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Unrecognized function or variable 'svmtrain'. Error in main>multisvm (line 991) models(k) = svmtrain(TrainingSet,G1vAll); Error in main>pushbutton14_Callback (line 866) result = multisvm(data_feat,data_label,test_data);
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plz help me tp solve the error :
Unrecognized function or variable 'svmtrain'.
Error in main>multisvm (line 991)
models(k) = svmtrain(TrainingSet,G1vAll);
Error in main>pushbutton14_Callback (line 866)
result = multisvm(data_feat,data_label,test_data);
Error in gui_mainfcn (line 95)
feval(varargin{:});
Error in main (line 18)
gui_mainfcn(gui_State, varargin{:});
Error in matlab.graphics.internal.figfile.FigFile/read>@(hObject,eventdata)main('pushbutton14_Callback',hObject,eventdata,guidata(hObject))
Error while evaluating UIControl Callback.
MY CODE IS:
function varargout = main(varargin)
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @main_OpeningFcn, ...
'gui_OutputFcn', @main_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before main is made visible.
function main_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to main (see VARARGIN)
% Choose default command line output for main
% Update handles structure
guidata(hObject, handles);
axes(handles.axes1); axis off
% UIWAIT makes main wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = main_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
%varargout{1} = handles.output;
% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
[filename, pathname, filterindex]=uigetfile( ...
{'*.jpg','JPEG File (*.jpg)'; ...
'*.*','Any Image file (*.*)'}, ...
'Pick an image file');
var=strcat(pathname,filename);
k=imread(var);
handles.YY = k;
set(handles.edit1,'String',var);
z=k;
guidata(hObject,handles);
%guidata(hObject,handles);
axes(handles.axes1);
imshow(k);
% set(handles.axes1);
title('Input Image');
set(handles.pushbutton1,'enable','off');
set(handles.pushbutton16,'enable','on');
% --- Executes on button press in pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
k=handles.YY;
j=rgb2gray(k);
handles.XX=j;
figure,subplot(2,2,2),imshow(j);title('gray Image');
set(handles.pushbutton1,'enable','off');
set(handles.pushbutton2,'enable','off');
set(handles.pushbutton4,'enable','on');
% --- Executes on button press in pushbutton3.
function pushbutton3_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton3 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
str=get(handles.edit1,'String');
I = imread(str);
h = ones(5,5)/25;
I2 = imfilter(I,h);
figure
imshow(I2)
title('Filtered Image')
set(handles.pushbutton3,'enable','off');
set(handles.pushbutton12,'enable','on');
% --- Executes on button press in pushbutton4.
function pushbutton4_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
ei=25;
st=35;
%k=10
k=ei*st;
I=handles.YY;
%I = imread('1.jpg');
%h=filter matrx
h = ones(ei,st) / k;
I1 = imfilter(I,h,'symmetric');
IG=rgb2gray(I1);
%Converting to BW
I11 = imadjust(IG,stretchlim(IG),[]);
level = graythresh(I11);
BWJ = im2bw(I11,level);
dim = size(BWJ)
IN=ones(dim(1),dim(2));
BW=xor(BWJ,IN); %inverting
figure,subplot(2,2,2), imshow(BW), title('Black and White');
set(handles.pushbutton1,'enable','off');
set(handles.pushbutton2,'enable','off');
set(handles.pushbutton3,'enable','on');
set(handles.pushbutton4,'enable','off');
% --- Executes on button press in pushbutton5.
function pushbutton5_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton5 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in pushbutton6.
