基于matlab车牌的定位与分割识别程序

1、基于Matlab的车牌定位与分割经典算法I=imread('car.jpg'); %读取图像figure(); subplot(3,2,1),imshow(I), title('原始图像');I1=rgb2gray(I);%转化为灰度图像subplot(3,2,2),imshow(I1),title('灰度图像');I2=edge(I1,'robert',0.09,'both');%采用robert算子进行边缘检测subplot(3,2,3),imshow(I2),title('边缘检测后图像');

2、se=1;1;1; %线型结构元素 I3=imerode(I2,se); %腐蚀图像subplot(3,2,4),imshow(I3),title('腐蚀后边缘图像');se=strel('rectangle',25,25); 矩形结构元素I4=imclose(I3,se);%图像聚类、填充图像subplot(3,2,5),imshow(I4),title('填充后图像');I5=bwareaopen(I4,2000);%去除聚团灰度值小于2000的部分subplot(3,2,6),imshow(I5),title('形态滤波后图像

3、9;);y,x,z=size(I5);I6=double(I5); Y1=zeros(y,1); for i=1:y for j=1:x if(I6(i,j,1)=1) Y1(i,1)= Y1(i,1)+1; end end end temp MaxY=max(Y1); figure(); subplot(3,2,1),plot(0:y-1,Y1),title('行方向像素点灰度值累计和'),xlabel('行值'),ylabel('像素'); %求的车牌的行起始位置和终止位置 PY1=MaxY; while (Y1(PY1,1)>=50)

4、&&(PY1>1) PY1=PY1-1; end PY2=MaxY; while (Y1(PY2,1)>=50)&&(PY2<y) PY2=PY2+1; end IY=I(PY1:PY2,:,:); X1=zeros(1,x); for j=1:x for i=PY1:PY2 if(I6(i,j,1)=1) X1(1,j)= X1(1,j)+1; end end end subplot(3,2,2),plot(0:x-1,X1),title('列方向像素点灰度值累计和'),xlabel('列值'),ylabel(

5、'像数');%求的车牌的列起始位置和终止位置PX1=1; while (X1(1,PX1)<3)&&(PX1<x) PX1=PX1+1; end PX2=x; while (X1(1,PX2)<3)&&(PX2>PX1) PX2=PX2-1; end PX1=PX1-1; PX2=PX2+1; %分割出车牌图像%dw=I(PY1:PY2,PX1:PX2,:); subplot(3,2,3),imshow(dw),title('定位剪切后的彩色车牌图像')4.2 车牌字符分割确定车牌位置后下一步的任务就是进行

6、字符切分分离出车牌号码的全部字符图像。if isrgb(I) I1 = rgb2gray(I); %将RGB图像转化为灰度图像else I1=I； endg_max=double(max(max(I1);g_min=double(min(min(I1);T=round(g_max-(g_max-g_min)/3); % T 为二值化的阈值m,n=size(I1);% d:二值图像%h=graythresh(I1);I1=im2bw(I1,T/256);subplot(3,2,4);imshow(I1),title('二值化车牌图像');I2=bwareaopen(I1,20);

7、subplot(3,2,5);imshow(I2),title('形态学滤波后的二值化图像');y1,x1,z1=size(I2);I3=double(I2);TT=1;%去除图像顶端和底端的不感兴趣区域%Y1=zeros(y1,1); for i=1:y1 for j=1:x1 if(I3(i,j,1)=1) Y1(i,1)= Y1(i,1)+1 ; end end endPy1=1;Py0=1;while (Y1(Py0,1)<20)&&(Py0<y1) Py0=Py0+1;endPy1=Py0; while(Y1(Py1,1)>=20)&

8、amp;&(Py1<y1) Py1=Py1+1; endI2=I2(Py0:Py1,:,:);subplot(3,2,6);imshow(I2),title('目标车牌区域');% 分割字符按行积累量%X1=zeros(1,x1);for j=1:x1 for i=1:y1 if(I3(i,j,1)=1) X1(1,j)= X1(1,j)+1; end end endfigure(5);plot(0:x1-1,X1),title('列方向像素点灰度值累计和'),xlabel('列值'),ylabel('累计像素量')

