View Source Evision.AKAZE (Evision v0.1.21)

Link to this section Summary

Types

t()

Type that represents an Evision.AKAZE struct.

Functions

The AKAZE constructor

The AKAZE constructor

defaultNorm

descriptorSize

descriptorType

Variant 1:

detect

Variant 1:

detect

empty

getDefaultName

getDescriptorChannels

getDescriptorSize

getDescriptorType

getDiffusivity

getNOctaveLayers

getNOctaves

getThreshold

Variant 1:

read

setDescriptorChannels

setDescriptorSize

setDescriptorType

Variant 1:

write

Link to this section Types

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

Type that represents an Evision.AKAZE struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section Functions

Link to this function

compute(self, images, keypoints)

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

Variant 1:

compute

Positional Arguments
  • self: Evision.AKAZE.t()

  • images: [Evision.Mat].

    Image set.

Return
  • keypoints: [[Evision.KeyPoint]].

    Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).

  • descriptors: [Evision.Mat].

    Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.

Has overloading in C++

Python prototype (for reference only):

compute(images, keypoints[, descriptors]) -> keypoints, descriptors

Variant 2:

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

Positional Arguments
Return
  • keypoints: [Evision.KeyPoint].

    Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).

  • descriptors: Evision.Mat.

    Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.

Python prototype (for reference only):

compute(image, keypoints[, descriptors]) -> keypoints, descriptors
Link to this function

compute(self, images, keypoints, opts)

View Source
@spec compute(
  t(),
  [Evision.Mat.maybe_mat_in()],
  [[Evision.KeyPoint.t()]],
  [{atom(), term()}, ...] | nil
) :: {[[Evision.KeyPoint.t()]], [Evision.Mat.t()]} | {:error, String.t()}
@spec compute(
  t(),
  Evision.Mat.maybe_mat_in(),
  [Evision.KeyPoint.t()],
  [{atom(), term()}, ...] | nil
) ::
  {[Evision.KeyPoint.t()], Evision.Mat.t()} | {:error, String.t()}

Variant 1:

compute

Positional Arguments
  • self: Evision.AKAZE.t()

  • images: [Evision.Mat].

    Image set.

Return
  • keypoints: [[Evision.KeyPoint]].

    Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).

  • descriptors: [Evision.Mat].

    Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.

Has overloading in C++

Python prototype (for reference only):

compute(images, keypoints[, descriptors]) -> keypoints, descriptors

Variant 2:

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

Positional Arguments
Return
  • keypoints: [Evision.KeyPoint].

    Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).

  • descriptors: Evision.Mat.

    Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.

Python prototype (for reference only):

compute(image, keypoints[, descriptors]) -> keypoints, descriptors
@spec create() :: t() | {:error, String.t()}

The AKAZE constructor

Keyword Arguments
  • descriptor_type: AKAZE_DescriptorType.

    Type of the extracted descriptor: DESCRIPTOR_KAZE, DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.

  • descriptor_size: int.

    Size of the descriptor in bits. 0 -> Full size

  • descriptor_channels: int.

    Number of channels in the descriptor (1, 2, 3)

  • threshold: float.

    Detector response threshold to accept point

  • nOctaves: int.

    Maximum octave evolution of the image

  • nOctaveLayers: int.

    Default number of sublevels per scale level

  • diffusivity: KAZE_DiffusivityType.

    Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or DIFF_CHARBONNIER

Return

Python prototype (for reference only):

create([, descriptor_type[, descriptor_size[, descriptor_channels[, threshold[, nOctaves[, nOctaveLayers[, diffusivity]]]]]]]) -> retval
@spec create([{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}

The AKAZE constructor

Keyword Arguments
  • descriptor_type: AKAZE_DescriptorType.

    Type of the extracted descriptor: DESCRIPTOR_KAZE, DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.

  • descriptor_size: int.

    Size of the descriptor in bits. 0 -> Full size

  • descriptor_channels: int.

    Number of channels in the descriptor (1, 2, 3)

  • threshold: float.

    Detector response threshold to accept point

  • nOctaves: int.

    Maximum octave evolution of the image

  • nOctaveLayers: int.

    Default number of sublevels per scale level

  • diffusivity: KAZE_DiffusivityType.

    Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or DIFF_CHARBONNIER

Return

Python prototype (for reference only):

create([, descriptor_type[, descriptor_size[, descriptor_channels[, threshold[, nOctaves[, nOctaveLayers[, diffusivity]]]]]]]) -> retval
@spec defaultNorm(t()) :: integer() | {:error, String.t()}

defaultNorm

Positional Arguments
  • self: Evision.AKAZE.t()
Return
  • retval: int

Python prototype (for reference only):

defaultNorm() -> retval
@spec descriptorSize(t()) :: integer() | {:error, String.t()}

descriptorSize

Positional Arguments
  • self: Evision.AKAZE.t()
Return
  • retval: int

Python prototype (for reference only):

descriptorSize() -> retval
@spec descriptorType(t()) :: integer() | {:error, String.t()}

descriptorType

Positional Arguments
  • self: Evision.AKAZE.t()
Return
  • retval: int

Python prototype (for reference only):

descriptorType() -> retval
@spec detect(t(), [Evision.Mat.maybe_mat_in()]) ::
  [[Evision.KeyPoint.t()]] | {:error, String.t()}
@spec detect(t(), Evision.Mat.maybe_mat_in()) ::
  [Evision.KeyPoint.t()] | {:error, String.t()}

Variant 1:

detect

Positional Arguments
  • self: Evision.AKAZE.t()

  • images: [Evision.Mat].

    Image set.

Keyword Arguments
  • masks: [Evision.Mat].

    Masks for each input image specifying where to look for keypoints (optional). masks[i] is a mask for images[i].

Return
  • keypoints: [[Evision.KeyPoint]].

    The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .

Has overloading in C++

Python prototype (for reference only):

detect(images[, masks]) -> keypoints

Variant 2:

Detects keypoints in an image (first variant) or image set (second variant).

Positional Arguments
Keyword Arguments
  • mask: Evision.Mat.

    Mask specifying where to look for keypoints (optional). It must be a 8-bit integer matrix with non-zero values in the region of interest.

Return
  • keypoints: [Evision.KeyPoint].

    The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .

Python prototype (for reference only):

detect(image[, mask]) -> keypoints
Link to this function

detect(self, images, opts)

View Source
@spec detect(t(), [Evision.Mat.maybe_mat_in()], [{atom(), term()}, ...] | nil) ::
  [[Evision.KeyPoint.t()]] | {:error, String.t()}
@spec detect(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) ::
  [Evision.KeyPoint.t()] | {:error, String.t()}

Variant 1:

detect

Positional Arguments
  • self: Evision.AKAZE.t()

  • images: [Evision.Mat].

    Image set.

Keyword Arguments
  • masks: [Evision.Mat].

    Masks for each input image specifying where to look for keypoints (optional). masks[i] is a mask for images[i].

Return
  • keypoints: [[Evision.KeyPoint]].

    The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .

Has overloading in C++

Python prototype (for reference only):

detect(images[, masks]) -> keypoints

Variant 2:

Detects keypoints in an image (first variant) or image set (second variant).

Positional Arguments
Keyword Arguments
  • mask: Evision.Mat.

    Mask specifying where to look for keypoints (optional). It must be a 8-bit integer matrix with non-zero values in the region of interest.

Return
  • keypoints: [Evision.KeyPoint].

    The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .

Python prototype (for reference only):

detect(image[, mask]) -> keypoints
Link to this function

detectAndCompute(self, image, mask)

View Source
@spec detectAndCompute(t(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) ::
  {[Evision.KeyPoint.t()], Evision.Mat.t()} | {:error, String.t()}

detectAndCompute

Positional Arguments
Keyword Arguments
  • useProvidedKeypoints: bool.
Return

Detects keypoints and computes the descriptors

Python prototype (for reference only):

detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
Link to this function

detectAndCompute(self, image, mask, opts)

View Source
@spec detectAndCompute(
  t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  [{atom(), term()}, ...] | nil
) :: {[Evision.KeyPoint.t()], Evision.Mat.t()} | {:error, String.t()}

detectAndCompute

Positional Arguments
Keyword Arguments
  • useProvidedKeypoints: bool.
Return

Detects keypoints and computes the descriptors

Python prototype (for reference only):

detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
@spec empty(t()) :: boolean() | {:error, String.t()}

empty

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

Python prototype (for reference only):

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

getDefaultName

Positional Arguments
  • self: Evision.AKAZE.t()
Return

Python prototype (for reference only):

getDefaultName() -> retval
Link to this function

getDescriptorChannels(self)

View Source
@spec getDescriptorChannels(t()) :: integer() | {:error, String.t()}

getDescriptorChannels

Positional Arguments
  • self: Evision.AKAZE.t()
Return
  • retval: int

Python prototype (for reference only):

getDescriptorChannels() -> retval
@spec getDescriptorSize(t()) :: integer() | {:error, String.t()}

getDescriptorSize

Positional Arguments
  • self: Evision.AKAZE.t()
Return
  • retval: int

Python prototype (for reference only):

getDescriptorSize() -> retval
@spec getDescriptorType(t()) :: integer() | {:error, String.t()}

getDescriptorType

Positional Arguments
  • self: Evision.AKAZE.t()
Return
  • retval: AKAZE::DescriptorType

