[License plate recognition] Parking space management system based on MATLAB simulation

function varargout = slyarh_main(varargin)
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @slyarh_main_OpeningFcn, ...
                   'gui_OutputFcn',  @slyarh_main_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
function slyarh_main_OpeningFcn(hObject, eventdata, handles, varargin)
handles.output = hObject;
guidata(hObject, handles);
function varargout = slyarh_main_OutputFcn(hObject, eventdata, handles) 
varargout{1} = handles.output;

%“※﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏ 启动画面 ﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏ ※”
ImageFile  = '.\字符模板\wel.png';
ScreenSize = get(0,'ScreenSize');
jImage     = im2java(imread(ImageFile));
jfBounds   = num2cell([...
    (ScreenSize(3)-jImage.getWidth)/2 ...
    (ScreenSize(4)-jImage.getHeight)/2 ...
    jImage.getWidth ...
    jImage.getHeight]);
jFrame     = javax.swing.JFrame;
icon       = javax.swing.ImageIcon(jImage);
label      = javax.swing.JLabel(icon);
jFrame.getContentPane.add(label);
jFrame.setUndecorated(true)
jFrame.setBounds(jfBounds{:});
jFrame.pack
jFrame.show
pause(3)
jFrame.dispose
%“※﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏  ﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏ ※”

%“※﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏ 背景和logo ﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏ ※”
h = handles.figure1; %创建一个Figure，并返回其句柄
newIcon = javax.swing.ImageIcon('.\字符模板\logo.png')
figFrame = get(h,'JavaFrame'); %取得Figure的JavaFrame。
figFrame.setFigureIcon(newIcon); %修改图标
ha=axes('units','normalized','pos',[0 0 1 1]);
uistack(ha,'down');
ii=imread('.\字符模板\bg1.png');
%设置程序的背景图为beijing1.jpg
image(ii);
colormap gray
set(ha,'handlevisibility','off','visible','off');
%“※﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏  ﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏ ※”

%“※﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏ 停车 ﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏ ※”
function bt_in_Callback(hObject, eventdata, handles)
[filename pathname]=uigetfile({'*.jpg';'*.png';'*.bmp'}, '正在拍摄入场的车辆~');
I=imread([pathname '\' filename]);
handles.I=I;
guidata(hObject, handles);
axes(handles.axes1);imshow(I);title('汽车靓照');
axes(handles.axes11);imshow(I);title('汽车靓照');
imwrite(I,'.\进车记录\原.jpg');
set(handles.bt_new,'visible','on');
set(handles.ui_in,'visible','off');
set(handles.ui_out,'visible','on');
%“※﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏ 进车 ﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏﹋﹏ ※”
I=handles.I;
I1=rgb2gray(I);
I2=edge(I1,'roberts',0.18,'both');
imwrite(I1,'.\进车记录\灰度.jpg');
imwrite(I2,'.\进车记录\边缘.jpg');
se=[1;1;1];
I3=imerode(I2,se);%腐蚀操作
imwrite(I2,'.\进车记录\腐蚀.jpg');
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)&&(PY2<y))
    PY2=PY2+1;
end
IY=I(PY1:PY2,:,:);
%X方向车牌区域确定
Blue_x=zeros(1,x);%进一步确认x方向的车牌区域
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))
    PX1=PX1+1;
end
PX2=x;
while((Blue_x(1,PX2)<3)&&(PX2>PX1))
    PX2=PX2-1;
end
PX1=PX1-1;%对车牌区域的矫正
PX2=PX2+1;
dw=I(PY1:PY2-8,PX1:PX2,:);
t=toc;
imwrite(dw,'.\进车记录\定位.jpg');
b=rgb2gray(dw);%将车牌图像转换为灰度图
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,'.\进车记录\二值.jpg');
% 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,'.\进车记录\均值.jpg');
% 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(I2,'.\进车记录\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 wide<y1 %认为是左干扰 
        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.axes2);imshow(word1);
axes(handles.axes3);imshow(word2);
axes(handles.axes4);imshow(word3);
axes(handles.axes5);imshow(word4);
axes(handles.axes6);imshow(word5);
axes(handles.axes7);imshow(word6);
axes(handles.axes8);imshow(word7);
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Version: 2014 a