Merge branch 4.x

doc/tutorials/objdetect/aruco_board_detection/aruco_board_detection.markdown

@@ -2,8 +2,7 @@ Detection of ArUco boards {#tutorial_aruco_board_detection}
 =========================
 
 @prev_tutorial{tutorial_aruco_detection}
-@next_tutorial{tutorial_barcode_detect_and_decode}
-
+@next_tutorial{tutorial_charuco_detection}
 
 |    |    |
 | -: | :- |

doc/tutorials/objdetect/aruco_calibration/aruco_calibration.markdown

@@ -0,0 +1,88 @@
+Calibration with ArUco and ChArUco {#tutorial_aruco_calibration}
+==================================
+
+@prev_tutorial{tutorial_charuco_diamond_detection}
+@next_tutorial{tutorial_aruco_faq}
+
+The ArUco module can also be used to calibrate a camera. Camera calibration consists in obtaining the
+camera intrinsic parameters and distortion coefficients. This parameters remain fixed unless the camera
+optic is modified, thus camera calibration only need to be done once.
+
+Camera calibration is usually performed using the OpenCV `cv::calibrateCamera()` function. This function
+requires some correspondences between environment points and their projection in the camera image from
+different viewpoints. In general, these correspondences are obtained from the corners of chessboard
+patterns. See `cv::calibrateCamera()` function documentation or the OpenCV calibration tutorial for
+more detailed information.
+
+Using the ArUco module, calibration can be performed based on ArUco markers corners or ChArUco corners.
+Calibrating using ArUco is much more versatile than using traditional chessboard patterns, since it
+allows occlusions or partial views.
+
+As it can be stated, calibration can be done using both, marker corners or ChArUco corners. However,
+it is highly recommended using the ChArUco corners approach since the provided corners are much
+more accurate in comparison to the marker corners. Calibration using a standard Board should only be
+employed in those scenarios where the ChArUco boards cannot be employed because of any kind of restriction.
+
+Calibration with ChArUco Boards
+-------------------------------
+
+To calibrate using a ChArUco board, it is necessary to detect the board from different viewpoints, in the
+same way that the standard calibration does with the traditional chessboard pattern. However, due to the
+benefits of using ChArUco, occlusions and partial views are allowed, and not all the corners need to be
+visible in all the viewpoints.
+
+![ChArUco calibration viewpoints](images/charucocalibration.jpg)
+
+The example of using `cv::calibrateCamera()` for cv::aruco::CharucoBoard:
+
+@snippet samples/cpp/tutorial_code/objectDetection/calibrate_camera_charuco.cpp CalibrationWithCharucoBoard1
+@snippet samples/cpp/tutorial_code/objectDetection/calibrate_camera_charuco.cpp CalibrationWithCharucoBoard2
+@snippet samples/cpp/tutorial_code/objectDetection/calibrate_camera_charuco.cpp CalibrationWithCharucoBoard3
+
+The ChArUco corners and ChArUco identifiers captured on each viewpoint are stored in the vectors
+`allCharucoCorners` and `allCharucoIds`, one element per viewpoint.
+
+The `calibrateCamera()` function will fill the `cameraMatrix` and `distCoeffs` arrays with the
+camera calibration parameters. It will return the reprojection error obtained from the calibration.
+The elements in `rvecs` and `tvecs` will be filled with the estimated pose of the camera
+(respect to the ChArUco board) in each of the viewpoints.
+
+Finally, the `calibrationFlags` parameter determines some of the options for the calibration.
+
+A full working example is included in the `calibrate_camera_charuco.cpp` inside the
+`samples/cpp/tutorial_code/objectDetection` folder.
+
+The samples now take input via commandline via the `cv::CommandLineParser`. For this file the example
+parameters will look like:
+@code{.cpp}
+    "camera_calib.txt" -w=5 -h=7 -sl=0.04 -ml=0.02 -d=10
+    -v=path/img_%02d.jpg
+@endcode
+
+The camera calibration parameters from `opencv/samples/cpp/tutorial_code/objectDetection/tutorial_camera_charuco.yml`
+were obtained by the `img_00.jpg-img_03.jpg` placed from this
+[folder](https://github.com/opencv/opencv_contrib/tree/4.6.0/modules/aruco/tutorials/aruco_calibration/images).
+
+Calibration with ArUco Boards
+-----------------------------
+
+As it has been stated, it is recommended the use of ChAruco boards instead of ArUco boards for camera
+calibration, since ChArUco corners are more accurate than marker corners. However, in some special cases
+it must be required to use calibration based on ArUco boards. As in the previous case, it requires
+the detections of an ArUco board from different viewpoints.
+
+![ArUco calibration viewpoints](images/arucocalibration.jpg)
+
+The example of using `cv::calibrateCamera()` for cv::aruco::GridBoard:
+
+@snippet samples/cpp/tutorial_code/objectDetection/calibrate_camera.cpp CalibrationWithArucoBoard1
+@snippet samples/cpp/tutorial_code/objectDetection/calibrate_camera.cpp CalibrationWithArucoBoard2
+@snippet samples/cpp/tutorial_code/objectDetection/calibrate_camera.cpp CalibrationWithArucoBoard3
+
+A full working example is included in the `calibrate_camera.cpp` inside the `samples/cpp/tutorial_code/objectDetection` folder.
+
+The samples now take input via commandline via the `cv::CommandLineParser`. For this file the example
+parameters will look like:
+@code{.cpp}
+    "camera_calib.txt" -w=5 -h=7 -l=100 -s=10 -d=10 -v=path/aruco_videos_or_images
+@endcode

