matlab车道线检测

1、%a%A=imread();r c d=size(A);r2g=zeros(r,c);red=zeros(r,c);green=zeros(r,c);blue=zeros(r,c);rg=zeros(r,c);for i=1:r;for j=1:c;red(i,j)=A(i,j,1);烟取图像的红色分量green(i,j)=A(i,j,2);%S取图像的绿色分量blue(i,j)=A(i,j,3);%g取图像的蓝色分量endendfor i=1:r;for j=1:c;rg(i,j)=*red(i,j)+*green(i,j);endendrg=uint8(rg);for i=1:r;for

2、j=1:c;if rg(i,j)178;rg(i,j)=255;endendendfigure;subplot(2,2,1);imshow(A);title( 原图)% 显示原图像subplot(222);imshow(rg);title( 彩色通道提取法-灰度图);% figurer2g=rg;i=r2g; %俞入灰度变换后的图像subplot(221);imshow(i);title( 原图)subplot(223);imhist(i);%1 示直方图h1=histeq(i);subplot(222);imshow(h1);title( 直方图均衡化后的图)subplot(224);imh

3、ist(h1);%i=h1;%:方图均衡化后的图像 j=imnoise(i, salt & pepper , k1=medfilt2(j);figure;subplot(121);imshow(j);title( 添加椒盐噪声图像)subplot(122);imshow(k1);title( 3*3 模板中值滤波)% clcr2g;figure;subplot(221);imshow(r2g);title(原图像)；W_H1=2 3 0; %选用自定义差分模板增强左车道标记线3 0 -3;0 -3 -2;W_V1= 0 3 2; %选用自定义差分模板增强右车道标记线-3 0 3;-2 -3 0

4、;T = ;% the threshold in the 2-valueI = r2g; % read the image height,width = size(I);I1 = double(I);13 = double(I);14 = double(I);I2 = zeros(height+2,width+2);% put the images data into a biggerarray to void the edgeI2(2:height+1,2:width+1) = I1;for i=2:height+1% move the window and calculate thegra

5、dsfor j=2:width+1sum3 = 0;%不同方向的模板算子sum4 = 0;for m=-1:1for n=-1:1sum3= sum3 + W_H1(m + 2,n + 2) * I2(i + m,j + n); end end for m=-1:1for n=-1:1sum4 = sum4 + W_V1(m + 2,n + 2) * I2(i + m,j + n); end endgrey1 = abs(sum3) + abs(sum4);I3(i-1,j-1) = grey1; end endbig = max(max(I3);% 归一化small = min(min(I3

6、);for i=1:heightfor j=1:widthI3(i,j) = (I3(i,j)-small)/(big - small);% 归一化if (I3(i,j) T)I3(i,j) = 1;% 二值化elseI3(i,j) = 0; end end end subplot(222); imshow(I3);title( sl、sr算子处理的图像)%figure;subplot(221);imshow(A);title( gg=bwmorph(I3, thin ,inf);subplot(222);imshow(gg);title(I = rg;x,y=size(I);height,w

7、idth = size(I);seedx=round(x);seedy=round(y/2);gr=I(seedx,seedy)W_H = 1 1 1;1 1 1;1 1 1;11 = double(I);12 = zeros(height+2,width+2);array to void the edgeI2(2:height+1,2:width+1) = I1;for i=2:height+1gradsfor j=2:width+1原图)细化的图像)% the model in the horizon direction% put the images data into a bigger

8、% move the window and calculate thesum1 = 0;% the cumulusfor m=-1:1for n=-1:1sum1 = sum1 + W_H(m + 2,n + 2) * I2(i + m,j + n);endendgrey=sum1/9;I1(i-1,j-1) = grey;endendI1=uint8(I1);%邻域平均化灰度图像%subplot(222);imshow(I1);title(区域生长-路面区域图像)x,y=size(I1);I2=zeros(x,y);I=double(I);I1=double(I1);for i=1:x;fo

9、r j=1:y;if abs(I1(i,j)-I(i,j)=70&abs(I(seedx,seedy)-I1(i,j)=90)I2(i,j)=1;endendendsubplot(223)imshow(I2);title( 区域生长-路面区域图像)I4=zeros(x,y);for i=round(5):x-4;for j=5:y-4;if gg(i,j)=1for m=i-4:i+4;for n=j-4:j+4;if I2(m,n)=0&sqrt(i-m)A2+(j-nA2)=2I4(i,j)=1;endendendendendendsubplot(224)imshow(I4);title(

10、 检测图像)%clc;t0=clockff=I4; %俞入检测的图像x,y=size(ff);a1=zeros(x,1);b1=zeros(y,1);k=1;for i=1:xfor j=1:round(y/2);if ff(i,j)=1;a1(k)=i;b1(k)=j;k=k+1;endendendm=length(a1);a2=max(a1)h=1;for i=1:m;if a1(i)=a2;jiaobiao(h)=i;h=h+1;endendb1=b1(jiaobiao);b11=max(b1);%ff(a1,b1)为选中的车道线第一个像素点 k=1;for i=round(1):rou

11、nd(x);for j=1:round(y/2 );if ff(i,j)=1&i=a2&j=b11;kkb(k)=(b11-j)/(a2-i);bbc(k)=b11-kkb(k)*a2;k=k+1;endend endtheta=atan(-1./kkb); theta1= theta+pi, roi=bbc.*sin(theta); roi1= roi+abs(roi); maxtheta=max( thetal); maxroi=max(roi1);accum=zeros(round(maxtheta)+1,round( maxroi)+1); for i=1:length(theta);

12、thetaint=round( maxtheta/2+theta1(i)/2);roiint=round( maxroi/2+roi1(i)/2)+1;accum(thetaint,roiint)=accum( thetaint,roiint)+1; endp=max(max(accum) %出现峰值处的累加器的值 for i=1:length(theta);thetaint=round( maxtheta/2+theta1(i)/2);roiint=round( maxroi/2+roi1(i)/2)+1;if accum(thetaint,roiint)=p;ji=i;endendk=1;m=1;for i=round(x/2):x;for j=1:round(y/2);if ff(i,j)=1&i=a2&j=b11;kk(k)=(b11-j)/(a2-i);bb(k)=b11-kk(k)*a2;theta(k)=atan(-1./kk(k); if theta(k) =theta