View Source Evision.CUDA.DescriptorMatcher (Evision v0.1.28)

Link to this section Summary

Types

t()

Type that represents an CUDA.DescriptorMatcher struct.

Functions

Adds descriptors to train a descriptor collection.

Clears the train descriptor collection.

Brute-force descriptor matcher.

Brute-force descriptor matcher.

Returns true if there are no train descriptors in the collection.

getDefaultName

Returns a constant link to the train descriptor collection.

Returns true if the descriptor matcher supports masking permissible matches.

Variant 1:

Finds the k best matches for each descriptor from a query set (blocking version).

Variant 1:

Finds the k best matches for each descriptor from a query set (blocking version).

Variant 1:

Finds the k best matches for each descriptor from a query set (asynchronous version).

Variant 1:

Finds the k best matches for each descriptor from a query set (asynchronous version).

Variant 1:

Converts matches array from internal representation to standard matches vector.

Variant 1:

Converts matches array from internal representation to standard matches vector.

Variant 1:

Finds the best match for each descriptor from a query set (blocking version).

Variant 1:

Finds the best match for each descriptor from a query set (blocking version).

Variant 1:

matchAsync

Variant 1:

Finds the best match for each descriptor from a query set (asynchronous version).

Variant 1:

Finds the best match for each descriptor from a query set (asynchronous version).

Variant 1:

Converts matches array from internal representation to standard matches vector.

Variant 1:

For each query descriptor, finds the training descriptors not farther than the specified distance (blocking version).

Variant 1:

For each query descriptor, finds the training descriptors not farther than the specified distance (blocking version).

Variant 1:

For each query descriptor, finds the training descriptors not farther than the specified distance (asynchronous version).

Variant 1:

For each query descriptor, finds the training descriptors not farther than the specified distance (asynchronous version).

Variant 1:

Converts matches array from internal representation to standard matches vector.

Variant 1:

Converts matches array from internal representation to standard matches vector.

Reads algorithm parameters from a file storage

Trains a descriptor matcher.

Stores algorithm parameters in a file storage

Link to this section Types

@type t() :: %Evision.CUDA.DescriptorMatcher{ref: reference()}

Type that represents an CUDA.DescriptorMatcher struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section Functions

Adds descriptors to train a descriptor collection.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • descriptors: [Evision.CUDA.GpuMat].

    Descriptors to add. Each descriptors[i] is a set of descriptors from the same train image.

If the collection is not empty, the new descriptors are added to existing train descriptors.

Python prototype (for reference only):

add(descriptors) -> None

Clears the train descriptor collection.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

Python prototype (for reference only):

clear() -> None
@spec createBFMatcher() :: t() | {:error, String.t()}

Brute-force descriptor matcher.

Keyword Arguments
  • normType: int.

    One of NORM_L1, NORM_L2, NORM_HAMMING. L1 and L2 norms are preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and BRIEF).

Return
  • retval: Evision.CUDA.DescriptorMatcher.t()

For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one. This descriptor matcher supports masking permissible matches of descriptor sets.

Python prototype (for reference only):

createBFMatcher([, normType]) -> retval
@spec createBFMatcher([{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}

Brute-force descriptor matcher.

Keyword Arguments
  • normType: int.

    One of NORM_L1, NORM_L2, NORM_HAMMING. L1 and L2 norms are preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and BRIEF).

Return
  • retval: Evision.CUDA.DescriptorMatcher.t()

For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one. This descriptor matcher supports masking permissible matches of descriptor sets.

Python prototype (for reference only):

createBFMatcher([, normType]) -> retval
@spec empty(Evision.DescriptorMatcher.t()) :: boolean() | {:error, String.t()}

Returns true if there are no train descriptors in the collection.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
Return
  • retval: bool

Python prototype (for reference only):

empty() -> retval
@spec getDefaultName(Evision.DescriptorMatcher.t()) :: binary() | {:error, String.t()}

getDefaultName

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
Return

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
Link to this function

getTrainDescriptors(self)

View Source
@spec getTrainDescriptors(Evision.DescriptorMatcher.t()) ::
  [Evision.CUDA.GpuMat.t()] | {:error, String.t()}

Returns a constant link to the train descriptor collection.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
Return
  • retval: [Evision.CUDA.GpuMat]

Python prototype (for reference only):

getTrainDescriptors() -> retval
@spec isMaskSupported(Evision.DescriptorMatcher.t()) ::
  boolean() | {:error, String.t()}

Returns true if the descriptor matcher supports masking permissible matches.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
Return
  • retval: bool

Python prototype (for reference only):

isMaskSupported() -> retval
Link to this function

knnMatch(self, queryDescriptors, k)

View Source

Variant 1:

knnMatch

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.Mat.t()
  • k: int
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • compactResult: bool.
Return
  • matches: [[Evision.DMatch]]

Has overloading in C++

Python prototype (for reference only):

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

Variant 2:

knnMatch

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.CUDA.GpuMat.t()
  • k: int
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • compactResult: bool.
Return
  • matches: [[Evision.DMatch]]

Has overloading in C++

Python prototype (for reference only):

knnMatch(queryDescriptors, k[, masks[, compactResult]]) -> matches
Link to this function

knnMatch(self, queryDescriptors, k, opts)

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

Variant 1:

Finds the k best matches for each descriptor from a query set (blocking version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.Mat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.Mat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • k: int.

    Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total.

Keyword Arguments
  • mask: Evision.Mat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Matches. Each matches[i] is k or less matches for the same query descriptor.

These extended variants of DescriptorMatcher::match methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match for the details about query and train descriptors.

Python prototype (for reference only):

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

Variant 2:

Finds the k best matches for each descriptor from a query set (blocking version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.CUDA.GpuMat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.CUDA.GpuMat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • k: int.

    Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total.

Keyword Arguments
  • mask: Evision.CUDA.GpuMat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Matches. Each matches[i] is k or less matches for the same query descriptor.

These extended variants of DescriptorMatcher::match methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match for the details about query and train descriptors.

Python prototype (for reference only):

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

Variant 3:

knnMatch

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.Mat.t()
  • k: int
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • compactResult: bool.
Return
  • matches: [[Evision.DMatch]]

Has overloading in C++

Python prototype (for reference only):

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

Variant 4:

knnMatch

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.CUDA.GpuMat.t()
  • k: int
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • compactResult: bool.
Return
  • matches: [[Evision.DMatch]]

Has overloading in C++

Python prototype (for reference only):

knnMatch(queryDescriptors, k[, masks[, compactResult]]) -> matches
Link to this function

knnMatch(self, queryDescriptors, trainDescriptors, k, opts)

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

Variant 1:

Finds the k best matches for each descriptor from a query set (blocking version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.Mat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.Mat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • k: int.

    Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total.

Keyword Arguments
  • mask: Evision.Mat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Matches. Each matches[i] is k or less matches for the same query descriptor.

These extended variants of DescriptorMatcher::match methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match for the details about query and train descriptors.

Python prototype (for reference only):

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

Variant 2:

Finds the k best matches for each descriptor from a query set (blocking version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.CUDA.GpuMat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.CUDA.GpuMat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • k: int.

    Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total.

Keyword Arguments
  • mask: Evision.CUDA.GpuMat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Matches. Each matches[i] is k or less matches for the same query descriptor.

These extended variants of DescriptorMatcher::match methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match for the details about query and train descriptors.

Python prototype (for reference only):

knnMatch(queryDescriptors, trainDescriptors, k[, mask[, compactResult]]) -> matches
Link to this function

knnMatchAsync(self, queryDescriptors, k)

View Source
@spec knnMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  integer()
) ::
  Evision.Mat.t() | {:error, String.t()}
@spec knnMatchAsync(Evision.DescriptorMatcher.t(), Evision.CUDA.GpuMat.t(), integer()) ::
  Evision.CUDA.GpuMat.t() | {:error, String.t()}

Variant 1:

knnMatchAsync

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.Mat.t()
  • k: int
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • stream: Evision.CUDA.Stream.t().
Return
  • matches: Evision.Mat.t().

Has overloading in C++

Python prototype (for reference only):

knnMatchAsync(queryDescriptors, k[, matches[, masks[, stream]]]) -> matches

Variant 2:

knnMatchAsync

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.CUDA.GpuMat.t()
  • k: int
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • stream: Evision.CUDA.Stream.t().
Return
  • matches: Evision.CUDA.GpuMat.t().

Has overloading in C++

Python prototype (for reference only):

knnMatchAsync(queryDescriptors, k[, matches[, masks[, stream]]]) -> matches
Link to this function

knnMatchAsync(self, queryDescriptors, k, opts)

View Source
@spec knnMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  integer(),
  [{atom(), term()}, ...] | nil
) :: Evision.Mat.t() | {:error, String.t()}
@spec knnMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  integer(),
  [{atom(), term()}, ...] | nil
) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
@spec knnMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  integer()
) :: Evision.Mat.t() | {:error, String.t()}
@spec knnMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  Evision.CUDA.GpuMat.t(),
  integer()
) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}

Variant 1:

Finds the k best matches for each descriptor from a query set (asynchronous version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.Mat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.Mat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • k: int.

    Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total.

Keyword Arguments
  • mask: Evision.Mat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • stream: Evision.CUDA.Stream.t().

    CUDA stream.

Return
  • matches: Evision.Mat.t().

    Matches array stored in GPU memory. Internal representation is not defined. Use DescriptorMatcher::knnMatchConvert method to retrieve results in standard representation.

These extended variants of DescriptorMatcher::matchAsync methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::matchAsync for the details about query and train descriptors.

Python prototype (for reference only):

knnMatchAsync(queryDescriptors, trainDescriptors, k[, matches[, mask[, stream]]]) -> matches

Variant 2:

Finds the k best matches for each descriptor from a query set (asynchronous version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.CUDA.GpuMat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.CUDA.GpuMat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • k: int.

    Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total.

Keyword Arguments
  • mask: Evision.CUDA.GpuMat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • stream: Evision.CUDA.Stream.t().

    CUDA stream.

Return
  • matches: Evision.CUDA.GpuMat.t().

