Merge pull request #23018 from stopmosk:move-aruco-tutorial

Move Aruco tutorials and samples to main repo #23018

merge with https://github.com/opencv/opencv_contrib/pull/3401
merge with https://github.com/opencv/opencv_extra/pull/1143

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

---------
Co-authored-by: AleksandrPanov <alexander.panov@xperience.ai>
Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>

doc/tutorials/objdetect/aruco_board_detection/aruco_board_detection.markdown

@@ -0,0 +1,201 @@
+Detection of ArUco boards {#tutorial_aruco_board_detection}
+=========================
+
+@prev_tutorial{tutorial_aruco_detection}
+
+|    |    |
+| -: | :- |
+| Original authors | Sergio Garrido, Alexander Panov |
+| Compatibility    | OpenCV >= 4.7.0 |
+
+An ArUco board is a set of markers that acts like a single marker in the sense that it provides a
+single pose for the camera.
+
+The most popular board is the one with all the markers in the same plane, since it can be easily printed:
+
+![](images/gboriginal.jpg)
+
+However, boards are not limited to this arrangement and can represent any 2d or 3d layout.
+
+The difference between a board and a set of independent markers is that the relative position between
+the markers in the board is known a priori. This allows that the corners of all the markers can be used for
+estimating the pose of the camera respect to the whole board.
+
+When you use a set of independent markers, you can estimate the pose for each marker individually,
+since you dont know the relative position of the markers in the environment.
+
+The main benefits of using boards are:
+
+- The pose estimation is much more versatile. Only some markers are necessary to perform pose estimation.
+Thus, the pose can be calculated even in the presence of occlusions or partial views.
+- The obtained pose is usually more accurate since a higher amount of point correspondences (marker
+corners) are employed.
+
+Board Detection
+---------------
+
+A board detection is similar to the standard marker detection. The only difference is in the pose estimation step.
+In fact, to use marker boards, a standard marker detection should be done before estimating the board pose.
+
+To perform pose estimation for boards, you should use `solvePnP()` function, as shown below
+in the `samples/cpp/tutorial_code/objectDetection/detect_board.cpp`.
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_board.cpp aruco_detect_board_full_sample
+
+
+The parameters are:
+
+- `objPoints`, `imgPoints` object and image points, matched with `cv::aruco::GridBoard::matchImagePoints()`
+   which, in turn, takes as input `markerCorners` and `markerIds` structures of detected markers from
+   `cv::aruco::ArucoDetector::detectMarkers()` function.
+- `board` the `cv::aruco::Board` object that defines the board layout and its ids
+- `cameraMatrix` and `distCoeffs`: camera calibration parameters necessary for pose estimation.
+- `rvec` and `tvec`: estimated pose of the board. If not empty then treated as initial guess.
+- The function returns the total number of markers employed for estimating the board pose.
+
+The drawFrameAxes() function can be used to check the obtained pose. For instance:
+
+![Board with axis](images/gbmarkersaxis.jpg)
+
+And this is another example with the board partially occluded:
+
+![Board with occlusions](images/gbocclusion.jpg)
+
+As it can be observed, although some markers have not been detected, the board pose can still be
+estimated from the rest of markers.
+
+Sample video:
+
+@youtube{Q1HlJEjW_j0}
+
+A full working example is included in the `detect_board.cpp` inside the `samples/cpp/tutorial_code/objectDetection/`.
+
+The samples now take input via command line via the `cv::CommandLineParser`. For this file the example
+parameters will look like:
+@code{.cpp}
+    -w=5 -h=7 -l=100 -s=10
+    -v=/path_to_opencv/opencv/doc/tutorials/objdetect/aruco_board_detection/gboriginal.jpg
+    -c=/path_to_opencv/opencv/samples/cpp/tutorial_code/objectDetection/tutorial_camera_params.yml
+    -cd=/path_to_opencv/opencv/samples/cpp/tutorial_code/objectDetection/tutorial_dict.yml
+@endcode
+Parameters for `detect_board.cpp`:
+@snippet samples/cpp/tutorial_code/objectDetection/detect_board.cpp aruco_detect_board_keys
+
+Grid Board
+----------
+
+Creating the `cv::aruco::Board` object requires specifying the corner positions for each marker in the environment.
+However, in many cases, the board will be just a set of markers in the same plane and in a grid layout,
+so it can be easily printed and used.
+
+Fortunately, the aruco module provides the basic functionality to create and print these types of markers
+easily.
+
+The `cv::aruco::GridBoard` class is a specialized class that inherits from the `cv::aruco::Board`
+class and which represents a Board with all the markers in the same plane and in a grid layout,
+as in the following image:
+
+![Image with aruco board](images/gboriginal.jpg)
+
+Concretely, the coordinate system in a grid board is positioned in the board plane, centered in the bottom left
+corner of the board and with the Z pointing out, like in the following image (X:red, Y:green, Z:blue):
+
+![Board with axis](images/gbaxis.jpg)
+
+A `cv::aruco::GridBoard` object can be defined using the following parameters:
+
+- Number of markers in the X direction.
+- Number of markers in the Y direction.
+- Length of the marker side.
+- Length of the marker separation.
+- The dictionary of the markers.
+- Ids of all the markers (X*Y markers).
+
+This object can be easily created from these parameters using the `cv::aruco::GridBoard` constructor:
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_board.cpp aruco_create_board
+
+- The first and second parameters are the number of markers in the X and Y direction respectively.
+- The third and fourth parameters are the marker length and the marker separation respectively.
+  They can be provided in any unit, having in mind that the estimated pose for this board will be
+  measured in the same units (in general, meters are used).
+- Finally, the dictionary of the markers is provided.
+
+So, this board will be composed by 5x7=35 markers. The ids of each of the markers are assigned, by default,
+in ascending order starting on 0, so they will be 0, 1, 2, ..., 34.
+
+After creating a grid board, we probably want to print it and use 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::GridBoard::generateImage()`.
+
+The function `cv::aruco::GridBoard::generateImage()` is provided in cv::aruco::GridBoard class and
+can be called by using the following code:
+
+@snippet samples/cpp/tutorial_code/objectDetection/create_board.cpp aruco_generate_board_image
+
+- The first parameter is the size of the output image in pixels. In this case 600x500 pixels. If this is not proportional
+to the board dimensions, it will be centered on the image.
+- `boardImage`: the output image with the board.
+- The third parameter is the (optional) margin in pixels, so none of the markers are touching the image border.
+In this case the margin is 10.
+- Finally, the size of the marker border, similarly to `generateImageMarker()` function. The default value is 1.
+
+A full working example of board creation is included in the `samples/cpp/tutorial_code/objectDetection/create_board.cpp`
+
+The output image will be something like this:
+
+![](images/board.png)
+
+The samples now take input via commandline via the `cv::CommandLineParser`. For this file the example
+parameters will look like:
+@code{.cpp}
+    "_output_path_/aboard.png" -w=5 -h=7 -l=100 -s=10 -d=10
+@endcode
+
+Refine marker detection
+-----------------------
+
+ArUco boards can also be used to improve the detection of markers. If we have detected a subset of the markers
+that belongs to the board, we can use these markers and the board layout information to try to find the
+markers that have not been previously detected.
+
+This can be done using the `cv::aruco::refineDetectedMarkers()` function, which should be called
+after calling `cv::aruco::ArucoDetector::detectMarkers()`.
+
+The main parameters of this function are the original image where markers were detected, the board object,
+the detected marker corners, the detected marker ids and the rejected marker corners.
+
+The rejected corners can be obtained from the `cv::aruco::ArucoDetector::detectMarkers()` function and
+are also known as marker candidates. This candidates are square shapes that have been found in the
+original image but have failed to pass the identification step (i.e. their inner codification presents
+too many errors) and thus they have not been recognized as markers.
+
+However, these candidates are sometimes actual markers that have not been correctly identified due to high
+noise in the image, very low resolution or other related problems that affect to the binary code extraction.
+The `cv::aruco::ArucoDetector::refineDetectedMarkers()` function finds correspondences between these
+candidates and the missing markers of the board. This search is based on two parameters:
+
+- Distance between the candidate and the projection of the missing marker. To obtain these projections,
+it is necessary to have detected at least one marker of the board. The projections are obtained using the
+camera parameters (camera matrix and distortion coefficients) if they are provided. If not, the projections
+are obtained from local homography and only planar board are allowed (i.e. the Z coordinate of all the
+marker corners should be the same). The `minRepDistance` parameter in `refineDetectedMarkers()`
+determines the minimum euclidean distance between the candidate corners and the projected marker corners
+(default value 10).
+
+- Binary codification. If a candidate surpasses the minimum distance condition, its internal bits
+are analyzed again to determine if it is actually the projected marker or not. However, in this case,
+the condition is not so strong and the number of allowed erroneous bits can be higher. This is indicated
+in the `errorCorrectionRate` parameter (default value 3.0). If a negative value is provided, the
+internal bits are not analyzed at all and only the corner distances are evaluated.
+
+This is an example of using the `cv::aruco::ArucoDetector::refineDetectedMarkers()` function:
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_board.cpp aruco_detect_and_refine
+
+It must also be noted that, in some cases, if the number of detected markers in the first place is
+too low (for instance only 1 or 2 markers), the projections of the missing markers can be of bad
+quality, producing erroneous correspondences.
+
+See module samples for a more detailed implementation.

