// // This file is auto-generated. Please don't modify it! // package org.opencv.face; import java.util.ArrayList; import java.util.List; import org.opencv.core.Mat; import org.opencv.face.FaceRecognizer; import org.opencv.face.LBPHFaceRecognizer; import org.opencv.utils.Converters; // C++: class LBPHFaceRecognizer public class LBPHFaceRecognizer extends FaceRecognizer { protected LBPHFaceRecognizer(long addr) { super(addr); } // internal usage only public static LBPHFaceRecognizer __fromPtr__(long addr) { return new LBPHFaceRecognizer(addr); } // // C++: int cv::face::LBPHFaceRecognizer::getGridX() // /** * SEE: setGridX * @return automatically generated */ public int getGridX() { return getGridX_0(nativeObj); } // // C++: void cv::face::LBPHFaceRecognizer::setGridX(int val) // /** * getGridX SEE: getGridX * @param val automatically generated */ public void setGridX(int val) { setGridX_0(nativeObj, val); } // // C++: int cv::face::LBPHFaceRecognizer::getGridY() // /** * SEE: setGridY * @return automatically generated */ public int getGridY() { return getGridY_0(nativeObj); } // // C++: void cv::face::LBPHFaceRecognizer::setGridY(int val) // /** * getGridY SEE: getGridY * @param val automatically generated */ public void setGridY(int val) { setGridY_0(nativeObj, val); } // // C++: int cv::face::LBPHFaceRecognizer::getRadius() // /** * SEE: setRadius * @return automatically generated */ public int getRadius() { return getRadius_0(nativeObj); } // // C++: void cv::face::LBPHFaceRecognizer::setRadius(int val) // /** * getRadius SEE: getRadius * @param val automatically generated */ public void setRadius(int val) { setRadius_0(nativeObj, val); } // // C++: int cv::face::LBPHFaceRecognizer::getNeighbors() // /** * SEE: setNeighbors * @return automatically generated */ public int getNeighbors() { return getNeighbors_0(nativeObj); } // // C++: void cv::face::LBPHFaceRecognizer::setNeighbors(int val) // /** * getNeighbors SEE: getNeighbors * @param val automatically generated */ public void setNeighbors(int val) { setNeighbors_0(nativeObj, val); } // // C++: double cv::face::LBPHFaceRecognizer::getThreshold() // /** * SEE: setThreshold * @return automatically generated */ public double getThreshold() { return getThreshold_0(nativeObj); } // // C++: void cv::face::LBPHFaceRecognizer::setThreshold(double val) // /** * getThreshold SEE: getThreshold * @param val automatically generated */ public void setThreshold(double val) { setThreshold_0(nativeObj, val); } // // C++: vector_Mat cv::face::LBPHFaceRecognizer::getHistograms() // public List getHistograms() { List retVal = new ArrayList(); Mat retValMat = new Mat(getHistograms_0(nativeObj)); Converters.Mat_to_vector_Mat(retValMat, retVal); return retVal; } // // C++: Mat cv::face::LBPHFaceRecognizer::getLabels() // public Mat getLabels() { return new Mat(getLabels_0(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__(create_0(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__(create_1(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__(create_2(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__(create_3(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__(create_4(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__(create_5()); } @Override protected void finalize() throws Throwable { delete(nativeObj); } // C++: int cv::face::LBPHFaceRecognizer::getGridX() private static native int getGridX_0(long nativeObj); // C++: void cv::face::LBPHFaceRecognizer::setGridX(int val) private static native void setGridX_0(long nativeObj, int val); // C++: int cv::face::LBPHFaceRecognizer::getGridY() private static native int getGridY_0(long nativeObj); // C++: void cv::face::LBPHFaceRecognizer::setGridY(int val) private static native void setGridY_0(long nativeObj, int val); // C++: int cv::face::LBPHFaceRecognizer::getRadius() private static native int getRadius_0(long nativeObj); // C++: void cv::face::LBPHFaceRecognizer::setRadius(int val) private static native void setRadius_0(long nativeObj, int val); // C++: int cv::face::LBPHFaceRecognizer::getNeighbors() private static native int getNeighbors_0(long nativeObj); // C++: void cv::face::LBPHFaceRecognizer::setNeighbors(int val) private static native void setNeighbors_0(long nativeObj, int val); // C++: double cv::face::LBPHFaceRecognizer::getThreshold() private static native double getThreshold_0(long nativeObj); // C++: void cv::face::LBPHFaceRecognizer::setThreshold(double val) private static native void setThreshold_0(long nativeObj, double val); // C++: vector_Mat cv::face::LBPHFaceRecognizer::getHistograms() private static native long getHistograms_0(long nativeObj); // C++: Mat cv::face::LBPHFaceRecognizer::getLabels() private static native long getLabels_0(long 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) private static native long create_0(int radius, int neighbors, int grid_x, int grid_y, double threshold); private static native long create_1(int radius, int neighbors, int grid_x, int grid_y); private static native long create_2(int radius, int neighbors, int grid_x); private static native long create_3(int radius, int neighbors); private static native long create_4(int radius); private static native long create_5(); // native support for java finalize() private static native void delete(long nativeObj); }