plz ans my question i want to segment the pic but this code give me erroe plz help?

%This code implements multi-region graph cut image segmentation according
%to the kernel-mapping formulation in M. Ben Salah, A. Mitiche, and 
%I. Ben Ayed, Multiregion Image Segmentation by Parametric Kernel Graph
%Cuts, IEEE Transactions on Image Processing, 20(2): 545-557 (2011).
%The code uses Veksler, Boykov, Zabih and Kolmogorov’s implementation
%of the Graph Cut algorithm. Written in C++, the graph cut algorithm comes
%bundled with a MATLAB wrapper by Shai Bagon (Weizmann). The kernel mapping
%part was implemented in MATLAB by M. Ben Salah (University
%of Alberta). If you use this code, please cite the papers mentioned in the
%accompanying bib file (citations.bib).

%%%%%%%%%%%%%%%%%%%%%%%%%%%Requirements%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%This code was tested with:
% • MATLAB Version: 7.12.0.635 (R2011a) for 32-bit wrapper
% • Microsoft Visual C++ 2010 Express

%%%%%%%%%%%%%%%%%%%Generating the mex files in MATLAB%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%>>mex -g GraphCutConstr.cpp graph.cpp GCoptimization.cpp Graph-
%Cut.cpp LinkedBlockList.cpp maxflow.cpp

%>>mex -g GraphCutMex.cpp graph.cpp GCoptimization.cpp GraphCut.cpp
%LinkedBlockList.cpp maxflow.cpp

clear all; close all; 

%%%%%%%%%%%%%%%%%%%%%%%Main inputs and parameters%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%Note: The RBF-kernel parameters are given in function kernel RBF.m%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%Example with a color image%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%path = 'Images\Color_image.jpg';
%im = im2double(imread(path)); 
%alpha=1; %The weight of the smoothness constraint
%k =8; %The number of regions

%%%%%%%Example with a SAR image corrupted with a multiplicative noise%%%%%%
%%%%%%%%%%%%%%%%Uncomment the following to run the example)%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

imread('wb.jpg');
alpha=0.6;
k =4;

%%%%%%%%%%%%%%%%%%%%%%%%%%Example with a brain image%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%Uncomment the following to run the example)%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%path = 'Images\Brain_image.tif';
%im = im2double(imread(path));
%alpha=0.1;
%k =4;

sz = size(image);
Hc=ones(sz(1:2));
Vc=Hc;
i_ground = 0; % rank of the bakground for plotting, 0: the darkest; 
%k-1 the brightest; 99: nowhere

diff=10000;
an_energy=999999999;
iter=0;
iter_v=0;
energy_global_min=99999999;

distance = 'sqEuclidean'; % Feature space distance

% Initialization: cluster the data into k regions
tic,
disp('Start kmeans');
data = ToVector(im);
[idx c] = kmeans(data, k, 'distance', distance,'EmptyAction','drop','maxiter',100);
c=c(find(isfinite(c(:,1))),:);
k=size(c,1);
k_max=k;
kmean_time=toc,

Dc = zeros([sz(1:2) k],'single');   
c,

tic
while iter < 5
    iter=iter+1;
    clear Dc
    clear K
    c;
    for ci=1:k
        K=kernel_RBF(im,c(ci,:));
        Dc(:,:,ci)=1-K;
    end   
    clear Sc
    clear K
    %%The smoothness term
    Sc = alpha*(ones(k) - eye(k)); 
    gch = GraphCut('open', Dc, Sc, Vc, Hc);
    [gch L] = GraphCut('swap',gch);
    [gch se de] = GraphCut('energy', gch);
    nv_energy=se+de;
    gch = GraphCut('close', gch);

      if (nv_energy<=energy_global_min)
          diff=abs(energy_global_min-nv_energy)/energy_global_min;
          energy_global_min=nv_energy;
          L_global_min=L;
          k_max=k;

