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AleksandrPanov
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move testCharucoCornersCollinear to CharucoBoard
move ArucoTutorial tests to new cpp file move estimatePoseBoard() to board class removed aruco_detector add matchImagePoints add getters to CharucoBoard
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‎modules/aruco/CMakeLists.txt‎

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set(the_description "ArUco Marker Detection")
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ocv_define_module(aruco opencv_core opencv_imgproc opencv_calib3d WRAP python java objc js)
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ocv_define_module(aruco opencv_core opencv_imgproc opencv_calib3d opencv_objdetect WRAP python java objc js)
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ocv_include_directories(${CMAKE_CURRENT_BINARY_DIR})
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ocv_add_testdata(samples/ contrib/aruco

‎modules/aruco/include/opencv2/aruco.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_HPP__
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#define __OPENCV_ARUCO_HPP__
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#ifndef OPENCV_ARUCO_HPP
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#define OPENCV_ARUCO_HPP
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#include "opencv2/aruco_detector.hpp"
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#include "opencv2/aruco/aruco_calib_pose.hpp"
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#include "opencv2/objdetect/aruco_detector.hpp"
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#include "opencv2/aruco/aruco_calib.hpp"
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namespace cv {
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namespace aruco {
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/**
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@deprecated Use class ArucoDetector
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@deprecated Use class ArucoDetector::detectMarkers
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*/
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CV_EXPORTS_W void detectMarkers(InputArray image, const Ptr<Dictionary> &dictionary, OutputArrayOfArrays corners,
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OutputArray ids, const Ptr<DetectorParameters> &parameters = DetectorParameters::create(),
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OutputArrayOfArrays rejectedImgPoints = noArray());
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/**
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@deprecated Use class ArucoDetector
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@deprecated Use class ArucoDetector::refineDetectedMarkers
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*/
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CV_EXPORTS_W void refineDetectedMarkers(InputArray image,const Ptr<Board> &board,
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InputOutputArrayOfArrays detectedCorners,
@@ -29,6 +29,124 @@ CV_EXPORTS_W void refineDetectedMarkers(InputArray image,const Ptr<Board> &boar
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bool checkAllOrders = true, OutputArray recoveredIdxs = noArray(),
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const Ptr<DetectorParameters> &parameters = DetectorParameters::create());
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/**
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@deprecated Use Board::draw
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*/
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CV_EXPORTS_W void drawPlanarBoard(const Ptr<Board> &board, Size outSize, OutputArray img, int marginSize,
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int borderBits);
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/**
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@deprecated Use Board::matchImagePoints
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*/
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CV_EXPORTS_W void getBoardObjectAndImagePoints(const Ptr<Board> &board, InputArrayOfArrays detectedCorners,
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InputArray detectedIds, OutputArray objPoints, OutputArray imgPoints);
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/**
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* @brief Pose estimation for a board of markers
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*
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* @param corners vector of already detected markers corners. For each marker, its four corners
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* are provided, (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers, the
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* dimensions of this array should be Nx4. The order of the corners should be clockwise.
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* @param ids list of identifiers for each marker in corners
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* @param board layout of markers in the board. The layout is composed by the marker identifiers
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* and the positions of each marker corner in the board reference system.
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* @param cameraMatrix input 3x3 floating-point camera matrix
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* \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
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* @param distCoeffs 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 rvec Output vector (e.g. cv::Mat) corresponding to the rotation vector of the board
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* (see cv::Rodrigues). Used as initial guess if not empty.
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* @param tvec Output vector (e.g. cv::Mat) corresponding to the translation vector of the board.
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* @param useExtrinsicGuess defines whether initial guess for \b rvec and \b tvec will be used or not.
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* Used as initial guess if not empty.
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*
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* This function receives the detected markers and returns the pose of a marker board composed
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* by those markers.
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* A Board of marker has a single world coordinate system which is defined by the board layout.
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* The returned transformation is the one that transforms points from the board coordinate system
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* to the camera coordinate system.
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* Input markers that are not included in the board layout are ignored.
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* The function returns the number of markers from the input employed for the board pose estimation.
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* Note that returning a 0 means the pose has not been estimated.
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* @sa use cv::drawFrameAxes to get world coordinate system axis for object points
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*/
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CV_EXPORTS_W int estimatePoseBoard(InputArrayOfArrays corners, InputArray ids, const Ptr<Board> &board,
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InputArray cameraMatrix, InputArray distCoeffs, InputOutputArray rvec,
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InputOutputArray tvec, bool useExtrinsicGuess = false);
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/**
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* @brief Pose estimation for a ChArUco board given some of their corners
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* @param charucoCorners vector of detected charuco corners
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* @param charucoIds list of identifiers for each corner in charucoCorners
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* @param board layout of ChArUco board.
