View Source Evision.XFeatures2D.HarrisLaplaceFeatureDetector (Evision v0.1.28)

Link to this section Summary

Types

t()

Type that represents an XFeatures2D.HarrisLaplaceFeatureDetector struct.

Functions

Creates a new implementation instance.

Creates a new implementation instance.

defaultNorm

descriptorSize

descriptorType

Variant 1:

detect

Variant 1:

detect

empty

getCornThresh

getDefaultName

getDOGThresh

getMaxCorners

getNumLayers

getNumOctaves

Variant 1:

read

Link to this section Types

@type t() :: %Evision.XFeatures2D.HarrisLaplaceFeatureDetector{ref: reference()}

Type that represents an XFeatures2D.HarrisLaplaceFeatureDetector 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.XFeatures2D.HarrisLaplaceFeatureDetector.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
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()

  • image: Evision.Mat.t().

    Image.

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.t().

    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.XFeatures2D.HarrisLaplaceFeatureDetector.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
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()

  • image: Evision.Mat.t().

    Image.

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.t().

    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()}

Creates a new implementation instance.

Keyword Arguments
  • numOctaves: int.

    the number of octaves in the scale-space pyramid

  • corn_thresh: float.

    the threshold for the Harris cornerness measure

  • dOG_thresh: float.

    the threshold for the Difference-of-Gaussians scale selection

  • maxCorners: int.

    the maximum number of corners to consider

  • num_layers: int.

    the number of intermediate scales per octave

Return
  • retval: HarrisLaplaceFeatureDetector

Python prototype (for reference only):

create([, numOctaves[, corn_thresh[, DOG_thresh[, maxCorners[, num_layers]]]]]) -> retval
@spec create([{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}

Creates a new implementation instance.

Keyword Arguments
  • numOctaves: int.

    the number of octaves in the scale-space pyramid

  • corn_thresh: float.

    the threshold for the Harris cornerness measure

  • dOG_thresh: float.

    the threshold for the Difference-of-Gaussians scale selection

  • maxCorners: int.

    the maximum number of corners to consider

  • num_layers: int.

    the number of intermediate scales per octave

Return
  • retval: HarrisLaplaceFeatureDetector

Python prototype (for reference only):

create([, numOctaves[, corn_thresh[, DOG_thresh[, maxCorners[, num_layers]]]]]) -> retval
@spec defaultNorm(t()) :: integer() | {:error, String.t()}

defaultNorm

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
  • retval: int

Python prototype (for reference only):

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

descriptorSize

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
  • retval: int

Python prototype (for reference only):

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

descriptorType

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.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.XFeatures2D.HarrisLaplaceFeatureDetector.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
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()

  • image: Evision.Mat.t().

    Image.

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

    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.XFeatures2D.HarrisLaplaceFeatureDetector.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
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()

  • image: Evision.Mat.t().

    Image.

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

    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
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
  • image: Evision.Mat.t()
  • mask: Evision.Mat.t()
Keyword Arguments
  • useProvidedKeypoints: bool.
Return
  • keypoints: [Evision.KeyPoint]
  • descriptors: Evision.Mat.t().

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
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
  • image: Evision.Mat.t()
  • mask: Evision.Mat.t()
Keyword Arguments
  • useProvidedKeypoints: bool.
Return
  • keypoints: [Evision.KeyPoint]
  • descriptors: Evision.Mat.t().

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.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
  • retval: bool

Python prototype (for reference only):

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

getCornThresh

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
  • retval: float

Python prototype (for reference only):

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

getDefaultName

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return

Python prototype (for reference only):

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

getDOGThresh

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
  • retval: float

Python prototype (for reference only):

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

getMaxCorners

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
  • retval: int

Python prototype (for reference only):

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

getNumLayers

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
  • retval: int

Python prototype (for reference only):

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

getNumOctaves

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
Return
  • retval: int

Python prototype (for reference only):

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

Variant 1:

read

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
  • arg1: Evision.FileNode.t()

Python prototype (for reference only):

read(arg1) -> None

Variant 2:

read

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
  • fileName: String

Python prototype (for reference only):

read(fileName) -> None
Link to this function

setCornThresh(self, corn_thresh_)

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

setCornThresh

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
  • cornthresh: float

Python prototype (for reference only):

setCornThresh(corn_thresh_) -> None
Link to this function

setDOGThresh(self, dOG_thresh_)

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

setDOGThresh

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
  • dOGthresh: float

Python prototype (for reference only):

setDOGThresh(DOG_thresh_) -> None
Link to this function

setMaxCorners(self, maxCorners_)

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

setMaxCorners

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
  • maxCorners_: int

Python prototype (for reference only):

setMaxCorners(maxCorners_) -> None
Link to this function

setNumLayers(self, num_layers_)

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

setNumLayers

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
  • numlayers: int

Python prototype (for reference only):

setNumLayers(num_layers_) -> None
Link to this function

setNumOctaves(self, numOctaves_)

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

setNumOctaves

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
  • numOctaves_: int

Python prototype (for reference only):

setNumOctaves(numOctaves_) -> None
@spec write(t(), binary()) :: t() | {:error, String.t()}

write

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
  • fileName: String

Python prototype (for reference only):

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

write

Positional Arguments
  • self: Evision.XFeatures2D.HarrisLaplaceFeatureDetector.t()
  • fs: Evision.FileStorage.t()
  • name: String

Python prototype (for reference only):

write(fs, name) -> None