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sharedmatting.cpp
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#include "sharedmatting.h"
#include <time.h>
using namespace cv;
//构造函数
SharedMatting::SharedMatting()
{
kI = 10;
kC = 5.0;
kG = 4; //each unknown p gathers at most kG forground and background samples
uT.clear();
tuples.clear();
}
//析构函数
SharedMatting::~SharedMatting()
{
// cvReleaseImage(&pImg);
// cvReleaseImage(&trimap);
// cvReleaseImage(&matte);
pImg.release();
trimap.release();
matte.release();
uT.clear();
tuples.clear();
ftuples.clear();
for (int i = 0; i < height; ++i)
{
delete[] tri[i];
delete[] unknownIndex[i];
delete[] alpha[i];
}
delete[] tri;
delete[] unknownIndex;
delete[] alpha;
}
//载入图像
void SharedMatting::loadImage(char * filename)
{
pImg = imread(filename);
if (!pImg.data)
{
cout << "Loading Image Failed!" << endl;
exit(-1);
}
//height = pImg->height;
height = pImg.rows;
//width = pImg->width;
width = pImg.cols;
//step = pImg->widthStep;
step = pImg.step1();
// channels = pImg->nChannels;
channels = pImg.channels();
// data = (uchar *)pImg->imageData;
data = (uchar *)pImg.data;
unknownIndex = new int*[height];
tri = new int*[height];
alpha = new int*[height];
for(int i = 0; i < height; ++i)
{
unknownIndex[i] = new int[width];
tri[i] = new int[width];
alpha[i] = new int[width];
}
// matte = cvCreateImage(cvSize(width, height), IPL_DEPTH_8U, 1);
matte.create(Size(width, height), CV_8UC1);
}
//载入第三方图像
void SharedMatting::loadTrimap(char * filename)
{
trimap = imread(filename);
if (!trimap.data)
{
cout << "Loading Trimap Failed!" << endl;
exit(-1);
}
/*cvNamedWindow("aa");
cvShowImage("aa", trimap);
cvWaitKey(0);*/
}
void SharedMatting::expandKnown()
{
vector<struct labelPoint> vp;
int kc2 = kC * kC;
vp.clear();
int s = trimap.step1();
int c = trimap.channels();
uchar * d = (uchar *)trimap.data;
for (int i = 0; i < height; ++i)
{
for (int j = 0; j < width; ++j)
{
tri[i][j] = d[i * step + j * channels];
}
}
for (int i = 0; i < height; ++i)
{
for (int j = 0; j < width; ++j)
{
if (tri[i][j] != 0 && tri[i][j] != 255)
{
int label = -1;
double dmin = 10000.0;
bool flag = false;
int pb = data[i * step + j * channels];
int pg = data[i * step + j * channels + 1];
int pr = data[i * step + j * channels + 2];
Scalar p = Scalar(pb, pg, pr);
//int i1 = max(0, i - kI);
//int i2 = min(i + kI, height - 1);
//int j1 = max(0, j - kI);
//int j2 = min(j + kI, width - 1);
//
//for (int k = i1; k <= i2; ++k)
//{
// for (int l = j1; l <= j2; ++l)
// {
// int temp = tri[k][l];
// if (temp != 0 && temp != 255)
// {
// continue;
// }
// double dis = dP(Point(i, j), Point(k, l));
// if (dis > dmin)
// {
// continue;
// }
//
// int qb = data[k * step + l * channels];
// int qg = data[k * step + l * channels + 1];
// int qr = data[k * step + l * channels + 2];
// Scalar q = Scalar(qb, qg, qr);
// double distanceColor = distanceColor2(p, q);
// if (distanceColor <= kc2)
// {
// dmin = dis;
// label = temp;
// }
// }
//}
for (int k = 0; (k <= kI) && !flag; ++k)
{
int k1 = max(0, i - k);
int k2 = min(i + k, height - 1);
int l1 = max(0, j - k);
