车道检测器分隔线 C ++ 与 OpenCV

现在我一直在用OpenCV进行图像分析，我想做的是识别车道分割线，我所做的如下：

1.I receive a image,
2. Then transform it to grayscale
3.I apply the GaussianBlur
4.After I place me in the ROI
5.I apply the canny
6.then I look for lines with hough transform Lines 
7.Draw the lines obtained from hough

但我遇到了一个问题： 既不识别分界线，也不识别黄线。

我希望能帮我解决这个问题，你会非常感谢的。 然后我把代码

#include "opencv2/highgui/highgui.hpp"
#include <opencv2/objdetect/objdetect.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
#include <vector>
#include <stdio.h>
#include "linefinder.h"

using namespace cv;

int main(int argc, char* argv[]) {
int houghVote = 200;
string arg = argv[1];
Mat image;
image = imread(argv[1]);    
    Mat gray;
    cvtColor(image,gray,CV_RGB2GRAY);
   GaussianBlur( gray, gray, Size( 5, 5 ), 0, 0 );
    vector<string> codes;
    Mat corners;
    findDataMatrix(gray, codes, corners);
    drawDataMatrixCodes(image, codes, corners);
 //Mat image = imread("");
    //Rect region_of_interest = Rect(x, y, w, h);
    //Mat image_roi = image(region_of_interest);
 std::cout << image.cols << "\n";
 std::cout << image.rows << "\n";
 Rect roi(0,290,640,190);// set the ROI for the image
 Mat imgROI = image(roi);
 // Display the image
 imwrite("original.bmp", imgROI);
// Canny algorithm
Mat contours;
Canny(imgROI, contours, 120, 300, 3); 
imwrite("canny.bmp", contours);
Mat contoursInv;
threshold(contours,contoursInv,128,255,THRESH_BINARY_INV);
// Display Canny image
imwrite("contours.bmp", contoursInv);

/* 
Hough tranform for line detection with feedback
Increase by 25 for the next frame if we found some lines.  
This is so we don't miss other lines that may crop up in the next frame
but at the same time we don't want to start the feed back loop from scratch. 
*/
std::vector<Vec2f> lines;
if (houghVote < 1 or lines.size() > 2){ // we lost all lines. reset 
    houghVote = 200; 
}else{ 
    houghVote += 25;
} 
while(lines.size() < 5 && houghVote > 0){
    HoughLines(contours,lines,1,PI/180, houghVote);
    houghVote -= 5;
}
std::cout << houghVote << "\n";
Mat result(imgROI.size(),CV_8U,Scalar(255));
imgROI.copyTo(result);
// Draw the limes
std::vector<Vec2f>::const_iterator it= lines.begin();
Mat hough(imgROI.size(),CV_8U,Scalar(0));
while (it!=lines.end()) {
    float rho= (*it)[0];   // first element is distance rho
    float theta= (*it)[1]; // second element is angle theta
    if ( theta > 0.09 && theta < 1.48 || theta < 3.14 && theta > 1.66 ) { 
    // filter to remove    vertical and horizontal lines
        // point of intersection of the line with first row
        Point pt1(rho/cos(theta),0);        
        // point of intersection of the line with last row
        Point pt2((rho-result.rows*sin(theta))/cos(theta),result.rows);
        // draw a white line
        line( result, pt1, pt2, Scalar(255), 8); 
        line( hough, pt1, pt2, Scalar(255), 8);
    }
    ++it;
   }

   // Display the detected line image
   std::cout << "line image:"<< "\n";
   namedWindow("Detected Lines with Hough");
   imwrite("hough.bmp", result);

   // Create LineFinder instance
   LineFinder ld;

  // Set probabilistic Hough parameters
   ld.setLineLengthAndGap(60,10);
   ld.setMinVote(4);

  // Detect lines
  std::vector<Vec4i> li= ld.findLines(contours);
  Mat houghP(imgROI.size(),CV_8U,Scalar(0));
  ld.setShift(0);
  ld.drawDetectedLines(houghP);
  std::cout << "First Hough" << "\n";
  imwrite("houghP.bmp", houghP);

  // bitwise AND of the two hough images
  bitwise_and(houghP,hough,houghP);
  Mat houghPinv(imgROI.size(),CV_8U,Scalar(0));
  Mat dst(imgROI.size(),CV_8U,Scalar(0));
  threshold(houghP,houghPinv,150,255,THRESH_BINARY_INV); // threshold and invert to black lines
  namedWindow("Detected Lines with Bitwise");
  imshow("Detected Lines with Bitwise", houghPinv);

  Canny(houghPinv,contours,100,350);
  li= ld.findLines(contours);
 // Display Canny image
 imwrite("contours.bmp", contoursInv);

 // Set probabilistic Hough parameters
  ld.setLineLengthAndGap(5,2);
  ld.setMinVote(1);
  ld.setShift(image.cols/3);
  ld.drawDetectedLines(image);

  std::stringstream stream;
  stream << "Lines Segments: " << lines.size();

  putText(image, stream.str(), Point(10,image.rows-10), 2, 0.8, Scalar(0,0,255),0); 
  imwrite("processed.bmp", image);

  char key = (char) waitKey(10);
  lines.clear();
  }

The following are the input images respectively:

这里我展示了两张照片，一张识别白线，另一张不识别黄线，我需要的是识别分界线，因为我监控车道，但对我来说很复杂，它不能识别所有的存在分界线，我希望能帮助我，因为我已经诚实地尝试了一切，但没有得到好的结果。

我认为这是因为您正在对概率霍夫变换和常规霍夫变换进行按位加法。这意味着输出的图像将仅包含在这两种变换中出现的线条。我很确定在常规变换中未检测到该线，但在概率霍夫输出中检测到该线。最好的办法是分别输出两个转换并进行调试。我正在做一个类似的项目，我想你可以包含一个单独的 ROI 以从按位加法中排除，并且该区域将沿着车道标记的中心。