/// <summary>
/// サンプル集合からガウス混合パラメータを推定する
/// </summary>
/// <param name="samples"></param>
/// <param name="means0"></param>
/// <param name="covs0"></param>
/// <param name="weights0"></param>
/// <param name="logLikelihoods"></param>
/// <param name="labels"></param>
/// <param name="probs"></param>
#else
/// <summary>
/// Estimates Gaussian mixture parameters from the sample set
/// </summary>
/// <param name="samples"></param>
/// <param name="means0"></param>
/// <param name="covs0"></param>
/// <param name="weights0"></param>
/// <param name="logLikelihoods"></param>
/// <param name="labels"></param>
/// <param name="probs"></param>
#endif
public virtual bool TrainE(
InputArray samples,
InputArray means0,
InputArray covs0 = null,
InputArray weights0 = null,
OutputArray logLikelihoods = null,
OutputArray labels = null,
OutputArray probs = null)
{
if (disposed)
{
throw new ObjectDisposedException("EM");
}
if (samples == null)
{
throw new ArgumentNullException("samples");
}
if (means0 == null)
{
throw new ArgumentNullException("means0");
}
samples.ThrowIfDisposed();
means0.ThrowIfDisposed();
if (logLikelihoods != null)
{
logLikelihoods.ThrowIfNotReady();
}
if (covs0 != null)
{
covs0.ThrowIfDisposed();
}
if (weights0 != null)
{
weights0.ThrowIfDisposed();
}
if (labels != null)
{
labels.ThrowIfNotReady();
}
if (probs != null)
{
probs.ThrowIfNotReady();
}
int ret = NativeMethods.ml_EM_trainE(
ptr,
samples.CvPtr,
means0.CvPtr,
Cv2.ToPtr(covs0),
Cv2.ToPtr(weights0),
Cv2.ToPtr(logLikelihoods),
Cv2.ToPtr(labels),
Cv2.ToPtr(probs));
if (logLikelihoods != null)
{
logLikelihoods.Fix();
}
if (labels != null)
{
labels.Fix();
}
if (probs != null)
{
probs.Fix();
}
return(ret != 0);
}
/// <summary> /// /// </summary> /// <param name="frame0"></param> /// <param name="frame1"></param> /// <param name="flow1"></param> /// <param name="flow2"></param> protected abstract void Calc(InputArray frame0, InputArray frame1, OutputArray flow1, OutputArray flow2 = null);
/// <summary> /// /// </summary> /// <param name="frame0"></param> /// <param name="frame1"></param> /// <param name="flow1"></param> /// <param name="flow2"></param> public abstract void Calc(InputArray frame0, InputArray frame1, OutputArray flow1, OutputArray flow2 = null);
/// <summary>
///
/// </summary>
/// <param name="mat"></param>
/// <param name="distType"></param>
/// <param name="a"></param>
/// <param name="b"></param>
/// <param name="saturateRange"></param>
public void Fill(InputOutputArray mat, DistributionType distType, InputArray a, InputArray b,
bool saturateRange = false)
{
if (mat == null)
{
throw new ArgumentNullException(nameof(mat));
}
if (a == null)
{
throw new ArgumentNullException(nameof(a));
}
if (b == null)
{
throw new ArgumentNullException(nameof(b));
}
mat.ThrowIfNotReady();
a.ThrowIfDisposed();
b.ThrowIfDisposed();
NativeMethods.core_RNG_fill(ref state, mat.CvPtr, (int)distType, a.CvPtr, b.CvPtr, saturateRange ? 1 : 0);
mat.Fix();
}
/// <summary>
/// Calculates all of the moments
/// up to the third order of a polygon or rasterized shape.
/// </summary>
/// <param name="array">A raster image (single-channel, 8-bit or floating-point
/// 2D array) or an array ( 1xN or Nx1 ) of 2D points ( Point or Point2f )</param>
/// <param name="binaryImage">If it is true, then all the non-zero image pixels are treated as 1’s</param>
/// <returns></returns>
private void InitializeFromInputArray(InputArray array, bool binaryImage)
{
WCvMoments m = NativeMethods.imgproc_moments(array.CvPtr, binaryImage ? 1 : 0);
Initialize(m.m00, m.m10, m.m01, m.m20, m.m11, m.m02, m.m30, m.m21, m.m12, m.m03);
}
