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1、基于matlab的车牌识别的源程序clcclearfilename='E:matlab7.0car.jpg';%图片的路径I=im2gray(filename);%调用自编函数读取图像，并转化为灰度图象；tic%计时开始[height,width]=size(I);%预处理I_edge=zeros(height,width);%创建height*width矩阵fori=1:width-1%对每一列进行遍历I_edge(:,i)=abs(I(:,i+1)-I(:,i));%每列的值赋为原图像中左右两列相减的绝对值(即梯度)end
2、%归一化处理(0～255)I_edge=(255/(max(max(I_edge))-min(min(I_edge))))*(I_edge-min(min(I_edge)));[I_edge,y1]=select(I_edge,height,width);%%%%%%调用select函数选择图像的某个区域BW2=I_edge;%%%%%%%%%%%%%%%%%一些形态学处理SE=strel('rectangle',[10,10]);%创建10*10的建构元素IM2=imerode(BW2,SE);%腐蚀IM2=bwareaopen
3、(IM2,20);%删除小面积IM3=imdilate(IM2,SE);%膨胀%先腐蚀再膨胀，进行了开运算，消除小物体%%%%%%%%%%%%%%%%%%投影以粗略估计车牌位置p_h=projection(double(IM3),'h');%调用projection函数，水平方向if(p_h(1)>0)p_h=[0,p_h];endp_v=projection(double(IM3),'v');%调用projection函数，垂直方向if(p_v(1)>0)p_v=[0,p_v];end%%%%%%p_h=double((p_h>
4、5));%水平方向p_h=find(((p_h(1:end-1)-p_h(2:end))~=0));len_h=length(p_h)/2;%%%%%p_v=double((p_v>5));%垂直方向p_v=find(((p_v(1:end-1)-p_v(2:end))~=0));len_v=length(p_v)/2;%%%%%%%%%%%%%%%%%%%%%%%%%%%%粗略计算车牌候选区k=1;fori=1:len_hforj=1:len_vs=IM3(p_h(2*i-1):p_h(2*i),p_v(2*j-1):p_v(2
5、*j));if(mean(mean(s))>0.1)p{k}=[p_h(2*i-1),p_h(2*i)+1,p_v(2*j-1),p_v(2*j)+1];k=k+1;endendendk=k-1;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%进一步缩小车牌候选区fori=1:kedge_IM3=double(edge(double(IM3(p{i}(1):p{i}(2),p{i}(3):p{i}(4))),'canny'));[x,y]=find(edge_IM3==1);p{i}=[
6、p{i}(1)+min(x),p{i}(2)-(p{i}(2)-p{i}(1)+1-max(x)),...p{i}(3)+min(y),p{i}(4)-(p{i}(4)-p{i}(3)+1-max(y))];p_center{i}=[fix((p{i}(1)+p{i}(2))/2),fix((p{i}(3)+p{i}(4))/2)];p_ratio(i)=(p{i}(4)-p{i}(3))/(p{i}(2)-p{i}(1));end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%对上面参数和变量的说明：p为一胞元，用于
7、存放每个图像块的左上和右下两个点的坐标；%存放格式为：p{k}=[x1,x2,y1,y2]；x1,x2分别为行坐标，y1,y2为列坐标%p_center为一胞元,用于存放每个图像块的中心坐标,p_center{k}=[x,y];x,y分别为行,列坐标%p_ratio为一矩阵，用来存放图像块的长宽比例%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%合并临近区域%%%%%%%%如果有多个区域则执行合并ifk>1n=0;ncount=zeros(1,k);fori=1:k-1%%%需要调整if条件中的比
8、例%%%需要调整%检查是否满足合并条件if(abs(p{i}(1)+p{i}(2)-p{i+1}(1)-p{i+1}(2))<=height/30&&abs(p{i+1}(3)-p{i}(4))<=width/15)p{i+1}(1)=min(p{