    Matches array stored in GPU memory. Internal representation is not defined. Use DescriptorMatcher::knnMatchConvert method to retrieve results in standard representation.

These extended variants of DescriptorMatcher::matchAsync methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::matchAsync for the details about query and train descriptors.

Python prototype (for reference only):

knnMatchAsync(queryDescriptors, trainDescriptors, k[, matches[, mask[, stream]]]) -> matches

Variant 3:

knnMatchAsync

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.Mat.t()
  • k: int
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • stream: Evision.CUDA.Stream.t().
Return
  • matches: Evision.Mat.t().

Has overloading in C++

Python prototype (for reference only):

knnMatchAsync(queryDescriptors, k[, matches[, masks[, stream]]]) -> matches

Variant 4:

knnMatchAsync

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.CUDA.GpuMat.t()
  • k: int
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • stream: Evision.CUDA.Stream.t().
Return
  • matches: Evision.CUDA.GpuMat.t().

Has overloading in C++

Python prototype (for reference only):

knnMatchAsync(queryDescriptors, k[, matches[, masks[, stream]]]) -> matches
Link to this function

knnMatchAsync(self, queryDescriptors, trainDescriptors, k, opts)

View Source
@spec knnMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  integer(),
  [{atom(), term()}, ...] | nil
) :: Evision.Mat.t() | {:error, String.t()}
@spec knnMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  Evision.CUDA.GpuMat.t(),
  integer(),
  [{atom(), term()}, ...] | nil
) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}

Variant 1:

Finds the k best matches for each descriptor from a query set (asynchronous version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.Mat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.Mat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • k: int.

    Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total.

Keyword Arguments
  • mask: Evision.Mat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • stream: Evision.CUDA.Stream.t().

    CUDA stream.

Return
  • matches: Evision.Mat.t().

    Matches array stored in GPU memory. Internal representation is not defined. Use DescriptorMatcher::knnMatchConvert method to retrieve results in standard representation.

These extended variants of DescriptorMatcher::matchAsync methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::matchAsync for the details about query and train descriptors.

Python prototype (for reference only):

knnMatchAsync(queryDescriptors, trainDescriptors, k[, matches[, mask[, stream]]]) -> matches

Variant 2:

Finds the k best matches for each descriptor from a query set (asynchronous version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.CUDA.GpuMat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.CUDA.GpuMat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • k: int.

    Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total.

Keyword Arguments
  • mask: Evision.CUDA.GpuMat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • stream: Evision.CUDA.Stream.t().

    CUDA stream.

Return
  • matches: Evision.CUDA.GpuMat.t().

    Matches array stored in GPU memory. Internal representation is not defined. Use DescriptorMatcher::knnMatchConvert method to retrieve results in standard representation.

These extended variants of DescriptorMatcher::matchAsync methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::matchAsync for the details about query and train descriptors.

Python prototype (for reference only):

knnMatchAsync(queryDescriptors, trainDescriptors, k[, matches[, mask[, stream]]]) -> matches
Link to this function

knnMatchConvert(self, gpu_matches)

View Source
@spec knnMatchConvert(Evision.DescriptorMatcher.t(), Evision.Mat.maybe_mat_in()) ::
  [[Evision.DMatch.t()]] | {:error, String.t()}
@spec knnMatchConvert(Evision.DescriptorMatcher.t(), Evision.CUDA.GpuMat.t()) ::
  [[Evision.DMatch.t()]] | {:error, String.t()}

Variant 1:

Converts matches array from internal representation to standard matches vector.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • gpu_matches: Evision.Mat.t().

    Matches, returned from DescriptorMatcher::knnMatchAsync.

Keyword Arguments
  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Vector of DMatch objects.

The method is supposed to be used with DescriptorMatcher::knnMatchAsync to get final result. Call this method only after DescriptorMatcher::knnMatchAsync is completed (ie. after synchronization).

Python prototype (for reference only):

knnMatchConvert(gpu_matches[, compactResult]) -> matches

Variant 2:

Converts matches array from internal representation to standard matches vector.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • gpu_matches: Evision.CUDA.GpuMat.t().

    Matches, returned from DescriptorMatcher::knnMatchAsync.

Keyword Arguments
  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Vector of DMatch objects.

The method is supposed to be used with DescriptorMatcher::knnMatchAsync to get final result. Call this method only after DescriptorMatcher::knnMatchAsync is completed (ie. after synchronization).

Python prototype (for reference only):

knnMatchConvert(gpu_matches[, compactResult]) -> matches
Link to this function

knnMatchConvert(self, gpu_matches, opts)

View Source
@spec knnMatchConvert(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  [{atom(), term()}, ...] | nil
) :: [[Evision.DMatch.t()]] | {:error, String.t()}
@spec knnMatchConvert(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  [{atom(), term()}, ...] | nil
) :: [[Evision.DMatch.t()]] | {:error, String.t()}

Variant 1:

Converts matches array from internal representation to standard matches vector.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • gpu_matches: Evision.Mat.t().

    Matches, returned from DescriptorMatcher::knnMatchAsync.

Keyword Arguments
  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Vector of DMatch objects.

