org.opencv.features2d.ORB Maven / Gradle / Ivy
The newest version!
//
// 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);
}