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added OpenCL optimization for LBP-based face detector

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This commit is contained in:
Vadim Pisarevsky
2013-12-23 15:28:50 +04:00
parent 1540910542
commit d084d19779
3 changed files with 102 additions and 53 deletions
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117
modules/objdetect/src/cascadedetect.cpp
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@@ -743,6 +743,14 @@ bool LBPEvaluator::setWindow( Point pt )
pwin = &sum.at<int>(pt);
return true;
}
void LBPEvaluator::getUMats(std::vector<UMat>& bufs)
{
bufs.clear();
bufs.push_back(usum);
bufs.push_back(ufbuf);
}
//---------------------------------------------- HOGEvaluator ---------------------------------------
bool HOGEvaluator::Feature :: read( const FileNode& node )
@@ -1162,50 +1170,84 @@ bool CascadeClassifierImpl::detectSingleScale( InputArray _image, Size processin
bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size processingRectSize,
int yStep, double factor, Size sumSize0 )
{
const int VECTOR_SIZE = 1;
Ptr<HaarEvaluator> haar = featureEvaluator.dynamicCast<HaarEvaluator>();
if( haar.empty() )
return false;
haar->setImage(_image, data.origWinSize, sumSize0);
if( cascadeKernel.empty() )
{
cascadeKernel.create("runHaarClassifierStump", ocl::objdetect::cascadedetect_oclsrc,
format("-D VECTOR_SIZE=%d", VECTOR_SIZE));
if( cascadeKernel.empty() )
return false;
}
int featureType = getFeatureType();
std::vector<UMat> bufs;
size_t globalsize[] = { processingRectSize.width/yStep, processingRectSize.height/yStep };
bool ok = false;
if( ustages.empty() )
{
copyVectorToUMat(data.stages, ustages);
copyVectorToUMat(data.stumps, ustumps);
if( !data.subsets.empty() )
copyVectorToUMat(data.subsets, usubsets);
}
std::vector<UMat> bufs;
haar->getUMats(bufs);
CV_Assert(bufs.size() == 3);
if( featureType == FeatureEvaluator::HAAR )
{
Ptr<HaarEvaluator> haar = featureEvaluator.dynamicCast<HaarEvaluator>();
if( haar.empty() )
return false;
Rect normrect = haar->getNormRect();
haar->setImage(_image, data.origWinSize, sumSize0);
if( haarKernel.empty() )
{
haarKernel.create("runHaarClassifierStump", ocl::objdetect::cascadedetect_oclsrc, "");
if( haarKernel.empty() )
return false;
}
haar->getUMats(bufs);
Rect normrect = haar->getNormRect();
//processingRectSize = Size(yStep, yStep);
size_t globalsize[] = { (processingRectSize.width/yStep + VECTOR_SIZE-1)/VECTOR_SIZE, processingRectSize.height/yStep };
haarKernel.args(ocl::KernelArg::ReadOnlyNoSize(bufs[0]), // sum
ocl::KernelArg::ReadOnlyNoSize(bufs[1]), // sqsum
ocl::KernelArg::PtrReadOnly(bufs[2]), // optfeatures
cascadeKernel.args(ocl::KernelArg::ReadOnlyNoSize(bufs[0]), // sum
ocl::KernelArg::ReadOnlyNoSize(bufs[1]), // sqsum
ocl::KernelArg::PtrReadOnly(bufs[2]), // optfeatures
// cascade classifier
(int)data.stages.size(),
ocl::KernelArg::PtrReadOnly(ustages),
ocl::KernelArg::PtrReadOnly(ustumps),
// cascade classifier
(int)data.stages.size(),
ocl::KernelArg::PtrReadOnly(ustages),
ocl::KernelArg::PtrReadOnly(ustumps),
ocl::KernelArg::PtrWriteOnly(ufacepos), // positions
processingRectSize,
yStep, (float)factor,
normrect, data.origWinSize, MAX_FACES);
bool ok = cascadeKernel.run(2, globalsize, 0, true);
ocl::KernelArg::PtrWriteOnly(ufacepos), // positions
processingRectSize,
yStep, (float)factor,
normrect, data.origWinSize, MAX_FACES);
ok = haarKernel.run(2, globalsize, 0, true);
}
else if( featureType == FeatureEvaluator::LBP )
{
Ptr<LBPEvaluator> lbp = featureEvaluator.dynamicCast<LBPEvaluator>();
if( lbp.empty() )
return false;
lbp->setImage(_image, data.origWinSize, sumSize0);
if( lbpKernel.empty() )
{
lbpKernel.create("runLBPClassifierStump", ocl::objdetect::cascadedetect_oclsrc, "");
if( lbpKernel.empty() )
return false;
}
lbp->getUMats(bufs);
int subsetSize = (data.ncategories + 31)/32;
lbpKernel.args(ocl::KernelArg::ReadOnlyNoSize(bufs[0]), // sum
ocl::KernelArg::PtrReadOnly(bufs[1]), // optfeatures
// cascade classifier
(int)data.stages.size(),
ocl::KernelArg::PtrReadOnly(ustages),
ocl::KernelArg::PtrReadOnly(ustumps),
ocl::KernelArg::PtrReadOnly(usubsets),
subsetSize,
ocl::KernelArg::PtrWriteOnly(ufacepos), // positions
processingRectSize,
yStep, (float)factor,
data.origWinSize, MAX_FACES);
ok = lbpKernel.run(2, globalsize, 0, true);
}
//CV_Assert(ok);
return ok;
}
@@ -1254,6 +1296,7 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::
double scaleFactor, Size minObjectSize, Size maxObjectSize,
bool outputRejectLevels )
{
int featureType = getFeatureType();
Size imgsz = _image.size();
int imgtype = _image.type();
@@ -1267,7 +1310,8 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::
maxObjectSize = imgsz;
bool use_ocl = ocl::useOpenCL() &&
getFeatureType() == FeatureEvaluator::HAAR &&
(featureType == FeatureEvaluator::HAAR ||
featureType == FeatureEvaluator::LBP) &&
!isOldFormatCascade() &&
data.isStumpBased() &&
maskGenerator.empty() &&
@@ -1593,7 +1637,8 @@ bool CascadeClassifierImpl::Data::read(const FileNode &root)
bool CascadeClassifierImpl::read_(const FileNode& root)
{
tryOpenCL = true;
cascadeKernel = ocl::Kernel();
haarKernel = ocl::Kernel();
lbpKernel = ocl::Kernel();
ustages.release();
ustumps.release();
if( !data.read(root) )