无人驾驶车道检测[C++]

个人兴趣爱好，最近在学习一些无人驾驶相关的技术，根基网上的教程用C++写了一下车道检测的实现，这里的车道检测是基础版本，需要满足几个先决条件：
（1）无人车保持在同车道的高速路中行驶
（2）车道线清晰可见
（3）无人车与同车道内前车保持足够远的距离。
现在的版本依旧有许多的问题，后续继续改进。
思路：
1.灰度转换 G=0.5*(B+R)，可以实现白线与黄线的增强
2.滤波
3.二值化
4.截取RIO
5.边缘检测
6.HoughLinesP直线检测
7.直线拟合
下面直接贴代码main.cpp
github

#include "process.h"#define endLine 320int main(){VideoCapture cap("./2.mp4");if(!cap.isOpened()){cout<< "open failed "<< endl;}int  thresh=160;Mat threshold_img,gray;Mat dstImg;Mat frame;Mat edges;Mat dilateImg;cap>>frame;gray=Mat(frame.size(),CV_8UC1);cout<< "row: "<< frame.rows << "\n"<<"cols:"<<frame.cols<<endl;int i,j;namedWindow("result");//RIO区域Mat mask = Mat(gray.size(),CV_8UC1);vector<vector<Point> > contour;vector<Point> pts;pts.push_back(Point(460,300));pts.push_back(Point(60,540));pts.push_back(Point(900,540));pts.push_back(Point(500,300));contour.push_back(pts);drawContours(mask,contour,0,Scalar::all(255),-1);VideoWriter videoWrite("video.mp4",CV_FOURCC('M','J','P','G'),30,frame.size());    while(1)    {        cap>>frame;        //G=0.5*(B+R)        for(i=0;i<gray.rows;i++)        {                uchar*  p1=gray.ptr<uchar>(i);                uchar*  p2=frame.ptr<uchar>(i);               for(j=0;j<gray.cols;j++)                {                p1[j]=0.5*(p2[3*j]+p2[3*j+2]);               }        }         //滤波        blur(gray,gray ,Size(3,3));        imshow("gray",gray);        //二值化        threshold(gray,threshold_img,thresh,255, CV_THRESH_OTSU  );        //imshow("thresh",threshold_img);        //截取RIO        threshold_img.copyTo(dstImg,mask);        //imshow("dstImg",dstImg);        //膨胀        Mat element = getStructuringElement(MORPH_RECT,Size(5,5));        dilate(dstImg,dilateImg,element);        //imshow("erodeImg",dilateImg);         //边缘检测        Canny(dilateImg,edges,120,200,3);        vector<Vec4i> lines;        Vec4i leftLine,rightLine;        vector<Vec2f>::iterator it;        //霍夫直线变换        HoughLinesP(edges, lines, 1, CV_PI/180, 50, 0,10);        cout<<"num of line is "<< lines.size()<<endl;        int m=0,n=0;        for(int j=0;j<4;j++)        {           leftLine[j]=0;           rightLine[j]=0;        }        for( size_t i = 0; i < lines.size(); i++ )        {              Vec4i  l;              l=lines[i];              float angle;              angle=atan2(l[2]-l[0],l[3]-l[1])*180/CV_PI;              if(angle>90)              {                  m++;                  for(int j=0;j<4;j++)                  {                     leftLine[j]+=l[j];                  }              }              else if(angle< 90)              {                   n++;                  for(int j=0;j<4;j++)                  {                     rightLine[j]+=l[j];                  }              }         }        //计算直线平均值        for(int j=0;j<4;j++)        {           rightLine[j]=rightLine[j]/n;           leftLine[j]=leftLine[j]/m;        }        float angle_left,angle_right;        //计算角度        angle_left=atan2(leftLine[2]-leftLine[0],leftLine[3]-leftLine[1]);        angle_right=atan2(rightLine[2]-rightLine[0],rightLine[3]-rightLine[1]);        cout << "left:"<< angle_left<<endl;        cout << "right"<< angle_right<<endl;        float leftK,rightK;        leftK=static_cast<float>(leftLine[1]-leftLine[3])/(leftLine[2]-leftLine[0]);        cout <<"leftLine 0 1 2 3 "<<leftLine[0]<<" "<<leftLine[1]<<" "<<leftLine[2]<<"  "<<leftLine[3]<<" "<<endl;        cout << "leftK= "<< leftK << endl;        leftLine[0]=leftLine[0]-(frame.cols-leftLine[1])/leftK;        leftLine[1]=frame.cols;        if(leftLine[3]>endLine)        {        leftLine[2]=leftLine[2]+(leftLine[3]-endLine)/leftK;        leftLine[3]=endLine;        }        rightK=static_cast<float>(rightLine[3]-rightLine[1])/(rightLine[2]-rightLine[0]);        rightLine[2]=rightLine[2]+(frame.cols-rightLine[3])/rightK;        rightLine[3]=frame.cols;        if(rightLine[1]>endLine)        {        rightLine[0]=rightLine[0]-(rightLine[1]-endLine)/rightK;        rightLine[1]=endLine;        }        Mat GreenImg=Mat(frame.size(),CV_8UC3,Scalar(0,255,0));        Mat maskGreen=Mat(frame.size(),CV_8UC3,Scalar(0,0,0));        vector<vector<Point> > maskContour;        vector<Point> maskPts;        maskPts.push_back(Point(leftLine[2],leftLine[3]));        maskPts.push_back(Point(leftLine[0],leftLine[1]));        maskPts.push_back(Point(rightLine[2],rightLine[3]));        maskPts.push_back(Point(rightLine[0],rightLine[1]));        maskContour.push_back(maskPts);        drawContours(maskGreen,maskContour,0,Scalar::all(255),-1);        GreenImg.copyTo(maskGreen,maskGreen);        //imshow("maskGreen",maskGreen);        addWeighted(frame,0.8,maskGreen,0.2,0,frame);        line( frame, Point(rightLine[0],rightLine[1]),Point(rightLine[2],rightLine[3]), Scalar(0,0,255), 3, CV_AA);        line( frame, Point(leftLine[0],leftLine[1]),Point(leftLine[2],leftLine[3]), Scalar(0,0,255), 3, CV_AA);        circle(frame,Point(rightLine[0],rightLine[1]),10,Scalar(0,0,255));        circle(frame,Point(leftLine[2],leftLine[3]),10,Scalar(0,0,255));        videoWrite<<frame;        imshow("result",frame);        waitKey(30);    }    return 0;}

在直线道路上效果还不错，但是受限于截取RIO的方法，一些问题暂时无法解决，这里只是提供一个简单的思路和实现。