/// <summary>
/// load the set of rectangular filters
/// </summary>
/// <param name="filename"></param>
/// <param name="rectangularFilter"></param>
public static RectangularFilter LoadFilterSet(string filename)
{
RectangularFilter rectangularFilter = null;
FileStream fstream = new FileStream(filename, FileMode.Open, FileAccess.Read);
BinaryFormatter fmt = new BinaryFormatter();
rectangularFilter = (RectangularFilter)fmt.Deserialize(fstream);
fstream.Close();
return(rectangularFilter);
}
/// <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;
}
/// <summary>
/// Get the filter responses based on the LBP integral histogram
/// </summary>
private SparseFilterExample CreateFilterResponses(LBPIntegralHistogrom iHist1, LBPIntegralHistogrom iHist2,
RectangularFilter rectangularFilters, List <int> listFilterIndices)
{
SparseFilterExample example = new SparseFilterExample();
System.Drawing.Rectangle rectangle;
float[] hist1;
float[] hist2;
foreach (int indexFilters in listFilterIndices)
{
rectangle = rectangularFilters.m_listRectangles[indexFilters];
hist1 = LBPIntegralHistogrom.Histogram(iHist1, rectangle);
hist2 = LBPIntegralHistogrom.Histogram(iHist2, rectangle);
example.Add(indexFilters, ArrayUtils.ChiSquare(hist1, hist2));
}
return(example);
}
