matlab 车道线检测

1、clc%读图最后得到灰度图像rgA=imread(999.png);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);%提取图像的红色分量 green(i,j)=A(i,j,2);%提取图像的绿色分量 blue(i,j)=A(i,j,3);%提取图像的蓝色分量 endendfor i=1:r; for j=1:c; rg(i,j)=0.5*red(i,j)+0.5*green(i,j)

2、; endendrg=uint8(rg); for i=1:r; for j=1:c; if rg(i,j)178; rg(i,j)=255; end endendfigure;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;%输入灰度变换后的图像subplot(221);imshow(i);title(原图)subplot(223);imhist(i);%显示直方图h1=histeq(i);subplot(222);imshow(h1

3、);title(直方图均衡化后的图)subplot(224);imhist(h1);%i=h1;%直方图均衡化后的图像j=imnoise(i,salt & pepper,0.02)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;

4、%选用自定义差分模板增强右车道标记线 -3 0 3; -2 -3 0;T = 0.28; % the threshold in the 2-valueI = r2g; % read the imageheight,width = size(I);I1 = double(I);I3 = double(I);I4 = double(I);I2 = zeros(height+2,width+2); % put the images data into a bigger array to void the edgeI2(2:height+1,2:width+1) = I1;for i=2:height

5、+1 % move the window and calculate the grads for j=2:width+1 sum3 = 0; % 不同方向的模板算子 sum4 = 0; for m=-1:1 for n=-1:1 sum3= sum3 + W_H1(m + 2,n + 2) * I2(i + m,j + n); end end for m=-1:1 for n=-1:1 sum4 = sum4 + W_V1(m + 2,n + 2) * I2(i + m,j + n); end end grey1 = abs(sum3) + abs(sum4); I3(i-1,j-1) = g

6、rey1; endendbig = max(max(I3); % 归一化small = min(min(I3);for i=1:height for j=1:width I3(i,j) = (I3(i,j)-small)/(big - small); % 归一化 if(I3(i,j) T) I3(i,j) = 1; % 二值化 else I3(i,j) = 0; end endend subplot(222);imshow(I3);title(sl、sr算子处理的图像)%figure;subplot(221);imshow(A);title(原图)gg=bwmorph(I3,thin,inf)

7、;subplot(222);imshow(gg);title(细化的图像)I = rg;x,y=size(I);height,width = size(I);seedx=round(x);seedy=round(y/2);gr=I(seedx,seedy)W_H = 1 1 1; % the model in the horizon direction 1 1 1; 1 1 1;I1 = double(I);I2 = zeros(height+2,width+2); % put the images data into a bigger array to void the edgeI2(2:h

8、eight+1,2:width+1) = I1;for i=2:height+1 % move the window and calculate the grads for j=2:width+1 sum1 = 0; % the cumulus for m=-1:1 for n=-1:1 sum1 = sum1 + W_H(m + 2,n + 2) * I2(i + m,j + n); end end grey=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

9、-路面区域图像)x,y=size(I1);I2=zeros(x,y);I=double(I);I1=double(I1);for i=1:x; for j=1:y; if abs(I1(i,j)-I(i,j)=70&abs(I(seedx,seedy)-I1(i,j)=90) I2(i,j)=1; end endendsubplot(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)=1 for m=i-4:i+4; for n=j-4:j+4; if I2(m,n

10、)=0&sqrt(i-m)2+(j-n2)=2 I4(i,j)=1; end endendendendendsubplot(224)imshow(I4);title(检测图像) %clc;t0=clockff=I4;%输入检测的图像x,y=size(ff);a1=zeros(x,1);b1=zeros(y,1);k=1;for i=1:x for j=1:round(y/2); if ff(i,j)=1; a1(k)=i; b1(k)=j; k=k+1; end endendm=length(a1);a2=max(a1)h=1;for i=1:m; if a1(i)=a2; jiaobiao(

11、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):round(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; end endend theta=atan(-1./kkb); theta1= theta+pi, roi=bbc.*sin(theta); roi1= roi+abs(roi); maxtheta=max

12、( theta1); maxroi=max(roi1); accum=zeros(round(maxtheta)+1,round( maxroi)+1); for i=1:length(theta); thetaint=round( maxtheta/2+theta1(i)/2); roiint=round( maxroi/2+roi1(i)/2)+1; accum(thetaint,roiint)=accum( thetaint,roiint)+1; end p=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; endend k=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) =the