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
- 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.
-
--# 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_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
+
+@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:
-
--# **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:
+#### Load an image:
+
+@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)
- *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)
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
------
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:
-
-
+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:
+
+And 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
projects / platform / upstream / opencv.git / blobdiff