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Merge pull request #12446 from alalek:imgproc_grabcut_numeric_issues

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Alexander Alekhin 2018-09-06 20:18:45 +00:00
commit bd98ed46bd
1 changed files with 29 additions and 29 deletions
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58
modules/imgproc/src/grabcut.cpp
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@ -69,7 +69,7 @@ public:
void endLearning();
private:
void calcInverseCovAndDeterm( int ci );
void calcInverseCovAndDeterm(int ci, double singularFix);
Mat model;
double* coefs;
double* mean;
@ -103,7 +103,7 @@ GMM::GMM( Mat& _model )
for( int ci = 0; ci < componentsCount; ci++ )
if( coefs[ci] > 0 )
calcInverseCovAndDeterm( ci );
calcInverseCovAndDeterm(ci, 0.0);
totalSampleCount = 0;
}
@ -175,7 +175,6 @@ void GMM::addSample( int ci, const Vec3d color )
void GMM::endLearning()
{
CV_Assert(totalSampleCount > 0);
const double variance = 0.01;
for( int ci = 0; ci < componentsCount; ci++ )
{
int n = sampleCounts[ci];
@ -183,48 +182,49 @@ void GMM::endLearning()
coefs[ci] = 0;
else
{
double inv_n = 1.0 / n;
coefs[ci] = (double)n/totalSampleCount;
double* m = mean + 3*ci;
m[0] = sums[ci][0]/n; m[1] = sums[ci][1]/n; m[2] = sums[ci][2]/n;
m[0] = sums[ci][0] * inv_n; m[1] = sums[ci][1] * inv_n; m[2] = sums[ci][2] * inv_n;
double* c = cov + 9*ci;
c[0] = prods[ci][0][0]/n - m[0]*m[0]; c[1] = prods[ci][0][1]/n - m[0]*m[1]; c[2] = prods[ci][0][2]/n - m[0]*m[2];
c[3] = prods[ci][1][0]/n - m[1]*m[0]; c[4] = prods[ci][1][1]/n - m[1]*m[1]; c[5] = prods[ci][1][2]/n - m[1]*m[2];
c[6] = prods[ci][2][0]/n - m[2]*m[0]; c[7] = prods[ci][2][1]/n - m[2]*m[1]; c[8] = prods[ci][2][2]/n - m[2]*m[2];
c[0] = prods[ci][0][0] * inv_n - m[0]*m[0]; c[1] = prods[ci][0][1] * inv_n - m[0]*m[1]; c[2] = prods[ci][0][2] * inv_n - m[0]*m[2];
c[3] = prods[ci][1][0] * inv_n - m[1]*m[0]; c[4] = prods[ci][1][1] * inv_n - m[1]*m[1]; c[5] = prods[ci][1][2] * inv_n - m[1]*m[2];
c[6] = prods[ci][2][0] * inv_n - m[2]*m[0]; c[7] = prods[ci][2][1] * inv_n - m[2]*m[1]; c[8] = prods[ci][2][2] * inv_n - m[2]*m[2];
double dtrm = c[0]*(c[4]*c[8]-c[5]*c[7]) - c[1]*(c[3]*c[8]-c[5]*c[6]) + c[2]*(c[3]*c[7]-c[4]*c[6]);
if( dtrm <= std::numeric_limits<double>::epsilon() )
{
// Adds the white noise to avoid singular covariance matrix.
c[0] += variance;
c[4] += variance;
c[8] += variance;
}
calcInverseCovAndDeterm(ci);
calcInverseCovAndDeterm(ci, 0.01);
}
}
}
void GMM::calcInverseCovAndDeterm( int ci )
void GMM::calcInverseCovAndDeterm(int ci, const double singularFix)
{
if( coefs[ci] > 0 )
{
double *c = cov + 9*ci;
double dtrm =
covDeterms[ci] = c[0]*(c[4]*c[8]-c[5]*c[7]) - c[1]*(c[3]*c[8]-c[5]*c[6]) + c[2]*(c[3]*c[7]-c[4]*c[6]);
double dtrm = c[0]*(c[4]*c[8]-c[5]*c[7]) - c[1]*(c[3]*c[8]-c[5]*c[6]) + c[2]*(c[3]*c[7]-c[4]*c[6]);
if (dtrm <= 1e-6 && singularFix > 0)
{
// Adds the white noise to avoid singular covariance matrix.
c[0] += singularFix;
c[4] += singularFix;
c[8] += singularFix;
dtrm = c[0] * (c[4] * c[8] - c[5] * c[7]) - c[1] * (c[3] * c[8] - c[5] * c[6]) + c[2] * (c[3] * c[7] - c[4] * c[6]);
}
covDeterms[ci] = dtrm;
CV_Assert( dtrm > std::numeric_limits<double>::epsilon() );
inverseCovs[ci][0][0] = (c[4]*c[8] - c[5]*c[7]) / dtrm;
inverseCovs[ci][1][0] = -(c[3]*c[8] - c[5]*c[6]) / dtrm;
inverseCovs[ci][2][0] = (c[3]*c[7] - c[4]*c[6]) / dtrm;
inverseCovs[ci][0][1] = -(c[1]*c[8] - c[2]*c[7]) / dtrm;
inverseCovs[ci][1][1] = (c[0]*c[8] - c[2]*c[6]) / dtrm;
inverseCovs[ci][2][1] = -(c[0]*c[7] - c[1]*c[6]) / dtrm;
inverseCovs[ci][0][2] = (c[1]*c[5] - c[2]*c[4]) / dtrm;
inverseCovs[ci][1][2] = -(c[0]*c[5] - c[2]*c[3]) / dtrm;
inverseCovs[ci][2][2] = (c[0]*c[4] - c[1]*c[3]) / dtrm;
double inv_dtrm = 1.0 / dtrm;
inverseCovs[ci][0][0] = (c[4]*c[8] - c[5]*c[7]) * inv_dtrm;
inverseCovs[ci][1][0] = -(c[3]*c[8] - c[5]*c[6]) * inv_dtrm;
inverseCovs[ci][2][0] = (c[3]*c[7] - c[4]*c[6]) * inv_dtrm;
inverseCovs[ci][0][1] = -(c[1]*c[8] - c[2]*c[7]) * inv_dtrm;
inverseCovs[ci][1][1] = (c[0]*c[8] - c[2]*c[6]) * inv_dtrm;
inverseCovs[ci][2][1] = -(c[0]*c[7] - c[1]*c[6]) * inv_dtrm;
inverseCovs[ci][0][2] = (c[1]*c[5] - c[2]*c[4]) * inv_dtrm;
inverseCovs[ci][1][2] = -(c[0]*c[5] - c[2]*c[3]) * inv_dtrm;
inverseCovs[ci][2][2] = (c[0]*c[4] - c[1]*c[3]) * inv_dtrm;
}
}