function pushbutton6_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton6 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
%I=imread('cancer.bmp');
I=handles.YY;
[y,x,z]=size(I);
myI=double(I);
H=zeros(y,x);
S=H;
HS_I=H;
for i=1:x
for j=1:y
HS_I(j,i)=((myI(j,i,1)+myI(j,i,2)+myI(j,i,3))/3);
S(j,i)=1-3*min(myI(j,i,:))/(myI(j,i,1)+myI(j,i,2)+myI(j,i,3));
if ((myI(j,i,1)==myI(j,i,2))&(myI(j,i,2)==myI(j,i,3)))
Hdegree=0;
else
Hdegree=acos(0.5*(2*myI(j,i,1)-myI(j,i,2)-myI(j,i,3))/((myI(j,i,1)-myI(j,i,2))^2+(myI(j,i,1)-myI(j,i,3))*(myI(j,i,2)-myI(j,i,3)))^0.5);
end
if (myI(j,i,2)>=myI(j,i,3))
H(j,i)=Hdegree;
else
H(j,i)=(2*pi-Hdegree);
end
end
end
Hth1=0.9*2*pi; Hth2=0.1*2*pi;
Nred=0;
for i=1:x
for j=1:y
if ((H(j,i)>=Hth1)||(H(j,i)<=Hth2))
Nred=Nred+1;
end
end
end
Ratio=Nred/(x*y);
if (Ratio>=0.6)
Red=1
else
Red=0
end
HS_I=uint8(HS_I);
figure(1);
imshow(I);
figure(2);
imshow(HS_I);
% --- Executes on button press in pushbutton7.
function pushbutton7_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton7 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
createffnn
% --- Executes on button press in pushbutton8.
function pushbutton8_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton8 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% NET = trainnet(net,IMGDB);
str=get(handles.edit1,'String');
I1 = imread(str);
I = im2double(I1);
HSV = rgb2hsv(I);
H = HSV(:,:,1); H = H(:);
S = HSV(:,:,2); S = S(:);
V = HSV(:,:,3); V = V(:);
idx = kmeans([H S V], 4);
imshow(I1);
figure,imshow(ind2rgb(reshape(idx, size(I,1), size(I, 2)), [0 0 1; 0 0.8 0]))
% --- Executes on button press in pushbutton9.
function pushbutton9_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton9 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in pushbutton10.
function pushbutton10_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton10 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
function edit1_Callback(hObject, eventdata, handles)
% hObject handle to edit1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit1 as text
% str2double(get(hObject,'String')) returns contents of edit1 as a double
% --- Executes during object creation, after setting all properties.
function edit1_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function [ Unow, center, now_obj_fcn ] = FCMforImage( img, clusterNum )
if nargin < 2
clusterNum = 2; % number of cluster
end
[row, col] = size(img);
expoNum = 2; % fuzzification parameter
epsilon = 0.001; % stopping condition
mat_iter = 100; % number of maximun iteration
Upre = rand(row, col, clusterNum);
dep_sum = sum(Upre, 3);
dep_sum = repmat(dep_sum, [1,1, clusterNum]);
Upre = Upre./dep_sum;
center = zeros(clusterNum,1);
for i=1:clusterNum
center(i,1) = sum(sum(Upre(:,:,i).*img))/sum(sum(Upre(:,:,i)));
end
pre_obj_fcn = 0;
for i=1:clusterNum
pre_obj_fcn = pre_obj_fcn + sum(sum((Upre(:,:,i) .*img - center(i)).^2));
end
%fprintf('Initial objective fcn = %f\n', pre_obj_fcn);
for iter = 1:mat_iter
Unow = zeros(size(Upre));
for i=1:row
for j=1:col
for uII = 1:clusterNum
tmp = 0;
for uJJ = 1:clusterNum
disUp = abs(img(i,j) - center(uII));
disDn = abs(img(i,j) - center(uJJ));
tmp = tmp + (disUp/disDn).^(2/(expoNum-1));
end
Unow(i,j, uII) = 1/(tmp);
end
end
end
now_obj_fcn = 0;
for i=1:clusterNum
now_obj_fcn = now_obj_fcn + sum(sum((Unow(:,:,i) .*img - center(i)).^2));
end
% fprintf('Iter = %d, Objective = %f\n', iter, now_obj_fcn);
if max(max(max(abs(Unow-Upre))))<epsilon || abs(now_obj_fcn - pre_obj_fcn)<epsilon
break;
else
Upre = Unow.^expoNum;
for i=1:clusterNum
center(i,1) = sum(sum(Upre(:,:,i).*img))/sum(sum(Upre(:,:,i)));
end
pre_obj_fcn = now_obj_fcn;
end
end
% --- Executes on button press in pushbutton11.
function pushbutton11_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton11 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in pushbutton12.