int l2 = min(j + k, width - 1);
for (int l = k1; (l <= k2) && !flag; ++l)
{
double dis;
double gray;
gray = tri[l][l1];
if (gray == 0 || gray == 255)
{
dis = dP(Point(i, j), Point(l, l1));
if (dis > kI)
{
continue;
}
int qb = data[l * step + l1 * channels];
int qg = data[l * step + l1 * channels + 1];
int qr = data[l * step + l1 * channels + 2];
Scalar q = Scalar(qb, qg, qr);
double distanceColor = distanceColor2(p, q);
if (distanceColor <= kc2)
{
flag = true;
label = gray;
}
}
if (flag)
{
break;
}
gray = tri[l][l2];
if (gray == 0 || gray == 255)
{
dis = dP(Point(i, j), Point(l, l2));
if (dis > kI)
{
continue;
}
int qb = data[l * step + l2 * channels];
int qg = data[l * step + l2 * channels + 1];
int qr = data[l * step + l2 * channels + 2];
Scalar q = Scalar(qb, qg, qr);
double distanceColor = distanceColor2(p, q);
if (distanceColor <= kc2)
{
flag = true;
label = gray;
}
}
}
for (int l = l1; (l <= l2) && !flag; ++l)
{
double dis;
double gray;
gray = tri[k1][l];
if (gray == 0 || gray == 255)
{
dis = dP(Point(i, j), Point(k1, l));
if (dis > kI)
{
continue;
}
int qb = data[k1 * step + l * channels];
int qg = data[k1 * step + l * channels + 1];
int qr = data[k1 * step + l * channels + 2];
Scalar q = Scalar(qb, qg, qr);
double distanceColor = distanceColor2(p, q);
if (distanceColor <= kc2)
{
flag = true;
label = gray;
}
}
gray = tri[k2][l];
if (gray == 0 || gray == 255)
{
dis = dP(Point(i, j), Point(k2, l));
if (dis > kI)
{
continue;
}
int qb = data[k2 * step + l * channels];
int qg = data[k2 * step + l * channels + 1];
int qr = data[k2 * step + l * channels + 2];
Scalar q = Scalar(qb, qg, qr);
double distanceColor = distanceColor2(p, q);
if (distanceColor <= kc2)
{
flag = true;
label = gray;
}
}
}
}
if (label != -1)
{
struct labelPoint lp;
lp.x = i;
lp.y = j;
lp.label = label;
vp.push_back(lp);
}
else
{
Point lp;
lp.x = i;
lp.y = j;
uT.push_back(lp);
}
}
}
}
vector<struct labelPoint>::iterator it;
for (it = vp.begin(); it != vp.end(); ++it)
{
int ti = it->x;
int tj = it->y;
int label = it->label;
//cvSet2D(trimap, ti, tj, ScalarAll(label));
tri[ti][tj] = label;
}
vp.clear();
/*cvNamedWindow("trimap");
cvShowImage("trimap", trimap);
cvWaitKey(0);*/
}
double SharedMatting::comalpha(Scalar c, Scalar f, Scalar b)
{
double alpha = ((c.val[0] - b.val[0]) * (f.val[0] - b.val[0]) +
(c.val[1] - b.val[1]) * (f.val[1] - b.val[1]) +
(c.val[2] - b.val[2]) * (f.val[2] - b.val[2]))
/ ((f.val[0] - b.val[0]) * (f.val[0] - b.val[0]) +
(f.val[1] - b.val[1]) * (f.val[1] - b.val[1]) +
(f.val[2] - b.val[2]) * (f.val[2] - b.val[2]) + 0.0000001);
return min(1.0, max(0.0, alpha));
}
double SharedMatting::mP(int i, int j, Scalar f, Scalar b)
{
int bc = data[i * step + j * channels];
int gc = data[i * step + j * channels + 1];
int rc = data[i * step + j * channels + 2];
Scalar c = Scalar(bc, gc, rc);
double alpha = comalpha(c, f, b);
double result = sqrt((c.val[0] - alpha * f.val[0] - (1 - alpha) * b.val[0]) * (c.val[0] - alpha * f.val[0] - (1 - alpha) * b.val[0]) +
(c.val[1] - alpha * f.val[1] - (1 - alpha) * b.val[1]) * (c.val[1] - alpha * f.val[1] - (1 - alpha) * b.val[1]) +
