车道线检测+车道线跟踪+车道线识别【持续更新】

原本打算用深度学习的，但是....各种原因。于是想着随便先实现以一下，发现效果还不错，还节约成本。美滋滋

现成代码美滋滋，先上个链接

http://blog.csdn.net/chongshangyunxiao321/article/details/50999212

再上图

用上面博客的代码，再改改参数，美滋滋。

但是会出现丢帧的情况。这对追求完美的我。。。。。。。。。的老板来说不行啊。

于是想用深度学习做一做。

怎么做？表急，一步一步来。。。。。

先看一下逆透视变换。这里是用的opencv的四点变换。

先上个链接，链接关了。直接上代码吧。

#include <cv.h>
#include <highgui.h>
using namespace std;
int main()
{
CvPoint2D32f srcQuad[4], dstQuad[4];
CvMat *warp_matrix = cvCreateMat(3, 3, CV_32FC1);
IplImage *src, *dst;
if (((src = cvLoadImage("D:\\路径\\3.png", 1)) != 0))
{
dst = cvCloneImage(src);
dst->origin = src->origin;//确定起点位置为座顶角
cvZero(dst);


srcQuad[0].x = 0;
srcQuad[0].y = 0;
srcQuad[1].x = src->width - 1.;
srcQuad[1].y = 0;
srcQuad[2].x = 0;
srcQuad[2].y = src->height - 1;
srcQuad[3].x = src->width - 1;
srcQuad[3].y = src->height - 1;


//dstQuad[0].x = src->width*0.05;
//dstQuad[0].y = src->height*0.33;
dstQuad[0].x = srcQuad[0].x;
dstQuad[0].y = srcQuad[0].y;
dstQuad[1].x = src->width;
dstQuad[1].y = 0;
dstQuad[2].x = src->width*0.45;
dstQuad[2].y = src->height;
dstQuad[3].x = src->width*0.55;
dstQuad[3].y = src->height;


//计算透视映射矩阵
cvGetPerspectiveTransform(srcQuad, dstQuad, warp_matrix);
//密集透视变换
cvWarpPerspective(src, dst, warp_matrix);
cvNamedWindow("Perspective_Warp", 1);
cvShowImage("Perspective_Warp", dst);
cvSaveImage("result3.png", dst);
cvWaitKey();
}
cvReleaseImage(&dst);
cvReleaseMat(&warp_matrix);
return 0;
}

//华丽丽的分割线，上面是车道线检测，下面是车道线跟踪

为什么要做跟踪，直接从上图中可以看出在车道检测（绿色）算法没有很好的检测出正确的车道线的时候，车道线跟踪（红色）能够很好的规避这种错误，达到理想效果。

咱这里用的kalman，我的代码太乱了，不好意思贴了。哇咔咔。这里贴一个kalman的鼠标跟踪代码。

#include <opencv/cv.h>  
#include <opencv/highgui.h>  

#include <iostream>  

using namespace cv;  
using namespace std;  

const int winWidth = 800;  
const int winHeight = 600;  

Point mousePosition = Point(winWidth>>1, winHeight>>1);  

//mouse call back  
void mouseEvent(int event, int x, int y, int flags, void *param)  
{  
    if(event==CV_EVENT_MOUSEMOVE)  
    {  
        mousePosition=Point(x,y);  
    }  
}  

int main()  
{  
    //1.kalman filter setup     
    const int stateNum=4;    
    const int measureNum=2;    

    KalmanFilter KF(stateNum, measureNum, 0);  
    Mat state (stateNum, 1, CV_32FC1); //state(x,y,detaX,detaY)  
    Mat processNoise(stateNum, 1, CV_32F);  
    Mat measurement = Mat::zeros(measureNum, 1, CV_32F);    //measurement(x,y)  


        randn( state, Scalar::all(0), Scalar::all(0.1) ); //随机生成一个矩阵，期望是0，标准差为0.1;  
        KF.transitionMatrix = *(Mat_<float>(4, 4) <<   
            1,0,1,0,   
            0,1,0,1,   
            0,0,1,0,   
            0,0,0,1 );//元素导入矩阵，按行;  

        //setIdentity: 缩放的单位对角矩阵;  
        //!< measurement matrix (H) 观测模型  
        setIdentity(KF.measurementMatrix);  

        //!< process noise covariance matrix (Q)  
        // wk 是过程噪声，并假定其符合均值为零，协方差矩阵为Qk(Q)的多元正态分布;  
        setIdentity(KF.processNoiseCov, Scalar::all(1e-5));  

        //!< measurement noise covariance matrix (R)  
        //vk 是观测噪声，其均值为零，协方差矩阵为Rk,且服从正态分布;  
        setIdentity(KF.measurementNoiseCov, Scalar::all(1e-1));  

        //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/  A代表F: transitionMatrix  
        //预测估计协方差矩阵;  
        setIdentity(KF.errorCovPost, Scalar::all(1));  


        //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))  
        //initialize post state of kalman filter at random   
        randn(KF.statePost, Scalar::all(0), Scalar::all(0.1));  
        Mat showImg(winWidth, winHeight,CV_8UC3);  

        for(;;)  
        {  
            setMouseCallback("Kalman", mouseEvent);  
            showImg.setTo(0);  

            Point statePt = Point( (int)KF.statePost.at<float>(0), (int)KF.statePost.at<float>(1));  

            //2.kalman prediction     
            Mat prediction = KF.predict();  
            Point predictPt = Point( (int)prediction.at<float>(0), (int)prediction.at<float>(1));  

            //3.update measurement  
            measurement.at<float>(0)= (float)mousePosition.x;  
            measurement.at<float>(1) = (float)mousePosition.y;  

            //4.update  
            KF.correct(measurement);  

            //randn( processNoise, Scalar(0), Scalar::all(sqrt(KF.processNoiseCov.at<float>(0, 0))));  
            //state = KF.transitionMatrix*state + processNoise;  
            //draw  
            circle(showImg, statePt, 5, CV_RGB(255,0,0),1);//former point  
            circle(showImg, predictPt, 5, CV_RGB(0,255,0),1);//predict point  
            circle(showImg, mousePosition, 5, CV_RGB(0,0,255),1);//ture point  


//          CvFont font;//字体  
//          cvInitFont(&font, CV_FONT_HERSHEY_SCRIPT_COMPLEX, 0.5f, 0.5f, 0, 1, 8);  
            putText(showImg, "Red: Former Point", cvPoint(10,30), FONT_HERSHEY_SIMPLEX, 1 ,Scalar :: all(255));  
            putText(showImg, "Green: Predict Point", cvPoint(10,60), FONT_HERSHEY_SIMPLEX, 1 ,Scalar :: all(255));  
            putText(showImg, "Blue: Ture Point", cvPoint(10,90), FONT_HERSHEY_SIMPLEX, 1 ,Scalar :: all(255));  

            imshow( "Kalman", showImg );  
            int key = waitKey(3);  
            if (key == 27)  
            {  
                break;  
            }  
        }  
}  

第三部分 车道线的识别。

随便弄个分类器做做分类，等有闲暇时间上文字描述。