//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) { }