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Adds OpenCV 4.1.0 dependencies for Windows

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/*
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License Agreement
For Open Source Computer Vision Library
(3-clause BSD License)
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* Redistributions of source code must retain the above copyright notice,
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#ifndef __OPENCV_ARUCO_HPP__
#define __OPENCV_ARUCO_HPP__
#include <opencv2/core.hpp>
#include <vector>
#include "opencv2/aruco/dictionary.hpp"
/**
* @defgroup aruco ArUco Marker Detection
* This module is dedicated to square fiducial markers (also known as Augmented Reality Markers)
* These markers are useful for easy, fast and robust camera pose estimation.ç
*
* The main functionalities are:
* - Detection of markers in an image
* - Pose estimation from a single marker or from a board/set of markers
* - Detection of ChArUco board for high subpixel accuracy
* - Camera calibration from both, ArUco boards and ChArUco boards.
* - Detection of ChArUco diamond markers
* The samples directory includes easy examples of how to use the module.
*
* The implementation is based on the ArUco Library by R. Muñoz-Salinas and S. Garrido-Jurado @cite Aruco2014.
*
* Markers can also be detected based on the AprilTag 2 @cite wang2016iros fiducial detection method.
*
* @sa 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
*
* @sa http://www.uco.es/investiga/grupos/ava/node/26
*
* This module has been originally developed by Sergio Garrido-Jurado as a project
* for Google Summer of Code 2015 (GSoC 15).
*
*
*/
namespace cv {
namespace aruco {
//! @addtogroup aruco
//! @{
enum CornerRefineMethod{
CORNER_REFINE_NONE, ///< Tag and corners detection based on the ArUco approach
CORNER_REFINE_SUBPIX, ///< ArUco approach and refine the corners locations using corner subpixel accuracy
CORNER_REFINE_CONTOUR, ///< ArUco approach and refine the corners locations using the contour-points line fitting
CORNER_REFINE_APRILTAG, ///< Tag and corners detection based on the AprilTag 2 approach @cite wang2016iros
};
/**
* @brief Parameters for the detectMarker process:
* - adaptiveThreshWinSizeMin: minimum window size for adaptive thresholding before finding
* contours (default 3).
* - adaptiveThreshWinSizeMax: maximum window size for adaptive thresholding before finding
* contours (default 23).
* - adaptiveThreshWinSizeStep: increments from adaptiveThreshWinSizeMin to adaptiveThreshWinSizeMax
* during the thresholding (default 10).
* - adaptiveThreshConstant: constant for adaptive thresholding before finding contours (default 7)
* - minMarkerPerimeterRate: determine minimum perimeter for marker contour to be detected. This
* is defined as a rate respect to the maximum dimension of the input image (default 0.03).
* - maxMarkerPerimeterRate: determine maximum perimeter for marker contour to be detected. This
* is defined as a rate respect to the maximum dimension of the input image (default 4.0).
* - polygonalApproxAccuracyRate: minimum accuracy during the polygonal approximation process to
* determine which contours are squares.
* - minCornerDistanceRate: minimum distance between corners for detected markers relative to its
* perimeter (default 0.05)
* - minDistanceToBorder: minimum distance of any corner to the image border for detected markers
* (in pixels) (default 3)
* - minMarkerDistanceRate: minimum mean distance beetween two marker corners to be considered
* similar, so that the smaller one is removed. The rate is relative to the smaller perimeter
* of the two markers (default 0.05).
* - cornerRefinementMethod: corner refinement method. (CORNER_REFINE_NONE, no refinement.
* CORNER_REFINE_SUBPIX, do subpixel refinement. CORNER_REFINE_CONTOUR use contour-Points,
* CORNER_REFINE_APRILTAG use the AprilTag2 approach)
* - cornerRefinementWinSize: window size for the corner refinement process (in pixels) (default 5).
* - cornerRefinementMaxIterations: maximum number of iterations for stop criteria of the corner
* refinement process (default 30).
