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Tutorial Hough Lines

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doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.markdown
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Hough Line Transform {#tutorial_hough_lines}
====================
@prev_tutorial{tutorial_canny_detector}
@next_tutorial{tutorial_hough_circle}
Goal
----
In this tutorial you will learn how to:
- Use the OpenCV functions @ref cv::HoughLines and @ref cv::HoughLinesP to detect lines in an
- Use the OpenCV functions **HoughLines()** and **HoughLinesP()** to detect lines in an
image.
Theory
@@ -79,54 +82,93 @@ 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 \f$(\theta, r_{\theta})\f$
- In OpenCV it is implemented with the function @ref cv::HoughLines
- In OpenCV it is implemented with the function **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 \f$(x_{0}, y_{0}, x_{1}, y_{1})\f$
- In OpenCV it is implemented with the function @ref cv::HoughLinesP
- In OpenCV it is implemented with the function **HoughLinesP()**
### What does this program do?
- Loads an image
- Applies a *Standard Hough Line Transform* and a *Probabilistic Line Transform*.
- Display the original image and the detected line in three windows.
Code
----
-# **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.
@add_toggle_cpp
The sample code that we will explain can be downloaded from
[here](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/ImgTrans/houghlines.cpp).
A slightly fancier version (which shows both Hough standard and probabilistic
with trackbars for changing the threshold values) can be found
[here](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp).
@include samples/cpp/tutorial_code/ImgTrans/houghlines.cpp
@end_toggle
-# The sample code that we will explain can be downloaded from [here](https://github.com/opencv/opencv/tree/master/samples/cpp/houghlines.cpp). A slightly fancier version
(which shows both Hough standard and probabilistic with trackbars for changing the threshold
values) can be found [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp).
@include samples/cpp/houghlines.cpp
@add_toggle_java
The sample code that we will explain can be downloaded from
[here](https://raw.githubusercontent.com/opencv/opencv/master/samples/java/tutorial_code/ImgTrans/HoughLine/HoughLines.java).
@include samples/java/tutorial_code/ImgTrans/HoughLine/HoughLines.java
@end_toggle
@add_toggle_python
The sample code that we will explain can be downloaded from
[here](https://raw.githubusercontent.com/opencv/opencv/master/samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py).
@include samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py
@end_toggle
Explanation
-----------
-# Load an image
@code{.cpp}
Mat src = imread(filename, 0);
if(src.empty())
{
help();
cout << "can not open " << filename << endl;
return -1;
}
@endcode
-# Detect the edges of the image by using a Canny detector
@code{.cpp}
Canny(src, dst, 50, 200, 3);
@endcode
Now we will apply the Hough Line Transform. We will explain how to use both OpenCV functions
available for this purpose:
#### Load an image:
-# **Standard Hough Line Transform**
-# First, you apply the Transform:
@code{.cpp}
vector<Vec2f> lines;
HoughLines(dst, lines, 1, CV_PI/180, 100, 0, 0 );
@endcode
with the following arguments:
@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghlines.cpp load
@end_toggle
@add_toggle_java
@snippet samples/java/tutorial_code/ImgTrans/HoughLine/HoughLines.java load
@end_toggle
@add_toggle_python
@snippet samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py load
@end_toggle
#### Detect the edges of the image by using a Canny detector:
@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghlines.cpp edge_detection
@end_toggle
@add_toggle_java
@snippet samples/java/tutorial_code/ImgTrans/HoughLine/HoughLines.java edge_detection
@end_toggle
@add_toggle_python
@snippet samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py edge_detection
@end_toggle
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:
@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghlines.cpp hough_lines
@end_toggle
@add_toggle_java
@snippet samples/java/tutorial_code/ImgTrans/HoughLine/HoughLines.java hough_lines
@end_toggle
@add_toggle_python
@snippet samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py hough_lines
@end_toggle
- with the following arguments:
- *dst*: Output of the edge detector. It should be a grayscale image (although in fact it
is a binary one)
@@ -137,28 +179,35 @@ Explanation
- *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.
@code{.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, LINE_AA);
}
@endcode
-# **Probabilistic Hough Line Transform**
-# First you apply the transform:
@code{.cpp}
vector<Vec4i> lines;
HoughLinesP(dst, lines, 1, CV_PI/180, 50, 50, 10 );
@endcode
with the arguments:
And then you display the result by drawing the lines.
@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghlines.cpp draw_lines
@end_toggle
@add_toggle_java
@snippet samples/java/tutorial_code/ImgTrans/HoughLine/HoughLines.java draw_lines
@end_toggle
@add_toggle_python
@snippet samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py draw_lines
@end_toggle
#### Probabilistic Hough Line Transform
First you apply the transform:
@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghlines.cpp hough_lines_p
@end_toggle
@add_toggle_java
@snippet samples/java/tutorial_code/ImgTrans/HoughLine/HoughLines.java hough_lines_p
@end_toggle
@add_toggle_python
@snippet samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py hough_lines_p
@end_toggle
- with the arguments:
- *dst*: Output of the edge detector. It should be a grayscale image (although in fact it
is a binary one)
@@ -172,23 +221,47 @@ Explanation
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.
@code{.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, LINE_AA);
}
@endcode
-# Display the original image and the detected lines:
@code{.cpp}
imshow("source", src);
imshow("detected lines", cdst);
@endcode
-# Wait until the user exits the program
@code{.cpp}
waitKey();
@endcode
And then you display the result by drawing the lines.
@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghlines.cpp draw_lines_p
@end_toggle
@add_toggle_java
@snippet samples/java/tutorial_code/ImgTrans/HoughLine/HoughLines.java draw_lines_p
@end_toggle
@add_toggle_python
@snippet samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py draw_lines_p
@end_toggle
#### Display the original image and the detected lines:
@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghlines.cpp imshow
@end_toggle
@add_toggle_java
@snippet samples/java/tutorial_code/ImgTrans/HoughLine/HoughLines.java imshow
@end_toggle
@add_toggle_python
@snippet samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py imshow
@end_toggle
#### Wait until the user exits the program
@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghlines.cpp exit
@end_toggle
@add_toggle_java
@snippet samples/java/tutorial_code/ImgTrans/HoughLine/HoughLines.java exit
@end_toggle
@add_toggle_python
@snippet samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py exit
@end_toggle
Result
------
@@ -198,13 +271,11 @@ Result
section. It still implements the same stuff as above, only adding the Trackbar for the
Threshold.
Using an input image such as:
![](images/Hough_Lines_Tutorial_Original_Image.jpg)
We get the following result by using the Probabilistic Hough Line Transform:
![](images/Hough_Lines_Tutorial_Result.jpg)
Using an input image such as a [sudoku image](https://raw.githubusercontent.com/opencv/opencv/master/samples/data/sudoku.png).
We get the following result by using the Standard Hough Line Transform:
![](images/hough_lines_result1.png)
And by using the Probabilistic Hough Line Transform:
![](images/hough_lines_result2.png)
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
doc/tutorials/imgproc/imgtrans/hough_lines/images/hough_lines_result1.png
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doc/tutorials/imgproc/table_of_content_imgproc.markdown
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@@ -125,6 +125,8 @@ In this section you will learn about the image processing (manipulation) functio
- @subpage tutorial_hough_lines
*Languages:* C++, Java, Python
*Compatibility:* \> OpenCV 2.0
*Author:* Ana Huamán