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⛄一、模糊逻辑(Fuzzy Logic)简介

理论知识参考：模糊逻辑(Fuzzy Logic)

⛄二、部分源代码

function varargout = LeafDiseaseGradingSystemGUI(varargin) % LeafDiseaseGradingSystemGUI MATLAB code for LeafDiseaseGradingSystemGUI.fig % LeafDiseaseGradingSystemGUI, by itself, creates a new LeafDiseaseGradingSystemGUI or raises the existing % singleton*. % % H = LeafDiseaseGradingSystemGUI returns the handle to a new LeafDiseaseGradingSystemGUI or the handle to % the existing singleton*. % % LeafDiseaseGradingSystemGUI(‘CALLBACK’,hObject,eventData,handles,…) calls the local % function named CALLBACK in LeafDiseaseGradingSystemGUI.M with the given input arguments. % % LeafDiseaseGradingSystemGUI(‘Property’,‘Value’,…) creates a new LeafDiseaseGradingSystemGUI or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the LeafDiseaseGradingSystemGUI before LeafDiseaseGradingSystemGUI_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to LeafDiseaseGradingSystemGUI_OpeningFcn via varargin. % % *See LeafDiseaseGradingSystemGUI Options on GUIDE’s Tools menu. Choose “LeafDiseaseGradingSystemGUI allows only one % instance to run (singleton)”. % % See also: GUIDE, GUIDATA, GUIHANDLES

% Edit the above text to modify the response to help LeafDiseaseGradingSystemGUI

% Last Modified by GUIDE v2.5 20-Jan-2015 14:49:28

% Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct(‘gui_Name’, mfilename, … ‘gui_Singleton’, gui_Singleton, … ‘gui_OpeningFcn’, @LeafDiseaseGradingSystemGUI_OpeningFcn, … ‘gui_OutputFcn’, @LeafDiseaseGradingSystemGUI_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 LeafDiseaseGradingSystemGUI is made visible. function LeafDiseaseGradingSystemGUI_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 LeafDiseaseGradingSystemGUI (see VARARGIN) set(gcf, ‘units’,‘normalized’,‘outerposition’,[0 0 1 1]);

Disease_Grading = readfis(‘Disease_Grading.fis’);

handles.Disease_Grading = Disease_Grading; guidata(hObject,handles);

% Choose default command line output for LeafDiseaseGradingSystemGUI handles.output = hObject;

% Update handles structure guidata(hObject, handles);

% UIWAIT makes LeafDiseaseGradingSystemGUI wait for user response (see UIRESUME) % uiwait(handles.figure1);

% — Outputs from this function are returned to the command line. function varargout = LeafDiseaseGradingSystemGUI_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 select_image. function select_image_Callback(hObject, eventdata, handles) % hObject handle to select_image (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)

[File_Name, Path_Name] = uigetfile('PATHNAME');

I = imread([Path_Name,File_Name]);

imshow([Path_Name,File_Name], 'Parent', handles.axes1); title('Original Leaf Image', 'Parent', handles.axes1);

%# store queryname, version 1

handles.I = I;

guidata(hObject,handles);

% — Executes on button press in segmentation. function segmentation_Callback(hObject, eventdata, handles) % hObject handle to segmentation (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)

I = handles.I;

% Creating color transformation from sRGB to Lab % cform = makecform(‘srgb2lab’);

lab_I = applycform(I,cform);

ab = double(lab_I(:,:,2:3));

nrows = size(ab,1); ncols = size(ab,2);

ab = reshape(ab,nrows*ncols,2); % No of clusters to be created with five iterations % nColors =5; [cluster_idx cluster_center] = kmeans(ab,nColors,‘EmptyAction’,‘singleton’,‘distance’,‘sqEuclidean’,‘start’,[128,128;128,128;128,128;128,128;128,128]);

pixel_labels = reshape(cluster_idx,nrows,ncols);

segmented_images = cell(5);

rgb_label = repmat(pixel_labels,[1 1 3]);

for k = 1:nColors color = I; color(rgb_label ~= k) = 0; segmented_images{k} = color; end

% displaying different show_clusters objects %

I_cluster_1 = segmented_images{1};

I_cluster_2 = segmented_images{2};

I_cluster_3 = segmented_images{3};

I_cluster_4 = segmented_images{4};

I_cluster_5 = segmented_images{5};

imshow(I_cluster_1,‘Parent’, handles.axes2); title(‘Cluster 1’);

handles.I_cluster_1 = I_cluster_1; handles.I_cluster_2 = I_cluster_2; handles.I_cluster_3 = I_cluster_3; handles.I_cluster_4 = I_cluster_4; handles.I_cluster_5 = I_cluster_5;

guidata(hObject,handles);

% — Executes on button press in disease_grade. function disease_grade_Callback(hObject, eventdata, handles) % hObject handle to disease_grade (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)

Disease_Grading = handles.Disease_Grading;

white_pixels_I = handles.white_pixels_I ;

white_pixels_I_selected = handles.white_pixels_I_selected ;

percentage_infected = (white_pixels_I_selected/white_pixels_I)*100;

grade = evalfis(percentage_infected,Disease_Grading);

figure();

plot(percentage_infected,grade,‘g*’);

legend(‘Percent - Grade of Disease’);

title(‘Disease Grade Classification Using Fuzzy Logic’); xlabel(‘Percentage’); ylabel(‘Disease Grade’);

% — Executes on button press in binary_original. function binary_original_Callback(hObject, eventdata, handles) % hObject handle to binary_original (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)

I = handles.I;

BW_I = im2bw(I,0.17);

white_pixels_I = sum(BW_I( == 1);

se = strel(‘disk’,1);

closeBW = imclose(BW_I,se);

imshow(closeBW,‘Parent’, handles.axes2); title(‘Binary of Original Image’);

handles.white_pixels_I = white_pixels_I;

guidata(hObject,handles);

⛄三、运行结果

⛄四、matlab版本及参考文献

1 matlab版本 2014a

2 参考文献 [1]张会孔,杨振霞,陈振东,刘汉舒.玉米粗缩病严重度分级标准的研究[J].植保技术与推广. 1998,(05)

3 备注 简介此部分摘自互联网，仅供参考，若侵权，联系删除

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