doc/tutorials/objdetect/aruco_detection/aruco_detection.markdown

@@ -0,0 +1,702 @@
+Detection of ArUco Markers {#tutorial_aruco_detection}
+==========================
+
+@next_tutorial{tutorial_aruco_board_detection}
+
+|    |    |
+| -: | :- |
+| Original authors | Sergio Garrido, Alexander Panov |
+| Compatibility    | OpenCV >= 4.7.0 |
+
+Pose estimation is of great importance in many computer vision applications: robot navigation,
+augmented reality, and many more. This process is based on finding correspondences between points in
+the real environment and their 2d image projection. This is usually a difficult step, and thus it is
+common to use synthetic or fiducial markers to make it easier.
+
+One of the most popular approaches is the use of binary square fiducial markers. The main benefit
+of these markers is that a single marker provides enough correspondences (its four corners)
+to obtain the camera pose. Also, the inner binary codification makes them specially robust, allowing
+the possibility of applying error detection and correction techniques.
+
+The aruco module is based on the [ArUco library](http://www.uco.es/investiga/grupos/ava/node/26),
+a popular library for detection of square fiducial markers developed by Rafael Muñoz and Sergio Garrido @cite Aruco2014.
+
+The aruco functionalities are included in:
+@code{.cpp}
+#include <opencv2/objdetect/aruco_detector.hpp>
+@endcode
+
+
+Markers and Dictionaries
+------------------------
+
+An ArUco marker is a synthetic square marker composed by a wide black border and an inner binary
+matrix which determines its identifier (id). The black border facilitates its fast detection in the
+image and the binary codification allows its identification and the application of error detection
+and correction techniques. The marker size determines the size of the internal matrix. For instance
+a marker size of 4x4 is composed by 16 bits.
+
+Some examples of ArUco markers:
+
+![Example of markers images](images/markers.jpg)
+
+It must be noted that a marker can be found rotated in the environment, however, the detection
+process needs to be able to determine its original rotation, so that each corner is identified
+unequivocally. This is also done based on the binary codification.
+
+A dictionary of markers is the set of markers that are considered in a specific application. It is
+simply the list of binary codifications of each of its markers.
+
+The main properties of a dictionary are the dictionary size and the marker size.
+
+- The dictionary size is the number of markers that compose the dictionary.
+- The marker size is the size of those markers (the number of bits/modules).
+
+The aruco module includes some predefined dictionaries covering a range of different dictionary
+sizes and marker sizes.
+
+One may think that the marker id is the number obtained from converting the binary codification to
+a decimal base number. However, this is not possible since for high marker sizes the number of bits
+is too high and managing such huge numbers is not practical. Instead, a marker id is simply
+the marker index within the dictionary it belongs to. For instance, the first 5 markers in a
+dictionary have the ids: 0, 1, 2, 3 and 4.
+
+More information about dictionaries is provided in the "Selecting a dictionary" section.
+
+
+Marker Creation
+---------------
+
+Before their detection, markers need to be printed in order to be placed in the environment.
+Marker images can be generated using the `generateImageMarker()` function.
+
+For example, lets analyze the following call:
+
+@code{.cpp}
+cv::Mat markerImage;
+cv::aruco::Dictionary dictionary = cv::aruco::getPredefinedDictionary(cv::aruco::DICT_6X6_250);
+cv::aruco::generateImageMarker(dictionary, 23, 200, markerImage, 1);
+cv::imwrite("marker23.png", markerImage);
+@endcode
+
+First, the `cv::aruco::Dictionary` object is created by choosing one of the predefined dictionaries in the aruco module.
+Concretely, this dictionary is composed of 250 markers and a marker size of 6x6 bits (`cv::aruco::DICT_6X6_250`).
+
+The parameters of `cv::aruco::generateImageMarker()` are:
+
+- The first parameter is the `cv::aruco::Dictionary` object previously created.
+- The second parameter is the marker id, in this case the marker 23 of the dictionary `cv::aruco::DICT_6X6_250`.
+Note that each dictionary is composed of a different number of markers. In this case, the valid ids
+go from 0 to 249. Any specific id out of the valid range will produce an exception.
+- The third parameter, 200, is the size of the output marker image. In this case, the output image
+will have a size of 200x200 pixels. Note that this parameter should be large enough to store the
+number of bits for the specific dictionary. So, for instance, you cannot generate an image of
+5x5 pixels for a marker size of 6x6 bits (and that is without considering the marker border).
+Furthermore, to avoid deformations, this parameter should be proportional to the number of bits +
+border size, or at least much higher than the marker size (like 200 in the example), so that
+deformations are insignificant.
+- The fourth parameter is the output image.
+- Finally, the last parameter is an optional parameter to specify the width of the marker black
+border. The size is specified proportional to the number of bits. For instance a value of 2 means
+that the border will have a width equivalent to the size of two internal bits. The default value
+is 1.
+
+The generated image is:
+
+![Generated marker](images/marker23.png)
+
+A full working example is included in the `create_marker.cpp` inside the `samples/cpp/tutorial_code/objectDetection/`.
+
+The samples now take input from the command line using cv::CommandLineParser. For this file the example
+parameters will look like:
+@code{.cpp}
+"marker23.png" -d=10 -id=23
+@endcode
+Parameters for `create_marker.cpp`:
+@snippet samples/cpp/tutorial_code/objectDetection/create_marker.cpp aruco_create_markers_keys
+
+Marker Detection
+----------------
+
+Given an image containing ArUco markers, the detection process has to return a list of
+detected markers. Each detected marker includes:
+
+- The position of its four corners in the image (in their original order).
+- The id of the marker.
+
+The marker detection process is comprised of two main steps:
+
+1. Detection of marker candidates. In this step the image is analyzed in order to find square shapes
+that are candidates to be markers. It begins with an adaptive thresholding to segment the markers,
+then contours are extracted from the thresholded image and those that are not convex or do not
+approximate to a square shape are discarded. Some extra filtering is also applied (removing contours
+that are too small or too big, removing contours too close to each other, etc).
+
+2. After the candidate detection, it is necessary to determine if they are actually markers by
+analyzing their inner codification. This step starts by extracting the marker bits of each marker.
+To do so, a perspective transformation is first applied to obtain the marker in its canonical form.
+Then, the canonical image is thresholded using Otsu to separate white and black bits. The image
+is divided into different cells according to the marker size and the border size. Then the number
+of black or white pixels in each cell is counted to determine if it is a white or a black bit.
+Finally, the bits are analyzed to determine if the marker belongs to the specific dictionary.
+Error correction techniques are employed when necessary.
+
+
+Consider the following image:
+
+![Image with an assortment of markers](images/singlemarkerssource.jpg)
+
+And a printout of this image in a photo:
+
+![Original image with markers](images/singlemarkersoriginal.jpg)
+
+These are the detected markers (in green). Note that some markers are rotated. The small red square
+indicates the marker’s top left corner:
+
+![Image with detected markers](images/singlemarkersdetection.jpg)
+
+And these are the marker candidates that have been rejected during the identification step (in pink):
+
+![Image with rejected candidates](images/singlemarkersrejected.jpg)
+
+In the aruco module, the detection is performed in the `cv::aruco::ArucoDetector::detectMarkers()`
+function. This function is the most important in the module, since all the rest of the functionality
+is based on the detected markers returned by `cv::aruco::ArucoDetector::detectMarkers()`.
+
+An example of marker detection:
+
+@code{.cpp}
+cv::Mat inputImage;
+// ... read inputImage ...
+std::vector<int> markerIds;
+std::vector<std::vector<cv::Point2f>> markerCorners, rejectedCandidates;
+cv::aruco::DetectorParameters detectorParams = cv::aruco::DetectorParameters();
+cv::aruco::Dictionary dictionary = cv::aruco::getPredefinedDictionary(cv::aruco::DICT_6X6_250);
+cv::aruco::ArucoDetector detector(dictionary, detectorParams);
+detector.detectMarkers(inputImage, markerCorners, markerIds, rejectedCandidates);
+@endcode
+
+When you create an `cv::aruco::ArucoDetector` object, you need to pass the following parameters to the constructor:
+
+- A dictionary object, in this case one of the predefined dictionaries (`cv::aruco::DICT_6X6_250`).
+- Object of type `cv::aruco::DetectorParameters`. This object includes all parameters that can be customized during the detection process.
+These parameters will be explained in the next section.
+
+The parameters of `cv::aruco::ArucoDetector::detectMarkers()` are:
+
+- The first parameter is the image containing the markers to be detected.
+- The detected markers are stored in the `markerCorners` and `markerIds` structures:
+    - `markerCorners` is the list of corners of the detected markers. For each marker, its four
+    corners are returned in their original order (which is clockwise starting with top left).
+    So, the first corner is the top left corner, followed by the top right, bottom right and bottom left.
+    - `markerIds` is the list of ids of each of the detected markers in `markerCorners`.
+    Note that the returned `markerCorners` and `markerIds` vectors have the same size.
+- The final optional parameter, `rejectedCandidates`, is a returned list of marker candidates, i.e.
+shapes that were found and considered but did not contain a valid marker. Each candidate is also
+defined by its four corners, and its format is the same as the `markerCorners` parameter. This
+parameter can be omitted and is only useful for debugging purposes and for ‘refind’ strategies
+(see `cv::aruco::ArucoDetector::refineDetectedMarkers()`).
+
+
+The next thing you probably want to do after `cv::aruco::ArucoDetector::detectMarkers()` is check that
+your markers have been correctly detected. Fortunately, the aruco module provides a function to draw
+the detected markers in the input image, this function is `drawDetectedMarkers()`. For example:
+
+@code{.cpp}
+cv::Mat outputImage = inputImage.clone();
+cv::aruco::drawDetectedMarkers(outputImage, markerCorners, markerIds);
+@endcode
+
+- `outputImage ` is the input/output image where the markers will be drawn (it will normally be
+  the same as the image where the markers were detected).
+- `markerCorners` and `markerIds` are the structures of the detected markers returned by the
+  `cv::aruco::ArucoDetector::detectMarkers()` function.
+
+![Image with detected markers](images/singlemarkersdetection.jpg)
+
+Note that this function is only provided for visualization and its use can be omitted.
+
+With these two functions we can create a basic marker detection loop to detect markers from our
+camera:
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_markers.cpp aruco_detect_markers
+
+Note that some of the optional parameters have been omitted, like the detection parameter object and the
+output vector of rejected candidates.
+
+A full working example is included in the `detect_markers.cpp` inside the `samples/cpp/tutorial_code/objectDetection/`.
+
