Package org.opencv.xfeatures2d
Class Xfeatures2d
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- org.opencv.xfeatures2d.Xfeatures2d
 
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 public class Xfeatures2d extends java.lang.Object
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Field SummaryFields Modifier and Type Field Description static intSURF_CUDA_ANGLE_ROWstatic intSURF_CUDA_HESSIAN_ROWstatic intSURF_CUDA_LAPLACIAN_ROWstatic intSURF_CUDA_OCTAVE_ROWstatic intSURF_CUDA_ROWS_COUNTstatic intSURF_CUDA_SIZE_ROWstatic intSURF_CUDA_X_ROWstatic intSURF_CUDA_Y_ROW
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Constructor SummaryConstructors Constructor Description Xfeatures2d()
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Method SummaryAll Methods Static Methods Concrete Methods Modifier and Type Method Description static voidmatchGMS(Size size1, Size size2, MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, MatOfDMatch matchesGMS)GMS (Grid-based Motion Statistics) feature matching strategy by CITE: Bian2017gms .static voidmatchGMS(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 voidmatchGMS(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 voidmatchGMS(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- 
SURF_CUDA_X_ROWpublic static final int SURF_CUDA_X_ROW - See Also:
- Constant Field Values
 
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SURF_CUDA_Y_ROWpublic static final int SURF_CUDA_Y_ROW - See Also:
- Constant Field Values
 
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SURF_CUDA_LAPLACIAN_ROWpublic static final int SURF_CUDA_LAPLACIAN_ROW - See Also:
- Constant Field Values
 
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SURF_CUDA_OCTAVE_ROWpublic static final int SURF_CUDA_OCTAVE_ROW - See Also:
- Constant Field Values
 
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SURF_CUDA_SIZE_ROWpublic static final int SURF_CUDA_SIZE_ROW - See Also:
- Constant Field Values
 
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SURF_CUDA_ANGLE_ROWpublic static final int SURF_CUDA_ANGLE_ROW - See Also:
- Constant Field Values
 
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SURF_CUDA_HESSIAN_ROWpublic static final int SURF_CUDA_HESSIAN_ROW - See Also:
- Constant Field Values
 
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SURF_CUDA_ROWS_COUNTpublic static final int SURF_CUDA_ROWS_COUNT - See Also:
- Constant Field Values
 
 
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Method Detail- 
matchGMSpublic 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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matchGMSpublic 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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matchGMSpublic 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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matchGMSpublic 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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