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+Object detection with Generalized Ballard and Guil Hough Transform {#tutorial_generalized_hough_ballard_guil}
+==================================================================
+
+@tableofcontents
+
+@prev_tutorial{tutorial_hough_circle}
+@next_tutorial{tutorial_remap}
+
+|    |    |
+| -: | :- |
+| Original author | Markus Heck |
+| Compatibility | OpenCV >= 3.4 |
+
+Goal
+----
+
+In this tutorial you will learn how to:
+
+- Use @ref cv::GeneralizedHoughBallard and @ref cv::GeneralizedHoughGuil to detect an object
+
+Example
+-------
+
+### What does this program do?
+
+1. Load the image and template
+
+![image](images/generalized_hough_mini_image.jpg)
+![template](images/generalized_hough_mini_template.jpg)
+
+2. Instantiate @ref cv::GeneralizedHoughBallard with the help of `createGeneralizedHoughBallard()`
+3. Instantiate @ref cv::GeneralizedHoughGuil with the help of `createGeneralizedHoughGuil()`
+4. Set the required parameters for both GeneralizedHough variants
+5. Detect and show found results
+
+@note
+- Both variants can't be instantiated directly. Using the create methods is required.
+- Guil Hough is very slow. Calculating the results for the "mini" files used in this tutorial
+  takes only a few seconds. With image and template in a higher resolution, as shown below,
+  my notebook requires about 5 minutes to calculate a result.
+
+![image](images/generalized_hough_image.jpg)
+![template](images/generalized_hough_template.jpg)
+
+### Code
+
+The complete code for this tutorial is shown below.
+@include samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp
+
+Explanation
+-----------
+
+### Load image, template and setup variables
+
+@snippet samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp generalized-hough-transform-load-and-setup
+
+The position vectors will contain the matches the detectors will find.
+Every entry contains four floating point values:
+position vector
+
+- *[0]*: x coordinate of center point
+- *[1]*: y coordinate of center point
+- *[2]*: scale of detected object compared to template
+- *[3]*: rotation of detected object in degree in relation to template
+
+An example could look as follows: `[200, 100, 0.9, 120]`
+
+### Setup parameters
+
+@snippet samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp generalized-hough-transform-setup-parameters
+
+Finding the optimal values can end up in trial and error and depends on many factors, such as the image resolution.
+
+### Run detection
+
+@snippet samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp generalized-hough-transform-run
+
+As mentioned above, this step will take some time, especially with larger images and when using Guil.
+
+### Draw results and show image
+
+@snippet samples/cpp/tutorial_code/ImgTrans/generalizedHoughTransform.cpp generalized-hough-transform-draw-results
+
+Result
+------
+
+![result image](images/generalized_hough_result_img.jpg)
+
+The blue rectangle shows the result of @ref cv::GeneralizedHoughBallard and the green rectangles the results of @ref
+cv::GeneralizedHoughGuil.
+
+Getting perfect results like in this example is unlikely if the parameters are not perfectly adapted to the sample.
+An example with less perfect parameters is shown below.
+For the Ballard variant, only the center of the result is marked as a black dot on this image. The rectangle would be
+the same as on the previous image.
+
+![less perfect result](images/generalized_hough_less_perfect_result_img.jpg)