          nv_energy;
          iter_v=0;
          % Calculate region Pl of label l
          if size(im, 3)==3 % Color image
          for l=0:k-1
              Pl=find(L==l);
              card=length(Pl);
              K1=kernel_RBF(im(Pl),c(l+1,1));K2=kernel_RBF(im(Pl),c(l+1,2));K3=kernel_RBF(im(Pl),c(l+1,3));
              smKI(1)=sum(im(Pl).*K1); smKI(2)=sum(im(Pl+prod(sz(1:2))).*K2); smKI(3)=sum(im(Pl+2*prod(sz(1:2))).*K3);
              smK1=sum(K1);smK2=sum(K2);smK3=sum(K3);
              if (card~=0)
                  c(l+1,1)=smKI(1)/smK1;c(l+1,2)=smKI(2)/smK2;c(l+1,3)=smKI(3)/smK3;
              else
                  c(l+1,1)=999999999;c(l+1,2)=999999999;c(l+1,3)=999999999;
              end
          end
          end

          if size(im, 1)==1 % Gray-level image
          for l=0:k-1
              Pl=find(L==l);
              card=length(Pl);
              K=kernel_RBF(im(Pl),c(l+1,1));
              smKI=sum(im(Pl).*K);
              smK=sum(K);
              if (card~=0)
                  c(l+1,1)=smKI/smK;
              else
                  c(l+1,1)=999999999;
              end
          end
          end

          c=c(find(c(:,1)~=999999999),:);
          c_global_min=c;
          k_global=length(c(:,1));
          k=k_global;

      else
          iter_v=iter_v+1;
          %---------------------------------
          %       Begin updating labels
          %---------------------------------
          % Calculate region Pl of label l
          if size(im, 3)==3 % Color image 
          for l=0:k-1           
              Pl=find(L==l);
              card=length(Pl);
              K1=kernel_RBF(im(Pl),c(l+1,1));K2=kernel_RBF(im(Pl),c(l+1,2));K3=kernel_RBF(im(Pl),c(l+1,3));
              smKI(1)=sum(im(Pl).*K1); smKI(2)=sum(im(Pl+prod(sz(1:2))).*K2); smKI(3)=sum(im(Pl+2*prod(sz(1:2))).*K3);
              smK1=sum(K1);smK2=sum(K2);smK3=sum(K3);
              % Calculate contour Cl of region Pl
              if (card~=0)
                  c(l+1,1)=smKI(1)/smK1;c(l+1,2)=smKI(2)/smK2;c(l+1,3)=smKI(3)/smK3;
              else
                  c(l+1,1)=999999999;c(l+1,2)=999999999;c(l+1,3)=999999999;
                  area(l+1)=999999999;
              end
          end
          end

          if size(im, 3)== 1 % Gray-level image 
          for l=0:k-1           
              Pl=find(L==l);
              card=length(Pl);
              K=kernel_RBF(im(Pl),c(l+1,1));
              smKI=sum(im(Pl).*K);
              smK=sum(K);
              % Calculate contour Cl of region Pl
              if (card~=0)
                  c(l+1,1)=smKI/smK;
              else
                  c(l+1,1)=999999999;
                  area(l+1)=999999999;
              end
          end
          end

          c=c(find(c(:,1)~=999999999),:);
          k=length(c(:,1));
      end
  end

L=L_global_min;
energy_global_min;
c=c_global_min;

size(c,1)
iter;

%Show the results

if size(im, 3)==3 % Color image 
img=zeros(sz(1),sz(2),3);
j=1;
imagesc(im); axis off; hold on; 

for i=0:k_max-1
    LL=(L_global_min==i);
    is_zero=sum(sum(LL));
    if is_zero
         img(:,:,1)=img(:,:,1)+LL*c(j,1);
         img(:,:,2)=img(:,:,2)+LL*c(j,2);
         img(:,:,3)=img(:,:,3)+LL*c(j,3);
         j=j+1;
    end
    if i~=i_ground
        color=[rand rand rand];
        contour(LL,[1 1],'LineWidth',2.5,'Color',color); hold on;
    end
end
figure(2);
imagesc(img); axis off;
end

if size(im, 3)==1 % Gray-level image 
img=zeros(sz(1),sz(2));
j=1;
imagesc(im); axis off; hold on; colormap gray; 

for i=0:k_max-1
    LL=(L_global_min==i);
    is_zero=sum(sum(LL));
    if is_zero
         img(:,:,1)=img(:,:,1)+LL*c(j,1);
         j=j+1;
    end
    if i~=i_ground
        color=[rand rand rand];
        contour(LL,[1 1],'LineWidth',2.5,'Color',color); hold on;
    end
end
figure(2);
imagesc(img); axis off;
end

[Walter Roberson]

We need to see the error message, everything in red.

