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

create

Keyword Arguments

• radius: int.

  The radius used for building the Circular Local Binary Pattern. The greater the radius, the smoother the image but more spatial information you can get.

• neighbors: int.

  The number of sample points to build a Circular Local Binary Pattern from. An appropriate value is to use 8 sample points. Keep in mind: the more sample points you include, the higher the computational cost.

• grid_x: int.

  The number of cells in the horizontal direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.

• grid_y: int.

  The number of cells in the vertical direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.

• threshold: double.

  The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.

Return

• retval: LBPHFaceRecognizer

Notes:

• The Circular Local Binary Patterns (used in training and prediction) expect the data given as grayscale images, use cvtColor to convert between the color spaces.

• This model supports updating.

Model internal data:

• radius see LBPHFaceRecognizer::create.

• neighbors see LBPHFaceRecognizer::create.

• grid_x see LLBPHFaceRecognizer::create.

• grid_y see LBPHFaceRecognizer::create.

• threshold see LBPHFaceRecognizer::create.

• histograms Local Binary Patterns Histograms calculated from the given training data (empty if none was given).

• labels Labels corresponding to the calculated Local Binary Patterns Histograms.

Python prototype (for reference only):

create([, radius[, neighbors[, grid_x[, grid_y[, threshold]]]]]) -> retval

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

create

Keyword Arguments

• radius: int.

  The radius used for building the Circular Local Binary Pattern. The greater the radius, the smoother the image but more spatial information you can get.

• neighbors: int.

  The number of sample points to build a Circular Local Binary Pattern from. An appropriate value is to use 8 sample points. Keep in mind: the more sample points you include, the higher the computational cost.

• grid_x: int.

  The number of cells in the horizontal direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.

• grid_y: int.

  The number of cells in the vertical direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.

• threshold: double.

  The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.

Return

• retval: LBPHFaceRecognizer

Notes:

• The Circular Local Binary Patterns (used in training and prediction) expect the data given as grayscale images, use cvtColor to convert between the color spaces.

• This model supports updating.

Model internal data:

• radius see LBPHFaceRecognizer::create.

• neighbors see LBPHFaceRecognizer::create.

• grid_x see LLBPHFaceRecognizer::create.

• grid_y see LBPHFaceRecognizer::create.

• threshold see LBPHFaceRecognizer::create.

• histograms Local Binary Patterns Histograms calculated from the given training data (empty if none was given).

• labels Labels corresponding to the calculated Local Binary Patterns Histograms.

Python prototype (for reference only):

create([, radius[, neighbors[, grid_x[, grid_y[, threshold]]]]]) -> retval

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

getGridX

Positional Arguments

• self: Evision.Face.LBPHFaceRecognizer.t()

Return

• retval: int

@see setGridX/2

Python prototype (for reference only):

getGridX() -> retval

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

getGridY

Positional Arguments

• self: Evision.Face.LBPHFaceRecognizer.t()

Return

• retval: int

@see setGridY/2

Python prototype (for reference only):

getGridY() -> retval

@spec getHistograms(t()) :: [Evision.Mat.t()] | {:error, String.t()}

getHistograms

Positional Arguments

• self: Evision.Face.LBPHFaceRecognizer.t()

Return

• retval: [Evision.Mat]

Python prototype (for reference only):

getHistograms() -> retval

@spec getLabels(t()) :: Evision.Mat.t() | {:error, String.t()}

getLabels

Positional Arguments

• self: Evision.Face.LBPHFaceRecognizer.t()

Return

• retval: Evision.Mat.t()

Python prototype (for reference only):

getLabels() -> retval

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

getNeighbors

Positional Arguments

• self: Evision.Face.LBPHFaceRecognizer.t()

Return

• retval: int

@see setNeighbors/2

Python prototype (for reference only):

getNeighbors() -> retval

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

getRadius

Positional Arguments

• self: Evision.Face.LBPHFaceRecognizer.t()

Return

• retval: int

@see setRadius/2

Python prototype (for reference only):

getRadius() -> retval

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

getThreshold

Positional Arguments

• self: Evision.Face.LBPHFaceRecognizer.t()

Return

• retval: double

@see setThreshold/2

Python prototype (for reference only):

getThreshold() -> retval

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

setGridX

Positional Arguments

• self: Evision.Face.LBPHFaceRecognizer.t()
• val: int

@see getGridX/1

Python prototype (for reference only):

setGridX(val) -> None

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

setGridY

Positional Arguments

• self: Evision.Face.LBPHFaceRecognizer.t()
• val: int

@see getGridY/1

Python prototype (for reference only):

setGridY(val) -> None

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

setNeighbors

Positional Arguments

• self: Evision.Face.LBPHFaceRecognizer.t()
• val: int

@see getNeighbors/1

Python prototype (for reference only):

setNeighbors(val) -> None

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

setRadius

Positional Arguments

• self: Evision.Face.LBPHFaceRecognizer.t()
• val: int

@see getRadius/1

Python prototype (for reference only):

setRadius(val) -> None

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

setThreshold

Positional Arguments

• self: Evision.Face.LBPHFaceRecognizer.t()
• val: double

@see getThreshold/1

Python prototype (for reference only):

setThreshold(val) -> None