9、;Px0=1;Px1=1;%分割字符for i=1:7 while (X1(1,Px0)<3)&&(Px0<x1) Px0=Px0+1; end Px1=Px0; while (X1(1,Px1)>=3)&&(Px1<x1)|(Px1-Px0)<10) Px1=Px1+1; end Z=I2(:,Px0:Px1,:); switch strcat('Z',num2str(i) case 'Z1' PIN0=Z; case 'Z2' PIN1=Z; case 'Z3' P

10、IN2=Z; case 'Z4' PIN3=Z; case 'Z5' PIN4=Z; case 'Z6' PIN5=Z; otherwise PIN6=Z; end figure(3); subplot(1,7,i); imshow(Z); Px0=Px1;Endfunction inpt = pretreatment(I)%YUCHULI Summary of this function goes here% Detailed explanation goes hereif isrgb(I) I1 = rgb2gray(I);else I1=I

11、;endI1=imresize(I1,50 20);%将图片统一划为50*20大小I1=im2bw(I1,0.9);m,n=size(I1);inpt=zeros(1,m*n);%将图像按列转换成一个行向量for j=1:n for i=1:m inpt(1,m*(j-1)+i)=I1(i,j); endend%神经网络进行识别。close all;clear all;%归一化训练样本%I0=pretreatment(imread('0.jpg');I1=pretreatment(imread('1.jpg');I2=pretreatment(imread(&#

12、39;2.jpg');I3=pretreatment(imread('3.jpg');I4=pretreatment(imread('4.jpg');I5=pretreatment(imread('5.jpg');I6=pretreatment(imread('6.jpg');I7=pretreatment(imread('7.jpg');I8=pretreatment(imread('8.jpg');I9=pretreatment(imread('9.jpg');I10=p

13、retreatment(imread('A.jpg');I11=pretreatment(imread('C.jpg');I12=pretreatment(imread('G.jpg');I13=pretreatment(imread('L.jpg');I14=pretreatment(imread('M.jpg');I15=pretreatment(imread('R.jpg');I16=pretreatment(imread('H.jpg');I17=pretreatment(i

14、mread('N.jpg');P=I0',I1',I2',I3',I4',I5',I6',I7',I8',I9',I10',I11',I12',I13',I14',I15',I16',I17'%输出样本%T=eye(18,18);%bp神经网络参数设置net=newff(minmax(P),1000,32,18,'logsig','logsig','logsig','trainrp

15、');net.inputWeights1,1.initFcn ='randnr'net.layerWeights2,1.initFcn ='randnr'net.trainparam.epochs=5000;net.trainparam.show=50;%net.trainparam.lr=0.003;net.trainparam.goal=0.0000000001;net=init(net);%训练样本%net,tr=train(net,P,T);%测试%I=imread('DSC01323.jpg');I=imread('DS

16、C01344.jpg');dw=location(I);%车牌定位PIN0,PIN1,PIN2,PIN3,PIN4,PIN5,PIN6=StringSplit(dw);%字符分割及处理%测试字符，得到识别数值%PIN0=pretreatment(PIN0);PIN1=pretreatment(PIN1);PIN2=pretreatment(PIN2);PIN3=pretreatment(PIN3);PIN4=pretreatment(PIN4);PIN5=pretreatment(PIN5);PIN6=pretreatment(PIN6);P0=PIN0',PIN1',

17、PIN2',PIN3',PIN4',PIN5',PIN6'for i=2:7 T0= sim(net ,P0(:,i); T1 = compet (T0) ; d =find(T1 = 1) - 1 if (d=10) str='A' elseif (d=11) str='C' elseif (d=12) str='G' elseif (d=13) str='L' elseif (d=14) str='M' elseif (d=15) str='R' elseif (d=16) str='H' elseif (d=17) str='N'