Python prototype (for reference only):

getDescriptorType() -> retval
@spec getDiffusivity(t()) :: integer() | {:error, String.t()}

getDiffusivity

Positional Arguments
  • self: Evision.AKAZE.t()
Return
  • retval: KAZE::DiffusivityType

Python prototype (for reference only):

getDiffusivity() -> retval
@spec getNOctaveLayers(t()) :: integer() | {:error, String.t()}

getNOctaveLayers

Positional Arguments
  • self: Evision.AKAZE.t()
Return
  • retval: int

Python prototype (for reference only):

getNOctaveLayers() -> retval
@spec getNOctaves(t()) :: integer() | {:error, String.t()}

getNOctaves

Positional Arguments
  • self: Evision.AKAZE.t()
Return
  • retval: int

Python prototype (for reference only):

getNOctaves() -> retval
@spec getThreshold(t()) :: number() | {:error, String.t()}

getThreshold

Positional Arguments
  • self: Evision.AKAZE.t()
Return
  • retval: double

Python prototype (for reference only):

getThreshold() -> retval
@spec read(t(), Evision.FileNode.t()) :: :ok | {:error, String.t()}
@spec read(t(), binary()) :: :ok | {:error, String.t()}

Variant 1:

read

Positional Arguments

Python prototype (for reference only):

read(arg1) -> None

Variant 2:

read

Positional Arguments
  • self: Evision.AKAZE.t()
  • fileName: String

Python prototype (for reference only):

read(fileName) -> None
Link to this function

setDescriptorChannels(self, dch)

View Source
@spec setDescriptorChannels(t(), integer()) :: :ok | {:error, String.t()}

setDescriptorChannels

Positional Arguments
  • self: Evision.AKAZE.t()
  • dch: int

Python prototype (for reference only):

setDescriptorChannels(dch) -> None
Link to this function

setDescriptorSize(self, dsize)

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

setDescriptorSize

Positional Arguments
  • self: Evision.AKAZE.t()
  • dsize: int

Python prototype (for reference only):

setDescriptorSize(dsize) -> None
Link to this function

setDescriptorType(self, dtype)

View Source
@spec setDescriptorType(t(), integer()) :: :ok | {:error, String.t()}

setDescriptorType

Positional Arguments
  • self: Evision.AKAZE.t()
  • dtype: AKAZE_DescriptorType

Python prototype (for reference only):

setDescriptorType(dtype) -> None
Link to this function

setDiffusivity(self, diff)

View Source
@spec setDiffusivity(t(), integer()) :: :ok | {:error, String.t()}

setDiffusivity

Positional Arguments
  • self: Evision.AKAZE.t()
  • diff: KAZE_DiffusivityType

Python prototype (for reference only):

setDiffusivity(diff) -> None
Link to this function

setNOctaveLayers(self, octaveLayers)

View Source
@spec setNOctaveLayers(t(), integer()) :: :ok | {:error, String.t()}

setNOctaveLayers

Positional Arguments
  • self: Evision.AKAZE.t()
  • octaveLayers: int

Python prototype (for reference only):

setNOctaveLayers(octaveLayers) -> None
Link to this function

setNOctaves(self, octaves)

View Source
@spec setNOctaves(t(), integer()) :: :ok | {:error, String.t()}

setNOctaves

Positional Arguments
  • self: Evision.AKAZE.t()
  • octaves: int

Python prototype (for reference only):

setNOctaves(octaves) -> None
Link to this function

setThreshold(self, threshold)

View Source
@spec setThreshold(t(), number()) :: :ok | {:error, String.t()}

setThreshold

Positional Arguments
  • self: Evision.AKAZE.t()
  • threshold: double

Python prototype (for reference only):

setThreshold(threshold) -> None
@spec write(t(), Evision.FileStorage.t()) :: :ok | {:error, String.t()}
@spec write(t(), binary()) :: :ok | {:error, String.t()}

Variant 1:

write

Positional Arguments
Keyword Arguments

Python prototype (for reference only):

write(fs[, name]) -> None

Variant 2:

write

Positional Arguments
  • self: Evision.AKAZE.t()
  • fileName: String

Python prototype (for reference only):

write(fileName) -> None
@spec write(t(), Evision.FileStorage.t(), [{atom(), term()}, ...] | nil) ::
  :ok | {:error, String.t()}

write

Positional Arguments
Keyword Arguments

Python prototype (for reference only):

write(fs[, name]) -> None