Class 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 Description
static int FAST_SCORE
static int HARRIS_SCORE
• Fields inherited from class org.opencv.core.Algorithm

nativeObj
• Constructor Summary

Constructors
Modifier Constructor Description
protected  ORB​(long addr)
• Method Summary

All 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 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()
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
• 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
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 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 class Feature2D
Returns:
automatically generated
• finalize

protected void finalize()
throws java.lang.Throwable
Overrides:
finalize in class Feature2D
Throws:
java.lang.Throwable