internal override double GeAssessment(AssesmentsSegment[] segments, IColorSheme colorSheme)
{
double bdc = GetBDC(segments, colorSheme);
double wcd = GetWDC(segments, colorSheme);
return(1 - 1d / Math.Exp(Math.Exp(bdc - wcd)));
}
private double GetWDC(AssesmentsSegment[] segments, IColorSheme colorSheme)
{
double wcd = 0;
for (int i = 0; i < segments.Length; i++)
{
for (int j = 0; j < segments[i].Length; j++)
{
wcd += colorSheme.AssessmentsDifference(segments[i].points[j], segments[i].center) / segments[i].Length;
}
}
return(wcd / segments.Length);
}
internal override double GeAssessment(AssesmentsSegment[] segments, IColorSheme colorSheme)
{
int k = segments.Length;
int n = segments[0].center.Length;
Matrix sumMatrix = new Matrix(n);
for (int i = 0; i < k; i++)
{
AssesmentsSegment current = segments[i];
sumMatrix += current.Length * GetMatrixOfCovariations(current, colorSheme);
}
return(sumMatrix.Determinant());
}
internal override double GeAssessment(AssesmentsSegment[] segments, IColorSheme colorSheme)
{
double distance = 0;
int k = segments.Length;
for (int j = 0; j < k; j++)
{
for (int l = 0; l < segments[j].Length; l++)
{
distance += Math.Pow(colorSheme.AssessmentsDifference(segments[j].points[l], segments[j].center), 2);
}
}
//distance += colorSheme.AssessmentsDifference(segments[j].points[l], segments[j].center);
return(distance);
}
internal override double GeAssessment(AssesmentsSegment[] segments, IColorSheme colorSheme)
{
double distance = 0;
int k = segments.Length;
for (int j = 0; j < k; j++)
{
for (int l = 0; l < segments[j].Length - 1; l++)
{
for (int h = l + 1; h < segments[j].Length; h++)
{
distance += colorSheme.AssessmentsDifference(segments[j].points[l], segments[j].points[h]);
}
}
}
return(distance);
}
internal override double GeAssessment(AssesmentsSegment[] segments, IColorSheme colorSheme)
{
int k = segments.Length;
SumOfPairwiseInternalDistancesAssessment tempassessment = new SumOfPairwiseInternalDistancesAssessment();
double a = tempassessment.GeAssessment(segments, colorSheme);
double denominatorToACoef = 0;
double denominatorToBCoef = 1;
for (int i = 0; i < k; i++)
{
var Nj = segments[i].Length;
denominatorToACoef += (Nj * (Nj - 1) / 2d);
denominatorToBCoef *= Nj;
}
a /= 1d / denominatorToACoef;
double b = 0;
for (int j = 0; j < k - 1; j++)
{
for (int l = 0; l < segments[j].Length; l++)
{
for (int m = j + 1; m < k; m++)
{
for (int h = 0; h < segments[m].Length; h++)
{
b += colorSheme.Difference(segments[j].points[l], segments[m].points[h]);
}
}
}
}
b /= 1d / denominatorToBCoef;
return(a / b);
}
private Matrix GetMatrixOfCovariations(AssesmentsSegment segment, IColorSheme colorSheme)
{
Func <double, double, int, double> d = null;
if (colorSheme is GrayScaleColorSheme || colorSheme is RGBColorSheme)
{
d = (p1, p2, dim) => (p1 - p2) / 255d;
}
else if (colorSheme is LabColorSheme)
{
d = (p1, p2, dim) => dim == 0 ? (p1 - p2) / 100d : (p2 - p2) / 128d;
}
int n = segment.center.Length;
double[] averages = segment.center;
double[][] cov = ArrayMatrix.GetJaggedArray(n, n);
for (int k = 0; k < n; k++)
{
for (int p = 0; p < n; p++)
{
double v = 0;
for (int l = 0; l < segment.Length; l++)
{
//v += ((segment.points[l][k] - averages[k])) * ((segment.points[l][p] - averages[p]));
v += d(segment.points[l][k], averages[k], k) * d(segment.points[l][p], averages[p], p);
}
cov[k][p] = v / segment.Length;
}
}
return(new Matrix(cov));
}
private void button4_Click(object sender, EventArgs e)
{
textBox1.Text = String.Empty;
double sigma = Convert.ToDouble(sigmaTextBox.Text.Replace(".", ","));
int k = Convert.ToInt32(KtextBox.Text);
int min = Convert.ToInt32(MinTextBox.Text);
IColorSheme colorSheme = null;
if (GreyScaleRadioButton.Checked)
{
colorSheme = new GrayScaleColorSheme();
}
if (RgbRadioButton.Checked)
{
colorSheme = new RGBColorSheme();
}
if (LabRadioButton.Checked)
{
colorSheme = new LabColorSheme();
}
Segmentation segmentObj = new Segmentation();
segmented = segmentObj.DoSegmentation(m_workImage, sigma, k, min, colorSheme);
OutputBitmapOnPictureBox(segmented);
outoutSegmented = true;
//вывод в текстбокс
textBox1.Text += $"Сегментів: {segmentObj.m_componentLength}" + Environment.NewLine + Environment.NewLine;
if (AssessmentCheckBox.Checked)
{
textBox1.Text += segmentObj.CalcAssessments();
}
}
private double GetBDC(AssesmentsSegment[] segments, IColorSheme colorSheme)
