/// <summary>
/// Creates instance from cv::Ptr<T> .
/// ptr is disposed when the wrapper disposes.
/// </summary>
/// <param name="ptr"></param>
internal static FaceRecognizer FromPtr(IntPtr ptr)
{
if (ptr == IntPtr.Zero)
throw new OpenCvSharpException("Invalid cv::Ptr<FaceRecognizer> pointer");
var ptrObj = new Ptr<FaceRecognizer>(ptr);
var detector = new FaceRecognizer
{
recognizerPtr = ptrObj,
ptr = ptrObj.Get()
};
return detector;
}
/// <summary>
/// Creates instance from cv::Ptr<T> .
/// ptr is disposed when the wrapper disposes.
/// </summary>
/// <param name="ptr"></param>
internal static FaceRecognizer FromPtr(IntPtr ptr)
{
if (ptr == IntPtr.Zero)
{
throw new OpenCvSharpException($"Invalid cv::Ptr<{nameof(FaceRecognizer)}> pointer");
}
var ptrObj = new Ptr(ptr);
var detector = new FaceRecognizer
{
recognizerPtr = ptrObj,
ptr = ptrObj.Get()
};
return(detector);
}
/// <summary>
///
/// </summary>
/// <param name="radius">The radius used for building the Circular Local Binary Pattern. The greater the radius, the</param>
/// <param name="neighbors">The number of sample points to build a Circular Local Binary Pattern from.
/// An appropriate value is to use `8` sample points.Keep in mind: the more sample points you include, the higher the computational cost.</param>
/// <param name="gridX">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>
/// <param name="gridY">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>
/// <param name="threshold">The threshold applied in the prediction. If the distance to the nearest neighbor
/// is larger than the threshold, this method returns -1.</param>
/// <returns></returns>
public static LBPHFaceRecognizer CreateLBPHFaceRecognizer(int radius = 1, int neighbors = 8,
int gridX = 8, int gridY = 8, double threshold = Double.MaxValue)
{
return(FaceRecognizer.CreateLBPHFaceRecognizer(radius, neighbors, gridX, gridY, threshold));
}
/// <summary>
///
/// </summary>
/// <param name="numComponents">The number of components (read: Fisherfaces) kept for this Linear Discriminant Analysis
/// with the Fisherfaces criterion. It's useful to keep all components, that means the number of your classes c
/// (read: subjects, persons you want to recognize). If you leave this at the default (0) or set it
/// to a value less-equal 0 or greater (c-1), it will be set to the correct number (c-1) automatically.</param>
/// <param name="threshold">The threshold applied in the prediction. If the distance to the nearest neighbor
/// is larger than the threshold, this method returns -1.</param>
/// <returns></returns>
public static BasicFaceRecognizer CreateFisherFaceRecognizer(
int numComponents = 0, double threshold = Double.MaxValue)
{
return(FaceRecognizer.CreateFisherFaceRecognizer(numComponents, threshold));
}
