/// <summary>
/// Load the classifier file
/// </summary>
/// <param name="loadFileName"></param>
/// <returns></returns>
public static StrongClassifier LoadClassifier(string loadFileName)
{
StreamReader sr = new StreamReader(loadFileName, System.Text.Encoding.Unicode);
XmlReader xr = new XmlTextReader(sr);
XmlSerializer ser = new XmlSerializer(typeof(StrongClassifier));
StrongClassifier classifier = (StrongClassifier)ser.Deserialize(xr);
xr.Close();
return(classifier);
}
/// <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 list of rectangular filter ID
/// </summary>
/// <param name="strongClassifier"></param>
/// <returns></returns>
private List <int> GetFilterIndex(StrongClassifier strongClassifier)
{
List <int> listRectangleIndex = new List <int>();
if (strongClassifier.Classifiers[0] is Feature)
{
foreach (Feature feature in strongClassifier.Classifiers)
{
if (!listRectangleIndex.Contains(feature.Nfilter))
{
listRectangleIndex.Add(feature.Nfilter);
}
}
}
else
{
if (strongClassifier.Classifiers[0] is DecisionTree)
{
foreach (DecisionTree decisionTree in strongClassifier.Classifiers)
{
DecisionTree.SubTreeCollection treeNodeCollection = new DecisionTree.SubTreeCollection(decisionTree);
foreach (DecisionTree treenode in treeNodeCollection)
{
if (treenode.Decision != null)
{
if (!listRectangleIndex.Contains(treenode.Decision.Nfilter))
{
listRectangleIndex.Add(treenode.Decision.Nfilter);
}
}
}
}
}
}
return(listRectangleIndex);
}
