//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.face;
import org.opencv.face.BasicFaceRecognizer;
import org.opencv.face.FisherFaceRecognizer;
// C++: class FisherFaceRecognizer
public class FisherFaceRecognizer extends BasicFaceRecognizer {
protected FisherFaceRecognizer(long addr) { super(addr); }
// internal usage only
public static FisherFaceRecognizer __fromPtr__(long addr) { return new FisherFaceRecognizer(addr); }
//
// C++: static Ptr_FisherFaceRecognizer cv::face::FisherFaceRecognizer::create(int num_components = 0, double threshold = DBL_MAX)
//
/**
* @param 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.
* @param 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.
*
*
* @return automatically generated
*/
public static FisherFaceRecognizer create(int num_components, double threshold) {
return FisherFaceRecognizer.__fromPtr__(create_0(num_components, threshold));
}
/**
* @param 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.
*
*
*
* ### 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.
*
*
* @return automatically generated
*/
public static FisherFaceRecognizer create(int num_components) {
return FisherFaceRecognizer.__fromPtr__(create_1(num_components));
}
/**
* 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.
*
*
*
* ### 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.
*
*
* @return automatically generated
*/
public static FisherFaceRecognizer create() {
return FisherFaceRecognizer.__fromPtr__(create_2());
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: static Ptr_FisherFaceRecognizer cv::face::FisherFaceRecognizer::create(int num_components = 0, double threshold = DBL_MAX)
private static native long create_0(int num_components, double threshold);
private static native long create_1(int num_components);
private static native long create_2();
// native support for java finalize()
private static native void delete(long nativeObj);
}