect ratio
%config.compIndSal=0; % Compute also saliency maps for individual feature channels (1 if 2 output arguments defined, 0 otherwise)
config.fullImage=[0 0 0 2]; % 0-> whole window inside image, 1-> kernel inside image, 2->full image. Corresponds to each feature in dataCell 

%%Assume following automatic configuration settings
% length(dataCell==4) % Assume input {L,a,b,absFlow}
% length(dataCell==3) % Assume input {L,a,b}
% length(dataCell==2) % Assume input {L,absFlow}
% length(dataCell==1) % Assume input {L}
if length(dataCell)==2
    featConf(2)=featConf(4);
    config.fullImage=[0 2];
end

%%Initialize
config.numRow=size(dataCell{1},1);
config.numCol=size(dataCell{1},2);
config.numFeat=length(dataCell);
if config.numCol>config.numRow && config.flipBox==1
    tmp=config.windowRows; config.windowRows=config.windowCols; config.windowCols=tmp;
end   
config=parseConfiguration_(config);
numFeat=length(dataCell);
numRow=size(dataCell{1},1);
numCol=size(dataCell{1},2);
salMat=zeros(numRow,numCol,length(config.windowRows));
intHist=cell(1,numFeat);
smoothingKernel=cell(1,numFeat);
salMatInd=cell(1,numFeat);
if nargout<2
    config.compIndSal=0;
else
    config.compIndSal=1;
end

%%Compute integral histogram image
for i=1:numFeat
    [intHist{i},dataCell{i}]=formIntegralHistogram_(dataCell{i},featConf(i).numBin,featConf(i).minVal,featConf(i).maxVal);
    smoothingKernel{i}=getSmoothKernel_(featConf(i).smoothFactor);
    if config.compIndSal>0
        salMatInd{i}=zeros(numRow,numCol);
    end
end

%%Run trough image using all sliding windows sizes
for winSizeId=1:length(config.windowRows)

      if config.verbose>=1
          fprintf('Kernel %i x %i, borders %i x %i, step %i.\n',config.windowRows(winSizeId)-2*config.verticalBorder(winSizeId),config.windowCols(winSizeId)-2*config.horizontalBorder(winSizeId),config.verticalBorder(winSizeId),config.horizontalBorder(winSizeId),config.sampleStep(winSizeId));
      end

    % Initialize window parameters
    halfWinRow=floor(config.windowRows(winSizeId)/2);
    halfWinCol=floor(config.windowCols(winSizeId)/2);
    halfKerRow=halfWinRow-config.verticalBorder(winSizeId);
    halfKerCol=halfWinCol-config.horizontalBorder(winSizeId);
    kernelRowIdx=-halfKerRow:halfKerRow;
    kernelColIdx=-halfKerCol:halfKerCol;

    % Initialize window positions
    if sum(config.fullImage(1:numFeat))>0
        rowCent=(config.sampleStep(winSizeId)+1):config.sampleStep(winSizeId):numRow;
        colCent=(config.sampleStep(winSizeId)+1):config.sampleStep(winSizeId):numCol;
        rowCent(rowCent>(numRow))=[];
        colCent(colCent>(numCol))=[];
        numWindow=length(rowCent)*length(colCent);
    else
        rowCent=(halfWinRow+1):config.sampleStep(winSizeId):numRow;
        colCent=(halfWinCol+1):config.sampleStep(winSizeId):numCol;
        rowCent(rowCent>(numRow-halfWinRow))=[];
        colCent(colCent>(numCol-halfWinCol))=[];
        numWindow=length(rowCent)*length(colCent);
    end

    % Initialize a priori probabilities
    pH1=config.pH1;
    pH0=1-pH1;

    % Loop trough all windows and calculate saliency
    cnt=1;
    for winRowPos=rowCent
        for winColPos=colCent

