new 3

  public DistribPack(Image_Project img)
        {
            distance = ND;
            norm = Brightness;
            volume_limit = 0.005;
            pack = new List<IStats>();
            List<IColor> prevcenters = CentersLoad(img);
            var tmplist = GetClustMask(img, prevcenters);

            for (int i = 0; i < tmplist.Count; i++)
            {
                pack.Add(new StatsForDistrib(img, tmplist[i], norm));
            }
           pack= UniteSame(img, pack);
        }
        public IPack Create(Image_Project img, Parts parts)
        {
            return new DistribPack(img, parts);
        }
        public DistribPack(Image_Project img, Parts parts)
        {
            distance = DN;
            norm = Evcklid;
            volume_limit = 0.015;
            var size = new Point(img.GetImage.Width, img.GetImage.Height);
            pack = new List<IStats>();
            int row = parts.Row;
            int column = parts.Column;
            IPack[,] tmppack = new IPack[row, column];
            for (int i = 0; i < row; i++)
            {
                for (int j = 0; j < column; j++)
                {
                    tmppack[i, j] = new DistribPack().Create(parts[i, j]);
                }
            }
        }
        List<IStats> UniteSame(Image_Project img, List<IStats> clust)
        {
            var res = clust;
            for (int i = 0; i < res.Count - 1; i++)
            {
                var s1 = res[i] as StatsForDistrib;
                for (int j = i + 1; j < res.Count; j++)
                {
                    var s2 = res[j] as StatsForDistrib;
                    // if(DispersionLimit(s1)&&DispersionLimit(s2))
                    if (Statistic_test(s1, s2))
                    {
                        res.Add(new StatsForDistrib().Create(img, UniteMaps(s1.GetMap, s2.GetMap), norm));
                        res.RemoveAt(j);
                        res.RemoveAt(i);
                        i=-1;
                        break;
                    }
                }
            }
            return res;
        }















        List<Mask> GetClustMask(Image_Project img, List<IColor> center)
        {
            var empty = new List<bool>();
            var clustvol = new List<int>();
            var res = new List<Mask>();
            int w = img.GetImage.Width;
            int h = img.GetImage.Height;
            int length = img.GetImage.GetLength();
            int numb_clust = 0;
            float dist = int.MaxValue;
            for (int i = 0; i < center.Count; i++)
            {
                clustvol.Add(0);
                empty.Add(true);
                res.Add(new Mask(w,h,true));
            }
            for (int i = 0; i < h; i++)
            {
                for (int j = 0; j < w; j++)
                {
                    Pixel tmp = img.GetImage[i,j];
                    numb_clust = -1;
                    dist = int.MaxValue;
                    for (int n = 0; n < center.Count; n++)
                    {
                        float tmpd = (float) distance(tmp.GetColor, center[n], norm);
                        if (tmpd < dist)
                        {
                            dist = tmpd;
                            numb_clust = n;
                        }
                    }
                    res[numb_clust].GetMask[i,j] = true;
                    clustvol[numb_clust]++;
                    empty[numb_clust] = false;
                }
            }
            EmptyCleaner(empty, center, clustvol, res);
            //SmallClear(res, center, clustvol, img.GetImage.GetLength());
            return res;
        }
        void SmallClear(List<Mask> maps, List<IColor> center, List<int> clustv, int length)
        {
            for (int i = 0; i < maps.Count; i++)
            {
                if (( (double) clustv[i] / (double) length ) < volume_limit)
                {
                    int tm = NearestCenter(i, center, DN);////////
                    Mask tmp = UniteMaps(maps[i], maps[tm]);
                    int tmpl = ( tm < i ) ? tm : i;
                    int tmpr = ( tm >= i ) ? tm : i;
                    maps.Add(tmp);
                    maps.RemoveAt(tmpr);
                    maps.RemoveAt(tmpl);
                    clustv.Add(clustv[tm] + clustv[i]);
                    clustv.RemoveAt(tmpr);
                    clustv.RemoveAt(tmpl);
                    center.Add(center[tm]);
                    center.RemoveAt(tmpr);
                    center.RemoveAt(tmpl);
                    i = -1;
                }
            }
        }
        void EmptyCleaner(List<bool> empty, List<IColor> center, List<int> clustv, List<Mask> maps)
        {
            for (int i = 0; i < empty.Count; i++)
            {
                if (empty[i])
                {
                    maps.RemoveAt(i);
                    clustv.RemoveAt(i);
                    center.RemoveAt(i);
                    empty.RemoveAt(i);
                    i--;
                }
            }
        }
        List<IColor> CentersUpdate(Image_Project img,List<Mask> Mask)
        {
            var res = new List<IColor>();
            for (int i = 0; i < Mask.Count; i++)
            {
                var tmp = new float[3];
                tmp[0] = 0;
                tmp[1] = 0;
                tmp[2] = 0;
                int l = 0;
                for (int j = 0; j < img.GetImage.Height; j++)
                    for (int n = 0; n < img.GetImage.Width; n++)
                    {
                        if (Mask[i][j, n])
                        {
                            l++;
                            tmp[0] += img.GetImage[j, n].GetColor.GetVal1;
                            tmp[1] += img.GetImage[j, n].GetColor.GetVal2;
                            tmp[2] += img.GetImage[j, n].GetColor.GetVal3;
                        }
                    }
                tmp[0] /= l;
                tmp[1] /= l;
                tmp[2] /= l;
                res.Add(img.GetState.ColorState.Create(tmp));
            }
            return res;
        }