%%%Matlab Code for K Means Segmentation
function varargout = guifinal(varargin)
% GUIFINAL M-file for guifinal.fig
% GUIFINAL, by itself, creates a new GUIFINAL or raises the existing
% singleton*.
%
% H = GUIFINAL returns the handle to a new GUIFINAL or the handle to
% the existing singleton*.
%
% GUIFINAL('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in GUIFINAL.M with the given input arguments.
%
% GUIFINAL('Property','Value',...) creates a new GUIFINAL or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before guifinal_OpeningFunction gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to guifinal_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help guifinal
% Last Modified by GUIDE v2.5 25-Sep-2006 12:53:17
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @guifinal_OpeningFcn, ...
'gui_OutputFcn', @guifinal_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin & isstr(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 guifinal is made visible.
function guifinal_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 guifinal (see VARARGIN)
% Choose default command line output for guifinal
handles.output = hObject;
a=ones(256,256);
axes(handles.one);
imshow(a);
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes guifinal wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = guifinal_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 Browse.
function Browse_Callback(hObject, eventdata, handles)
% hObject handle to Browse (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
[filename, pathname] = uigetfile('*.bmp', 'Pick an Image');
if isequal(filename,0) | isequal(pathname,0)
warndlg('User pressed cancel')
else
a=imread(filename);
axes(handles.one);
imshow(a);
handles.file=filename;
% Update handles structure
guidata(hObject, handles);
end
% --- 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)
bb=get(handles.e1,'String');
bb=str2num(bb);
filename = handles.file;
if bb == 3
wd=256;
Input = imread(filename);
% imshow(Input);
figure;
Input =rgb2gray(Input);
Input =double(Input);
[r c] = size(Input);
Length = r*c;
Dataset = reshape(Input,[Length,1]);
Clusters=3 %k CLUSTERS
% Cluster1=[];
% Cluster2=[];
% Cluster3=[];
% Cluster4=[];
% Cluster5=[];
Cluster1=zeros(Length,1);
Cluster2=zeros(Length,1);
Cluster3=zeros(Length,1);
% Cluster4=zeros(Length,1);
% Cluster5=zeros(Length,1);
% Initial Centroids
% ARBITARLY CHOOSE K OBJECTS AS INITIAL CENTROIDS
miniv = min(min(Input));
maxiv = max(max(Input));
range = maxiv - miniv;
stepv = range/Clusters;
incrval = stepv;
for i = 1:Clusters
K(i).centroid = incrval;
incrval = incrval + stepv;
end
%BEGIN LOOP
% L=1;
update1=0;
update2=0;
update3=0;
update4=0;
update5=0;
mean1=2;
mean2=2;
mean3=2;
mean4=2;
mean5=2;
while ((mean1 ~= update1) & (mean2 ~= update2) & (mean3 ~= update3) )
mean1=K(1).centroid;
mean2=K(2).centroid;
mean3=K(3).centroid;
% mean4=K(4).centroid;
% mean5=K(5).centroid;
for i=1:Length
for j = 1:Clusters
temp= Dataset(i);
difference(j) = abs(temp-K(j).centroid);
end
[y,ind]=min(difference);
if ind==1
Cluster1(i) =temp;
end
if ind==2
Cluster2(i) =temp;
end
if ind==3
Cluster3(i) =temp;
end
% if ind==4
% Cluster4(i) =temp;
% end
% if ind==5
% Cluster5(i) =temp;
% end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%UPDATE CENTROIDS
cout1=0;
cout2=0;
cout3=0;
cout4=0;
cout5=0;
for i=1:Length
Load1=Cluster1(i);
Load2=Cluster2(i);
Load3=Cluster3(i);
% Load4=Cluster4(i);
% Load5=Cluster5(i);
if Load1 ~= 0
cout1=cout1+1;
end