The method is supposed to be used with DescriptorMatcher::knnMatchAsync to get final result. Call this method only after DescriptorMatcher::knnMatchAsync is completed (ie. after synchronization).

Python prototype (for reference only):

knnMatchConvert(gpu_matches[, compactResult]) -> matches

Variant 2:

Converts matches array from internal representation to standard matches vector.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • gpu_matches: Evision.CUDA.GpuMat.t().

    Matches, returned from DescriptorMatcher::knnMatchAsync.

Keyword Arguments
  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Vector of DMatch objects.

The method is supposed to be used with DescriptorMatcher::knnMatchAsync to get final result. Call this method only after DescriptorMatcher::knnMatchAsync is completed (ie. after synchronization).

Python prototype (for reference only):

knnMatchConvert(gpu_matches[, compactResult]) -> matches
Link to this function

match(self, queryDescriptors)

View Source

Variant 1:

match

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.Mat.t()
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
Return
  • matches: [Evision.DMatch]

Has overloading in C++

Python prototype (for reference only):

match(queryDescriptors[, masks]) -> matches

Variant 2:

match

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.CUDA.GpuMat.t()
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
Return
  • matches: [Evision.DMatch]

Has overloading in C++

Python prototype (for reference only):

match(queryDescriptors[, masks]) -> matches
Link to this function

match(self, queryDescriptors, opts)

View Source

Variant 1:

Finds the best match for each descriptor from a query set (blocking version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.Mat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.Mat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

Keyword Arguments
  • mask: Evision.Mat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

Return
  • matches: [Evision.DMatch].

    Matches. If a query descriptor is masked out in mask , no match is added for this descriptor. So, matches size may be smaller than the query descriptors count.

In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if mask.at\<uchar>(i,j) is non-zero.

Python prototype (for reference only):

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

Variant 2:

Finds the best match for each descriptor from a query set (blocking version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.CUDA.GpuMat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.CUDA.GpuMat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

Keyword Arguments
  • mask: Evision.CUDA.GpuMat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

Return
  • matches: [Evision.DMatch].

    Matches. If a query descriptor is masked out in mask , no match is added for this descriptor. So, matches size may be smaller than the query descriptors count.

In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if mask.at\<uchar>(i,j) is non-zero.

Python prototype (for reference only):

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

Variant 3:

match

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.Mat.t()
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
Return
  • matches: [Evision.DMatch]

Has overloading in C++

Python prototype (for reference only):

match(queryDescriptors[, masks]) -> matches

Variant 4:

match

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.CUDA.GpuMat.t()
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
Return
  • matches: [Evision.DMatch]

Has overloading in C++

Python prototype (for reference only):

match(queryDescriptors[, masks]) -> matches
Link to this function

match(self, queryDescriptors, trainDescriptors, opts)

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

Variant 1:

Finds the best match for each descriptor from a query set (blocking version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.Mat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.Mat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

Keyword Arguments
  • mask: Evision.Mat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

Return
  • matches: [Evision.DMatch].

    Matches. If a query descriptor is masked out in mask , no match is added for this descriptor. So, matches size may be smaller than the query descriptors count.

In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if mask.at\<uchar>(i,j) is non-zero.

Python prototype (for reference only):

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

Variant 2:

Finds the best match for each descriptor from a query set (blocking version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.CUDA.GpuMat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.CUDA.GpuMat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

Keyword Arguments
  • mask: Evision.CUDA.GpuMat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

Return
  • matches: [Evision.DMatch].

    Matches. If a query descriptor is masked out in mask , no match is added for this descriptor. So, matches size may be smaller than the query descriptors count.

In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if mask.at\<uchar>(i,j) is non-zero.

Python prototype (for reference only):

match(queryDescriptors, trainDescriptors[, mask]) -> matches
Link to this function

matchAsync(self, queryDescriptors)

View Source

Variant 1:

matchAsync

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.Mat.t()
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • stream: Evision.CUDA.Stream.t().
Return
  • matches: Evision.Mat.t().

Has overloading in C++

Python prototype (for reference only):

matchAsync(queryDescriptors[, matches[, masks[, stream]]]) -> matches

Variant 2:

matchAsync

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.CUDA.GpuMat.t()
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • stream: Evision.CUDA.Stream.t().
Return
  • matches: Evision.CUDA.GpuMat.t().

Has overloading in C++

Python prototype (for reference only):

matchAsync(queryDescriptors[, matches[, masks[, stream]]]) -> matches
Link to this function

matchAsync(self, queryDescriptors, opts)

View Source
@spec matchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  [{atom(), term()}, ...] | nil
) :: Evision.Mat.t() | {:error, String.t()}
@spec matchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  [{atom(), term()}, ...] | nil
) ::
  Evision.CUDA.GpuMat.t() | {:error, String.t()}
@spec matchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in()
) ::
  Evision.Mat.t() | {:error, String.t()}
@spec matchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  Evision.CUDA.GpuMat.t()
) ::
  Evision.CUDA.GpuMat.t() | {:error, String.t()}

Variant 1:

Finds the best match for each descriptor from a query set (asynchronous version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.Mat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.Mat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

Keyword Arguments
  • mask: Evision.Mat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • stream: Evision.CUDA.Stream.t().

