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

* Move objdetect parts to contrib

* Move objdetect parts to contrib

* Minor fixes.

doc/tutorials/others/barcode_detect_and_decode.markdown

@@ -1,76 +0,0 @@
-Barcode Recognition    {#tutorial_barcode_detect_and_decode}
-===================
-
-@tableofcontents
-
-@prev_tutorial{tutorial_cascade_classifier}
-@next_tutorial{tutorial_introduction_to_pca}
-
-|    |    |
-| -: | :- |
-| Compatibility | OpenCV >= 4.8 |
-
-Goal
-----
-
-In this chapter we will familiarize with the barcode detection and decoding methods available in OpenCV.
-
-Basics
-----
-
-Barcode is major technique to identify commodity in real life. A common barcode is a pattern of parallel lines arranged by black bars and white bars with vastly different reflectivity. Barcode recognition is to scan the barcode in the horizontal direction to get a string of binary codes composed of bars of different widths and colors, that is, the code information of the barcode. The content of barcode can be decoded by matching with various barcode encoding methods. Currently, we support EAN-8, EAN-13, UPC-A and UPC-E standards.
-
-See https://en.wikipedia.org/wiki/Universal_Product_Code and https://en.wikipedia.org/wiki/International_Article_Number
-
-Related papers: @cite Xiangmin2015research , @cite kass1987analyzing , @cite bazen2002systematic
-
-Code example
-------------
-
-### Main class
-Several algorithms were introduced for barcode recognition.
-
-While coding, we firstly need to create a cv::barcode::BarcodeDetector object. It has mainly three member functions, which will be introduced in the following.
-
-#### Initialization
-
-Optionally user can construct barcode detector with super resolution model which should be downloaded from https://github.com/WeChatCV/opencv_3rdparty/tree/wechat_qrcode (`sr.caffemodel`, `sr.prototxt`).
-
-@snippet cpp/barcode.cpp initialize
-
-We need to create variables to store the outputs.
-
-@snippet cpp/barcode.cpp output
-
-#### Detecting
-
-cv::barcode::BarcodeDetector::detect method uses an algorithm based on directional coherence. First, we compute the average squared gradients of every pixel, @cite bazen2002systematic . Then we divide an image into square patches and compute the **gradient orientation coherence** and **mean gradient direction** of each patch. Then, we connect all patches that have **high gradient orientation coherence** and **similar gradient direction**. At this stage we use multiscale patches to capture the gradient distribution of multi-size barcodes, and apply non-maximum suppression to filter duplicate proposals. At last, we use cv::minAreaRect to bound the ROI, and output the corners of the rectangles.
-
-Detect codes in the input image, and output the corners of detected rectangles:
-
-@snippet cpp/barcode.cpp detect
-
-#### Decoding
-
-cv::barcode::BarcodeDetector::decode method first super-scales the image (_optionally_) if it is smaller than threshold, sharpens the image and then binaries it by OTSU or local binarization. Then it reads the contents of the barcode by matching the similarity of the specified barcode pattern.
-
-#### Detecting and decoding
-
-cv::barcode::BarcodeDetector::detectAndDecode combines `detect` and `decode` in a single call. A simple example below shows how to use this function:
-
-@snippet cpp/barcode.cpp detectAndDecode
-
-Visualize the results:
-
-@snippet cpp/barcode.cpp visualize
-
-Results
--------
-
-Original image:
-
-![image](images/barcode_book.jpg)
-
-After detection:
-
-![image](images/barcode_book_res.jpg)

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