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.
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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 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).
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static |
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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 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 |
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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 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 |
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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 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 |
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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 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 |
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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 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 |
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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 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 |
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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 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 |
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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 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 |
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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 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 |
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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 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 |
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protectedvirtual |
Reimplemented from OpenCVForUnity.Features2dModule.Feature2D.
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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 |