* - cornerRefinementMinAccuracy: minimum error for the stop cristeria of the corner refinement
* process (default: 0.1)
* - markerBorderBits: number of bits of the marker border, i.e. marker border width (default 1).
* - perspectiveRemovePixelPerCell: number of bits (per dimension) for each cell of the marker
* when removing the perspective (default 8).
* - perspectiveRemoveIgnoredMarginPerCell: width of the margin of pixels on each cell not
* considered for the determination of the cell bit. Represents the rate respect to the total
* size of the cell, i.e. perspectiveRemovePixelPerCell (default 0.13)
* - maxErroneousBitsInBorderRate: maximum number of accepted erroneous bits in the border (i.e.
* number of allowed white bits in the border). Represented as a rate respect to the total
* number of bits per marker (default 0.35).
* - minOtsuStdDev: minimun standard deviation in pixels values during the decodification step to
* apply Otsu thresholding (otherwise, all the bits are set to 0 or 1 depending on mean higher
* than 128 or not) (default 5.0)
* - errorCorrectionRate error correction rate respect to the maximun error correction capability
* for each dictionary. (default 0.6).
* - aprilTagMinClusterPixels: reject quads containing too few pixels.
* - aprilTagMaxNmaxima: how many corner candidates to consider when segmenting a group of pixels into a quad.
* - aprilTagCriticalRad: Reject quads where pairs of edges have angles that are close to straight or close to
* 180 degrees. Zero means that no quads are rejected. (In radians).
* - aprilTagMaxLineFitMse: When fitting lines to the contours, what is the maximum mean squared error
* allowed? This is useful in rejecting contours that are far from being quad shaped; rejecting
* these quads "early" saves expensive decoding processing.
* - aprilTagMinWhiteBlackDiff: When we build our model of black & white pixels, we add an extra check that
* the white model must be (overall) brighter than the black model. How much brighter? (in pixel values, [0,255]).
* - aprilTagDeglitch: should the thresholded image be deglitched? Only useful for very noisy images
* - aprilTagQuadDecimate: Detection of quads can be done on a lower-resolution image, improving speed at a
* cost of pose accuracy and a slight decrease in detection rate. Decoding the binary payload is still
* done at full resolution.
* - aprilTagQuadSigma: What Gaussian blur should be applied to the segmented image (used for quad detection?)
* Parameter is the standard deviation in pixels. Very noisy images benefit from non-zero values (e.g. 0.8).
*/
struct CV_EXPORTS_W DetectorParameters {
DetectorParameters();
CV_WRAP static Ptr<DetectorParameters> create();
CV_PROP_RW int adaptiveThreshWinSizeMin;
CV_PROP_RW int adaptiveThreshWinSizeMax;
CV_PROP_RW int adaptiveThreshWinSizeStep;
CV_PROP_RW double adaptiveThreshConstant;
CV_PROP_RW double minMarkerPerimeterRate;
CV_PROP_RW double maxMarkerPerimeterRate;
CV_PROP_RW double polygonalApproxAccuracyRate;
CV_PROP_RW double minCornerDistanceRate;
CV_PROP_RW int minDistanceToBorder;
CV_PROP_RW double minMarkerDistanceRate;
CV_PROP_RW int cornerRefinementMethod;
CV_PROP_RW int cornerRefinementWinSize;
CV_PROP_RW int cornerRefinementMaxIterations;
CV_PROP_RW double cornerRefinementMinAccuracy;
CV_PROP_RW int markerBorderBits;
CV_PROP_RW int perspectiveRemovePixelPerCell;
CV_PROP_RW double perspectiveRemoveIgnoredMarginPerCell;
CV_PROP_RW double maxErroneousBitsInBorderRate;
CV_PROP_RW double minOtsuStdDev;
CV_PROP_RW double errorCorrectionRate;
// April :: User-configurable parameters.