The code appears to rely upon some programs having been compiled. Have you compiled the programs?

 mex -g GraphCutConstr.cpp graph.cpp GCoptimization.cpp Graph-Cut.cpp LinkedBlockList.cpp maxflow.cpp

 mex -g GraphCutMex.cpp graph.cpp GCoptimization.cpp GraphCut.cpp LinkedBlockList.cpp maxflow.cpp

[diya]

yes i compile but it give error

Answer 1

[diya]

%This code implements multi-region graph cut image segmentation according %to the kernel-mapping formulation in M. Ben Salah, A. Mitiche, and %I. Ben Ayed, Multiregion Image Segmentation by Parametric Kernel Graph %Cuts, IEEE Transactions on Image Processing, 20(2): 545-557 (2011). %The code uses Veksler, Boykov, Zabih and Kolmogorov’s implementation %of the Graph Cut algorithm. Written in C++, the graph cut algorithm comes %bundled with a MATLAB wrapper by Shai Bagon (Weizmann). The kernel mapping %part was implemented in MATLAB by M. Ben Salah (University %of Alberta). If you use this code, please cite the papers mentioned in the %accompanying bib file (citations.bib).

%%%%%%%%%%%%%%%%%%%%%%%%%%%Requirements%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %This code was tested with: % • MATLAB Version: 7.12.0.635 (R2011a) for 32-bit wrapper % • Microsoft Visual C++ 2010 Express

%%%%%%%%%%%%%%%%%%%Generating the mex files in MATLAB%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%>>mex -g GraphCutConstr.cpp graph.cpp GCoptimization.cpp Graph- %Cut.cpp LinkedBlockList.cpp maxflow.cpp

%>>mex -g GraphCutMex.cpp graph.cpp GCoptimization.cpp GraphCut.cpp %LinkedBlockList.cpp maxflow.cpp

clear all; close all;

%%%%%%%%%%%%%%%%%%%%%%%Main inputs and parameters%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%Note: The RBF-kernel parameters are given in function kernel RBF.m%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%Example with a color image%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %path = 'Images\Color_image.jpg'; %im = im2double(imread(path)); %alpha=1; %The weight of the smoothness constraint %k =8; %The number of regions

%%%%%%%Example with a SAR image corrupted with a multiplicative noise%%%%%% %%%%%%%%%%%%%%%%Uncomment the following to run the example)%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

imread('wb.jpg'); alpha=0.6; k =4;

%%%%%%%%%%%%%%%%%%%%%%%%%%Example with a brain image%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%Uncomment the following to run the example)%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%path = 'Images\Brain_image.tif'; %im = im2double(imread(path)); %alpha=0.1; %k =4;

sz = size(image); Hc=ones(sz(1:2)); Vc=Hc; i_ground = 0; % rank of the bakground for plotting, 0: the darkest; %k-1 the brightest; 99: nowhere

diff=10000; an_energy=999999999; iter=0; iter_v=0; energy_global_min=99999999;

distance = 'sqEuclidean'; % Feature space distance

% Initialization: cluster the data into k regions tic, disp('Start kmeans'); data = ToVector(im); [idx c] = kmeans(data, k, 'distance', distance,'EmptyAction','drop','maxiter',100); c=c(find(isfinite(c(:,1))),:); k=size(c,1); k_max=k; kmean_time=toc,

Dc = zeros([sz(1:2) k],'single'); c,

tic while iter < 5 iter=iter+1; clear Dc clear K c; for ci=1:k K=kernel_RBF(im,c(ci,:)); Dc(:,:,ci)=1-K; end clear Sc clear K %% The smoothness term Sc = alpha*(ones(k) - eye(k)); gch = GraphCut('open', Dc, Sc, Vc, Hc); [gch L] = GraphCut('swap',gch); [gch se de] = GraphCut('energy', gch); nv_energy=se+de; gch = GraphCut('close', gch);

    if (nv_energy<=energy_global_min)
        diff=abs(energy_global_min-nv_energy)/energy_global_min;
        energy_global_min=nv_energy;
        L_global_min=L;
        k_max=k;