function pushbutton12_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton12 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
str=get(handles.edit1,'String');
I1 = imread(str);
str=rgb2gray(I1);
imwrite(str,'img.tif','tiff');
img = double(imread('img.tif'));
clusterNum = 2;
[ Unow, center, now_obj_fcn ] = FCMforImage( img, clusterNum );
figure;
subplot(2,2,1); imshow(img,[]);
for i=1:clusterNum
subplot(2,2,i+1);
imshow(Unow(:,:,i),[]);
imwrite(Unow(:,:,i),'seg.jpg');
end
imshow('seg.jpg'); title('Segmented Image');
set(handles.pushbutton13,'enable','on');
set(handles.pushbutton12,'enable','off');
% --- Executes on button press in pushbutton13.
function pushbutton13_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton13 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
s=get(handles.edit1,'String');
I = imread(s);
I=rgb2gray(I);
glcms = graycomatrix(I);
getData()
% Derive Statistics from GLCM
stats = graycoprops(glcms,'Contrast Correlation Energy Homogeneity');
Contrast = stats.Contrast;
Correlation = stats.Correlation;
Energy = stats.Energy;
Homogeneity = stats.Homogeneity;
seg_img=imread('seg.jpg');
Mean = mean2(seg_img);
Standard_Deviation = std2(seg_img);
Entropy = entropy(seg_img);
RMS = mean2(rms(seg_img));
%Skewness = skewness(img)
Variance = mean2(var(double(seg_img)));
a = sum(double(seg_img(:)));
Smoothness = 1-(1/(1+a));
Kurtosis = kurtosis(double(seg_img(:)));
Skewness = skewness(double(seg_img(:)));
% Inverse Difference Movement
m = size(seg_img,1);
n = size(seg_img,2);
in_diff = 0;
for i = 1:m
for j = 1:n
temp = seg_img(i,j)./(1+(i-j).^2);
in_diff = in_diff+temp;
end
end
IDM = double(in_diff);
feat = [Contrast,Correlation,Energy,Homogeneity, Mean, Standard_Deviation, Entropy, RMS, Variance, Smoothness, Kurtosis, Skewness, IDM];
img = imread(s);
gaborArray = gaborFilterBank(5,8,39,39); % Generates the Gabor filter bank
featureVector = gaborFeatures(img,gaborArray,4,4);
disp('Gabor Feature');
featureVector
save('Gaborfeature.mat','featureVector');
% GLCM2 = graycomatrix(I,'Offset',[2 0;0 2]);
% features = GLCM_Features1(GLCM2,0)
set(handles.pushbutton14,'enable','on');
set(handles.pushbutton13,'enable','off');
msgbox('Feature Extracted Done');
clear all;
function [out] = GLCM_Features1(glcmin,pairs)
if ((nargin > 2) || (nargin == 0))
error('Too many or too few input arguments. Enter GLCM and pairs.');
elseif ( (nargin == 2) )
if ((size(glcmin,1) <= 1) || (size(glcmin,2) <= 1))
error('The GLCM should be a 2-D or 3-D matrix.');
elseif ( size(glcmin,1) ~= size(glcmin,2) )
error('Each GLCM should be square with NumLevels rows and NumLevels cols');
end
elseif (nargin == 1)
pairs = 0;
if ((size(glcmin,1) <= 1) || (size(glcmin,2) <= 1))
error('The GLCM should be a 2-D or 3-D matrix.');
elseif ( size(glcmin,1) ~= size(glcmin,2) )
error('Each GLCM should be square with NumLevels rows and NumLevels cols');
