matlab 车牌识别 带GUI界面

function varargout = mygui(varargin) % MYGUI MATLAB code for mygui.fig % MYGUI, by itself, creates a new MYGUI or raises the existing % singleton*. % % H = MYGUI returns the handle to a new MYGUI or the handle to % the existing singleton*. % % MYGUI('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in MYGUI.M with the given input arguments. % % MYGUI('Property','Value',...) creates a new MYGUI or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before mygui_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to mygui_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 mygui % Last Modified by GUIDE v2.5 27-Jun-2020 05:56:16 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @mygui_OpeningFcn, ... 'gui_OutputFcn', @mygui_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 mygui is made visible. function mygui_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 mygui (see VARARGIN) % Choose default command line output for mygui handles.output = hObject; % Update handles structure guidata(hObject, handles); % UIWAIT makes mygui wait for user response (see UIRESUME) % uiwait(handles.figure1); % --- Outputs from this function are returned to the command line. function varargout = mygui_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; % 按钮1为输入车牌读取按钮 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) [filename, pathname]=uigetfile({'*.jpg';'*.bmp'}, 'File Selector'); I=imread([pathname '\' filename]); handles.I=I; guidata(hObject, handles); axes(handles.axes1); imshow(I);title('原图'); %矫正识别 function pushbutton2_Callback(hObject, eventdata, handles) % 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) I=handles.I; P=rgb2gray(I); %真彩色图像转化为灰度图像 %车牌矫正 %用Radon变换矫正倾斜的车牌 P1 = wiener2(P, [5, 5]); % 对灰度图像进行维纳滤波 P2 = edge(P1, 'canny'); % 边缘检测 theta = 0 : 179; r = radon(P2, theta); %Radon变换 [m, n] = size(r); %compute the size of the image c = 1; for i = 1 : m for j = 1 : n if r(1,1) < r(i,j) r(1,1) = r(i,j); c = j; end end end rot_theta=0; rot_theta = 90 - c; P3 = imrotate(I, rot_theta, 'crop'); % 对图像进行旋转，校正图像 fprintf('图像倾斜角是：%d\n',rot_theta) %输出图像倾斜角 axes(handles.axes16); imshow(P3);title('车牌矫正') I1=rgb2gray(P3); %真彩色图像转化为灰度图像 I2=edge(I1,'roberts',0.18,'both'); %沿两个方向进行边缘检测，阈值为0.18（忽略小于阈值的边缘），返回图像大小相同 axes(handles.axes2); %选定句柄axes2在其上面作图 imshow(I1);title('彩色图像灰度化'); axes(handles.axes4); imshow(I2);title('边缘检测处理'); axes(handles.axes3);imhist(I1);title('灰度化直方图'); %腐蚀操作 se=[1;1;1]; %构造一个三维单位列向量 