CV_PROP_RW float aprilTagQuadDecimate;
CV_PROP_RW float aprilTagQuadSigma;
// April :: Internal variables
CV_PROP_RW int aprilTagMinClusterPixels;
CV_PROP_RW int aprilTagMaxNmaxima;
CV_PROP_RW float aprilTagCriticalRad;
CV_PROP_RW float aprilTagMaxLineFitMse;
CV_PROP_RW int aprilTagMinWhiteBlackDiff;
CV_PROP_RW int aprilTagDeglitch;
};
/**
* @brief Basic marker detection
*
* @param image input image
* @param dictionary indicates the type of markers that will be searched
* @param corners vector of detected marker corners. For each marker, its four corners
* are provided, (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers,
* the dimensions of this array is Nx4. The order of the corners is clockwise.
* @param ids vector of identifiers of the detected markers. The identifier is of type int
* (e.g. std::vector<int>). For N detected markers, the size of ids is also N.
* The identifiers have the same order than the markers in the imgPoints array.
* @param parameters marker detection parameters
* @param rejectedImgPoints contains the imgPoints of those squares whose inner code has not a
* correct codification. Useful for debugging purposes.
* @param cameraMatrix optional input 3x3 floating-point camera matrix
* \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
* @param distCoeff optional vector of distortion coefficients
* \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
*
* Performs marker detection in the input image. Only markers included in the specific dictionary
* are searched. For each detected marker, it returns the 2D position of its corner in the image
* and its corresponding identifier.
* Note that this function does not perform pose estimation.
* @sa estimatePoseSingleMarkers, estimatePoseBoard
*
*/
CV_EXPORTS_W void detectMarkers(InputArray image, const Ptr<Dictionary> &dictionary, OutputArrayOfArrays corners,
OutputArray ids, const Ptr<DetectorParameters> &parameters = DetectorParameters::create(),
OutputArrayOfArrays rejectedImgPoints = noArray(), InputArray cameraMatrix= noArray(), InputArray distCoeff= noArray());
/**
* @brief Pose estimation for single markers
*
* @param corners vector of already detected markers corners. For each marker, its four corners
* are provided, (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers,
* the dimensions of this array should be Nx4. The order of the corners should be clockwise.
* @sa detectMarkers
* @param markerLength the length of the markers' side. The returning translation vectors will
* be in the same unit. Normally, unit is meters.
* @param cameraMatrix input 3x3 floating-point camera matrix
* \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
* @param distCoeffs vector of distortion coefficients
* \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
* @param rvecs array of output rotation vectors (@sa Rodrigues) (e.g. std::vector<cv::Vec3d>).
* Each element in rvecs corresponds to the specific marker in imgPoints.
* @param tvecs array of output translation vectors (e.g. std::vector<cv::Vec3d>).
* Each element in tvecs corresponds to the specific marker in imgPoints.
* @param _objPoints array of object points of all the marker corners
*
* This function receives the detected markers and returns their pose estimation respect to
* the camera individually. So for each marker, one rotation and translation vector is returned.
* The returned transformation is the one that transforms points from each marker coordinate system
* to the camera coordinate system.
* The marker corrdinate system is centered on the middle of the marker, with the Z axis
* perpendicular to the marker plane.
* The coordinates of the four corners of the marker in its own coordinate system are:
* (-markerLength/2, markerLength/2, 0), (markerLength/2, markerLength/2, 0),
* (markerLength/2, -markerLength/2, 0), (-markerLength/2, -markerLength/2, 0)
*/
CV_EXPORTS_W void estimatePoseSingleMarkers(InputArrayOfArrays corners, float markerLength,
InputArray cameraMatrix, InputArray distCoeffs,
OutputArray rvecs, OutputArray tvecs, OutputArray _objPoints = noArray());
/**
* @brief Board of markers
*
* A board is a set of markers in the 3D space with a common cordinate system.
* The common form of a board of marker is a planar (2D) board, however any 3D layout can be used.
* A Board object is composed by:
* - The object points of the marker corners, i.e. their coordinates respect to the board system.
* - The dictionary which indicates the type of markers of the board
* - The identifier of all the markers in the board.