doc/tutorials/objdetect/aruco_faq/aruco_faq.markdown

@@ -0,0 +1,191 @@
+Aruco module FAQ {#tutorial_aruco_faq}
+================
+
+@prev_tutorial{tutorial_aruco_calibration}
+@next_tutorial{tutorial_barcode_detect_and_decode}
+
+This is a compilation of questions that can be useful for those that want to use the aruco module.
+
+- I only want to label some objects, what should I use?
+
+In this case, you only need single ArUco markers. You can place one or several markers with different
+ids in each of the object you want to identify.
+
+
+- Which algorithm is used for marker detection?
+
+The aruco module is based on the original ArUco library. A full description of the detection process
+can be found in:
+
+> S. Garrido-Jurado, R. Muñoz-Salinas, F. J. Madrid-Cuevas, and M. J. Marín-Jiménez. 2014.
+> "Automatic generation and detection of highly reliable fiducial markers under occlusion".
+> Pattern Recogn. 47, 6 (June 2014), 2280-2292. DOI=10.1016/j.patcog.2014.01.005
+
+
+- My markers are not being detected correctly, what can I do?
+
+There can be many factors that avoid the correct detection of markers. You probably need to adjust
+some of the parameters in the `cv::aruco::DetectorParameters` object. The first thing you can do is
+checking if your markers are returned as rejected candidates by the `cv::aruco::ArucoDetector::detectMarkers()`
+function. Depending on this, you should try to modify different parameters.
+
+If you are using a ArUco board, you can also try the `cv::aruco::ArucoDetector::refineDetectedMarkers()` function.
+If you are [using big markers](https://github.com/opencv/opencv_contrib/issues/2811) (400x400 pixels and more), try
+increasing `cv::aruco::DetectorParameters::adaptiveThreshWinSizeMax` value.
+Also avoid [narrow borders around the ArUco marker](https://github.com/opencv/opencv_contrib/issues/2492)
+(5% or less of the marker perimeter, adjusted by `cv::aruco::DetectorParameters::minMarkerDistanceRate`)
+around markers.
+
+
+- What are the benefits of ArUco boards? What are the drawbacks?
+
+Using a board of markers you can obtain the camera pose from a set of markers, instead of a single one.
+This way, the detection is able to handle occlusion of partial views of the Board, since only one
+marker is necessary to obtain the pose.
+
+Furthermore, as in most cases you are using more corners for pose estimation, it will be more
+accurate than using a single marker.
+
+The main drawback is that a Board is not as versatile as a single marker.
+
+
+
+- What are the benefits of ChArUco boards over ArUco boards? And the drawbacks?
+
+ChArUco boards combines chessboards with ArUco boards. Thanks to this, the corners provided by
+ChArUco boards are more accurate than those provided by ArUco Boards (or single markers).
+
+The main drawback is that ChArUco boards are not as versatile as ArUco board. For instance,
+a ChArUco board is a planar board with a specific marker layout while the ArUco boards can have
+any layout, even in 3d. Furthermore, the markers in the ChArUco board are usually smaller and
+more difficult to detect.
+
+
+- I do not need pose estimation, should I use ChArUco boards?
+
+No. The main goal of ChArUco boards is provide high accurate corners for pose estimation or camera
+calibration.
+
+
+- Should all the markers in an ArUco board be placed in the same plane?
+
+No, the marker corners in a ArUco board can be placed anywhere in its 3d coordinate system.
+
+
+- Should all the markers in an ChArUco board be placed in the same plane?
+
+Yes, all the markers in a ChArUco board need to be in the same plane and their layout is fixed by
+the chessboard shape.
+
+
+- What is the difference between a `cv::aruco::Board` object and a `cv::aruco::GridBoard` object?
+
+The `cv::aruco::GridBoard` class is a specific type of board that inherits from `cv::aruco::Board` class.
+A `cv::aruco::GridBoard` object is a board whose markers are placed in the same plane and in a grid layout.
+
+
+- What are Diamond markers?
+
+Diamond markers are very similar to a ChArUco board of 3x3 squares. However, contrary to ChArUco boards,
+the detection of diamonds is based on the relative position of the markers.
+They are useful when you want to provide a conceptual meaning to any (or all) of the markers in
+the diamond. An example is using one of the marker to provide the diamond scale.
+
+
+- Do I need to detect marker before board detection, ChArUco board detection or Diamond detection?
+
+Yes, the detection of single markers is a basic tool in the aruco module. It is done using the
+`cv::aruco::DetectorParameters::detectMarkers()` function. The rest of functionalities receives
+a list of detected markers from this function.