    CUDA stream.

Return
  • matches: Evision.Mat.t().

    Matches array stored in GPU memory. Internal representation is not defined. Use DescriptorMatcher::matchConvert method to retrieve results in standard representation.

In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if mask.at\<uchar>(i,j) is non-zero.

Python prototype (for reference only):

matchAsync(queryDescriptors, trainDescriptors[, matches[, mask[, stream]]]) -> matches

Variant 2:

Finds the best match for each descriptor from a query set (asynchronous version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.CUDA.GpuMat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.CUDA.GpuMat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

Keyword Arguments
  • mask: Evision.CUDA.GpuMat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • stream: Evision.CUDA.Stream.t().

    CUDA stream.

Return
  • matches: Evision.CUDA.GpuMat.t().

    Matches array stored in GPU memory. Internal representation is not defined. Use DescriptorMatcher::matchConvert method to retrieve results in standard representation.

In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if mask.at\<uchar>(i,j) is non-zero.

Python prototype (for reference only):

matchAsync(queryDescriptors, trainDescriptors[, matches[, mask[, stream]]]) -> matches

Variant 3:

matchAsync

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.Mat.t()
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • stream: Evision.CUDA.Stream.t().
Return
  • matches: Evision.Mat.t().

Has overloading in C++

Python prototype (for reference only):

matchAsync(queryDescriptors[, matches[, masks[, stream]]]) -> matches

Variant 4:

matchAsync

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.CUDA.GpuMat.t()
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • stream: Evision.CUDA.Stream.t().
Return
  • matches: Evision.CUDA.GpuMat.t().

Has overloading in C++

Python prototype (for reference only):

matchAsync(queryDescriptors[, matches[, masks[, stream]]]) -> matches
Link to this function

matchAsync(self, queryDescriptors, trainDescriptors, opts)

View Source
@spec matchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  [{atom(), term()}, ...] | nil
) :: Evision.Mat.t() | {:error, String.t()}
@spec matchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  Evision.CUDA.GpuMat.t(),
  [{atom(), term()}, ...] | nil
) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}

Variant 1:

Finds the best match for each descriptor from a query set (asynchronous version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.Mat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.Mat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

Keyword Arguments
  • mask: Evision.Mat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • stream: Evision.CUDA.Stream.t().

    CUDA stream.

Return
  • matches: Evision.Mat.t().

    Matches array stored in GPU memory. Internal representation is not defined. Use DescriptorMatcher::matchConvert method to retrieve results in standard representation.

In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if mask.at\<uchar>(i,j) is non-zero.

Python prototype (for reference only):

matchAsync(queryDescriptors, trainDescriptors[, matches[, mask[, stream]]]) -> matches

Variant 2:

Finds the best match for each descriptor from a query set (asynchronous version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.CUDA.GpuMat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.CUDA.GpuMat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

Keyword Arguments
  • mask: Evision.CUDA.GpuMat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • stream: Evision.CUDA.Stream.t().

    CUDA stream.

Return
  • matches: Evision.CUDA.GpuMat.t().

    Matches array stored in GPU memory. Internal representation is not defined. Use DescriptorMatcher::matchConvert method to retrieve results in standard representation.

In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if mask.at\<uchar>(i,j) is non-zero.

Python prototype (for reference only):

matchAsync(queryDescriptors, trainDescriptors[, matches[, mask[, stream]]]) -> matches
Link to this function

matchConvert(self, gpu_matches)

View Source
@spec matchConvert(Evision.DescriptorMatcher.t(), Evision.Mat.maybe_mat_in()) ::
  [Evision.DMatch.t()] | {:error, String.t()}
@spec matchConvert(Evision.DescriptorMatcher.t(), Evision.CUDA.GpuMat.t()) ::
  [Evision.DMatch.t()] | {:error, String.t()}

Variant 1:

Converts matches array from internal representation to standard matches vector.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • gpu_matches: Evision.Mat.t().

    Matches, returned from DescriptorMatcher::matchAsync.

Return
  • matches: [Evision.DMatch].

    Vector of DMatch objects.

The method is supposed to be used with DescriptorMatcher::matchAsync to get final result. Call this method only after DescriptorMatcher::matchAsync is completed (ie. after synchronization).

Python prototype (for reference only):

matchConvert(gpu_matches) -> matches

Variant 2:

Converts matches array from internal representation to standard matches vector.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • gpu_matches: Evision.CUDA.GpuMat.t().

    Matches, returned from DescriptorMatcher::matchAsync.

Return
  • matches: [Evision.DMatch].

    Vector of DMatch objects.

The method is supposed to be used with DescriptorMatcher::matchAsync to get final result. Call this method only after DescriptorMatcher::matchAsync is completed (ie. after synchronization).