(c.val[2] - alpha * f.val[2] - (1 - alpha) * b.val[2]) * (c.val[2] - alpha * f.val[2] - (1 - alpha) * b.val[2]));
return result / 255.0;
}
double SharedMatting::nP(int i, int j, Scalar f, Scalar b)
{
int i1 = max(0, i - 1);
int i2 = min(i + 1, height - 1);
int j1 = max(0, j - 1);
int j2 = min(j + 1, width - 1);
double result = 0;
for (int k = i1; k <= i2; ++k)
{
for (int l = j1; l <= j2; ++l)
{
double m = mP(k, l, f, b);
result += m * m;
}
}
return result;
}
double SharedMatting::eP(int i1, int j1, int i2, int j2)
{
//int flagi = 1, flagj = 1;
double ci = i2 - i1;
double cj = j2 - j1;
double z = sqrt(ci * ci + cj * cj);
double ei = ci / (z + 0.0000001);
double ej = cj / (z + 0.0000001);
double stepinc = min(1 / (abs(ei) + 1e-10), 1 / (abs(ej) + 1e-10));
double result = 0;
int b = data[i1 * step + j1 * channels];
int g = data[i1 * step + j1 * channels + 1];
int r = data[i1 * step + j1 * channels + 2];
Scalar pre = Scalar(b, g, r);
int ti = i1;
int tj = j1;
for (double t = 1; ;t += stepinc)
{
double inci = ei * t;
double incj = ej * t;
int i = int(i1 + inci + 0.5);
int j = int(j1 + incj + 0.5);
double z = 1;
int b = data[i * step + j * channels];
int g = data[i * step + j * channels + 1];
int r = data[i * step + j * channels + 2];
Scalar cur = Scalar(b, g, r);
if (ti - i > 0 && tj - j == 0)
{
z = ej;
}
else if(ti - i == 0 && tj - j > 0)
{
z = ei;
}
result += ((cur.val[0] - pre.val[0]) * (cur.val[0] - pre.val[0]) +
(cur.val[1] - pre.val[1]) * (cur.val[1] - pre.val[1]) +
(cur.val[2] - pre.val[2]) * (cur.val[2] - pre.val[2])) * z;
pre = cur;
ti = i;
tj = j;
if(abs(ci) >= abs(inci) || abs(cj) >= abs(incj))
break;
}
return result;
}
double SharedMatting::pfP(Point p, vector<Point>& f, vector<Point>& b)
{
double fmin = 1e10;
vector<Point>::iterator it;
for (it = f.begin(); it != f.end(); ++it)
{
double fp = eP(p.x, p.y, it->x, it->y);
if (fp < fmin)
{
fmin = fp;
}
}
double bmin = 1e10;
for (it = b.begin(); it != b.end(); ++it)
{
double bp = eP(p.x, p.y, it->x, it->y);
if (bp < bmin)
{
bmin = bp;
}
}
return bmin / (fmin + bmin + 1e-10);
}
double SharedMatting::aP(int i, int j, double pf, Scalar f, Scalar b)
{
int bc = data[i * step + j * channels];
int gc = data[i * step + j * channels + 1];
int rc = data[i * step + j * channels + 2];
Scalar c = Scalar(bc, gc, rc);
double alpha = comalpha(c, f, b);
return pf + (1 - 2 * pf) * alpha;
}
double SharedMatting::dP(Point s, Point d)
{
return sqrt(double((s.x - d.x) * (s.x - d.x) + (s.y - d.y) * (s.y - d.y)));
}
double SharedMatting::gP(Point p, Point fp, Point bp, double pf)
{
int bc, gc, rc;
bc = data[fp.x * step + fp.y * channels];
gc = data[fp.x * step + fp.y * channels + 1];
rc = data[fp.x * step + fp.y * channels + 2];
Scalar f = Scalar(bc, gc, rc);
bc = data[bp.x * step + bp.y * channels];
gc = data[bp.x * step + bp.y * channels + 1];
rc = data[bp.x * step + bp.y * channels + 2];
Scalar b = Scalar(bc, gc, rc);
double tn = pow(nP(p.x, p.y, f, b), 3);
double ta = pow(aP(p.x, p.y, pf, f, b), 2);
double tf = dP(p, fp);
double tb = pow(dP(p, bp), 4);
//cout << "tn:" << tn << "ta:" << ta << "tf:" << tf << "tb:" << tb << endl;
return tn * ta * tf * tb;
}
