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Health/Assets/OpenCVForUnity/org/opencv_contrib/face/LBPHFaceRecognizer.cs

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create base
2023-11-07 13:55:35 +00:00
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<Mat> getHistograms()
{
ThrowIfDisposed();
List<Mat> retVal = new List<Mat>();
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:
*
* <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__(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:
*
* <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__(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:
*
* <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)
{
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:
*
* <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__(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:
*
* <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__(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:
*
* <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__(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);
}
}