+
+
+- I want to calibrate my camera, can I use this module?
+
+Yes, the aruco module provides functionalities to calibrate the camera using both, ArUco boards and
+ChArUco boards.
+
+
+- Should I calibrate using a ChArUco board or an ArUco board?
+
+It is highly recommended the calibration using ChArUco board due to the high accuracy.
+
+
+- Should I use a predefined dictionary or generate my own dictionary?
+
+In general, it is easier to use one of the predefined dictionaries. However, if you need a bigger
+dictionary (in terms of number of markers or number of bits) you should generate your own dictionary.
+Dictionary generation is also useful if you want to maximize the inter-marker distance to achieve
+a better error correction during the identification step.
+
+- I am generating my own dictionary but it takes too long
+
+Dictionary generation should only be done once at the beginning of your application and it should take
+some seconds. If you are generating the dictionary on each iteration of your detection loop, you are
+doing it wrong.
+
+Furthermore, it is recommendable to save the dictionary to a file with `cv::aruco::Dictionary::writeDictionary()`
+and read it with `cv::aruco::Dictionary::readDictionary()` on every execution, so you don't need
+to generate it.
+
+
+- I would like to use some markers of the original ArUco library that I have already printed, can I use them?
+
+Yes, one of the predefined dictionary is `cv::aruco::DICT_ARUCO_ORIGINAL`, which detects the marker
+of the original ArUco library with the same identifiers.
+
+
+- Can I use the Board configuration file of the original ArUco library in this module?
+
+Not directly, you will need to adapt the information of the ArUco file to the aruco module Board format.
+
+
+- Can I use this module to detect the markers of other libraries based on binary fiducial markers?
+
+Probably yes, however you will need to port the dictionary of the original library to the aruco module format.
+
+
+- Do I need to store the Dictionary information in a file so I can use it in different executions?
+
+If you are using one of the predefined dictionaries, it is not necessary. Otherwise, it is recommendable
+that you save it to file.
+
+
+- Do I need to store the Board information in a file so I can use it in different executions?
+
+If you are using a `cv::aruco::GridBoard` or a `cv::aruco::CharucoBoard` you only need to store
+the board measurements that are provided to the `cv::aruco::GridBoard::GridBoard()` constructor or
+in or `cv::aruco::CharucoBoard` constructor. If you manually modify the marker ids of the boards,
+or if you use a different type of board, you should save your board object to file.
+
+- Does the aruco module provide functions to save the Dictionary or Board to file?
+
+You can use `cv::aruco::Dictionary::writeDictionary()` and `cv::aruco::Dictionary::readDictionary()`
+for `cv::aruco::Dictionary`. The data member of board classes are public and can be easily stored.
+
+
+- Alright, but how can I render a 3d model to create an augmented reality application?
+
+To do so, you will need to use an external rendering engine library, such as OpenGL. The aruco module
+only provides the functionality to obtain the camera pose, i.e. the rotation and traslation vectors,
+which is necessary to create the augmented reality effect. However, you will need to adapt the rotation
+and traslation vectors from the OpenCV format to the format accepted by your 3d rendering library.
+The original ArUco library contains examples of how to do it for OpenGL and Ogre3D.
+
+
+- I have use this module in my research work, how can I cite it?
+
+You can cite the original ArUco library:
+
+> S. Garrido-Jurado, R. Muñoz-Salinas, F. J. Madrid-Cuevas, and M. J. Marín-Jiménez. 2014.
+> "Automatic generation and detection of highly reliable fiducial markers under occlusion".
+> Pattern Recogn. 47, 6 (June 2014), 2280-2292. DOI=10.1016/j.patcog.2014.01.005
+
+- Pose estimation markers are not being detected correctly, what can I do?
+
+It is important to remark that the estimation of the pose using only 4 coplanar points is subject to ambiguity.
+In general, the ambiguity can be solved, if the camera is near to the marker.
+However, as the marker becomes small, the errors in the corner estimation grows and ambiguity comes
+as a problem. Try increasing the size of the marker you're using, and you can also try non-symmetrical
+(aruco_dict_utils.cpp) markers to avoid collisions. Use multiple markers (ArUco/ChArUco/Diamonds boards)
+and pose estimation with solvePnP() with the `cv::SOLVEPNP_IPPE_SQUARE` option.
+More in [this issue](https://github.com/opencv/opencv/issues/8813).