Python prototype (for reference only):

matchConvert(gpu_matches) -> matches
Link to this function

radiusMatch(self, queryDescriptors, maxDistance)

View Source
@spec radiusMatch(Evision.DescriptorMatcher.t(), Evision.Mat.maybe_mat_in(), number()) ::
  [[Evision.DMatch.t()]] | {:error, String.t()}
@spec radiusMatch(Evision.DescriptorMatcher.t(), Evision.CUDA.GpuMat.t(), number()) ::
  [[Evision.DMatch.t()]] | {:error, String.t()}

Variant 1:

radiusMatch

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.Mat.t()
  • maxDistance: float
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • compactResult: bool.
Return
  • matches: [[Evision.DMatch]]

Has overloading in C++

Python prototype (for reference only):

radiusMatch(queryDescriptors, maxDistance[, masks[, compactResult]]) -> matches

Variant 2:

radiusMatch

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.CUDA.GpuMat.t()
  • maxDistance: float
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • compactResult: bool.
Return
  • matches: [[Evision.DMatch]]

Has overloading in C++

Python prototype (for reference only):

radiusMatch(queryDescriptors, maxDistance[, masks[, compactResult]]) -> matches
Link to this function

radiusMatch(self, queryDescriptors, maxDistance, opts)

View Source
@spec radiusMatch(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  number(),
  [{atom(), term()}, ...] | nil
) :: [[Evision.DMatch.t()]] | {:error, String.t()}
@spec radiusMatch(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  number(),
  [{atom(), term()}, ...] | nil
) :: [[Evision.DMatch.t()]] | {:error, String.t()}
@spec radiusMatch(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  number()
) :: [[Evision.DMatch.t()]] | {:error, String.t()}
@spec radiusMatch(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  Evision.CUDA.GpuMat.t(),
  number()
) :: [[Evision.DMatch.t()]] | {:error, String.t()}

Variant 1:

For each query descriptor, finds the training descriptors not farther than the specified distance (blocking version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.Mat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.Mat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • maxDistance: float.

    Threshold for the distance between matched descriptors. Distance means here metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured in Pixels)!

Keyword Arguments
  • mask: Evision.Mat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Found matches.

For each query descriptor, the methods find such training descriptors that the distance between the query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are returned in the distance increasing order.

Python prototype (for reference only):

radiusMatch(queryDescriptors, trainDescriptors, maxDistance[, mask[, compactResult]]) -> matches

Variant 2:

For each query descriptor, finds the training descriptors not farther than the specified distance (blocking version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.CUDA.GpuMat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.CUDA.GpuMat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • maxDistance: float.

    Threshold for the distance between matched descriptors. Distance means here metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured in Pixels)!

Keyword Arguments
  • mask: Evision.CUDA.GpuMat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Found matches.

For each query descriptor, the methods find such training descriptors that the distance between the query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are returned in the distance increasing order.

Python prototype (for reference only):

radiusMatch(queryDescriptors, trainDescriptors, maxDistance[, mask[, compactResult]]) -> matches

Variant 3:

radiusMatch

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.Mat.t()
  • maxDistance: float
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • compactResult: bool.
Return
  • matches: [[Evision.DMatch]]

Has overloading in C++

Python prototype (for reference only):

radiusMatch(queryDescriptors, maxDistance[, masks[, compactResult]]) -> matches

Variant 4:

radiusMatch

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.CUDA.GpuMat.t()
  • maxDistance: float
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • compactResult: bool.
Return
  • matches: [[Evision.DMatch]]

Has overloading in C++

Python prototype (for reference only):

radiusMatch(queryDescriptors, maxDistance[, masks[, compactResult]]) -> matches
Link to this function

radiusMatch(self, queryDescriptors, trainDescriptors, maxDistance, opts)

View Source
@spec radiusMatch(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  number(),
  [{atom(), term()}, ...] | nil
) :: [[Evision.DMatch.t()]] | {:error, String.t()}
@spec radiusMatch(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  Evision.CUDA.GpuMat.t(),
  number(),
  [{atom(), term()}, ...] | nil
) :: [[Evision.DMatch.t()]] | {:error, String.t()}

Variant 1:

For each query descriptor, finds the training descriptors not farther than the specified distance (blocking version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.Mat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.Mat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • maxDistance: float.

    Threshold for the distance between matched descriptors. Distance means here metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured in Pixels)!

Keyword Arguments
  • mask: Evision.Mat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Found matches.

For each query descriptor, the methods find such training descriptors that the distance between the query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are returned in the distance increasing order.

Python prototype (for reference only):

radiusMatch(queryDescriptors, trainDescriptors, maxDistance[, mask[, compactResult]]) -> matches

Variant 2:

For each query descriptor, finds the training descriptors not farther than the specified distance (blocking version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.CUDA.GpuMat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.CUDA.GpuMat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • maxDistance: float.

    Threshold for the distance between matched descriptors. Distance means here metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured in Pixels)!

Keyword Arguments
  • mask: Evision.CUDA.GpuMat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Found matches.

For each query descriptor, the methods find such training descriptors that the distance between the query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are returned in the distance increasing order.

