基于MATLAB的柑橘果实分类图像朴素贝叶斯分类器

function varargout = NaiveBayesClassifier(varargin) % NAIVEBAYESCLASSIFIER M-file for NaiveBayesClassifier.fig % NAIVEBAYESCLASSIFIER, by itself, creates a new NAIVEBAYESCLASSIFIER or raises the existing % singleton*. % % H = NAIVEBAYESCLASSIFIER returns the handle to a new NAIVEBAYESCLASSIFIER or the handle to % the existing singleton*. % % NAIVEBAYESCLASSIFIER('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in NAIVEBAYESCLASSIFIER.M with the given input arguments. % % NAIVEBAYESCLASSIFIER('Property','Value',...) creates a new NAIVEBAYESCLASSIFIER or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before NaiveBayesClassifier_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to NaiveBayesClassifier_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 NaiveBayesClassifier % Last Modified by GUIDE v2.5 09-May-2013 06:54:52 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @NaiveBayesClassifier_OpeningFcn, ... 'gui_OutputFcn', @NaiveBayesClassifier_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); clc; % set window potition (get_size_screen/gsl_) gsl_ = get(0,'ScreenSize'); 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 NaiveBayesClassifier is made visible. function NaiveBayesClassifier_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 NaiveBayesClassifier (see VARARGIN) % Choose default command line output for NaiveBayesClassifier handles.output = hObject; % Update handles structure guidata(hObject, handles); % UIWAIT makes NaiveBayesClassifier wait for user response (see UIRESUME) % uiwait(handles.NaiveBayesClassifier); % Set the figure icon by matlabfreecode.wordpress.com warning('off','MATLAB:HandleGraphics:ObsoletedProperty:JavaFrame'); jframe=get(handles.NaiveBayesClassifier,'javaframe'); jIcon=javax.swing.ImageIcon('citrus-icon.png'); jframe.setFigureIcon(jIcon); % --- Outputs from this function are returned to the command line. function varargout = NaiveBayesClassifier_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 NaiveBayesClassifier. function trainingdata_Callback(hObject, eventdata, handles) % hObject handle to NaiveBayesClassifier (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) NaiveBayesClassifierProject=guidata(gcbo); GetImageTraining=get(NaiveBayesClassifierProject.ImageTraining,'Userdata'); % determine the training data path path_data_train=strrep(cd,... 'Matlab_Code_To_Classification_Citrus','CitrusImage\Data Training'); % data training of Citrus nipis (jn) lots_of_data_train_jn=7; % data training of Citrus lemon (jl) lots_of_data_train_jl=3; % data training of Citrus orange (jm) lots_of_data_train_jm=5; lots_of_feature=4; lots_of_class=3; % initialization of matrix dataset dataset=zeros(lots_of_data_train_jn,lots_of_feature); for i=1:(lots_of_data_train_jn+lots_of_data_train_jl+lots_of_data_train_jm) if(i<=lots_of_data_train_jn) % membaca setiap file citra jeruk nipis filename=strcat(path_data_train,'\','CitrusNipis',... num2str(i),'.jpg'); class{i}='Nipis'; elseif(i<=(lots_of_data_train_jn+lots_of_data_train_jl)) % membaca setiap file citra jeruk lemon filename=strcat(path_data_train,'\','CitrusLemon',... num2str(i-lots_of_data_train_jn),'.jpg'); class{i}='Lemon'; else % membaca setiap file citra jeruk manis filename=strcat(path_data_train,'\','CitrusOrange',... num2str(i-(lots_of_data_train_jn+lots_of_data_train_jl)),'.jpg'); class{i}='Orange'; end I = imread (filename); % merisize image size % I = imresize(I,[256 256]); if(size(I,3)==4) I(:,:,1)=[]; % convert to I = [MxNx3] end % count mean Red, Green, Blue mean_red=mean(mean(I(:,:,1))); mean_green=mean(mean(I(:,:,2))); mean_blue=mean(mean(I(:,:,3))); axes(handles.ImageTraining); imshow(I); set(NaiveBayesClassifierProject.ImageTraining,'Userdata',I); SizeImageTraining=size(I); StringSizeImageTraining=sprintf(strcat(num2str(SizeImageTraining(1)),'x',num2str(SizeImageTraining(2)),'x',num2str(SizeImageTraining(3)))); StringSizeImageTraining = strrep(StringSizeImageTraining,'x',' x '); set(handles.SizeImageTraining,'String',StringSizeImageTraining); % set(handles.SizeImageTraining,'String',StringSizeImageTraining); set(handles.NameImageTraining,'String',sprintf(strcat('Image Training (Color) ->',num2str(i)))); %% create gray-scale image I_gray=Function_ColorToGray(I); axes(handles.ImageGray); imshow(I_gray); %% %% create binary image % level=graythresh(I); % I_biner=im2bw(I_gray,level); I_biner=zeros(size(I_gray,1),size(I_gray,2)); I_biner(find(I_gray<255))=1; axes(handles.ImageBinary); imshow(I_biner); %% create max filter image from binary image %windowing_size must be valued an odd number >=3 windowing_size=5; max_filter_I_biner=Function_MaxFilterBiner_(I_biner,windowing_size); axes(handles.ImageMaxFilter); imshow(max_filter_I_biner); %% count diameter with unit length horizontal each pixel % determine index which contains the value 1 [idx_,idy_]=find(max_filter_I_biner==1); diameter=idy_(numel(idy_))-idy_(1)+1; %% create boundary line in diameter horizontal %numel((XY2Index(1,idy_(1),size(I_gray,1)):XY2Index(size(I_gray,1),idy_(numel(idy_)),size(I_gray,1)))') % replace pixel value I_red=I(:,:,1); I_red(:,idy_(1):(idy_(1)+10))=105; I_red(:,(idy_(numel(idy_))-10):idy_(numel(idy_)))=105; I(:,:,1)=I_red; I_green=I(:,:,2); I_green(:,idy_(1):(idy_(1)+10))=75; I_green(:,(idy_(numel(idy_))-10):idy_(numel(idy_)))=75; I(:,:,2)=I_green; I_blue=I(:,:,3); I_blue(:,idy_(1):(idy_(1)+10))=245; I_blue(:,(idy_(numel(idy_))-10):idy_(numel(idy_)))=245; I(:,:,3)=I_blue; axes(handles.ImageTraining); imshow(I); % %% count diameter with unit length vertical each pixel % % determine index which contains the value 1 % [idx_v,idy_v]=find(max_filter_I_biner==1); % [value_max,idx_v_max]=max(idx_v); % [value_min,idx_v_min]=mi