function []=main(jpg)
close all
clc
tic %测定算法执行的时间
[fn,pn,fi]=uigetfile('BMW.jpg','选择图片');%读入图片
I=imread([pn fn]);
figure,imshow(I);title('原始图像');%显示原始图像
Im1=rgb2gray(I);
figure(2),subplot(1,2,1),
imshow(Im1);
title('灰度图');
figure(2),
subplot(1,2,2),
imhist(Im1);
title('灰度图的直方图');%显示图像的直方图
Tiao=imadjust(Im1,[0.19,0.78],[0,1]); %调整图片
figure(3),
subplot(1,2,1),
imshow(Tiao);title('增强灰度图');
figure(3),
subplot(1,2,2),
imhist(Tiao);
title('增强灰度图的直方图');
Im2=edge(Im1,'sobel',0.15,'both'); %使用sobel算子进行边缘检测
figure(4),
imshow(Im2);
title('sobel算子实现边缘检测')
se=[1;1;1];
Im3=imerode(Im2,se); %图像腐蚀
figure(5),
imshow(Im3);
title('腐蚀效果图');
se=strel('rectangle',[25,25]); % 创建由指定形状的结构元素，矩形
Im4=imclose(Im3,se);%对图像实现闭运算figure(6),
imshow(Im4);
title('平滑图像的轮廓');
Im5=bwareaopen(Im4,2000); %删除小面积图形
figure(7),
imshow(Im5);
title('移除小对象');
[y,x,z]=size(Im5);
Im6=double(Im5);
Blue_y=zeros(y,1);%创建元素为零的数组或矩阵y*1
for i=1:y
for j=1:x
if(Im6(i,j,1)==1)
Blue_y(i,1)= Blue_y(i,1)+1; %根据Im5的y值确定
end
end
end
[temp MaxY]=max(Blue_y);%垂直方向车牌区域确定
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,:,:);
Blue_x=zeros(1,x);
for j=1:x
for i=PY1:PY2
if(Im6(i,j,1)==1)
Blue_x(1,j)= Blue_x(1,j)+1; %根据Im5的x值确定
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;%对车牌区域的校正
PX2=PX2+1;
dw=I(PY1:PY2-6,PX1:PX2 ,:);
figure(8),
subplot(1,2,1),
imshow(IY),
title('垂直方向合理区域');
figure(8),
subplot(1,2,2),
imshow(dw),
title('定位剪切后的彩色车牌图像')
imwrite(dw,'dw.jpg'); %把图像写入图形文件中
a=imread('dw.jpg');
b=rgb2gray(a);
imwrite(b,'车牌灰度图像.jpg');
figure(9);
subplot(3,2,1),
imshow(b),
title('1.车牌灰度图像')
g_max=double(max(max(b)));
g_min=double(min(min(b)));
T=round(g_max-(g_max-g_min)/3); %T为设定的二值化的阈值
[m,n]=size(b);
d=(double(b)>=T); % d为二值图像
imwrite(d,'车牌二值图像.jpg');
figure(9);
subplot(3,2,2),
imshow(d),
title('2.车牌二值图像')
figure(9),
subplot(3,2,3),
imshow(d),
title('3.均值滤波前')
h=fspecial('average',3);
d=im2bw(round(filter2(h,d)));
imwrite(d,'均值滤波后.jpg');
figure(9),
subplot(3,2,4),
imshow(d),
title('4.均值滤波后')
se=eye(2);
[m,n]=size(d); % d为二值图像
if bwarea(d)/m/n>=0.365
d=imerode(d,se);
elseif bwarea(d)/m/n<=0.235
d=imdilate(d,se);
end
imwrite(d,'膨胀或腐蚀处理后.jpg');
figure(9),
subplot(3,2,5),
imshow(d),
title('5.膨胀或腐蚀处理后')
d=QieGe(d);%寻找连续有文字的块
[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;
end
end
d=QieGe(d);
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
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;
end
d(:,[1:wide])=0;d=QieGe(d);
end
end
[word2,d]=FenGe(d);%分割出第二个字符
[word3,d]=FenGe(d);%分割出第三个字符
[word4,d]=FenGe(d);%分割出第四个字符
[word5,d]=FenGe(d);%分割出第五个字符
[word6,d]=FenGe(d);%分割出第六个字符
[word7,d]=FenGe(d);%分割出第七个字符
word1=imresize(word1,[40 20]);%模板字符大小统一为40*20为字符辨认做准备
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]);
figure(10)
subplot(2,7,1),
imshow(word1),
title('1');
subplot(2,7,2),
imshow(word2),
title('2');
subplot(2,7,3),
imshow(word3),
title('3');
subplot(2,7,4),
imshow(word4),
title('4');
subplot(2,7,5),
imshow(word5),
title('5');
subplot(2,7,6),
imshow(word6),
title('6');
subplot(2,7,7),
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' '京辽鲁陕苏浙']); %建立自动识别字符代码表，顺序应与文件夹中的相同
l=1;
for I=1:7
ii=int2str(I); %将整数转换为字符串
t=imread([ii,'.jpg']);
SegBw2=imresize(t,[40 20],'nearest'); %改变图像的大小
if l==1 %第一位汉字识别
kmin=37;
kmax=40;
elseif l>=2&&l<=3 %第二、三位 A~Z 字母识别，可根据车牌情况做修改
kmin=11;
kmax=36;
elseif l>=4 & l<=7%第三、四位 0~9 A~Z字母和数字识别，可根据车牌情况做修改
kmin=1;
kmax=10;
end
for k2=kmin:kmax
fname=strcat('字符模板\',liccode(k2),'.jpg');
SamBw2 = imread(fname);
Dm=0;
for k1=1:40
for l1=1:20
if SegBw2(k1,l1)==SamBw2(k1,l1)
Dm=Dm+1; %判断分割字符与模板字符的相似度
end
end
end
Error(k2)=Dm;
end
Error1=Error(kmin:kmax);
MinError=max(Error1);
findc=find(Error1==MinError);%返回矩阵中非0项的坐标
Resault(l*2-1)=liccode(findc(1)+kmin-1);
Resault(l*2)=' ';
l=l+1;
end
t=toc
Resault
msgbox(Resault,'识别结果')
fid=fopen('Data.xls','a+');
fprintf(fid,'%s\r\n',Resault,datestr(now));
fclose(fid); %将识别结果保存在Data.xls中
function [word,result]=FenGe(d) %定义分割字符用函数（1）
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]));
[m1,n1]=size(temp);
if wide<y1 && n1/m1>y2
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;
function e=QieGe(d) %定义分割字符用函数（2）
[m,n]=size(d);
top=1;bottom=m;left=1;right=n; % init
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<=n
left=left+1;
end
while sum(d(:,right))==0 && right>=1
right=right-1;
end
dd=right-left;
hh=bottom-top;
e=imcrop(d,[left top dd hh]);
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