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

Link to this section 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

Link to this section Types

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

Type that represents an XFeatures2D.TEBLID struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section 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