            % Display info if needed
            if (cnt>1 && config.verbose>=1)
                fprintf('\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bWindow %0.6i of %0.6i',cnt,numWindow);
            elseif(config.verbose>=1)
                fprintf('Window %0.6i of %0.6i',cnt,numWindow);
            end
            cnt=cnt+1;

            % Initialize critical points
            k11=[winRowPos-halfKerRow-1,winColPos-halfKerCol-1]+1; 
            k12=[winRowPos-halfKerRow-1,winColPos+halfKerCol]+1;
            k21=[winRowPos+halfKerRow,winColPos-halfKerCol-1]+1;
            k22=[winRowPos+halfKerRow,winColPos+halfKerCol]+1;
            w11=[winRowPos-halfWinRow-1,winColPos-halfWinCol-1]+1;
            w12=[winRowPos-halfWinRow-1,winColPos+halfWinCol]+1;
            w21=[winRowPos+halfWinRow,winColPos-halfWinCol-1]+1;
            w22=[winRowPos+halfWinRow,winColPos+halfWinCol]+1;

            if sum([k11<1 k12(1)<1 k12(2)>(numCol+1) k21(1)>(numRow+1) k21(2)<1 k22(1)>(numRow+1) k22(2)>(numCol+1)])>0
                innerWindowFlag=2;
            elseif sum([w11<1 w12(1)<1 w12(2)>(numCol+1) w21(1)>(numRow+1) w21(2)<1 w22(1)>(numRow+1) w22(2)>(numCol+1)])>0
                innerWindowFlag=1;
            else
                innerWindowFlag=0;
            end

            k11=min(max(k11,[1,1]),[numRow+1,numCol+1]);
            k12=min(max(k12,[1,1]),[numRow+1,numCol+1]);
            k21=min(max(k21,[1,1]),[numRow+1,numCol+1]);
            k22=min(max(k22,[1,1]),[numRow+1,numCol+1]);

            w11=min(max(w11,[1,1]),[numRow+1,numCol+1]);
            w12=min(max(w12,[1,1]),[numRow+1,numCol+1]);
            w21=min(max(w21,[1,1]),[numRow+1,numCol+1]);
            w22=min(max(w22,[1,1]),[numRow+1,numCol+1]);

            featureRowIndex=kernelRowIdx+winRowPos;
            featureRowIndex(featureRowIndex<1 | featureRowIndex>numRow)=[];
            featureColIndex=kernelColIdx+winColPos;
            featureColIndex(featureColIndex<1 | featureColIndex>numCol)=[];

            % Calculate histograms
            PrH1=pH1;
            PrH0=pH0;
            for i=1:numFeat
                if innerWindowFlag==0 || config.fullImage(i)>=innerWindowFlag
                    pX_H1=double(intHist{i}(k22(1),k22(2),:)+intHist{i}(k11(1),k11(2),:)-intHist{i}(k12(1),k12(2),:)-intHist{i}(k21(1),k21(2),:));
                    pX_H0=double(intHist{i}(w22(1),w22(2),:)+intHist{i}(w11(1),w11(2),:)-intHist{i}(w12(1),w12(2),:)-intHist{i}(w21(1),w21(2),:))-pX_H1;

                    pX_H1=filterDistribution_(smoothingKernel{i},pX_H1(:)',featConf(i).numBin);
                    pX_H0=filterDistribution_(smoothingKernel{i},pX_H0(:)',featConf(i).numBin);

                    PrX_H1=pX_H1(dataCell{i}(featureRowIndex,featureColIndex));
                    PrX_H0=pX_H0(dataCell{i}(featureRowIndex,featureColIndex));

                    PrH1=PrH1.*PrX_H1;
                    PrH0=PrH0.*PrX_H0;

                    if config.compIndSal>0
                        PrH1_X_perFeat=(PrX_H1*pH1)./(PrX_H1*pH1+PrX_H0*pH0);
                        salMatInd{i}(featureRowIndex,featureColIndex)=max(PrH1_X_perFeat,salMatInd{i}(featureRowIndex,featureColIndex));
                    end
                end
            end