*/
class CV_EXPORTS_W Board {
public:
/**
* @brief Provide way to create Board by passing nessesary data. Specially needed in Python.
*
* @param objPoints array of object points of all the marker corners in the board
* @param dictionary the dictionary of markers employed for this board
* @param ids vector of the identifiers of the markers in the board
*
*/
CV_WRAP static Ptr<Board> create(InputArrayOfArrays objPoints, const Ptr<Dictionary> &dictionary, InputArray ids);
/// array of object points of all the marker corners in the board
/// each marker include its 4 corners in CCW order. For M markers, the size is Mx4.
CV_PROP std::vector< std::vector< Point3f > > objPoints;
/// the dictionary of markers employed for this board
CV_PROP Ptr<Dictionary> dictionary;
/// vector of the identifiers of the markers in the board (same size than objPoints)
/// The identifiers refers to the board dictionary
CV_PROP std::vector< int > ids;
};
/**
* @brief Planar board with grid arrangement of markers
* More common type of board. All markers are placed in the same plane in a grid arrangment.
* The board can be drawn using drawPlanarBoard() function (@sa drawPlanarBoard)
*/
class CV_EXPORTS_W GridBoard : public Board {
public:
/**
* @brief Draw a GridBoard
*
* @param outSize size of the output image in pixels.
* @param img output image with the board. The size of this image will be outSize
* and the board will be on the center, keeping the board proportions.
* @param marginSize minimum margins (in pixels) of the board in the output image
* @param borderBits width of the marker borders.
*
* This function return the image of the GridBoard, ready to be printed.
*/
CV_WRAP void draw(Size outSize, OutputArray img, int marginSize = 0, int borderBits = 1);
/**
* @brief Create a GridBoard object
*
* @param markersX number of markers in X direction
* @param markersY number of markers in Y direction
* @param markerLength marker side length (normally in meters)
* @param markerSeparation separation between two markers (same unit as markerLength)
* @param dictionary dictionary of markers indicating the type of markers
* @param firstMarker id of first marker in dictionary to use on board.
* @return the output GridBoard object
*
* This functions creates a GridBoard object given the number of markers in each direction and
* the marker size and marker separation.
*/
CV_WRAP static Ptr<GridBoard> create(int markersX, int markersY, float markerLength,
float markerSeparation, const Ptr<Dictionary> &dictionary, int firstMarker = 0);
/**
*
*/
CV_WRAP Size getGridSize() const { return Size(_markersX, _markersY); }
/**
*
*/
CV_WRAP float getMarkerLength() const { return _markerLength; }
/**
*
*/
CV_WRAP float getMarkerSeparation() const { return _markerSeparation; }
private:
// number of markers in X and Y directions
int _markersX, _markersY;
// marker side lenght (normally in meters)
float _markerLength;
// separation between markers in the grid
float _markerSeparation;
};
/**
* @brief Pose estimation for a board of markers
*
* @param corners vector of already detected markers corners. For each marker, its four corners
* are provided, (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers, the
* dimensions of this array should be Nx4. The order of the corners should be clockwise.
* @param ids list of identifiers for each marker in corners
* @param board layout of markers in the board. The layout is composed by the marker identifiers
* and the positions of each marker corner in the board reference system.
* @param cameraMatrix input 3x3 floating-point camera matrix
* \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
* @param distCoeffs vector of distortion coefficients
* \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
* @param rvec Output vector (e.g. cv::Mat) corresponding to the rotation vector of the board
* (see cv::Rodrigues). Used as initial guess if not empty.
* @param tvec Output vector (e.g. cv::Mat) corresponding to the translation vector of the board.
* @param useExtrinsicGuess defines whether initial guess for \b rvec and \b tvec will be used or not.
* Used as initial guess if not empty.
*
* This function receives the detected markers and returns the pose of a marker board composed
* by those markers.
* A Board of marker has a single world coordinate system which is defined by the board layout.
* The returned transformation is the one that transforms points from the board coordinate system
* to the camera coordinate system.
