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some attempts to tune the performance

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This commit is contained in:
Vadim Pisarevsky
2013-12-19 14:09:44 +04:00
parent 02fb3f0a77
commit b7553d4e2e
26 changed files with 187 additions and 150 deletions
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56
modules/objdetect/src/cascadedetect.cpp
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@@ -954,7 +954,7 @@ int CascadeClassifierImpl::runAt( Ptr<FeatureEvaluator>& evaluator, Point pt, do
if( !evaluator->setWindow(pt) )
return -1;
if( data.isStumpBased )
if( data.isStumpBased() )
{
if( data.featureType == FeatureEvaluator::HAAR )
return predictOrderedStump<HaarEvaluator>( *this, evaluator, weight );
@@ -1133,6 +1133,7 @@ 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 = 4;
Ptr<HaarEvaluator> haar = featureEvaluator.dynamicCast<HaarEvaluator>();
if( haar.empty() )
return false;
@@ -1142,7 +1143,7 @@ bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size proce
if( cascadeKernel.empty() )
{
cascadeKernel.create("runHaarClassifierStump", ocl::objdetect::haarobjectdetect_oclsrc,
format("-D MAX_FACES=%d", MAX_FACES));
format("-D VECTOR_SIZE=%d", VECTOR_SIZE));
if( cascadeKernel.empty() )
return false;
}
@@ -1150,9 +1151,7 @@ bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size proce
if( ustages.empty() )
{
copyVectorToUMat(data.stages, ustages);
copyVectorToUMat(data.classifiers, uclassifiers);
copyVectorToUMat(data.nodes, unodes);
copyVectorToUMat(data.leaves, uleaves);
copyVectorToUMat(data.stumps, ustumps);
}
std::vector<UMat> bufs;
@@ -1162,7 +1161,7 @@ bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size proce
Rect normrect = haar->getNormRect();
//processingRectSize = Size(yStep, yStep);
size_t globalsize[] = { processingRectSize.width/yStep, processingRectSize.height/yStep };
size_t globalsize[] = { (processingRectSize.width/yStep + VECTOR_SIZE-1)/VECTOR_SIZE, processingRectSize.height/yStep };
cascadeKernel.args(ocl::KernelArg::ReadOnlyNoSize(bufs[0]), // sum
ocl::KernelArg::ReadOnlyNoSize(bufs[1]), // sqsum
@@ -1171,14 +1170,12 @@ bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size proce
// cascade classifier
(int)data.stages.size(),
ocl::KernelArg::PtrReadOnly(ustages),
ocl::KernelArg::PtrReadOnly(uclassifiers),
ocl::KernelArg::PtrReadOnly(unodes),
ocl::KernelArg::PtrReadOnly(uleaves),
ocl::KernelArg::PtrReadOnly(ustumps),
ocl::KernelArg::PtrWriteOnly(ufacepos), // positions
processingRectSize,
yStep, (float)factor,
normrect, data.origWinSize);
normrect, data.origWinSize, MAX_FACES);
bool ok = cascadeKernel.run(2, globalsize, 0, true);
//CV_Assert(ok);
return ok;
@@ -1243,7 +1240,7 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::
bool use_ocl = ocl::useOpenCL() &&
getFeatureType() == FeatureEvaluator::HAAR &&
!isOldFormatCascade() &&
data.isStumpBased &&
data.isStumpBased() &&
maskGenerator.empty() &&
!outputRejectLevels &&
tryOpenCL;
@@ -1345,7 +1342,6 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::
Mat facepos = ufacepos.getMat(ACCESS_READ);
const int* fptr = facepos.ptr<int>();
int i, nfaces = fptr[0];
printf("nfaces = %d\n", nfaces);
for( i = 0; i < nfaces; i++ )
{
candidates.push_back(Rect(fptr[i*4+1], fptr[i*4+2], fptr[i*4+3], fptr[i*4+4]));
@@ -1428,6 +1424,12 @@ void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rec
}
}
CascadeClassifierImpl::Data::Data()
{
stageType = featureType = ncategories = maxNodesPerTree = 0;
}
bool CascadeClassifierImpl::Data::read(const FileNode &root)
{
static const float THRESHOLD_EPS = 1e-5f;
@@ -1471,9 +1473,10 @@ bool CascadeClassifierImpl::Data::read(const FileNode &root)
stages.reserve(fn.size());
classifiers.clear();
nodes.clear();
stumps.clear();
FileNodeIterator it = fn.begin(), it_end = fn.end();
isStumpBased = true;
maxNodesPerTree = 0;
for( int si = 0; it != it_end; si++, ++it )
{
@@ -1499,9 +1502,8 @@ bool CascadeClassifierImpl::Data::read(const FileNode &root)
DTree tree;
tree.nodeCount = (int)internalNodes.size()/nodeStep;
if( tree.nodeCount > 1 )
isStumpBased = false;
maxNodesPerTree = std::max(maxNodesPerTree, tree.nodeCount);
classifiers.push_back(tree);
nodes.reserve(nodes.size() + tree.nodeCount);
@@ -1536,6 +1538,24 @@ bool CascadeClassifierImpl::Data::read(const FileNode &root)
leaves.push_back((float)*internalNodesIter);
}
}
if( isStumpBased() )
{
int nodeOfs = 0, leafOfs = 0;
size_t nstages = stages.size();
for( size_t stageIdx = 0; stageIdx < nstages; stageIdx++ )
{
const Stage& stage = stages[stageIdx];
int ntrees = stage.ntrees;
for( int i = 0; i < ntrees; i++, nodeOfs++, leafOfs+= 2 )
{
const DTreeNode& node = nodes[nodeOfs];
stumps.push_back(Stump(node.featureIdx, node.threshold,
leaves[leafOfs], leaves[leafOfs+1]));
}
}
}
return true;
}
@@ -1546,9 +1566,7 @@ bool CascadeClassifierImpl::read_(const FileNode& root)
tryOpenCL = true;
cascadeKernel = ocl::Kernel();
ustages.release();
uclassifiers.release();
unodes.release();
uleaves.release();
ustumps.release();
if( !data.read(root) )
return false;