1. Introduction to SVM
Support Vector Machine (SVM) was first proposed by Cortes and Vapnik in 1995. It shows many unique advantages in solving small sample size, nonlinear and high-dimensional pattern recognition, and can be generalized to other Machine learning problems such as function fitting. 1 Mathematics section 1.1 Two-dimensional space
Two, some source code
function varargout = DetectDisease_GUI(varargin)
% DETECTDISEASE_GUI MATLAB code for DetectDisease_GUI.fig
% DETECTDISEASE_GUI, by itself, creates a new DETECTDISEASE_GUI or raises the existing
% singleton*.
%
% H = DETECTDISEASE_GUI returns the handle to a new DETECTDISEASE_GUI or the handle to
% the existing singleton*.
%
% DETECTDISEASE_GUI('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in DETECTDISEASE_GUI.M with the given input arguments.
%
% DETECTDISEASE_GUI('Property'.'Value',...). creates anew DETECTDISEASE_GUI or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before DetectDisease_GUI_OpeningFcn gets called. An
% unrecognized property name orinvalid value makes property application % stop. All inputs are passed to DetectDisease_GUI_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 DetectDisease_GUI
% Last Modified by GUIDE v2. 5 26-Aug- 2021. 17:06:52
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @DetectDisease_GUI_OpeningFcn, ...
'gui_OutputFcn', @DetectDisease_GUI_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 DetectDisease_GUI is made visible.
function DetectDisease_GUI_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 DetectDisease_GUI (see VARARGIN)
% Choose default command line output for DetectDisease_GUI
handles.output = hObject;
ss = ones(300.400);
axes(handles.axes1);
imshow(ss);
axes(handles.axes2);
imshow(ss);
axes(handles.axes3);
imshow(ss);
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes DetectDisease_GUI wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = DetectDisease_GUI_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)
%clear all
%close all
clc
[filename, pathname] = uigetfile({'*. *';'*.bmp';'*.jpg';'*.gif'}, 'Pick a Leaf Image File');
I = imread([pathname,filename]);
I = imresize(I,[256.256]);
I2 = imresize(I,[300.400]); axes(handles.axes1); imshow(I2); title('Query Image');
ss = ones(300.400);
axes(handles.axes2);
imshow(ss);
axes(handles.axes3);
imshow(ss);
handles.ImgData1 = I;
guidata(hObject,handles);
% --- 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)
I3 = handles.ImgData1;
I4 = imadjust(I3,stretchlim(I3));
I5 = imresize(I4,[300.400]); axes(handles.axes2); imshow(I5); title(' Contrast Enhanced ');
handles.ImgData2 = I4;
guidata(hObject,handles);
% --- 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)
I6 = handles.ImgData2;
I = I6;
%% Extract Features
% Function call to evaluate features
%[feat_disease seg_img] = EvaluateFeatures(I)
% Color Image Segmentation
% Use of K Means clustering for segmentation
% Convert Image from RGB Color Space to L*a*b* Color Space
% The L*a*b* space consists of a luminosity layer 'L*', chromaticity-layer 'a*' and 'b*'.
% All of the color information is in the 'a*' and 'b*' layers.
cform = makecform('srgb2lab');
% Apply the colorform
lab_he = applycform(I,cform);
% Classify the colors in a*b* colorspace using K means clustering.
% Since the image has 3 colors create 3 clusters.
% Measure the distance using Euclidean Distance Metric.
