using OpenCVForUnity.CoreModule;
using OpenCVForUnity.UtilsModule;
using System;
using System.Collections.Generic;
using System.Runtime.InteropServices;
namespace OpenCVForUnity.FaceModule
{
// C++: class EigenFaceRecognizer
public class EigenFaceRecognizer : BasicFaceRecognizer
{
protected override void Dispose(bool disposing)
{
try
{
if (disposing)
{
}
if (IsEnabledDispose)
{
if (nativeObj != IntPtr.Zero)
face_EigenFaceRecognizer_delete(nativeObj);
nativeObj = IntPtr.Zero;
}
}
finally
{
base.Dispose(disposing);
}
}
protected internal EigenFaceRecognizer(IntPtr addr) : base(addr) { }
// internal usage only
public static new EigenFaceRecognizer __fromPtr__(IntPtr addr) { return new EigenFaceRecognizer(addr); }
//
// C++: static Ptr_EigenFaceRecognizer cv::face::EigenFaceRecognizer::create(int num_components = 0, double threshold = DBL_MAX)
//
/**
* param num_components The number of components (read: Eigenfaces) kept for this Principal
* Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be
* kept for good reconstruction capabilities. It is based on your input data, so experiment with the
* number. Keeping 80 components should almost always be sufficient.
* param threshold The threshold applied in the prediction.
*
* ### Notes:
*
*
* -
* Training and prediction must be done on grayscale images, use cvtColor to convert between the
* color spaces.
*
* -
* THE EIGENFACES 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 EigenFaceRecognizer::create.
*
* -
* threshold see EigenFaceRecognizer::create.
*
* -
* eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
*
* -
* eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their
* eigenvalue).
*
* -
* mean The sample mean calculated from the training data.
*
* -
* projections The projections of the training data.
*
* -
* labels The threshold applied in the prediction. If the distance to the nearest neighbor is
* larger than the threshold, this method returns -1.
*
*
* return automatically generated
*/
public static EigenFaceRecognizer create(int num_components, double threshold)
{
return EigenFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(face_EigenFaceRecognizer_create_10(num_components, threshold)));
}
/**
* param num_components The number of components (read: Eigenfaces) kept for this Principal
* Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be
* kept for good reconstruction capabilities. It is based on your input data, so experiment with the
* number. Keeping 80 components should almost always be sufficient.
*
* ### Notes:
*
*
* -
* Training and prediction must be done on grayscale images, use cvtColor to convert between the
* color spaces.
*
* -
* THE EIGENFACES 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 EigenFaceRecognizer::create.
*
* -
* threshold see EigenFaceRecognizer::create.
*
* -
* eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
*
* -
* eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their
* eigenvalue).
*
* -
* mean The sample mean calculated from the training data.
*
* -
* projections The projections of the training data.
*
* -
* labels The threshold applied in the prediction. If the distance to the nearest neighbor is
* larger than the threshold, this method returns -1.
*
*
* return automatically generated
*/
public static EigenFaceRecognizer create(int num_components)
{
return EigenFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(face_EigenFaceRecognizer_create_11(num_components)));
}
/**
* Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be
* kept for good reconstruction capabilities. It is based on your input data, so experiment with the
* number. Keeping 80 components should almost always be sufficient.
*
* ### Notes:
*
*
* -
* Training and prediction must be done on grayscale images, use cvtColor to convert between the
* color spaces.
*
* -
* THE EIGENFACES 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 EigenFaceRecognizer::create.
*
* -
* threshold see EigenFaceRecognizer::create.
*
* -
* eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
*
* -
* eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their
* eigenvalue).
*
* -
* mean The sample mean calculated from the training data.
*
* -
* projections The projections of the training data.
*
* -
* labels The threshold applied in the prediction. If the distance to the nearest neighbor is
* larger than the threshold, this method returns -1.
*
*
* return automatically generated
*/
public static EigenFaceRecognizer create()
{
return EigenFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(face_EigenFaceRecognizer_create_12()));
}
#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
const string LIBNAME = "__Internal";
#else
const string LIBNAME = "opencvforunity";
#endif
// C++: static Ptr_EigenFaceRecognizer cv::face::EigenFaceRecognizer::create(int num_components = 0, double threshold = DBL_MAX)
[DllImport(LIBNAME)]
private static extern IntPtr face_EigenFaceRecognizer_create_10(int num_components, double threshold);
[DllImport(LIBNAME)]
private static extern IntPtr face_EigenFaceRecognizer_create_11(int num_components);
[DllImport(LIBNAME)]
private static extern IntPtr face_EigenFaceRecognizer_create_12();
// native support for java finalize()
[DllImport(LIBNAME)]
private static extern void face_EigenFaceRecognizer_delete(IntPtr nativeObj);
}
}