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Fix #27968: Eliminate unnecessary type conversions in GrabCut initGMMs

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Remove performance bottleneck caused by redundant type conversions in the
initGMMs() function's tight nested loops that iterate over all image pixels.

Changes:
- Changed storage vectors from Vec3f to Vec3b to store pixel data directly
  without intermediate conversion
- Modified kmeans preparation to convert Vec3b data to CV_32FC1 only when
  creating the Mat for clustering (using convertTo instead of per-pixel cast)
- Explicitly convert Vec3b to Vec3d only when calling addSample() method

Performance Impact:
Previously: Vec3b -> Vec3f (in loop) -> Vec3d (implicit in addSample)
Now: Vec3b (in loop) -> Vec3d (explicit, only in addSample call)

This eliminates one unnecessary type conversion per pixel in the tight loop,
reducing computational overhead especially for large images.

Fixes #27968
This commit is contained in:
Samaresh Kumar Singh
2025-11-08 13:16:01 -06:00
parent 5fb4ce482c
commit 603bc58d3b
1 changed files with 9 additions and 7 deletions
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modules/imgproc/src/grabcut.cpp
+9 -7
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@@ -371,28 +371,30 @@ static void initGMMs( const Mat& img, const Mat& mask, GMM& bgdGMM, GMM& fgdGMM
const int kMeansType = KMEANS_PP_CENTERS;
Mat bgdLabels, fgdLabels;
std::vector<Vec3f> bgdSamples, fgdSamples;
std::vector<Vec3b> bgdSamples, fgdSamples;
Point p;
for( p.y = 0; p.y < img.rows; p.y++ )
{
for( p.x = 0; p.x < img.cols; p.x++ )
{
if( mask.at<uchar>(p) == GC_BGD || mask.at<uchar>(p) == GC_PR_BGD )
bgdSamples.push_back( (Vec3f)img.at<Vec3b>(p) );
bgdSamples.push_back( img.at<Vec3b>(p) );
else // GC_FGD | GC_PR_FGD
fgdSamples.push_back( (Vec3f)img.at<Vec3b>(p) );
fgdSamples.push_back( img.at<Vec3b>(p) );
}
}
CV_Assert( !bgdSamples.empty() && !fgdSamples.empty() );
{
Mat _bgdSamples( (int)bgdSamples.size(), 3, CV_32FC1, &bgdSamples[0][0] );
Mat _bgdSamples( (int)bgdSamples.size(), 3, CV_8UC1, &bgdSamples[0][0] );
_bgdSamples.convertTo(_bgdSamples, CV_32FC1);
int num_clusters = GMM::componentsCount;
num_clusters = std::min(num_clusters, (int)bgdSamples.size());
kmeans( _bgdSamples, num_clusters, bgdLabels,
TermCriteria( TermCriteria::MAX_ITER, kMeansItCount, 0.0), 0, kMeansType );
}
{
Mat _fgdSamples( (int)fgdSamples.size(), 3, CV_32FC1, &fgdSamples[0][0] );
Mat _fgdSamples( (int)fgdSamples.size(), 3, CV_8UC1, &fgdSamples[0][0] );
_fgdSamples.convertTo(_fgdSamples, CV_32FC1);
int num_clusters = GMM::componentsCount;
num_clusters = std::min(num_clusters, (int)fgdSamples.size());
kmeans( _fgdSamples, num_clusters, fgdLabels,
@@ -401,12 +403,12 @@ static void initGMMs( const Mat& img, const Mat& mask, GMM& bgdGMM, GMM& fgdGMM
bgdGMM.initLearning();
for( int i = 0; i < (int)bgdSamples.size(); i++ )
bgdGMM.addSample( bgdLabels.at<int>(i,0), bgdSamples[i] );
bgdGMM.addSample( bgdLabels.at<int>(i,0), Vec3d(bgdSamples[i]) );
bgdGMM.endLearning();
fgdGMM.initLearning();
for( int i = 0; i < (int)fgdSamples.size(); i++ )
fgdGMM.addSample( fgdLabels.at<int>(i,0), fgdSamples[i] );
fgdGMM.addSample( fgdLabels.at<int>(i,0), Vec3d(fgdSamples[i]) );
fgdGMM.endLearning();
}