Matlab BlackBox Uji SVM dengan diawali proses segmentasi dan Invers Citra

Sbb Uji datalatih terhadap sistem SVM yang dibuat:

w=100;
h=100;
wh=w*h;

banyak=552; %ada 552 huruf yang hendak di latih
dataLatih=[];
for i=1:banyak
    AL=['datalatih/' num2str(i) '.PNG'];
    I=imread(AL);
    R=double(genData(I));
    RN=imresize(R,[w,h]);
 
    S=reshape(RN,[1,wh]);
    dataLatih(i,:)=S;
    dataTarget(i)=i;
end

% dataLatih       552x10000            44160000  double            
% dataTarget        1x552                  4416  double      

save dataLatih.mat dataLatih;
save dataTarget.mat dataTarget;


for i=1:size(dataLatih,1)
     dataUji=dataLatih(i,:);
     r=multisvmNew(dataLatih,dataTarget,double(dataUji))
end  

hasilnya adalah harus sama urutannnya dengan data Target

Selanjutnya kita akan menguji kalimat :

artinya langkahnya adalah sbb:

1.segmentasi 

2.mencocokan setiap karakter yang telah terbentuk ke sistem SVM

3 manganalsia hasilnya...

sebagaia berikut kodenya:

function btnProcess_Callback(hObject, eventdata, handles)

ff=get(handles.axes1,'Userdata');

f=imresize(ff,[400 NaN]);

g=rgb2gray(f);

    

g=medfilt2(g,[3 3]);

    set(handles.figure1,'CurrentAxes',handles.axes4);

    imshow(g);  

    set(handles.axes4,'Userdata',g);

%**********************************

conc=strel('disk',1);

gi=imdilate(g,conc);

ge=imerode(g,conc);            %%%% morphological image processing

gdiff=imsubtract(gi,ge);

gdiff=mat2gray(gdiff);

gdiff=conv2(gdiff,[1 1;1 1]);

gdiff=imadjust(gdiff,[0.5 0.7],[0 1],.1);

B=logical(gdiff);

[a1 b1]=size(B);

    set(handles.figure1,'CurrentAxes',handles.axes5);

    imshow(B);  

    set(handles.axes5,'Userdata',B);

er=imerode(B,strel('line',100,0));

    set(handles.figure1,'CurrentAxes',handles.axes6);

    imshow(er);  

    set(handles.axes6,'Userdata',er);

    

out1=imsubtract(B,er);

F=imfill(out1,'holes');      %%%filling the object

H=bwmorph(F,'thin',1);

H=imerode(H,strel('line',3,90));

    set(handles.figure1,'CurrentAxes',handles.axes7);

    imshow(H);  

    set(handles.axes7,'Userdata',H);

%%

final=bwareaopen(H,floor((a1/15)*(b1/15)));  

final(1:floor(.9*a1),1:2)=1;

final(a1:-1:(a1-20),b1:-1:(b1-2))=1;

    set(handles.figure1,'CurrentAxes',handles.axes8);

    imshow(final);  

    set(handles.axes7,'Userdata',final);

   

    

w1=get(handles.edw,'String');

h1=get(handles.edh,'String');

w=str2num(w1);

h=str2num(h1);

wh=w*h;

load dataLatih.mat;

load dataTarget.mat;

% dataLatih      6x40000            1920000  double              

% dataTarget      1x6                48  double              

% dataUji      1x40000            40000  logical    

  

Iprops=regionprops(final,'BoundingBox','Image');

hold on

 mm=1;

for n=1:size(Iprops,1)

    PPN=Iprops(n);

    IM=PPN.Image;

    IB=PPN.BoundingBox;

    rectangle('Position',PPN.BoundingBox,'EdgeColor','g','LineWidth',2); 

    

    wv=size(IM,1);

    hv=size(IM,2);

    whv=wv*hv;

   

   if whv>20000

        %figure     

        mm

        Citra=imresize(IM,[w,h]);

        ICitra=imcomplement(Citra);

        ALB=['datacapture/H' num2str(mm) '.png'];

        ALW=['datacapture/H' num2str(mm) '.png'];

        imwrite(Citra,ALB);

        imwrite(ICitra,ALW);

        dataUji=reshape(ICitra,[1,wh]);     

            %         whos dataLatih

            %         whos dataTarget

            %         whos dataUji

        r=multisvmNew(dataLatih,dataTarget,double(dataUji))

        mm=mm+1;

    end

end

hold off

Akan menghasilkan 3 gambar hasil segmentasi sbb:

Selanjutnya diinvers gambar tersebuat agar sesuai dengan pola datalatih:

 ICitra=imcomplement(Citra);

adapun engine svm yang bs digunakan adalah sbb:

function [itrfin] = multisvmNew( T,C,test )

%MULTISVM(3.0) classifies the class of given training vector according to the 

% given group and gives us result that which class it belongs.

