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

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diya
diya 2016-2-3
评论： diya 2016-2-3
%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
  2 个评论
Walter Roberson
Walter Roberson 2016-2-3
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

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回答（1 个）

diya
diya 2016-2-3
%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

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