// // This file is auto-generated. Please don't modify it! // package org.opencv.features2d; import org.opencv.features2d.Feature2D; import org.opencv.features2d.ORB; // C++: class ORB /** * Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor * * described in CITE: RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects * the strongest features using FAST or Harris response, finds their orientation using first-order * moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or * k-tuples) are rotated according to the measured orientation). */ public class ORB extends Feature2D { protected ORB(long addr) { super(addr); } // internal usage only public static ORB __fromPtr__(long addr) { return new ORB(addr); } // C++: enum ScoreType (cv.ORB.ScoreType) public static final int HARRIS_SCORE = 0, FAST_SCORE = 1; // // C++: static Ptr_ORB cv::ORB::create(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31, int firstLevel = 0, int WTA_K = 2, ORB_ScoreType scoreType = ORB::HARRIS_SCORE, int patchSize = 31, int fastThreshold = 20) // /** * The ORB constructor * * @param nfeatures The maximum number of features to retain. * @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical * pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor * will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor * will mean that to cover certain scale range you will need more pyramid levels and so the speed * will suffer. * @param nlevels The number of pyramid levels. The smallest level will have linear size equal to * input_image_linear_size/pow(scaleFactor, nlevels - firstLevel). * @param edgeThreshold This is size of the border where the features are not detected. It should * roughly match the patchSize parameter. * @param firstLevel The level of pyramid to put source image to. Previous layers are filled * with upscaled source image. * @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The * default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, * so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 * random points (of course, those point coordinates are random, but they are generated from the * pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel * rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such * output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, * denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each * bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). * @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features * (the score is written to KeyPoint::score and is used to retain best nfeatures features); * FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, * but it is a little faster to compute. * @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller * pyramid layers the perceived image area covered by a feature will be larger. * @param fastThreshold the fast threshold * @return automatically generated */ public static ORB create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize, int fastThreshold) { return ORB.__fromPtr__(create_0(nfeatures, scaleFactor, nlevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize, fastThreshold)); } /** * The ORB constructor * * @param nfeatures The maximum number of features to retain. * @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical * pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor * will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor * will mean that to cover certain scale range you will need more pyramid levels and so the speed * will suffer. * @param nlevels The number of pyramid levels. The smallest level will have linear size equal to * input_image_linear_size/pow(scaleFactor, nlevels - firstLevel). * @param edgeThreshold This is size of the border where the features are not detected. It should * roughly match the patchSize parameter. * @param firstLevel The level of pyramid to put source image to. Previous layers are filled * with upscaled source image. * @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The * default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, * so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 * random points (of course, those point coordinates are random, but they are generated from the * pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel * rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such * output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, * denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each * bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). * @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features * (the score is written to KeyPoint::score and is used to retain best nfeatures features); * FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, * but it is a little faster to compute. * @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller * pyramid layers the perceived image area covered by a feature will be larger. * @return automatically generated */ public static ORB create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize) { return ORB.__fromPtr__(create_1(nfeatures, scaleFactor, nlevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize)); } /** * The ORB constructor * * @param nfeatures The maximum number of features to retain. * @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical * pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor * will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor * will mean that to cover certain scale range you will need more pyramid levels and so the speed * will suffer. * @param nlevels The number of pyramid levels. The smallest level will have linear size equal to * input_image_linear_size/pow(scaleFactor, nlevels - firstLevel). * @param edgeThreshold This is size of the border where the features are not detected. It should * roughly match the patchSize parameter. * @param firstLevel The level of pyramid to put source image to. Previous layers are filled * with upscaled source