doc/tutorials/objdetect/barcode_detect_and_decode/barcode_detect_and_decode.markdown

@@ -3,7 +3,7 @@ Barcode Recognition    {#tutorial_barcode_detect_and_decode}
 
 @tableofcontents
 
-@prev_tutorial{tutorial_aruco_board_detection}
+@prev_tutorial{tutorial_aruco_faq}
 
 |    |    |
 | -: | :- |

doc/tutorials/objdetect/charuco_detection/charuco_detection.markdown

@@ -0,0 +1,265 @@
+Detection of ChArUco Boards {#tutorial_charuco_detection}
+===========================
+
+@prev_tutorial{tutorial_aruco_board_detection}
+@next_tutorial{tutorial_charuco_diamond_detection}
+
+ArUco markers and boards are very useful due to their fast detection and their versatility.
+However, one of the problems of ArUco markers is that the accuracy of their corner positions is not
+too high, even after applying subpixel refinement.
+
+On the contrary, the corners of chessboard patterns can be refined more accurately since each corner
+is surrounded by two black squares. However, finding a chessboard pattern is not as versatile as
+finding an ArUco board: it has to be completely visible and occlusions are not permitted.
+
+A ChArUco board tries to combine the benefits of these two approaches:
+
+![Charuco definition](images/charucodefinition.png)
+
+The ArUco part is used to interpolate the position of the chessboard corners, so that it has the
+versatility of marker boards, since it allows occlusions or partial views. Moreover, since the
+interpolated corners belong to a chessboard, they are very accurate in terms of subpixel accuracy.
+
+When high precision is necessary, such as in camera calibration, Charuco boards are a better option
+than standard ArUco boards.
+
+Goal
+----
+
+In this tutorial you will learn:
+
+- How to create a charuco board ?
+- How to detect the charuco corners without performing camera calibration ?
+- How to detect the charuco corners with camera calibration and pose estimation ?
+
+Source code
+-----------
+
+You can find this code in `samples/cpp/tutorial_code/objectDetection/detect_board_charuco.cpp`
+
+Here's a sample code of how to achieve all the stuff enumerated at the goal list.
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_board_charuco.cpp charuco_detect_board_full_sample
+
+ChArUco Board Creation
+----------------------
+
+The aruco module provides the `cv::aruco::CharucoBoard` class that represents a Charuco Board and
+which inherits from the `cv::aruco::Board` class.
+
+This class, as the rest of ChArUco functionalities, are defined in:
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_board_charuco.cpp charucohdr
+
+To define a `cv::aruco::CharucoBoard`, it is necessary:
+
+- Number of chessboard squares in X and Y directions.
+- Length of square side.
+- Length of marker side.
+- The dictionary of the markers.
+- Ids of all the markers.
+
+As for the `cv::aruco::GridBoard` objects, the aruco module provides to create `cv::aruco::CharucoBoard`
+easily. This object can be easily created from these parameters using the `cv::aruco::CharucoBoard`
+constructor:
+
+@snippet samples/cpp/tutorial_code/objectDetection/create_board_charuco.cpp create_charucoBoard
+
+- The first parameter is the number of squares in X and Y direction respectively.
+- The second and third parameters are the length of the squares and the markers respectively. They can
+  be provided in any unit, having in mind that the estimated pose for this board would be measured
+  in the same units (usually meters are used).
+- Finally, the dictionary of the markers is provided.
+
+The ids of each of the markers are assigned by default in ascending order and starting on 0, like in
+`cv::aruco::GridBoard` constructor. This can be easily customized by accessing to the ids vector
+through `board.ids`, like in the `cv::aruco::Board` parent class.
+
+Once we have our `cv::aruco::CharucoBoard` object, we can create an image to print it. There are
+two ways to do this:
+1. By using the script `doc/patter_tools/gen_pattern.py `, see @subpage tutorial_camera_calibration_pattern.
+2. By using the function `cv::aruco::CharucoBoard::generateImage()`.
+
+The function `cv::aruco::CharucoBoard::generateImage()` is provided in cv::aruco::CharucoBoard class
+and can be called by using the following code:
+@snippet samples/cpp/tutorial_code/objectDetection/create_board_charuco.cpp generate_charucoBoard
+
+- The first parameter is the size of the output image in pixels. If this is not proportional
