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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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96
modules/objdetect/src/cascadedetect.hpp
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@@ -48,7 +48,7 @@ public:
Ptr<MaskGenerator> getMaskGenerator();
protected:
enum { SUM_ALIGN = 16 };
enum { SUM_ALIGN = 64 };
bool detectSingleScale( InputArray image, Size processingRectSize,
int yStep, double factor, std::vector<Rect>& candidates,
@@ -109,14 +109,29 @@ protected:
int ntrees;
float threshold;
};
struct Stump
{
Stump() {};
Stump(int _featureIdx, float _threshold, float _left, float _right)
: featureIdx(_featureIdx), threshold(_threshold), left(_left), right(_right) {}
int featureIdx;
float threshold;
float left;
float right;
};
Data();
bool read(const FileNode &node);
bool isStumpBased;
bool isStumpBased() const { return maxNodesPerTree == 1; }
int stageType;
int featureType;
int ncategories;
int maxNodesPerTree;
Size origWinSize;
std::vector<Stage> stages;
@@ -124,6 +139,7 @@ protected:
std::vector<DTreeNode> nodes;
std::vector<float> leaves;
std::vector<int> subsets;
std::vector<Stump> stumps;
};
Data data;
@@ -132,7 +148,7 @@ protected:
Ptr<MaskGenerator> maskGenerator;
UMat ugrayImage, uimageBuffer;
UMat ufacepos, ustages, uclassifiers, unodes, uleaves, usubsets;
UMat ufacepos, ustages, ustumps, usubsets;
ocl::Kernel cascadeKernel;
bool tryOpenCL;
@@ -592,30 +608,36 @@ template<class FEval>
inline int predictOrderedStump( CascadeClassifierImpl& cascade,
Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
int nodeOfs = 0, leafOfs = 0;
CV_Assert(!cascade.data.stumps.empty());
FEval& featureEvaluator = (FEval&)*_featureEvaluator;
float* cascadeLeaves = &cascade.data.leaves[0];
CascadeClassifierImpl::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
const CascadeClassifierImpl::Data::Stump* cascadeStumps = &cascade.data.stumps[0];
const CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
int nstages = (int)cascade.data.stages.size();
double tmp = 0;
for( int stageIdx = 0; stageIdx < nstages; stageIdx++ )
{
CascadeClassifierImpl::Data::Stage& stage = cascadeStages[stageIdx];
sum = 0.0;
const CascadeClassifierImpl::Data::Stage& stage = cascadeStages[stageIdx];
tmp = 0;
int ntrees = stage.ntrees;
for( int i = 0; i < ntrees; i++, nodeOfs++, leafOfs+= 2 )
for( int i = 0; i < ntrees; i++ )
{
CascadeClassifierImpl::Data::DTreeNode& node = cascadeNodes[nodeOfs];
double value = featureEvaluator(node.featureIdx);
sum += cascadeLeaves[ value < node.threshold ? leafOfs : leafOfs + 1 ];
const CascadeClassifierImpl::Data::Stump& stump = cascadeStumps[i];
double value = featureEvaluator(stump.featureIdx);
tmp += value < stump.threshold ? stump.left : stump.right;
}
if( sum < stage.threshold )
if( tmp < stage.threshold )
{
sum = (double)tmp;
return -stageIdx;
}
cascadeStumps += ntrees;
}
sum = (double)tmp;
return 1;
}
@@ -623,56 +645,44 @@ template<class FEval>
inline int predictCategoricalStump( CascadeClassifierImpl& cascade,
Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
CV_Assert(!cascade.data.stumps.empty());
int nstages = (int)cascade.data.stages.size();
int nodeOfs = 0, leafOfs = 0;
FEval& featureEvaluator = (FEval&)*_featureEvaluator;
size_t subsetSize = (cascade.data.ncategories + 31)/32;
int* cascadeSubsets = &cascade.data.subsets[0];
float* cascadeLeaves = &cascade.data.leaves[0];
CascadeClassifierImpl::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
const int* cascadeSubsets = &cascade.data.subsets[0];
const CascadeClassifierImpl::Data::Stump* cascadeStumps = &cascade.data.stumps[0];
const CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
#ifdef HAVE_TEGRA_OPTIMIZATION
float tmp = 0; // float accumulator -- float operations are quicker
#else
double tmp = 0;
#endif
for( int si = 0; si < nstages; si++ )
{
CascadeClassifierImpl::Data::Stage& stage = cascadeStages[si];
const CascadeClassifierImpl::Data::Stage& stage = cascadeStages[si];
int wi, ntrees = stage.ntrees;
#ifdef HAVE_TEGRA_OPTIMIZATION
tmp = 0;
#else
sum = 0;
#endif
for( wi = 0; wi < ntrees; wi++ )
{
CascadeClassifierImpl::Data::DTreeNode& node = cascadeNodes[nodeOfs];
int c = featureEvaluator(node.featureIdx);
const int* subset = &cascadeSubsets[nodeOfs*subsetSize];
#ifdef HAVE_TEGRA_OPTIMIZATION
tmp += cascadeLeaves[ subset[c>>5] & (1 << (c & 31)) ? leafOfs : leafOfs+1];
#else
sum += cascadeLeaves[ subset[c>>5] & (1 << (c & 31)) ? leafOfs : leafOfs+1];
#endif
nodeOfs++;
leafOfs += 2;
const CascadeClassifierImpl::Data::Stump& stump = cascadeStumps[wi];
int c = featureEvaluator(stump.featureIdx);
const int* subset = &cascadeSubsets[wi*subsetSize];
tmp += (subset[c>>5] & (1 << (c & 31))) ? stump.left : stump.right;
}
#ifdef HAVE_TEGRA_OPTIMIZATION
if( tmp < stage.threshold ) {
if( tmp < stage.threshold )
{
sum = (double)tmp;
return -si;
}
#else
if( sum < stage.threshold )
return -si;
#endif
cascadeStumps += ntrees;
cascadeSubsets += ntrees*subsetSize;
}
#ifdef HAVE_TEGRA_OPTIMIZATION
sum = (double)tmp;
#endif
return 1;
}
}