Python prototype (for reference only):

radiusMatch(queryDescriptors, trainDescriptors, maxDistance[, mask[, compactResult]]) -> matches
Link to this function

radiusMatchAsync(self, queryDescriptors, maxDistance)

View Source
@spec radiusMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  number()
) ::
  Evision.Mat.t() | {:error, String.t()}
@spec radiusMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  number()
) ::
  Evision.CUDA.GpuMat.t() | {:error, String.t()}

Variant 1:

radiusMatchAsync

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.Mat.t()
  • maxDistance: float
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • stream: Evision.CUDA.Stream.t().
Return
  • matches: Evision.Mat.t().

Has overloading in C++

Python prototype (for reference only):

radiusMatchAsync(queryDescriptors, maxDistance[, matches[, masks[, stream]]]) -> matches

Variant 2:

radiusMatchAsync

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.CUDA.GpuMat.t()
  • maxDistance: float
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • stream: Evision.CUDA.Stream.t().
Return
  • matches: Evision.CUDA.GpuMat.t().

Has overloading in C++

Python prototype (for reference only):

radiusMatchAsync(queryDescriptors, maxDistance[, matches[, masks[, stream]]]) -> matches
Link to this function

radiusMatchAsync(self, queryDescriptors, maxDistance, opts)

View Source
@spec radiusMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  number(),
  [{atom(), term()}, ...] | nil
) :: Evision.Mat.t() | {:error, String.t()}
@spec radiusMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  number(),
  [{atom(), term()}, ...] | nil
) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
@spec radiusMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  number()
) :: Evision.Mat.t() | {:error, String.t()}
@spec radiusMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  Evision.CUDA.GpuMat.t(),
  number()
) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}

Variant 1:

For each query descriptor, finds the training descriptors not farther than the specified distance (asynchronous version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.Mat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.Mat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • maxDistance: float.

    Threshold for the distance between matched descriptors. Distance means here metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured in Pixels)!

Keyword Arguments
  • mask: Evision.Mat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • stream: Evision.CUDA.Stream.t().

    CUDA stream.

Return
  • matches: Evision.Mat.t().

    Matches array stored in GPU memory. Internal representation is not defined. Use DescriptorMatcher::radiusMatchConvert method to retrieve results in standard representation.

For each query descriptor, the methods find such training descriptors that the distance between the query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are returned in the distance increasing order.

Python prototype (for reference only):

radiusMatchAsync(queryDescriptors, trainDescriptors, maxDistance[, matches[, mask[, stream]]]) -> matches

Variant 2:

For each query descriptor, finds the training descriptors not farther than the specified distance (asynchronous version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.CUDA.GpuMat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.CUDA.GpuMat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • maxDistance: float.

    Threshold for the distance between matched descriptors. Distance means here metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured in Pixels)!

Keyword Arguments
  • mask: Evision.CUDA.GpuMat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • stream: Evision.CUDA.Stream.t().

    CUDA stream.

Return
  • matches: Evision.CUDA.GpuMat.t().

    Matches array stored in GPU memory. Internal representation is not defined. Use DescriptorMatcher::radiusMatchConvert method to retrieve results in standard representation.

For each query descriptor, the methods find such training descriptors that the distance between the query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are returned in the distance increasing order.

Python prototype (for reference only):

radiusMatchAsync(queryDescriptors, trainDescriptors, maxDistance[, matches[, mask[, stream]]]) -> matches

Variant 3:

radiusMatchAsync

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.Mat.t()
  • maxDistance: float
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • stream: Evision.CUDA.Stream.t().
Return
  • matches: Evision.Mat.t().

Has overloading in C++

Python prototype (for reference only):

radiusMatchAsync(queryDescriptors, maxDistance[, matches[, masks[, stream]]]) -> matches

Variant 4:

radiusMatchAsync

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • queryDescriptors: Evision.CUDA.GpuMat.t()
  • maxDistance: float
Keyword Arguments
  • masks: [Evision.CUDA.GpuMat].
  • stream: Evision.CUDA.Stream.t().
Return
  • matches: Evision.CUDA.GpuMat.t().

Has overloading in C++

Python prototype (for reference only):

radiusMatchAsync(queryDescriptors, maxDistance[, matches[, masks[, stream]]]) -> matches
Link to this function

radiusMatchAsync(self, queryDescriptors, trainDescriptors, maxDistance, opts)

View Source
@spec radiusMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  number(),
  [{atom(), term()}, ...] | nil
) :: Evision.Mat.t() | {:error, String.t()}
@spec radiusMatchAsync(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  Evision.CUDA.GpuMat.t(),
  number(),
  [{atom(), term()}, ...] | nil
) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}

Variant 1:

For each query descriptor, finds the training descriptors not farther than the specified distance (asynchronous version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.Mat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.Mat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • maxDistance: float.

    Threshold for the distance between matched descriptors. Distance means here metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured in Pixels)!

Keyword Arguments
  • mask: Evision.Mat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • stream: Evision.CUDA.Stream.t().

    CUDA stream.

Return
  • matches: Evision.Mat.t().

    Matches array stored in GPU memory. Internal representation is not defined. Use DescriptorMatcher::radiusMatchConvert method to retrieve results in standard representation.

For each query descriptor, the methods find such training descriptors that the distance between the query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are returned in the distance increasing order.

