org.opencv.features2d

Class ORB

java.lang.Object

org.opencv.core.Algorithm

org.opencv.features2d.Feature2D

org.opencv.features2d.ORB

public class ORB
extends Feature2D

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).

Field Summary

Fields

Modifier and Type	Field and Description
static int	FAST_SCORE
static int	HARRIS_SCORE

Fields inherited from class org.opencv.core.Algorithm

nativeObj

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	ORB(long addr)

Method Summary

Modifier and Type	Method and Description
static ORB	__fromPtr__(long addr)
static ORB	create() 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.
static ORB	create(int nfeatures) The ORB constructor
static ORB	create(int nfeatures, float scaleFactor) The ORB constructor
static ORB	create(int nfeatures, float scaleFactor, int nlevels) The ORB constructor
static ORB	create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold) The ORB constructor
static ORB	create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel) The ORB constructor
static ORB	create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K) The ORB constructor
static ORB	create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType) The ORB constructor
static ORB	create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize) The ORB constructor
static ORB	create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize, int fastThreshold) The ORB constructor
protected void	finalize()
String	getDefaultName() Returns the algorithm string identifier.
int	getEdgeThreshold()
int	getFastThreshold()
int	getFirstLevel()
int	getMaxFeatures()
int	getNLevels()
int	getPatchSize()
double	getScaleFactor()
int	getScoreType()
int	getWTA_K()
void	setEdgeThreshold(int edgeThreshold)
void	setFastThreshold(int fastThreshold)
void	setFirstLevel(int firstLevel)
void	setMaxFeatures(int maxFeatures)
void	setNLevels(int nlevels)
void	setPatchSize(int patchSize)
void	setScaleFactor(double scaleFactor)
void	setScoreType(int scoreType)
void	setWTA_K(int wta_k)

Methods inherited from class org.opencv.features2d.Feature2D

compute, compute, defaultNorm, descriptorSize, descriptorType, detect, detect, detect, detect, detectAndCompute, detectAndCompute, empty, read, write

Methods inherited from class org.opencv.core.Algorithm

clear, getNativeObjAddr, save

Methods inherited from class java.lang.Object

clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

HARRIS_SCORE

public static final int HARRIS_SCORE

See Also:

Constant Field Values

FAST_SCORE

public static final int FAST_SCORE

See Also:

Constant Field Values

Constructor Detail

ORB

protected ORB(long addr)

Method Detail

__fromPtr__

public static ORB __fromPtr__(long addr)

create

public static ORB create(int nfeatures,
                         float scaleFactor,
                         int nlevels,
                         int edgeThreshold,
                         int firstLevel,
                         int WTA_K,
                         int scoreType,
                         int patchSize,
                         int fastThreshold)

The ORB constructor

Parameters:

nfeatures - The maximum number of features to retain.

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.

nlevels - The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).

edgeThreshold - This is size of the border where the features are not detected. It should roughly match the patchSize parameter.

firstLevel - The level of pyramid to put source image to. Previous layers are filled with upscaled source image.

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).

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.

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.

fastThreshold - the fast threshold

Returns:

automatically generated

create

public static ORB create(int nfeatures,
                         float scaleFactor,
                         int nlevels,
                         int edgeThreshold,
                         int firstLevel,
                         int WTA_K,
                         int scoreType,
                         int patchSize)

The ORB constructor

Parameters:

nfeatures - The maximum number of features to retain.

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.

nlevels - The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).

edgeThreshold - This is size of the border where the features are not detected. It should roughly match the patchSize parameter.

firstLevel - The level of pyramid to put source image to. Previous layers are filled with upscaled source image.

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).

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.

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.

Returns:

automatically generated

create

public static ORB create(int nfeatures,
                         float scaleFactor,
                         int nlevels,
                         int edgeThreshold,
                         int firstLevel,
                         int WTA_K,
                         int scoreType)

The ORB constructor

Parameters:

nfeatures - The maximum number of features to retain.

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.

nlevels - The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).

edgeThreshold - This is size of the border where the features are not detected. It should roughly match the patchSize parameter.

firstLevel - The level of pyramid to put source image to. Previous layers are filled with upscaled source image.

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).

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.

Returns:

automatically generated

create

public static ORB create(int nfeatures,
                         float scaleFactor,
                         int nlevels,
                         int edgeThreshold,
                         int firstLevel,
                         int WTA_K)

The ORB constructor

Parameters:

nfeatures - The maximum number of features to retain.

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.

nlevels - The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).

edgeThreshold - This is size of the border where the features are not detected. It should roughly match the patchSize parameter.

firstLevel - The level of pyramid to put source image to. Previous layers are filled with upscaled source image.

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.

Returns:

automatically generated

create

public static ORB create(int nfeatures,
                         float scaleFactor,
                         int nlevels,
                         int edgeThreshold,
                         int firstLevel)

The ORB constructor

Parameters:

nfeatures - The maximum number of features to retain.

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.

nlevels - The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).

edgeThreshold - This is size of the border where the features are not detected. It should roughly match the patchSize parameter.

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.

Returns:

automatically generated

create

public static ORB create(int nfeatures,
                         float scaleFactor,
                         int nlevels,
                         int edgeThreshold)

The ORB constructor

Parameters:

nfeatures - The maximum number of features to retain.

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.

nlevels - The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).

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.

Returns:

automatically generated

create

public static ORB create(int nfeatures,
                         float scaleFactor,
                         int nlevels)

The ORB constructor

Parameters:

nfeatures - The maximum number of features to retain.

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.

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.

Returns:

automatically generated

create

public static ORB create(int nfeatures,
                         float scaleFactor)

The ORB constructor

Parameters:

nfeatures - The maximum number of features to retain.

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.

Returns:

automatically generated

create

public static ORB create(int nfeatures)

The ORB constructor

Parameters:

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.

Returns:

automatically generated

create

public static ORB create()

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.

Returns:

automatically generated

setMaxFeatures

public void setMaxFeatures(int maxFeatures)

getMaxFeatures

public int getMaxFeatures()

setScaleFactor

public void setScaleFactor(double scaleFactor)

getScaleFactor

public double getScaleFactor()

setNLevels

public void setNLevels(int nlevels)

getNLevels

public int getNLevels()

setEdgeThreshold

public void setEdgeThreshold(int edgeThreshold)

getEdgeThreshold

public int getEdgeThreshold()

setFirstLevel

public void setFirstLevel(int firstLevel)

getFirstLevel

public int getFirstLevel()

setWTA_K

public void setWTA_K(int wta_k)

getWTA_K

public int getWTA_K()

setScoreType

public void setScoreType(int scoreType)

getScoreType

public int getScoreType()

setPatchSize

public void setPatchSize(int patchSize)

getPatchSize

public int getPatchSize()

setFastThreshold

public void setFastThreshold(int fastThreshold)

getFastThreshold

public int getFastThreshold()

getDefaultName

public String getDefaultName()

Description copied from class: Algorithm

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Overrides:

getDefaultName in class Feature2D

Returns:

automatically generated

finalize

protected void finalize()
                 throws Throwable

Overrides:

finalize in class Feature2D

Throws:

Throwable