+to the board dimensions, it will be centered on the image.
+- The second parameter is the output image with the charuco board.
+- The third parameter is the (optional) margin in pixels, so none of the markers are touching the
+  image border.
+- Finally, the size of the marker border, similarly to `cv::aruco::generateImageMarker()` function.
+  The default value is 1.
+
+The output image will be something like this:
+
+![](images/charucoboard.png)
+
+A full working example is included in the `create_board_charuco.cpp` inside the `samples/cpp/tutorial_code/objectDetection/`.
+
+The samples `create_board_charuco.cpp` now take input via commandline via the `cv::CommandLineParser`.
+For this file the example
+parameters will look like:
+@code{.cpp}
+    "_output_path_/chboard.png" -w=5 -h=7 -sl=100 -ml=60 -d=10
+@endcode
+
+
+ChArUco Board Detection
+-----------------------
+
+When you detect a ChArUco board, what you are actually detecting is each of the chessboard corners
+of the board.
+
+Each corner on a ChArUco board has a unique identifier (id) assigned. These ids go from 0 to the total
+number of corners in the board.
+The steps of charuco board detection can be broken down to the following steps:
+
+- **Taking input Image**
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_board_charuco.cpp inputImg
+
+The original image where the markers are to be detected. The image is necessary to perform subpixel
+refinement in the ChArUco corners.
+
+- **Reading the camera calibration Parameters(only for detection with camera calibration)**
+
+@snippet samples/cpp/tutorial_code/objectDetection/aruco_samples_utility.hpp camDistCoeffs
+
+The parameters of `readCameraParameters` are:
+- The first parameter is the path to the camera intrinsic matrix and distortion coefficients.
+- The second and third parameters are cameraMatrix and distCoeffs.
+
+This function takes these parameters as input and returns a boolean value of whether the camera
+calibration parameters are valid or not. For detection of charuco corners without calibration,
+this step is not required.
+
+- **Detecting the markers and interpolation of charuco corners from markers**
+
+The detection of the ChArUco corners is based on the previous detected markers.
+So that, first markers are detected, and then ChArUco corners are interpolated from markers.
+The method that detect the ChArUco corners is `cv::aruco::CharucoDetector::detectBoard()`.
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_board_charuco.cpp interpolateCornersCharuco
+
+The parameters of detectBoard are:
+- `image` - Input image.
+- `charucoCorners` - output list of image positions of the detected corners.
+- `charucoIds` - output ids for each of the detected corners in `charucoCorners`.
+- `markerCorners` - input/output vector of detected marker corners.
+- `markerIds` - input/output vector of identifiers of the detected markers
+
+If markerCorners and markerIds are empty, the function will detect aruco markers and ids.
+
+If calibration parameters are provided, the ChArUco corners are interpolated by, first, estimating
+a rough pose from the ArUco markers and, then, reprojecting the ChArUco corners back to the image.
+
+On the other hand, if calibration parameters are not provided, the ChArUco corners are interpolated
+by calculating the corresponding homography between the ChArUco plane and the ChArUco image projection.
+
+The main problem of using homography is that the interpolation is more sensible to image distortion.
+Actually, the homography is only performed using the closest markers of each ChArUco corner to reduce
+the effect of distortion.
+
+When detecting markers for ChArUco boards, and specially when using homography, it is recommended to
+disable the corner refinement of markers. The reason of this is that, due to the proximity of the
+chessboard squares, the subpixel process can produce important deviations in the corner positions and
+these deviations are propagated to the ChArUco corner interpolation, producing poor results.
+
+@note To avoid deviations, the margin between chessboard square and aruco marker should be greater
+than 70% of one marker module.
+
+Furthermore, only those corners whose two surrounding markers have be found are returned. If any of
+the two surrounding markers has not been detected, this usually means that there is some occlusion
