基于Matlab的车牌识别系统（带界面、模板库）

matlab版本：R2018A

字符模板＋工程下载：https://download.csdn.net/download/qq_43224338/11248794

GithubPage：https://github.com/zhuyinyin

建立几个需要的文件夹：

GUI界面参考例程
https://wenku.baidu.com/view/d4e31fd152d380eb63946d49.html
下面是代码需求的GUI基本界面：

代码参考博客
https://blog.csdn.net/zhangquan2015/article/details/78994551
做好界面后就会出现gui.m文件，写进如下代码：

function varargout = gui(varargin)
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @gui_OpeningFcn, ...
                   'gui_OutputFcn',  @gui_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
%结束初始化
function gui_OpeningFcn(hObject, eventdata, handles, varargin)
handles.output = hObject;
guidata(hObject, handles);
function varargout = gui_OutputFcn(hObject, eventdata, handles) 
varargout{1} = handles.output;

% ======================输入图像===============================
function pushbutton1_Callback(hObject, eventdata, handles)
[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');

% ======================图像处理===============================
function pushbutton2_Callback(hObject, eventdata, handles)
I=handles.I;
I1=rgb2gray(I);
I2=edge(I1,'roberts',0.18,'both');
axes(handles.axes2);
imshow(I1);title('灰度图');
axes(handles.axes3);
imshow(I2);title('边缘检测');
se=[1;1;1];
I3=imerode(I2,se);%腐蚀操作
se=strel('rectangle',[25,25]);
I4=imclose(I3,se);%图像聚类，填充图像
I5=bwareaopen(I4,2000);%去除聚团灰度值小于2000的部分
[y,x,z]=size(I5);%返回15各维的尺寸，存储在x,y,z中
myI=double(I5);
tic      %tic计时开始，toc结束
Blue_y=zeros(y,1);%产生一个y*1的零针
for i=1:y
    for j=1:x
        if(myI(i,j,1)==1)%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
        end
    end
end
[temp MaxY]=max(Blue_y);
%Y方向车牌区域确定
%temp为向量yellow_y的元素中的最大值，MaxY为该值得索引
PY1=MaxY;
while((Blue_y(PY1,1)>=5)&&(PY1>1))
    PY1=PY1-1;
end
PY2=MaxY;
while((Blue_y(PY2,1)>=5)&&(PY2PX1))
    PX2=PX2-1;
end
PX1=PX1-1;%对车牌区域的矫正
PX2=PX2+1;
dw=I(PY1:PY2,PX1:PX2,:);
t=toc;
axes(handles.axes4);imshow(dw),title('定位车牌');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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)));
g_min=double(min(min(b)));
T=round(g_max-(g_max-g_min)/3);%T为二值化的阈值
[m,n]=size(b);
d=(double(b)>=T);%d:二值图像
imwrite(d,'二值化.jpg');
%均值滤波前
%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)));%使用指定的滤波器h对h进行d即均值滤波
imwrite(d,'均值滤波.jpg');
%某些图像进行操作
%膨胀或腐蚀
se=eye(2);%单位矩阵
[m,n]=size(d);%返回信息矩阵
if bwarea(d)/m/n>=0.365%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
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
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=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);
%切割出7个字符
y1=10;
y2=0.25;
flag=0;
word1=[];
while flag==0
    [m,n]=size(d);
    left=1;
    wide=0;
    while sum(d(:,wide+1))~=0
        wide=wide+1;
    end
    if widey2
            flag=1;word1=temp;%word1
        end
        d(:,[1:wide])=0;d=qiege(d);
    end
end
%分割出第二至七个字符
[word2,d]=getword(d);
[word3,d]=getword(d);
[word4,d]=getword(d);
[word5,d]=getword(d);
[word6,d]=getword(d);
[word7,d]=getword(d);
[m,n]=size(word1);
%商用系统程序中归一化大小为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]);
axes(handles.axes5);imshow(word1),title('1');
axes(handles.axes6);imshow(word2),title('2');
axes(handles.axes7);imshow(word3),title('3');
axes(handles.axes8);imshow(word4),title('4');
axes(handles.axes9);imshow(word5),title('5');
axes(handles.axes10);imshow(word6),title('6');
axes(handles.axes11);imshow(word7),title('7');
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');
liccode=char(['0':'9' 'A':'Z' '辽粤豫鄂鲁陕京津']);%京津沪渝冀豫云辽黑湘皖鲁新苏浙赣鄂桂甘晋蒙陕吉闽贵粤青藏川宁琼
SubBw2=zeros(40,20);
l=1;
for I=1:7
    ii=int2str(I);
    t=imread([ii,'.jpg']);
    SegBw2=imresize(t,[40 20],'nearest');
    SegBw2=double(SegBw2)>20;
    if l==1 %第一位汉字识别
        kmin=37;
        kmax=43;
    elseif l==2 %第二位字母识别
        kmin=11;
        kmax=36;
    elseif l>=3   %第三位后字母或数字识别
        kmin=1;
        kmax=36;
    end
    for k2=kmin:kmax
        fname=strcat('字符模板\',liccode(k2),'.bmp');
        SamBw2=imread(fname);
        SamBw2=double(SamBw2)>1;
        for i=1:40
            for j=1:20
                SubBw2(i,j)=SegBw2(i,j)-SamBw2(i,j);
            end
        end
        %相当于两幅图相减得第三幅图
        Dmax=0;
        for k1=1:40
            for l1=1:20
                if(SubBw2(k1,l1)>0 || SubBw2(k1,l1)<0)
                    Dmax=Dmax+1;
                end
            end
        end
        Error(k2)=Dmax;
    end
    Error1=Error(kmin:kmax);
    MinError=min(Error1);
    findc=find(Error1==MinError);
    Code(l*2-1)=liccode(findc(1)+kmin-1);
    Code(l*2)=' ';
    l=l+1;
end
axes(handles.axes12);imshow(dw),title(['车牌号码：',Code],'Color','b');
axes(handles.axes13);imhist(I1);title('灰度化直方图');

% ==========================退出系统============================
function pushbutton3_Callback(hObject, eventdata, handles)
close(gcf);

建立子程序qiege.m用于切割处理好的车牌号码：

function e=qiege(d)
%% 功能：
%% 切割字符
[m,n]=size(d);
top=1;
bottom=m;
left=1;
right=n;
while sum(d(top,:))==0 && top<=m
    top=top+1;
end
while sum(d(bottom,:))==0 && bottom>1
    bottom=bottom-1;
end
while sum(d(:,left))==0 && left=1
    right=right-1;
end
dd=right-left;
hh=bottom-top;
e=imcrop(d,[left top dd hh]);

建立子程序getword.m用于获取字符：

function[word,result]=getword(d)
%% 功能：
%% 获取字符
word=[];
flag=0;
y1=8;
y2=0.5;
while flag==0
    [m,n]=size(d);
    wide=0;
    while sum(d(:,wide+1))~=0 && wide<=n-2
        wide=wide+1;
    end
    temp=qiege(imcrop(d,[1 1 wide m]));%用于返回图像的一个裁剪区域
    [m1,n1]=size(temp);
    if widey2
        d(:,[1:wide])=0;
        if sum(sum(d))~=0
            d=qiege(d);%切割出最小范围
        else word=[];flag=1;
        end
    else
        word=qiege(imcrop(d,[1 1 wide m]));
        d(:,[1:wide])=0;
        if sum(sum(d))~=0;
            d=qiege(d);
            flag=1;
        else d=[];
        end
    end
end
result=d;

测试结果：


ps ：只能对蓝底车牌进行识别，车牌必须比较正，斜着的无法识别。