chepaishibie.zip_GUI界面_GUI界面显示_基于matlab的车牌识别系统_车牌识别 字符

function varargout = chepaishibie(varargin) gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @chepaishibie_OpeningFcn, ... 'gui_OutputFcn', @chepaishibie_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 % --- Executes just before chepaishibie is made visible. function chepaishibie_OpeningFcn(hObject, eventdata, handles, varargin) % Choose default command line output for chepaishibie handles.output = hObject; % Update handles structure guidata(hObject, handles); % --- Outputs from this function are returned to the command line. function varargout = chepaishibie_OutputFcn(hObject, eventdata, handles) % Get default command line output from handles structure varargout{1} = handles.output; % --- 选择车牌 function pushbutton1_Callback(hObject, eventdata, handles) cla; global I; [filename,pathname]=uigetfile({'*.jpg;*.png'},'select car:');%选择车牌 str=[pathname,filename]; I=imread(str);%读取车牌图片 axes(handles.axes1);%使用第1个axes imshow(I);%显示原车牌图片 axes(handles.axes2); reset(gca); % --- 灰度化 function pushbutton3_Callback(hObject, eventdata, handles) global I; global I1; I1=rgb2gray(I); axes(handles.axes2);%使用第2个axes imshow(I1);%显示灰度化后的车牌图片 % --- 边缘检测 function pushbutton4_Callback(hObject, eventdata, handles) global I1; global I2; I2=edge(I1,'roberts',0.08,'both');%roberts算子边缘检测 axes(handles.axes2);%使用第2个axes imshow(I2);%显示经过roberts算子边缘检测后的车牌图片 % --- 车牌定位 function pushbutton5_Callback(hObject, eventdata, handles) global I; global I2; global dw; se=[1;1;1]; I3=imerode(I2,se);%腐蚀后图像 se=strel('rectangle',[40,40]);%生成一个矩阵 I4=imclose(I3,se); %闭运算 I5=bwareaopen(I4,2000);%从对象中移除小对象 [y,x]=size(I5); myI=double(I5); %begin横向扫描 white_y=zeros(y,1); for i=1:y for j=1:x if(myI(i,j,1)==1) %如果myI(i,j,1)即myI图像中坐标为(i,j)的点为白色 %则相应行的元素white_y(i,1)值加1 white_y(i,1)= white_y(i,1)+1;%白色像素点统计 end end end [temp, MaxY]=max(white_y);%temp为向量white_y的元素中的最大值，MaxY为该值的索引 PY1=MaxY; while ((white_y(PY1,1)>=120)&&(PY1>1)) PY1=PY1-1; end PY2=MaxY; while ((white_y(PY2,1)>=40)&&(PY2<y)) PY2=PY2+1; end %end横向扫描 %begin纵向扫描 white_x=zeros(1,x);%进一步确定x方向的车牌区域 for j=1:x for i=PY1:PY2 if(myI(i,j,1)==1) white_x(1,j)= white_x(1,j)+1; end end end PX1=1; while ((white_x(1,PX1)<3)&&(PX1<x)) PX1=PX1+1; end PX2=x; while ((white_x(1,PX2)<3)&&(PX2>PX1)) PX2=PX2-1; end %end纵向扫描 dw=I(PY1:PY2,PX1:PX2,:);%截取原图像I中的部分 axes(handles.axes2);%使用第2个axes imshow(dw);%显示经过定位后的车牌图片 % --- 车牌预处理 function pushbutton6_Callback(hObject, eventdata, handles) global dw; global d; b=rgb2gray(dw);%灰度化 %二值化 g_max=double(max(max(b))); g_min=double(min(min(b))); T=round(g_max-(g_max-g_min)/3); % T 为二值化的阈值 d=(double(b)>=T); % d为车牌二值图像 % 滤波 h=fspecial('average',3);%均值滤波器 d=im2bw(round(filter2(h,d)));%im2bw(I, level) 将灰度图像 I 转换为二进制图像， %将输入图像中亮度值大于 level 的像素替换为值1 (白色)，其他替换为值0(黑色） % 膨胀或腐蚀 se=eye(2); % eye(n) returns the n-by-n identity matrix 单位矩阵 [m,n]=size(d); if bwarea(d)/m/n>=0.365 %计算面积 d=imerode(d,se); %imerode 实现图像腐蚀 d为待处理图像，se是结构元素对象 elseif bwarea(d)/m/n<=0.235 d=imdilate(d,se); %imdilate 图像膨胀 end