+or the image quality is not good in that zone. In any case, it is preferable not to consider that
+corner, since what we want is to be sure that the interpolated ChArUco corners are very accurate.
+
+After the ChArUco corners have been interpolated, a subpixel refinement is performed.
+
+Once we have interpolated the ChArUco corners, we would probably want to draw them to see if their
+detections are correct. This can be easily done using the `cv::aruco::drawDetectedCornersCharuco()`
+function:
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_board_charuco.cpp drawDetectedCornersCharuco
+
+- `imageCopy` is the image where the corners will be drawn (it will normally be the same image where
+   the corners were detected).
+- The `outputImage` will be a clone of `inputImage` with the corners drawn.
+- `charucoCorners` and `charucoIds` are the detected Charuco corners from the `cv::aruco::CharucoDetector::detectBoard()`
+  function.
+- Finally, the last parameter is the (optional) color we want to draw the corners with, of type `cv::Scalar`.
+
+For this image:
+
+![Image with Charuco board](images/choriginal.jpg)
+
+The result will be:
+
+![Charuco board detected](images/chcorners.jpg)
+
+In the presence of occlusion. like in the following image, although some corners are clearly visible,
+not all their surrounding markers have been detected due occlusion and, thus, they are not interpolated:
+
+![Charuco detection with occlusion](images/chocclusion.jpg)
+
+Sample video:
+
+@youtube{Nj44m_N_9FY}
+
+A full working example is included in the `detect_board_charuco.cpp` inside the
+`samples/cpp/tutorial_code/objectDetection/`.
+
+The samples `detect_board_charuco.cpp` now take input via commandline via the `cv::CommandLineParser`.
+For this file the example parameters will look like:
+@code{.cpp}
+    -w=5 -h=7 -sl=0.04 -ml=0.02 -d=10 -v=/path_to_opencv/opencv/doc/tutorials/objdetect/charuco_detection/images/choriginal.jpg
+@endcode
+
+ChArUco Pose Estimation
+-----------------------
+
+The final goal of the ChArUco boards is finding corners very accurately for a high precision calibration
+or pose estimation.
+
+The aruco module provides a function to perform ChArUco pose estimation easily. As in the
+`cv::aruco::GridBoard`, the coordinate system of the `cv::aruco::CharucoBoard` is placed in
+the board plane with the Z axis pointing in, and centered in the bottom left corner of the board.
+
+@note After OpenCV 4.6.0, there was an incompatible change in the coordinate systems of the boards,
+now the coordinate systems are placed in the boards plane with the Z axis pointing in the plane
+(previously the axis pointed out the plane).
+`objPoints` in CW order correspond to the Z-axis pointing in the plane.
+`objPoints` in CCW order correspond to the Z-axis pointing out the plane.
+See PR https://github.com/opencv/opencv_contrib/pull/3174
+
+
+To perform pose estimation for charuco boards, you should use `cv::aruco::CharucoBoard::matchImagePoints()`
+and `cv::solvePnP()`:
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_board_charuco.cpp poseCharuco
+
+- The `charucoCorners` and `charucoIds` parameters are the detected charuco corners from the
+  `cv::aruco::CharucoDetector::detectBoard()` function.
+- The `cameraMatrix` and `distCoeffs` are the camera calibration parameters which are necessary
+  for pose estimation.
+- Finally, the `rvec` and `tvec` parameters are the output pose of the Charuco Board.
+- `cv::solvePnP()` returns true if the pose was correctly estimated and false otherwise.
+  The main reason of failing is that there are not enough corners for pose estimation or
+  they are in the same line.
+
+The axis can be drawn using `cv::drawFrameAxes()` to check the pose is correctly estimated.
+The result would be: (X:red, Y:green, Z:blue)
+
+![Charuco Board Axis](images/chaxis.jpg)
+
+A full working example is included in the `detect_board_charuco.cpp` inside the
+`samples/cpp/tutorial_code/objectDetection/`.
+
+The samples `detect_board_charuco.cpp` now take input via commandline via the `cv::CommandLineParser`.
+For this file the example parameters will look like:
+@code{.cpp}
+    -w=5 -h=7 -sl=0.04 -ml=0.02 -d=10
+    -v=/path_to_opencv/opencv/doc/tutorials/objdetect/charuco_detection/images/choriginal.jpg
+    -c=/path_to_opencv/opencv/samples/cpp/tutorial_code/objectDetection/tutorial_camera_charuco.yml
+@endcode

