//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.objdetect;
import org.opencv.core.Algorithm;
import org.opencv.core.Mat;
import org.opencv.utils.Converters;
import java.util.List;
// C++: class ArucoDetector
/**
* The main functionality of ArucoDetector class is detection of markers in an image with detectMarkers() method.
*
* After detecting some markers in the image, you can try to find undetected markers from this dictionary with
* refineDetectedMarkers() method.
*
* SEE: DetectorParameters, RefineParameters
*/
public class ArucoDetector extends Algorithm {
protected ArucoDetector(long addr) { super(addr); }
// internal usage only
public static ArucoDetector __fromPtr__(long addr) { return new ArucoDetector(addr); }
//
// C++: cv::aruco::ArucoDetector::ArucoDetector(Dictionary dictionary = getPredefinedDictionary(cv::aruco::DICT_4X4_50), DetectorParameters detectorParams = DetectorParameters(), RefineParameters refineParams = RefineParameters())
//
/**
* Basic ArucoDetector constructor
*
* @param dictionary indicates the type of markers that will be searched
* @param detectorParams marker detection parameters
* @param refineParams marker refine detection parameters
*/
public ArucoDetector(Dictionary dictionary, DetectorParameters detectorParams, RefineParameters refineParams) {
super(ArucoDetector_0(dictionary.nativeObj, detectorParams.nativeObj, refineParams.nativeObj));
}
/**
* Basic ArucoDetector constructor
*
* @param dictionary indicates the type of markers that will be searched
* @param detectorParams marker detection parameters
*/
public ArucoDetector(Dictionary dictionary, DetectorParameters detectorParams) {
super(ArucoDetector_1(dictionary.nativeObj, detectorParams.nativeObj));
}
/**
* Basic ArucoDetector constructor
*
* @param dictionary indicates the type of markers that will be searched
*/
public ArucoDetector(Dictionary dictionary) {
super(ArucoDetector_2(dictionary.nativeObj));
}
/**
* Basic ArucoDetector constructor
*
*/
public ArucoDetector() {
super(ArucoDetector_3());
}
//
// C++: void cv::aruco::ArucoDetector::detectMarkers(Mat image, vector_Mat& corners, Mat& ids, vector_Mat& rejectedImgPoints = vector_Mat())
//
/**
* Basic marker detection
*
* @param image input image
* @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 rejectedImgPoints contains the imgPoints of those squares whose inner code has not a
* correct codification. Useful for debugging purposes.
*
* 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.
* <b>Note:</b> The function does not correct lens distortion or takes it into account. It's recommended to undistort
* input image with corresponding camera model, if camera parameters are known
* SEE: undistort, estimatePoseSingleMarkers, estimatePoseBoard
*/
public void detectMarkers(Mat image, List<Mat> corners, Mat ids, List<Mat> rejectedImgPoints) {
Mat corners_mat = new Mat();
Mat rejectedImgPoints_mat = new Mat();
detectMarkers_0(nativeObj, image.nativeObj, corners_mat.nativeObj, ids.nativeObj, rejectedImgPoints_mat.nativeObj);
Converters.Mat_to_vector_Mat(corners_mat, corners);
corners_mat.release();
Converters.Mat_to_vector_Mat(rejectedImgPoints_mat, rejectedImgPoints);
rejectedImgPoints_mat.release();
}
/**
* Basic marker detection
*
* @param image input image
* @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.
* correct codification. Useful for debugging purposes.
*
* 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.
* <b>Note:</b> The function does not correct lens distortion or takes it into account. It's recommended to undistort
* input image with corresponding camera model, if camera parameters are known
* SEE: undistort, estimatePoseSingleMarkers, estimatePoseBoard
*/
public void detectMarkers(Mat image, List<Mat> corners, Mat ids) {
Mat corners_mat = new Mat();
detectMarkers_1(nativeObj, image.nativeObj, corners_mat.nativeObj, ids.nativeObj);
Converters.Mat_to_vector_Mat(corners_mat, corners);
corners_mat.release();
}
//
// C++: void cv::aruco::ArucoDetector::refineDetectedMarkers(Mat image, Board board, vector_Mat& detectedCorners, Mat& detectedIds, vector_Mat& rejectedCorners, Mat cameraMatrix = Mat(), Mat distCoeffs = Mat(), Mat& recoveredIdxs = Mat())
//
/**
* Refine 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
* \(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)
* @param distCoeffs optional vector of distortion coefficients
* \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
* @param recoveredIdxs Optional array to returns the indexes of the recovered candidates in the
* original rejectedCorners array.
*
* 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.
