@spec matchGMS(
  {number(), number()},
  {number(), number()},
  [Evision.KeyPoint.t()],
  [Evision.KeyPoint.t()],
  [Evision.DMatch.t()]
) :: [Evision.DMatch.t()] | {:error, String.t()}

GMS (Grid-based Motion Statistics) feature matching strategy described in @cite Bian2017gms .

Positional Arguments

• size1: Size.

  Input size of image1.

• size2: Size.

  Input size of image2.

• keypoints1: [Evision.KeyPoint].

  Input keypoints of image1.

• keypoints2: [Evision.KeyPoint].

  Input keypoints of image2.

• matches1to2: [Evision.DMatch].

  Input 1-nearest neighbor matches.

Keyword Arguments

• withRotation: bool.

  Take rotation transformation into account.

• withScale: bool.

  Take scale transformation into account.

• thresholdFactor: double.

  The higher, the less matches.

Return

• matchesGMS: [Evision.DMatch].

  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.

Python prototype (for reference only):

matchGMS(size1, size2, keypoints1, keypoints2, matches1to2[, withRotation[, withScale[, thresholdFactor]]]) -> matchesGMS

@spec matchGMS(
  {number(), number()},
  {number(), number()},
  [Evision.KeyPoint.t()],
  [Evision.KeyPoint.t()],
  [Evision.DMatch.t()],
  [{atom(), term()}, ...] | nil
) :: [Evision.DMatch.t()] | {:error, String.t()}

GMS (Grid-based Motion Statistics) feature matching strategy described in @cite Bian2017gms .

Positional Arguments

• size1: Size.

  Input size of image1.

• size2: Size.

  Input size of image2.

• keypoints1: [Evision.KeyPoint].

  Input keypoints of image1.

• keypoints2: [Evision.KeyPoint].

  Input keypoints of image2.

• matches1to2: [Evision.DMatch].

  Input 1-nearest neighbor matches.

Keyword Arguments

• withRotation: bool.

  Take rotation transformation into account.

• withScale: bool.

  Take scale transformation into account.

• thresholdFactor: double.

  The higher, the less matches.

Return

• matchesGMS: [Evision.DMatch].

  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.

Python prototype (for reference only):

matchGMS(size1, size2, keypoints1, keypoints2, matches1to2[, withRotation[, withScale[, thresholdFactor]]]) -> matchesGMS

@spec matchLOGOS([Evision.KeyPoint.t()], [Evision.KeyPoint.t()], [integer()], [
  integer()
]) ::
  [Evision.DMatch.t()] | {:error, String.t()}

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

Positional Arguments

• keypoints1: [Evision.KeyPoint].

  Input keypoints of image1.

• keypoints2: [Evision.KeyPoint].

  Input keypoints of image2.

• nn1: [int].

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

• nn2: [int].

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

Return

• matches1to2: [Evision.DMatch].

  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).

Python prototype (for reference only):

matchLOGOS(keypoints1, keypoints2, nn1, nn2) -> matches1to2