+The samples now take input from the command line using cv::CommandLineParser. For this file
+the example parameters will look like:
+@code{.cpp}
+-v=/path_to_opencv/opencv/doc/tutorials/objdetect/aruco_detection/images/singlemarkersoriginal.jpg -d=10
+@endcode
+Parameters for `detect_markers.cpp`:
+@snippet samples/cpp/tutorial_code/objectDetection/detect_markers.cpp aruco_detect_markers_keys
+
+
+Pose Estimation
+---------------
+
+The next thing you'll probably want to do after detecting the markers is to use them to get the camera pose.
+
+To perform camera pose estimation, you need to know your camera's calibration parameters. These are
+the camera matrix and distortion coefficients. If you do not know how to calibrate your camera,
+you can take a look at the `calibrateCamera()` function and the Calibration tutorial of OpenCV.
+You can also calibrate your camera using the aruco module as explained in the **Calibration with ArUco and ChArUco**
+tutorial. Note that this only needs to be done once unless the camera optics are modified
+(for instance changing its focus).
+
+As a result of the calibration, you get a camera matrix: a matrix of 3x3 elements with the
+focal distances and the camera center coordinates (a.k.a intrinsic parameters), and the distortion
+coefficients: a vector of 5 or more elements that models the distortion produced by your camera.
+
+When you estimate the pose with ArUco markers, you can estimate the pose of each marker individually.
+If you want to estimate one pose from a set of markers, use ArUco Boards (see the **Detection of ArUco
+Boards** tutorial). Using ArUco boards instead of single markers allows some markers to be occluded.
+
+The camera pose relative to the marker is a 3d transformation from the marker coordinate system to the
+camera coordinate system. It is specified by rotation and translation vectors. OpenCV provides
+`cv::solvePnP()` function to do that.
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_markers.cpp aruco_pose_estimation1
+@snippet samples/cpp/tutorial_code/objectDetection/detect_markers.cpp aruco_pose_estimation2
+@snippet samples/cpp/tutorial_code/objectDetection/detect_markers.cpp aruco_pose_estimation3
+
+- The `corners` parameter is the vector of marker corners returned by the `cv::aruco::ArucoDetector::detectMarkers()` function.
+- The second parameter is the size of the marker side in meters or in any other unit. Note that the
+  translation vectors of the estimated poses will be in the same units.
+- `camMatrix` and `distCoeffs` are the camera calibration parameters that were created during
+  the camera calibration process.
+- The output parameters `rvecs` and `tvecs` are the rotation and translation vectors respectively,
+  for each of the detected markers in `corners`.
+
+The marker coordinate system that is assumed by this function is placed in the center (by default) or
+in the top left corner of the marker with the Z axis pointing out, as in the following image.
+Axis-color correspondences are X: red, Y: green, Z: blue. Note the axis directions of the rotated
+markers in this image.
+
+![Image with axes drawn](images/singlemarkersaxes.jpg)
+
+OpenCV provides a function to draw the axis as in the image above, so pose estimation can be
+checked:
+
+@snippet samples/cpp/tutorial_code/objectDetection/detect_markers.cpp aruco_draw_pose_estimation
+
+- `imageCopy` is the input/output image where the detected markers will be shown.
+- `camMatrix` and `distCoeffs` are the camera calibration parameters.
+- `rvecs[i]` and `tvecs[i]` are the rotation and translation vectors respectively, for each of the detected markers.
+- The last parameter is the length of the axis, in the same unit as tvec (usually meters).
+
+Sample video:
+
+@youtube{IsXWrcB_Hvs}
+
+A full working example is included in the `detect_markers.cpp` inside the `samples/cpp/tutorial_code/objectDetection/`.
+
+The samples now take input from the command line using cv::CommandLineParser. For this file
+the example parameters will look like:
+@code{.cpp}
+-v=/path_to_opencv/opencv/doc/tutorials/objdetect/aruco_detection/images/singlemarkersoriginal.jpg -d=10
+-c=/path_to_opencv/opencv/samples/cpp/tutorial_code/objectDetection/tutorial_camera_params.yml
+@endcode
+Parameters for `detect_markers.cpp`:
+@snippet samples/cpp/tutorial_code/objectDetection/detect_markers.cpp aruco_detect_markers_keys
+
+Selecting a dictionary
+----------------------
+
+The aruco module provides the `Dictionary` class to represent a dictionary of markers.
+
+In addition to the marker size and the number of markers in the dictionary, there is another important
+parameter of the dictionary - the inter-marker distance. The inter-marker distance is the minimum
+Hamming distance between dictionary markers that determines the dictionary's ability to detect and
+correct errors.
+
+In general, smaller dictionary sizes and larger marker sizes increase the inter-marker distance and
+vice versa. However, the detection of markers with larger sizes is more difficult due to the higher
+number of bits that need to be extracted from the image.
+
+For instance, if you need only 10 markers in your application, it is better to use a dictionary composed
+only of those 10 markers than using a dictionary composed of 1000 markers. The reason is that
+the dictionary composed of 10 markers will have a higher inter-marker distance and, thus, it will be
+more robust to errors.
+
+As a consequence, the aruco module includes several ways to select your dictionary of markers, so that
+you can increase your system robustness:
+
+### Predefined dictionaries
+
+This is the easiest way to select a dictionary. The aruco module includes a set of predefined
+dictionaries in a variety of marker sizes and number of markers. For instance:
+
+@code{.cpp}
+cv::aruco::Dictionary dictionary = cv::aruco::getPredefinedDictionary(cv::aruco::DICT_6X6_250);
+@endcode
+
+`cv::aruco::DICT_6X6_250` is an example of predefined dictionary of markers with 6x6 bits and a total of 250
+markers.
+
+From all the provided dictionaries, it is recommended to choose the smallest one that fits your application.
+For instance, if you need 200 markers of 6x6 bits, it is better to use `cv::aruco::DICT_6X6_250` than `cv::aruco::DICT_6X6_1000`.
+The smaller the dictionary, the higher the inter-marker distance.
+
+The list of available predefined dictionaries can be found in the documentation for the `PredefinedDictionaryType` enum.
+
+### Automatic dictionary generation
+
+A dictionary can be generated automatically to adjust the desired number of markers and bits
+to optimize the inter-marker distance:
+
+@code{.cpp}
+cv::aruco::Dictionary dictionary = cv::aruco::extendDictionary(36, 5);
+@endcode
+
+This will generate a customized dictionary composed of 36 markers of 5x5 bits. The process can take several
+seconds, depending on the parameters (it is slower for larger dictionaries and higher numbers of bits).
+
+Also you could use `aruco_dict_utils.cpp` sample inside the `opencv/samples/cpp`. This sample calculates
+the minimum Hamming distance for the generated dictionary and also allows you to create markers that are
+resistant to reflection.
+
+### Manual dictionary definition
+
+Finally, the dictionary can be configured manually, so that any encoding can be used. To do that,
+the `cv::aruco::Dictionary` object parameters need to be assigned manually. It must be noted that,
+unless you have a special reason to do this manually, it is preferable to use one of the previous alternatives.
+
+The `cv::aruco::Dictionary` parameters are:
+
+@code{.cpp}
+    class Dictionary {
+    public:
+
+        cv::Mat bytesList;      // marker code information
+        int markerSize;         // number of bits per dimension
+        int maxCorrectionBits;  // maximum number of bits that can be corrected
+
+        ...
+
+    }
+@endcode
+
+`bytesList` is the array that contains all the information about the marker codes. `markerSize` is the size
+ of each marker dimension (for instance, 5 for markers with 5x5 bits). Finally, `maxCorrectionBits` is
+the maximum number of erroneous bits that can be corrected during the marker detection. If this value is too
+high, it can lead to a high number of false positives.
+
+Each row in `bytesList` represents one of the dictionary markers. However, the markers are not stored in their
+binary form, instead they are stored in a special format to simplify their detection.
+
+Fortunately, a marker can be easily transformed to this form using the static method `Dictionary::getByteListFromBits()`.
+
+For example:
+
+@code{.cpp}
+    cv::aruco::Dictionary dictionary;
+
+    // Markers of 6x6 bits
+    dictionary.markerSize = 6;
+
+    // Maximum number of bit corrections
+    dictionary.maxCorrectionBits = 3;
+
+    // Let's create a dictionary of 100 markers
+    for(int i = 0; i < 100; i++)
+    {
+        // Assume generateMarkerBits() generates a new marker in binary format, so that
+        // markerBits is a 6x6 matrix of CV_8UC1 type, only containing 0s and 1s
+        cv::Mat markerBits = generateMarkerBits();
+        cv::Mat markerCompressed = cv::aruco::Dictionary::getByteListFromBits(markerBits);
+
+        // Add the marker as a new row
+        dictionary.bytesList.push_back(markerCompressed);
+    }
+@endcode
+
+Detector Parameters
+-------------------
+
+One of the parameters of `cv::aruco::ArucoDetector` is a `cv::aruco::DetectorParameters` object. This object
+includes all the options that can be customized during the marker detection process.
+
+This section describes each detector parameter. The parameters can be classified depending on
+the process in which they’re involved:
+
+### Thresholding
+
+One of the first steps in the marker detection process is adaptive thresholding of the input image.
+
+For instance, the thresholded image for the sample image used above is:
+
+![Thresholded image](images/singlemarkersthresh.png)
+
+This thresholding can be customized with the following parameters:
+
+#### adaptiveThreshWinSizeMin, adaptiveThreshWinSizeMax, and adaptiveThreshWinSizeStep
+
+The `adaptiveThreshWinSizeMin` and `adaptiveThreshWinSizeMax` parameters represent the interval where the
+thresholding window sizes (in pixels) are selected for the adaptive thresholding (see OpenCV
+`threshold()` and `adaptiveThreshold()` functions for more details).
+
+The parameter `adaptiveThreshWinSizeStep` indicates the increments of the window size from
+`adaptiveThreshWinSizeMin` to `adaptiveThreshWinSizeMax`.
+
+For instance, for the values `adaptiveThreshWinSizeMin` = 5 and `adaptiveThreshWinSizeMax` = 21 and
+`adaptiveThreshWinSizeStep` = 4, there will be 5 thresholding steps with window sizes 5, 9, 13, 17 and 21.
+On each thresholding image, marker candidates will be extracted.
+
+Low values of window size can "break" the marker border if the marker size is too large, causing it to not be detected, as in the following image:
+
+![Broken marker image](images/singlemarkersbrokenthresh.png)
+
+On the other hand, too large values can produce the same effect if the markers are too small, and can also
+reduce the performance. Moreover the process will tend to global thresholding, resulting in a loss of adaptive benefits.
+
+The simplest case is using the same value for `adaptiveThreshWinSizeMin` and
+ `adaptiveThreshWinSizeMax`, which produces a single thresholding step. However, it is usually better to use a
+ range of values for the window size, although many thresholding steps can also reduce the performance considerably.
+
+@see cv::aruco::DetectorParameters::adaptiveThreshWinSizeMin, cv::aruco::DetectorParameters::adaptiveThreshWinSizeMax,
+cv::aruco::DetectorParameters::adaptiveThreshWinSizeStep
+
+#### adaptiveThreshConstant
+
+The `adaptiveThreshConstant` parameter represents the constant value added in the thresholding operation (see OpenCV
+`threshold()` and `adaptiveThreshold()` functions for more details). Its default value is a good option in most cases.
