车牌智能识别_车牌识别_matlab

clc; clear; close all; I=imread('1.png'); figure(1),imshow(I);title('原图'); I1=rgb2gray(I); figure(2),subplot(1,2,1),imshow(I1);title('灰度图'); figure(2),subplot(1,2,2),imhist(I1);title('灰度图直方图'); I2=edge(I1,'canny',[0.2,0.55]); figure(3),imshow(I2);title('canny算子边缘检测') se=[1;1;1]; I3=imerode(I2,se); figure(4),imshow(I3);title('腐蚀后图像'); se=strel('rectangle',[40,40]); I4=imclose(I3,se); figure(5),imshow(I4);title('平滑图像的轮廓'); I5=bwareaopen(I4,2000); figure(6),imshow(I5);title('从对象中移除小对象'); [y,x,z]=size(I5); myI=double(I5); %begin横向扫描 Blue_y=zeros(y,1); for i=1:y for j=1:x if(myI(i,j,1)==1) %如果myI(i,j,1)即myI图像中坐标为(i,j)的点为蓝色 %则Blue_y的相应行的元素white_y(i,1)值加1 Blue_y(i,1)= Blue_y(i,1)+1;%蓝色像素点统计 end end end [temp MaxY]=max(Blue_y);%temp为向量white_y的元素中的最大值，MaxY为该值的索引（ 在向量中的位置） PY1=MaxY; while ((Blue_y(PY1,1)>=50)&&(PY1>1)) PY1=PY1-1; end PY2=MaxY; while ((Blue_y(PY2,1)>=10)&&(PY2<y)) PY2=PY2+1; end IY=I(PY1:PY2,:,:); %IY为原始图像I中截取的纵坐标在PY1：PY2之间的部分 %end横向扫描 %begin纵向扫描 Blue_x=zeros(1,x);%进一步确定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; PX2=PX2+15; PY1=PY1-2.5; %end纵向扫描 dw=I(PY1:PY2,PX1:PX2,:); imwrite(dw,'dw.jpg'); figure(7),subplot(1,2,1),imshow(IY),title('行方向合理区域'); figure(7),subplot(1,2,2),imshow(dw),title('定位剪切后的彩色车牌图像') %利用radon变换做水平矫正 bw=rgb2gray(dw); bw1=edge(bw,'sobel','horizontal'); theta=0:179; r=radon(bw1,theta); [m,n]=size(r); c=1; for i=1:m for j=1:n if r(1,1)<r(i,j) r(1,1)=r(i,j); c=j; end end end rot=90-c+2; pic=imrotate(bw,rot,'crop'); figure(8),subplot(3,1,1),imshow(bw),title('1.定位后的这牌灰度图像'); figure(8),subplot(3,1,2),imshow(pic),title('2.利用radon算子做水平方向矫正'); %利用radon变换做垂直方向的矫正 binaryImage = edge(pic,'canny'); binaryImage = bwmorph(binaryImage,'thicken'); theta = -90:89; [R,xp] = radon(binaryImage,theta); %%%%% [R1,r_max] = max(R); theta_max = 90; while (theta_max > 50 || theta_max<-50) [R2,theta_max] = max(R1); R1(theta_max) = 0; theta_max = theta_max - 91; end %角度计算完毕 H=[1,0,0; tan(-theta_max),1,0;0,0,1]; T=maketform('affine',H); pic=imtransform(pic,T); figure(8),subplot(3,1,3), imshow(pic);title('3.利用radon算子做垂直方向矫正'); %字符分割前的预处理 g_max=double(max(max(pic))); g_min=double(min(min(pic))); T=round(g_max-(g_max-g_min)/3); % T 为二值化的阈值 [m,n]=size(pic); d=im2bw(pic,T/256); % d:二值图像 figure(9);subplot(2,2,1),imshow(d),title('1.车牌二值图像') % 滤波 h=fspecial('average',3); d=im2bw(round(filter2(h,d))); figure(9),subplot(2,2,2),imshow(d),title('2.均值滤波后') % 某些图像进行操作 % 膨胀或腐蚀 se=strel('square',3); % 使用一个3X3的正方形结果元素对象对创建的图像膨胀 se=eye(2); % eye(n) returns the n-by-n identity matrix 单位矩阵 [m,n]=size(d); if bwarea(d)/m/n>=0.365 d=imerode(d,se); elseif bwarea(d)/m/n<=0.235 d=imdilate(d,se); end figure(9),subplot(2,2,3),imshow(d),title('3.膨胀或腐蚀处理后') %对图像的边框进行裁剪，只留下有效字符部分 [y1,x1,z1]=size(d); I3=double(d); TT=1; 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 end Py1=1; Py0=1; while ((Y1(Py0,1)<30)&&(Py0<y1)) Py0=Py0+1; end Py1=Py0; while((Y1(Py1,1)>=30)&&(Py1<y1)) Py1=Py1+1; end d=d(Py0:Py1,:,:); figure(9),subplot(2,2,4); imshow(d),title('4.目标车牌区域'); %在列方向上进行灰度值累加 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 end figure(10); 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'); Px0=1; Px1=1; %%%%%%%%%%%%分割字符%%%%%%%%%%%%%%%%%% for i=1:7 while ((X1(1,Px0)<2)&&(Px0<x1)) Px0=Px0+1; end Px1=Px0; while (((X1(1,Px1)>=3)&&(Px1<x1))||((Px1-Px0)<10)) Px1=Px1+1; end Z=d(:,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(11); subplot(1,7,i); imshow(Z); Px0=Px1; end %载入训练好的神经网络% load('-mat','ym'); %将分割后的后的车牌字符归一化处理% PIN0=pretreatment(PIN0); PIN1=pretreatment(PIN1); PIN2=pretreatment(PIN2); PIN3=pretreatment(PIN3); PIN4=pretreatment(PIN4); PIN5=pretreatment(PIN5); PIN6=pretreatment(PIN6); P0=[PIN0',PIN1',PIN2',PIN3',PIN4',PIN5',PIN6']; %开始识别字符 for i=2:7 T0= sim(net ,P0(:,i)); T1 = compet (T0) ; d =find(T1 == 1)-1 if (d==0) str='A'; elseif (d==1) str='B'; elseif (d==2) str='C'; elseif (d==3) str='D'; elseif (d==4) str='E'; elseif (d==5) str='F'; elseif (d==6) str='G'; elseif (d==7) str='H'; elseif (d==8) str='I'; elseif (d==9) str='J'; elseif (d==10) str='K'; elseif (d==11) str='L'; elseif (d==12) str='M'; elseif (d==13) str='N'; elseif (d==14) str='O'; elseif (d==15) str='P'; elseif (d==16) str='Q'; elseif (d==17) str='R'; elseif (d==18) str='S'; elseif (d==19) str='T'; elseif (d==20) str='U'; elseif (d==21) str='V'; elseif (d==22) str='W'; elseif (d==23) str='X'; elseif (d==24) str='Y'; elseif (d==25) str='Z'; else str=num2str(d); end switch i case 2 str1=str; case 3 str2=str; case 4 str3=str; case 5 str4=str; case 6 str5=str; otherwise str6=str; end end s=strcat('京',str1,'.',str2,str3,str4,str5,str6); figure(12); imshow('dw.jpg'),title(s);