% We have also to input the testing matrix

%Inputs: T=Training Matrix, C=Group, test=Testing matrix

%Outputs: itrfin=Resultant class(Group,USE ROW VECTOR MATRIX) to which tst set belongs 

%----------------------------------------------------------------------%

% IMPORTANT: DON'T USE THIS PROGRAM FOR CLASS LESS THAN 3,             %

%            OTHERWISE USE svmtrain,svmclassify DIRECTLY or            %

%            add an else condition also for that case in this program. %

%            Modify required data to use Kernel Functions and Plot also%

%----------------------------------------------------------------------%

%                       Date:11-08-2011(DD-MM-YYYY)                    %

% This function for multiclass Support Vector Machine is written by

% ANAND MISHRA (Machine Vision Lab. CEERI, Pilani, India) 

% and this is free to use. email: anand.mishra2k88@gmail.com

% Updated version 2.0 Date:14-10-2011(DD-MM-YYYY)

% Updated version 3.0 Date:04-04-2012(DD-MM-YYYY)

itrind=size(test,1);

itrfin=[];

Cb=C;

Tb=T;

for tempind=1:itrind

    tst=test(tempind,:);

    C=Cb;

    T=Tb;

    u=unique(C);

    N=length(u);

    c4=[];

    c3=[];

    j=1;

    k=1;

    if(N>2)

        itr=1;

        classes=0;

        cond=max(C)-min(C);

        while((classes~=1)&&(itr<=length(u))&& size(C,2)>1 && cond>0)

        %This while loop is the multiclass SVM Trick

            c1=(C==u(itr));

            newClass=c1;

            svmStruct = svmtrain(T,newClass,'kernel_function','rbf');   % I am using rbf kernel function, you must change it also

            classes = svmclassify(svmStruct,tst);

        

            % This is the loop for Reduction of Training Set

            for i=1:size(newClass,2)

                if newClass(1,i)==0;

                    c3(k,:)=T(i,:);

                    k=k+1;

                end

            end

        T=c3;

        c3=[];

        k=1;

        

            % This is the loop for reduction of group

            for i=1:size(newClass,2)

                if newClass(1,i)==0;

                    c4(1,j)=C(1,i);

                    j=j+1;

                end

            end

        C=c4;

        c4=[];

        j=1;

        

        cond=max(C)-min(C); % Condition for avoiding group 

                            %to contain similar type of values 

                            %and the reduce them to process

        

            % This condition can select the particular value of iteration

            % base on classes

            if classes~=1

                itr=itr+1;

            end    

        end

    end

valt=Cb==u(itr); % This logic is used to allow classification

val=Cb(valt==1); % of multiple rows testing matrix

val=unique(val);

itrfin(tempind,:)=val;  

end

end

% Give more suggestions for improving the program.

Tambahan Button:

function btnBrowse_Callback(hObject, eventdata, handles)

[handles.namafile,handles.direktori]=uigetfile({'*.png;*.jpg','file citra(*.bmp,*.jpg)';'*.bmp','file bitmap(*.bmp)';'*.jpg','file jpg(*.jpg)';'*.*','semua file(*.*)'},'buka file citra host/asli');

I=imread(fullfile(handles.direktori,handles.namafile)); 

if size(handles.namafile,2)>0   

    set(handles.btnReset,'Enable','on');

    set(handles.btnProcess,'Enable','on');

    set(handles.figure1,'CurrentAxes',handles.axes1);

    set(imshow(I));  

    set(handles.axes1,'Userdata',I);

    mywidth=int2str(size(I,1));

    myheight=int2str(size(I,2));

    mysize=(['size image:',mywidth,'x',myheight]);

    mypath=(['path image:',handles.direktori,handles.namafile]);

    

    [infogambar]=([mypath ' Resolusi:',mysize]);

    set(handles.edPath,'string',infogambar);

else

    set(handles.edPath,'string','');

    set(handles.edProses,'string','');

    set(handles.edResult,'string','00 0000 00');

    return;

end

% --- Executes on button press in btnReset.

function btnReset_Callback(hObject, eventdata, handles)

mau=questdlg(['Reset ' get(handles.figure1,'Name')''],['Anda ingin Reset Aplikasi ini?'],'Ya','Tidak','Ya');

if strcmp(mau,'Tidak')

return;

else

    arrayfun(@cla,findall(0,'type','axes'))

       set(handles.edPath,'string','');

       

    set(handles.edProses,'string','');

    set(handles.edResult,'string','00 0000 00');