+
+@see cv::aruco::DetectorParameters::adaptiveThreshConstant
+
+
+### Contour filtering
+
+After thresholding, contours are detected. However, not all contours
+are considered as marker candidates. They are filtered out in different steps so that contours that are
+very unlikely to be markers are discarded. The parameters in this section customize
+this filtering process.
+
+It must be noted that in most cases it is a question of balance between detection capacity
+and performance. All the considered contours will be processed in the following stages, which usually have
+a higher computational cost. So, it is preferred to discard invalid candidates in this stage than in the later stages.
+
+On the other hand, if the filtering conditions are too strict, the real marker contours could be discarded and,
+hence, not detected.
+
+#### minMarkerPerimeterRate and maxMarkerPerimeterRate
+
+These parameters determine the minimum and maximum size of a marker, specifically the minimum and
+maximum marker perimeter. They are not specified in absolute pixel values, instead they are specified
+relative to the maximum dimension of the input image.
+
+For instance, a image with size 640x480 and a minimum relative marker perimeter of 0.05 will lead
+to a minimum marker perimeter of 640x0.05 = 32 pixels, since 640 is the maximum dimension of the
+image. The same applies for the `maxMarkerPerimeterRate` parameter.
+
+If the `minMarkerPerimeterRate` is too low, detection performance can be significantly reduced,
+as many more contours will be considered for future stages.
+This penalization is not so noticeable for the `maxMarkerPerimeterRate` parameter, since there are
+usually many more small contours than big contours.
+A `minMarkerPerimeterRate` value of 0 and a `maxMarkerPerimeterRate` value of 4 (or more) will be
+equivalent to consider all the contours in the image, however this is not recommended for
+performance reasons.
+
+@see cv::aruco::DetectorParameters::minMarkerPerimeterRate, cv::aruco::DetectorParameters::maxMarkerPerimeterRate
+
+#### polygonalApproxAccuracyRate
+
+A polygonal approximation is applied to each candidate and only those that approximate to a square
+shape are accepted. This value determines the maximum error that the polygonal approximation can
+produce (see `approxPolyDP()` function for more information).
+
+This parameter is relative to the candidate length (in pixels). So if the candidate has
+a perimeter of 100 pixels and the value of `polygonalApproxAccuracyRate` is 0.04, the maximum error
+would be 100x0.04=5.4 pixels.
+
+In most cases, the default value works fine, but higher error values could be necessary for highly
+distorted images.
+
+@see cv::aruco::DetectorParameters::polygonalApproxAccuracyRate
+
+#### minCornerDistanceRate
+
+Minimum distance between any pair of corners in the same marker. It is expressed relative to the marker
+perimeter. Minimum distance in pixels is Perimeter * minCornerDistanceRate.
+
+@see cv::aruco::DetectorParameters::minCornerDistanceRate
+
+#### minMarkerDistanceRate
+
+Minimum distance between any pair of corners from two different markers. It is expressed relative to
+the minimum marker perimeter of the two markers. If two candidates are too close, the smaller one is ignored.
+
+@see cv::aruco::DetectorParameters::minMarkerDistanceRate
+
+#### minDistanceToBorder
+
+Minimum distance to any of the marker corners to the image border (in pixels). Markers partially occluded
+by the image border can be correctly detected if the occlusion is small. However, if one of the corners
+is occluded, the returned corner is usually placed in a wrong position near the image border.
+
+If the position of marker corners is important, for instance if you want to do pose estimation, it is
+better to discard any markers whose corners are too close to the image border. Elsewhere, it is not necessary.
+
+@see cv::aruco::DetectorParameters::minDistanceToBorder
+
+### Bits Extraction
+
+After candidate detection, the bits of each candidate are analyzed in order to determine if they
+are markers or not.
+
+Before analyzing the binary code itself, the bits need to be extracted. To do this, perspective
+distortion is corrected and the resulting image is thresholded using Otsu threshold to separate
+black and white pixels.
+
+This is an example of the image obtained after removing the perspective distortion of a marker:
+
+![Perspective removing](images/removeperspective.jpg)
+
+Then, the image is divided into a grid with the same number of cells as the number of bits in the marker.
+In each cell, the number of black and white pixels are counted to determine the bit value assigned
+to the cell (from the majority value):
+
+![Marker cells](images/bitsextraction1.png)
+
+There are several parameters that can customize this process:
+
+#### markerBorderBits
+
+This parameter indicates the width of the marker border. It is relative to the size of each bit. So, a
+value of 2 indicates the border has the width of two internal bits.
+
+This parameter needs to coincide with the border size of the markers you are using. The border size
+can be configured in the marker drawing functions such as `generateImageMarker()`.
+
+@see cv::aruco::DetectorParameters::markerBorderBits
+
+#### minOtsuStdDev
+
+This value determines the minimum standard deviation of the pixel values to perform Otsu
+thresholding. If the deviation is low, it probably means that all the square is black (or white)
+and applying Otsu does not make sense. If this is the case, all the bits are set to 0 (or 1)
+depending on whether the mean value is higher or lower than 128.
+
+@see cv::aruco::DetectorParameters::minOtsuStdDev
+
+#### perspectiveRemovePixelPerCell
+
+This parameter determines the number of pixels (per cell) in the obtained image after correcting perspective
+distortion (including the border). This is the size of the red squares in the image above.
+
+For instance, let’s assume we are dealing with markers of 5x5 bits and border size of 1 bit
+(see `markerBorderBits`). Then, the total number of cells/bits per dimension is 5 + 2*1 = 7 (the border
+has to be counted twice). The total number of cells is 7x7.
+
+If the value of `perspectiveRemovePixelPerCell` is 10, then the size of the obtained image will be
+10*7 = 70 -> 70x70 pixels.
+
+A higher value of this parameter can improve the bits extraction process (up to some degree),
+however it can penalize the performance.
+
+@see cv::aruco::DetectorParameters::perspectiveRemovePixelPerCell
+
+#### perspectiveRemoveIgnoredMarginPerCell
+
+When extracting the bits of each cell, the numbers of black and white pixels are counted. In general, it is
+not recommended to consider all the cell pixels. Instead it is better to ignore some pixels in the
+margins of the cells.
+
+The reason for this is that, after removing the perspective distortion, the cells’ colors are, in general, not
+perfectly separated and white cells can invade some pixels of black cells (and vice versa). Thus, it is
+better to ignore some pixels just to avoid counting erroneous pixels.
+
+For instance, in the following image:
+
+![Marker cell margins](images/bitsextraction2.png)
+
+only the pixels inside the green squares are considered. It can be seen in the right image that
+the resulting pixels contain a lower amount of noise from neighbor cells.
+The `perspectiveRemoveIgnoredMarginPerCell` parameter indicates the difference between the red and
+the green squares.
+
+This parameter is relative to the total size of the cell. For instance if the cell size is 40 pixels and the
+value of this parameter is 0.1, a margin of 40*0.1=4 pixels is ignored in the cells. This means that the total
+number of pixels that would be analyzed in each cell would actually be 32x32, instead of 40x40.
+
+@see cv::aruco::DetectorParameters::perspectiveRemoveIgnoredMarginPerCell
+
+
+### Marker identification
+
+After the bits have been extracted, the next step is checking whether the extracted code belongs to the marker
+dictionary and, if necessary, error correction can be performed.
+
+#### maxErroneousBitsInBorderRate
+
+The bits of the marker border should be black. This parameter specifies the allowed number of erroneous
+bits in the border, i.e. the maximum number of white bits in the border. It is represented
+relative to the total number of bits in the marker.
+
+@see cv::aruco::DetectorParameters::maxErroneousBitsInBorderRate
+
+#### errorCorrectionRate
+
+Each marker dictionary has a theoretical maximum number of bits that can be corrected (`Dictionary.maxCorrectionBits`).
+However, this value can be modified by the `errorCorrectionRate` parameter.
+
+For instance, if the allowed number of bits that can be corrected (for the used dictionary) is 6 and the value of `errorCorrectionRate` is
+0.5, the real maximum number of bits that can be corrected is 6*0.5=3 bits.
+
+This value is useful to reduce the error correction capabilities in order to avoid false positives.
+
+@see cv::aruco::DetectorParameters::errorCorrectionRate
+
+
+### Corner Refinement
+
+After markers have been detected and identified, the last step is performing subpixel refinement
+of the corner positions (see OpenCV `cornerSubPix()` and `cv::aruco::CornerRefineMethod`).
+
+Note that this step is optional and it only makes sense if the positions of the marker corners have to
+be accurate, for instance for pose estimation. It is usually a time-consuming step and therefore is disabled by default.
+
+#### cornerRefinementMethod
+
+This parameter determines whether the corner subpixel process is performed or not and which method to use
+if it is being performed. It can be disabled if accurate corners are not necessary. Possible values are
+`CORNER_REFINE_NONE`, `CORNER_REFINE_SUBPIX`, `CORNER_REFINE_CONTOUR`, and `CORNER_REFINE_APRILTAG`.
+
+@see cv::aruco::DetectorParameters::cornerRefinementMethod
+
+#### cornerRefinementWinSize
+
+This parameter determines the maximum window size for the corner refinement process.
+
+High values can cause close corners of the image to be included in the window area, so that the corner
+of the marker moves to a different and incorrect location during the process. Also, it may affect performance.
+The window size may decrease if the ArUco marker is too small, check cv::aruco::DetectorParameters::relativeCornerRefinmentWinSize.
+The final window size is calculated as: min(cornerRefinementWinSize, averageArucoModuleSize*relativeCornerRefinmentWinSize),
+where averageArucoModuleSize is average module size of ArUco marker in pixels.
+
+@see cv::aruco::DetectorParameters::cornerRefinementWinSize
+
+#### relativeCornerRefinmentWinSize
+
+Dynamic window size for corner refinement relative to Aruco module size (default 0.3).
+
+The final window size is calculated as: min(cornerRefinementWinSize, averageArucoModuleSize*relativeCornerRefinmentWinSize),
+where averageArucoModuleSize is average module size of ArUco marker in pixels.
+In the case of markers located far from each other, it may be useful to increase the value of the parameter to 0.4-0.5.
+In the case of markers located close to each other, it may be useful to decrease the parameter value to 0.1-0.2.
+
+@see cv::aruco::DetectorParameters::relativeCornerRefinmentWinSize
+
+#### cornerRefinementMaxIterations and cornerRefinementMinAccuracy
+
+These two parameters determine the stop criteria of the subpixel refinement process. The
+`cornerRefinementMaxIterations` indicates the maximum number of iterations and
+`cornerRefinementMinAccuracy` the minimum error value before stopping the process.
+
+If the number of iterations is too high, it may affect the performance. On the other hand, if it is
+too low, it can result in poor subpixel refinement.
+
+@see cv::aruco::DetectorParameters::cornerRefinementMaxIterations, cv::aruco::DetectorParameters::cornerRefinementMinAccuracy