axes(handles.axes2);%使用第2个axes imshow(d);%显示经过预处理后的车牌图片 % --- 车牌提取 function pushbutton7_Callback(hObject, eventdata, handles) global d; %切割 d=qiege(d); [m,n]=size(d); k1=1;k2=1;s=sum(d);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 k2-k1>=round(n/6.5) [val,num]=min(sum(d(:,k1+5:k2-5))); d(:,k1+num+5)=0; % 分割 end end % 再切割 d=qiege(d); axes(handles.axes2);%使用第2个axes imshow(d);%显示提取的车牌图片 % --- 字符分割 function pushbutton8_Callback(hObject, eventdata, handles) global d; global word1; global word2; global word3; global word4; global word5; global word6; global word7; global h1; global h2; global h3; global h4; global h5; global h6; % 切割出 7 个字符 y1=10;y2=0.25;flag=0;word1=[]; while flag==0 [m,n]=size(d); wide=0; while sum(d(:,wide+1))~=0 wide=wide+1; end if wide<y1 % 认为是左侧干扰 d(:,1:wide)=0; d=qiege(d); else temp=qiege(imcrop(d,[1 1 wide m])); [m,n]=size(temp); all=sum(sum(temp)); two_thirds=sum(sum(temp([round(m/3):2*round(m/3)],:))); if two_thirds/all>y2 flag=1;word1=temp; % word1 end d(:,1:wide)=0;d=qiege(d); end end % 分割出第二个字符 [word2,d]=getword(d); % 分割出第三个字符 [word3,d]=getword(d); % 分割出第四个字符 [word4,d]=getword(d); % 分割出第五个字符 [word5,d]=getword(d); % 分割出第六个字符 [word6,d]=getword(d); % 分割出第七个字符 [word7,d]=getword(d); %归一化大小为 40*20 word1=imresize(word1,[40 20]); word2=imresize(word2,[40 20]); word3=imresize(word3,[40 20]); word4=imresize(word4,[40 20]); word5=imresize(word5,[40 20]); word6=imresize(word6,[40 20]); word7=imresize(word7,[40 20]); %保存车牌字符 imwrite(word1,'1.jpg'); imwrite(word2,'2.jpg'); imwrite(word3,'3.jpg'); imwrite(word4,'4.jpg'); imwrite(word5,'5.jpg'); imwrite(word6,'6.jpg'); imwrite(word7,'7.jpg'); axes(handles.axes2);%使用第2个axes cla;%清除当前坐标轴对象 axes('position',[0.06,0.35,0.02,0.06]); imshow(word1);%显示第1个字符 h1=gca();%获得当前坐标轴句柄 axes('position',[0.08,0.35,0.02,0.06]); imshow(word2);%显示第2个字符 h2=gca();%获得当前坐标轴句柄 axes('position',[0.1,0.35,0.02,0.06]); imshow(word3);%显示第3个字符 h3=gca();%获得当前坐标轴句柄 axes('position',[0.12,0.35,0.02,0.06]); imshow(word4);%显示第4个字符 h4=gca();%获得当前坐标轴句柄 axes('position',[0.14,0.35,0.02,0.06]); imshow(word5);%显示第5个字符 h5=gca();%获得当前坐标轴句柄 axes('position',[0.16,0.35,0.02,0.06]); imshow(word6);%显示第6个字符 h6=gca();%获得当前坐标轴句柄 axes('position',[0.18,0.35,0.02,0.06]); imshow(word7);%显示第7个字符 % --- 字符识别 function pushbutton9_Callback(hObject, eventdata, handles) global h1; global h2; global h3; global h4; global h5; global h6; delete(h1); delete(h2); delete(h3); delete(h4); delete(h5); delete(h6);%清除坐标轴中的对象 global dw; liccode=char(['0':'9' 'ABCDEFGHJKLMNOPQRSTUVWXYZ' '京陕苏吉晋桂鲁鄂浙湘赣云豫辽粤渝冀闽']); %建立自动识别字符代码表 for n=1:7 m=int2str(n); %将整数转换为字符串 ii=strcat('F:\MATLAB\workspace\',m,'.jpg'); t=imread(ii); SegBw2=imresize(t,[40 20],'nearest'); %使用最近邻插值法放大图像 SegBw=bwdist(SegBw2); %待识别字符 if n==1 %第一位汉字识别 kmin=36; kmax=53; elseif n==2 %第二位 A~Z 字母识别 kmin=11; kmax=35; else %第三位以后是字母或数字识别 kmin=1; kmax=35; end p=zeros(1,40); for k2=kmin:kmax fname=strcat('F:\MATLAB\workspace\字符模板\',liccode(k2),'.jpg'); SamBw2 = imread(fname); SamBw=bwdist(SamBw2);%模板字符 p(1,k2)=corr2(SegBw,SamBw);%计算相关系数 end [dest,I