Struct opencv::face::EigenFaceRecognizer

pub struct EigenFaceRecognizer { /* fields omitted */ }

Methods

impl EigenFaceRecognizer

pub fn as_raw_EigenFaceRecognizer(&self) -> *mut c_void

pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self

impl EigenFaceRecognizer

pub fn create(
    num_components: i32,
    threshold: f64
) -> Result<PtrOfEigenFaceRecognizer>

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

Trait Implementations

impl AlgorithmTrait for EigenFaceRecognizer

fn as_raw_Algorithm(&self) -> *mut c_void

fn clear(&mut self) -> Result<()>

Clears the algorithm state

fn write(&self, fs: &mut FileStorage) -> Result<()>

Stores algorithm parameters in a file storage

fn write_1(&self, fs: &PtrOfFileStorage, name: &str) -> Result<()>

simplified API for language bindings

fn read(&mut self, _fn: &FileNode) -> Result<()>

Reads algorithm parameters from a file storage

fn empty(&self) -> Result<bool>

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

fn save(&self, filename: &str) -> Result<()>

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

fn get_default_name(&self) -> Result<String>

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

impl BasicFaceRecognizerTrait for EigenFaceRecognizer

fn as_raw_BasicFaceRecognizer(&self) -> *mut c_void

fn get_num_components(&self) -> Result<i32>

@see setNumComponents

fn set_num_components(&mut self, val: i32) -> Result<()>

@copybrief getNumComponents @see getNumComponents

fn get_threshold(&self) -> Result<f64>

@see setThreshold

fn set_threshold(&mut self, val: f64) -> Result<()>

@copybrief getThreshold @see getThreshold

fn get_projections(&self) -> Result<VectorOfMat>

fn get_labels(&self) -> Result<Mat>

fn get_eigen_values(&self) -> Result<Mat>

fn get_eigen_vectors(&self) -> Result<Mat>

fn get_mean(&self) -> Result<Mat>

fn read(&mut self, _fn: &FileNode) -> Result<()>

fn write(&self, fs: &mut FileStorage) -> Result<()>

fn empty(&self) -> Result<bool>

impl Drop for EigenFaceRecognizer

fn drop(&mut self)

Executes the destructor for this type.

impl FaceRecognizer for EigenFaceRecognizer

fn as_raw_FaceRecognizer(&self) -> *mut c_void

fn train(
    &mut self,
    src: &dyn ToInputArray,
    labels: &dyn ToInputArray
) -> Result<()>

Trains a FaceRecognizer with given data and associated labels.

fn update(
    &mut self,
    src: &dyn ToInputArray,
    labels: &dyn ToInputArray
) -> Result<()>

Updates a FaceRecognizer with given data and associated labels.

fn predict(&self, src: &dyn ToInputArray) -> Result<i32>

fn predict_1(
    &self,
    src: &dyn ToInputArray,
    label: &mut i32,
    confidence: &mut f64
) -> Result<()>

Predicts a label and associated confidence (e.g. distance) for a given input image.

fn write(&self, filename: &str) -> Result<()>

Saves a FaceRecognizer and its model state.

fn read(&mut self, filename: &str) -> Result<()>

Loads a FaceRecognizer and its model state.

fn write_1(&self, fs: &mut FileStorage) -> Result<()>

Saves this model to a given FileStorage. ## Parameters * fs: The FileStorage to store this FaceRecognizer to.

fn read_1(&mut self, _fn: &FileNode) -> Result<()>

fn empty(&self) -> Result<bool>

fn set_label_info(&mut self, label: i32, str_info: &str) -> Result<()>

Sets string info for the specified model's label.

fn get_label_info(&self, label: i32) -> Result<String>

Gets string information by label.

fn get_labels_by_string(&self, str: &str) -> Result<VectorOfint>

Gets vector of labels by string.

fn get_threshold(&self) -> Result<f64>

threshold parameter accessor - required for default BestMinDist collector

fn set_threshold(&mut self, val: f64) -> Result<()>

Sets threshold of model

impl Send for EigenFaceRecognizer