{
int N = segments.Sum(s => s.points.Count);
Vector v = new Vector(new double[3]);
for (int i = 0; i < segments.Length; i++)
{
v += new Vector(segments[i].center);
}
v /= segments.Length;
double[] centerOfCenters = v.GetCloneOfData();
double bcd = 0;
for (int i = 0; i < segments.Length; i++)
{
bcd += colorSheme.AssessmentsDifference(segments[i].center, centerOfCenters) * segments[i].Length;
}
return(bcd / (N * segments.Length));
}
public Bitmap DoSegmentation(double[,,] arrayImage, double sigma, double k, int minSize, IColorSheme colorSheme)
{
m_height = arrayImage.GetLength(1);
m_width = arrayImage.GetLength(2);
//debug
System.Diagnostics.Debug.WriteLine("Reading done: " + DateTime.Now);
m_colorSheme = colorSheme;
//препроцессинг иображения
arrayImage = colorSheme.Convert(arrayImage);
//сохранение для проведения оценки качества сегментации
m_arrayImageCopy = (double[, , ])arrayImage.Clone();
//debug
System.Diagnostics.Debug.WriteLine("color sheme changed: " + DateTime.Now);
//DebugImageInfo(arrayImage);
//smoothing
GaussianBlur gaussianBlur = new GaussianBlur();
double[][] filter = gaussianBlur.getKernel(sigma);
double[,,] blurredImage = DoubleArrayImageOperations.ConvolutionFilter(arrayImage, filter);
//debug
System.Diagnostics.Debug.WriteLine("Smooting done: " + DateTime.Now);
//тест размещения преобразования цвета
//arrayImage = colorSheme.Convert(arrayImage);
//построение графа
Edge[] edges = buildGraphByImage(blurredImage)
.OrderBy(el => el.w)
.ToArray();
//debug
System.Diagnostics.Debug.WriteLine("graph builded: " + DateTime.Now);
//debugging
System.Diagnostics.Debug.WriteLine("edges total: " + edges.Length);
//double minWeight = edges.Min(el => el.w);
//double maxWeight = edges.Max(el => el.w);
//Edge[] EdgesMoreThanMin = edges.Where(el => el.w > minWeight + 0.1).ToArray();
//Edge[] EdgesZeroWidth = edges.Where(el => el.w < 0.01).ToArray();
//
//Edge[] edgesHor = edges.Where(el => el.neightbourType == NeightbourType.Horizontal).ToArray();
//Edge[] edgesVer = edges.Where(el => el.neightbourType == NeightbourType.Vertical).ToArray();
//Edge[] edgesTopDiag = edges.Where(el => el.neightbourType == NeightbourType.TopDiagonal).ToArray();
//Edge[] edgesBottom = edges.Where(el => el.neightbourType == NeightbourType.BottomDiagonal).ToArray();
//сегментированный лес непересекающихся деревьев
DisjointSet segmentedSet = SegmentOnDisjointSet(k, m_height * m_width, edges); //картинка тут только для передачи размера потому осталась arrayImage
//запоминание в поле для проведения оценки
m_segmentedSet = segmentedSet;
//debug
System.Diagnostics.Debug.WriteLine("Segmented: " + DateTime.Now);
//присоеденить те, что меньше min_size к соседу по ребру
PostProcessSmallComponents(edges, segmentedSet, minSize);
//debug
System.Diagnostics.Debug.WriteLine("Small Component Merged: " + DateTime.Now);
return(SegmentedSetConverter.ConvertToBitmap(segmentedSet, m_height, m_width, out m_componentLength));
//var a = SegmentedSetConverter.ConvertToRealCoordsSegments(segmentedSet, height, width);
//return SegmentedSetConverter.RealCoordsSegmentResultToBitmap(a);
}
public Bitmap DoSegmentation(Bitmap bmp, double sigma, double k, int minSize, IColorSheme colorSheme)
{
double[,,] arrayImage = BitmapConverter.BitmapToDoubleRgb(bmp);
return(DoSegmentation(arrayImage, sigma, k, minSize, colorSheme));
}
public static AssesmentsSegment[] ConvertToAssessmentSegments(DisjointSet segmentedSet, int height, int width, double[,,] arrayImage, IColorSheme colorSheme)
{
Dictionary <int, List <double[]> > segments = new Dictionary <int, List <double[]> >();
for (int h = 0; h < height; h++)
{
for (int w = 0; w < width; w++)
{
int comp = segmentedSet.Find(h * width + w);
List <double[]> currentSegmentPointsList;
if (!segments.TryGetValue(comp, out currentSegmentPointsList))
{
currentSegmentPointsList = new List <double[]>();
segments.Add(comp, currentSegmentPointsList);
}
currentSegmentPointsList.Add(new double[] { arrayImage[0, h, w], arrayImage[1, h, w], arrayImage[2, h, w] });
}
}
return(segments.Values.ToList()
.Select(segmentPoints => new AssesmentsSegment(segmentPoints))
.ToArray());
}
internal string name; //если вдруг будет нужно написать нормальное название internal abstract double GeAssessment(AssesmentsSegment[] segments, IColorSheme colorSheme);
