@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

@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

@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

@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

@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

• retval: String

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

@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

@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

@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

@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

@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