if Load2 ~= 0
cout2=cout2+1;
end
%
if Load3 ~= 0
cout3=cout3+1;
end
% if Load4 ~= 0
% cout4=cout4+1;
% end
%
% if Load5 ~= 0
% cout5=cout5+1;
% end
end
% find the new mean of centroids
% Mean_Cluster(1)=sum(Cluster1)/Length;
% Mean_Cluster(2)=sum(Cluster2)/Length;
% Mean_Cluster(3)=sum(Cluster3)/Length;
% Mean_Cluster(4)=sum(Cluster4)/Length;
% Mean_Cluster(5)=sum(Cluster5)/Length;
Mean_Cluster(1)=sum(Cluster1)/cout1;
Mean_Cluster(2)=sum(Cluster2)/cout2;
Mean_Cluster(3)=sum(Cluster3)/cout3;
% Mean_Cluster(4)=sum(Cluster4)/cout4;
% Mean_Cluster(5)=sum(Cluster5)/cout5;
%reload
for i = 1:Clusters
K(i).centroid = Mean_Cluster(i);
end
update1=K(1).centroid;
update2=K(2).centroid;
update3=K(3).centroid;
% update4=K(4).centroid;
% update5=K(5).centroid;
end
AA1=reshape(Cluster1,[wd wd]);
AA2=reshape(Cluster2,[wd wd]);
AA3=reshape(Cluster3,[wd wd]);
% AA4=reshape(Cluster4,[wd wd]);
% AA5=reshape(Cluster5,[wd wd]);
subplot(1,3,1);imshow(AA1,[]);
%figure;
subplot(1,3,2);imshow(AA2,[]);
%figure;
subplot(1,3,3);imshow(AA3,[]);
% figure;
% imshow(AA4,[]);
% figure;
% imshow(AA5,[]);
elseif bb == 4
wd=256;
Input = imread(filename);
imshow(Input);
figure;
Input =rgb2gray(Input);
Input =double(Input);
[r c] = size(Input);
Length = r*c;
Dataset = reshape(Input,[Length,1]);
Clusters=4 %k CLUSTERS
% Cluster1=[];
% Cluster2=[];
% Cluster3=[];
% Cluster4=[];
% Cluster5=[];
Cluster1=zeros(Length,1);
Cluster2=zeros(Length,1);
Cluster3=zeros(Length,1);
Cluster4=zeros(Length,1);
% Cluster5=zeros(Length,1);
% Initial Centroids
% ARBITARLY CHOOSE K OBJECTS AS INITIAL CENTROIDS
miniv = min(min(Input));
maxiv = max(max(Input));
range = maxiv - miniv;
stepv = range/Clusters;
incrval = stepv;
for i = 1:Clusters
K(i).centroid = incrval;
incrval = incrval + stepv;
end
%BEGIN LOOP
% L=1;
update1=0;
update2=0;
update3=0;
update4=0;
update5=0;
mean1=2;
mean2=2;
mean3=2;
mean4=2;
mean5=2;
while ((mean1 ~= update1) & (mean2 ~= update2) & (mean3 ~= update3) & (mean4 ~= update4))
mean1=K(1).centroid;
mean2=K(2).centroid;
mean3=K(3).centroid;
mean4=K(4).centroid;
% mean5=K(5).centroid;
for i=1:Length
for j = 1:Clusters
temp= Dataset(i);
difference(j) = abs(temp-K(j).centroid);
end
[y,ind]=min(difference);
if ind==1
Cluster1(i) =temp;
end
if ind==2
Cluster2(i) =temp;
end
if ind==3
Cluster3(i) =temp;
end
if ind==4
Cluster4(i) =temp;
end
% if ind==5
% Cluster5(i) =temp;
% end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%UPDATE CENTROIDS
cout1=0;
cout2=0;
cout3=0;
cout4=0;
cout5=0;
for i=1:Length
Load1=Cluster1(i);
Load2=Cluster2(i);
Load3=Cluster3(i);
Load4=Cluster4(i);
% Load5=Cluster5(i);
if Load1 ~= 0
cout1=cout1+1;
end
if Load2 ~= 0
cout2=cout2+1;
end
%
if Load3 ~= 0
cout3=cout3+1;
end
if Load4 ~= 0
cout4=cout4+1;
end
%
% if Load5 ~= 0
% cout5=cout5+1;
% end
end
% find the new mean of centroids
% Mean_Cluster(1)=sum(Cluster1)/Length;
% Mean_Cluster(2)=sum(Cluster2)/Length;
% Mean_Cluster(3)=sum(Cluster3)/Length;
% Mean_Cluster(4)=sum(Cluster4)/Length;
% Mean_Cluster(5)=sum(Cluster5)/Length;
Mean_Cluster(1)=sum(Cluster1)/cout1;
Mean_Cluster(2)=sum(Cluster2)/cout2;
Mean_Cluster(3)=sum(Cluster3)/cout3;
Mean_Cluster(4)=sum(Cluster4)/cout4;
% Mean_Cluster(5)=sum(Cluster5)/cout5;
%reload
for i = 1:Clusters
K(i).centroid = Mean_Cluster(i);
end
update1=K(1).centroid;
update2=K(2).centroid;
update3=K(3).centroid;
update4=K(4).centroid;
% update5=K(5).centroid;
end
AA1=reshape(Cluster1,[wd wd]);
AA2=reshape(Cluster2,[wd wd]);
AA3=reshape(Cluster3,[wd wd]);
