//javadoc: LBPHFaceRecognizer::create(radius, neighbors, grid_x) public static LBPHFaceRecognizer create(int radius, int neighbors, int grid_x) { #if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER LBPHFaceRecognizer retVal = LBPHFaceRecognizer.__fromPtr__(face_LBPHFaceRecognizer_create_12(radius, neighbors, grid_x)); return(retVal); #else return(null); #endif }
//javadoc: LBPHFaceRecognizer::create() public static LBPHFaceRecognizer create() { #if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER LBPHFaceRecognizer retVal = LBPHFaceRecognizer.__fromPtr__(face_LBPHFaceRecognizer_create_15()); return(retVal); #else return(null); #endif }
void DoProcess() { if (!(owner.Value is OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer)) { LogError("owner is not initialized. Add Action \"newLBPHFaceRecognizer\"."); return; } OpenCVForUnity.FaceModule.LBPHFaceRecognizer wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer, OpenCVForUnity.FaceModule.LBPHFaceRecognizer>(owner); storeResult.Value = wrapped_owner.getGridX(); }
void DoProcess() { if (!(owner.Value is OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer)) { LogError("owner is not initialized. Add Action \"newLBPHFaceRecognizer\"."); return; } OpenCVForUnity.FaceModule.LBPHFaceRecognizer wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer, OpenCVForUnity.FaceModule.LBPHFaceRecognizer>(owner); wrapped_owner.setThreshold((float)val.Value); }
void DoProcess() { if (!(owner.Value is OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer)) { LogError("owner is not initialized. Add Action \"newLBPHFaceRecognizer\"."); return; } OpenCVForUnity.FaceModule.LBPHFaceRecognizer wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer, OpenCVForUnity.FaceModule.LBPHFaceRecognizer>(owner); if (!(storeResult.Value is OpenCVForUnityPlayMakerActions.Double)) { storeResult.Value = new OpenCVForUnityPlayMakerActions.Double(); } ((OpenCVForUnityPlayMakerActions.Double)storeResult.Value).wrappedObject = wrapped_owner.getThreshold(); }
void DoProcess() { if (!(owner.Value is OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer)) { LogError("owner is not initialized. Add Action \"newLBPHFaceRecognizer\"."); return; } OpenCVForUnity.FaceModule.LBPHFaceRecognizer wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer, OpenCVForUnity.FaceModule.LBPHFaceRecognizer>(owner); List <OpenCVForUnity.CoreModule.Mat> wrapped_storeResult = wrapped_owner.getHistograms(); if (!storeResult.IsNone) { OpenCVForUnityPlayMakerActionsUtils.ConvertListToFsmArray <OpenCVForUnity.CoreModule.Mat, OpenCVForUnityPlayMakerActions.Mat>(wrapped_storeResult, storeResult); } }
void DoProcess() { if (!(owner.Value is OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer)) { LogError("owner is not initialized. Add Action \"newLBPHFaceRecognizer\"."); return; } OpenCVForUnity.FaceModule.LBPHFaceRecognizer wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.LBPHFaceRecognizer, OpenCVForUnity.FaceModule.LBPHFaceRecognizer>(owner); if (!(val.Value is OpenCVForUnityPlayMakerActions.Double)) { LogError("val is not initialized. Add Action \"newDouble\"."); return; } System.Double wrapped_val = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.Double, System.Double>(val); wrapped_owner.setThreshold(wrapped_val); }
public LBPHFaceRecognizer(OpenCVForUnity.FaceModule.LBPHFaceRecognizer nativeObj) : base(nativeObj) { }
/** * radius, the smoother the image but more spatial information you can get. * appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include, * the higher the computational cost. * publications. The more cells, the finer the grid, the higher the dimensionality of the resulting * feature vector. * publications. The more cells, the finer the grid, the higher the dimensionality of the resulting * feature vector. * is larger than the threshold, this method returns -1. * * ### Notes: * * <ul> * <li> * The Circular Local Binary Patterns (used in training and prediction) expect the data given as * grayscale images, use cvtColor to convert between the color spaces. * </li> * <li> * This model supports updating. * </li> * </ul> * * ### Model internal data: * * <ul> * <li> * radius see LBPHFaceRecognizer::create. * </li> * <li> * neighbors see LBPHFaceRecognizer::create. * </li> * <li> * grid_x see LLBPHFaceRecognizer::create. * </li> * <li> * grid_y see LBPHFaceRecognizer::create. * </li> * <li> * threshold see LBPHFaceRecognizer::create. * </li> * <li> * histograms Local Binary Patterns Histograms calculated from the given training data (empty if * none was given). * </li> * <li> * labels Labels corresponding to the calculated Local Binary Patterns Histograms. * </li> * </ul> * return automatically generated */ public static LBPHFaceRecognizer create() { return(LBPHFaceRecognizer.__fromPtr__(face_LBPHFaceRecognizer_create_15())); }
/** * param radius The radius used for building the Circular Local Binary Pattern. The greater the * radius, the smoother the image but more spatial information you can get. * appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include, * the higher the computational cost. * publications. The more cells, the finer the grid, the higher the dimensionality of the resulting * feature vector. * publications. The more cells, the finer the grid, the higher the dimensionality of the resulting * feature vector. * is larger than the threshold, this method returns -1. * * ### Notes: * * <ul> * <li> * The Circular Local Binary Patterns (used in training and prediction) expect the data given as * grayscale images, use cvtColor to convert between the color spaces. * </li> * <li> * This model supports updating. * </li> * </ul> * * ### Model internal data: * * <ul> * <li> * radius see LBPHFaceRecognizer::create. * </li> * <li> * neighbors see LBPHFaceRecognizer::create. * </li> * <li> * grid_x see LLBPHFaceRecognizer::create. * </li> * <li> * grid_y see LBPHFaceRecognizer::create. * </li> * <li> * threshold see LBPHFaceRecognizer::create. * </li> * <li> * histograms Local Binary Patterns Histograms calculated from the given training data (empty if * none was given). * </li> * <li> * labels Labels corresponding to the calculated Local Binary Patterns Histograms. * </li> * </ul> * return automatically generated */ public static LBPHFaceRecognizer create(int radius) { return(LBPHFaceRecognizer.__fromPtr__(face_LBPHFaceRecognizer_create_14(radius))); }