doc/tutorials/objdetect/table_of_content_objdetect.markdown

@@ -0,0 +1,5 @@
+Object Detection (objdetect module) {#tutorial_table_of_content_objdetect}
+==========================================================
+
+-   @subpage tutorial_aruco_detection
+-   @subpage tutorial_aruco_board_detection

doc/tutorials/tutorials.markdown

@@ -6,6 +6,7 @@ OpenCV Tutorials {#tutorial_root}
 - @subpage tutorial_table_of_content_imgproc - image processing functions
 - @subpage tutorial_table_of_content_app - application utils (GUI, image/video input/output)
 - @subpage tutorial_table_of_content_calib3d - extract 3D world information from 2D images
+- @subpage tutorial_table_of_content_objdetect - INSERT OBJDETECT MODULE INFO
 - @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

modules/objdetect/include/opencv2/objdetect/charuco_detector.hpp

@@ -77,6 +77,9 @@ public:
      * If camera parameters are provided, the process is based in an approximated pose estimation, else it is based on local homography.
      * Only visible corners are returned. For each corner, its corresponding identifier is also returned in charucoIds.
      * @sa findChessboardCorners
+     * @note After OpenCV 4.6.0, there was an incompatible change in the ChArUco pattern generation algorithm for even row counts.
+     * Use cv::aruco::CharucoBoard::setLegacyPattern() to ensure compatibility with patterns created using OpenCV versions prior to 4.6.0.
+     * For more information, see the issue: https://github.com/opencv/opencv/issues/23152
      */
     CV_WRAP void detectBoard(InputArray image, OutputArray charucoCorners, OutputArray charucoIds,
                              InputOutputArrayOfArrays markerCorners = noArray(),

samples/cpp/aruco_dict_utils.cpp

@@ -283,7 +283,7 @@ int main(int argc, char *argv[])
     int markerSize = parser.get<int>("markerSize");
     bool checkFlippedMarkers = parser.get<bool>("r");
 
