【瑕疵检测】基于matlab GUI Gabor滤波布匹瑕疵检测【含Matlab源码 407期】

⛄一、Gabor滤波布匹瑕疵检测简介

1 Gabor滤波器

2 Gabor函数结合代码分析：

⛄二、部分源代码

function varargout = bupi(varargin)
% BUPI MATLAB code for bupi.fig
% BUPI, by itself, creates a new BUPI or raises the existing
% singleton*.
%
% H = BUPI returns the handle to a new BUPI or the handle to
% the existing singleton*.
%
% BUPI(‘CALLBACK’,hObject,eventData,handles,…) calls the local
% function named CALLBACK in BUPI.M with the given input arguments.
%
% BUPI(‘Property’,‘Value’,…) creates a new BUPI or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before bupi_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to bupi_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 bupi

% Last Modified by GUIDE v2.5 28-May-2015 22:11:59

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct(‘gui_Name’, mfilename, …
‘gui_Singleton’, gui_Singleton, …
‘gui_OpeningFcn’, @bupi_OpeningFcn, …
‘gui_OutputFcn’, @bupi_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 bupi is made visible.
function bupi_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 bupi (see VARARGIN)
axes(handles.axes1);
imshow([255]);
axes(handles.axes2);
imshow([255]);
axes(handles.axes3);
imshow([255]);
axes(handles.axes4);
imshow([255]);
axes(handles.axes5);
imshow([255]);
axes(handles.axes6);
imshow([255]);
% Choose default command line output for bupi
handles.output = hObject;

% Update handles structure
guidata(hObject, handles);

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

% — Outputs from this function are returned to the command line.
function varargout = bupi_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)
global tu
axes(handles.axes1)
[filename,pathname]=uigetfile({ …
‘.’,‘All Files(.)’;},…
‘选择文件’);
%
if isequal([filename,pathname],[0,0])
return
else
%读取图片
pic = fullfile(pathname,filename);
b = imread(pic);
imshow(b);%上面是打开图片的步骤，这一句是显示图片
tu=b;

title('原始图像');
%handle.axes1=b;

end
% 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)

% — Executes on button press in pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
global tu;
tu2 = gabor2(tu,0);
axes(handles.axes2);
imshow(tu2);
title(‘0度滤波结果’);

tu2 = gabor2(tu,pi/4);
axes(handles.axes3);
imshow(tu2);
title(‘pi/4度滤波结果’);

tu2 = gabor2(tu,pi/2);
axes(handles.axes4);
imshow(tu2);
title(‘pi/2度滤波结果’);

tu2 = gabor2(tu,pi/43);
axes(handles.axes5);
imshow(tu2);
title('pi/43度滤波结果’);
% 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)

% — Executes on button press in pushbutton3.
function pushbutton3_Callback(hObject, eventdata, handles)
global tu;
P = 5;
T1 =8;
T2 = 100;
I = tu;
I0=I;
%预处理
I=double(I0); %数据类型的转换
[M,N]=size(I);%得到待检测图像的大小
J=junzhicaiyang(I,M,N,P); %调用均值下采样函数
J=uint8(J);
%双线性插值，恢复原来的图像大小
I1=imresize(J,P,‘bilinear’); %双线性插值，恢复原来图像的大小。
%进行方差下采样，用于增强图像疵点信息
I1=double(I1);
J1=fangchacaiyang(I1,M,N,P); %调用方差下采样函数
J1=uint8(J1);
%双线性插值，恢复原来图像的大小。
I=imresize(J1,P,‘bilinear’);
%进行二值化及其后处理
T=Otsu(I);
%解决了利用大津法不能分辨是否含有疵点的缺陷的缺点，统计发现无疵点图像otsu得到阈值均小于8
if T<=T1
T=T1+1;
end

⛄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]薛婷婷,刘秀平,张凯兵,王珍,闫亚娣,闫焕营.基于学习的Gabor滤波器多样式布匹瑕疵检测[J].西安工程大学学报. 2017,31(06)

3 备注
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