Class Xfeatures2d

java.lang.Object
org.opencv.xfeatures2d.Xfeatures2d

public class Xfeatures2d extends Object
  • Constructor Details

    • Xfeatures2d

      public Xfeatures2d()
  • Method Details

    • 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).