车牌识别程序及说明

1、数字图像处理综合实验报告 车牌识别技术（LPR）1.图像灰度化及滤波I=imread('yuan.bmp');figure; subplot(2,2,1);imshow(I);title('原图');I1=rgb2gray(I);%图象灰度化subplot(2,2,2);imshow(I1);title('灰度化图');m2=medfilt2(I1,5,5);subplot(2,2,3);imshow(m2);title('中值滤波');w2=wiener2(I1,5,5);%维纳滤波subplot(2,2,4);imshow(w

2、2);title('维纳滤波');2.图像二值化fmax1=double(max(max(I1);fmin1=double(min(min(I1);level=(fmax1-(fmax1-fmin1)/3)/255;bw2=im2bw(I1,level);bw22=double(bw2);figure,imshow(bw22),title('图像二值化');3.边缘检测及开运算闭运算lubo=edge(bw22,'log');figure;subplot(2,2,1);imshow(bw2);title('Log算子边缘检测');

3、%图象边缘检测bg1=imclose(lubo,strel('rectangle',1,33);subplot(2,2,2),imshow(bg1);title('图像闭运算1,33');bg3=imopen(bg1,strel('rectangle',1,33);subplot(2,2,3),imshow(bg3);title('图像开运算1,33');bg2=imopen(bg3,strel('rectangle',12,1);subplot(2,2,4),imshow(bg2);title('图像开1

4、5,1');4. 图像标记L,num = bwlabel(bg2,8);%标注二进制图像中已连接的部分%Feastats = imfeature(L,'basic');%计算图像区域的特征尺寸%Area=Feastats.Area;%区域面积%BoundingBox=Feastats.BoundingBox;%x y width height车牌的框架大小%RGB = label2rgb(L, 'spring', 'k', 'shuffle'); %标志图像向RGB图像转换figure,imshow(L);title(&#

5、39;图像标记');%输出框架的彩色图像5. 形态滤波I5=bwareaopen(L,1000);%去除聚团灰度值小于2000的部分figure,imshow(I5),title('形态滤波后图像');6. 形态滤波车牌定位及字符分割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)

6、,title('行方向像素点灰度值累计和'),xlabel('行值'),ylabel('像素'); PY1=MaxY; while (Y1(PY1,1)>=50)&&(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(

7、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('像数');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,PX

8、1:PX2,:); subplot(3,2,3),imshow(dw),title('定位剪切后的彩色车牌图像')if isrgb(dw) II1 = rgb2gray(dw); %将RGB图像转化为灰度图像else II1=dw;endg_max=double(max(max( II1);g_min=double(min(min( II1);T=round(g_max-(g_max-g_min)/3); % T 为二值化的阈值m,n=size(II1);% d:二值图像%h=graythresh(I1);II=im2bw(II1,T/256);subplot(3,2,4);i

9、mshow(II),title('二值化车牌图像');I2=bwareaopen(II,20);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)&&(P

10、y0<y1) Py0=Py0+1;endPy1=Py0; while(Y1(Py1,1)>=20)&&(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;plot(0:x1-1,X1),title('列方向像素点灰度值累

11、计和'),xlabel('列值'),ylabel('累计像素量');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)>=4)&&(Px1<x1)|(Px1-Px0)<10) Px1=Px1+1; end Z=I2(:,Px0:Px1,:); switch strcat('Z',num2str(i) case 'Z1'

12、 PIN0=Z; case 'Z2' PIN1=Z; case 'Z3' PIN2=Z; case 'Z4' PIN3=Z; case 'Z5' PIN4=Z; case 'Z6' PIN5=Z; otherwise PIN6=Z; end figure(8); subplot(1,7,i); imshow(Z);Px0=Px1;end Px0=1;Px1=1;%分割字符%for i=1:7 while (X1(1,Px0)<3)&&(Px0<x1) Px0=Px0+1; end Px1

13、=Px0; while (X1(1,Px1)>=4)&&(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' PIN2=Z; case 'Z4' PIN3=Z; case 'Z5' PIN4=Z; case 'Z6' PIN5=Z; otherwise PIN6=Z; end figure(8); subplot(1,7,i); imshow(Z);Px0=Px1;end附录：子函数function inpt = pretreatment(dw)%YUCHULI Summary of this function goes here% Detailed explanation