end
end
format long e
if (pairs == 1)
newn = 1;
for nglcm = 1:2:size(glcmin,3)
glcm(:,:,newn) = glcmin(:,:,nglcm) + glcmin(:,:,nglcm+1);
newn = newn + 1;
end
elseif (pairs == 0)
glcm = glcmin;
end
size_glcm_1 = size(glcm,1);
size_glcm_2 = size(glcm,2);
size_glcm_3 = size(glcm,3);
out.autoc = zeros(1,size_glcm_3);
out.contr = zeros(1,size_glcm_3);
out.corrm = zeros(1,size_glcm_3);
out.corrp = zeros(1,size_glcm_3);
out.cprom = zeros(1,size_glcm_3);
out.cshad = zeros(1,size_glcm_3);
out.dissi = zeros(1,size_glcm_3);
out.energ = zeros(1,size_glcm_3);
out.entro = zeros(1,size_glcm_3);
out.homom = zeros(1,size_glcm_3);
out.homop = zeros(1,size_glcm_3);
out.maxpr = zeros(1,size_glcm_3);
out.sosvh = zeros(1,size_glcm_3);
out.savgh = zeros(1,size_glcm_3);
out.svarh = zeros(1,size_glcm_3);
out.senth = zeros(1,size_glcm_3);
out.dvarh = zeros(1,size_glcm_3);
out.denth = zeros(1,size_glcm_3);
out.inf1h = zeros(1,size_glcm_3);
out.inf2h = zeros(1,size_glcm_3);
out.indnc = zeros(1,size_glcm_3);
out.idmnc = zeros(1,size_glcm_3);
glcm_sum = zeros(size_glcm_3,1);
glcm_mean = zeros(size_glcm_3,1);
glcm_var = zeros(size_glcm_3,1);
u_x = zeros(size_glcm_3,1);
u_y = zeros(size_glcm_3,1);
s_x = zeros(size_glcm_3,1);
s_y = zeros(size_glcm_3,1);
p_x = zeros(size_glcm_1,size_glcm_3);
p_y = zeros(size_glcm_2,size_glcm_3);
p_xplusy = zeros((size_glcm_1*2 - 1),size_glcm_3);
p_xminusy = zeros((size_glcm_1),size_glcm_3);
hxy = zeros(size_glcm_3,1);
hxy1 = zeros(size_glcm_3,1);
hx = zeros(size_glcm_3,1);
hy = zeros(size_glcm_3,1);
hxy2 = zeros(size_glcm_3,1);
for k = 1:size_glcm_3
glcm_sum(k) = sum(sum(glcm(:,:,k)));
glcm(:,:,k) = glcm(:,:,k)./glcm_sum(k);
glcm_mean(k) = mean2(glcm(:,:,k));
glcm_var(k) = (std2(glcm(:,:,k)))^2;
for i = 1:size_glcm_1
for j = 1:size_glcm_2
out.contr(k) = out.contr(k) + (abs(i - j))^2.*glcm(i,j,k);
out.dissi(k) = out.dissi(k) + (abs(i - j)*glcm(i,j,k));
out.energ(k) = out.energ(k) + (glcm(i,j,k).^2);
out.entro(k) = out.entro(k) - (glcm(i,j,k)*log(glcm(i,j,k) + eps));
out.homom(k) = out.homom(k) + (glcm(i,j,k)/( 1 + abs(i-j) ));
out.homop(k) = out.homop(k) + (glcm(i,j,k)/( 1 + (i - j)^2));
out.sosvh(k) = out.sosvh(k) + glcm(i,j,k)*((i - glcm_mean(k))^2);
out.indnc(k) = out.indnc(k) + (glcm(i,j,k)/( 1 + (abs(i-j)/size_glcm_1) ));
out.idmnc(k) = out.idmnc(k) + (glcm(i,j,k)/( 1 + ((i - j)/size_glcm_1)^2));
u_x(k) = u_x(k) + (i)*glcm(i,j,k);
u_y(k) = u_y(k) + (j)*glcm(i,j,k);
end
end
out.maxpr(k) = max(max(glcm(:,:,k)));
end
for k = 1:size_glcm_3
for i = 1:size_glcm_1
for j = 1:size_glcm_2
p_x(i,k) = p_x(i,k) + glcm(i,j,k);
p_y(i,k) = p_y(i,k) + glcm(j,i,k); % taking i for j and j for i
if (ismember((i + j),[2:2*size_glcm_1]))
p_xplusy((i+j)-1,k) = p_xplusy((i+j)-1,k) + glcm(i,j,k);
end
if (ismember(abs(i-j),[0:(size_glcm_1-1)]))
p_xminusy((abs(i-j))+1,k) = p_xminusy((abs(i-j))+1,k) +...