Python prototype (for reference only):

radiusMatchAsync(queryDescriptors, trainDescriptors, maxDistance[, matches[, mask[, stream]]]) -> matches

Variant 2:

For each query descriptor, finds the training descriptors not farther than the specified distance (asynchronous version).

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • queryDescriptors: Evision.CUDA.GpuMat.t().

    Query set of descriptors.

  • trainDescriptors: Evision.CUDA.GpuMat.t().

    Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

  • maxDistance: float.

    Threshold for the distance between matched descriptors. Distance means here metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured in Pixels)!

Keyword Arguments
  • mask: Evision.CUDA.GpuMat.t().

    Mask specifying permissible matches between an input query and train matrices of descriptors.

  • stream: Evision.CUDA.Stream.t().

    CUDA stream.

Return
  • matches: Evision.CUDA.GpuMat.t().

    Matches array stored in GPU memory. Internal representation is not defined. Use DescriptorMatcher::radiusMatchConvert method to retrieve results in standard representation.

For each query descriptor, the methods find such training descriptors that the distance between the query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are returned in the distance increasing order.

Python prototype (for reference only):

radiusMatchAsync(queryDescriptors, trainDescriptors, maxDistance[, matches[, mask[, stream]]]) -> matches
Link to this function

radiusMatchConvert(self, gpu_matches)

View Source
@spec radiusMatchConvert(Evision.DescriptorMatcher.t(), Evision.Mat.maybe_mat_in()) ::
  [[Evision.DMatch.t()]] | {:error, String.t()}
@spec radiusMatchConvert(Evision.DescriptorMatcher.t(), Evision.CUDA.GpuMat.t()) ::
  [[Evision.DMatch.t()]] | {:error, String.t()}

Variant 1:

Converts matches array from internal representation to standard matches vector.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • gpu_matches: Evision.Mat.t().

    Matches, returned from DescriptorMatcher::radiusMatchAsync.

Keyword Arguments
  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Vector of DMatch objects.

The method is supposed to be used with DescriptorMatcher::radiusMatchAsync to get final result. Call this method only after DescriptorMatcher::radiusMatchAsync is completed (ie. after synchronization).

Python prototype (for reference only):

radiusMatchConvert(gpu_matches[, compactResult]) -> matches

Variant 2:

Converts matches array from internal representation to standard matches vector.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • gpu_matches: Evision.CUDA.GpuMat.t().

    Matches, returned from DescriptorMatcher::radiusMatchAsync.

Keyword Arguments
  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Vector of DMatch objects.

The method is supposed to be used with DescriptorMatcher::radiusMatchAsync to get final result. Call this method only after DescriptorMatcher::radiusMatchAsync is completed (ie. after synchronization).

Python prototype (for reference only):

radiusMatchConvert(gpu_matches[, compactResult]) -> matches
Link to this function

radiusMatchConvert(self, gpu_matches, opts)

View Source
@spec radiusMatchConvert(
  Evision.DescriptorMatcher.t(),
  Evision.Mat.maybe_mat_in(),
  [{atom(), term()}, ...] | nil
) :: [[Evision.DMatch.t()]] | {:error, String.t()}
@spec radiusMatchConvert(
  Evision.DescriptorMatcher.t(),
  Evision.CUDA.GpuMat.t(),
  [{atom(), term()}, ...] | nil
) :: [[Evision.DMatch.t()]] | {:error, String.t()}

Variant 1:

Converts matches array from internal representation to standard matches vector.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • gpu_matches: Evision.Mat.t().

    Matches, returned from DescriptorMatcher::radiusMatchAsync.

Keyword Arguments
  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Vector of DMatch objects.

The method is supposed to be used with DescriptorMatcher::radiusMatchAsync to get final result. Call this method only after DescriptorMatcher::radiusMatchAsync is completed (ie. after synchronization).

Python prototype (for reference only):

radiusMatchConvert(gpu_matches[, compactResult]) -> matches

Variant 2:

Converts matches array from internal representation to standard matches vector.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

  • gpu_matches: Evision.CUDA.GpuMat.t().

    Matches, returned from DescriptorMatcher::radiusMatchAsync.

Keyword Arguments
  • compactResult: bool.

    Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Return
  • matches: [[Evision.DMatch]].

    Vector of DMatch objects.

The method is supposed to be used with DescriptorMatcher::radiusMatchAsync to get final result. Call this method only after DescriptorMatcher::radiusMatchAsync is completed (ie. after synchronization).

Python prototype (for reference only):

radiusMatchConvert(gpu_matches[, compactResult]) -> matches

Reads algorithm parameters from a file storage

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • fn_: Evision.FileNode.t()

Python prototype (for reference only):

read(fn_) -> None

save

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.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

Trains a descriptor matcher.

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()

Trains a descriptor matcher (for example, the flann index). In all methods to match, the method train() is run every time before matching.

Python prototype (for reference only):

train() -> None

Stores algorithm parameters in a file storage

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • fs: Evision.FileStorage.t()

Python prototype (for reference only):

write(fs) -> None

write

Positional Arguments
  • self: Evision.CUDA.DescriptorMatcher.t()
  • fs: Evision.FileStorage.t()
  • name: String

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None