            PrH1_X=PrH1./(PrH1+PrH0);

            % Read saliency values for datapoints
            salMat(featureRowIndex,featureColIndex,winSizeId)=max(PrH1_X,salMat(featureRowIndex,featureColIndex,winSizeId));

        end
    end
    if(config.verbose>=1)
        fprintf('\n');
    end
end

% Concatenate saliency images with different resolutions salMat=max(salMat,[],3);

%% Display result if(config.verbose>=2)

    if config.compIndSal>0
        visImg=[];
        for i=1:numFeat
            dataMat=double(dataCell{i})/max(dataCell{i}(:));
            visImg=[visImg,[dataMat;salMatInd{i}]];
        end
        visImg=[visImg,[double(salMat>0.7);salMat]];
    else
        visImg=[];
        for i=1:numFeat
            dataMat=double(dataCell{i})/max(dataCell{i}(:));
            visImg=[visImg,dataMat];
        end
        visImg=[visImg,salMat];
    end

    figure; imshow(visImg,[]);
    if config.compIndSal==0
        title('Left to right: Each feature channel and rightmost is resulting saliency map.');
    else
        title('Top row left to right: Each feature channel and rightmost is thresholded (>0.7) saliency map. Bottom row corresponding feature channel saliency maps and rightmost is combined map.');
    end

end

%%%%%%%%%%%%%%%%%%%%%%%% % Additional functions % %%%%%%%%%%%%%%%%%%%%%%%%

%% Make integral histogram image function [intHist,binInd]=formIntegralHistogram_(dataMat,numBin,minVal,maxVal) % currently only 1 histograms

% Compute bin indices %binInd=ceil(numBin*(double(dataMat)+1e-5)/maxVal); %binInd(binInd>numBin)=numBin;

if isempty(dataMat) error('Datamatrix is empty'); end

% If only one numBin/minVal/maxVal is given, but dataMat has more feature, use same numBin/minVal/maxVal for all dimensions. if(length(numBin)<size(dataMat,3)) numBin=repmat(numBin(1),[1,size(dataMat,3)]); end if(length(minVal)<size(dataMat,3)) minVal=repmat(minVal(1),[1,size(dataMat,3)]); end if(length(maxVal)<size(dataMat,3)) maxVal=repmat(maxVal(1),[1,size(dataMat,3)]); end

% Compute bin indices binInd=ones(size(dataMat,1),size(dataMat,2)); binOffset=[1 numBin]; for i=1:size(dataMat,3) tempInd=max(min(ceil(numBin(i)*(double(dataMat(:,:,i))-minVal(i))/(maxVal(i)-minVal(i))),numBin(i)),1); binInd=binInd+prod(binOffset(1:i))*(tempInd-1); end

%% Matlab version r=size(binInd,1); c=size(binInd,2); intHist=zeros(r+1,c+1,prod(numBin),'int32');

intHist(2,2,binInd(1,1))=1; for i=2:c intHist(2,i+1,:)=intHist(2,i,:); intHist(2,i+1,binInd(1,i))=intHist(2,i+1,binInd(1,i))+1; end for j=2:r intHist(j+1,2,:)=intHist(j,2,:); intHist(j+1,2,binInd(j,1))=intHist(j+1,2,binInd(j,1))+1; end

for i=2:c for j=2:r intHist(j+1,i+1,:)=intHist(j,i+1,:)+intHist(j+1,i,:)-intHist(j,i,:); intHist(j+1,i+1,binInd(j,i))=intHist(j+1,i+1,binInd(j,i))+1; end end