glcm(i,j,k);
end
end
end
end
for k = 1:(size_glcm_3)
for i = 1:(2*(size_glcm_1)-1)
out.savgh(k) = out.savgh(k) + (i+1)*p_xplusy(i,k);
out.senth(k) = out.senth(k) - (p_xplusy(i,k)*log(p_xplusy(i,k) + eps));
end
end
for k = 1:(size_glcm_3)
for i = 1:(2*(size_glcm_1)-1)
out.svarh(k) = out.svarh(k) + (((i+1) - out.senth(k))^2)*p_xplusy(i,k);
end
end
for k = 1:size_glcm_3
for i = 0:(size_glcm_1-1)
out.denth(k) = out.denth(k) - (p_xminusy(i+1,k)*log(p_xminusy(i+1,k) + eps));
out.dvarh(k) = out.dvarh(k) + (i^2)*p_xminusy(i+1,k);
end
end
for k = 1:size_glcm_3
hxy(k) = out.entro(k);
for i = 1:size_glcm_1
for j = 1:size_glcm_2
hxy1(k) = hxy1(k) - (glcm(i,j,k)*log(p_x(i,k)*p_y(j,k) + eps));
hxy2(k) = hxy2(k) - (p_x(i,k)*p_y(j,k)*log(p_x(i,k)*p_y(j,k) + eps));
end
hx(k) = hx(k) - (p_x(i,k)*log(p_x(i,k) + eps));
hy(k) = hy(k) - (p_y(i,k)*log(p_y(i,k) + eps));
end
out.inf1h(k) = ( hxy(k) - hxy1(k) ) / ( max([hx(k),hy(k)]) );
out.inf2h(k) = ( 1 - exp( -2*( hxy2(k) - hxy(k) ) ) )^0.5;
end
corm = zeros(size_glcm_3,1);
corp = zeros(size_glcm_3,1);
for k = 1:size_glcm_3
for i = 1:size_glcm_1
for j = 1:size_glcm_2
s_x(k) = s_x(k) + (((i) - u_x(k))^2)*glcm(i,j,k);
s_y(k) = s_y(k) + (((j) - u_y(k))^2)*glcm(i,j,k);
corp(k) = corp(k) + ((i)*(j)*glcm(i,j,k));
corm(k) = corm(k) + (((i) - u_x(k))*((j) - u_y(k))*glcm(i,j,k));
out.cprom(k) = out.cprom(k) + (((i + j - u_x(k) - u_y(k))^4)*...
glcm(i,j,k));
out.cshad(k) = out.cshad(k) + (((i + j - u_x(k) - u_y(k))^3)*...
glcm(i,j,k));
end
end
s_x(k) = s_x(k) ^ 0.5;
s_y(k) = s_y(k) ^ 0.5;
out.autoc(k) = corp(k);
out.corrp(k) = (corp(k) - u_x(k)*u_y(k))/(s_x(k)*s_y(k));
out.corrm(k) = corm(k) / (s_x(k)*s_y(k));
end
function gaborArray = gaborFilterBank(u,v,m,n)
if (nargin ~= 4) % Check correct number of arguments
error('There must be four input arguments (Number of scales and orientations and the 2-D size of the filter)!')
end
%% Create Gabor filters
% Create u*v gabor filters each being an m by n matrix
gaborArray = cell(u,v);
fmax = 0.25;
gama = sqrt(2);
eta = sqrt(2);
for i = 1:u
fu = fmax/((sqrt(2))^(i-1));
alpha = fu/gama;
beta = fu/eta;
for j = 1:v
tetav = ((j-1)/v)*pi;
gFilter = zeros(m,n);
for x = 1:m
for y = 1:n
xprime = (x-((m+1)/2))*cos(tetav)+(y-((n+1)/2))*sin(tetav);
yprime = -(x-((m+1)/2))*sin(tetav)+(y-((n+1)/2))*cos(tetav);
gFilter(x,y) = (fu^2/(pi*gama*eta))*exp(-((alpha^2)*(xprime^2)+(beta^2)*(yprime^2)))*exp(1i*2*pi*fu*xprime);
end
end
gaborArray{i,j} = gFilter;
end
end
%% Show Gabor filters (Please comment this section if not needed!)