-    aruco::Dictionary dictionary = aruco::getPredefinedDictionary(0);
+    aruco::Dictionary dictionary = aruco::getPredefinedDictionary(cv::aruco::DICT_4X4_50);
 
     if (parser.has("d")) {
         string arucoDictName = parser.get<string>("d");

samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp

@@ -354,7 +354,7 @@ int main(int argc, char* argv[])
     }
     else {
         // default dictionary
-        dictionary = cv::aruco::getPredefinedDictionary(0);
+        dictionary = cv::aruco::getPredefinedDictionary(cv::aruco::DICT_4X4_50);
     }
     cv::aruco::CharucoBoard ch_board({s.boardSize.width, s.boardSize.height}, s.squareSize, s.markerSize, dictionary);
     cv::aruco::CharucoDetector ch_detector(ch_board);

samples/cpp/tutorial_code/objectDetection/aruco_samples_utility.hpp

@@ -0,0 +1,48 @@
+#include <opencv2/highgui.hpp>
+#include <opencv2/objdetect/aruco_detector.hpp>
+#include <opencv2/calib3d.hpp>
+#include <ctime>
+
+namespace {
+inline static bool readCameraParameters(const std::string& filename, cv::Mat &camMatrix, cv::Mat &distCoeffs) {
+    cv::FileStorage fs(filename, cv::FileStorage::READ);
+    if (!fs.isOpened())
+        return false;
+    fs["camera_matrix"] >> camMatrix;
+    fs["distortion_coefficients"] >> distCoeffs;
+    return true;
+}
+
+inline static bool saveCameraParams(const std::string &filename, cv::Size imageSize, float aspectRatio, int flags,
+                                    const cv::Mat &cameraMatrix, const cv::Mat &distCoeffs, double totalAvgErr) {
+    cv::FileStorage fs(filename, cv::FileStorage::WRITE);
+    if (!fs.isOpened())
+        return false;
+
+    time_t tt;
+    time(&tt);
+    struct tm *t2 = localtime(&tt);
+    char buf[1024];
+    strftime(buf, sizeof(buf) - 1, "%c", t2);
+
+    fs << "calibration_time" << buf;
+    fs << "image_width" << imageSize.width;
+    fs << "image_height" << imageSize.height;
+
+    if (flags & cv::CALIB_FIX_ASPECT_RATIO) fs << "aspectRatio" << aspectRatio;
+
+    if (flags != 0) {
+        sprintf(buf, "flags: %s%s%s%s",
+                flags & cv::CALIB_USE_INTRINSIC_GUESS ? "+use_intrinsic_guess" : "",
+                flags & cv::CALIB_FIX_ASPECT_RATIO ? "+fix_aspectRatio" : "",
+                flags & cv::CALIB_FIX_PRINCIPAL_POINT ? "+fix_principal_point" : "",
+                flags & cv::CALIB_ZERO_TANGENT_DIST ? "+zero_tangent_dist" : "");
+    }
+    fs << "flags" << flags;
+    fs << "camera_matrix" << cameraMatrix;
+    fs << "distortion_coefficients" << distCoeffs;
+    fs << "avg_reprojection_error" << totalAvgErr;
+    return true;
+}
+
+}

samples/cpp/tutorial_code/objectDetection/create_board.cpp

@@ -0,0 +1,95 @@
+#include <opencv2/highgui.hpp>
+#include <opencv2/objdetect/aruco_detector.hpp>
+#include <iostream>
+#include "aruco_samples_utility.hpp"
+
+using namespace cv;
+
+namespace {
+const char* about = "Create an ArUco grid board image";
+const char* keys  =
+        "{@outfile |<none> | Output image }"
+        "{w        |       | Number of markers in X direction }"
+        "{h        |       | Number of markers in Y direction }"
+        "{l        |       | Marker side length (in pixels) }"
+        "{s        |       | Separation between two consecutive markers in the grid (in pixels)}"
+        "{d        |       | dictionary: DICT_4X4_50=0, DICT_4X4_100=1, DICT_4X4_250=2,"
+        "DICT_4X4_1000=3, DICT_5X5_50=4, DICT_5X5_100=5, DICT_5X5_250=6, DICT_5X5_1000=7, "
+        "DICT_6X6_50=8, DICT_6X6_100=9, DICT_6X6_250=10, DICT_6X6_1000=11, DICT_7X7_50=12,"
+        "DICT_7X7_100=13, DICT_7X7_250=14, DICT_7X7_1000=15, DICT_ARUCO_ORIGINAL = 16}"
+        "{cd       |       | Input file with custom dictionary }"
+        "{m        |       | Margins size (in pixels). Default is marker separation (-s) }"
+        "{bb       | 1     | Number of bits in marker borders }"
+        "{si       | false | show generated image }";
+}
+
+
+int main(int argc, char *argv[]) {
+    CommandLineParser parser(argc, argv, keys);
+    parser.about(about);
+
+    if(argc < 7) {
+        parser.printMessage();
+        return 0;
+    }
+
+    int markersX = parser.get<int>("w");
+    int markersY = parser.get<int>("h");
+    int markerLength = parser.get<int>("l");
+    int markerSeparation = parser.get<int>("s");
+    int margins = markerSeparation;
+    if(parser.has("m")) {
+        margins = parser.get<int>("m");
+    }
+
+    int borderBits = parser.get<int>("bb");
+    bool showImage = parser.get<bool>("si");
+
+    String out = parser.get<String>(0);
+
+    if(!parser.check()) {
+        parser.printErrors();
+        return 0;
+    }
+
+    Size imageSize;
+    imageSize.width = markersX * (markerLength + markerSeparation) - markerSeparation + 2 * margins;
+    imageSize.height =
+        markersY * (markerLength + markerSeparation) - markerSeparation + 2 * margins;
+
+    aruco::Dictionary dictionary = aruco::getPredefinedDictionary(cv::aruco::DICT_4X4_50);
+    if (parser.has("d")) {
+        int dictionaryId = parser.get<int>("d");
+        dictionary = aruco::getPredefinedDictionary(aruco::PredefinedDictionaryType(dictionaryId));
+    }
+    else if (parser.has("cd")) {
+        FileStorage fs(parser.get<std::string>("cd"), FileStorage::READ);
+        bool readOk = dictionary.readDictionary(fs.root());
+        if(!readOk)
+        {
+            std::cerr << "Invalid dictionary file" << std::endl;
+            return 0;
+        }
+    }
+    else {
+        std::cerr << "Dictionary not specified" << std::endl;
+        return 0;
+    }
+
+    aruco::GridBoard board(Size(markersX, markersY), float(markerLength), float(markerSeparation), dictionary);
+
+    // show created board
+    //! [aruco_generate_board_image]
+    Mat boardImage;
+    board.generateImage(imageSize, boardImage, margins, borderBits);
+    //! [aruco_generate_board_image]
+
+    if(showImage) {
+        imshow("board", boardImage);
+        waitKey(0);
+    }
+
+    imwrite(out, boardImage);
+
+    return 0;
+}

samples/cpp/tutorial_code/objectDetection/create_marker.cpp

@@ -0,0 +1,77 @@
+#include <opencv2/highgui.hpp>
+#include <opencv2/objdetect/aruco_detector.hpp>
+#include <iostream>
+#include "aruco_samples_utility.hpp"
+
+using namespace cv;
+
+namespace {
+const char* about = "Create an ArUco marker image";
+
+//! [aruco_create_markers_keys]
+const char* keys  =
+        "{@outfile |<none> | Output image }"
+        "{d        |       | dictionary: DICT_4X4_50=0, DICT_4X4_100=1, DICT_4X4_250=2,"
+        "DICT_4X4_1000=3, DICT_5X5_50=4, DICT_5X5_100=5, DICT_5X5_250=6, DICT_5X5_1000=7, "
+        "DICT_6X6_50=8, DICT_6X6_100=9, DICT_6X6_250=10, DICT_6X6_1000=11, DICT_7X7_50=12,"
+        "DICT_7X7_100=13, DICT_7X7_250=14, DICT_7X7_1000=15, DICT_ARUCO_ORIGINAL = 16}"
+        "{cd       |       | Input file with custom dictionary }"
+        "{id       |       | Marker id in the dictionary }"
+        "{ms       | 200   | Marker size in pixels }"
+        "{bb       | 1     | Number of bits in marker borders }"
+        "{si       | false | show generated image }";
+}
+//! [aruco_create_markers_keys]
+
+
+int main(int argc, char *argv[]) {
+    CommandLineParser parser(argc, argv, keys);
+    parser.about(about);
+
+    if(argc < 4) {
+        parser.printMessage();
+        return 0;
+    }
+
+    int markerId = parser.get<int>("id");
+    int borderBits = parser.get<int>("bb");
+    int markerSize = parser.get<int>("ms");
+    bool showImage = parser.get<bool>("si");
+
+    String out = parser.get<String>(0);
+
+    if(!parser.check()) {
+        parser.printErrors();
+        return 0;
+    }
+
+    aruco::Dictionary dictionary = aruco::getPredefinedDictionary(cv::aruco::DICT_4X4_50);
+    if (parser.has("d")) {
+        int dictionaryId = parser.get<int>("d");
+        dictionary = aruco::getPredefinedDictionary(aruco::PredefinedDictionaryType(dictionaryId));
+    }
+    else if (parser.has("cd")) {
+        FileStorage fs(parser.get<std::string>("cd"), FileStorage::READ);
+        bool readOk = dictionary.readDictionary(fs.root());
+        if(!readOk) {
+            std::cerr << "Invalid dictionary file" << std::endl;
+            return 0;
+        }
+    }
+    else {
+        std::cerr << "Dictionary not specified" << std::endl;
+        return 0;
+    }
+
+    Mat markerImg;
+    aruco::generateImageMarker(dictionary, markerId, markerSize, markerImg, borderBits);
+
+    if(showImage) {
+        imshow("marker", markerImg);
+        waitKey(0);
+    }
+
+    imwrite(out, markerImg);
+
+    return 0;
+}