%% Get smoothing filter function [smKer]=getSmoothKernel_(sigma)

if sigma==0 smKer=1; return; end

dim=length(sigma); % row, column, third dimension sz=max(ceil(sigma*2),1); sigma=2*sigma.^2;

if dim==1 d1=-sz(1):sz(1);

    smKer=exp(-((d1.^2)/sigma));

elseif dim==2 [d2,d1]=meshgrid(-sz(2):sz(2),-sz(1):sz(1));

    smKer=exp(-((d1.^2)/sigma(1)+(d2.^2)/sigma(2)));

elseif dim==3 [d2,d1,d3]=meshgrid(-sz(2):sz(2),-sz(1):sz(1),-sz(3):sz(3));

    smKer=exp(-((d1.^2)/sigma(1)+(d2.^2)/sigma(2)+(d3.^2)/sigma(3)));

else error('Not implemented'); end

smKer=smKer/sum(smKer(:));

%% Smooth distribution function dist=filterDistribution_(filterKernel,dist,numBin)

if numel(filterKernel)==1 dist=dist(:)/sum(dist(:)); return; end

numDim=length(numBin);

if numDim==1 %smoothDist=conv(dist,filterKernel,'same');

    lenDist=length(dist);
    hlenKernel=(length(filterKernel)-1)/2;

    dist=[dist(1)*ones(1,hlenKernel),dist,dist(end)*ones(1,hlenKernel)];
    dist=conv(dist,filterKernel);
    lenSmoothDist=length(dist);
    offset=(lenSmoothDist-lenDist)/2;
    dist=dist((offset+1):(lenSmoothDist-offset));

elseif numDim==2 dist=reshape(dist,numBin);

    dist=conv2(filterKernel,filterKernel,dist,'same');

else dist=reshape(dist,numBin);

    for i=1:numDim
        fker=ones(1,numDim);
        fker(i)=length(filterKernel);
        fker=zeros(fker);
        fker(:)=filterKernel(:);

        dist=convn(dist,fker,'same');

    end

end

dist=dist(:)/sum(dist(:));

%% Parse config function config=parseConfiguration_(config)

numWindow=length(config.windowRows);

% Window rows and columns config.windowRows=round(config.windowRows*max(config.numRow,config.numCol)); config.windowCols=round(config.windowCols*max(config.numRow,config.numCol));

% Window step size if(length(config.sampleStep)<numWindow) config.sampleStep=repmat(config.sampleStep,1,numWindow); end config.sampleStep=ceil(config.sampleStep*max(config.numRow,config.numCol));

% Border lengths if(length(config.verticalBorder)<numWindow) config.verticalBorder=repmat(config.verticalBorder,1,numWindow); end if(length(config.horizontalBorder)<numWindow) config.horizontalBorder=repmat(config.horizontalBorder,1,numWindow); end config.verticalBorder=round(config.verticalBorder.*max(config.windowRows,config.windowCols)); config.horizontalBorder=round(config.horizontalBorder.*max(config.windowRows,config.windowCols));

% Increase window sizes by border sizes config.windowRows=config.windowRows+2*config.verticalBorder; config.windowCols=config.windowCols+2*config.horizontalBorder;

% Full image flag if(length(config.fullImage)<numWindow) config.fullImage=repmat(config.fullImage(1),[1,numWindow]); end ........................................ The error it is giving: Error using applycform Too many output arguments.

Error in saliencyMeasure (line 19) [l,a,b] = applycform(img,cform);

Please help.......

[Geoff Hayes]

Pritesh - please attach the code (that is the m file) to your post using the paper clip button. Do not copy and paste the code into a comment, especially if the code is not formatted which makes it difficult to decipher. Please also include any relevant details on how you are calling this function and the full error message.

[pritesh patel]

I have attached (.m) file. The error it gives is:

---------------------------%ERROR%-------------------------------------------------------------------------------------------- In applycform (line 89) out = c.c_func(columndata, cdata{:});

In applycformsequence (line 12) out = applycform(out, cforms{k});

In applycform (line 89) out = c.c_func(columndata, cdata{:});

In applycformsequence (line 12) out = applycform(out, cforms{k});

In applycform (line 89) out = c.c_func(columndata, cdata{:});

In saliencyMeasure (line 21) lab = applycform(img,cform);

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

-----------------------%ERROR ENDS%-------------------- Thank You Geoff Hayes for your continuous help. Please check that..........

