[−][src]Trait opencv::hub_prelude::EigenFaceRecognizer

pub trait EigenFaceRecognizer: BasicFaceRecognizer {
    pub fn as_raw_EigenFaceRecognizer(&self) -> *const c_void;

    pub fn as_raw_mut_EigenFaceRecognizer(&mut self) -> *mut c_void;
}

Required methods

pub fn as_raw_EigenFaceRecognizer(&self) -> *const c_void[src]

pub fn as_raw_mut_EigenFaceRecognizer(&mut self) -> *mut c_void[src]

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Implementations

impl<'_> dyn EigenFaceRecognizer + '_[src]

pub fn create(
    num_components: i32,
    threshold: f64
) -> Result<Ptr<dyn EigenFaceRecognizer>>[src]

Parameters

  • num_components: The number of components (read: Eigenfaces) kept for this Principal Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient.
  • threshold: The threshold applied in the prediction.

Notes:

  • Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
  • THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE. (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use resize to resize the images.
  • This model does not support updating.

Model internal data:

  • num_components see EigenFaceRecognizer::create.
  • threshold see EigenFaceRecognizer::create.
  • eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
  • eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue).
  • mean The sample mean calculated from the training data.
  • projections The projections of the training data.
  • labels The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.

C++ default parameters

  • num_components: 0
  • threshold: DBL_MAX

Implementors

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