@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.SIFT.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.SIFT.t()

• image: Evision.Mat.

  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.SIFT.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.SIFT.t()

• image: Evision.Mat.

  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()}

create

Keyword Arguments

• nfeatures: integer().

  The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)

• nOctaveLayers: integer().

  The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.

• contrastThreshold: double.

  The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.

• edgeThreshold: double.

  The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).

• sigma: double.

  The sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.

• enable_precise_upscale: bool.

  Whether to enable precise upscaling in the scale pyramid, which maps index \f$\texttt{x}\f$ to \f$\texttt{2x}\f$. This prevents localization bias. The option is disabled by default.

Return

• retval: Evision.SIFT.t()

Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.

Python prototype (for reference only):

create([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma[, enable_precise_upscale]]]]]]) -> retval

@spec create(Keyword.t()) :: any() | {:error, String.t()}

@spec create(
  [
    contrastThreshold: term(),
    edgeThreshold: term(),
    enable_precise_upscale: term(),
    nOctaveLayers: term(),
    nfeatures: term(),
    sigma: term()
  ]
  | nil
) :: t() | {:error, String.t()}

create

Keyword Arguments

• nfeatures: integer().

  The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)

• nOctaveLayers: integer().

  The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.

• contrastThreshold: double.

  The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.

• edgeThreshold: double.

  The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).

• sigma: double.

  The sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.

• enable_precise_upscale: bool.

  Whether to enable precise upscaling in the scale pyramid, which maps index \f$\texttt{x}\f$ to \f$\texttt{2x}\f$. This prevents localization bias. The option is disabled by default.

Return

• retval: Evision.SIFT.t()

Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.

Python prototype (for reference only):

create([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma[, enable_precise_upscale]]]]]]) -> retval

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

Create SIFT with specified descriptorType.

Positional Arguments

• nfeatures: integer().

  The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)

• nOctaveLayers: integer().

  The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.

• contrastThreshold: double.

  The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.

• edgeThreshold: double.

  The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).

• sigma: double.

  The sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.

• descriptorType: integer().

  The type of descriptors. Only CV_32F and CV_8U are supported.

Keyword Arguments

• enable_precise_upscale: bool.

  Whether to enable precise upscaling in the scale pyramid, which maps index \f$\texttt{x}\f$ to \f$\texttt{2x}\f$. This prevents localization bias. The option is disabled by default.

Return

• retval: Evision.SIFT.t()

Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.

Python prototype (for reference only):

create(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, descriptorType[, enable_precise_upscale]) -> retval

@spec create(
  integer(),
  integer(),
  number(),
  number(),
  number(),
  integer(),
  [{:enable_precise_upscale, term()}] | nil
) :: t() | {:error, String.t()}

Create SIFT with specified descriptorType.

Positional Arguments

• nfeatures: integer().

  The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)

• nOctaveLayers: integer().

  The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.

• contrastThreshold: double.

  The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.

• edgeThreshold: double.

  The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).

• sigma: double.

  The sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.

• descriptorType: integer().

  The type of descriptors. Only CV_32F and CV_8U are supported.

Keyword Arguments

• enable_precise_upscale: bool.

  Whether to enable precise upscaling in the scale pyramid, which maps index \f$\texttt{x}\f$ to \f$\texttt{2x}\f$. This prevents localization bias. The option is disabled by default.

Return

• retval: Evision.SIFT.t()

Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.

Python prototype (for reference only):

create(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, descriptorType[, enable_precise_upscale]) -> retval

@spec defaultNorm(Keyword.t()) :: any() | {:error, String.t()}

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

defaultNorm

Positional Arguments

• self: Evision.SIFT.t()

Return

• retval: integer()

Python prototype (for reference only):

defaultNorm() -> retval

@spec descriptorSize(Keyword.t()) :: any() | {:error, String.t()}

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

descriptorSize

Positional Arguments

• self: Evision.SIFT.t()

Return

• retval: integer()

Python prototype (for reference only):

descriptorSize() -> retval

@spec descriptorType(Keyword.t()) :: any() | {:error, String.t()}

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

descriptorType

Positional Arguments

• self: Evision.SIFT.t()

Return

• retval: integer()

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.SIFT.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.SIFT.t()

• image: Evision.Mat.

  Image.

