Package 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 firstorder moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or ktuples) are rotated according to the measured orientation).


Field Summary
Fields Modifier and Type Field Description static int
FAST_SCORE
static int
HARRIS_SCORE
static int
kBytes

Constructor Summary
Constructors Modifier Constructor Description protected
ORB(long addr)

Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method 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 constructorstatic ORB
create(int nfeatures, float scaleFactor)
The ORB constructorstatic ORB
create(int nfeatures, float scaleFactor, int nlevels)
The ORB constructorstatic ORB
create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold)
The ORB constructorstatic ORB
create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel)
The ORB constructorstatic ORB
create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K)
The ORB constructorstatic ORB
create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType)
The ORB constructorstatic ORB
create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize)
The ORB constructorstatic ORB
create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize, int fastThreshold)
The ORB constructorprotected void
finalize()
java.lang.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




Field Detail

kBytes
public static final int kBytes
 See Also:
 Constant Field Values

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


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 predefined 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 predefined 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 predefined 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 predefined 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 predefined 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 predefined 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 predefined 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 predefined 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 predefined 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 predefined 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 java.lang.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 classFeature2D
 Returns:
 automatically generated

