Move objdetect HaarCascadeClassifier and HOGDescriptor to contrib xobjdetect (#25198)

* Move objdetect parts to contrib

* Move objdetect parts to contrib

* Minor fixes.

doc/tutorials/introduction/desktop_java/java_dev_intro.markdown

@@ -259,10 +259,10 @@ Next, create the directory `src/main/resources` and download this Lena image int
 Make sure it's called `"lena.png"`. Items in the resources directory are available to the Java
 application at runtime.
 
-Next, copy `lbpcascade_frontalface.xml` from `opencv/data/lbpcascades/` into the `resources`
+Next, copy `lbpcascade_frontalface.xml` from `opencv_contrib/modules/xobjdetect/data/lbpcascades/` into the `resources`
 directory:
 @code{.bash}
-cp <opencv_dir>/data/lbpcascades/lbpcascade_frontalface.xml src/main/resources/
+cp <xobjdetect_dir>/data/lbpcascades/lbpcascade_frontalface.xml src/main/resources/
 @endcode
 Now modify src/main/java/HelloOpenCV.java so it contains the following Java code:
 @code{.java}
@@ -273,7 +273,7 @@ import org.opencv.core.Point;
 import org.opencv.core.Rect;
 import org.opencv.core.Scalar;
 import org.opencv.imgcodecs.Imgcodecs;
-import org.opencv.objdetect.CascadeClassifier;
+import org.opencv.xobjdetect.CascadeClassifier;
 
 //
 // Detects faces in an image, draws boxes around them, and writes the results

doc/tutorials/objdetect/aruco_board_detection/aruco_board_detection.markdown

@@ -2,6 +2,8 @@ Detection of ArUco boards {#tutorial_aruco_board_detection}
 =========================
 
 @prev_tutorial{tutorial_aruco_detection}
+@next_tutorial{tutorial_barcode_detect_and_decode}
+
 
 |    |    |
 | -: | :- |

doc/tutorials/objdetect/barcode_detect_and_decode/barcode_detect_and_decode.markdown

@@ -3,8 +3,7 @@ Barcode Recognition    {#tutorial_barcode_detect_and_decode}
 
 @tableofcontents
 
-@prev_tutorial{tutorial_cascade_classifier}
-@next_tutorial{tutorial_introduction_to_pca}
+@prev_tutorial{tutorial_aruco_board_detection}
 
 |    |    |
 | -: | :- |

doc/tutorials/objdetect/table_of_content_objdetect.markdown

@@ -3,3 +3,4 @@ Object Detection (objdetect module) {#tutorial_table_of_content_objdetect}
 
 -   @subpage tutorial_aruco_detection
 -   @subpage tutorial_aruco_board_detection
+-   @subpage tutorial_barcode_detect_and_decode