samples/cpp/tutorial_code/objectDetection/detect_board.cpp

@@ -0,0 +1,201 @@
+#include <iostream>
+#include <vector>
+#include <opencv2/highgui.hpp>
+#include <opencv2/objdetect/aruco_detector.hpp>
+#include "aruco_samples_utility.hpp"
+
+using namespace std;
+using namespace cv;
+
+namespace {
+const char* about = "Pose estimation using a ArUco Planar Grid board";
+
+//! [aruco_detect_board_keys]
+const char* keys  =
+        "{w        |       | Number of squares in X direction }"
+        "{h        |       | Number of squares in Y direction }"
+        "{l        |       | Marker side length (in pixels) }"
+        "{s        |       | Separation between two consecutive markers in the grid (in pixels)}"
+        "{d        |       | dictionary: DICT_4X4_50=0, DICT_4X4_100=1, DICT_4X4_250=2,"
+        "DICT_4X4_1000=3, DICT_5X5_50=4, DICT_5X5_100=5, DICT_5X5_250=6, DICT_5X5_1000=7, "
+        "DICT_6X6_50=8, DICT_6X6_100=9, DICT_6X6_250=10, DICT_6X6_1000=11, DICT_7X7_50=12,"
+        "DICT_7X7_100=13, DICT_7X7_250=14, DICT_7X7_1000=15, DICT_ARUCO_ORIGINAL = 16}"
+        "{cd       |       | Input file with custom dictionary }"
+        "{c        |       | Output file with calibrated camera parameters }"
+        "{v        |       | Input from video or image file, if omitted, input comes from camera }"
+        "{ci       | 0     | Camera id if input doesnt come from video (-v) }"
+        "{dp       |       | File of marker detector parameters }"
+        "{rs       |       | Apply refind strategy }"
+        "{r        |       | show rejected candidates too }";
+}
+//! [aruco_detect_board_keys]
+
+static void readDetectorParamsFromCommandLine(CommandLineParser &parser, aruco::DetectorParameters& detectorParams) {
+    if(parser.has("dp")) {
+        FileStorage fs(parser.get<string>("dp"), FileStorage::READ);
+        bool readOk = detectorParams.readDetectorParameters(fs.root());
+        if(!readOk) {
+            cerr << "Invalid detector parameters file" << endl;
+            throw -1;
+        }
+    }
+}
+
+static void readCameraParamsFromCommandLine(CommandLineParser &parser, Mat& camMatrix, Mat& distCoeffs) {
+    if(parser.has("c")) {
+        bool readOk = readCameraParameters(parser.get<string>("c"), camMatrix, distCoeffs);
+        if(!readOk) {
+            cerr << "Invalid camera file" << endl;
+            throw -1;
+        }
+    }
+}
+
+static void readDictionatyFromCommandLine(CommandLineParser &parser, aruco::Dictionary& dictionary) {
+    if (parser.has("d")) {
+        int dictionaryId = parser.get<int>("d");
+        dictionary = aruco::getPredefinedDictionary(aruco::PredefinedDictionaryType(dictionaryId));
+    }
+    else if (parser.has("cd")) {
+        FileStorage fs(parser.get<string>("cd"), FileStorage::READ);
+        bool readOk = dictionary.readDictionary(fs.root());
+        if(!readOk) {
+            cerr << "Invalid dictionary file" << endl;
+            throw -1;
+        }
+    }
+    else {
+        cerr << "Dictionary not specified" << endl;
+        throw -1;
+    }
+}
+
+int main(int argc, char *argv[]) {
+    CommandLineParser parser(argc, argv, keys);
+    parser.about(about);
+
+    if(argc < 7) {
+        parser.printMessage();
+        return 0;
+    }
+
+    //! [aruco_detect_board_full_sample]
+    int markersX = parser.get<int>("w");
+    int markersY = parser.get<int>("h");
+    float markerLength = parser.get<float>("l");
+    float markerSeparation = parser.get<float>("s");
+    bool showRejected = parser.has("r");
+    bool refindStrategy = parser.has("rs");
+    int camId = parser.get<int>("ci");
+
+
+    Mat camMatrix, distCoeffs;
+    readCameraParamsFromCommandLine(parser, camMatrix, distCoeffs);
+
+    aruco::DetectorParameters detectorParams;
+    detectorParams.cornerRefinementMethod = aruco::CORNER_REFINE_SUBPIX; // do corner refinement in markers
+    readDetectorParamsFromCommandLine(parser, detectorParams);
+
+    String video;
+    if(parser.has("v")) {
+        video = parser.get<String>("v");
+    }
+
+    if(!parser.check()) {
+        parser.printErrors();
+        return 0;
+    }
+
+    aruco::Dictionary dictionary = aruco::getPredefinedDictionary(cv::aruco::DICT_4X4_50);
+    readDictionatyFromCommandLine(parser, dictionary);
+
+    aruco::ArucoDetector detector(dictionary, detectorParams);
+    VideoCapture inputVideo;
+    int waitTime;
+    if(!video.empty()) {
+        inputVideo.open(video);
+        waitTime = 0;
+    } else {
+        inputVideo.open(camId);
+        waitTime = 10;
+    }
+
+    float axisLength = 0.5f * ((float)min(markersX, markersY) * (markerLength + markerSeparation) +
+                               markerSeparation);
+
+    // Create GridBoard object
+    //! [aruco_create_board]
+    aruco::GridBoard board(Size(markersX, markersY), markerLength, markerSeparation, dictionary);
+    //! [aruco_create_board]
+
+    // Also you could create Board object
+    //vector<vector<Point3f> > objPoints; // array of object points of all the marker corners in the board
+    //vector<int> ids; // vector of the identifiers of the markers in the board
+    //aruco::Board board(objPoints, dictionary, ids);
+
+    double totalTime = 0;
+    int totalIterations = 0;
+
+    while(inputVideo.grab()) {
+        Mat image, imageCopy;
+        inputVideo.retrieve(image);
+
+        double tick = (double)getTickCount();
+
+        vector<int> ids;
+        vector<vector<Point2f>> corners, rejected;
+        Vec3d rvec, tvec;
+
+        //! [aruco_detect_and_refine]
+
+        // Detect markers
+        detector.detectMarkers(image, corners, ids, rejected);
+
+        // Refind strategy to detect more markers
+        if(refindStrategy)
+            detector.refineDetectedMarkers(image, board, corners, ids, rejected, camMatrix,
+                                           distCoeffs);
+
+        //! [aruco_detect_and_refine]
+
+        // Estimate board pose
+        int markersOfBoardDetected = 0;
+        if(!ids.empty()) {
+            // Get object and image points for the solvePnP function
+            cv::Mat objPoints, imgPoints;
+            board.matchImagePoints(corners, ids, objPoints, imgPoints);
+
+            // Find pose
+            cv::solvePnP(objPoints, imgPoints, camMatrix, distCoeffs, rvec, tvec);
+
+            markersOfBoardDetected = (int)objPoints.total() / 4;
+        }
+
+        double currentTime = ((double)getTickCount() - tick) / getTickFrequency();
+        totalTime += currentTime;
+        totalIterations++;
+        if(totalIterations % 30 == 0) {
+            cout << "Detection Time = " << currentTime * 1000 << " ms "
+                 << "(Mean = " << 1000 * totalTime / double(totalIterations) << " ms)" << endl;
+        }
+
+        // Draw results
+        image.copyTo(imageCopy);
+        if(!ids.empty()) {
+            aruco::drawDetectedMarkers(imageCopy, corners, ids);
+        }
+
+        if(showRejected && !rejected.empty())
+            aruco::drawDetectedMarkers(imageCopy, rejected, noArray(), Scalar(100, 0, 255));
+
+        if(markersOfBoardDetected > 0)
+            cv::drawFrameAxes(imageCopy, camMatrix, distCoeffs, rvec, tvec, axisLength);
+
+        imshow("out", imageCopy);
+        char key = (char)waitKey(waitTime);
+        if(key == 27) break;
+    //! [aruco_detect_board_full_sample]
+    }
+
+    return 0;
+}

