Merge branch 4.x

doc/tutorials/calib3d/camera_calibration/camera_calibration.markdown

@@ -60,6 +60,7 @@ done through basic geometrical equations. The equations used depend on the chose
 objects. Currently OpenCV supports three types of objects for calibration:
 
 -   Classical black-white chessboard
+-   ChArUco board pattern
 -   Symmetrical circle pattern
 -   Asymmetrical circle pattern
 
@@ -88,7 +89,8 @@ Source code
 
 You may also find the source code in the `samples/cpp/tutorial_code/calib3d/camera_calibration/`
 folder of the OpenCV source library or [download it from here
-](https://github.com/opencv/opencv/tree/5.x/samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp). For the usage of the program, run it with `-h` argument. The program has an
+](https://github.com/opencv/opencv/tree/5.x/samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp).
+For the usage of the program, run it with `-h` argument. The program has an
 essential argument: the name of its configuration file. If none is given then it will try to open the
 one named "default.xml". [Here's a sample configuration file
 ](https://github.com/opencv/opencv/tree/5.x/samples/cpp/tutorial_code/calib3d/camera_calibration/in_VID5.xml) in XML format. In the
@@ -128,14 +130,23 @@ Explanation
 
     The formation of the equations I mentioned above aims
     to finding major patterns in the input: in case of the chessboard this are corners of the
-    squares and for the circles, well, the circles themselves. The position of these will form the
+    squares and for the circles, well, the circles themselves. ChArUco board is equivalent to
+    chessboard, but corners are mached by ArUco markers. The position of these will form the
     result which will be written into the *pointBuf* vector.
     @snippet samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp find_pattern
     Depending on the type of the input pattern you use either the @ref cv::findChessboardCorners or
-    the @ref cv::findCirclesGrid function. For both of them you pass the current image and the size
-    of the board and you'll get the positions of the patterns. Furthermore, they return a boolean
-    variable which states if the pattern was found in the input (we only need to take into account
-    those images where this is true!).
+    the @ref cv::findCirclesGrid function or @ref cv::aruco::CharucoDetector::detectBoard method.
+    For all of them you pass the current image and the size of the board and you'll get the positions
+    of the patterns. cv::findChessboardCorners and cv::findCirclesGrid return a boolean variable
+    which states if the pattern was found in the input (we only need to take into account
+    those images where this is true!). `CharucoDetector::detectBoard` may detect partially visible
+    pattern and returns coordunates and ids of visible inner corners.
+
+    @note Board size and amount of matched points is different for chessboard, circles grid and ChArUco.
+    All chessboard related algorithm expects amount of inner corners as board width and height.
+    Board size of circles grid is just amount of circles by both grid dimentions. ChArUco board size
+    is defined in squares, but detection result is list of inner corners and that's why is smaller
+    by 1 in both dimentions.
 
     Then again in case of cameras we only take camera images when an input delay time is passed.
     This is done in order to allow user moving the chessboard around and getting different images.

doc/tutorials/calib3d/camera_calibration_pattern/camera_calibration_pattern.markdown

@@ -17,6 +17,9 @@ You can find a chessboard pattern in https://github.com/opencv/opencv/blob/5.x/d
 
 You can find a circleboard pattern in https://github.com/opencv/opencv/blob/5.x/doc/acircles_pattern.png
 
+You can find a ChAruco board pattern in https://github.com/opencv/opencv/blob/5.x/doc/charuco_board_pattern.png
+(7X5 ChAruco board, square size: 30 mm , marker size: 15 mm, aruco dict: DICT_5X5_100, page width: 210 mm, page height: 297 mm)
+
 Create your own pattern
 ---------------
 
@@ -28,7 +31,7 @@ create a checkerboard pattern in file chessboard.svg with 9 rows, 6 columns and
 
         python gen_pattern.py -o chessboard.svg --rows 9 --columns 6 --type checkerboard --square_size 20
 
-create a circle board pattern in file circleboard.svg with 7 rows, 5 columns and a radius of 15mm:
+create a circle board pattern in file circleboard.svg with 7 rows, 5 columns and a radius of 15 mm:
 
         python gen_pattern.py -o circleboard.svg --rows 7 --columns 5 --type circles --square_size 15
 
@@ -40,13 +43,18 @@ create a radon checkerboard for findChessboardCornersSB() with markers in (7 4),
 
         python gen_pattern.py -o radon_checkerboard.svg --rows 10 --columns 15 --type radon_checkerboard -s 12.1 -m 7 4 7 5 8 5
 
+create a ChAruco board pattern in charuco_board.svg with 7 rows, 5 columns, square size 30 mm, aruco marker size 15 mm and using DICT_5X5_100 as dictionary for aruco markers (it contains in DICT_ARUCO.json file):
+
+        python gen_pattern.py -o charuco_board.svg --rows 7 --columns 5 -T charuco_board --square_size 30 --marker_size 15 -f DICT_5X5_100.json.gz
+
 If you want to change unit use -u option (mm inches, px, m)
 
 If you want to change page size use -w and -h options
 
-@cond HAVE_opencv_aruco
-If you want to create a ChArUco board read @ref tutorial_charuco_detection "tutorial Detection of ChArUco Corners" in opencv_contrib tutorial.
-@endcond
-@cond !HAVE_opencv_aruco
-If you want to create a ChArUco board read tutorial Detection of ChArUco Corners in opencv_contrib tutorial.
-@endcond
+If you want to use your own dictionary for ChAruco board your should write name of file with your dictionary. For example
+
+        python gen_pattern.py -o charuco_board.svg --rows 7 --columns 5 -T charuco_board -f my_dictionary.json
+
+You can generate your dictionary in my_dictionary.json file with number of markers 30 and markers size 5 bits by using opencv/samples/cpp/aruco_dict_utils.cpp.
+
+        bin/example_cpp_aruco_dict_utils.exe my_dict.json -nMarkers=30 -markerSize=5