AA4=reshape(Cluster4,[wd wd]);
% AA5=reshape(Cluster5,[wd wd]);
subplot(2,2,1);imshow(AA1,[]);
%figure;
subplot(2,2,2);imshow(AA2,[]);
%figure;
subplot(2,2,3);imshow(AA3,[]);
%figure;
subplot(2,2,4);imshow(AA4,[]);
% figure;
% imshow(AA5,[]);
elseif bb== 5
% % number of clusters k and dataset containing n objects
% clc;
% clear;
% close all;
wd=256;
Input = imread(filename);
% imshow(Input);
figure;
Input =rgb2gray(Input);
Input =double(Input);
[r c] = size(Input);
Length = r*c;
Dataset = reshape(Input,[Length,1]);
Clusters=5 %k CLUSTERS
% Cluster1=[];
% Cluster2=[];
% Cluster3=[];
% Cluster4=[];
% Cluster5=[];
Cluster1=zeros(Length,1);
Cluster2=zeros(Length,1);
Cluster3=zeros(Length,1);
Cluster4=zeros(Length,1);
Cluster5=zeros(Length,1);
% Initial Centroids
% ARBITARLY CHOOSE K OBJECTS AS INITIAL CENTROIDS
miniv = min(min(Input));
maxiv = max(max(Input));
range = maxiv - miniv;
stepv = range/Clusters;
incrval = stepv;
for i = 1:Clusters
K(i).centroid = incrval;
incrval = incrval + stepv;
end
%BEGIN LOOP
% L=1;
update1=0;
update2=0;
update3=0;
update4=0;
update5=0;
mean1=2;
mean2=2;
mean3=2;
mean4=2;
mean5=2;
while ((mean1 ~= update1) & (mean2 ~= update2) & (mean3 ~= update3) & (mean4 ~= update4) & (mean5 ~= update5))
mean1=K(1).centroid;
mean2=K(2).centroid;
mean3=K(3).centroid;
mean4=K(4).centroid;
mean5=K(5).centroid;
for i=1:Length
for j = 1:Clusters
temp= Dataset(i);
difference(j) = abs(temp-K(j).centroid);
end
[y,ind]=min(difference);
if ind==1
Cluster1(i) =temp;
end
if ind==2
Cluster2(i) =temp;
end
if ind==3
Cluster3(i) =temp;
end
if ind==4
Cluster4(i) =temp;
end
if ind==5
Cluster5(i) =temp;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%UPDATE CENTROIDS
cout1=0;
cout2=0;
cout3=0;
cout4=0;
cout5=0;
for i=1:Length
Load1=Cluster1(i);
Load2=Cluster2(i);
Load3=Cluster3(i);
Load4=Cluster4(i);
Load5=Cluster5(i);
if Load1 ~= 0
cout1=cout1+1;
end
if Load2 ~= 0
cout2=cout2+1;
end
%
if Load3 ~= 0
cout3=cout3+1;
end
if Load4 ~= 0
cout4=cout4+1;
end
if Load5 ~= 0
cout5=cout5+1;
end
end
% find the new mean of centroids
% Mean_Cluster(1)=sum(Cluster1)/Length;
% Mean_Cluster(2)=sum(Cluster2)/Length;
% Mean_Cluster(3)=sum(Cluster3)/Length;
% Mean_Cluster(4)=sum(Cluster4)/Length;
% Mean_Cluster(5)=sum(Cluster5)/Length;
Mean_Cluster(1)=sum(Cluster1)/cout1;
Mean_Cluster(2)=sum(Cluster2)/cout2;
Mean_Cluster(3)=sum(Cluster3)/cout3;
Mean_Cluster(4)=sum(Cluster4)/cout4;
Mean_Cluster(5)=sum(Cluster5)/cout5;
%reload
for i = 1:Clusters
K(i).centroid = Mean_Cluster(i);
end
update1=K(1).centroid;
update2=K(2).centroid;
update3=K(3).centroid;
update4=K(4).centroid;
update5=K(5).centroid;
end
AA1=reshape(Cluster1,[wd wd]);
AA2=reshape(Cluster2,[wd wd]);
AA3=reshape(Cluster3,[wd wd]);
AA4=reshape(Cluster4,[wd wd]);
AA5=reshape(Cluster5,[wd wd]);
subplot(3,2,1);imshow(AA1,[]);
% figure;
subplot(3,2,2);imshow(AA2,[]);
% figure;
subplot(3,2,3);imshow(AA3,[]);
% figure;
subplot(3,2,4);imshow(AA4,[]);
% figure;
subplot(3,2,5);imshow(AA5,[]);
else
helpdlg('The no of cluster should be 3,4 or 5');
end
%
function e1_Callback(hObject, eventdata, handles)
% hObject handle to e1 (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 e1 as text
% str2double(get(hObject,'String')) returns contents of e1 as a double
%%Error Occured%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Error in gui_mainfcn (line 158)
gui_hFigure = local_openfig(gui_State.gui_Name, gui_SingletonOpt, gui_Visible);
Error in K_Means_NN (line 43)
gui_mainfcn(gui_State, varargin{:});
