//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))); }