Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor. More...

Public Member Functions
override string	getDefaultName ()

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)

Public Member Functions inherited from OpenCVForUnity.Features2dModule.Feature2D
void	compute (List< Mat > images, List< MatOfKeyPoint > keypoints, List< Mat > descriptors)

void	compute (Mat image, MatOfKeyPoint keypoints, Mat descriptors)
	Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

int	defaultNorm ()

int	descriptorSize ()

int	descriptorType ()

void	detect (List< Mat > images, List< MatOfKeyPoint > keypoints)

void	detect (List< Mat > images, List< MatOfKeyPoint > keypoints, List< Mat > masks)

void	detect (Mat image, MatOfKeyPoint keypoints)
	Detects keypoints in an image (first variant) or image set (second variant).

void	detect (Mat image, MatOfKeyPoint keypoints, Mat mask)
	Detects keypoints in an image (first variant) or image set (second variant).

void	detectAndCompute (Mat image, Mat mask, MatOfKeyPoint keypoints, Mat descriptors)

void	detectAndCompute (Mat image, Mat mask, MatOfKeyPoint keypoints, Mat descriptors, bool useProvidedKeypoints)

override bool	empty ()
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.

void	read (string fileName)

void	write (string fileName)

Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
virtual void	clear ()
	Clears the algorithm state.

IntPtr	getNativeObjAddr ()

void	save (string filename)

Public Member Functions inherited from OpenCVForUnity.DisposableObject
void	Dispose ()

void	ThrowIfDisposed ()

Static Public Member Functions
static new ORB	__fromPtr__ (IntPtr addr)

static ORB	create ()
	The ORB constructor.

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.

Static Public Member Functions inherited from OpenCVForUnity.Features2dModule.Feature2D
static new Feature2D	__fromPtr__ (IntPtr addr)

Static Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
static Algorithm	__fromPtr__ (IntPtr addr)

Static Public Member Functions inherited from OpenCVForUnity.DisposableObject
static IntPtr	ThrowIfNullIntPtr (IntPtr ptr)

Static Public Attributes
const int	FAST_SCORE = 1

const int	HARRIS_SCORE = 0

Protected Member Functions
override void	Dispose (bool disposing)

Protected Member Functions inherited from OpenCVForUnity.Features2dModule.Feature2D
Protected Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
	DisposableOpenCVObject ()

	DisposableOpenCVObject (bool isEnabledDispose)

	DisposableOpenCVObject (IntPtr ptr)

	DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)

Protected Member Functions inherited from OpenCVForUnity.DisposableObject
	DisposableObject ()

	DisposableObject (bool isEnabledDispose)

Additional Inherited Members
Package Functions inherited from OpenCVForUnity.Features2dModule.Feature2D
Package Functions inherited from OpenCVForUnity.CoreModule.Algorithm
Package Attributes inherited from OpenCVForUnity.DisposableOpenCVObject
Properties inherited from OpenCVForUnity.DisposableObject
bool	IsDisposed `[get, protected set]`

bool	IsEnabledDispose `[get, set]`

Detailed Description

Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor.

described in [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).

Member Function Documentation

◆ fromPtr()

static new ORB OpenCVForUnity.Features2dModule.ORB.__fromPtr__ ( IntPtr addr )

static

◆ create() [1/10]

static ORB OpenCVForUnity.Features2dModule.ORB.create ( )

static

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

◆ create() [2/10]

static ORB OpenCVForUnity.Features2dModule.ORB.create ( int nfeatures )

static

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

◆ create() [3/10]

static ORB OpenCVForUnity.Features2dModule.ORB.create	(	int	nfeatures,
		float	scaleFactor )

static

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

◆ create() [4/10]

static ORB OpenCVForUnity.Features2dModule.ORB.create	(	int	nfeatures,
		float	scaleFactor,
		int	nlevels )

static

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

◆ create() [5/10]

static ORB OpenCVForUnity.Features2dModule.ORB.create	(	int	nfeatures,
		float	scaleFactor,
		int	nlevels,
		int	edgeThreshold )

static

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

◆ create() [6/10]

static ORB OpenCVForUnity.Features2dModule.ORB.create	(	int	nfeatures,
		float	scaleFactor,
		int	nlevels,
		int	edgeThreshold,
		int	firstLevel )

static

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

◆ create() [7/10]

static ORB OpenCVForUnity.Features2dModule.ORB.create	(	int	nfeatures,
		float	scaleFactor,
		int	nlevels,
		int	edgeThreshold,
		int	firstLevel,
		int	WTA_K )

static

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

◆ create() [8/10]

static ORB OpenCVForUnity.Features2dModule.ORB.create	(	int	nfeatures,
		float	scaleFactor,
		int	nlevels,
		int	edgeThreshold,
		int	firstLevel,
		int	WTA_K,
		int	scoreType )