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

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

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

Variant 1:

detect

Positional Arguments

• self: Evision.SIFT.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.SIFT.t()

• image: Evision.Mat.

  Image.

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

@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.SIFT.t()
• image: Evision.Mat
• mask: Evision.Mat

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(),
  [{:useProvidedKeypoints, term()}] | nil
) :: {[Evision.KeyPoint.t()], Evision.Mat.t()} | {:error, String.t()}

detectAndCompute

Positional Arguments

• self: Evision.SIFT.t()
• image: Evision.Mat
• mask: Evision.Mat

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(Keyword.t()) :: any() | {:error, String.t()}

@spec empty(t()) :: boolean() | {:error, String.t()}

empty

Positional Arguments

• self: Evision.SIFT.t()

Return

• retval: bool

Python prototype (for reference only):

empty() -> retval

@spec getContrastThreshold(Keyword.t()) :: any() | {:error, String.t()}

@spec getContrastThreshold(t()) :: number() | {:error, String.t()}

getContrastThreshold

Positional Arguments

• self: Evision.SIFT.t()

Return

• retval: double

Python prototype (for reference only):

getContrastThreshold() -> retval

@spec getDefaultName(Keyword.t()) :: any() | {:error, String.t()}

@spec getDefaultName(t()) :: binary() | {:error, String.t()}

getDefaultName

Positional Arguments

• self: Evision.SIFT.t()

Return

• retval: String

Python prototype (for reference only):

getDefaultName() -> retval

@spec getEdgeThreshold(Keyword.t()) :: any() | {:error, String.t()}

@spec getEdgeThreshold(t()) :: number() | {:error, String.t()}

getEdgeThreshold

Positional Arguments

• self: Evision.SIFT.t()

Return

• retval: double

Python prototype (for reference only):

getEdgeThreshold() -> retval

@spec getNFeatures(Keyword.t()) :: any() | {:error, String.t()}

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

getNFeatures

Positional Arguments

• self: Evision.SIFT.t()

Return

• retval: integer()

Python prototype (for reference only):

getNFeatures() -> retval

@spec getNOctaveLayers(Keyword.t()) :: any() | {:error, String.t()}

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

getNOctaveLayers

Positional Arguments

• self: Evision.SIFT.t()

Return

• retval: integer()

Python prototype (for reference only):

getNOctaveLayers() -> retval

@spec getSigma(Keyword.t()) :: any() | {:error, String.t()}

@spec getSigma(t()) :: number() | {:error, String.t()}

getSigma

Positional Arguments

• self: Evision.SIFT.t()

Return

• retval: double

Python prototype (for reference only):

getSigma() -> 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.SIFT.t()
• arg1: Evision.FileNode

Python prototype (for reference only):

read(arg1) -> None

Variant 2:

read

Positional Arguments

• self: Evision.SIFT.t()
• fileName: String

Python prototype (for reference only):

read(fileName) -> None

@spec setContrastThreshold(t(), number()) :: t() | {:error, String.t()}

setContrastThreshold

Positional Arguments

• self: Evision.SIFT.t()
• contrastThreshold: double

Python prototype (for reference only):

setContrastThreshold(contrastThreshold) -> None

@spec setEdgeThreshold(t(), number()) :: t() | {:error, String.t()}

setEdgeThreshold

Positional Arguments

• self: Evision.SIFT.t()
• edgeThreshold: double

Python prototype (for reference only):

setEdgeThreshold(edgeThreshold) -> None

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

setNFeatures

Positional Arguments

• self: Evision.SIFT.t()
• maxFeatures: integer()

Python prototype (for reference only):

setNFeatures(maxFeatures) -> None

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

setNOctaveLayers

Positional Arguments

• self: Evision.SIFT.t()
• nOctaveLayers: integer()

Python prototype (for reference only):

setNOctaveLayers(nOctaveLayers) -> None

@spec setSigma(t(), number()) :: t() | {:error, String.t()}

setSigma

Positional Arguments

• self: Evision.SIFT.t()
• sigma: double

Python prototype (for reference only):

setSigma(sigma) -> None

@spec write(t(), binary()) :: t() | {:error, String.t()}

write

Positional Arguments

• self: Evision.SIFT.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.SIFT.t()
• fs: Evision.FileStorage
• name: String

Python prototype (for reference only):

write(fs, name) -> None