Package org.opencv.xfeatures2d
Class Xfeatures2d
- java.lang.Object
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- org.opencv.xfeatures2d.Xfeatures2d
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public class Xfeatures2d extends java.lang.Object
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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
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Constructor Summary
Constructors Constructor Description Xfeatures2d()
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Method Summary
All Methods Static Methods Concrete Methods 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 by 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 by 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 by 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 by CITE: Bian2017gms .
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Field Detail
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SURF_CUDA_X_ROW
public static final int SURF_CUDA_X_ROW
- See Also:
- Constant Field Values
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SURF_CUDA_Y_ROW
public static final int SURF_CUDA_Y_ROW
- See Also:
- Constant Field Values
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SURF_CUDA_LAPLACIAN_ROW
public static final int SURF_CUDA_LAPLACIAN_ROW
- See Also:
- Constant Field Values
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SURF_CUDA_OCTAVE_ROW
public static final int SURF_CUDA_OCTAVE_ROW
- See Also:
- Constant Field Values
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SURF_CUDA_SIZE_ROW
public static final int SURF_CUDA_SIZE_ROW
- See Also:
- Constant Field Values
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SURF_CUDA_ANGLE_ROW
public static final int SURF_CUDA_ANGLE_ROW
- See Also:
- Constant Field Values
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SURF_CUDA_HESSIAN_ROW
public static final int SURF_CUDA_HESSIAN_ROW
- See Also:
- Constant Field Values
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SURF_CUDA_ROWS_COUNT
public static final int SURF_CUDA_ROWS_COUNT
- See Also:
- Constant Field Values
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Method Detail
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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 by 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.
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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 by 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.
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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 by 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.
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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 by 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.
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