I want to use the GrabCut algorithm implemented on OpenCV.
As shown in the documentation this is the function signature:
void grabCut(
InputArra
In opencv/sources/modules/imgproc/src/grabcut.cpp you can have a look how the models (GMMs) are encoded:
GMM::GMM( Mat& _model )
{
const int modelSize = 3/*mean*/ + 9/*covariance*/ + 1/*component weight*/;
if( _model.empty() )
{
_model.create( 1, modelSize*componentsCount, CV_64FC1 );
_model.setTo(Scalar(0));
}
else if( (_model.type() != CV_64FC1) || (_model.rows != 1) || (_model.cols != modelSize*componentsCount) )
CV_Error( CV_StsBadArg, "_model must have CV_64FC1 type, rows == 1 and cols == 13*componentsCount" );
model = _model;
coefs = model.ptr<double>(0);
mean = coefs + componentsCount;
cov = mean + 3*componentsCount;
for( int ci = 0; ci < componentsCount; ci++ )
if( coefs[ci] > 0 )
calcInverseCovAndDeterm( ci );
}
So you need for every model a cv::Mat of 1 x 65 doubles (componentsCount equals 5). There are 3 means per component because its computing in RGB colorspace. Using GC_EVAL would indeed leave the models intact but I never tried it with pre-computed models.
Even I had a similar problem. This is how I solved it. I edited the GC_EVAL condition in the grabcut source code to this -
if( mode == GC_EVAL )
{ checkMask( img, mask );
for( int i = 0; i < iterCount; i++ )
{
GCGraph<double> graph;
assignGMMsComponents( img, mask, bgdGMM, fgdGMM, compIdxs );
constructGCGraph(img, mask, bgdGMM, fgdGMM, lambda, leftW, upleftW, upW, uprightW, graph );
estimateSegmentation( graph, mask );
return;
}
}
Notice that the function learnGMMs is not called here. This is done because the Foreground and Background GMMs are precomputed.
You can save models in a .xml file using the following code snippet.
FileStorage fs("mymodels.xml", FileStorage::WRITE);
fs << "BgdModel" << bgdModel;
fs << "FgdModel" << fgdModel;
fs.release();
You can retrieve the models using the following code.
FileStorage fs1("mymodels.xml", FileStorage::READ);
fs1["BgdModel"] >> bgdModel1;
fs1["FgdModel"] >> fgdModel1;
This worked for me.