        nv_energy;
        iter_v=0;
        % Calculate region Pl of label l
        if size(im, 3)==3 % Color image
        for l=0:k-1
            Pl=find(L==l);
            card=length(Pl);
            K1=kernel_RBF(im(Pl),c(l+1,1));K2=kernel_RBF(im(Pl),c(l+1,2));K3=kernel_RBF(im(Pl),c(l+1,3));
            smKI(1)=sum(im(Pl).*K1); smKI(2)=sum(im(Pl+prod(sz(1:2))).*K2); smKI(3)=sum(im(Pl+2*prod(sz(1:2))).*K3);
            smK1=sum(K1);smK2=sum(K2);smK3=sum(K3);
            if (card~=0)
                c(l+1,1)=smKI(1)/smK1;c(l+1,2)=smKI(2)/smK2;c(l+1,3)=smKI(3)/smK3;
            else
                c(l+1,1)=999999999;c(l+1,2)=999999999;c(l+1,3)=999999999;
            end
        end
        end

        if size(im, 1)==1 % Gray-level image
        for l=0:k-1
            Pl=find(L==l);
            card=length(Pl);
            K=kernel_RBF(im(Pl),c(l+1,1));
            smKI=sum(im(Pl).*K);
            smK=sum(K);
            if (card~=0)
                c(l+1,1)=smKI/smK;
            else
                c(l+1,1)=999999999;
            end
        end
        end

        c=c(find(c(:,1)~=999999999),:);
        c_global_min=c;
        k_global=length(c(:,1));
        k=k_global;

    else
        iter_v=iter_v+1;
        %---------------------------------
        %       Begin updating labels
        %---------------------------------
        % Calculate region Pl of label l
        if size(im, 3)==3 % Color image 
        for l=0:k-1           
            Pl=find(L==l);
            card=length(Pl);
            K1=kernel_RBF(im(Pl),c(l+1,1));K2=kernel_RBF(im(Pl),c(l+1,2));K3=kernel_RBF(im(Pl),c(l+1,3));
            smKI(1)=sum(im(Pl).*K1); smKI(2)=sum(im(Pl+prod(sz(1:2))).*K2); smKI(3)=sum(im(Pl+2*prod(sz(1:2))).*K3);
            smK1=sum(K1);smK2=sum(K2);smK3=sum(K3);
            % Calculate contour Cl of region Pl
            if (card~=0)
                c(l+1,1)=smKI(1)/smK1;c(l+1,2)=smKI(2)/smK2;c(l+1,3)=smKI(3)/smK3;
            else
                c(l+1,1)=999999999;c(l+1,2)=999999999;c(l+1,3)=999999999;
                area(l+1)=999999999;
            end
        end
        end

        if size(im, 3)== 1 % Gray-level image 
        for l=0:k-1           
            Pl=find(L==l);
            card=length(Pl);
            K=kernel_RBF(im(Pl),c(l+1,1));
            smKI=sum(im(Pl).*K);
            smK=sum(K);
            % Calculate contour Cl of region Pl
            if (card~=0)
                c(l+1,1)=smKI/smK;
            else
                c(l+1,1)=999999999;
                area(l+1)=999999999;
            end
        end
        end

        c=c(find(c(:,1)~=999999999),:);
        k=length(c(:,1));
    end
end

L=L_global_min; energy_global_min; c=c_global_min;

size(c,1) iter;

%Show the results

if size(im, 3)==3 % Color image img=zeros(sz(1),sz(2),3); j=1; imagesc(im); axis off; hold on;

for i=0:k_max-1 LL=(L_global_min==i); is_zero=sum(sum(LL)); if is_zero img(:,:,1)=img(:,:,1)+LL*c(j,1); img(:,:,2)=img(:,:,2)+LL*c(j,2); img(:,:,3)=img(:,:,3)+LL*c(j,3); j=j+1; end if i~=i_ground color=[rand rand rand]; contour(LL,[1 1],'LineWidth',2.5,'Color',color); hold on; end end figure(2); imagesc(img); axis off; end

if size(im, 3)==1 % Gray-level image img=zeros(sz(1),sz(2)); j=1; imagesc(im); axis off; hold on; colormap gray;

for i=0:k_max-1 LL=(L_global_min==i); is_zero=sum(sum(LL)); if is_zero img(:,:,1)=img(:,:,1)+LL*c(j,1); j=j+1; end if i~=i_ground color=[rand rand rand]; contour(LL,[1 1],'LineWidth',2.5,'Color',color); hold on; end end figure(2); imagesc(img); axis off; end