using OpenCVForUnity.CoreModule; using OpenCVForUnity.UtilsModule; using System; using System.Collections.Generic; using System.Runtime.InteropServices; namespace OpenCVForUnity.FaceModule { // C++: class LBPHFaceRecognizer public class LBPHFaceRecognizer : FaceRecognizer { protected override void Dispose(bool disposing) { try { if (disposing) { } if (IsEnabledDispose) { if (nativeObj != IntPtr.Zero) face_LBPHFaceRecognizer_delete(nativeObj); nativeObj = IntPtr.Zero; } } finally { base.Dispose(disposing); } } protected internal LBPHFaceRecognizer(IntPtr addr) : base(addr) { } // internal usage only public static new LBPHFaceRecognizer __fromPtr__(IntPtr addr) { return new LBPHFaceRecognizer(addr); } // // C++: int cv::face::LBPHFaceRecognizer::getGridX() // /** * SEE: setGridX * return automatically generated */ public int getGridX() { ThrowIfDisposed(); return face_LBPHFaceRecognizer_getGridX_10(nativeObj); } // // C++: void cv::face::LBPHFaceRecognizer::setGridX(int val) // /** * getGridX SEE: getGridX * param val automatically generated */ public void setGridX(int val) { ThrowIfDisposed(); face_LBPHFaceRecognizer_setGridX_10(nativeObj, val); } // // C++: int cv::face::LBPHFaceRecognizer::getGridY() // /** * SEE: setGridY * return automatically generated */ public int getGridY() { ThrowIfDisposed(); return face_LBPHFaceRecognizer_getGridY_10(nativeObj); } // // C++: void cv::face::LBPHFaceRecognizer::setGridY(int val) // /** * getGridY SEE: getGridY * param val automatically generated */ public void setGridY(int val) { ThrowIfDisposed(); face_LBPHFaceRecognizer_setGridY_10(nativeObj, val); } // // C++: int cv::face::LBPHFaceRecognizer::getRadius() // /** * SEE: setRadius * return automatically generated */ public int getRadius() { ThrowIfDisposed(); return face_LBPHFaceRecognizer_getRadius_10(nativeObj); } // // C++: void cv::face::LBPHFaceRecognizer::setRadius(int val) // /** * getRadius SEE: getRadius * param val automatically generated */ public void setRadius(int val) { ThrowIfDisposed(); face_LBPHFaceRecognizer_setRadius_10(nativeObj, val); } // // C++: int cv::face::LBPHFaceRecognizer::getNeighbors() // /** * SEE: setNeighbors * return automatically generated */ public int getNeighbors() { ThrowIfDisposed(); return face_LBPHFaceRecognizer_getNeighbors_10(nativeObj); } // // C++: void cv::face::LBPHFaceRecognizer::setNeighbors(int val) // /** * getNeighbors SEE: getNeighbors * param val automatically generated */ public void setNeighbors(int val) { ThrowIfDisposed(); face_LBPHFaceRecognizer_setNeighbors_10(nativeObj, val); } // // C++: double cv::face::LBPHFaceRecognizer::getThreshold() // /** * SEE: setThreshold * return automatically generated */ public double getThreshold() { ThrowIfDisposed(); return face_LBPHFaceRecognizer_getThreshold_10(nativeObj); } // // C++: void cv::face::LBPHFaceRecognizer::setThreshold(double val) // /** * getThreshold SEE: getThreshold * param val automatically generated */ public void setThreshold(double val) { ThrowIfDisposed(); face_LBPHFaceRecognizer_setThreshold_10(nativeObj, val); } // // C++: vector_Mat cv::face::LBPHFaceRecognizer::getHistograms() // public List getHistograms() { ThrowIfDisposed(); List retVal = new List(); Mat retValMat = new Mat(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_getHistograms_10(nativeObj))); Converters.Mat_to_vector_Mat(retValMat, retVal); return retVal; } // // C++: Mat cv::face::LBPHFaceRecognizer::getLabels() // public Mat getLabels() { ThrowIfDisposed(); return new Mat(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_getLabels_10(nativeObj))); } // // 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: * *
    *
  • * 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. *
    • *
  • * This model supports updating. *
    • *
* * ### Model internal data: * *
    *
  • * radius see LBPHFaceRecognizer::create. *
    • *
  • * neighbors see LBPHFaceRecognizer::create. *
    • *
  • * grid_x see LLBPHFaceRecognizer::create. *
    • *
  • * grid_y see LBPHFaceRecognizer::create. *
    • *
  • * threshold see LBPHFaceRecognizer::create. *
    • *
