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

Constructor.

Return

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()

The BinaryDescriptorMatcher constructed is able to store and manage 256-bits long entries.

Python prototype (for reference only):

BinaryDescriptorMatcher() -> <line_descriptor_BinaryDescriptorMatcher object>

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

Clears the algorithm state

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()

Python prototype (for reference only):

clear() -> None

@spec empty(t()) :: boolean() | {:error, String.t()}

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()

Return

• retval: bool

Python prototype (for reference only):

empty() -> retval

@spec getDefaultName(t()) :: binary() | {:error, String.t()}

getDefaultName

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()

Return

• retval: String

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Python prototype (for reference only):

getDefaultName() -> retval

@spec knnMatch(t(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer()) ::
  [[Evision.DMatch.t()]] | {:error, String.t()}

For every input query descriptor, retrieve the best k matching ones from a dataset provided from user or from the one internal to class

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()

• queryDescriptors: Evision.Mat.t().

  query descriptors

• trainDescriptors: Evision.Mat.t().

  dataset of descriptors furnished by user

• k: int.

  number of the closest descriptors to be returned for every input query

Keyword Arguments

• mask: Evision.Mat.t().

  mask to select which input descriptors must be matched to ones in dataset

• compactResult: bool.

  flag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result)

Return

• matches: [[Evision.DMatch]].

  vector to host retrieved matches

Python prototype (for reference only):

knnMatch(queryDescriptors, trainDescriptors, k[, mask[, compactResult]]) -> matches

@spec knnMatch(
  t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  integer(),
  [{atom(), term()}, ...] | nil
) :: [[Evision.DMatch.t()]] | {:error, String.t()}

For every input query descriptor, retrieve the best k matching ones from a dataset provided from user or from the one internal to class

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()

• queryDescriptors: Evision.Mat.t().

  query descriptors

• trainDescriptors: Evision.Mat.t().

  dataset of descriptors furnished by user

• k: int.

  number of the closest descriptors to be returned for every input query

Keyword Arguments

• mask: Evision.Mat.t().

  mask to select which input descriptors must be matched to ones in dataset

• compactResult: bool.

  flag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result)

Return

• matches: [[Evision.DMatch]].

  vector to host retrieved matches

Python prototype (for reference only):

knnMatch(queryDescriptors, trainDescriptors, k[, mask[, compactResult]]) -> matches

@spec knnMatchQuery(
  t(),
  Evision.Mat.maybe_mat_in(),
  [[Evision.DMatch.t()]],
  integer()
) ::
  t() | {:error, String.t()}

knnMatchQuery

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()

• queryDescriptors: Evision.Mat.t().

  query descriptors

• matches: [[Evision.DMatch]].

  vector to host retrieved matches

• k: int.

  number of the closest descriptors to be returned for every input query

Keyword Arguments

• masks: [Evision.Mat].

  vector of masks to select which input descriptors must be matched to ones in dataset (the i-th mask in vector indicates whether each input query can be matched with descriptors in dataset relative to i-th image)

• compactResult: bool.

  flag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result)

Has overloading in C++

Python prototype (for reference only):

knnMatchQuery(queryDescriptors, matches, k[, masks[, compactResult]]) -> None

@spec knnMatchQuery(
  t(),
  Evision.Mat.maybe_mat_in(),
  [[Evision.DMatch.t()]],
  integer(),
  [{atom(), term()}, ...] | nil
) :: t() | {:error, String.t()}

knnMatchQuery

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()

• queryDescriptors: Evision.Mat.t().

  query descriptors

• matches: [[Evision.DMatch]].

  vector to host retrieved matches

• k: int.

  number of the closest descriptors to be returned for every input query

Keyword Arguments

• masks: [Evision.Mat].

  vector of masks to select which input descriptors must be matched to ones in dataset (the i-th mask in vector indicates whether each input query can be matched with descriptors in dataset relative to i-th image)

• compactResult: bool.

  flag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result)

Has overloading in C++

Python prototype (for reference only):

knnMatchQuery(queryDescriptors, matches, k[, masks[, compactResult]]) -> None

@spec match(t(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) ::
  [Evision.DMatch.t()] | {:error, String.t()}

For every input query descriptor, retrieve the best matching one from a dataset provided from user or from the one internal to class

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()

• queryDescriptors: Evision.Mat.t().

  query descriptors

• trainDescriptors: Evision.Mat.t().

  dataset of descriptors furnished by user

Keyword Arguments

• mask: Evision.Mat.t().

  mask to select which input descriptors must be matched to one in dataset

Return

• matches: [Evision.DMatch].

  vector to host retrieved matches

Python prototype (for reference only):

match(queryDescriptors, trainDescriptors[, mask]) -> matches

@spec match(
  t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  [{atom(), term()}, ...] | nil
) ::
  [Evision.DMatch.t()] | {:error, String.t()}

For every input query descriptor, retrieve the best matching one from a dataset provided from user or from the one internal to class

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()

• queryDescriptors: Evision.Mat.t().

  query descriptors

• trainDescriptors: Evision.Mat.t().

  dataset of descriptors furnished by user

Keyword Arguments

• mask: Evision.Mat.t().

  mask to select which input descriptors must be matched to one in dataset

Return

• matches: [Evision.DMatch].

  vector to host retrieved matches

Python prototype (for reference only):

match(queryDescriptors, trainDescriptors[, mask]) -> matches

@spec matchQuery(t(), Evision.Mat.maybe_mat_in()) ::
  [Evision.DMatch.t()] | {:error, String.t()}

matchQuery

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()

• queryDescriptors: Evision.Mat.t().

  query descriptors

Keyword Arguments

• masks: [Evision.Mat].

  vector of masks to select which input descriptors must be matched to one in dataset (the i-th mask in vector indicates whether each input query can be matched with descriptors in dataset relative to i-th image)

Return

• matches: [Evision.DMatch].

  vector to host retrieved matches

Has overloading in C++

Python prototype (for reference only):

matchQuery(queryDescriptors[, masks]) -> matches

@spec matchQuery(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) ::
  [Evision.DMatch.t()] | {:error, String.t()}

matchQuery

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()

• queryDescriptors: Evision.Mat.t().

  query descriptors

Keyword Arguments

• masks: [Evision.Mat].

  vector of masks to select which input descriptors must be matched to one in dataset (the i-th mask in vector indicates whether each input query can be matched with descriptors in dataset relative to i-th image)

Return

• matches: [Evision.DMatch].

  vector to host retrieved matches

Has overloading in C++

Python prototype (for reference only):

matchQuery(queryDescriptors[, masks]) -> matches

@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}

Reads algorithm parameters from a file storage

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()
• fn_: Evision.FileNode.t()

Python prototype (for reference only):

read(fn_) -> None

@spec save(t(), binary()) :: t() | {:error, String.t()}

save

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()
• filename: String

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Python prototype (for reference only):

save(filename) -> None

@spec write(t(), Evision.FileStorage.t()) :: t() | {:error, String.t()}

Stores algorithm parameters in a file storage

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()
• fs: Evision.FileStorage.t()

Python prototype (for reference only):

write(fs) -> None

@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}

write

Positional Arguments

• self: Evision.LineDescriptor.BinaryDescriptorMatcher.t()
• fs: Evision.FileStorage.t()
• name: String

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None