* Input markers that are not included in the board layout are ignored.
* The function returns the number of markers from the input employed for the board pose estimation.
* Note that returning a 0 means the pose has not been estimated.
*/
CV_EXPORTS_W int estimatePoseBoard(InputArrayOfArrays corners, InputArray ids, const Ptr<Board> &board,
InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec,
OutputArray tvec, bool useExtrinsicGuess = false);
/**
* @brief Refind not detected markers based on the already detected and the board layout
*
* @param image input image
* @param board layout of markers in the board.
* @param detectedCorners vector of already detected marker corners.
* @param detectedIds vector of already detected marker identifiers.
* @param rejectedCorners vector of rejected candidates during the marker detection process.
* @param cameraMatrix optional input 3x3 floating-point camera matrix
* \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
* @param distCoeffs optional vector of distortion coefficients
* \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
* @param minRepDistance minimum distance between the corners of the rejected candidate and the
* reprojected marker in order to consider it as a correspondence.
* @param errorCorrectionRate rate of allowed erroneous bits respect to the error correction
* capability of the used dictionary. -1 ignores the error correction step.
* @param checkAllOrders Consider the four posible corner orders in the rejectedCorners array.
* If it set to false, only the provided corner order is considered (default true).
* @param recoveredIdxs Optional array to returns the indexes of the recovered candidates in the
* original rejectedCorners array.
* @param parameters marker detection parameters
*
* This function tries to find markers that were not detected in the basic detecMarkers function.
* First, based on the current detected marker and the board layout, the function interpolates
* the position of the missing markers. Then it tries to find correspondence between the reprojected
* markers and the rejected candidates based on the minRepDistance and errorCorrectionRate
* parameters.
* If camera parameters and distortion coefficients are provided, missing markers are reprojected
* using projectPoint function. If not, missing marker projections are interpolated using global
* homography, and all the marker corners in the board must have the same Z coordinate.
*/
CV_EXPORTS_W void refineDetectedMarkers(
InputArray image,const Ptr<Board> &board, InputOutputArrayOfArrays detectedCorners,
InputOutputArray detectedIds, InputOutputArrayOfArrays rejectedCorners,
InputArray cameraMatrix = noArray(), InputArray distCoeffs = noArray(),
float minRepDistance = 10.f, float errorCorrectionRate = 3.f, bool checkAllOrders = true,
OutputArray recoveredIdxs = noArray(), const Ptr<DetectorParameters> &parameters = DetectorParameters::create());
/**
* @brief Draw detected markers in image
*
* @param image input/output image. It must have 1 or 3 channels. The number of channels is not
* altered.
* @param corners positions of marker corners on input image.
* (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers, the dimensions of
* this array should be Nx4. The order of the corners should be clockwise.
* @param ids vector of identifiers for markers in markersCorners .
* Optional, if not provided, ids are not painted.
* @param borderColor color of marker borders. Rest of colors (text color and first corner color)
* are calculated based on this one to improve visualization.
*
* Given an array of detected marker corners and its corresponding ids, this functions draws
* the markers in the image. The marker borders are painted and the markers identifiers if provided.
* Useful for debugging purposes.
*/
CV_EXPORTS_W void drawDetectedMarkers(InputOutputArray image, InputArrayOfArrays corners,
InputArray ids = noArray(),
Scalar borderColor = Scalar(0, 255, 0));
/**
* @brief Draw coordinate system axis from pose estimation
*
* @param image input/output image. It must have 1 or 3 channels. The number of channels is not
* altered.
* @param cameraMatrix input 3x3 floating-point camera matrix
* \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
* @param distCoeffs vector of distortion coefficients
* \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
* @param rvec rotation vector of the coordinate system that will be drawn. (@sa Rodrigues).
* @param tvec translation vector of the coordinate system that will be drawn.
* @param length length of the painted axis in the same unit than tvec (usually in meters)
*
* Given the pose estimation of a marker or board, this function draws the axis of the world
* coordinate system, i.e. the system centered on the marker/board. Useful for debugging purposes.