doc/tutorials/others/cascade_classifier.markdown

@@ -1,148 +0,0 @@
-Cascade Classifier {#tutorial_cascade_classifier}
-==================
-
-@tableofcontents
-
-@prev_tutorial{tutorial_optical_flow}
-@next_tutorial{tutorial_barcode_detect_and_decode}
-
-|    |    |
-| -: | :- |
-| Original author | Ana Huamán |
-| Compatibility | OpenCV >= 3.0 |
-
-Goal
-----
-
-In this tutorial,
-
--   We will learn how the Haar cascade object detection works.
--   We will see the basics of face detection and eye detection using the Haar Feature-based Cascade Classifiers
--   We will use the @ref cv::CascadeClassifier class to detect objects in a video stream. Particularly, we
-    will use the functions:
-    -   @ref cv::CascadeClassifier::load to load a .xml classifier file. It can be either a Haar or a LBP classifier
-    -   @ref cv::CascadeClassifier::detectMultiScale to perform the detection.
-
-Theory
-------
-
-Object Detection using Haar feature-based cascade classifiers is an effective object detection
-method proposed by Paul Viola and Michael Jones in their paper, "Rapid Object Detection using a
-Boosted Cascade of Simple Features" in 2001. It is a machine learning based approach where a cascade
-function is trained from a lot of positive and negative images. It is then used to detect objects in
-other images.
-
-Here we will work with face detection. Initially, the algorithm needs a lot of positive images
-(images of faces) and negative images (images without faces) to train the classifier. Then we need
-to extract features from it. For this, Haar features shown in the below image are used. They are just
-like our convolutional kernel. Each feature is a single value obtained by subtracting sum of pixels
-under the white rectangle from sum of pixels under the black rectangle.
-
-![image](images/haar_features.jpg)
-
-Now, all possible sizes and locations of each kernel are used to calculate lots of features. (Just
-imagine how much computation it needs? Even a 24x24 window results over 160000 features). For each
-feature calculation, we need to find the sum of the pixels under white and black rectangles. To solve
-this, they introduced the integral image. However large your image, it reduces the calculations for a
-given pixel to an operation involving just four pixels. Nice, isn't it? It makes things super-fast.
-
-But among all these features we calculated, most of them are irrelevant. For example, consider the
-image below. The top row shows two good features. The first feature selected seems to focus on the
-property that the region of the eyes is often darker than the region of the nose and cheeks. The
-second feature selected relies on the property that the eyes are darker than the bridge of the nose.
-But the same windows applied to cheeks or any other place is irrelevant. So how do we select the
-best features out of 160000+ features? It is achieved by **Adaboost**.
-
-![image](images/haar.png)
-
-For this, we apply each and every feature on all the training images. For each feature, it finds the
-best threshold which will classify the faces to positive and negative. Obviously, there will be
-errors or misclassifications. We select the features with minimum error rate, which means they are
-the features that most accurately classify the face and non-face images. (The process is not as simple as
-this. Each image is given an equal weight in the beginning. After each classification, weights of
-misclassified images are increased. Then the same process is done. New error rates are calculated.
-Also new weights. The process is continued until the required accuracy or error rate is achieved or
-the required number of features are found).
-
-The final classifier is a weighted sum of these weak classifiers. It is called weak because it alone
-can't classify the image, but together with others forms a strong classifier. The paper says even
-200 features provide detection with 95% accuracy. Their final setup had around 6000 features.
-(Imagine a reduction from 160000+ features to 6000 features. That is a big gain).
-
-So now you take an image. Take each 24x24 window. Apply 6000 features to it. Check if it is face or
-not. Wow.. Isn't it a little inefficient and time consuming? Yes, it is. The authors have a good
-solution for that.
-
-In an image, most of the image is non-face region. So it is a better idea to have a simple
-method to check if a window is not a face region. If it is not, discard it in a single shot, and don't
-process it again. Instead, focus on regions where there can be a face. This way, we spend more time
-checking possible face regions.
-
-For this they introduced the concept of **Cascade of Classifiers**. Instead of applying all 6000
-features on a window, the features are grouped into different stages of classifiers and applied one-by-one.
-(Normally the first few stages will contain very many fewer features). If a window fails the first
-stage, discard it. We don't consider the remaining features on it. If it passes, apply the second stage
-of features and continue the process. The window which passes all stages is a face region. How is
-that plan!
-
-The authors' detector had 6000+ features with 38 stages with 1, 10, 25, 25 and 50 features in the first five
-stages. (The two features in the above image are actually obtained as the best two features from
-Adaboost). According to the authors, on average 10 features out of 6000+ are evaluated per
-sub-window.
-
-So this is a simple intuitive explanation of how Viola-Jones face detection works. Read the paper for
-more details or check out the references in the Additional Resources section.
-
-Haar-cascade Detection in OpenCV
---------------------------------
-OpenCV provides pretrained models that can be read using the @ref cv::CascadeClassifier::load method.
-These models are located in the data folder in the OpenCV installation or can be found [here](https://github.com/opencv/opencv/tree/5.x/data).
-
-The following code example will use pretrained Haar cascade models to detect faces and eyes in an image.
-First, a @ref cv::CascadeClassifier is created and the necessary XML file is loaded using the @ref cv::CascadeClassifier::load method.
-Afterwards, the detection is done using the @ref cv::CascadeClassifier::detectMultiScale method, which returns boundary rectangles for the detected faces or eyes.
-
-@add_toggle_cpp
-This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/opencv/opencv/tree/5.x/samples/cpp/tutorial_code/objectDetection/objectDetection.cpp)
-@include samples/cpp/tutorial_code/objectDetection/objectDetection.cpp
-@end_toggle
-
-@add_toggle_java
-This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/opencv/opencv/tree/5.x/samples/java/tutorial_code/objectDetection/cascade_classifier/ObjectDetectionDemo.java)
-@include samples/java/tutorial_code/objectDetection/cascade_classifier/ObjectDetectionDemo.java
-@end_toggle
-
-@add_toggle_python
-This tutorial code's is shown lines below. You can also download it from
-[here](https://github.com/opencv/opencv/tree/5.x/samples/python/tutorial_code/objectDetection/cascade_classifier/objectDetection.py)
-@include samples/python/tutorial_code/objectDetection/cascade_classifier/objectDetection.py
-@end_toggle
-
-Result
-------
-
--#  Here is the result of running the code above and using as input the video stream of a built-in
-    webcam:
-
-    ![](images/Cascade_Classifier_Tutorial_Result_Haar.jpg)
-
-    Be sure the program will find the path of files *haarcascade_frontalface_alt.xml* and
-    *haarcascade_eye_tree_eyeglasses.xml*. They are located in
-    *opencv/data/haarcascades*
-
--#  This is the result of using the file *lbpcascade_frontalface.xml* (LBP trained) for the face
-    detection. For the eyes we keep using the file used in the tutorial.
-
-    ![](images/Cascade_Classifier_Tutorial_Result_LBP.jpg)
-
-Additional Resources
---------------------
-
--#  Paul Viola and Michael J. Jones. Robust real-time face detection. International Journal of Computer Vision, 57(2):137–154, 2004. @cite Viola04
--#  Rainer Lienhart and Jochen Maydt. An extended set of haar-like features for rapid object detection. In Image Processing. 2002. Proceedings. 2002 International Conference on, volume 1, pages I–900. IEEE, 2002. @cite Lienhart02
--#  Video Lecture on [Face Detection and Tracking](https://www.youtube.com/watch?v=WfdYYNamHZ8)
--#  An interesting interview regarding Face Detection by [Adam
-    Harvey](https://web.archive.org/web/20171204220159/http://www.makematics.com/research/viola-jones/)
--#  [OpenCV Face Detection: Visualized](https://vimeo.com/12774628) on Vimeo by Adam Harvey

