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AleksandrPanov
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move board aruco pose
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‎modules/aruco/include/opencv2/aruco.hpp

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#define __OPENCV_ARUCO_HPP__
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#include "opencv2/objdetect/aruco_detector.hpp"
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#include "opencv2/aruco/aruco_calib_pose.hpp"
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#include "opencv2/aruco/aruco_calib.hpp"
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namespace cv {
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namespace aruco {

‎modules/aruco/include/opencv2/aruco/aruco_calib.hpp

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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html
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#ifndef __OPENCV_ARUCO_CALIB_POSE_HPP__
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#define __OPENCV_ARUCO_CALIB_POSE_HPP__
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#include <opencv2/objdetect/aruco_board.hpp>
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namespace cv {
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namespace aruco {
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//! @addtogroup aruco
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//! @{
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/**
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* @brief Calibrate a camera using aruco markers
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*
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* @param corners vector of detected marker corners in all frames.
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* The corners should have the same format returned by detectMarkers (see #detectMarkers).
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* @param ids list of identifiers for each marker in corners
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* @param counter number of markers in each frame so that corners and ids can be split
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* @param board Marker Board layout
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* @param imageSize Size of the image used only to initialize the intrinsic camera matrix.
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* @param cameraMatrix Output 3x3 floating-point camera matrix
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* \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS
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* and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be
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* initialized before calling the function.
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* @param distCoeffs Output vector of distortion coefficients
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* \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
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* @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each board view
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* (e.g. std::vector<cv::Mat>>). That is, each k-th rotation vector together with the corresponding
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* k-th translation vector (see the next output parameter description) brings the board pattern
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* from the model coordinate space (in which object points are specified) to the world coordinate
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* space, that is, a real position of the board pattern in the k-th pattern view (k=0.. *M* -1).
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* @param tvecs Output vector of translation vectors estimated for each pattern view.
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* @param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters.
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* Order of deviations values:
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* \f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3,
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* s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero.
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* @param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters.
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* Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views,
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* \f$R_i, T_i\f$ are concatenated 1x3 vectors.
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* @param perViewErrors Output vector of average re-projection errors estimated for each pattern view.
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* @param flags flags Different flags for the calibration process (see #calibrateCamera for details).
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* @param criteria Termination criteria for the iterative optimization algorithm.
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*
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* This function calibrates a camera using an Aruco Board. The function receives a list of
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* detected markers from several views of the Board. The process is similar to the chessboard
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* calibration in calibrateCamera(). The function returns the final re-projection error.
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*/
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CV_EXPORTS_AS(calibrateCameraArucoExtended)
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double calibrateCameraAruco(InputArrayOfArrays corners, InputArray ids, InputArray counter, const Ptr<Board> &board,
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Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
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OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, OutputArray stdDeviationsIntrinsics,
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OutputArray stdDeviationsExtrinsics, OutputArray perViewErrors, int flags = 0,
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const TermCriteria& criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON));
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/** @overload
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* @brief It's the same function as #calibrateCameraAruco but without calibration error estimation.
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*/
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CV_EXPORTS_W double calibrateCameraAruco(InputArrayOfArrays corners, InputArray ids, InputArray counter,
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const Ptr<Board> &board, Size imageSize, InputOutputArray cameraMatrix,
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InputOutputArray distCoeffs, OutputArrayOfArrays rvecs = noArray(),
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OutputArrayOfArrays tvecs = noArray(), int flags = 0,
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const TermCriteria& criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS,
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30, DBL_EPSILON));
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/**
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* @brief Calibrate a camera using Charuco corners
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*
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* @param charucoCorners vector of detected charuco corners per frame
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* @param charucoIds list of identifiers for each corner in charucoCorners per frame
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* @param board Marker Board layout
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* @param imageSize input image size
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* @param cameraMatrix Output 3x3 floating-point camera matrix
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* \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS
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* and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be
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* initialized before calling the function.
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* @param distCoeffs Output vector of distortion coefficients
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* \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
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* @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each board view
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* (e.g. std::vector<cv::Mat>>). That is, each k-th rotation vector together with the corresponding
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* k-th translation vector (see the next output parameter description) brings the board pattern
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* from the model coordinate space (in which object points are specified) to the world coordinate
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* space, that is, a real position of the board pattern in the k-th pattern view (k=0.. *M* -1).
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* @param tvecs Output vector of translation vectors estimated for each pattern view.
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* @param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters.
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* Order of deviations values:
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* \f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3,
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* s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero.
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* @param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters.
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* Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views,
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* \f$R_i, T_i\f$ are concatenated 1x3 vectors.
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* @param perViewErrors Output vector of average re-projection errors estimated for each pattern view.
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* @param flags flags Different flags for the calibration process (see #calibrateCamera for details).
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* @param criteria Termination criteria for the iterative optimization algorithm.
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*
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* This function calibrates a camera using a set of corners of a Charuco Board. The function
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* receives a list of detected corners and its identifiers from several views of the Board.
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* The function returns the final re-projection error.
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*/
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CV_EXPORTS_AS(calibrateCameraCharucoExtended)
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double calibrateCameraCharuco(InputArrayOfArrays charucoCorners, InputArrayOfArrays charucoIds,
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const Ptr<CharucoBoard> &board, Size imageSize, InputOutputArray cameraMatrix,
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InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
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OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics,
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OutputArray perViewErrors, int flags = 0, const TermCriteria& criteria = TermCriteria(
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TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON));
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/**
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* @brief It's the same function as #calibrateCameraCharuco but without calibration error estimation.
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*/
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CV_EXPORTS_W double calibrateCameraCharuco(InputArrayOfArrays charucoCorners, InputArrayOfArrays charucoIds,
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const Ptr<CharucoBoard> &board, Size imageSize,
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InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
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OutputArrayOfArrays rvecs = noArray(),
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OutputArrayOfArrays tvecs = noArray(), int flags = 0,
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const TermCriteria& criteria=TermCriteria(TermCriteria::COUNT +
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TermCriteria::EPS, 30, DBL_EPSILON));
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//! @}
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}
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}
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#endif

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