% Show magnitudes of Gabor filters:
figure('NumberTitle','Off','Name','Magnitudes of Gabor filters');
for i = 1:u
for j = 1:v
subplot(u,v,(i-1)*v+j);
imshow(abs(gaborArray{i,j}),[]);
end
end
% Show real parts of Gabor filters:
figure('NumberTitle','Off','Name','Real parts of Gabor filters');
for i = 1:u
for j = 1:v
subplot(u,v,(i-1)*v+j);
imshow(real(gaborArray{i,j}),[]);
end
end
function featureVector = gaborFeatures(img,gaborArray,d1,d2)
if (nargin ~= 4)
error('Please use the correct number of input arguments!')
end
if size(img,3) == 3
warning('The input RGB image is converted to grayscale!')
img = rgb2gray(img);
end
img = double(img);
[u,v] = size(gaborArray);
gaborResult = cell(u,v);
for i = 1:u
for j = 1:v
gaborResult{i,j} = imfilter(img, gaborArray{i,j});
end
end
featureVector = [];
for i = 1:u
for j = 1:v
gaborAbs = abs(gaborResult{i,j});
gaborAbs = downsample(gaborAbs,d1);
gaborAbs = downsample(gaborAbs.',d2);
gaborAbs = gaborAbs(:);
gaborAbs = (gaborAbs-mean(gaborAbs))/std(gaborAbs,1);
featureVector = [featureVector; gaborAbs];
end
end
function getData()
%adds an extraction angle per pixel
offsets = [0 1; -1 1;-1 0;-1 -1;2 2];
jpgImagesDir = fullfile('Dataset/Train', '*.jpg');
total = numel( dir(jpgImagesDir) );
jpg_files = dir(jpgImagesDir);
jpg_counter = 0;
%total=length(filename);
gambar={total};
data_feat={total};
stats={total};
data_label={total};
label=1;
limit=5;
j=1;
for i=1:total
%msgbox(jpg_files(j).name)
s=strcat(num2str(i),'.jpg');
file=fullfile('Dataset/Train',s);
%file=fullfile(pathname,filename{i});
gambar{i}=imread(file);
gambar{i}=imresize(gambar{i},[600 600]);
gambar{i}=rgb2gray(gambar{i});
% gambar{i}=imadjust(gambar{i},stretchlim(gambar{i} ));
% gambar{i}=imsharpen(gambar{i},'Radius',1,'Amount',0.5);
glcm=graycomatrix(gambar{i}, 'Offset', offsets, 'Symmetric', true);
stats{i}=graycoprops(glcm);
iglcm=1;
for x=1:5
data_feat{i,x}=stats{i}.Contrast(iglcm);
iglcm=iglcm+1;
end
iglcm=1;
for x=6:10
data_feat{i,x}=stats{i}.Correlation(iglcm);
iglcm=iglcm+1;
end
iglcm=1;
for x=12:16
data_feat{i,x}=stats{i}.Energy(iglcm);
iglcm=iglcm+1;
end
iglcm=1;
for x=18:22
data_feat{i,x}=stats{i}.Homogeneity(iglcm);
iglcm=iglcm+1;
end
data_feat{i,24}=mean2(gambar{i});
data_feat{i,25}=std2(gambar{i});
data_feat{i,26}=entropy(gambar{i});
data_feat{i,27}= mean2(var(double(gambar{i}))); %average image variance
data_feat{i,28}=kurtosis(double(gambar{i}(:)));
data_feat{i,29}=skewness(double(gambar{i}(:)));
%labeling
if i>limit
label=label+1;
data_label{i}=label;
limit=limit+5;
else
data_label{1,i}=label;
end
end
% data is converted to the appropriate data type so that svm is not confused
data_feat=cell2mat(data_feat);
disp(data_feat);
data_label=cell2mat(data_label);
save('data_1.mat','data_feat','data_label');
% --- Executes on button press in pushbutton14.
function pushbutton14_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton14 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
s=get(handles.edit1,'String');
test=imread(s);
test=imresize(test,[600 600]);
test=rgb2gray(test);
offsets = [0 1; -1 1;-1 0;-1 -1;2 2];
glcm=graycomatrix(test, 'Offset', offsets, 'Symmetric', true);
stats=graycoprops(glcm);
data_glcm=struct2array(stats);
iglcm=1;
glcm_contrast={5};
glcm_correlation={5};
glcm_energy={5};
glcm_homogeneity={5};
for x=1:5
glcm_contrast{x}=data_glcm(iglcm);
iglcm=iglcm+1;
end
for x=1:5
glcm_correlation{x}=data_glcm(iglcm);
iglcm=iglcm+1;
end
for x=1:5
glcm_energy{x}=data_glcm(iglcm);
iglcm=iglcm+1;
end
for x=1:5
glcm_homogeneity{x}=data_glcm(iglcm);
iglcm=iglcm+1;
end
rata2=mean2(test);
std_deviation=std2(test);
glcm_entropy=entropy(test);
rata2_variance= mean2(var(double(test)));
glcm_kurtosis=kurtosis(double(test(:)));
glcm_skewness=skewness(double(test(:)));
buat_train=[glcm_contrast(1:5),glcm_correlation(1:5),glcm_energy(1:5),glcm_homogeneity(1:5),rata2,std_deviation,glcm_entropy,rata2_variance,glcm_kurtosis,glcm_skewness];
test_data=cell2mat(buat_train);
disp('GLCM Feature')
disp('Contrast(1) Correlation(2) Energy(3) Homogeneity(4) Mean(5) Standard_Deviation(6) Entropy(7) RMS(8) Variance(9) smoothness(10) Kurtosis(11) Skewness(12) IDM(13)')
input_Feature=test_data
load('data_1.mat');
%load('Test_data.mat');
%disp('Gabor Feature');
load('Gaborfeature.mat');
%Input_Feat=test_data
result = multisvm(data_feat,data_label,test_data);
%result1 = multisvm(data_feat,data_label,data_feat1);
%[Cmat,Accuracy]= confusion_matrix(result1,data_label,{'Desert','Forest','Mountain','Residential','River'});
%[c_matrixp,Result]= confusion.getMatrix(data_label,result1);
%C = confusionmat(data_label,result1)
%disp(result);
if result == 1
A1 = 'actinic keratosis';
set(handles.edit2,'string',A1);
helpdlg('actinic keratosis');
disp('actinic keratosis');
elseif result == 2
A2 = 'Basel cell carcinoma';
set(handles.edit2,'string',A2);
helpdlg('Basel cell carcinoma');
disp('Basel cell carcinoma');
elseif result == 3
A3 = 'cherry nevus';
set(handles.edit2,'string',A3);
helpdlg('cherry nevus');
disp('cherry nevus');
elseif result == 4
A4 = 'dermatofibroma';
set(handles.edit2,'string',A4);
helpdlg('dermatofibroma');
disp('dermatofibroma');
elseif result == 5
A5 = 'Melanocytic nevus';
set(handles.edit2,'string',A5);
helpdlg('Melanocytic nevus');
disp('Melanocytic nevus');
elseif result == 6
A5 = 'Melanoma';
set(handles.edit2,'string',A5);
helpdlg('Melanoma');
disp('Melanoma');
end
% function [itrfin] = multisvm( T,C,test )
%
%
% itrind=size(test,1);
% itrfin=[];
% Cb=C;
% Tb=T;
% itr=1;
%
% 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
% Give more suggestions for improving the program.
function [result] = multisvm(TrainingSet,GroupTrain,TestSet)
u=unique(GroupTrain);
numClasses=length(u);
result = zeros(length(TestSet(:,1)),1);
%build models
for k=1:numClasses
%Vectorized statement that binarizes Group
%where 1 is the current class and 0 is all other classes
G1vAll=(GroupTrain==u(k));
models(k) = svmtrain(TrainingSet,G1vAll);
end
%classify test cases
for j=1:size(TestSet,1)
for k=1:numClasses
if(svmclassify(models(k),TestSet(j,:)))
break;
end
end
result(j) = k;
%[Cmat,Accuracy]= confusion_matrix(TestSet(j,:),models(k),{'A','B','C','D','E','F'});
%disp('Result')
%disp(result(j));
end
function exCode1()
folder = 'Dataset';
dirImage = dir( folder );
numData = size(dirImage,1);
M ={} ;
for i=1:numData
nama = dirImage(i).name;
if regexp(nama, '(D|F)-[0-9]{1,2}.jpg')
B = cell(1,2);
if regexp(nama, 'D-[0-9]{1,2}.jpg')
B{1,1} = double(imread([folder, '/', nama]));
B{1,2} = 1;
elseif regexp(nama, 'F-[0-9]{1,2}.jpg')