double SharedMatting::gP(Point p, Point fp, Point bp, double dpf, double pf)
{
int bc, gc, rc;
bc = data[fp.x * step + fp.y * channels];
gc = data[fp.x * step + fp.y * channels + 1];
rc = data[fp.x * step + fp.y * channels + 2];
Scalar f = Scalar(bc, gc, rc);
bc = data[bp.x * step + bp.y * channels];
gc = data[bp.x * step + bp.y * channels + 1];
rc = data[bp.x * step + bp.y * channels + 2];
Scalar b = Scalar(bc, gc, rc);
double tn = pow(nP(p.x, p.y, f, b), 3);
double ta = pow(aP(p.x, p.y, pf, f, b), 2);
double tf = dpf;
double tb = pow(dP(p, bp), 4);
return tn * ta * tf * tb;
}
double SharedMatting::sigma2(Point p)
{
int xi = p.x;
int yj = p.y;
int bc, gc, rc;
bc = data[xi * step + yj * channels];
gc = data[xi * step + yj * channels + 1];
rc = data[xi * step + yj * channels + 2];
Scalar pc = Scalar(bc, gc, rc);
int i1 = max(0, xi - 2);
int i2 = min(xi + 2, height - 1);
int j1 = max(0, yj - 2);
int j2 = min(yj + 2, width - 1);
double result = 0;
int num = 0;
for (int i = i1; i <= i2; ++i)
{
for (int j = j1; j <= j2; ++j)
{
int bc, gc, rc;
bc = data[i * step + j * channels];
gc = data[i * step + j * channels + 1];
rc = data[i * step + j * channels + 2];
Scalar temp = Scalar(bc, gc, rc);
result += distanceColor2(pc, temp);
++num;
}
}
return result / (num + 1e-10);
}
double SharedMatting::distanceColor2(Scalar cs1, Scalar cs2)
{
return (cs1.val[0] - cs2.val[0]) * (cs1.val[0] - cs2.val[0]) +
(cs1.val[1] - cs2.val[1]) * (cs1.val[1] - cs2.val[1]) +
(cs1.val[2] - cs2.val[2]) * (cs1.val[2] - cs2.val[2]);
}
void SharedMatting::sample(Point p, std::vector<Point> &f, std::vector<Point> &b)
{
int i = p.x;
int j = p.y;
double inc = 360.0 / kG;
//cout << inc << endl;
double ca = inc / 9;
double angle = (i % 3 * 3 + j % 9) * ca;
for (int k = 0; k < kG; ++k)
{
bool flagf = false;
bool flagb = false;
double z = (angle + k * inc) / 180 * 3.1415926;
double ei = sin(z);
double ej = cos(z);
double step = min(1.0 / (abs(ei) + 1e-10), 1.0 / (abs(ej) + 1e-10));
for (double t = 1; ;t += step)
{
int ti = int(i + ei * t + 0.5);
int tj = int(j + ej * t + 0.5);
if(ti >= height || ti < 0 || tj >= width || tj < 0)
{
break;
}
int gray = tri[ti][tj];
if (!flagf && gray == 255)
{
Point tp = Point(ti, tj);
f.push_back(tp);
flagf = true;
}
else if (!flagb && gray == 0)
{
Point tp = Point(ti, tj);
b.push_back(tp);
flagb = true;
}
if (flagf && flagb)
{
break;
}
}
}
}
void SharedMatting::Sample(std::vector<vector<Point> > &F, std::vector<vector<Point> > &B)
{
int a,b,i;
int x,y,p,q;
int w,h,gray;
int angle;
double z,ex,ey,t,step;
vector<Point>::iterator iter;
a=360/kG;
b=1.7f*a/9;
F.clear();
B.clear();
w=pImg.cols;
h=pImg.rows;
for(iter=uT.begin();iter!=uT.end();++iter)
{
vector<Point> fPts,bPts;
x=iter->x;
y=iter->y;
angle=(x+y)*b % a;
for(i=0;i<kG;++i)
{
bool f1(false),f2(false);
z=(angle+i*a)/180.0f*3.1415926f;
ex=sin(z);
ey=cos(z);
step=min(1.0f/(abs(ex)+1e-10f),
1.0f/(abs(ey)+1e-10f));
for(t=0;;t+=step)
{
p=(int)(x+ex*t+0.5f);
q=(int)(y+ey*t+0.5f);
if(p<0 || p>=h || q<0 || q>=w)
break;
gray=tri[p][q];
if(!f1 && gray<50)
{
Point pt = Point(p, q);
bPts.push_back(pt);
f1=true;
}
else
if(!f2 && gray>200)
