[−][src]Trait opencv::hub_prelude::FisherFaceRecognizer
Required methods
pub fn as_raw_FisherFaceRecognizer(&self) -> *const c_void
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pub fn as_raw_mut_FisherFaceRecognizer(&mut self) -> *mut c_void
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Implementations
impl<'_> dyn FisherFaceRecognizer + '_
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pub fn create(
num_components: i32,
threshold: f64
) -> Result<Ptr<dyn FisherFaceRecognizer>>
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num_components: i32,
threshold: f64
) -> Result<Ptr<dyn FisherFaceRecognizer>>
Parameters
- num_components: The number of components (read: Fisherfaces) kept for this Linear Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that means the number of your classes c (read: subjects, persons you want to recognize). If you leave this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the correct number (c-1) automatically.
- threshold: The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
Notes:
- Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
- THE FISHERFACES 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 FisherFaceRecognizer::create.
- threshold see FisherFaceRecognizer::create.
- eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
- eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their eigenvalue).
- mean The sample mean calculated from the training data.
- projections The projections of the training data.
- labels The labels corresponding to the projections.
C++ default parameters
- num_components: 0
- threshold: DBL_MAX