View Source Evision.XFeatures2D.TEBLID (Evision v0.1.34)

Summary

Types

t()

Type that represents an XFeatures2D.TEBLID struct.

Functions

Creates the TEBLID descriptor.

Creates the TEBLID descriptor.

defaultNorm

descriptorSize

descriptorType

Variant 1:

detect

Variant 1:

detect

empty

getDefaultName

Variant 1:

read

Types

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

Type that represents an XFeatures2D.TEBLID struct.

  • ref. reference()

    The underlying erlang resource variable.

Functions

Link to this function

compute(self, images, keypoints)

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

Creates the TEBLID descriptor.

Positional Arguments
  • scale_factor: float.Adjust the sampling window around detected keypoints:
    • <b> 1.00f </b> should be the scale for ORB keypoints
    • <b> 6.75f </b> should be the scale for SIFT detected keypoints
    • <b> 6.25f </b> is default and fits for KAZE, SURF detected keypoints
    • <b> 5.00f </b> should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints
Keyword Arguments
  • n_bits: int.

    Determine the number of bits in the descriptor. Should be either TEBLID::SIZE_256_BITS or TEBLID::SIZE_512_BITS.

Return
  • retval: TEBLID

Python prototype (for reference only):

create(scale_factor[, n_bits]) -> retval
Link to this function

create(scale_factor, opts)

View Source
@spec create(number(), [{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}

Creates the TEBLID descriptor.

Positional Arguments
  • scale_factor: float.Adjust the sampling window around detected keypoints:
    • <b> 1.00f </b> should be the scale for ORB keypoints
    • <b> 6.75f </b> should be the scale for SIFT detected keypoints
    • <b> 6.25f </b> is default and fits for KAZE, SURF detected keypoints
    • <b> 5.00f </b> should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints
Keyword Arguments
  • n_bits: int.

    Determine the number of bits in the descriptor. Should be either TEBLID::SIZE_256_BITS or TEBLID::SIZE_512_BITS.

Return
  • retval: TEBLID

Python prototype (for reference only):

create(scale_factor[, n_bits]) -> retval
@spec defaultNorm(t()) :: integer() | {:error, String.t()}

defaultNorm

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

Python prototype (for reference only):

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

descriptorSize

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

Python prototype (for reference only):

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

descriptorType

Positional Arguments
  • self: Evision.XFeatures2D.TEBLID.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.TEBLID.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.TEBLID.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.TEBLID.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.TEBLID.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.TEBLID.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.TEBLID.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.TEBLID.t()
Return
  • retval: bool

Python prototype (for reference only):

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

getDefaultName

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

Python prototype (for reference only):

getDefaultName() -> 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.TEBLID.t()
  • arg1: Evision.FileNode.t()

Python prototype (for reference only):

read(arg1) -> None

Variant 2:

read

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

Python prototype (for reference only):

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

write

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

Python prototype (for reference only):

write(fs, name) -> None