[Geoff Hayes]

And how are you calling this function? What are the inputs that you are using?

[pritesh patel]

The input is an image and passing {l,a,b} values to function. The code is just giving saliency map for an input image. This is what I have to do with this code. Hello Geoff Hayes , if you have checked it please let me know. Thank You

Answer 1

[Geoff Hayes]

Pritesh - rather than pasting all of the code into your question body, please just attach the code to your question using the paperclip button. That way, you can save the body for the full error message and the way in which you are calling the function.

As for the error message, which version of MATLAB are you using? According to the R2014b documentation for applycform there is only one output parameter...and so the error message makes sense since your (?) code is expecting three output parameters. To find out more about your implementation for this function, in the Command Window, type

 doc applycform

[Geoff Hayes]

Pritesh's answer moved here

I am using R2012a MATLAB version. The part of code is:

------------------------------------%Code%--------------------------------------------- function [salMat,salMatInd]=saliencyMeasure(dataCell)

% You need some function RGB2Lab to perform the conversion 
img=imread('Simplicity_By_The_Lake5.jpg'); %[L,a,b]=RGB2Lab(img); 

lab = makecform('srgb2lab'); [L,a,b] = applycform(img,lab);

% >>salMat=saliencyMeasure({L,a,b}); % or [salMat,salMatInd]=saliencyMeasure({L,a,b}); . . . This code is giving error (below)

--------------------------------------%ERROR%----------------------------------------------

Error using applycform Too many output arguments.

Error in saliencyMeasure (line 21) [L,a,b] = applycform(img,lab); --------------------------------------------------------------------------------------------

Thank you Geoff Hayes for your interest. Please see this.....

[Geoff Hayes]

Yes, the error message makes sense because even in R2012a, the applycform function has only one output parameter. See http://www.mathworks.com/help/releases/R2012a/toolbox/images/ref/applycform.html for details.

Is the line of code

 [L,a,b] = applycform(img,lab);

something that you have written, or have obtained from somewhere else? Even going back to the R2008a documentation, the applycform function only returns one output value. What are you attempting to do with this line of code? If you are trying to break the image into its L, a, and b components, then use the solution from Image Analyst at http://www.mathworks.com/matlabcentral/answers/47601-how-to-convert-rgb-to-cie-lab-color-space:

 colorTransform = makecform('srgb2lab'); 
 lab = applycform(img, colorTransform);
 L_Image = lab(:, :, 1);  % Extract the L image.
 A_Image = lab(:, :, 2);  % Extract the A image.
 B_Image = lab(:, :, 3);  % Extract the B image.

[Geoff Hayes]

Pritesh's answer moved here

Actually the problem is not of (l,a,b) extracting separately, the problem is in the line below: ------------------------------------------------------------------- [salMat,salMatInd]=saliencyMeasure({l,a,b}); . . . and rest of the code after it is given (in question above) -------------------------------------------------------------------------- --------------------------%The error is%---------------------------------------------------------

In applycform (line 89) out = c.c_func(columndata, cdata{:});

In applycformsequence (line 12) out = applycform(out, cforms{k});

In applycform (line 89) out = c.c_func(columndata, cdata{:});

In applycformsequence (line 12) out = applycform(out, cforms{k});

In applycform (line 89) out = c.c_func(columndata, cdata{:});

In saliencyMeasure (line 21) lab = applycform(img,cform);

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b});

In saliencyMeasure (line 27) [salMat,salMatInd]=saliencyMeasure({l,a,b}); ------------------------------------------------------------------------------------------------ Here I am stuck that how this values can be passed to function.please lool at it..... Thank You Geoff Hayes

[Geoff Hayes]

Pritesh - please stop adding answers to your question. If you want to further the dialog to this answer, then add a comment.

You neglected to add the error message. Please copy and paste it as a comment below this one.

As well, please attach the code to your question as it is difficult to decipher since it is not formatted and there are too many lines. Includes also the line of code that you are using to call this function.