Matlab函数句柄调用图像K均值分类

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#matlab #gui #image #K-means分类

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本文介绍如何在Matlab中使用函数句柄简化GUI设计，并通过实例展示如何利用K-means算法进行图像颜色聚类分类。

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Matlab函数句柄

参考：《Matlab实用教程》

Matlab中提供了函数句柄（function handle）的使用，定义方法为：

h_function1 = @function1;

或

h_function1 = str2func(function1);

函数句柄包含了函数的路径、函数名、类型及可能存在的重载方法。

函数句柄有什么作用呢？

（1）函数的调用，范围更广、速度更快；

（2）使函数调用像使用变量一样方便简单；

例如在GUI设计中，

以函数句柄作为结构数组元素：

gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @guideTemp2_OpeningFcn, ...
                   'gui_OutputFcn',  @guideTemp2_OutputFcn, ...
                   'gui_LayoutFcn',  [] , ...
                   'gui_Callback',   []);

点击按钮，使用函数句柄执行子函数 ------ K均值图像分类

function pushbutton1_Callback(hObject, eventdata, handles)
[fname,pname] = uigetfile('*.*','Select Image');
I_rgb = imread([pname,fname]);
feval(@KmeansTemp1, I_rgb, handles );

GUI完整代码：

function varargout = guideTemp2(varargin)

% Last Modified by GUIDE v2.5 15-Nov-2016 15:14:25

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @guideTemp2_OpeningFcn, ...
                   'gui_OutputFcn',  @guideTemp2_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 guideTemp2 is made visible.
function guideTemp2_OpeningFcn(hObject, eventdata, handles, varargin)
% Choose default command line output for guideTemp2
handles.output = hObject;

% Update handles structure
guidata(hObject, handles);

% UIWAIT makes guideTemp2 wait for user response (see UIRESUME)
% uiwait(handles.figure1);
global axes1_h; 
axes1_h = handles.axes1;

% --- Outputs from this function are returned to the command line.
function varargout = guideTemp2_OutputFcn(hObject, eventdata, handles) 
% Get default command line output from handles structure
varargout{1} = handles.output;


% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
[fname,pname] = uigetfile('*.*','Select Image');
I_rgb = imread([pname,fname]);
feval(@KmeansTemp1, I_rgb, handles );



% --- Executes during object creation, after setting all properties.
function axes1_CreateFcn(hObject, eventdata, handles)
% Hint: place code in OpeningFcn to populate axes1

k均值图像分类函数代码：

function   KmeansTemp1( I_rgb, handles )
% 
%   Image Classification by K-means

axes(handles.axes1);  imshow(I_rgb);  hold on;  title('Origin Image');
% RGB to lab 
C = makecform('srgb2lab');        
I_lab = applycform(I_rgb, C);
 
ab = double(I_lab(:,:,2:3));      
nrows = size(ab,1);               
ncols = size(ab,2);               
ab = reshape(ab,nrows*ncols,2);   
nColors = 4;                      
[cluster_idx,cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean','Replicates',3); 
%使用k均值聚类算法得到的结果对图像进行标记
pixel_labels = reshape(cluster_idx,nrows,ncols);  
axes(handles.axes2);   imshow(pixel_labels,[]);    title('Cluster Image');
%--------segmented_images------
segmented_images = cell(1,4);               
rgb_label = repmat(pixel_labels,[1 1 3]);    
for k = 1:nColors
    color = I_rgb;
    color(rgb_label ~= k) = 0;
    segmented_images{k} = color;
end

axes(handles.axes3);
ntemp = round(sqrt(nColors));
for i=1:nColors
    h(i)=subplot(ntemp,ntemp,i); 
    imshow(segmented_images{i});
end
%annotation(gcf,'textbox','String',{'Segmented Images'},'FontSize',15,...
%           'Position',[0.38 0.92 0.36 0.06],'edgecolor',get(gcf,'color'));

end

实验结果：