OpenCVForUnity.FaceModule.EigenFaceRecognizer Class Reference

Static Public Member Functions
static new EigenFaceRecognizer	__fromPtr__ (IntPtr addr)
static EigenFaceRecognizer	create ()
static EigenFaceRecognizer	create (int num_components)
static EigenFaceRecognizer	create (int num_components, double threshold)
Static Public Member Functions inherited from OpenCVForUnity.FaceModule.BasicFaceRecognizer
static new BasicFaceRecognizer	__fromPtr__ (IntPtr addr)
Static Public Member Functions inherited from OpenCVForUnity.FaceModule.FaceRecognizer
static new FaceRecognizer	__fromPtr__ (IntPtr addr)
Static Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
static Algorithm	__fromPtr__ (IntPtr addr)
Static Public Member Functions inherited from OpenCVForUnity.DisposableObject
static IntPtr	ThrowIfNullIntPtr (IntPtr ptr)

Protected Member Functions
override void	Dispose (bool disposing)
Protected Member Functions inherited from OpenCVForUnity.FaceModule.BasicFaceRecognizer
Protected Member Functions inherited from OpenCVForUnity.FaceModule.FaceRecognizer
override void	Dispose (bool disposing)
Protected Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
	DisposableOpenCVObject ()
	DisposableOpenCVObject (bool isEnabledDispose)
	DisposableOpenCVObject (IntPtr ptr)
	DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)
Protected Member Functions inherited from OpenCVForUnity.DisposableObject
	DisposableObject ()
	DisposableObject (bool isEnabledDispose)

Additional Inherited Members
Public Member Functions inherited from OpenCVForUnity.FaceModule.BasicFaceRecognizer
Mat	getEigenValues ()
Mat	getEigenVectors ()
Mat	getLabels ()
Mat	getMean ()
int	getNumComponents ()
List< Mat >	getProjections ()
double	getThreshold ()
void	setNumComponents (int val)
void	setThreshold (double val)
Public Member Functions inherited from OpenCVForUnity.FaceModule.FaceRecognizer
string	getLabelInfo (int label)
	Gets string information by label.
MatOfInt	getLabelsByString (string str)
	Gets vector of labels by string.
void	predict (Mat src, int[] label, double[] confidence)
	Predicts a label and associated confidence (e.g. distance) for a given input image.
void	predict_collect (Mat src, PredictCollector collector)
int	predict_label (Mat src)
void	read (string filename)
	Loads a FaceRecognizer and its model state.
void	setLabelInfo (int label, string strInfo)
	Sets string info for the specified model's label.
void	train (List< Mat > src, Mat labels)
	Trains a FaceRecognizer with given data and associated labels.
void	update (List< Mat > src, Mat labels)
	Updates a FaceRecognizer with given data and associated labels.
void	write (string filename)
	Saves a FaceRecognizer and its model state.
Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
virtual void	clear ()
	Clears the algorithm state.
virtual bool	empty ()
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
virtual string	getDefaultName ()
IntPtr	getNativeObjAddr ()
void	save (string filename)
Public Member Functions inherited from OpenCVForUnity.DisposableObject
void	Dispose ()
void	ThrowIfDisposed ()
Package Functions inherited from OpenCVForUnity.FaceModule.BasicFaceRecognizer
Package Functions inherited from OpenCVForUnity.FaceModule.FaceRecognizer
Package Functions inherited from OpenCVForUnity.CoreModule.Algorithm
Package Attributes inherited from OpenCVForUnity.DisposableOpenCVObject
Properties inherited from OpenCVForUnity.DisposableObject
bool	IsDisposed [get, protected set]
bool	IsEnabledDispose [get, set]

Member Function Documentation

__fromPtr__()

static new EigenFaceRecognizer OpenCVForUnity.FaceModule.EigenFaceRecognizer.__fromPtr__ ( IntPtr addr )

static

create() [1/3]

static EigenFaceRecognizer OpenCVForUnity.FaceModule.EigenFaceRecognizer.create ( )

static

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.

create() [2/3]

static EigenFaceRecognizer OpenCVForUnity.FaceModule.EigenFaceRecognizer.create ( int num_components )

static

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.

create() [3/3]

static EigenFaceRecognizer OpenCVForUnity.FaceModule.EigenFaceRecognizer.create ( int num_components, double threshold )

static

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.

Dispose()

override void OpenCVForUnity.FaceModule.EigenFaceRecognizer.Dispose ( bool disposing )

protectedvirtual

Reimplemented from OpenCVForUnity.FaceModule.BasicFaceRecognizer.

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The documentation for this class was generated from the following file:

• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv_contrib/face/EigenFaceRecognizer.cs