*/
public void refineDetectedMarkers(Mat image, Board board, List<Mat> detectedCorners, Mat detectedIds, List<Mat> rejectedCorners, Mat cameraMatrix, Mat distCoeffs, Mat recoveredIdxs) {
Mat detectedCorners_mat = Converters.vector_Mat_to_Mat(detectedCorners);
Mat rejectedCorners_mat = Converters.vector_Mat_to_Mat(rejectedCorners);
refineDetectedMarkers_0(nativeObj, image.nativeObj, board.nativeObj, detectedCorners_mat.nativeObj, detectedIds.nativeObj, rejectedCorners_mat.nativeObj, cameraMatrix.nativeObj, distCoeffs.nativeObj, recoveredIdxs.nativeObj);
Converters.Mat_to_vector_Mat(detectedCorners_mat, detectedCorners);
detectedCorners_mat.release();
Converters.Mat_to_vector_Mat(rejectedCorners_mat, rejectedCorners);
rejectedCorners_mat.release();
}
/**
* Refine 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
* \(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)
* @param distCoeffs optional vector of distortion coefficients
* \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
* original rejectedCorners array.
*
* 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.
*/
public void refineDetectedMarkers(Mat image, Board board, List<Mat> detectedCorners, Mat detectedIds, List<Mat> rejectedCorners, Mat cameraMatrix, Mat distCoeffs) {
Mat detectedCorners_mat = Converters.vector_Mat_to_Mat(detectedCorners);
Mat rejectedCorners_mat = Converters.vector_Mat_to_Mat(rejectedCorners);
refineDetectedMarkers_1(nativeObj, image.nativeObj, board.nativeObj, detectedCorners_mat.nativeObj, detectedIds.nativeObj, rejectedCorners_mat.nativeObj, cameraMatrix.nativeObj, distCoeffs.nativeObj);
Converters.Mat_to_vector_Mat(detectedCorners_mat, detectedCorners);
detectedCorners_mat.release();
Converters.Mat_to_vector_Mat(rejectedCorners_mat, rejectedCorners);
rejectedCorners_mat.release();
}
/**
* Refine 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
* \(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)
* \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
* original rejectedCorners array.
*
* 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.
*/
public void refineDetectedMarkers(Mat image, Board board, List<Mat> detectedCorners, Mat detectedIds, List<Mat> rejectedCorners, Mat cameraMatrix) {
Mat detectedCorners_mat = Converters.vector_Mat_to_Mat(detectedCorners);
Mat rejectedCorners_mat = Converters.vector_Mat_to_Mat(rejectedCorners);
refineDetectedMarkers_2(nativeObj, image.nativeObj, board.nativeObj, detectedCorners_mat.nativeObj, detectedIds.nativeObj, rejectedCorners_mat.nativeObj, cameraMatrix.nativeObj);
Converters.Mat_to_vector_Mat(detectedCorners_mat, detectedCorners);
detectedCorners_mat.release();
Converters.Mat_to_vector_Mat(rejectedCorners_mat, rejectedCorners);
rejectedCorners_mat.release();
}
/**
* Refine 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.
* \(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\)
* \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\) of 4, 5, 8 or 12 elements
* original rejectedCorners array.
*
* 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.
*/
public void refineDetectedMarkers(Mat image, Board board, List<Mat> detectedCorners, Mat detectedIds, List<Mat> rejectedCorners) {
Mat detectedCorners_mat = Converters.vector_Mat_to_Mat(detectedCorners);
Mat rejectedCorners_mat = Converters.vector_Mat_to_Mat(rejectedCorners);
refineDetectedMarkers_3(nativeObj, image.nativeObj, board.nativeObj, detectedCorners_mat.nativeObj, detectedIds.nativeObj, rejectedCorners_mat.nativeObj);
Converters.Mat_to_vector_Mat(detectedCorners_mat, detectedCorners);
detectedCorners_mat.release();
Converters.Mat_to_vector_Mat(rejectedCorners_mat, rejectedCorners);
rejectedCorners_mat.release();
}
//
// C++: Dictionary cv::aruco::ArucoDetector::getDictionary()
//
public Dictionary getDictionary() {
return new Dictionary(getDictionary_0(nativeObj));
}
//
// C++: void cv::aruco::ArucoDetector::setDictionary(Dictionary dictionary)
//
public void setDictionary(Dictionary dictionary) {
setDictionary_0(nativeObj, dictionary.nativeObj);
}
//
// C++: DetectorParameters cv::aruco::ArucoDetector::getDetectorParameters()
//
public DetectorParameters getDetectorParameters() {
return new DetectorParameters(getDetectorParameters_0(nativeObj));
}
//
