Package org.opencv.xfeatures2d

Class Xfeatures2d

java.lang.Object

org.opencv.xfeatures2d.Xfeatures2d

public class Xfeatures2d
extends java.lang.Object

Field Summary

Fields

Modifier and Type	Field	Description
static int	SURF_CUDA_ANGLE_ROW
static int	SURF_CUDA_HESSIAN_ROW
static int	SURF_CUDA_LAPLACIAN_ROW
static int	SURF_CUDA_OCTAVE_ROW
static int	SURF_CUDA_ROWS_COUNT
static int	SURF_CUDA_SIZE_ROW
static int	SURF_CUDA_X_ROW
static int	SURF_CUDA_Y_ROW

Constructor Summary

Constructors

Constructor	Description
Xfeatures2d()

Method Summary

Modifier and Type	Method	Description
static void	matchGMS(Size size1, Size size2, MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, MatOfDMatch matchesGMS)	GMS (Grid-based Motion Statistics) feature matching strategy described in CITE: Bian2017gms .
static void	matchGMS(Size size1, Size size2, MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, MatOfDMatch matchesGMS, boolean withRotation)	GMS (Grid-based Motion Statistics) feature matching strategy described in CITE: Bian2017gms .
static void	matchGMS(Size size1, Size size2, MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, MatOfDMatch matchesGMS, boolean withRotation, boolean withScale)	GMS (Grid-based Motion Statistics) feature matching strategy described in CITE: Bian2017gms .
static void	matchGMS(Size size1, Size size2, MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, MatOfDMatch matchesGMS, boolean withRotation, boolean withScale, double thresholdFactor)	GMS (Grid-based Motion Statistics) feature matching strategy described in CITE: Bian2017gms .
static void	matchLOGOS(MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfInt nn1, MatOfInt nn2, MatOfDMatch matches1to2)	LOGOS (Local geometric support for high-outlier spatial verification) feature matching strategy described in CITE: Lowry2018LOGOSLG .

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

SURF_CUDA_X_ROW

public static final int SURF_CUDA_X_ROW

See Also:

Constant Field Values

SURF_CUDA_Y_ROW

public static final int SURF_CUDA_Y_ROW

See Also:

Constant Field Values

SURF_CUDA_LAPLACIAN_ROW

public static final int SURF_CUDA_LAPLACIAN_ROW

See Also:

Constant Field Values

SURF_CUDA_OCTAVE_ROW

public static final int SURF_CUDA_OCTAVE_ROW

See Also:

Constant Field Values

SURF_CUDA_SIZE_ROW

public static final int SURF_CUDA_SIZE_ROW

See Also:

Constant Field Values

SURF_CUDA_ANGLE_ROW

public static final int SURF_CUDA_ANGLE_ROW

See Also:

Constant Field Values

SURF_CUDA_HESSIAN_ROW

public static final int SURF_CUDA_HESSIAN_ROW

See Also:

Constant Field Values

SURF_CUDA_ROWS_COUNT

public static final int SURF_CUDA_ROWS_COUNT

See Also:

Constant Field Values

Constructor Detail

Xfeatures2d

public Xfeatures2d()

Method Detail

matchGMS

public static void matchGMS(Size size1,
                            Size size2,
                            MatOfKeyPoint keypoints1,
                            MatOfKeyPoint keypoints2,
                            MatOfDMatch matches1to2,
                            MatOfDMatch matchesGMS,
                            boolean withRotation,
                            boolean withScale,
                            double thresholdFactor)

GMS (Grid-based Motion Statistics) feature matching strategy described in CITE: Bian2017gms .

