move tutorial to imgproc and example to ImgTrans

2026-07-29 23:33:05 +04:00 · 2022-09-10 17:40:31 +02:00
parent 5408949951
commit 619e038de9
13 changed files with 19 additions and 19 deletions
@@ -1,91 +0,0 @@
-Object detection with Generalized Ballard and Guil Hough Transform {#tutorial_generalized_hough_ballard_guil}
-==================================================================
-
-@tableofcontents
-
-@prev_tutorial{tutorial_traincascade}
-
-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/objectDetection/generalizedHoughTransform.cpp
-
-Explanation
-----------
-
-### Load image, template and setup variables
-
-@snippet samples/cpp/tutorial_code/objectDetection/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/objectDetection/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/objectDetection/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/objectDetection/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)
@@ -16,13 +16,3 @@ Ever wondered how your digital camera detects peoples and faces? Look here to fi
 -   @subpage tutorial_traincascade

    This tutorial describes _opencv_traincascade_ application and its parameters.
-
-   @subpage tutorial_generalized_hough_ballard_guil
-
-    *Languages:* C++
-
-    *Compatibility:* \>= OpenCV 3.4
-
-    *Author:* Markus Heck
-
-    Detect an object in a picture with the help of GeneralizedHoughBallard and GeneralizedHoughGuil.
@@ -2,7 +2,6 @@ Cascade Classifier Training {#tutorial_traincascade}
 ===========================

@prev_tutorial{tutorial_cascade_classifier}
-@next_tutorial{tutorial_generalized_hough_ballard_guil}


 Introduction