image. * @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The * default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, * so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 * random points (of course, those point coordinates are random, but they are generated from the * pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel * rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such * output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, * denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each * bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). * @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features * (the score is written to KeyPoint::score and is used to retain best nfeatures features); * FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, * but it is a little faster to compute. * pyramid layers the perceived image area covered by a feature will be larger. * @return automatically generated */ public static ORB create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType) { return ORB.__fromPtr__(create_2(nfeatures, scaleFactor, nlevels, edgeThreshold, firstLevel, WTA_K, scoreType)); } /** * The ORB constructor * * @param nfeatures The maximum number of features to retain. * @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical * pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor * will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor * will mean that to cover certain scale range you will need more pyramid levels and so the speed * will suffer. * @param nlevels The number of pyramid levels. The smallest level will have linear size equal to * input_image_linear_size/pow(scaleFactor, nlevels - firstLevel). * @param edgeThreshold This is size of the border where the features are not detected. It should * roughly match the patchSize parameter. * @param firstLevel The level of pyramid to put source image to. Previous layers are filled * with upscaled source image. * @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The * default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, * so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 * random points (of course, those point coordinates are random, but they are generated from the * pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel * rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such * output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, * denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each * bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). * (the score is written to KeyPoint::score and is used to retain best nfeatures features); * FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, * but it is a little faster to compute. * pyramid layers the perceived image area covered by a feature will be larger. * @return automatically generated */ public static ORB create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K) { return ORB.__fromPtr__(create_3(nfeatures, scaleFactor, nlevels, edgeThreshold, firstLevel, WTA_K)); } /** * The ORB constructor * * @param nfeatures The maximum number of features to retain. * @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical * pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor * will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor * will mean that to cover certain scale range you will need more pyramid levels and so the speed * will suffer. * @param nlevels The number of pyramid levels. The smallest level will have linear size equal to * input_image_linear_size/pow(scaleFactor, nlevels - firstLevel). * @param edgeThreshold This is size of the border where the features are not detected. It should * roughly match the patchSize parameter. * @param firstLevel The level of pyramid to put source image to. Previous layers are filled * with upscaled source image. * default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, * so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 * random points (of course, those point coordinates are random, but they are generated from the * pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel * rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such * output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, * denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each * bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). * (the score is written to KeyPoint::score and is used to retain best nfeatures features); * FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, * but it is a little faster to compute. * pyramid layers the perceived image area covered by a feature will be larger. * @return automatically generated */ public static ORB create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel) { return ORB.__fromPtr__(create_4(nfeatures, scaleFactor, nlevels, edgeThreshold, firstLevel)); } /** * The ORB constructor * * @param nfeatures The maximum number of features to retain. * @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical * pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor * will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor * will mean that to cover certain scale range you will need more pyramid levels and so the speed * will suffer. * @param nlevels The number of pyramid levels. The smallest level will have linear size equal to * input_image_linear_size/pow(scaleFactor, nlevels - firstLevel). * @param edgeThreshold This is size of the border where the features are not detected. It should * roughly match the patchSize parameter. * with upscaled source image. * default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, * so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 * random points (of course, those point coordinates are random, but they are generated from the * pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel * rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such * output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, * denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each * bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). * (the score is written to KeyPoint::score and is used to retain best nfeatures features); * FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, * but it is a little faster to compute. * pyramid layers the perceived image area covered by a feature will be larger. * @return automatically generated */ public static ORB create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold) { return ORB.