Parameters:

size1 - Input size of image1.

size2 - Input size of image2.

keypoints1 - Input keypoints of image1.

keypoints2 - Input keypoints of image2.

matches1to2 - Input 1-nearest neighbor matches.

matchesGMS - Matches returned by the GMS matching strategy.

withRotation - Take rotation transformation into account.

withScale - Take scale transformation into account.

thresholdFactor - The higher, the less matches. Note: Since GMS works well when the number of features is large, we recommend to use the ORB feature and set FastThreshold to 0 to get as many as possible features quickly. If matching results are not satisfying, please add more features. (We use 10000 for images with 640 X 480). If your images have big rotation and scale changes, please set withRotation or withScale to true.

matchGMS

public static void matchGMS(Size size1,
                            Size size2,
                            MatOfKeyPoint keypoints1,
                            MatOfKeyPoint keypoints2,
                            MatOfDMatch matches1to2,
                            MatOfDMatch matchesGMS,
                            boolean withRotation,
                            boolean withScale)

GMS (Grid-based Motion Statistics) feature matching strategy described in CITE: Bian2017gms .

Parameters:

size1 - Input size of image1.

size2 - Input size of image2.

keypoints1 - Input keypoints of image1.

keypoints2 - Input keypoints of image2.

matches1to2 - Input 1-nearest neighbor matches.

matchesGMS - Matches returned by the GMS matching strategy.

withRotation - Take rotation transformation into account.

withScale - Take scale transformation into account. Note: Since GMS works well when the number of features is large, we recommend to use the ORB feature and set FastThreshold to 0 to get as many as possible features quickly. If matching results are not satisfying, please add more features. (We use 10000 for images with 640 X 480). If your images have big rotation and scale changes, please set withRotation or withScale to true.

matchGMS

public static void matchGMS(Size size1,
                            Size size2,
                            MatOfKeyPoint keypoints1,
                            MatOfKeyPoint keypoints2,
                            MatOfDMatch matches1to2,
                            MatOfDMatch matchesGMS,
                            boolean withRotation)

GMS (Grid-based Motion Statistics) feature matching strategy described in CITE: Bian2017gms .

Parameters:

size1 - Input size of image1.

size2 - Input size of image2.

keypoints1 - Input keypoints of image1.

keypoints2 - Input keypoints of image2.

matches1to2 - Input 1-nearest neighbor matches.

matchesGMS - Matches returned by the GMS matching strategy.

withRotation - Take rotation transformation into account. Note: Since GMS works well when the number of features is large, we recommend to use the ORB feature and set FastThreshold to 0 to get as many as possible features quickly. If matching results are not satisfying, please add more features. (We use 10000 for images with 640 X 480). If your images have big rotation and scale changes, please set withRotation or withScale to true.

matchGMS

public static void matchGMS(Size size1,
                            Size size2,
                            MatOfKeyPoint keypoints1,
                            MatOfKeyPoint keypoints2,
                            MatOfDMatch matches1to2,
                            MatOfDMatch matchesGMS)

GMS (Grid-based Motion Statistics) feature matching strategy described in CITE: Bian2017gms .

Parameters:

size1 - Input size of image1.

size2 - Input size of image2.

keypoints1 - Input keypoints of image1.

keypoints2 - Input keypoints of image2.

matches1to2 - Input 1-nearest neighbor matches.

matchesGMS - Matches returned by the GMS matching strategy. Note: Since GMS works well when the number of features is large, we recommend to use the ORB feature and set FastThreshold to 0 to get as many as possible features quickly. If matching results are not satisfying, please add more features. (We use 10000 for images with 640 X 480). If your images have big rotation and scale changes, please set withRotation or withScale to true.

matchLOGOS

public static void matchLOGOS(MatOfKeyPoint keypoints1,
                              MatOfKeyPoint keypoints2,
                              MatOfInt nn1,
                              MatOfInt nn2,
                              MatOfDMatch matches1to2)

LOGOS (Local geometric support for high-outlier spatial verification) feature matching strategy described in CITE: Lowry2018LOGOSLG .

Parameters:

keypoints1 - Input keypoints of image1.

keypoints2 - Input keypoints of image2.

nn1 - Index to the closest BoW centroid for each descriptors of image1.

nn2 - Index to the closest BoW centroid for each descriptors of image2.

matches1to2 - Matches returned by the LOGOS matching strategy. Note: This matching strategy is suitable for features matching against large scale database. First step consists in constructing the bag-of-words (BoW) from a representative image database. Image descriptors are then represented by their closest codevector (nearest BoW centroid).