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// Targeted by JavaCPP version 1.5.10: DO NOT EDIT THIS FILE

package org.bytedeco.opencv.opencv_face;

import java.nio.*;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.annotation.*;

import static org.bytedeco.javacpp.presets.javacpp.*;
import static org.bytedeco.openblas.global.openblas_nolapack.*;
import static org.bytedeco.openblas.global.openblas.*;
import org.bytedeco.opencv.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_core.*;
import org.bytedeco.opencv.opencv_imgproc.*;
import static org.bytedeco.opencv.global.opencv_imgproc.*;
import static org.bytedeco.opencv.global.opencv_imgcodecs.*;
import org.bytedeco.opencv.opencv_videoio.*;
import static org.bytedeco.opencv.global.opencv_videoio.*;
import org.bytedeco.opencv.opencv_highgui.*;
import static org.bytedeco.opencv.global.opencv_highgui.*;
import org.bytedeco.opencv.opencv_flann.*;
import static org.bytedeco.opencv.global.opencv_flann.*;
import org.bytedeco.opencv.opencv_features2d.*;
import static org.bytedeco.opencv.global.opencv_features2d.*;
import org.bytedeco.opencv.opencv_calib3d.*;
import static org.bytedeco.opencv.global.opencv_calib3d.*;
import org.bytedeco.opencv.opencv_dnn.*;
import static org.bytedeco.opencv.global.opencv_dnn.*;
import org.bytedeco.opencv.opencv_objdetect.*;
import static org.bytedeco.opencv.global.opencv_objdetect.*;
import org.bytedeco.opencv.opencv_photo.*;
import static org.bytedeco.opencv.global.opencv_photo.*;

import static org.bytedeco.opencv.global.opencv_face.*;


@Namespace("cv::face") @Properties(inherit = org.bytedeco.opencv.presets.opencv_face.class)
public class FisherFaceRecognizer extends BasicFaceRecognizer {
    static { Loader.load(); }
    /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
    public FisherFaceRecognizer(Pointer p) { super(p); }
    /** Downcast constructor. */
    public FisherFaceRecognizer(Algorithm pointer) { super((Pointer)null); allocate(pointer); }
    @Namespace private native @Name("static_cast") void allocate(Algorithm pointer);

    /**
    @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. */ public static native @Ptr FisherFaceRecognizer create(int num_components/*=0*/, double threshold/*=DBL_MAX*/); public static native @Ptr FisherFaceRecognizer create(); }





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