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modified grabCut: noise is added only if covariance determinant = 0

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This commit is contained in:
Maria Dimashova 2010-11-11 12:04:40 +00:00
parent ef3171dc65
commit 762cf182ef
1 changed files with 14 additions and 36 deletions
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50
modules/imgproc/src/grabcut.cpp
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@ -51,35 +51,6 @@ This is implementation of image segmentation algorithm GrabCut described in
Carsten Rother, Vladimir Kolmogorov, Andrew Blake. Carsten Rother, Vladimir Kolmogorov, Andrew Blake.
*/ */
class Noise3DGenerator
{
public:
Noise3DGenerator( float var=0.1f ) : rng(theRNG())
{
var = std::min( std::max( 0.01f, var ), 1.f ) ;
double meanData[] = { 0., 0., 0. };
double covData[] = { var, 0., 0.,
0., var, 0.,
0., 0., var };
Mat( 1, 3, CV_64FC1, meanData ).copyTo( mean );
Mat( 3, 3, CV_64FC1, covData ).copyTo( cov );
}
Vec3d generateNoise()
{
Mat noise( 1, 3, CV_64FC1 );
rng.fill( noise, RNG::NORMAL, Scalar::all(0.0), Scalar(1.0) );
noise = noise * cov + mean;
return Vec3d( noise.ptr<double>() );
}
private:
RNG& rng;
Mat mean;
Mat cov;
};
/* /*
GMM - Gaussian Mixture Model GMM - Gaussian Mixture Model
*/ */
@ -111,8 +82,6 @@ private:
double prods[componentsCount][3][3]; double prods[componentsCount][3][3];
int sampleCounts[componentsCount]; int sampleCounts[componentsCount];
int totalSampleCount; int totalSampleCount;
Noise3DGenerator noiseGenerator;
}; };
GMM::GMM( Mat& _model ) GMM::GMM( Mat& _model )
@ -194,17 +163,17 @@ void GMM::initLearning()
void GMM::addSample( int ci, const Vec3d color ) void GMM::addSample( int ci, const Vec3d color )
{ {
Vec3d nClr = color + noiseGenerator.generateNoise(); sums[ci][0] += color[0]; sums[ci][1] += color[1]; sums[ci][2] += color[2];
sums[ci][0] += nClr[0]; sums[ci][1] += nClr[1]; sums[ci][2] += nClr[2]; prods[ci][0][0] += color[0]*color[0]; prods[ci][0][1] += color[0]*color[1]; prods[ci][0][2] += color[0]*color[2];
prods[ci][0][0] += nClr[0]*nClr[0]; prods[ci][0][1] += nClr[0]*nClr[1]; prods[ci][0][2] += nClr[0]*nClr[2]; prods[ci][1][0] += color[1]*color[0]; prods[ci][1][1] += color[1]*color[1]; prods[ci][1][2] += color[1]*color[2];
prods[ci][1][0] += nClr[1]*nClr[0]; prods[ci][1][1] += nClr[1]*nClr[1]; prods[ci][1][2] += nClr[1]*nClr[2]; prods[ci][2][0] += color[2]*color[0]; prods[ci][2][1] += color[2]*color[1]; prods[ci][2][2] += color[2]*color[2];
prods[ci][2][0] += nClr[2]*nClr[0]; prods[ci][2][1] += nClr[2]*nClr[1]; prods[ci][2][2] += nClr[2]*nClr[2];
sampleCounts[ci]++; sampleCounts[ci]++;
totalSampleCount++; totalSampleCount++;
} }
void GMM::endLearning() void GMM::endLearning()
{ {
const double variance = 0.01;
for( int ci = 0; ci < componentsCount; ci++ ) for( int ci = 0; ci < componentsCount; ci++ )
{ {
int n = sampleCounts[ci]; int n = sampleCounts[ci];
@ -222,6 +191,15 @@ void GMM::endLearning()
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[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[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];
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);
} }
} }