OpenCVForUnity.FaceModule.FisherFaceRecognizer Class Reference

Inheritance diagram for OpenCVForUnity.FaceModule.FisherFaceRecognizer:

Static Public Member Functions
static new FisherFaceRecognizer	__fromPtr__ (IntPtr addr)
static FisherFaceRecognizer	create (int num_components, double threshold)
static FisherFaceRecognizer	create (int num_components)
static FisherFaceRecognizer	create ()
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.DisposableOpenCVObject
	DisposableOpenCVObject ()
	DisposableOpenCVObject (IntPtr ptr)
	DisposableOpenCVObject (bool isEnabledDispose)
	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
int	getNumComponents ()
void	setNumComponents (int val)
double	getThreshold ()
void	setThreshold (double val)
List< Mat >	getProjections ()
Mat	getLabels ()
Mat	getEigenValues ()
Mat	getEigenVectors ()
Mat	getMean ()
Public Member Functions inherited from OpenCVForUnity.FaceModule.FaceRecognizer
void	train (List< Mat > src, Mat labels)
	Trains a FaceRecognizer with given data and associated labels. More...
void	update (List< Mat > src, Mat labels)
	Updates a FaceRecognizer with given data and associated labels. More...
int	predict_label (Mat src)
void	predict (Mat src, int[] label, double[] confidence)
	Predicts a label and associated confidence (e.g. distance) for a given input image. More...
void	predict_collect (Mat src, PredictCollector collector)
	if implemented - send all result of prediction to collector that can be used for somehow custom result handling More...
void	write (string filename)
	Saves a FaceRecognizer and its model state. More...
void	read (string filename)
	Loads a FaceRecognizer and its model state. More...
void	setLabelInfo (int label, string strInfo)
	Sets string info for the specified model's label. More...
string	getLabelInfo (int label)
	Gets string information by label. More...
MatOfInt	getLabelsByString (string str)
	Gets vector of labels by string. More...
Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
IntPtr	getNativeObjAddr ()
virtual void	clear ()
	Clears the algorithm state. More...
virtual bool	empty ()
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...
void	save (string filename)
virtual string	getDefaultName ()
Public Member Functions inherited from OpenCVForUnity.DisposableObject
void	Dispose ()
void	ThrowIfDisposed ()
Properties inherited from OpenCVForUnity.DisposableObject
bool	IsDisposed [get, protected set]
bool	IsEnabledDispose [get, set]

Member Function Documentation

__fromPtr__()

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

static

create() [1/3]

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

static

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.

create() [2/3]

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

static

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.

create() [3/3]

static FisherFaceRecognizer OpenCVForUnity.FaceModule.FisherFaceRecognizer.create ( )

static

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.

Dispose()

override void OpenCVForUnity.FaceModule.FisherFaceRecognizer.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/FisherFaceRecognizer.cs