Package org.opencv.face
Class FisherFaceRecognizer
- java.lang.Object
-
- org.opencv.core.Algorithm
-
- org.opencv.face.FaceRecognizer
-
- org.opencv.face.BasicFaceRecognizer
-
- org.opencv.face.FisherFaceRecognizer
-
public class FisherFaceRecognizer extends BasicFaceRecognizer
-
-
Constructor Summary
Constructors Modifier Constructor Description protected
FisherFaceRecognizer(long addr)
-
Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static FisherFaceRecognizer
__fromPtr__(long addr)
static FisherFaceRecognizer
create()
Discriminant Analysis with the Fisherfaces criterion.static FisherFaceRecognizer
create(int num_components)
static FisherFaceRecognizer
create(int num_components, double threshold)
protected void
finalize()
-
Methods inherited from class org.opencv.face.BasicFaceRecognizer
getEigenValues, getEigenVectors, getLabels, getMean, getNumComponents, getProjections, getThreshold, setNumComponents, setThreshold
-
Methods inherited from class org.opencv.face.FaceRecognizer
getLabelInfo, getLabelsByString, predict, predict_collect, predict_label, read, setLabelInfo, train, update, write
-
Methods inherited from class org.opencv.core.Algorithm
clear, empty, getDefaultName, getNativeObjAddr, save
-
-
-
-
Method Detail
-
__fromPtr__
public static FisherFaceRecognizer __fromPtr__(long addr)
-
create
public static FisherFaceRecognizer create(int num_components, double threshold)
- 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.
- 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.
- Returns:
- automatically generated
-
create
public static FisherFaceRecognizer create(int num_components)
- 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. 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.
- 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.
- Returns:
- automatically generated
-
create
public static FisherFaceRecognizer create()
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. 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.
- 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.
- Returns:
- automatically generated
-
finalize
protected void finalize() throws java.lang.Throwable
- Overrides:
finalize
in classBasicFaceRecognizer
- Throws:
java.lang.Throwable
-
-