//javadoc: FisherFaceRecognizer::create()
public static FisherFaceRecognizer create()
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
FisherFaceRecognizer retVal = FisherFaceRecognizer.__fromPtr__(face_FisherFaceRecognizer_create_12());
return(retVal);
#else
return(null);
#endif
}
//
// C++: static Ptr_FisherFaceRecognizer cv::face::FisherFaceRecognizer::create(int num_components = 0, double threshold = DBL_MAX)
//
//javadoc: FisherFaceRecognizer::create(num_components, threshold)
public static FisherFaceRecognizer create(int num_components, double threshold)
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
FisherFaceRecognizer retVal = FisherFaceRecognizer.__fromPtr__(face_FisherFaceRecognizer_create_10(num_components, threshold));
return(retVal);
#else
return(null);
#endif
}
/**
* 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:
*
* <ul>
* <li>
* Training and prediction must be done on grayscale images, use cvtColor to convert between the
* color spaces.
* </li>
* <li>
* <b>THE FISHERFACES 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 FisherFaceRecognizer::create.
* </li>
* <li>
* threshold see FisherFaceRecognizer::create.
* </li>
* <li>
* eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
* </li>
* <li>
* eigenvectors The eigenvectors for this Linear Discriminant 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 labels corresponding to the projections.
* </li>
* </ul>
* return automatically generated
*/
public static FisherFaceRecognizer create()
{
return(FisherFaceRecognizer.__fromPtr__(face_FisherFaceRecognizer_create_12()));
}
/**
* 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:
*
* <ul>
* <li>
* Training and prediction must be done on grayscale images, use cvtColor to convert between the
* color spaces.
* </li>
* <li>
* <b>THE FISHERFACES 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 FisherFaceRecognizer::create.
* </li>
* <li>
* threshold see FisherFaceRecognizer::create.
* </li>
* <li>
* eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
* </li>
* <li>
* eigenvectors The eigenvectors for this Linear Discriminant 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 labels corresponding to the projections.
* </li>
* </ul>
* return automatically generated
*/
public static FisherFaceRecognizer create(int num_components)
{
return(FisherFaceRecognizer.__fromPtr__(face_FisherFaceRecognizer_create_11(num_components)));
}
//
// 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:
*
* <ul>
* <li>
* Training and prediction must be done on grayscale images, use cvtColor to convert between the
* color spaces.
* </li>
* <li>
* <b>THE FISHERFACES 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 FisherFaceRecognizer::create.
* </li>
* <li>
* threshold see FisherFaceRecognizer::create.
* </li>
* <li>
* eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
* </li>
* <li>
* eigenvectors The eigenvectors for this Linear Discriminant 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 labels corresponding to the projections.
* </li>
* </ul>
* return automatically generated
*/
public static FisherFaceRecognizer create(int num_components, double threshold)
{
return(FisherFaceRecognizer.__fromPtr__(face_FisherFaceRecognizer_create_10(num_components, threshold)));
}
public FisherFaceRecognizer(OpenCVForUnity.FaceModule.FisherFaceRecognizer nativeObj) : base(nativeObj)
{
}