doc/tutorials/objdetect/charuco_diamond_detection/charuco_diamond_detection.markdown

@@ -0,0 +1,143 @@
+Detection of Diamond Markers {#tutorial_charuco_diamond_detection}
+==============================
+
+@prev_tutorial{tutorial_charuco_detection}
+@next_tutorial{tutorial_aruco_calibration}
+
+A ChArUco diamond marker (or simply diamond marker) is a chessboard composed by 3x3 squares and 4 ArUco markers inside the white squares.
+It is similar to a ChArUco board in appearance, however they are conceptually different.
+
+![Diamond marker examples](images/diamondmarkers.jpg)
+
+In both, ChArUco board and Diamond markers, their detection is based on the previous detected ArUco
+markers. In the ChArUco case, the used markers are selected by directly looking their identifiers. This means
+that if a marker (included in the board) is found on a image, it will be automatically assumed to belong to the board. Furthermore,
+if a marker board is found more than once in the image, it will produce an ambiguity since the system wont
+be able to know which one should be used for the Board.
+
+On the other hand, the detection of Diamond marker is not based on the identifiers. Instead, their detection
+is based on the relative position of the markers. As a consequence, marker identifiers can be repeated in the
+same diamond or among different diamonds, and they can be detected simultaneously without ambiguity. However,
+due to the complexity of finding marker based on their relative position, the diamond markers are limited to
+a size of 3x3 squares and 4 markers.
+
+As in a single ArUco marker, each Diamond marker is composed by 4 corners and a identifier. The four corners
+correspond to the 4 chessboard corners in the marker and the identifier is actually an array of 4 numbers, which are
+the identifiers of the four ArUco markers inside the diamond.
+
+Diamond markers are useful in those scenarios where repeated markers should be allowed. For instance:
+
+- To increase the number of identifiers of single markers by using diamond marker for labeling. They would allow
+up to N^4 different ids, being N the number of markers in the used dictionary.
+
+- Give to each of the four markers a conceptual meaning. For instance, one of the four marker ids could be
+used to indicate the scale of the marker (i.e. the size of the square), so that the same diamond can be found
+in the environment with different sizes just by changing one of the four markers and the user does not need
+to manually indicate the scale of each of them. This case is included in the `detect_diamonds.cpp` file inside
+the samples folder of the module.
+
+Furthermore, as its corners are chessboard corners, they can be used for accurate pose estimation.
+
+The diamond functionalities are included in `<opencv2/objdetect/charuco_detector.hpp>`
+
+
+ChArUco Diamond Creation
+------
+
+The image of a diamond marker can be easily created using the `cv::aruco::CharucoBoard::generateImage()` function.
+For instance:
+
+@snippet samples/cpp/tutorial_code/objectDetection/create_diamond.cpp generate_diamond
+
+This will create a diamond marker image with a square size of 200 pixels and a marker size of 120 pixels.
+The marker ids are given in the second parameter as a `cv::Vec4i` object. The order of the marker ids
+in the diamond layout are the same as in a standard ChArUco board, i.e. top, left, right and bottom.
+
+The image produced will be:
+
+![Diamond marker](images/diamondmarker.png)
+
+A full working example is included in the `create_diamond.cpp` inside the `samples/cpp/tutorial_code/objectDetection/`.
+
+The samples `create_diamond.cpp` now take input via commandline via the `cv::CommandLineParser`. For this file the example
+parameters will look like:
+@code{.cpp}
+    "_path_/mydiamond.png" -sl=200 -ml=120 -d=10 -ids=0,1,2,3
+@endcode
+
+ChArUco Diamond Detection
+------
+
+As in most cases, the detection of diamond markers requires a previous detection of ArUco markers.
+After detecting markers, diamond are detected using the `cv::aruco::CharucoDetector::detectDiamonds()` function:
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_diamonds.cpp detect_diamonds
+
+The `cv::aruco::CharucoDetector::detectDiamonds()` function receives the original image and the previous detected marker corners and ids.
+If markerCorners and markerIds are empty, the function will detect aruco markers and ids.
