cv::face::BasicFaceRecognizerを継承しています。

静的公開メンバ関数
static CV_WRAP Ptr< EigenFaceRecognizer >	create (int num_components=0, double threshold=DBL_MAX)

基底クラス cv::Algorithm に属する継承静的公開メンバ関数
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node [詳解]

template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file [詳解]

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String [詳解]

その他の継承メンバ
基底クラス cv::face::BasicFaceRecognizer に属する継承公開メンバ関数
CV_WRAP int	getNumComponents () const

CV_WRAP void	setNumComponents (int val)

CV_WRAP double	getThreshold () const CV_OVERRIDE

CV_WRAP void	setThreshold (double val) CV_OVERRIDE

CV_WRAP std::vector< cv::Mat >	getProjections () const

CV_WRAP cv::Mat	getLabels () const

CV_WRAP cv::Mat	getEigenValues () const

CV_WRAP cv::Mat	getEigenVectors () const

CV_WRAP cv::Mat	getMean () const

virtual void	read (const FileNode &fn) CV_OVERRIDE

virtual void	write (FileStorage &fs) const CV_OVERRIDE

virtual bool	empty () const CV_OVERRIDE

virtual CV_WRAP void	read (const String &filename)
	Loads a FaceRecognizer and its model state. [詳解]

virtual void	read (const FileNode &fn) CV_OVERRIDE=0

virtual CV_WRAP void	write (const String &filename) const
	Saves a FaceRecognizer and its model state. [詳解]

virtual void	write (FileStorage &fs) const CV_OVERRIDE=0

基底クラス cv::face::FaceRecognizer に属する継承公開メンバ関数
virtual CV_WRAP void	train (InputArrayOfArrays src, InputArray labels)=0
	Trains a FaceRecognizer with given data and associated labels. [詳解]

virtual CV_WRAP void	update (InputArrayOfArrays src, InputArray labels)
	Updates a FaceRecognizer with given data and associated labels. [詳解]

	CV_WRAP_AS (predict_label) int predict(InputArray src) const

CV_WRAP void	predict (InputArray src, CV_OUT int &label, CV_OUT double &confidence) const
	Predicts a label and associated confidence (e.g. distance) for a given input image. [詳解]

	CV_WRAP_AS (predict_collect) virtual void predict(InputArray src
	if implemented - send all result of prediction to collector that can be used for somehow custom result handling [詳解]

virtual CV_WRAP void	setLabelInfo (int label, const String &strInfo)
	Sets string info for the specified model's label. [詳解]

virtual CV_WRAP String	getLabelInfo (int label) const
	Gets string information by label. [詳解]

virtual CV_WRAP std::vector< int >	getLabelsByString (const String &str) const
	Gets vector of labels by string. [詳解]

基底クラス cv::Algorithm に属する継承公開メンバ関数
virtual CV_WRAP void	clear ()
	Clears the algorithm state [詳解]

CV_WRAP void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
	simplified API for language bindings これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。

virtual CV_WRAP void	save (const String &filename) const

virtual CV_WRAP String	getDefaultName () const

基底クラス cv::face::FaceRecognizer に属する継承公開変数類
Ptr< PredictCollector > collector	const = 0

基底クラス cv::Algorithm に属する継承限定公開メンバ関数
void	writeFormat (FileStorage &fs) const

基底クラス cv::face::BasicFaceRecognizer に属する継承限定公開変数類
int	_num_components

double	_threshold

std::vector< Mat >	_projections

Mat	_labels

Mat	_eigenvectors

Mat	_eigenvalues

Mat	_mean

基底クラス cv::face::FaceRecognizer に属する継承限定公開変数類
std::map< int, String >	_labelsInfo

関数詳解

◆ create()

static CV_WRAP Ptr< EigenFaceRecognizer > cv::face::EigenFaceRecognizer::create	(	int	num_components = `0`,
		double	threshold = `DBL_MAX`
	)

static

引数

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.

このクラス詳解は次のファイルから抽出されました:

facerec.hpp

静的公開メンバ関数

その他の継承メンバ

関数詳解

◆ create()

Notes:

Model internal data: