public LBPCreator(ImageGrayData imageData, bool isTeaching, bool isDivideToFragments, bool isMultithread = true)
{
var needSize = RecognitionParameters.RecognitionFragmentSize;
if (isTeaching)
{
needSize = RecognitionParameters.FragmentsSize;
}
if ((imageData.Width < needSize) ||
(imageData.Height < needSize))
{
throw new ArgumentException("Размер изображения меньше размера фрагмента для разбиения. \n" +
"Невозможно создать LBP-гистограмму");
}
fragmentSize = needSize;
this.isTeaching = isTeaching;
this.isDivideToFragments = isDivideToFragments;
this.isMultithread = isMultithread;
data = imageData.Data;
width = imageData.Width;
height = imageData.Height;
feature = null;
ConstructFeature();
}
public LBPCreator(ImageGrayData imageData, bool isTeaching, bool isDivideToFragments, bool isMultithread = true)
{
var needSize = RecognitionParameters.RecognitionFragmentSize;
if (isTeaching)
{
needSize = RecognitionParameters.FragmentsSize;
}
if ((imageData.Width < needSize) ||
(imageData.Height < needSize))
{
throw new ArgumentException("Размер изображения меньше размера фрагмента для разбиения. \n" +
"Невозможно создать LBP-гистограмму");
}
fragmentSize = needSize;
this.isTeaching = isTeaching;
this.isDivideToFragments = isDivideToFragments;
this.isMultithread = isMultithread;
data = imageData.Data;
width = imageData.Width;
height = imageData.Height;
feature = null;
ConstructFeature();
}
public TextureSample(ImageGrayData imageData, bool isDivideToFragments)
{
sample = imageData;
var glcmCreator = new GLCMCreator(sample, false, isDivideToFragments);
var lbpCreator = new LBPCreator(sample, false, isDivideToFragments);
glcm = glcmCreator.Feature;
lbp = lbpCreator.Feature;
}
public TextureSample(ImageGrayData imageData, bool isDivideToFragments)
{
sample = imageData;
var glcmCreator = new GLCMCreator(sample, false, isDivideToFragments);
var lbpCreator = new LBPCreator(sample, false, isDivideToFragments);
glcm = glcmCreator.Feature;
lbp = lbpCreator.Feature;
}
private void AddFeatures(GLCMFeature glcm, LBPFeature lbp, string path)
{
lock ((knownFiles as ICollection).SyncRoot)
{
++featuresCount;
glcmFeatures.Add(glcm);
lbpFeatures.Add(lbp);
knownFiles.Add(path);
}
}
public double GetDistance(LBPFeature other)
{
double result = 0;
for (int i = 0; i < 18; ++i)
{
result += MathHelpers.Sqr(data[i] - other[i]) / (data[i] + other[i]);
}
return(result);
}
private void ConstructFeature()
{
if (isDivideToFragments)
{
var list = GetSubfeatures();
feature = LBPFeature.BuildStandart(list);
}
else
{
feature = GetLBPFeature(new Fragment(0, 0, width, height));
}
}
public LBPFeature PrepareLBP(out double average, out double variance)
{
var lStandart = LBPFeature.BuildStandart(lbpFeatures);
var lDistances = new double[lbpFeatures.Count];
for (int i = 0; i < lbpFeatures.Count; ++i)
{
lDistances[i] = lStandart.GetDistance(lbpFeatures[i]);
}
average = MathHelpers.GetAverage(lDistances, 0, lbpFeatures.Count);
variance = MathHelpers.GetVariance(lDistances, average, 0, lbpFeatures.Count);
return(lStandart);
}
public List <int> DischargeLBP(LBPFeature standart, double average, double variance)
{
var result = new List <int>();
var tValue = alglib.studenttdistribution(lbpFeatures.Count - 1, RecognitionParameters.CompactFactor);
var deviation = Math.Sqrt(variance);
for (int i = 0; i < lbpFeatures.Count; ++i)
{
var value = Math.Abs(standart.GetDistance(lbpFeatures[i]) - average) / deviation;
if (value > tValue)
{
result.Add(i);
}
}
return(result);
}
internal void LoadKnowledges(XmlNode node)
{
Name = node.Attributes["name"].Value;
int r, g, b;
int.TryParse(node.Attributes["R"].Value, out r);
int.TryParse(node.Attributes["G"].Value, out g);
int.TryParse(node.Attributes["B"].Value, out b);
RegionColor = Color.FromArgb(r, g, b);
var samples = node.ChildNodes;
foreach (XmlNode item in samples)
{
var file = item.ChildNodes[0].Attributes["path"].Value;
var glcm = new GLCMFeature();
glcm.LoadKnowledges(item.ChildNodes[1]);
var lbp = new LBPFeature();
lbp.LoadKnowledges(item.ChildNodes[2]);
AddFeatures(glcm, lbp, file);
}
}
private void ConstructFeature()
{
if (isDivideToFragments)
{
var list = GetSubfeatures();
feature = LBPFeature.BuildStandart(list);
}
else
{
feature = GetLBPFeature(new Fragment(0, 0, width, height));
}
}
public double GetDistance(LBPFeature other)
{
double result = 0;
for (int i = 0; i < 18; ++i)
{
result += MathHelpers.Sqr(data[i] - other[i]) / (data[i] + other[i]);
}
return result;
}
private void AddFeatures(GLCMFeature glcm, LBPFeature lbp, string path)
{
lock ((knownFiles as ICollection).SyncRoot)
{
++featuresCount;
glcmFeatures.Add(glcm);
lbpFeatures.Add(lbp);
knownFiles.Add(path);
}
}
internal void LoadKnowledges(XmlNode node)
{
Name = node.Attributes["name"].Value;
int r, g, b;
int.TryParse(node.Attributes["R"].Value, out r);
int.TryParse(node.Attributes["G"].Value, out g);
int.TryParse(node.Attributes["B"].Value, out b);
RegionColor = Color.FromArgb(r, g, b);
var samples = node.ChildNodes;
foreach (XmlNode item in samples)
{
var file = item.ChildNodes[0].Attributes["path"].Value;
var glcm = new GLCMFeature();
glcm.LoadKnowledges(item.ChildNodes[1]);
var lbp = new LBPFeature();
lbp.LoadKnowledges(item.ChildNodes[2]);
AddFeatures(glcm, lbp, file);
}
}
public List<int> DischargeLBP(LBPFeature standart, double average, double variance)
{
var result = new List<int>();
var tValue = alglib.studenttdistribution(lbpFeatures.Count - 1, RecognitionParameters.CompactFactor);
var deviation = Math.Sqrt(variance);
for (int i = 0; i < lbpFeatures.Count; ++i)
{
var value = Math.Abs(standart.GetDistance(lbpFeatures[i]) - average) / deviation;
if (value > tValue)
{
result.Add(i);
}
}
return result;
}