__fromPtr__(create_5(nfeatures, scaleFactor, nlevels, edgeThreshold)); } /** * The ORB constructor * * @param nfeatures The maximum number of features to retain. * @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical * pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor * will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor * will mean that to cover certain scale range you will need more pyramid levels and so the speed * will suffer. * @param nlevels The number of pyramid levels. The smallest level will have linear size equal to * input_image_linear_size/pow(scaleFactor, nlevels - firstLevel). * roughly match the patchSize parameter. * with upscaled source image. * default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, * so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 * random points (of course, those point coordinates are random, but they are generated from the * pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel * rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such * output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, * denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each * bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). * (the score is written to KeyPoint::score and is used to retain best nfeatures features); * FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, * but it is a little faster to compute. * pyramid layers the perceived image area covered by a feature will be larger. * @return automatically generated */ public static ORB create(int nfeatures, float scaleFactor, int nlevels) { return ORB.__fromPtr__(create_6(nfeatures, scaleFactor, nlevels)); } /** * The ORB constructor * * @param nfeatures The maximum number of features to retain. * @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical * pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor * will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor * will mean that to cover certain scale range you will need more pyramid levels and so the speed * will suffer. * input_image_linear_size/pow(scaleFactor, nlevels - firstLevel). * roughly match the patchSize parameter. * with upscaled source image. * default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, * so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 * random points (of course, those point coordinates are random, but they are generated from the * pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel * rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such * output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, * denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each * bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). * (the score is written to KeyPoint::score and is used to retain best nfeatures features); * FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, * but it is a little faster to compute. * pyramid layers the perceived image area covered by a feature will be larger. * @return automatically generated */ public static ORB create(int nfeatures, float scaleFactor) { return ORB.__fromPtr__(create_7(nfeatures, scaleFactor)); } /** * The ORB constructor * * @param nfeatures The maximum number of features to retain. * pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor * will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor * will mean that to cover certain scale range you will need more pyramid levels and so the speed * will suffer. * input_image_linear_size/pow(scaleFactor, nlevels - firstLevel). * roughly match the patchSize parameter. * with upscaled source image. * default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, * so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 * random points (of course, those point coordinates are random, but they are generated from the * pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel * rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such * output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, * denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each * bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). * (the score is written to KeyPoint::score and is used to retain best nfeatures features); * FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, * but it is a little faster to compute. * pyramid layers the perceived image area covered by a feature will be larger. * @return automatically generated */ public static ORB create(int nfeatures) { return ORB.__fromPtr__(create_8(nfeatures)); } /** * The ORB constructor * * pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor * will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor * will mean that to cover certain scale range you will need more pyramid levels and so the speed * will suffer. * input_image_linear_size/pow(scaleFactor, nlevels - firstLevel). * roughly match the patchSize parameter. * with upscaled source image. * default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, * so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 * random points (of course, those point coordinates are random, but they are generated from the * pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel * rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such * output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, * denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each * bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). * (the score is written to KeyPoint::score and is used to retain best nfeatures features); * FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, * but it is a little faster to compute. * pyramid layers the perceived image area covered by a feature will be larger. * @return automatically generated */ public static ORB create() { return ORB.