.. _hough_lines:
Hough Line Transform
*********************
Goal
=====
In this tutorial you will learn how to:
* Use the OpenCV functions :hough_lines:`HoughLines <>` and :hough_lines_p:`HoughLinesP <>` to detect lines in an image.
Theory
=======
.. note::
The explanation below belongs to the book **Learning OpenCV** by Bradski and Kaehler.
Hough Line Transform
---------------------
#. The Hough Line Transform is a transform used to detect straight lines.
#. To apply the Transform, first an edge detection pre-processing is desirable.
How does it work?
^^^^^^^^^^^^^^^^^^
#. As you know, a line in the image space can be expressed with two variables. For example:
a. In the **Cartesian coordinate system:** Parameters: :math:`(m,b)`.
b. In the **Polar coordinate system:** Parameters: :math:`(r,\theta)`
.. image:: images/Hough_Lines_Tutorial_Theory_0.jpg
:alt: Line variables
:align: center
For Hough Transforms, we will express lines in the *Polar system*. Hence, a line equation can be written as:
.. math::
y = \left ( -\dfrac{\cos \theta}{\sin \theta} \right ) x + \left ( \dfrac{r}{\sin \theta} \right )
Arranging the terms: :math:`r = x \cos \theta + y \sin \theta`
#. In general for each point :math:`(x_{0}, y_{0})`, we can define the family of lines that goes through that point as:
.. math::
r_{\theta} = x_{0} \cdot \cos \theta + y_{0} \cdot \sin \theta
Meaning that each pair :math:`(r_{\theta},\theta)` represents each line that passes by :math:`(x_{0}, y_{0})`.
#. If for a given :math:`(x_{0}, y_{0})` we plot the family of lines that goes through it, we get a sinusoid. For instance, for :math:`x_{0} = 8` and :math:`y_{0} = 6` we get the following plot (in a plane :math:`\theta` - :math:`r`):
.. image:: images/Hough_Lines_Tutorial_Theory_1.jpg
:alt: Polar plot of a the family of lines of a point
:align: center
We consider only points such that :math:`r > 0` and :math:`0< \theta < 2 \pi`.
#. 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 :math:`\theta` - :math:`r`, 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: :math:`x_{1} = 4`, :math:`y_{1} = 9` and :math:`x_{2} = 12`, :math:`y_{2} = 3`, we get:
.. image:: images/Hough_Lines_Tutorial_Theory_2.jpg
:alt: Polar plot of the family of lines for three points
:align: center
The three plots intersect in one single point :math:`(0.925, 9.6)`, these coordinates are the parameters (:math:`\theta, r`) or the line in which :math:`(x_{0}, y_{0})`, :math:`(x_{1}, y_{1})` and :math:`(x_{2}, y_{2})` 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 :math:`(\theta, r_{\theta})` of the intersection point.
Standard and Probabilistic Hough Line Transform
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
OpenCV implements two kind of Hough Line Transforms:
a. **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 :math:`(\theta, r_{\theta})`
* In OpenCV it is implemented with the function :hough_lines:`HoughLines <>`
b. **The Probabilistic Hough Line Transform**
* A more efficient implementation of the Hough Line Transform. It gives as output the extremes of the detected lines :math:`(x_{0}, y_{0}, x_{1}, y_{1})`
* In OpenCV it is implemented with the function :hough_lines_p:`HoughLinesP <>`
Code
======
.. |TutorialHoughLinesSimpleDownload| replace:: here
.. _TutorialHoughLinesSimpleDownload: https://github.com/opencv/opencv/tree/master/samples/cpp/houghlines.cpp
.. |TutorialHoughLinesFancyDownload| replace:: here
.. _TutorialHoughLinesFancyDownload: https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp
#. **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 |TutorialHoughLinesSimpleDownload|_. A slightly fancier version (which shows both Hough standard and probabilistic with trackbars for changing the threshold values) can be found |TutorialHoughLinesFancyDownload|_.
.. code-block:: cpp
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include
using namespace cv;
using namespace std;
void help()
{
cout << "\nThis program demonstrates line finding with the Hough transform.\n"
"Usage:\n"
"./houghlines , 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 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 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;
}
Explanation
=============
#. Load an image
.. code-block:: cpp
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
.. code-block:: cpp
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**
a. First, you apply the Transform:
.. code-block:: cpp
vector 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 :math:`(r,\theta)` of the detected lines
* *rho* : The resolution of the parameter :math:`r` in pixels. We use **1** pixel.
* *theta*: The resolution of the parameter :math:`\theta` 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.
b. And then you display the result by drawing the lines.
.. code-block:: cpp
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**
a. First you apply the transform:
.. code-block:: cpp
vector 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 :math:`(x_{start}, y_{start}, x_{end}, y_{end})` of the detected lines
* *rho* : The resolution of the parameter :math:`r` in pixels. We use **1** pixel.
* *theta*: The resolution of the parameter :math:`\theta` 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.
b. And then you display the result by drawing the lines.
.. code-block:: cpp
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:
.. code-block:: cpp
imshow("source", src);
imshow("detected lines", cdst);
#. Wait until the user exits the program
.. code-block:: cpp
waitKey();
Result
=======
.. 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:
.. image:: images/Hough_Lines_Tutorial_Original_Image.jpg
:alt: Result of detecting lines with Hough Transform
:align: center
We get the following result by using the Probabilistic Hough Line Transform:
.. image:: images/Hough_Lines_Tutorial_Result.jpg
:alt: Result of detecting lines with Hough Transform
:align: center
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).