pub trait EigenFaceRecognizer: BasicFaceRecognizer + EigenFaceRecognizerConst {
// Required method
fn as_raw_mut_EigenFaceRecognizer(&mut self) -> *mut c_void;
}
- 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.
- 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.
- 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.
- num_components: 0
- threshold: DBL_MAX