In this tutorial you will learn how to:

- Use the OpenCV functions HoughLines and HoughLinesP to detect lines in an image.

Note

The explanation below belongs to the book **Learning OpenCV** by Bradski and Kaehler.

- The Hough Line Transform is a transform used to detect straight lines.
- To apply the Transform, first an edge detection pre-processing is desirable.

As you know, a line in the image space can be expressed with two variables. For example:

- In the
**Cartesian coordinate system:**Parameters: . - In the
**Polar coordinate system:**Parameters:

For Hough Transforms, we will express lines in the

*Polar system*. Hence, a line equation can be written as:- In the

Arranging the terms:

In general for each point , we can define the family of lines that goes through that point as:

Meaning that each pair represents each line that passes by .

If for a given we plot the family of lines that goes through it, we get a sinusoid. For instance, for and we get the following plot (in a plane - ):

We consider only points such that and .

We can do the same operation above for all the points in an image. If the curves of two different points intersect in the plane - , that means that both points belong to a same line. For instance, following with the example above and drawing the plot for two more points: , and , , we get:

The three plots intersect in one single point , these coordinates are the parameters () or the line in which , and lay.

What does all the stuff above mean? It means that in general, a line can be

*detected*by finding the number of intersections between curves.The more curves intersecting means that the line represented by that intersection have more points. In general, we can define a*threshold*of the minimum number of intersections needed to*detect*a line.This is what the Hough Line Transform does. It keeps track of the intersection between curves of every point in the image. If the number of intersections is above some

*threshold*, then it declares it as a line with the parameters of the intersection point.

OpenCV implements two kind of Hough Line Transforms:

**The Standard Hough Transform**

- It consists in pretty much what we just explained in the previous section. It gives you as result a vector of couples
- In OpenCV it is implemented with the function HoughLines

**The Probabilistic Hough Line Transform**

- A more efficient implementation of the Hough Line Transform. It gives as output the extremes of the detected lines
- In OpenCV it is implemented with the function HoughLinesP

**What does this program do?**- Loads an image
- Applies either a
*Standard Hough Line Transform*or a*Probabilistic Line Transform*. - Display the original image and the detected line in two windows.

- The sample code that we will explain can be downloaded from here. A slightly fancier version (which shows both Hough standard and probabilistic with trackbars for changing the threshold values) can be found here.

```
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
using namespace cv;
using namespace std;
void help()
{
cout << "\nThis program demonstrates line finding with the Hough transform.\n"
"Usage:\n"
"./houghlines <image_name>, Default is pic1.jpg\n" << endl;
}
int main(int argc, char** argv)
{
const char* filename = argc >= 2 ? argv[1] : "pic1.jpg";
Mat src = imread(filename, 0);
if(src.empty())
{
help();
cout << "can not open " << filename << endl;
return -1;
}
Mat dst, cdst;
Canny(src, dst, 50, 200, 3);
cvtColor(dst, cdst, CV_GRAY2BGR);
#if 0
vector<Vec2f> lines;
HoughLines(dst, lines, 1, CV_PI/180, 100, 0, 0 );
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);
}
#else
vector<Vec4i> lines;
HoughLinesP(dst, lines, 1, CV_PI/180, 50, 50, 10 );
for( size_t i = 0; i < lines.size(); i++ )
{
Vec4i l = lines[i];
line( cdst, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0,0,255), 3, CV_AA);
}
#endif
imshow("source", src);
imshow("detected lines", cdst);
waitKey();
return 0;
}
```

Load an image

Mat src = imread(filename, 0); if(src.empty()) { help(); cout << "can not open " << filename << endl; return -1; }

Detect the edges of the image by using a Canny detector

Canny(src, dst, 50, 200, 3);

Now we will apply the Hough Line Transform. We will explain how to use both OpenCV functions available for this purpose:

**Standard Hough Line Transform**First, you apply the Transform:

vector<Vec2f> lines; HoughLines(dst, lines, 1, CV_PI/180, 100, 0, 0 );

with the following arguments:

*dst*: Output of the edge detector. It should be a grayscale image (although in fact it is a binary one)*lines*: A vector that will store the parameters of the detected lines*rho*: The resolution of the parameter in pixels. We use**1**pixel.*theta*: The resolution of the parameter in radians. We use**1 degree**(CV_PI/180)*threshold*: The minimum number of intersections to “*detect*” a line*srn*and*stn*: Default parameters to zero. Check OpenCV reference for more info.

And then you display the result by drawing the 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); }

**Probabilistic Hough Line Transform**First you apply the transform:

vector<Vec4i> lines; HoughLinesP(dst, lines, 1, CV_PI/180, 50, 50, 10 );

with the arguments:

*dst*: Output of the edge detector. It should be a grayscale image (although in fact it is a binary one)*lines*: A vector that will store the parameters of the detected lines*rho*: The resolution of the parameter in pixels. We use**1**pixel.*theta*: The resolution of the parameter in radians. We use**1 degree**(CV_PI/180)*threshold*: The minimum number of intersections to “*detect*” a line*minLinLength*: The minimum number of points that can form a line. Lines with less than this number of points are disregarded.*maxLineGap*: The maximum gap between two points to be considered in the same line.

And then you display the result by drawing the lines.

for( size_t i = 0; i < lines.size(); i++ ) { Vec4i l = lines[i]; line( cdst, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0,0,255), 3, CV_AA); }

Display the original image and the detected lines:

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

Wait until the user exits the program

waitKey();

Note

The results below are obtained using the slightly fancier version we mentioned in the *Code* section. It still implements the same stuff as above, only adding the Trackbar for the Threshold.

Using an input image such as:

We get the following result by using the Probabilistic Hough Line Transform:

You may observe that the number of lines detected vary while you change the *threshold*. The explanation is sort of evident: If you establish a higher threshold, fewer lines will be detected (since you will need more points to declare a line detected).

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