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

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

Clears the algorithm state

Positional Arguments

• self: Evision.Face.FacemarkAAM.t()

Python prototype (for reference only):

clear() -> None

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

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

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

Positional Arguments

• self: Evision.Face.FacemarkAAM.t()

Return

• retval: bool

Python prototype (for reference only):

empty() -> retval

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

Detect facial landmarks from an image.

Positional Arguments

• self: Evision.Face.FacemarkAAM.t()

• image: Evision.Mat.

  Input image.

• faces: Evision.Mat.

  Output of the function which represent region of interest of the detected faces. Each face is stored in cv::Rect container.

Return

• retval: bool

• landmarks: [Evision.Mat].

  The detected landmark points for each faces.

Mat image = imread("image.jpg");
std::vector<Rect> faces;
std::vector<std::vector<Point2f> > landmarks;
facemark->fit(image, faces, landmarks);

Python prototype (for reference only):

fit(image, faces[, landmarks]) -> retval, landmarks

@spec fit(
  t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  [{atom(), term()}, ...] | nil
) ::
  [Evision.Mat.t()] | false | {:error, String.t()}

Detect facial landmarks from an image.

Positional Arguments

• self: Evision.Face.FacemarkAAM.t()

• image: Evision.Mat.

  Input image.

• faces: Evision.Mat.

  Output of the function which represent region of interest of the detected faces. Each face is stored in cv::Rect container.

Return

• retval: bool

• landmarks: [Evision.Mat].

  The detected landmark points for each faces.

Mat image = imread("image.jpg");
std::vector<Rect> faces;
std::vector<std::vector<Point2f> > landmarks;
facemark->fit(image, faces, landmarks);

Python prototype (for reference only):

fit(image, faces[, landmarks]) -> retval, landmarks

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

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

getDefaultName

Positional Arguments

• self: Evision.Face.FacemarkAAM.t()

Return

• retval: String

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Python prototype (for reference only):

getDefaultName() -> retval

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

A function to load the trained model before the fitting process.

Positional Arguments

• self: Evision.Face.FacemarkAAM.t()

• model: String.

  A string represent the filename of a trained model.

facemark->loadModel("../data/lbf.model");

Python prototype (for reference only):

loadModel(model) -> None

@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}

Reads algorithm parameters from a file storage

Positional Arguments

• self: Evision.Face.FacemarkAAM.t()
• func: Evision.FileNode

Python prototype (for reference only):

read(fn) -> None

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

save

Positional Arguments

• self: Evision.Face.FacemarkAAM.t()
• filename: String

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Python prototype (for reference only):

save(filename) -> None

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

Stores algorithm parameters in a file storage

Positional Arguments

• self: Evision.Face.FacemarkAAM.t()
• fs: Evision.FileStorage

Python prototype (for reference only):

write(fs) -> None

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

write

Positional Arguments

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

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None