static

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

◆ create() [9/10]

static ORB OpenCVForUnity.Features2dModule.ORB.create	(	int	nfeatures,
		float	scaleFactor,
		int	nlevels,
		int	edgeThreshold,
		int	firstLevel,
		int	WTA_K,
		int	scoreType,
		int	patchSize )

static

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

◆ create() [10/10]

static ORB OpenCVForUnity.Features2dModule.ORB.create	(	int	nfeatures,
		float	scaleFactor,
		int	nlevels,
		int	edgeThreshold,
		int	firstLevel,
		int	WTA_K,
		int	scoreType,
		int	patchSize,
		int	fastThreshold )

static

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

◆ Dispose()

override void OpenCVForUnity.Features2dModule.ORB.Dispose ( bool disposing )

protectedvirtual

Reimplemented from OpenCVForUnity.Features2dModule.Feature2D.

◆ getDefaultName()

override string OpenCVForUnity.Features2dModule.ORB.getDefaultName ( )

virtual

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.

Reimplemented from OpenCVForUnity.Features2dModule.Feature2D.

◆ getEdgeThreshold()

int OpenCVForUnity.Features2dModule.ORB.getEdgeThreshold ( )

◆ getFastThreshold()

int OpenCVForUnity.Features2dModule.ORB.getFastThreshold ( )

◆ getFirstLevel()

int OpenCVForUnity.Features2dModule.ORB.getFirstLevel ( )

◆ getMaxFeatures()

int OpenCVForUnity.Features2dModule.ORB.getMaxFeatures ( )

◆ getNLevels()

int OpenCVForUnity.Features2dModule.ORB.getNLevels ( )

◆ getPatchSize()

int OpenCVForUnity.Features2dModule.ORB.getPatchSize ( )

◆ getScaleFactor()

double OpenCVForUnity.Features2dModule.ORB.getScaleFactor ( )

◆ getScoreType()

int OpenCVForUnity.Features2dModule.ORB.getScoreType ( )

◆ getWTA_K()

int OpenCVForUnity.Features2dModule.ORB.getWTA_K ( )

◆ setEdgeThreshold()

void OpenCVForUnity.Features2dModule.ORB.setEdgeThreshold ( int edgeThreshold )

◆ setFastThreshold()

void OpenCVForUnity.Features2dModule.ORB.setFastThreshold ( int fastThreshold )

◆ setFirstLevel()

void OpenCVForUnity.Features2dModule.ORB.setFirstLevel ( int firstLevel )

◆ setMaxFeatures()

void OpenCVForUnity.Features2dModule.ORB.setMaxFeatures ( int maxFeatures )

◆ setNLevels()

void OpenCVForUnity.Features2dModule.ORB.setNLevels ( int nlevels )

◆ setPatchSize()

void OpenCVForUnity.Features2dModule.ORB.setPatchSize ( int patchSize )

◆ setScaleFactor()

void OpenCVForUnity.Features2dModule.ORB.setScaleFactor ( double scaleFactor )

◆ setScoreType()

void OpenCVForUnity.Features2dModule.ORB.setScoreType ( int scoreType )

◆ setWTA_K()

void OpenCVForUnity.Features2dModule.ORB.setWTA_K ( int wta_k )

Member Data Documentation

◆ FAST_SCORE

const int OpenCVForUnity.Features2dModule.ORB.FAST_SCORE = 1

static

◆ HARRIS_SCORE

const int OpenCVForUnity.Features2dModule.ORB.HARRIS_SCORE = 0

static

The documentation for this class was generated from the following file:

OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/features2d/ORB.cs

Public Member Functions

Static Public Member Functions

Static Public Attributes

Protected Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ __fromPtr__()

◆ create() [1/10]

◆ create() [2/10]

◆ create() [3/10]

◆ create() [4/10]

◆ create() [5/10]

◆ create() [6/10]

◆ create() [7/10]

◆ create() [8/10]

◆ create() [9/10]

◆ create() [10/10]

◆ Dispose()

◆ getDefaultName()

◆ getEdgeThreshold()

◆ getFastThreshold()

◆ getFirstLevel()

◆ getMaxFeatures()

◆ getNLevels()

◆ getPatchSize()

◆ getScaleFactor()

◆ getScoreType()

◆ getWTA_K()

◆ setEdgeThreshold()

◆ setFastThreshold()

◆ setFirstLevel()

◆ setMaxFeatures()

◆ setNLevels()

◆ setPatchSize()

◆ setScaleFactor()

◆ setScoreType()

◆ setWTA_K()

Member Data Documentation

◆ FAST_SCORE

◆ HARRIS_SCORE

◆ fromPtr()