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* @param cameraMatrix input 3x3 floating-point camera matrix
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* \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
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* @param distCoeffs 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 rvec Output vector (e.g. cv::Mat) corresponding to the rotation vector of the board
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* (see cv::Rodrigues).
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* @param tvec Output vector (e.g. cv::Mat) corresponding to the translation vector of the board.
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* @param useExtrinsicGuess defines whether initial guess for \b rvec and \b tvec will be used or not.
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*
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* This function estimates a Charuco board pose from some detected corners.
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* The function checks if the input corners are enough and valid to perform pose estimation.
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* If pose estimation is valid, returns true, else returns false.
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* @sa use cv::drawFrameAxes to get world coordinate system axis for object points
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*/
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CV_EXPORTS_W bool estimatePoseCharucoBoard(InputArray charucoCorners, InputArray charucoIds,
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const Ptr<CharucoBoard> &board, InputArray cameraMatrix,
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InputArray distCoeffs, InputOutputArray rvec,
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InputOutputArray tvec, bool useExtrinsicGuess = false);
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/**
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* @brief Pose estimation for single markers
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*
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* @param corners vector of already detected markers corners. For each marker, its four corners
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* are provided, (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers,
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* the dimensions of this array should be Nx4. The order of the corners should be clockwise.
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* @sa detectMarkers
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* @param markerLength the length of the markers' side. The returning translation vectors will
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* be in the same unit. Normally, unit is meters.
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* @param cameraMatrix input 3x3 floating-point camera matrix
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* \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
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* @param distCoeffs 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 array of output rotation vectors (@sa Rodrigues) (e.g. std::vector<cv::Vec3d>).
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* Each element in rvecs corresponds to the specific marker in imgPoints.
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* @param tvecs array of output translation vectors (e.g. std::vector<cv::Vec3d>).
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* Each element in tvecs corresponds to the specific marker in imgPoints.
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* @param objPoints array of object points of all the marker corners
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* @param estimateParameters set the origin of coordinate system and the coordinates of the four corners of the marker
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* (default estimateParameters.pattern = PatternPositionType::ARUCO_CCW_CENTER, estimateParameters.useExtrinsicGuess = false,
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* estimateParameters.solvePnPMethod = SOLVEPNP_ITERATIVE).
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*
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* This function receives the detected markers and returns their pose estimation respect to
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* the camera individually. So for each marker, one rotation and translation vector is returned.
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* The returned transformation is the one that transforms points from each marker coordinate system
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* to the camera coordinate system.
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* The marker coordinate system is centered on the middle (by default) or on the top-left corner of the marker,
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* with the Z axis perpendicular to the marker plane.
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* estimateParameters defines the coordinates of the four corners of the marker in its own coordinate system (by default) are:
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* (-markerLength/2, markerLength/2, 0), (markerLength/2, markerLength/2, 0),
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* (markerLength/2, -markerLength/2, 0), (-markerLength/2, -markerLength/2, 0)
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* @sa use cv::drawFrameAxes to get world coordinate system axis for object points
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* @sa @ref tutorial_aruco_detection
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* @sa EstimateParameters
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* @sa PatternPositionType
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*/
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CV_EXPORTS_W void estimatePoseSingleMarkers(InputArrayOfArrays corners, float markerLength,
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InputArray cameraMatrix, InputArray distCoeffs,
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OutputArray rvecs, OutputArray tvecs, OutputArray objPoints = noArray(),
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const Ptr<EstimateParameters>& estimateParameters = EstimateParameters::create());
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/**
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@deprecated Use CharucoBoard::testCharucoCornersCollinear
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*/
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CV_EXPORTS_W bool testCharucoCornersCollinear(const Ptr<CharucoBoard> &board, InputArray charucoIds);
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}
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}
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‎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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8+
namespace cv {
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namespace aruco {
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11+
//! @addtogroup aruco
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//! @{
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/**
15+
* @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
48+
* calibration in calibrateCamera(). The function returns the final re-projection error.
49+
*/
50+
CV_EXPORTS_AS(calibrateCameraArucoExtended)
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double calibrateCameraAruco(InputArrayOfArrays corners, InputArray ids, InputArray counter, const Ptr<Board> &board,
52+
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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