B{1,1} = double(imread([folder, '/', nama]));
B{1,2} = -1;
end
M = cat(1,M,B);
end
end
numDataTrain = size(M,1);
class = zeros(numDataTrain,1);
arrayImage = zeros(numDataTrain, 300 * 300);
for i=1:numDataTrain
im = M{i,1} ;
im = rgb2gray(im);
im = imresize(im, [300 300]);
im = reshape(im', 1, 300*300);
arrayImage(i,:) = im;
class(i) = M{i,2};
end
SVMStruct = svmtrain(arrayImage, class);
imTest = double(imread(s));
imTest = rgb2gray(imTest);
imTest = imresize(imTest, [300 300]);
imTest = reshape(imTest',1, 300*300);
result = svmclassify(SVMStruct, imTest);
if(result==1)
msgbox('Desert');
else
msgbox('Forest');
end
function exCode2()
folder = 'Dataset';
dirImage = dir( folder );
numData = size(dirImage,1);
M ={} ;
for i=1:numData
nama = dirImage(i).name;
if regexp(nama, '(M|R)-[0-9]{1,2}.jpg')
B = cell(1,2);
if regexp(nama, 'M-[0-9]{1,2}.jpg')
B{1,1} = double(imread([folder, '/', nama]));
B{1,2} = 1;
elseif regexp(nama, 'R-[0-9]{1,2}.jpg')
B{1,1} = double(imread([folder, '/', nama]));
B{1,2} = -1;
end
M = cat(1,M,B);
end
end
numDataTrain = size(M,1);
class = zeros(numDataTrain,1);
arrayImage = zeros(numDataTrain, 300 * 300);
for i=1:numDataTrain
im = M{i,1} ;
im = rgb2gray(im);
im = imresize(im, [300 300]);
im = reshape(im', 1, 300*300);
arrayImage(i,:) = im;
class(i) = M{i,2};
end
SVMStruct = svmtrain(arrayImage, class);
imTest = double(imread(s));
imTest = rgb2gray(imTest);
imTest = imresize(imTest, [300 300]);
imTest = reshape(imTest',1, 300*300);
result = svmclassify(SVMStruct, imTest);
if(result==1)
msgbox('Mountain');
else
msgbox('Residential');
end
function exCode3()
folder = 'Dataset';
dirImage = dir( folder );
numData = size(dirImage,1);
M ={} ;
for i=1:numData
nama = dirImage(i).name;
if regexp(nama, '(RI|D)-[0-9]{1,2}.jpg')
B = cell(1,2);
if regexp(nama, 'RI-[0-9]{1,2}.jpg')
B{1,1} = double(imread([folder, '/', nama]));
B{1,2} = 1;
elseif regexp(nama, 'D-[0-9]{1,2}.jpg')
B{1,1} = double(imread([folder, '/', nama]));
B{1,2} = -1;
end
M = cat(1,M,B);
end
end
numDataTrain = size(M,1);
class = zeros(numDataTrain,1);
arrayImage = zeros(numDataTrain, 300 * 300);
for i=1:numDataTrain
im = M{i,1} ;
im = rgb2gray(im);
im = imresize(im, [300 300]);
im = reshape(im', 1, 300*300);
arrayImage(i,:) = im;
class(i) = M{i,2};
end
SVMStruct = svmtrain(arrayImage, class);
imTest = double(imread(s));
imTest = rgb2gray(imTest);
imTest = imresize(imTest, [300 300]);
imTest = reshape(imTest',1, 300*300);
result = svmclassify(SVMStruct, imTest);
if(result==1)
msgbox('River');
else
msgbox('Desert');
end
function edit2_Callback(hObject, eventdata, handles)
% hObject handle to edit2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit2 as text
% str2double(get(hObject,'String')) returns contents of edit2 as a double
% --- Executes during object creation, after setting all properties.
function edit2_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in pushbutton15.
function pushbutton15_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton15 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in pushbutton16.
function pushbutton16_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton16 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
str=get(handles.edit1,'String');
I1 = imread(str);
I4 = imadjust(I1,stretchlim(I1));
I5 = imresize(I4,[300,400]);
figure
imshow(I5);title(' Processed Image ');
set(handles.pushbutton1,'enable','off');
set(handles.pushbutton16,'enable','off');
set(handles.pushbutton12,'enable','on');
% --- Executes on button press in pushbutton17.
function pushbutton17_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton17 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
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