mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2025-08-06 14:36:36 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Merge pull request #14886 from alalek:fix_grabcut_kmeans_call_14879

Browse Source
This commit is contained in:
Alexander Alekhin 2019-06-26 20:03:04 +00:00
commit 4112866821
2 changed files with 19 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
modules/core/src/kmeans.cpp
View File

@ -238,7 +238,7 @@ double cv::kmeans( InputArray _data, int K,
attempts = std::max(attempts, 1);
CV_Assert( data0.dims <= 2 && type == CV_32F && K > 0 );
CV_Assert( N >= K );
CV_CheckGE(N, K, "Number of clusters should be more than number of elements");
Mat data(N, dims, CV_32F, data0.ptr(), isrow ? dims * sizeof(float) : static_cast<size_t>(data0.step));

24
modules/imgproc/src/grabcut.cpp
View File

@ -45,6 +45,8 @@
using namespace cv;
namespace {
/*
This is implementation of image segmentation algorithm GrabCut described in
"GrabCut - Interactive Foreground Extraction using Iterated Graph Cuts".
@ -228,6 +230,8 @@ void GMM::calcInverseCovAndDeterm(int ci, const double singularFix)
}
}
} // namespace
/*
Calculate beta - parameter of GrabCut algorithm.
beta = 1/(2*avg(sqr(||color[i] - color[j]||)))
@ -379,12 +383,20 @@ static void initGMMs( const Mat& img, const Mat& mask, GMM& bgdGMM, GMM& fgdGMM
}
}
CV_Assert( !bgdSamples.empty() && !fgdSamples.empty() );
Mat _bgdSamples( (int)bgdSamples.size(), 3, CV_32FC1, &bgdSamples[0][0] );
kmeans( _bgdSamples, GMM::componentsCount, bgdLabels,
TermCriteria( CV_TERMCRIT_ITER, kMeansItCount, 0.0), 0, kMeansType );
Mat _fgdSamples( (int)fgdSamples.size(), 3, CV_32FC1, &fgdSamples[0][0] );
kmeans( _fgdSamples, GMM::componentsCount, fgdLabels,
TermCriteria( CV_TERMCRIT_ITER, kMeansItCount, 0.0), 0, kMeansType );
{
Mat _bgdSamples( (int)bgdSamples.size(), 3, CV_32FC1, &bgdSamples[0][0] );
int num_clusters = GMM::componentsCount;
num_clusters = std::min(num_clusters, (int)bgdSamples.size());
kmeans( _bgdSamples, num_clusters, bgdLabels,
TermCriteria( CV_TERMCRIT_ITER, kMeansItCount, 0.0), 0, kMeansType );
}
{
Mat _fgdSamples( (int)fgdSamples.size(), 3, CV_32FC1, &fgdSamples[0][0] );
int num_clusters = GMM::componentsCount;
num_clusters = std::min(num_clusters, (int)fgdSamples.size());
kmeans( _fgdSamples, num_clusters, fgdLabels,
TermCriteria( CV_TERMCRIT_ITER, kMeansItCount, 0.0), 0, kMeansType );
}
bgdGMM.initLearning();
for( int i = 0; i < (int)bgdSamples.size(); i++ )