+The input image is necessary to perform subpixel refinement in the ChArUco corners.
+It also receives the rate between the square size and the marker sizes which is required for both, detecting the diamond
+from the relative positions of the markers and interpolating the ChArUco corners.
+
+The function returns the detected diamonds in two parameters. The first parameter, `diamondCorners`, is an array containing
+all the four corners of each detected diamond. Its format is similar to the detected corners by the `cv::aruco::ArucoDetector::detectMarkers()`
+function and, for each diamond, the corners are represented in the same order than in the ArUco markers, i.e. clockwise order
+starting with the top-left corner. The second returned parameter, `diamondIds`, contains all the ids of the returned
+diamond corners in `diamondCorners`. Each id is actually an array of 4 integers that can be represented with `cv::Vec4i`.
+
+The detected diamond can be visualized using the function `cv::aruco::drawDetectedDiamonds()` which simply receives the image and the diamond
+corners and ids:
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_diamonds.cpp draw_diamonds
+
+The result is the same that the one produced by `cv::aruco::drawDetectedMarkers()`, but printing the four ids of the diamond:
+
+![Detected diamond markers](images/detecteddiamonds.jpg)
+
+A full working example is included in the `detect_diamonds.cpp` inside the `samples/cpp/tutorial_code/objectDetection/`.
+
+The samples `detect_diamonds.cpp` now take input via commandline via the `cv::CommandLineParser`. For this file the example
+parameters will look like:
+@code{.cpp}
+    -dp=path_to_opencv/opencv/samples/cpp/tutorial_code/objectDetection/detector_params.yml -sl=0.4 -ml=0.25 -refine=3
+    -v=path_to_opencv/opencv/doc/tutorials/objdetect/charuco_diamond_detection/images/diamondmarkers.jpg
+    -cd=path_to_opencv/opencv/samples/cpp/tutorial_code/objectDetection/tutorial_dict.yml
+@endcode
+
+ChArUco Diamond Pose Estimation
+------
+
+Since a ChArUco diamond is represented by its four corners, its pose can be estimated in the same way than in a single ArUco marker,
+i.e. using the `cv::solvePnP()` function. For instance:
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_diamonds.cpp diamond_pose_estimation
+@snippet samples/cpp/tutorial_code/objectDetection/detect_diamonds.cpp draw_diamond_pose_estimation
+
+The function will obtain the rotation and translation vector for each of the diamond marker and store them
+in `rvecs` and `tvecs`. Note that the diamond corners are a chessboard square corners and thus, the square length
+has to be provided for pose estimation, and not the marker length. Camera calibration parameters are also required.
+
+Finally, an axis can be drawn to check the estimated pose is correct using `drawFrameAxes()`:
+
+![Detected diamond axis](images/diamondsaxis.jpg)
+
+The coordinate system of the diamond pose will be in the center of the marker with the Z axis pointing out,
+as in a simple ArUco marker pose estimation.
+
+Sample video:
+
+@youtube{OqKpBnglH7k}
+
+Also ChArUco diamond pose can be estimated as ChArUco board:
+@snippet samples/cpp/tutorial_code/objectDetection/detect_diamonds.cpp diamond_pose_estimation_as_charuco
+
+A full working example is included in the `detect_diamonds.cpp` inside the `samples/cpp/tutorial_code/objectDetection/`.
+
+The samples `detect_diamonds.cpp` now take input via commandline via the `cv::CommandLineParser`. For this file the example
+parameters will look like:
+@code{.cpp}
+    -dp=path_to_opencv/opencv/samples/cpp/tutorial_code/objectDetection/detector_params.yml -sl=0.4 -ml=0.25 -refine=3
+    -v=path_to_opencv/opencv/doc/tutorials/objdetect/charuco_diamond_detection/images/diamondmarkers.jpg
+    -cd=path_to_opencv/opencv/samples/cpp/tutorial_code/objectDetection/tutorial_dict.yml
+    -c=path_to_opencv/opencv/samples/cpp/tutorial_code/objectDetection/tutorial_camera_params.yml
+@endcode

doc/tutorials/objdetect/table_of_content_objdetect.markdown

@@ -3,4 +3,8 @@ Object Detection (objdetect module) {#tutorial_table_of_content_objdetect}
 
 -   @subpage tutorial_aruco_detection
 -   @subpage tutorial_aruco_board_detection
+-   @subpage tutorial_charuco_detection
+-   @subpage tutorial_charuco_diamond_detection
+-   @subpage tutorial_aruco_calibration
+-   @subpage tutorial_aruco_faq
 -   @subpage tutorial_barcode_detect_and_decode