{
Point pt = Point(p, q);
fPts.push_back(pt);
f2=true;
}
else
if(f1 && f2)
break;
}
}
F.push_back(fPts);
B.push_back(bPts);
}
}
void SharedMatting::gathering()
{
vector<Point> f;
vector<Point> b;
vector<Point>::iterator it;
vector<Point>::iterator it1;
vector<Point>::iterator it2;
vector<vector<Point> > F,B;
Sample(F, B);
int index = 0;
double a;
int size = uT.size();
for (int m = 0; m < size; ++m)
{
int i = uT[m].x;
int j = uT[m].y;
/*f.clear();
b.clear();*/
//sample(Point(i, j), f, b);
/*double pfp = pfP(Point(i, j), f, b);*/
double pfp = pfP(Point(i, j), F[m], B[m]);
double gmin = 1.0e10;
Point tf;
Point tb;
bool flag = false;
bool first = true;
for (it1 = F[m].begin(); it1 != F[m].end(); ++it1)
{
double dpf = dP(Point(i, j), *(it1));
for (it2 = B[m].begin(); it2 < B[m].end(); ++it2)
{
double gp = gP(Point(i, j), *(it1), *(it2), dpf, pfp);
if (gp < gmin)
{
gmin = gp;
tf = *(it1);
tb = *(it2);
flag = true;
}
}
}
struct Tuple st;
st.flag = -1;
if (flag)
{
int bc, gc, rc;
bc = data[tf.x * step + tf.y * channels];
gc = data[tf.x * step + tf.y * channels + 1];
rc = data[tf.x * step + tf.y * channels + 2];
st.flag = 1;
st.f = Scalar(bc, gc, rc);
bc = data[tb.x * step + tb.y * channels];
gc = data[tb.x * step + tb.y * channels + 1];
rc = data[tb.x * step + tb.y * channels + 2];
st.b = Scalar(bc, gc, rc);
st.sigmaf = sigma2(tf);
st.sigmab = sigma2(tb);
}
tuples.push_back(st);
unknownIndex[i][j] = index;
++index;
}
f.clear();
b.clear();
}
void SharedMatting::refineSample()
{
ftuples.resize(width * height + 1);
for (int i = 0; i < height; ++i)
{
for (int j = 0; j < width; ++j)
{
int b, g, r;
b = data[i * step + j* channels];
g = data[i * step + j * channels + 1];
r = data[i * step + j * channels + 2];
Scalar c = Scalar(b, g, r);
int indexf = i * width + j;
int gray = tri[i][j];
if (gray == 0 )
{
ftuples[indexf].f = c;
ftuples[indexf].b = c;
ftuples[indexf].alphar = 0;
ftuples[indexf].confidence = 1;
alpha[i][j] = 0;
}
else if (gray == 255)
{
ftuples[indexf].f = c;
ftuples[indexf].b = c;
ftuples[indexf].alphar = 1;
ftuples[indexf].confidence = 1;
alpha[i][j] = 255;
}
}
}
vector<Point>::iterator it;
for (it = uT.begin(); it != uT.end(); ++it)
{
int xi = it->x;
int yj = it->y;
int i1 = max(0, xi - 5);
int i2 = min(xi + 5, height - 1);
int j1 = max(0, yj - 5);
int j2 = min(yj + 5, width - 1);
double minvalue[3] = {1e10, 1e10, 1e10};
Point * p = new Point[3];
int num = 0;
for (int k = i1; k <= i2; ++k)
{
for (int l = j1; l <= j2; ++l)
{
int temp = tri[k][l];
if (temp == 0 || temp == 255)
{
continue;
}
int index = unknownIndex[k][l];
Tuple t = tuples[index];
if (t.flag == -1)
{
continue;
}
double m = mP(xi, yj, t.f, t.b);
if (m > minvalue[2])
{
continue;
}
if (m < minvalue[0])
{
minvalue[2] = minvalue[1];
p[2] = p[1];
minvalue[1] = minvalue[0];
p[1] = p[0];
minvalue[0] = m;
p[0].x = k;
p[0].y = l;
++num;
}
else if (m < minvalue[1])
{
minvalue[2] = minvalue[1];
p[2] = p[1];
minvalue[1] = m;
p[1].x = k;
p[1].y = l;
++num;
}
else if (m < minvalue[2])
{
minvalue[2] = m;
p[2].x = k;
p[2].y = l;
++num;
}
}
}
num = min(num, 3);
double fb = 0;
double fg = 0;
double fr = 0;
