In the last few days I implemented an adaptive skin classifier in make it easier for the tracking algorithms to find the objects. Emgu already has an adaptive skin classifier but you cannot change any parameters and the results are horrible.
Anyway, mixing two histograms in Emgu is not as straightforward as one might think.
The internal representation of the histograms is the N-th dimensional matrix class , MatND and the OpenCV does not overload any operators.
Thankfully there is a function in the cvInvoke that does the job.
I wasted about half an hour trying to use the cvAdd function but it crashed the whole thing about a dozen times, so I ended up using the cvAddWeighted function.
Below is the code snippet for histogram mixing :
PS. On a following post I will post the adaptive skin classifier.
Anyway, mixing two histograms in Emgu is not as straightforward as one might think.
The internal representation of the histograms is the N-th dimensional matrix class , MatND and the OpenCV does not overload any operators.
Thankfully there is a function in the cvInvoke that does the job.
I wasted about half an hour trying to use the cvAdd function but it crashed the whole thing about a dozen times, so I ended up using the cvAddWeighted function.
Below is the code snippet for histogram mixing :
/// <summary>
/// Mixes two histograms , new histogram is _mixCoeff * _histTarget + (1 - _mixCoeff) * _histMix
/// Old target histogram is destroyed
/// </summary>
/// <param name="_histTarget">Target histogram</param>
/// <param name="_hist1">Histogram 1</param>
/// <param name="_hist2">Histogram 2</param>
/// <param name="_mixCoeff">Mixing Coefficient</param>
public static void MixHistograms(
DenseHistogram _histTarget,
DenseHistogram _hist1,
DenseHistogram _hist2,
float _mixCoeff)
{
CvInvoke.cvAddWeighted(
_hist1.MatND.Ptr,
_mixCoeff,
_hist2.MatND.Ptr,
1.0d - _mixCoeff,
0,
_histTarget.MatND.Ptr);
}
PS. On a following post I will post the adaptive skin classifier.
