@spec getDistanceFlag(Keyword.t()) :: any() | {:error, String.t()}

@spec getDistanceFlag(t()) :: integer() | {:error, String.t()}

getDistanceFlag

Positional Arguments

• self: Evision.HausdorffDistanceExtractor.t()

Return

• retval: integer()

Python prototype (for reference only):

getDistanceFlag() -> retval

@spec getRankProportion(Keyword.t()) :: any() | {:error, String.t()}

@spec getRankProportion(t()) :: number() | {:error, String.t()}

getRankProportion

Positional Arguments

• self: Evision.HausdorffDistanceExtractor.t()

Return

• retval: float

Python prototype (for reference only):

getRankProportion() -> retval

@spec setDistanceFlag(t(), integer()) :: t() | {:error, String.t()}

Set the norm used to compute the Hausdorff value between two shapes. It can be L1 or L2 norm.

Positional Arguments

• self: Evision.HausdorffDistanceExtractor.t()

• distanceFlag: integer().

  Flag indicating which norm is used to compute the Hausdorff distance (NORM_L1, NORM_L2).

Python prototype (for reference only):

setDistanceFlag(distanceFlag) -> None

@spec setRankProportion(t(), number()) :: t() | {:error, String.t()}

This method sets the rank proportion (or fractional value) that establish the Kth ranked value of the partial Hausdorff distance. Experimentally had been shown that 0.6 is a good value to compare shapes.

Positional Arguments

• self: Evision.HausdorffDistanceExtractor.t()

• rankProportion: float.

  fractional value (between 0 and 1).

Python prototype (for reference only):

setRankProportion(rankProportion) -> None