I3=imerode(I2,se);%腐蚀图像 消除噪点，使目标缩小 axes(handles.axes5); %选定句柄axes2在其上面作图 imshow(I3);title('腐蚀处理'); se=strel('rectangle',[25,25]); %构造结构元素对象se，长方形（rectangle），大小25X25 %strel用于膨胀腐蚀及开闭运算等操作的结构元素对象 %闭运算 I4=imclose(I3,se);%图像聚类，填充图像，闭操作可使轮廓线更光滑，填充图像细小空间，平滑边界 I5=bwareaopen(I4,2000);%删除小面积对象（移除所有少于2000像素的连接的组件（对象）） axes(handles.axes14); %选定句柄axes2在其上面作图 imshow(I5);title('闭运算处理'); [y,x]=size(I5);%返回图像各维的尺寸，存储在x,y中（矩阵的行数y和列数x） myI=double(I5); %数据类型转换 tic %tic计时开始，toc结束 %Y方向车牌区域确定 %横向扫描 Blue_y=zeros(y,1);%产生一个y维1列的零矩阵（y维零向量），水平方向 for i=1:y %for i=1:k:100 表示每次循环过后，i= i+k，如果不指定k，就默认i=i+1 for j=1:x if(myI(i,j,1)==1) %扫描图像如果myI图像坐标为（i，j）点值为1，即背景颜色为蓝色，blue加一 Blue_y(i,1)=Blue_y(i,1)+1; %蓝色像素点统计（按行统计） end end end [~, MaxY]=max(Blue_y); %[Y,U]=max(A)：返回行向量Y和U，Y向量记录A的每列的最大值，U向量记录每列最大值的行号。 %temp为向量Blue_y的元素中的最大值，MaxY为该值得索引（行号），即Blue_y的第MaxY行值最大最大值为temp PY1=MaxY; while((Blue_y(PY1,1)>=5)&&(PY1>1)) %从上向下扫描 PY1=PY1-1; end PY2=MaxY; while((Blue_y(PY2,1)>=5)&&(PY2<y)) %从下向上扫描 PY2=PY2+1; end %X方向车牌区域确定 %纵向扫描 Blue_x=zeros(1,x);%进一步确认x方向的车牌区域竖直方向 for j=1:x for i=PY1:PY2 if(myI(i,j,1)==1) Blue_x(1,j)=Blue_x(1,j)+1; end end end PX1=1; while((Blue_x(1,PX1)<3)&&(PX1<x)) %从左向右扫描 PX1=PX1+1; end PX2=x; while((Blue_x(1,PX2)<3)&&(PX2>PX1)) %从右向左扫描 PX2=PX2-1; end PX1=PX1-1;%对车牌区域的矫正 PX2=PX2+1; dw=P3(PY1:PY2-8,PX1:PX2,:); %定位矫正后的彩色车牌图像，存入dw中 toc; %计时 axes(handles.axes15);imshow(dw),title('定位车牌'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %车牌字符分割 imwrite(dw,'dw.jpg');%将彩色车牌写入dw文件中 a=imread('dw.jpg');%读取车牌 b=rgb2gray(a);%将车牌图像转换为灰度图 imwrite(b,'灰度车牌.jpg');%将灰度图写入文件 g_max=double(max(max(b))); %b的最大值并转化为双精度型 g_min=double(min(min(b))); %b的最小值并转化为双精度型 T=round(g_max-(g_max-g_min)/3);%T为二值化的阈值（round为取整函数，最近的方向取整） %[m,n]=size(b); d=(double(b)>=T);%d:二值图像（灰度图大于阈值的部分即为二值图像）imbw imwrite(d,'二值化.jpg'); %均值滤波前 %均值滤波 h=fspecial('average',3); %建立预定义的滤波算子，average为均值滤波，模板尺寸为3*3 d=im2bw(round(filter2(h,d)));%filter2使用指定的滤波器h对h进行d即均值滤波、 % im2bw使用阈值变换法把灰度图像转换成二值图像 imwrite(d,'均值滤波.jpg'); %膨胀或腐蚀 se=eye(2);%eye(n)即n维单位矩阵 [m,n]=size(d);%返回信息矩阵 if bwarea(d)/m/n>=0.365%bwarea计算二值图像中对象的总面积与整个面积的比是否大于0.365 d=imerode(d,se);%如果大于0.365则进行腐蚀、去除边缘检测后图像中的无关结构 elseif bwarea(d)/m/n<=0.235%计算二值图像中对象的总面积与整个面积的比值是否小于0.235 d=imdilate(d,se);%%如果小于则实现膨胀操作、放大图片以便于后期处理 end imwrite(d,'膨胀.jpg'); %寻找连续有文字的块，若长度大于某阈值，则认为该块有两个字符组成，需要分割 d=cut_char(d);%调用切割函数 [m,n]=size(d); k1=1; k2=1; s=sum(d);%列求和返回一个行向量 j=1; while j~=n while s(j)==0 j=j+1; end k1=j; while s(j)~=0 && j<=n-1 j=j+1; end k2=j-1; if