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how to prune lines detected by houghtransformp ?

asked 2014-09-07 03:10:58 -0500

giorgyg gravatar image

updated 2020-11-17 04:02:45 -0500

I am trying to detect quadrilateral and going to do a perspective correction based on http://opencv-code.com/tutorials/automatic-perspective-correction-for-quadrilateral-objects/ by using probabilistic hough transform.

I only need 4 lines, but i ended up with other things (as you can see in the picture)

it's on purpose that i take the object of interest with other square environment ( to see if my program is adaptive)

i tried to play with the threshold and other parameter but these 2 images are the best that i can end up with.

can anyone help me to prune other lines except the 4 lines that i need (the ones that make a quadrilateral) thanks.

image description

image description

    int main()
{
//initialize the mat and variables
cv::Mat bw,rsz,roi,circleroi,result,dst,cannyimg;
int t1min =109;
//we want to adjust the size to 800x600
Size size(800,600); //the dst image size,e.g.800x600

//load the image
    cv::Mat src = cv::imread("D:\\image\\perspective.jpg");
    if (src.empty())
    return -1;

//create windows
    cvNamedWindow( "houghlinep", CV_WINDOW_AUTOSIZE );

while(1)
    {
    //create trackbars
        char TrackbarName1[50]="t1min";

        cvCreateTrackbar(TrackbarName1, "houghlinep", &t1min, 260 , NULL );
        if (t1min<=10)
            t1min=10;
    //resize image
    resize(src,rsz,size);
    //adjust the image boundary
    roi = rsz(cv::Range(110,560),cv::Range(70,685));
    //make the image grayscale to make it easier to canny   
    cv::cvtColor(roi, bw, CV_BGR2GRAY);
    //blur it to make it easier to canny
    cv::blur(bw, bw, cv::Size(2,2));
    //edge detect with canny operator
    cv::Canny(bw, dst, 50,100, 3);
    //copy the result to cannyimg
    dst.copyTo(cannyimg);

    //make lines object
    vector<Vec4i> lines;
    //use houghlinesp to dst
    HoughLinesP(dst, lines, 1, CV_PI/180,t1min, 140, 80 );
    //illustrate the line on the black n white image

    char count=1;
      for( size_t i = 0; i < lines.size(); i++ )
      {
        Vec4i l = lines[i];
        line(bw, Point(l[0], l[1]), Point(l[2], l[3]), CV_RGB(0,255,255), 1, CV_AA);

      }

    /*for (int i = 0; i < lines.size(); i++)
{
cv::Vec4i l = lines[i];
lines[i][0] = 0;
lines[i][1] = ((float)l[1] - l[3]) / (l[0] - l[2]) * -l[0] + l[1];
lines[i][2] = bw.cols;
lines[i][3] = ((float)l[1] - l[3]) / (l[0] - l[2]) * (bw.cols - l[2]) + l[3];
}*/



      //show the images

    cv::imshow("canny",cannyimg);
    cv::imshow("houghlinep",bw);
    cv::imshow("yeah",roi);


    if(t1min=t1min) cvWaitKey(0);

   if( (cvWaitKey(10) & 255) == 27 ) break;
   cvReleaseFileStorage;
}
return 1;


}
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answered 2014-09-07 07:34:32 -0500

I don't think you can improve your result by fine tuning the parameters of your edge detection. You could only learn the fitting parameters for this situation but your algorithm would fail anywhere else.

I would try to do it RANSAC style: you choose randomly four lines, compute the intersections and call T = getPerspectiveTransform(intersections, goal)

where intersections are your points and goal the expected new positions (in your case a rectangle). To check if you have chosen the right points, you compute projected = T*intersections . In the good case, the projected points are very close to the goal points (1 or 2 pixels). If you have chosen the wrong intersections, these distances are much higher (and your projected points don't form a rectangle) and you start the next iteration with another group of four lines.

You don't have to many lines so that this should terminate rather fast (especially if include some intelligence into the random choice (e.g. adjoint sides should have an angle of about 90deg (+-20 even in the original image,...)

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Asked: 2014-09-07 03:10:58 -0500

Seen: 1,228 times

Last updated: Sep 07 '14