//javadoc: EigenFaceRecognizer::create()
public static EigenFaceRecognizer create()
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
EigenFaceRecognizer retVal = EigenFaceRecognizer.__fromPtr__(face_EigenFaceRecognizer_create_12());
return(retVal);
#else
return(null);
#endif
}
//
// C++: static Ptr_EigenFaceRecognizer cv::face::EigenFaceRecognizer::create(int num_components = 0, double threshold = DBL_MAX)
//
//javadoc: EigenFaceRecognizer::create(num_components, threshold)
public static EigenFaceRecognizer create(int num_components, double threshold)
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
EigenFaceRecognizer retVal = EigenFaceRecognizer.__fromPtr__(face_EigenFaceRecognizer_create_10(num_components, threshold));
return(retVal);
#else
return(null);
#endif
}
public EigenFaceRecognizer(OpenCVForUnity.FaceModule.EigenFaceRecognizer nativeObj) : base(nativeObj)
{
}
/**
* 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:
*
* <ul>
* <li>
* Training and prediction must be done on grayscale images, use cvtColor to convert between the
* color spaces.
* </li>
* <li>
* <b>THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL
* SIZE.</b> (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.
* </li>
* <li>
* This model does not support updating.
* </li>
* </ul>
*
* ### Model internal data:
*
* <ul>
* <li>
* num_components see EigenFaceRecognizer::create.
* </li>
* <li>
* threshold see EigenFaceRecognizer::create.
* </li>
* <li>
* eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
* </li>
* <li>
* eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their
* eigenvalue).
* </li>
* <li>
* mean The sample mean calculated from the training data.
* </li>
* <li>
* projections The projections of the training data.
* </li>
* <li>
* labels The threshold applied in the prediction. If the distance to the nearest neighbor is
* larger than the threshold, this method returns -1.
* </li>
* </ul>
* return automatically generated
*/
public static EigenFaceRecognizer create()
{
return(EigenFaceRecognizer.__fromPtr__(face_EigenFaceRecognizer_create_12()));
}
/**
* 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:
*
* <ul>
* <li>
* Training and prediction must be done on grayscale images, use cvtColor to convert between the
* color spaces.
* </li>
* <li>
* <b>THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL
* SIZE.</b> (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.
* </li>
* <li>
* This model does not support updating.
* </li>
* </ul>
*
* ### Model internal data:
*
* <ul>
* <li>
* num_components see EigenFaceRecognizer::create.
* </li>
* <li>
* threshold see EigenFaceRecognizer::create.
* </li>
* <li>
* eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
* </li>
* <li>
* eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their
* eigenvalue).
* </li>
* <li>
* mean The sample mean calculated from the training data.
* </li>
* <li>
* projections The projections of the training data.
* </li>
* <li>
* labels The threshold applied in the prediction. If the distance to the nearest neighbor is
* larger than the threshold, this method returns -1.
* </li>
* </ul>
* return automatically generated
*/
public static EigenFaceRecognizer create(int num_components)
{
return(EigenFaceRecognizer.__fromPtr__(face_EigenFaceRecognizer_create_11(num_components)));
}
//
// 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:
*
* <ul>
* <li>
* Training and prediction must be done on grayscale images, use cvtColor to convert between the
* color spaces.
* </li>
* <li>
* <b>THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL
* SIZE.</b> (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.
* </li>
* <li>
* This model does not support updating.
* </li>
* </ul>
*
* ### Model internal data:
*
* <ul>
* <li>
* num_components see EigenFaceRecognizer::create.
* </li>
* <li>
* threshold see EigenFaceRecognizer::create.
* </li>
* <li>
* eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
* </li>
* <li>
* eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their
* eigenvalue).
* </li>
* <li>
* mean The sample mean calculated from the training data.
* </li>
* <li>
* projections The projections of the training data.
* </li>
* <li>
* labels The threshold applied in the prediction. If the distance to the nearest neighbor is
* larger than the threshold, this method returns -1.
* </li>
* </ul>
* return automatically generated
*/
public static EigenFaceRecognizer create(int num_components, double threshold)
{
return(EigenFaceRecognizer.__fromPtr__(face_EigenFaceRecognizer_create_10(num_components, threshold)));
}