    

         axes(handles.axes1);cla;

         axes(handles.axes4);cla;

         set(handles.btnReset,'Enable','off');%on

         set(handles.btnProcess,'Enable','off');%on

    hm = msgbox('Reset','Reset Sukses','help');

end

% --- Executes on button press in btnClose.

function btnClose_Callback(hObject, eventdata, handles)

mau=questdlg(['Keluar ' get(handles.figure1,'Name')''],['Keluar ' get(handles.figure1,'Name')''],'Ya','Tidak','Ya');

if strcmp(mau,'Tidak')

return;

end

delete(handles.figure1);%close;

% --- Executes on button press in btnGenerate.

function btnGenerate_Callback(hObject, eventdata, handles)

w1=get(handles.edw,'String');

h1=get(handles.edh,'String');

w=str2num(w1);

h=str2num(h1);

wh=w*h;

banyak=552;

dataLatih=[];

for i=1:banyak

    AL=['datalatih/' num2str(i) '.PNG'];

    I=imread(AL);

    R=double(genData(I));

    RN=imresize(R,[w,h]);

    

    S=reshape(RN,[1,wh]);

    dataLatih(i,:)=S;

    dataTarget(i)=i;

end 

whos dataLatih

save dataLatih.mat dataLatih;

save dataTarget.mat dataTarget;

msgbox('Generate Sukses','Sukses',help);

Selamat Mentjoba....

Literatur Pengujian Black Box


w=100;
h=100;
wh=w*h;

banyak=552;
dataLatih=[];
for i=1:banyak
    AL=['datalatih/' num2str(i) '.PNG'];
    I=imread(AL);
    R=double(genData(I));
    RN=imresize(R,[w,h]);
 
    S=reshape(RN,[1,wh]);
    dataLatih(i,:)=S;
    dataTarget(i)=i;
end

save dataLatih.mat dataLatih;
save dataTarget.mat dataTarget;


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
load dataLatih.mat
load dataTarget.mat

for i=1:1
    dataUji=dataLatih(58,:);
    r=multisvmNew(dataLatih,dataTarget',double(dataUji))
    %r=multisvm(dataLatih(50:100,:),dataTarget(:,50:100),double(dataLatih(1
    ,:)))
end

%%%%%%%%%%%%%%%%%%%%%%
AL=['datacapture/H1.png'];
I=imread(AL);
    R=double(genData(I));
    RN=imresize(R,[w,h]);
    S=reshape(RN,[1,wh]);
    dataUji=S;
    r=multisvm(dataLatih(50:100,:),dataTarget(:,50:100),double(dataUji))
    r=multisvm(dataLatih(50:100,:),dataTarget(:,50:100),double(dataLatih(70,:)))
 
 
TrainingSet=[ 1 10;2 20;3 30;4 40;5 50;6 66;3 30;4.1 42];
TestSet=[3 34; 1 14; 2.2 25; 6.2 63];
GroupTrain=[1;1;2;2;3;3;2;2];
results = multisvm(TrainingSet, GroupTrain, TestSet);
disp('multi class problem');
disp(results);

%   GroupTrain       8x1                64  double            
%   TestSet          4x2                64  double            
%   TrainingSet      8x2               128  double            

%   GroupTrain       8x1                64  double            
%   TestSet          1x200                64  double            
%   TrainingSet      8x200               128  double

M=5;
TrainingSet=dataLatih(1:552,:);
TestSet=double(dataLatih(M,:));
GroupTrain=dataTarget(:,1:552)';
results = multisvmNew(TrainingSet, GroupTrain, TestSet);
disp('multi class problem');
disp(results);

%   GroupTrain       1b, banyak C                      
%   TestSet          1b, cbebas                        
%   TrainingSet      banyak b , cbebas          


 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%Kasus Just   two group

%figure;
%   Xnew             2x2                32  double            
%   group          100x1             13100  cell              
%   xdata          100x2              1600  double    
% two group
xdata=dataLatih(50:100,:)';
group=dataTarget(:,50:100)';
Xnew=double(dataLatih(5,:))';

svmStruct = svmtrain(xdata,group,'ShowPlot',true);
Xnew = [5 2; 4 1.5];
species = svmclassify(svmStruct,Xnew,'ShowPlot',true)


%%%http://www.codeforge.com/read/11659212/oaasvm.m__html
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  
load fisheriris
xdata = meas(51:end,3:4);
group = species(51:end);
%figure;
svmStruct = svmtrain(xdata,group,'ShowPlot',true);
Xnew = [5 2; 4 1.5];
species = svmclassify(svmStruct,Xnew,'ShowPlot',true)
% hold on;
% plot(Xnew(:,1),Xnew(:,2),'ro','MarkerSize',12);
% hold off