doc/tutorials/others/introduction_to_pca.markdown

@@ -3,7 +3,7 @@ Introduction to Principal Component Analysis (PCA) {#tutorial_introduction_to_pc
 
 @tableofcontents
 
-@prev_tutorial{tutorial_barcode_detect_and_decode}
+@prev_tutorial{tutorial_optical_flow}
 
 |    |    |
 | -: | :- |

doc/tutorials/others/optical_flow.markdown

@@ -4,7 +4,7 @@ Optical Flow {#tutorial_optical_flow}
 @tableofcontents
 
 @prev_tutorial{tutorial_meanshift}
-@next_tutorial{tutorial_cascade_classifier}
+@next_tutorial{tutorial_introduction_to_pca}
 
 Goal
 ----

doc/tutorials/others/table_of_content_other.markdown

@@ -1,4 +1,4 @@
-Other tutorials (objdetect, photo, stitching, video) {#tutorial_table_of_content_other}
+Other tutorials (photo, stitching, video) {#tutorial_table_of_content_other}
 ========================================================
 
 -   photo. @subpage tutorial_hdr_imaging
@@ -6,6 +6,4 @@ Other tutorials (objdetect, photo, stitching, video) {#tutorial_table_of_content
 -   video. @subpage tutorial_background_subtraction
 -   video. @subpage tutorial_meanshift
 -   video. @subpage tutorial_optical_flow
--   objdetect. @subpage tutorial_cascade_classifier
--   objdetect. @subpage tutorial_barcode_detect_and_decode
 -   ml. @subpage tutorial_introduction_to_pca

doc/tutorials/tutorials.markdown

@@ -10,7 +10,7 @@ OpenCV Tutorials {#tutorial_root}
 - @subpage tutorial_table_of_content_features2d - feature detectors, descriptors and matching framework
 - @subpage tutorial_table_of_content_dnn - infer neural networks using built-in _dnn_ module
 - @subpage tutorial_table_of_content_gapi - graph-based approach to computer vision algorithms building
-- @subpage tutorial_table_of_content_other - other modules (objdetect, stitching, video, photo)
+- @subpage tutorial_table_of_content_other - other modules (stitching, video, photo)
 - @subpage tutorial_table_of_content_ios - running OpenCV on an iDevice
 - @subpage tutorial_table_of_content_3d - 3d objects processing and visualisation
 @cond CUDA_MODULES