pub trait FacemarkKazemi: Facemark + FacemarkKazemiConst {
    fn as_raw_mut_FacemarkKazemi(&mut self) -> *mut c_void;

    fn training(
        &mut self,
        images: &mut Vector<Mat>,
        landmarks: &mut Vector<Vector<Point2f>>,
        configfile: &str,
        scale: Size,
        model_filename: &str
    ) -> Result<bool> { ... } fn set_face_detector(
        &mut self,
        f: Option<Box<dyn FnMut(*const c_void, *const c_void) -> bool + Send + Sync + 'static>>
    ) -> Result<bool> { ... } fn get_faces(
        &mut self,
        image: &dyn ToInputArray,
        faces: &mut dyn ToOutputArray
    ) -> Result<bool> { ... } }

Required Methods§

Provided Methods§

This function is used to train the model using gradient boosting to get a cascade of regressors which can then be used to predict shape.

Parameters
  • images: A vector of type cv::Mat which stores the images which are used in training samples.
  • landmarks: A vector of vectors of type cv::Point2f which stores the landmarks detected in a particular image.
  • scale: A size of type cv::Size to which all images and landmarks have to be scaled to.
  • configfile: A variable of type std::string which stores the name of the file storing parameters for training the model.
  • modelFilename: A variable of type std::string which stores the name of the trained model file that has to be saved.
Returns

A boolean value. The function returns true if the model is trained properly or false if it is not trained.

C++ default parameters
  • model_filename: “face_landmarks.dat”

set the custom face detector

get faces using the custom detector

Implementations§

C++ default parameters
  • parameters: FacemarkKazemi::Params()

Implementors§