using OpenCVForUnity.CoreModule;
using OpenCVForUnity.UtilsModule;
using System;
using System.Collections.Generic;
using System.Runtime.InteropServices;
namespace OpenCVForUnity.FaceModule
{
// C++: class FisherFaceRecognizer
public class FisherFaceRecognizer : BasicFaceRecognizer
{
protected override void Dispose(bool disposing)
{
try
{
if (disposing)
{
}
if (IsEnabledDispose)
{
if (nativeObj != IntPtr.Zero)
face_FisherFaceRecognizer_delete(nativeObj);
nativeObj = IntPtr.Zero;
}
}
finally
{
base.Dispose(disposing);
}
}
protected internal FisherFaceRecognizer(IntPtr addr) : base(addr) { }
// internal usage only
public static new FisherFaceRecognizer __fromPtr__(IntPtr 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__(DisposableObject.ThrowIfNullIntPtr(face_FisherFaceRecognizer_create_10(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__(DisposableObject.ThrowIfNullIntPtr(face_FisherFaceRecognizer_create_11(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__(DisposableObject.ThrowIfNullIntPtr(face_FisherFaceRecognizer_create_12()));
}
#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
const string LIBNAME = "__Internal";
#else
const string LIBNAME = "opencvforunity";
#endif
// C++: static Ptr_FisherFaceRecognizer cv::face::FisherFaceRecognizer::create(int num_components = 0, double threshold = DBL_MAX)
[DllImport(LIBNAME)]
private static extern IntPtr face_FisherFaceRecognizer_create_10(int num_components, double threshold);
[DllImport(LIBNAME)]
private static extern IntPtr face_FisherFaceRecognizer_create_11(int num_components);
[DllImport(LIBNAME)]
private static extern IntPtr face_FisherFaceRecognizer_create_12();
// native support for java finalize()
[DllImport(LIBNAME)]
private static extern void face_FisherFaceRecognizer_delete(IntPtr nativeObj);
}
}