samples/cpp/tutorial_code/objectDetection/detect_markers.cpp

@@ -0,0 +1,177 @@
+#include <opencv2/highgui.hpp>
+#include <opencv2/objdetect/aruco_detector.hpp>
+#include <iostream>
+#include "aruco_samples_utility.hpp"
+
+using namespace std;
+using namespace cv;
+
+namespace {
+const char* about = "Basic marker detection";
+
+//! [aruco_detect_markers_keys]
+const char* keys  =
+        "{d        |       | dictionary: DICT_4X4_50=0, DICT_4X4_100=1, DICT_4X4_250=2,"
+        "DICT_4X4_1000=3, DICT_5X5_50=4, DICT_5X5_100=5, DICT_5X5_250=6, DICT_5X5_1000=7, "
+        "DICT_6X6_50=8, DICT_6X6_100=9, DICT_6X6_250=10, DICT_6X6_1000=11, DICT_7X7_50=12,"
+        "DICT_7X7_100=13, DICT_7X7_250=14, DICT_7X7_1000=15, DICT_ARUCO_ORIGINAL = 16,"
+        "DICT_APRILTAG_16h5=17, DICT_APRILTAG_25h9=18, DICT_APRILTAG_36h10=19, DICT_APRILTAG_36h11=20}"
+        "{cd       |       | Input file with custom dictionary }"
+        "{v        |       | Input from video or image file, if ommited, input comes from camera }"
+        "{ci       | 0     | Camera id if input doesnt come from video (-v) }"
+        "{c        |       | Camera intrinsic parameters. Needed for camera pose }"
+        "{l        | 0.1   | Marker side length (in meters). Needed for correct scale in camera pose }"
+        "{dp       |       | File of marker detector parameters }"
+        "{r        |       | show rejected candidates too }"
+        "{refine   |       | Corner refinement: CORNER_REFINE_NONE=0, CORNER_REFINE_SUBPIX=1,"
+        "CORNER_REFINE_CONTOUR=2, CORNER_REFINE_APRILTAG=3}";
+}
+//! [aruco_detect_markers_keys]
+
+int main(int argc, char *argv[]) {
+    CommandLineParser parser(argc, argv, keys);
+    parser.about(about);
+
+    if(argc < 2) {
+        parser.printMessage();
+        return 0;
+    }
+
+    bool showRejected = parser.has("r");
+    bool estimatePose = parser.has("c");
+    float markerLength = parser.get<float>("l");
+
+    cv::aruco::DetectorParameters detectorParams;
+    if(parser.has("dp")) {
+        cv::FileStorage fs(parser.get<string>("dp"), FileStorage::READ);
+        bool readOk = detectorParams.readDetectorParameters(fs.root());
+        if(!readOk) {
+            cerr << "Invalid detector parameters file" << endl;
+            return 0;
+        }
+    }
+
+    if (parser.has("refine")) {
+        // override cornerRefinementMethod read from config file
+        detectorParams.cornerRefinementMethod = parser.get<aruco::CornerRefineMethod>("refine");
+    }
+    std::cout << "Corner refinement method (0: None, 1: Subpixel, 2:contour, 3: AprilTag 2): " << (int)detectorParams.cornerRefinementMethod << std::endl;
+
+    int camId = parser.get<int>("ci");
+
+    String video;
+    if(parser.has("v")) {
+        video = parser.get<String>("v");
+    }
+
+    if(!parser.check()) {
+        parser.printErrors();
+        return 0;
+    }
+
+    aruco::Dictionary dictionary = aruco::getPredefinedDictionary(cv::aruco::DICT_4X4_50);
+    if (parser.has("d")) {
+        int dictionaryId = parser.get<int>("d");
+        dictionary = aruco::getPredefinedDictionary(aruco::PredefinedDictionaryType(dictionaryId));
+    }
+    else if (parser.has("cd")) {
+        cv::FileStorage fs(parser.get<std::string>("cd"), FileStorage::READ);
+        bool readOk = dictionary.readDictionary(fs.root());
+        if(!readOk) {
+            std::cerr << "Invalid dictionary file" << std::endl;
+            return 0;
+        }
+    }
+    else {
+        std::cerr << "Dictionary not specified" << std::endl;
+        return 0;
+    }
+
+    //! [aruco_pose_estimation1]
+    cv::Mat camMatrix, distCoeffs;
+    if(estimatePose) {
+        // You can read camera parameters from tutorial_camera_params.yml
+        bool readOk = readCameraParameters(parser.get<string>("c"), camMatrix, distCoeffs);
+        if(!readOk) {
+            cerr << "Invalid camera file" << endl;
+            return 0;
+        }
+    }
+    //! [aruco_pose_estimation1]
+    //! [aruco_detect_markers]
+    cv::aruco::ArucoDetector detector(dictionary, detectorParams);
+    cv::VideoCapture inputVideo;
+    int waitTime;
+    if(!video.empty()) {
+        inputVideo.open(video);
+        waitTime = 0;
+    } else {
+        inputVideo.open(camId);
+        waitTime = 10;
+    }
+
+    double totalTime = 0;
+    int totalIterations = 0;
+
+    //! [aruco_pose_estimation2]
+    // set coordinate system
+    cv::Mat objPoints(4, 1, CV_32FC3);
+    objPoints.ptr<Vec3f>(0)[0] = Vec3f(-markerLength/2.f, markerLength/2.f, 0);
+    objPoints.ptr<Vec3f>(0)[1] = Vec3f(markerLength/2.f, markerLength/2.f, 0);
+    objPoints.ptr<Vec3f>(0)[2] = Vec3f(markerLength/2.f, -markerLength/2.f, 0);
+    objPoints.ptr<Vec3f>(0)[3] = Vec3f(-markerLength/2.f, -markerLength/2.f, 0);
+    //! [aruco_pose_estimation2]
+
+    while(inputVideo.grab()) {
+        cv::Mat image, imageCopy;
+        inputVideo.retrieve(image);
+
+        double tick = (double)getTickCount();
+
+        //! [aruco_pose_estimation3]
+        vector<int> ids;
+        vector<vector<Point2f> > corners, rejected;
+
+        // detect markers and estimate pose
+        detector.detectMarkers(image, corners, ids, rejected);
+
+        size_t nMarkers = corners.size();
+        vector<Vec3d> rvecs(nMarkers), tvecs(nMarkers);
+
+        if(estimatePose && !ids.empty()) {
+            // Calculate pose for each marker
+            for (size_t i = 0; i < nMarkers; i++) {
+                solvePnP(objPoints, corners.at(i), camMatrix, distCoeffs, rvecs.at(i), tvecs.at(i));
+            }
+        }
+        //! [aruco_pose_estimation3]
+        double currentTime = ((double)getTickCount() - tick) / getTickFrequency();
+        totalTime += currentTime;
+        totalIterations++;
+        if(totalIterations % 30 == 0) {
+            cout << "Detection Time = " << currentTime * 1000 << " ms "
+                 << "(Mean = " << 1000 * totalTime / double(totalIterations) << " ms)" << endl;
+        }
+        //! [aruco_draw_pose_estimation]
+        // draw results
+        image.copyTo(imageCopy);
+        if(!ids.empty()) {
+            cv::aruco::drawDetectedMarkers(imageCopy, corners, ids);
+
+            if(estimatePose) {
+                for(unsigned int i = 0; i < ids.size(); i++)
+                    cv::drawFrameAxes(imageCopy, camMatrix, distCoeffs, rvecs[i], tvecs[i], markerLength * 1.5f, 2);
+            }
+        }
+        //! [aruco_draw_pose_estimation]
+
+        if(showRejected && !rejected.empty())
+            cv::aruco::drawDetectedMarkers(imageCopy, rejected, noArray(), Scalar(100, 0, 255));
+
+        imshow("out", imageCopy);
+        char key = (char)waitKey(waitTime);
+        if(key == 27) break;
+    }
+    //! [aruco_detect_markers]
+    return 0;
+}

samples/cpp/tutorial_code/objectDetection/tutorial_camera_params.yml

@@ -0,0 +1,14 @@
+%YAML:1.0
+camera_matrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 628.158, 0., 324.099,
+       0., 628.156, 260.908,
+       0., 0., 1. ]
+distortion_coefficients: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ 0.0995485, -0.206384,
+       0.00754589, 0.00336531, 0 ]

samples/cpp/tutorial_code/objectDetection/tutorial_dict.yml

@@ -0,0 +1,38 @@
+%YAML:1.0
+nmarkers: 35
+markersize: 6
+marker_0: "101011111011111001001001101100000000"
+marker_1: "000000000010011001010011111010111000"
+marker_2: "011001100000001010000101111101001101"
+marker_3: "001000111111000111011001110000011111"
+marker_4: "100110110100101111000000111101110011"
+marker_5: "010101101110111000111010111100010111"
+marker_6: "101001000110011110101001010100110100"
+marker_7: "011010100100110000011101110110100010"
+marker_8: "111110001000101000110001010010111101"
+marker_9: "011101101100110111001100100001010100"
+marker_10: "100001100001010001110001011000000111"
+marker_11: "110010010010011100101111111000001111"
+marker_12: "110101001001010110011111010110001101"
+marker_13: "001111000001000100010001101001010001"
+marker_14: "000000010010101010111110110011010011"
+marker_15: "110001110111100101110011111100111010"
+marker_16: "101011001110001010110011111011001110"
+marker_17: "101110111101110100101101011001010111"
+marker_18: "000100111000111101010011010101000101"
+marker_19: "001110001110001101100101110100000011"
+marker_20: "100101101100010110110110110001100011"
+marker_21: "010110001001011010000100111000110110"
+marker_22: "001000000000100100000000010100010010"
+marker_23: "101001110010100110000111111010010000"
+marker_24: "111001101010001100011010010001011100"
+marker_25: "101000010001010000110100111101101001"
+marker_26: "101010000001010011001010110110000001"
+marker_27: "100101001000010101001000111101111110"
+marker_28: "010010100110010011110001110101011100"
+marker_29: "011001000101100001101111010001001111"
+marker_30: "000111011100011110001101111011011001"
+marker_31: "010100001011000100111101110001101010"
+marker_32: "100101101001101010111111101101110100"
+marker_33: "101101001010111000000100110111010101"
+marker_34: "011111010000111011111110110101100101"