Struct opencv::face::EigenFaceRecognizer

pub struct EigenFaceRecognizer { /* private fields */ }

Implementations

impl EigenFaceRecognizer

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

pub fn create_def() -> Result<Ptr<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.

Note

This alternative version of EigenFaceRecognizer::create function uses the following default values for its arguments:

• num_components: 0
• threshold: DBL_MAX

Trait Implementations

impl AlgorithmTrait for EigenFaceRecognizer

fn as_raw_mut_Algorithm(&mut self) -> *mut c_void

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

Clears the algorithm state

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

Reads algorithm parameters from a file storage

impl AlgorithmTraitConst for EigenFaceRecognizer

fn as_raw_Algorithm(&self) -> *const c_void

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

Stores algorithm parameters in a file storage

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

Stores algorithm parameters in a file storage

fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>

@deprecated

fn write_with_name_def(&self, fs: &Ptr<FileStorage>) -> Result<()>

👎Deprecated:

Note

Deprecated: ## Note This alternative version of AlgorithmTraitConst::write_with_name function uses the following default values for its arguments:

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_mut_BasicFaceRecognizer(&mut self) -> *mut c_void

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

See also

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

See also

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

impl BasicFaceRecognizerTraitConst for EigenFaceRecognizer

fn as_raw_BasicFaceRecognizer(&self) -> *const c_void

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

See also

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

See also

fn get_projections(&self) -> Result<Vector<Mat>>

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

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

impl Boxed for EigenFaceRecognizer

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

Wrap the specified raw pointer

fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

fn as_raw_mut(&mut self) -> *mut c_void

Return the underlying mutable raw pointer

impl Debug for EigenFaceRecognizer

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Drop for EigenFaceRecognizer

fn drop(&mut self)

Executes the destructor for this type.

impl EigenFaceRecognizerTrait for EigenFaceRecognizer

fn as_raw_mut_EigenFaceRecognizer(&mut self) -> *mut c_void

impl EigenFaceRecognizerTraitConst for EigenFaceRecognizer

fn as_raw_EigenFaceRecognizer(&self) -> *const c_void

impl FaceRecognizerTrait for EigenFaceRecognizer

fn as_raw_mut_FaceRecognizer(&mut self) -> *mut c_void

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

Trains a FaceRecognizer with given data and associated labels.

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

Updates a FaceRecognizer with given data and associated labels.

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

Loads a FaceRecognizer and its model state.

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

Loads a FaceRecognizer and its model state.

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

Sets string info for the specified model’s label.

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

Sets threshold of model

impl FaceRecognizerTraitConst for EigenFaceRecognizer

fn as_raw_FaceRecognizer(&self) -> *const c_void

fn predict_label(&self, src: &impl ToInputArray) -> Result<i32>

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

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

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

fn predict_collect( &self, src: &impl ToInputArray, collector: Ptr<PredictCollector> ) -> Result<()>

if implemented - send all result of prediction to collector that can be used for somehow custom result handling

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

Saves a FaceRecognizer and its model state.

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

Saves a FaceRecognizer and its model state.

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

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

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

Gets string information by label.

fn get_labels_by_string(&self, str: &str) -> Result<Vector<i32>>

Gets vector of labels by string.

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

threshold parameter accessor - required for default BestMinDist collector

impl From<EigenFaceRecognizer> for Algorithm

fn from(s: EigenFaceRecognizer) -> Self

Converts to this type from the input type.

impl From<EigenFaceRecognizer> for BasicFaceRecognizer

fn from(s: EigenFaceRecognizer) -> Self

Converts to this type from the input type.

impl From<EigenFaceRecognizer> for FaceRecognizer

fn from(s: EigenFaceRecognizer) -> Self

Converts to this type from the input type.

impl TryFrom<BasicFaceRecognizer> for EigenFaceRecognizer

type Error = Error

The type returned in the event of a conversion error.

fn try_from(s: BasicFaceRecognizer) -> Result<Self>

Performs the conversion.

impl TryFrom<FaceRecognizer> for EigenFaceRecognizer

type Error = Error

The type returned in the event of a conversion error.

fn try_from(s: FaceRecognizer) -> Result<Self>

Performs the conversion.

impl Send for EigenFaceRecognizer