  • * histograms Local Binary Patterns Histograms calculated from the given training data (empty if * none was given). *
    • *
  • * labels Labels corresponding to the calculated Local Binary Patterns Histograms. *
    • *
* return automatically generated */ public static LBPHFaceRecognizer create(int radius, int neighbors, int grid_x, int grid_y, double threshold) { return LBPHFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_create_10(radius, neighbors, grid_x, grid_y, threshold))); } /** * 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: * *
    *
  • * 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. *
    • *
  • * This model supports updating. *
    • *
* * ### Model internal data: * *
    *
  • * radius see LBPHFaceRecognizer::create. *
    • *
  • * neighbors see LBPHFaceRecognizer::create. *
    • *
  • * grid_x see LLBPHFaceRecognizer::create. *
    • *
  • * grid_y see LBPHFaceRecognizer::create. *
    • *
  • * threshold see LBPHFaceRecognizer::create. *
    • *
  • * histograms Local Binary Patterns Histograms calculated from the given training data (empty if * none was given). *
    • *
  • * labels Labels corresponding to the calculated Local Binary Patterns Histograms. *
    • *
* return automatically generated */ public static LBPHFaceRecognizer create(int radius, int neighbors, int grid_x, int grid_y) { return LBPHFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_create_11(radius, neighbors, grid_x, grid_y))); } /** * 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. * 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: * *
    *
  • * 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. *
    • *
  • * This model supports updating. *
    • *
* * ### Model internal data: * *
    *
  • * radius see LBPHFaceRecognizer::create. *
    • *
  • * neighbors see LBPHFaceRecognizer::create. *
    • *
  • * grid_x see LLBPHFaceRecognizer::create. *
    • *
  • * grid_y see LBPHFaceRecognizer::create. *
    • *
  • * threshold see LBPHFaceRecognizer::create. *
    • *
  • * histograms Local Binary Patterns Histograms calculated from the given training data (empty if * none was given). *
    • *
  • * labels Labels corresponding to the calculated Local Binary Patterns Histograms. *
    • *
* return automatically generated */ public static LBPHFaceRecognizer create(int radius, int neighbors, int grid_x) { return LBPHFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_create_12(radius, neighbors, grid_x))); } /** * 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: * *
    *
  • * 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. *
    • *
  • * This model supports updating. *
    • *
* * ### Model internal data: * *
    *
  • * radius see LBPHFaceRecognizer::create. *
    • *
  • * neighbors see LBPHFaceRecognizer::create. *
    • *
  • * grid_x see LLBPHFaceRecognizer::create. *
    • *
  • * grid_y see LBPHFaceRecognizer::create. *
    • *
  • * threshold see LBPHFaceRecognizer::create. *
    • *
  • * histograms Local Binary Patterns Histograms calculated from the given training data (empty if * none was given). *
    • *
  • * labels Labels corresponding to the calculated Local Binary Patterns Histograms. *
    • *
* return automatically generated */ public static LBPHFaceRecognizer create(int radius, int neighbors) { return LBPHFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(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. * 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: * *
    *
  • * 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. *
    • *
  • * This model supports updating. *
    • *
* * ### Model internal data: * *
    *
  • * radius see LBPHFaceRecognizer::create. *
    • *
  • * neighbors see LBPHFaceRecognizer::create. *
    • *
  • * grid_x see LLBPHFaceRecognizer::create. *
    • *
  • * grid_y see LBPHFaceRecognizer::create. *
    • *
  • * threshold see LBPHFaceRecognizer::create. *
    • *
  • * histograms Local Binary Patterns Histograms calculated from the given training data (empty if * none was given). *
    • *
  • * labels Labels corresponding to the calculated Local Binary Patterns Histograms. *
    • *
