Line Detection by Hough Line Transform

void HoughLines(InputArray image, OutputArray lines, double rho, double theta, int threshold, double srn=0, double stn=0 )
Parameters:

• image – 8-bit, single-channel binary source image (use edge detectors)
• lines – Output vector of lines. Each line is represented by a two-element vector . is the distance from the coordinate origin (top-left corner of the image). is the line rotation angle in radians ( ).
• rho – Distance resolution of the accumulator in pixels.
• theta – Angle resolution of the accumulator in radians.
• threshold – Accumulator threshold parameter. Only those lines are returned that get enough votes (>threshold ).
• srn – For the multi-scale Hough transform, it is a divisor for the distance resolution rho . The coarse accumulator distance resolution is rho and the accurate accumulator resolution is rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these parameters should be positive.
• stn – For the multi-scale Hough transform, it is a divisor for the distance resolution theta.

A good example for Hough Line Transform is provided in OpenCV Documentation.

Steps:

1. Load image and convert to gray-scale.
2. Apply the Hough Transform to find the lines.(HoughLines)
3. Draw the detected lines.(line)
4. Show the result

Functions:

HoughLines, line, Canny, cvtColor, imshow, imread, namedWindow, waitKey.

Example:

-------------

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>

using namespace cv;
using namespace std;

int main()
{
    Mat src = imread("building.jpg", 0);

    Mat dst, cdst;
    Canny(src, dst, 50, 200, 3); 
    cvtColor(dst, cdst, CV_GRAY2BGR); 

    vector<Vec2f> lines;
    // detect lines
    HoughLines(dst, lines, 1, CV_PI/180, 150, 0, 0 );

    // draw lines
    for( size_t i = 0; i < lines.size(); i++ )
    {
        float rho = lines[i][0], theta = lines[i][1];
        Point pt1, pt2;
        double a = cos(theta), b = sin(theta);
        double x0 = a*rho, y0 = b*rho;
        pt1.x = cvRound(x0 + 1000*(-b));
        pt1.y = cvRound(y0 + 1000*(a));
        pt2.x = cvRound(x0 - 1000*(-b));
        pt2.y = cvRound(y0 - 1000*(a));
        line( cdst, pt1, pt2, Scalar(0,0,255), 3, CV_AA);
    }

    imshow("source", src);
    imshow("detected lines", cdst);

    waitKey();
    return 0;
}

-------------

Result:

Extra Stuffs:

• What if you need to select some lines on the basis of prior knowledge of range of angles?

So, you know the range of angles in which your lines may present. Now, what you need to do is to add a conditional statement to filter out the lines detected in that angle range. For example,

if you want to detect vertical lines, use the following conditional statement after line 23 of the above example.

if( theta>CV_PI/180*170 || theta<CV_PI/180*10)
        { Point pt1, pt2;
        ..........
        line( cdst, pt1, pt2, Scalar(0,0,255), 3, CV_AA);
        }

if you want to detect horizontal lines, use

if( theta>CV_PI/180*80 && theta<CV_PI/180*100)
        { Point pt1, pt2;
        ..........
        line( cdst, pt1, pt2, Scalar(0,0,255), 3, CV_AA);
        }