ab = double(lab_he(:,:,2:3));
nrows = size(ab,1);
ncols = size(ab,2);
ab = reshape(ab,nrows*ncols,2);
nColors = 3;
[cluster_idx cluster_center] = kmeans(ab,nColors,'distance'.'sqEuclidean'.'Replicates'.3);
%[cluster_idx cluster_center] = kmeans(ab,nColors,'distance'.'sqEuclidean'.'Replicates'.3);
% Label every pixel in tha image using results from K means
pixel_labels = reshape(cluster_idx,nrows,ncols);
%figure,imshow(pixel_labels,[]), title('Image Labeled by Cluster Index');
% Create a blank cell array to store the results of clustering
segmented_images = cell(1.3);
% Create RGB label using pixel_labels
rgb_label = repmat(pixel_labels,[1.1.3]);
for k = 1:nColors
colors = I;
colors(rgb_label ~= k) = 0;
segmented_images{k} = colors;
end
figure,subplot(2.3.2); imshow(I); title('Original Image'); subplot(2.3.4); imshow(segmented_images{1}); title('Cluster 1'); subplot(2.3.5); imshow(segmented_images{2}); title('Cluster 2');
subplot(2.3.6); imshow(segmented_images{3}); title('Cluster 3');
set(gcf, 'Position', get(0.'Screensize'));
set(gcf, 'name'.'Segmented by K Means'.'numbertitle'.'off')
% Feature Extraction
pause(2)
x = inputdlg('Enter the cluster no. containing the ROI only:');
i = str2double(x);
% Extract the features from the segmented image
seg_img = segmented_images{i};
% Convert to grayscale if image is RGB
if ndims(seg_img) = =3
img = rgb2gray(seg_img);
end
%figure, imshow(img); title('Gray Scale Image'); % Evaluate the disease affected area black = im2bw(seg_img,graythresh(seg_img)); %figure, imshow(black); title('Black & White Image');
m = size(seg_img,1);
n = size(seg_img,2);
zero_image = zeros(m,n);
%G = imoverlay(zero_image,seg_img,[1 0 0]);
cc = bwconncomp(seg_img,6);
diseasedata = regionprops(cc,'basic');
A1 = diseasedata.Area;
sprintf('Area of the disease affected region is : %g%',A1);
I_black = im2bw(I,graythresh(I));
kk = bwconncomp(I,6);
leafdata = regionprops(kk,'basic');
A2 = leafdata.Area;
sprintf(' Total leaf area is : %g%',A2);
%Affected_Area = 1-(A1/A2);
Affected_Area = (A1/A2);
if Affected_Area < 0.1
Affected_Area = Affected_Area+0.15;
end
sprintf('Affected Area is: %g%%',(Affected_Area*100))
Affect = Affected_Area*100;
% Create the Gray Level Cooccurance Matrices (GLCMs)
glcms = graycomatrix(img);
% Derive Statistics from GLCM
stats = graycoprops(glcms,'Contrast Correlation Energy Homogeneity');
Contrast = stats.Contrast;
Correlation = stats.Correlation;
Energy = stats.Energy;
Homogeneity = stats.Homogeneity;
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_disease = [Contrast,Correlation,Energy,Homogeneity, Mean, Standard_Deviation, Entropy, RMS, Variance, Smoothness, Kurtosis, Skewness, IDM];
I7 = imresize(seg_img,[300.400]); axes(handles.axes3); imshow(I7); title('Segmented ROI');
%set(handles.edit3,'string',Affect);
set(handles.edit5,'string',Mean);
set(handles.edit6,'string',Standard_Deviation);
set(handles.edit7,'string',Entropy);
set(handles.edit8,'string',RMS);
set(handles.edit9,'string',Variance);
set(handles.edit10,'string',Smoothness);
set(handles.edit11,'string',Kurtosis);
set(handles.edit12,'string',Skewness);
set(handles.edit13,'string',IDM);
set(handles.edit14,'string',Contrast);
set(handles.edit15,'string',Correlation);
set(handles.edit16,'string',Energy);
set(handles.edit17,'string',Homogeneity);
handles.ImgData3 = feat_disease;
handles.ImgData4 = Affect;
% Update GUI
guidata(hObject,handles);
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
Copy the code3. Operation results
Matlab version and references
1 matlab version 2014A
2 Reference [1] CAI Limei. MATLAB Image Processing — Theory, Algorithm and Case Analysis [M]. Tsinghua University Press, 2020. [2] Yang Dan, ZHAO Haibin, LONG Zhe. Examples of MATLAB Image Processing In detail [M]. Tsinghua University Press, 2013. [3] Zhou Pin. MATLAB Image Processing and Graphical User Interface Design [M]. Tsinghua University Press, 2013. [4] LIU Chenglong. Proficient in MATLAB Image Processing [M]. Tsinghua University Press, 2015.