* return automatically generated */ public static LBPHFaceRecognizer create(int radius) { return LBPHFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_create_14(radius))); } /** * 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: * *
    *
  • * 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. *
    • *
  • * This model supports updating. *
    • *
* * ### Model internal data: * *
    *
  • * radius see LBPHFaceRecognizer::create. *
    • *
  • * neighbors see LBPHFaceRecognizer::create. *
    • *
  • * grid_x see LLBPHFaceRecognizer::create. *
    • *
  • * grid_y see LBPHFaceRecognizer::create. *
    • *
  • * threshold see LBPHFaceRecognizer::create. *
    • *
  • * histograms Local Binary Patterns Histograms calculated from the given training data (empty if * none was given). *
    • *
  • * labels Labels corresponding to the calculated Local Binary Patterns Histograms. *
    • *
* return automatically generated */ public static LBPHFaceRecognizer create() { return LBPHFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_create_15())); } #if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR const string LIBNAME = "__Internal"; #else const string LIBNAME = "opencvforunity"; #endif // C++: int cv::face::LBPHFaceRecognizer::getGridX() [DllImport(LIBNAME)] private static extern int face_LBPHFaceRecognizer_getGridX_10(IntPtr nativeObj); // C++: void cv::face::LBPHFaceRecognizer::setGridX(int val) [DllImport(LIBNAME)] private static extern void face_LBPHFaceRecognizer_setGridX_10(IntPtr nativeObj, int val); // C++: int cv::face::LBPHFaceRecognizer::getGridY() [DllImport(LIBNAME)] private static extern int face_LBPHFaceRecognizer_getGridY_10(IntPtr nativeObj); // C++: void cv::face::LBPHFaceRecognizer::setGridY(int val) [DllImport(LIBNAME)] private static extern void face_LBPHFaceRecognizer_setGridY_10(IntPtr nativeObj, int val); // C++: int cv::face::LBPHFaceRecognizer::getRadius() [DllImport(LIBNAME)] private static extern int face_LBPHFaceRecognizer_getRadius_10(IntPtr nativeObj); // C++: void cv::face::LBPHFaceRecognizer::setRadius(int val) [DllImport(LIBNAME)] private static extern void face_LBPHFaceRecognizer_setRadius_10(IntPtr nativeObj, int val); // C++: int cv::face::LBPHFaceRecognizer::getNeighbors() [DllImport(LIBNAME)] private static extern int face_LBPHFaceRecognizer_getNeighbors_10(IntPtr nativeObj); // C++: void cv::face::LBPHFaceRecognizer::setNeighbors(int val) [DllImport(LIBNAME)] private static extern void face_LBPHFaceRecognizer_setNeighbors_10(IntPtr nativeObj, int val); // C++: double cv::face::LBPHFaceRecognizer::getThreshold() [DllImport(LIBNAME)] private static extern double face_LBPHFaceRecognizer_getThreshold_10(IntPtr nativeObj); // C++: void cv::face::LBPHFaceRecognizer::setThreshold(double val) [DllImport(LIBNAME)] private static extern void face_LBPHFaceRecognizer_setThreshold_10(IntPtr nativeObj, double val); // C++: vector_Mat cv::face::LBPHFaceRecognizer::getHistograms() [DllImport(LIBNAME)] private static extern IntPtr face_LBPHFaceRecognizer_getHistograms_10(IntPtr nativeObj); // C++: Mat cv::face::LBPHFaceRecognizer::getLabels() [DllImport(LIBNAME)] private static extern IntPtr face_LBPHFaceRecognizer_getLabels_10(IntPtr nativeObj); // 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) [DllImport(LIBNAME)] private static extern IntPtr face_LBPHFaceRecognizer_create_10(int radius, int neighbors, int grid_x, int grid_y, double threshold); [DllImport(LIBNAME)] private static extern IntPtr face_LBPHFaceRecognizer_create_11(int radius, int neighbors, int grid_x, int grid_y); [DllImport(LIBNAME)] private static extern IntPtr face_LBPHFaceRecognizer_create_12(int radius, int neighbors, int grid_x); [DllImport(LIBNAME)] private static extern IntPtr face_LBPHFaceRecognizer_create_13(int radius, int neighbors); [DllImport(LIBNAME)] private static extern IntPtr face_LBPHFaceRecognizer_create_14(int radius); [DllImport(LIBNAME)] private static extern IntPtr face_LBPHFaceRecognizer_create_15(); // native support for java finalize() [DllImport(LIBNAME)] private static extern void face_LBPHFaceRecognizer_delete(IntPtr nativeObj); } }