__fromPtr__(create_9()); } // // C++: void cv::ORB::setMaxFeatures(int maxFeatures) // public void setMaxFeatures(int maxFeatures) { setMaxFeatures_0(nativeObj, maxFeatures); } // // C++: int cv::ORB::getMaxFeatures() // public int getMaxFeatures() { return getMaxFeatures_0(nativeObj); } // // C++: void cv::ORB::setScaleFactor(double scaleFactor) // public void setScaleFactor(double scaleFactor) { setScaleFactor_0(nativeObj, scaleFactor); } // // C++: double cv::ORB::getScaleFactor() // public double getScaleFactor() { return getScaleFactor_0(nativeObj); } // // C++: void cv::ORB::setNLevels(int nlevels) // public void setNLevels(int nlevels) { setNLevels_0(nativeObj, nlevels); } // // C++: int cv::ORB::getNLevels() // public int getNLevels() { return getNLevels_0(nativeObj); } // // C++: void cv::ORB::setEdgeThreshold(int edgeThreshold) // public void setEdgeThreshold(int edgeThreshold) { setEdgeThreshold_0(nativeObj, edgeThreshold); } // // C++: int cv::ORB::getEdgeThreshold() // public int getEdgeThreshold() { return getEdgeThreshold_0(nativeObj); } // // C++: void cv::ORB::setFirstLevel(int firstLevel) // public void setFirstLevel(int firstLevel) { setFirstLevel_0(nativeObj, firstLevel); } // // C++: int cv::ORB::getFirstLevel() // public int getFirstLevel() { return getFirstLevel_0(nativeObj); } // // C++: void cv::ORB::setWTA_K(int wta_k) // public void setWTA_K(int wta_k) { setWTA_K_0(nativeObj, wta_k); } // // C++: int cv::ORB::getWTA_K() // public int getWTA_K() { return getWTA_K_0(nativeObj); } // // C++: void cv::ORB::setScoreType(ORB_ScoreType scoreType) // public void setScoreType(int scoreType) { setScoreType_0(nativeObj, scoreType); } // // C++: ORB_ScoreType cv::ORB::getScoreType() // public int getScoreType() { return getScoreType_0(nativeObj); } // // C++: void cv::ORB::setPatchSize(int patchSize) // public void setPatchSize(int patchSize) { setPatchSize_0(nativeObj, patchSize); } // // C++: int cv::ORB::getPatchSize() // public int getPatchSize() { return getPatchSize_0(nativeObj); } // // C++: void cv::ORB::setFastThreshold(int fastThreshold) // public void setFastThreshold(int fastThreshold) { setFastThreshold_0(nativeObj, fastThreshold); } // // C++: int cv::ORB::getFastThreshold() // public int getFastThreshold() { return getFastThreshold_0(nativeObj); } // // C++: String cv::ORB::getDefaultName() // public String getDefaultName() { return getDefaultName_0(nativeObj); } @Override protected void finalize() throws Throwable { delete(nativeObj); } // C++: static Ptr_ORB cv::ORB::create(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31, int firstLevel = 0, int WTA_K = 2, ORB_ScoreType scoreType = ORB::HARRIS_SCORE, int patchSize = 31, int fastThreshold = 20) private static native long create_0(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize, int fastThreshold); private static native long create_1(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize); private static native long create_2(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType); private static native long create_3(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K); private static native long create_4(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel); private static native long create_5(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold); private static native long create_6(int nfeatures, float scaleFactor, int nlevels); private static native long create_7(int nfeatures, float scaleFactor); private static native long create_8(int nfeatures); private static native long create_9(); // C++: void cv::ORB::setMaxFeatures(int maxFeatures) private static native void setMaxFeatures_0(long nativeObj, int maxFeatures); // C++: int cv::ORB::getMaxFeatures() private static native int getMaxFeatures_0(long nativeObj); // C++: void cv::ORB::setScaleFactor(double scaleFactor) private static native void setScaleFactor_0(long nativeObj, double scaleFactor); // C++: double cv::ORB::getScaleFactor() private static native double getScaleFactor_0(long nativeObj); // C++: void cv::ORB::setNLevels(int nlevels) private static native void setNLevels_0(long nativeObj, int nlevels); // C++: int cv::ORB::getNLevels() private static native int getNLevels_0(long nativeObj); // C++: void cv::ORB::setEdgeThreshold(int edgeThreshold) private static native void setEdgeThreshold_0(long nativeObj, int edgeThreshold); // C++: int cv::ORB::getEdgeThreshold() private static native int getEdgeThreshold_0(long nativeObj); // C++: void cv::ORB::setFirstLevel(int firstLevel) private static native void setFirstLevel_0(long nativeObj, int firstLevel); // C++: int cv::ORB::getFirstLevel() private static native int getFirstLevel_0(long nativeObj); // C++: void cv::ORB::setWTA_K(int wta_k) private static native void setWTA_K_0(long nativeObj, int wta_k); // C++: int cv::ORB::getWTA_K() private static native int getWTA_K_0(long nativeObj); // C++: void cv::ORB::setScoreType(ORB_ScoreType scoreType) private static native void setScoreType_0(long nativeObj, int scoreType); // C++: ORB_ScoreType cv::ORB::getScoreType() private static native int getScoreType_0(long nativeObj); // C++: void cv::ORB::setPatchSize(int patchSize) private static native void setPatchSize_0(long nativeObj, int patchSize); // C++: int cv::ORB::getPatchSize() private static native int getPatchSize_0(long nativeObj); // C++: void cv::ORB::setFastThreshold(int fastThreshold) private static native void setFastThreshold_0(long nativeObj, int fastThreshold); // C++: int cv::ORB::getFastThreshold() private static native int getFastThreshold_0(long nativeObj); // C++: String cv::ORB::getDefaultName() private static native String getDefaultName_0(long nativeObj); // native support for java finalize() private static native void delete(long nativeObj); }