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).