/// <summary>
/// Get the filter response images from exEl
/// </summary>
/// <param name="exEl"></param>
/// <returns></returns>
private Image GetData(ExchangeElement exEl, LBPFilter lbpFilter)
{
int nHeight = exEl.Height;
int nWidth = exEl.Width;
int i;
char[] charArr = new char[exEl.ByteData.GetLength(0)];
for (i = 0; i < exEl.ByteData.GetLength(0); i++)
{
charArr[i] = Convert.ToChar(exEl.ByteData[i]);
}
Image dpuImgData = new Image(charArr, nWidth, nHeight);
Image rstImgData = null;
if ((_imgWidth != nWidth) || (_imgHeight != nHeight))
{
rstImgData = new Dpu.ImageProcessing.Image(_imgWidth, _imgHeight);
Image.BilinearResample(dpuImgData, rstImgData);
}
else
{
rstImgData = dpuImgData;
}
dpuImgData = new Image(_imgWidth, _imgHeight);
lbpFilter.FilterImage(rstImgData, dpuImgData);
return(dpuImgData);
}
/// <summary>
/// Calculate the LBP distance
/// </summary>
/// <param name="frontUIData"></param>
public void LBPDistance(DataExchange frontUIData)
{
ParseConfig(frontUIData);
List <Image> listFilteredImage = new List <Image>();
int nEl = frontUIData.ElementCount;
ExchangeElement exEl = null;
Image filteredImage = null;
LBPFilter lbpFilter = new LBPFilter(1, 8, true, true);
int i, j;
for (i = 0; i < nEl; i++)
{
exEl = frontUIData.GetElement(i);
filteredImage = GetData(exEl, lbpFilter);
listFilteredImage.Add(filteredImage);
}
SparseFilterExample example;
LBPIntegralHistogrom lbpHist1 = new LBPIntegralHistogrom();
LBPIntegralHistogrom lbpHist2 = new LBPIntegralHistogrom();
StrongClassifier strongClassifier = LoadClassifier(_classifierFileName);
RectangularFilter rectangularFilters = LoadFilterSet(_rectangularFileName);
List <int> listRectangularIndices = GetFilterIndex(strongClassifier);
float[] score = new float[2];
double[] expScore = new double[2];
double[,] distanceMatrix = new double[nEl, nEl];
for (i = 0; i < nEl; i++)
{
lbpHist1.Create(listFilteredImage[i], lbpFilter.m_nFilterRange + 1);
for (j = 0; j < i; j++)
{
lbpHist2.Create(listFilteredImage[j], lbpFilter.m_nFilterRange + 1);
example = CreateFilterResponses(lbpHist1, lbpHist2,
rectangularFilters, listRectangularIndices);
score[0] = score[1] = 0;
score = strongClassifier.Vote(example, score, _stage);
expScore[0] = Math.Exp(Convert.ToDouble(score[0]));
expScore[1] = Math.Exp(Convert.ToDouble(score[1]));
distanceMatrix[i, j] = expScore[0] / (expScore[0] + expScore[1]) + 0.05;
distanceMatrix[j, i] = distanceMatrix[i, j];
}
}
frontUIData.DistanceMatrix = distanceMatrix;
}