// C++: void cv::aruco::ArucoDetector::setDetectorParameters(DetectorParameters detectorParameters)
//
public void setDetectorParameters(DetectorParameters detectorParameters) {
setDetectorParameters_0(nativeObj, detectorParameters.nativeObj);
}
//
// C++: RefineParameters cv::aruco::ArucoDetector::getRefineParameters()
//
public RefineParameters getRefineParameters() {
return new RefineParameters(getRefineParameters_0(nativeObj));
}
//
// C++: void cv::aruco::ArucoDetector::setRefineParameters(RefineParameters refineParameters)
//
public void setRefineParameters(RefineParameters refineParameters) {
setRefineParameters_0(nativeObj, refineParameters.nativeObj);
}
//
// C++: void cv::aruco::ArucoDetector::write(FileStorage fs, String name)
//
// Unknown type 'FileStorage' (I), skipping the function
//
// C++: void cv::aruco::ArucoDetector::read(FileNode fn)
//
// Unknown type 'FileNode' (I), skipping the function
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: cv::aruco::ArucoDetector::ArucoDetector(Dictionary dictionary = getPredefinedDictionary(cv::aruco::DICT_4X4_50), DetectorParameters detectorParams = DetectorParameters(), RefineParameters refineParams = RefineParameters())
private static native long ArucoDetector_0(long dictionary_nativeObj, long detectorParams_nativeObj, long refineParams_nativeObj);
private static native long ArucoDetector_1(long dictionary_nativeObj, long detectorParams_nativeObj);
private static native long ArucoDetector_2(long dictionary_nativeObj);
private static native long ArucoDetector_3();
// C++: void cv::aruco::ArucoDetector::detectMarkers(Mat image, vector_Mat& corners, Mat& ids, vector_Mat& rejectedImgPoints = vector_Mat())
private static native void detectMarkers_0(long nativeObj, long image_nativeObj, long corners_mat_nativeObj, long ids_nativeObj, long rejectedImgPoints_mat_nativeObj);
private static native void detectMarkers_1(long nativeObj, long image_nativeObj, long corners_mat_nativeObj, long ids_nativeObj);
// C++: void cv::aruco::ArucoDetector::refineDetectedMarkers(Mat image, Board board, vector_Mat& detectedCorners, Mat& detectedIds, vector_Mat& rejectedCorners, Mat cameraMatrix = Mat(), Mat distCoeffs = Mat(), Mat& recoveredIdxs = Mat())
private static native void refineDetectedMarkers_0(long nativeObj, long image_nativeObj, long board_nativeObj, long detectedCorners_mat_nativeObj, long detectedIds_nativeObj, long rejectedCorners_mat_nativeObj, long cameraMatrix_nativeObj, long distCoeffs_nativeObj, long recoveredIdxs_nativeObj);
private static native void refineDetectedMarkers_1(long nativeObj, long image_nativeObj, long board_nativeObj, long detectedCorners_mat_nativeObj, long detectedIds_nativeObj, long rejectedCorners_mat_nativeObj, long cameraMatrix_nativeObj, long distCoeffs_nativeObj);
private static native void refineDetectedMarkers_2(long nativeObj, long image_nativeObj, long board_nativeObj, long detectedCorners_mat_nativeObj, long detectedIds_nativeObj, long rejectedCorners_mat_nativeObj, long cameraMatrix_nativeObj);
private static native void refineDetectedMarkers_3(long nativeObj, long image_nativeObj, long board_nativeObj, long detectedCorners_mat_nativeObj, long detectedIds_nativeObj, long rejectedCorners_mat_nativeObj);
// C++: Dictionary cv::aruco::ArucoDetector::getDictionary()
private static native long getDictionary_0(long nativeObj);
// C++: void cv::aruco::ArucoDetector::setDictionary(Dictionary dictionary)
private static native void setDictionary_0(long nativeObj, long dictionary_nativeObj);
// C++: DetectorParameters cv::aruco::ArucoDetector::getDetectorParameters()
private static native long getDetectorParameters_0(long nativeObj);
// C++: void cv::aruco::ArucoDetector::setDetectorParameters(DetectorParameters detectorParameters)
private static native void setDetectorParameters_0(long nativeObj, long detectorParameters_nativeObj);
// C++: RefineParameters cv::aruco::ArucoDetector::getRefineParameters()
private static native long getRefineParameters_0(long nativeObj);
// C++: void cv::aruco::ArucoDetector::setRefineParameters(RefineParameters refineParameters)
private static native void setRefineParameters_0(long nativeObj, long refineParameters_nativeObj);
// native support for java finalize()
private static native void delete(long nativeObj);
}