double bb = 0;
double bg = 0;
double br = 0;
double sf = 0;
double sb = 0;
for (int k = 0; k < num; ++k)
{
int i = unknownIndex[p[k].x][p[k].y];
fb += tuples[i].f.val[0];
fg += tuples[i].f.val[1];
fr += tuples[i].f.val[2];
bb += tuples[i].b.val[0];
bg += tuples[i].b.val[1];
br += tuples[i].b.val[2];
sf += tuples[i].sigmaf;
sb += tuples[i].sigmab;
}
fb /= (num + 1e-10);
fg /= (num + 1e-10);
fr /= (num + 1e-10);
bb /= (num + 1e-10);
bg /= (num + 1e-10);
br /= (num + 1e-10);
sf /= (num + 1e-10);
sb /= (num + 1e-10);
Scalar fc = Scalar(fb, fg, fr);
Scalar bc = Scalar(bb, bg, br);
int b, g, r;
b = data[xi * step + yj* channels];
g = data[xi * step + yj * channels + 1];
r = data[xi * step + yj * channels + 2];
Scalar pc = Scalar(b, g, r);
double df = distanceColor2(pc, fc);
double db = distanceColor2(pc, bc);
Scalar tf = fc;
Scalar tb = bc;
int index = xi * width + yj;
if (df < sf)
{
fc = pc;
}
if (db < sb)
{
bc = pc;
}
if (fc.val[0] == bc.val[0] && fc.val[1] == bc.val[1] && fc.val[2] == bc.val[2])
{
ftuples[index].confidence = 0.00000001;
}
else
{
ftuples[index].confidence = exp(-10 * mP(xi, yj, tf, tb));
}
ftuples[index].f = fc;
ftuples[index].b = bc;
ftuples[index].alphar = max(0.0, min(1.0,comalpha(pc, fc, bc)));
//cvSet2D(matte, xi, yj, ScalarAll(ftuples[index].alphar * 255));
}
/*cvNamedWindow("1");
cvShowImage("1", matte);*/
/*cvNamedWindow("2");
cvShowImage("2", trimap);*/
/*cvWaitKey(0);*/
tuples.clear();
}
void SharedMatting::localSmooth()
{
vector<Point>::iterator it;
double sig2 = 100.0 / (9 * 3.1415926);
double r = 3 * sqrt(sig2);
for (it = uT.begin(); it != uT.end(); ++it)
{
int xi = it->x;
int yj = it->y;
int i1 = max(0, int(xi - r));
int i2 = min(int(xi + r), height - 1);
int j1 = max(0, int(yj - r));
int j2 = min(int(yj + r), width - 1);
int indexp = xi * width + yj;
Ftuple ptuple = ftuples[indexp];
Scalar wcfsumup = Scalar::all(0);
Scalar wcbsumup = Scalar::all(0);
double wcfsumdown = 0;
double wcbsumdown = 0;
double wfbsumup = 0;
double wfbsundown = 0;
double wasumup = 0;
double wasumdown = 0;
for (int k = i1; k <= i2; ++k)
{
for (int l = j1; l <= j2; ++l)
{
int indexq = k * width + l;
Ftuple qtuple = ftuples[indexq];
double d = dP(Point(xi, yj), Point(k, l));
if (d > r)
{
continue;
}
double wc;
if (d == 0)
{
wc = exp(-(d * d) / sig2) * qtuple.confidence;
}
else
{
wc = exp(-(d * d) / sig2) * qtuple.confidence * abs(qtuple.alphar - ptuple.alphar);
}
wcfsumdown += wc * qtuple.alphar;
wcbsumdown += wc * (1 - qtuple.alphar);
wcfsumup.val[0] += wc * qtuple.alphar * qtuple.f.val[0];
wcfsumup.val[1] += wc * qtuple.alphar * qtuple.f.val[1];
wcfsumup.val[2] += wc * qtuple.alphar * qtuple.f.val[2];
wcbsumup.val[0] += wc * (1 - qtuple.alphar) * qtuple.b.val[0];
wcbsumup.val[1] += wc * (1 - qtuple.alphar) * qtuple.b.val[1];
wcbsumup.val[2] += wc * (1 - qtuple.alphar) * qtuple.b.val[2];
double wfb = qtuple.confidence * qtuple.alphar * (1 - qtuple.alphar);
wfbsundown += wfb;
wfbsumup += wfb * sqrt(distanceColor2(qtuple.f, qtuple.b));
double delta = 0;
double wa;
if (tri[k][l] == 0 || tri[k][l] == 255)
{
delta = 1;
}
wa = qtuple.confidence * exp(-(d * d) / sig2) + delta;