/** * param radius The radius used for building the Circular Local Binary Pattern. The greater the * radius, the smoother the image but more spatial information you can get. * param neighbors The number of sample points to build a Circular Local Binary Pattern from. An * appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include, * the higher the computational cost. * publications. The more cells, the finer the grid, the higher the dimensionality of the resulting * feature vector. * publications. The more cells, the finer the grid, the higher the dimensionality of the resulting * feature vector. * is larger than the threshold, this method returns -1. * * ### Notes: * * <ul> * <li> * The Circular Local Binary Patterns (used in training and prediction) expect the data given as * grayscale images, use cvtColor to convert between the color spaces. * </li> * <li> * This model supports updating. * </li> * </ul> * * ### Model internal data: * * <ul> * <li> * radius see LBPHFaceRecognizer::create. * </li> * <li> * neighbors see LBPHFaceRecognizer::create. * </li> * <li> * grid_x see LLBPHFaceRecognizer::create. * </li> * <li> * grid_y see LBPHFaceRecognizer::create. * </li> * <li> * threshold see LBPHFaceRecognizer::create. * </li> * <li> * histograms Local Binary Patterns Histograms calculated from the given training data (empty if * none was given). * </li> * <li> * labels Labels corresponding to the calculated Local Binary Patterns Histograms. * </li> * </ul> * return automatically generated */ public static LBPHFaceRecognizer create(int radius, int neighbors) { return(LBPHFaceRecognizer.__fromPtr__(face_LBPHFaceRecognizer_create_13(radius, neighbors))); }
/** * param radius The radius used for building the Circular Local Binary Pattern. The greater the * radius, the smoother the image but more spatial information you can get. * param neighbors The number of sample points to build a Circular Local Binary Pattern from. An * appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include, * the higher the computational cost. * param grid_x The number of cells in the horizontal direction, 8 is a common value used in * publications. The more cells, the finer the grid, the higher the dimensionality of the resulting * feature vector. * param grid_y The number of cells in the vertical direction, 8 is a common value used in * publications. The more cells, the finer the grid, the higher the dimensionality of the resulting * feature vector. * is larger than the threshold, this method returns -1. * * ### Notes: * * <ul> * <li> * The Circular Local Binary Patterns (used in training and prediction) expect the data given as * grayscale images, use cvtColor to convert between the color spaces. * </li> * <li> * This model supports updating. * </li> * </ul> * * ### Model internal data: * * <ul> * <li> * radius see LBPHFaceRecognizer::create. * </li> * <li> * neighbors see LBPHFaceRecognizer::create. * </li> * <li> * grid_x see LLBPHFaceRecognizer::create. * </li> * <li> * grid_y see LBPHFaceRecognizer::create. * </li> * <li> * threshold see LBPHFaceRecognizer::create. * </li> * <li> * histograms Local Binary Patterns Histograms calculated from the given training data (empty if * none was given). * </li> * <li> * labels Labels corresponding to the calculated Local Binary Patterns Histograms. * </li> * </ul> * return automatically generated */ public static LBPHFaceRecognizer create(int radius, int neighbors, int grid_x, int grid_y) { return(LBPHFaceRecognizer.__fromPtr__(face_LBPHFaceRecognizer_create_11(radius, neighbors, grid_x, grid_y))); }
// // C++: static Ptr_LBPHFaceRecognizer cv::face::LBPHFaceRecognizer::create(int radius = 1, int neighbors = 8, int grid_x = 8, int grid_y = 8, double threshold = DBL_MAX) // /** * param radius The radius used for building the Circular Local Binary Pattern. The greater the * radius, the smoother the image but more spatial information you can get. * param neighbors The number of sample points to build a Circular Local Binary Pattern from. An * appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include, * the higher the computational cost. * param grid_x The number of cells in the horizontal direction, 8 is a common value used in * publications. The more cells, the finer the grid, the higher the dimensionality of the resulting * feature vector. * param grid_y The number of cells in the vertical direction, 8 is a common value used in * publications. The more cells, the finer the grid, the higher the dimensionality of the resulting * feature vector. * 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> * The Circular Local Binary Patterns (used in training and prediction) expect the data given as * grayscale images, use cvtColor to convert between the color spaces. * </li> * <li> * This model supports updating. * </li> * </ul> * * ### Model internal data: * * <ul> * <li> * radius see LBPHFaceRecognizer::create. * </li> * <li> * neighbors see LBPHFaceRecognizer::create. * </li> * <li> * grid_x see LLBPHFaceRecognizer::create. * </li> * <li> * grid_y see LBPHFaceRecognizer::create. * </li> * <li> * threshold see LBPHFaceRecognizer::create. * </li> * <li> * histograms Local Binary Patterns Histograms calculated from the given training data (empty if * none was given). * </li> * <li> * labels Labels corresponding to the calculated Local Binary Patterns Histograms. * </li> * </ul> * return automatically generated */ public static LBPHFaceRecognizer create(int radius, int neighbors, int grid_x, int grid_y, double threshold) { return(LBPHFaceRecognizer.__fromPtr__(face_LBPHFaceRecognizer_create_10(radius, neighbors, grid_x, grid_y, threshold))); }