[−][src]Trait opencv::face::prelude::EigenFaceRecognizer
Required methods
pub fn as_raw_EigenFaceRecognizer(&self) -> *const c_void
[src]
pub fn as_raw_mut_EigenFaceRecognizer(&mut self) -> *mut c_void
[src]
Implementations
impl<'_> dyn EigenFaceRecognizer + '_
[src]
pub fn create(
num_components: i32,
threshold: f64
) -> Result<Ptr<dyn EigenFaceRecognizer>>
[src]
num_components: i32,
threshold: f64
) -> Result<Ptr<dyn EigenFaceRecognizer>>
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