OpenCV for Unity
2.6.3
Enox Software / Please refer to OpenCV official document ( http://docs.opencv.org/4.10.0/index.html ) for the details of the argument of the method.

Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor. More...
Public Member Functions  
void  setMaxFeatures (int maxFeatures) 
int  getMaxFeatures () 
void  setScaleFactor (double scaleFactor) 
double  getScaleFactor () 
void  setNLevels (int nlevels) 
int  getNLevels () 
void  setEdgeThreshold (int edgeThreshold) 
int  getEdgeThreshold () 
void  setFirstLevel (int firstLevel) 
int  getFirstLevel () 
void  setWTA_K (int wta_k) 
int  getWTA_K () 
void  setScoreType (int scoreType) 
int  getScoreType () 
void  setPatchSize (int patchSize) 
int  getPatchSize () 
void  setFastThreshold (int fastThreshold) 
int  getFastThreshold () 
override string  getDefaultName () 
Public Member Functions inherited from OpenCVForUnity.Features2dModule.Feature2D  
void  detect (Mat image, MatOfKeyPoint keypoints, Mat mask) 
Detects keypoints in an image (first variant) or image set (second variant). More...  
void  detect (Mat image, MatOfKeyPoint keypoints) 
Detects keypoints in an image (first variant) or image set (second variant). More...  
void  detect (List< Mat > images, List< MatOfKeyPoint > keypoints, List< Mat > masks) 
void  detect (List< Mat > images, List< MatOfKeyPoint > keypoints) 
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). More...  
void  compute (List< Mat > images, List< MatOfKeyPoint > keypoints, List< Mat > descriptors) 
void  detectAndCompute (Mat image, Mat mask, MatOfKeyPoint keypoints, Mat descriptors, bool useProvidedKeypoints) 
void  detectAndCompute (Mat image, Mat mask, MatOfKeyPoint keypoints, Mat descriptors) 
int  descriptorSize () 
int  descriptorType () 
int  defaultNorm () 
void  write (string fileName) 
void  read (string fileName) 
override bool  empty () 
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...  
Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm  
IntPtr  getNativeObjAddr () 
virtual void  clear () 
Clears the algorithm state. More...  
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 (int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize, int fastThreshold) 
The ORB constructor. More...  
static ORB  create (int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize) 
The ORB constructor. More...  
static ORB  create (int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType) 
The ORB constructor. More...  
static ORB  create (int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K) 
The ORB constructor. More...  
static ORB  create (int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel) 
The ORB constructor. More...  
static ORB  create (int nfeatures, float scaleFactor, int nlevels, int edgeThreshold) 
The ORB constructor. More...  
static ORB  create (int nfeatures, float scaleFactor, int nlevels) 
The ORB constructor. More...  
static ORB  create (int nfeatures, float scaleFactor) 
The ORB constructor. More...  
static ORB  create (int nfeatures) 
The ORB constructor. More...  
static ORB  create () 
The ORB constructor. More...  
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) 
Public Attributes  
const int  HARRIS_SCORE = 0 
const int  FAST_SCORE = 1 
Protected Member Functions  
override void  Dispose (bool disposing) 
Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject  
DisposableOpenCVObject ()  
DisposableOpenCVObject (IntPtr ptr)  
DisposableOpenCVObject (bool isEnabledDispose)  
DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)  
Protected Member Functions inherited from OpenCVForUnity.DisposableObject  
DisposableObject ()  
DisposableObject (bool isEnabledDispose)  
Additional Inherited Members  
Properties inherited from OpenCVForUnity.DisposableObject  
bool  IsDisposed [get, protected set] 
bool  IsEnabledDispose [get, set] 
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 firstorder moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or ktuples) are rotated according to the measured orientation).

static 

static 
The ORB constructor.
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 

static 
The ORB constructor.
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 

static 
The ORB constructor.
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 

static 
The ORB constructor.
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 

static 
The ORB constructor.
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 

static 
The ORB constructor.
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 

static 
The ORB constructor.
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 

static 
The ORB constructor.
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 

static 
The ORB constructor.
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 

static 
The ORB constructor.
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 

protectedvirtual 
Reimplemented from OpenCVForUnity.Features2dModule.Feature2D.

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.
int OpenCVForUnity.Features2dModule.ORB.getEdgeThreshold  (  ) 
int OpenCVForUnity.Features2dModule.ORB.getFastThreshold  (  ) 
int OpenCVForUnity.Features2dModule.ORB.getFirstLevel  (  ) 
int OpenCVForUnity.Features2dModule.ORB.getMaxFeatures  (  ) 
int OpenCVForUnity.Features2dModule.ORB.getNLevels  (  ) 
int OpenCVForUnity.Features2dModule.ORB.getPatchSize  (  ) 
double OpenCVForUnity.Features2dModule.ORB.getScaleFactor  (  ) 
int OpenCVForUnity.Features2dModule.ORB.getScoreType  (  ) 
int OpenCVForUnity.Features2dModule.ORB.getWTA_K  (  ) 
void OpenCVForUnity.Features2dModule.ORB.setEdgeThreshold  (  int  edgeThreshold  ) 
void OpenCVForUnity.Features2dModule.ORB.setFastThreshold  (  int  fastThreshold  ) 
void OpenCVForUnity.Features2dModule.ORB.setFirstLevel  (  int  firstLevel  ) 
void OpenCVForUnity.Features2dModule.ORB.setMaxFeatures  (  int  maxFeatures  ) 
void OpenCVForUnity.Features2dModule.ORB.setNLevels  (  int  nlevels  ) 
void OpenCVForUnity.Features2dModule.ORB.setPatchSize  (  int  patchSize  ) 
void OpenCVForUnity.Features2dModule.ORB.setScaleFactor  (  double  scaleFactor  ) 
void OpenCVForUnity.Features2dModule.ORB.setScoreType  (  int  scoreType  ) 
void OpenCVForUnity.Features2dModule.ORB.setWTA_K  (  int  wta_k  ) 
const int OpenCVForUnity.Features2dModule.ORB.FAST_SCORE = 1 
const int OpenCVForUnity.Features2dModule.ORB.HARRIS_SCORE = 0 