/// <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)
{
IntPtr p = NativeMethods.face_createFisherFaceRecognizer(numComponents, threshold);
return(BasicFaceRecognizer.FromPtr(p));
}
/// <summary>
/// Creates instance from cv::Ptr<T> .
/// ptr is disposed when the wrapper disposes.
/// </summary>
/// <param name="ptr"></param>
internal new static BasicFaceRecognizer FromPtr(IntPtr ptr)
{
if (ptr == IntPtr.Zero)
{
throw new OpenCvSharpException($"Invalid cv::Ptr<{nameof(BasicFaceRecognizer)}> pointer");
}
var ptrObj = new Ptr(ptr);
var detector = new BasicFaceRecognizer
{
recognizerPtr = ptrObj,
ptr = ptrObj.Get()
};
return(detector);
}