*/
CV_EXPORTS_W void drawAxis(InputOutputArray image, InputArray cameraMatrix, InputArray distCoeffs,
InputArray rvec, InputArray tvec, float length);
/**
* @brief Draw a canonical marker image
*
* @param dictionary dictionary of markers indicating the type of markers
* @param id identifier of the marker that will be returned. It has to be a valid id
* in the specified dictionary.
* @param sidePixels size of the image in pixels
* @param img output image with the marker
* @param borderBits width of the marker border.
*
* This function returns a marker image in its canonical form (i.e. ready to be printed)
*/
CV_EXPORTS_W void drawMarker(const Ptr<Dictionary> &dictionary, int id, int sidePixels, OutputArray img,
int borderBits = 1);
/**
* @brief Draw a planar board
* @sa _drawPlanarBoardImpl
*
* @param board layout of the board that will be drawn. The board should be planar,
* z coordinate is ignored
* @param outSize size of the output image in pixels.
* @param img output image with the board. The size of this image will be outSize
* and the board will be on the center, keeping the board proportions.
* @param marginSize minimum margins (in pixels) of the board in the output image
* @param borderBits width of the marker borders.
*
* This function return the image of a planar board, ready to be printed. It assumes
* the Board layout specified is planar by ignoring the z coordinates of the object points.
*/
CV_EXPORTS_W void drawPlanarBoard(const Ptr<Board> &board, Size outSize, OutputArray img,
int marginSize = 0, int borderBits = 1);
/**
* @brief Implementation of drawPlanarBoard that accepts a raw Board pointer.
*/
void _drawPlanarBoardImpl(Board *board, Size outSize, OutputArray img,
int marginSize = 0, int borderBits = 1);
/**
* @brief Calibrate a camera using aruco markers
*
* @param corners vector of detected marker corners in all frames.
* The corners should have the same format returned by detectMarkers (see #detectMarkers).
* @param ids list of identifiers for each marker in corners
* @param counter number of markers in each frame so that corners and ids can be split
* @param board Marker Board layout
* @param imageSize Size of the image used only to initialize the intrinsic camera matrix.
* @param cameraMatrix Output 3x3 floating-point camera matrix
* \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS
* and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be
* initialized before calling the function.
* @param distCoeffs Output vector of distortion coefficients
* \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
* @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each board view
* (e.g. std::vector<cv::Mat>>). That is, each k-th rotation vector together with the corresponding
* k-th translation vector (see the next output parameter description) brings the board pattern
* from the model coordinate space (in which object points are specified) to the world coordinate
* space, that is, a real position of the board pattern in the k-th pattern view (k=0.. *M* -1).
* @param tvecs Output vector of translation vectors estimated for each pattern view.
* @param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters.
* Order of deviations values:
* \f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3,
* s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero.
* @param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters.
* Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views,
* \f$R_i, T_i\f$ are concatenated 1x3 vectors.
* @param perViewErrors Output vector of average re-projection errors estimated for each pattern view.
* @param flags flags Different flags for the calibration process (see #calibrateCamera for details).
* @param criteria Termination criteria for the iterative optimization algorithm.
*
* This function calibrates a camera using an Aruco Board. The function receives a list of
* detected markers from several views of the Board. The process is similar to the chessboard
* calibration in calibrateCamera(). The function returns the final re-projection error.
*/
CV_EXPORTS_AS(calibrateCameraArucoExtended) double calibrateCameraAruco(
InputArrayOfArrays corners, InputArray ids, InputArray counter, const Ptr<Board> &board,
Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics,
OutputArray perViewErrors, int flags = 0,
TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON));
/** @brief It's the same function as #calibrateCameraAruco but without calibration error estimation.
*/
CV_EXPORTS_W double calibrateCameraAruco(
InputArrayOfArrays corners, InputArray ids, InputArray counter, const Ptr<Board> &board,
Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs,