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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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1
data/haarcascades/haarcascade_eye.xml
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@ -48,7 +48,6 @@
<height>20</height>
<width>20</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>93</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_eye_tree_eyeglasses.xml
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@ -48,7 +48,6 @@
<height>20</height>
<width>20</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>47</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_frontalface_alt.xml
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@ -48,7 +48,6 @@
<height>20</height>
<width>20</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>213</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_frontalface_alt2.xml
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@ -48,7 +48,6 @@
<height>20</height>
<width>20</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>109</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_frontalface_alt_tree.xml
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@ -49,7 +49,6 @@
<height>20</height>
<width>20</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>406</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_frontalface_default.xml
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@ -48,7 +48,6 @@
<height>24</height>
<width>24</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>211</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_fullbody.xml
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@ -142,7 +142,6 @@ Thanks to Martin Spengler, ETH Zurich, for providing the demo movie.
<height>14</height>
<width>28</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>107</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_lefteye_2splits.xml
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@ -49,7 +49,6 @@
<height>20</height>
<width>20</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>33</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_lowerbody.xml
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@ -142,7 +142,6 @@ Thanks to Martin Spengler, ETH Zurich, for providing the demo movie.
<height>19</height>
<width>23</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>89</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_mcs_eyepair_big.xml
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@ -88,7 +88,6 @@ mcastrillon@iusiani.ulpgc.es
<height>45</height>
<width>11</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>85</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_mcs_eyepair_small.xml
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@ -87,7 +87,6 @@ mcastrillon@iusiani.ulpgc.es
<height>22</height>
<width>5</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>133</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_mcs_leftear.xml
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@ -67,7 +67,6 @@ mcastrillon@iusiani.ulpgc.es
<height>12</height>
<width>20</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>65</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_mcs_lefteye.xml
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@ -87,7 +87,6 @@ mcastrillon@iusiani.ulpgc.es
<height>18</height>
<width>12</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>279</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_mcs_mouth.xml
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@ -87,7 +87,6 @@ mcastrillon@iusiani.ulpgc.es
<height>25</height>
<width>15</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>218</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_mcs_nose.xml
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@ -87,7 +87,6 @@ mcastrillon@iusiani.ulpgc.es
<height>18</height>
<width>15</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>377</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_mcs_rightear.xml
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@ -67,7 +67,6 @@ mcastrillon@iusiani.ulpgc.es
<height>12</height>
<width>20</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>61</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_mcs_righteye.xml
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@ -87,7 +87,6 @@ mcastrillon@iusiani.ulpgc.es
<height>18</height>
<width>12</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>415</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_mcs_upperbody.xml
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@ -85,7 +85,6 @@ mcastrillon@iusiani.ulpgc.es
<height>22</height>
<width>20</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>334</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_profileface.xml
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@ -48,7 +48,6 @@
<height>20</height>
<width>20</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>195</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_righteye_2splits.xml
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@ -49,7 +49,6 @@
<height>20</height>
<width>20</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>34</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_smile.xml
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@ -50,7 +50,6 @@
<height>36</height>
<width>18</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>53</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

1
data/haarcascades/haarcascade_upperbody.xml
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@ -142,7 +142,6 @@ Thanks to Martin Spengler, ETH Zurich, for providing the demo movie.
<height>22</height>
<width>18</width>
<stageParams>
<maxDepth>0</maxDepth>
<maxWeakCount>152</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount></featureParams>

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;

96
modules/objdetect/src/cascadedetect.hpp
View File

@ -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;
}
}

1
modules/objdetect/src/cascadedetect_convert.cpp
View File

@ -209,7 +209,6 @@ static bool convert(const String& oldcascade, const String& newcascade)
<< "height" << cascadesize.width
<< "width" << cascadesize.height
<< "stageParams" << "{"
<< "maxDepth" << maxdepth
<< "maxWeakCount" << (int)maxWeakCount
<< "}"
<< "featureParams" << "{"

162
modules/objdetect/src/opencl/haarobjectdetect.cl
View File

@ -1,43 +1,5 @@
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Niko Li, newlife20080214@gmail.com
// Wang Weiyan, wangweiyanster@gmail.com
// Jia Haipeng, jiahaipeng95@gmail.com
// Nathan, liujun@multicorewareinc.com
// Peng Xiao, pengxiao@outlook.com
// Erping Pang, erping@multicorewareinc.com
// Vadim Pisarevsky, vadim.pisarevsky@itseez.com
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors as is and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//
///////////////////////////// OpenCL kernels for face detection //////////////////////////////
////////////////////////////// see the opencv/doc/license.txt ///////////////////////////////
typedef struct __attribute__((aligned(4))) OptFeature
{
@ -46,20 +8,14 @@ typedef struct __attribute__((aligned(4))) OptFeature
}
OptFeature;
typedef struct __attribute__((aligned(4))) DTreeNode
typedef struct __attribute__((aligned(4))) Stump
{
int featureIdx __attribute__((aligned (4)));
float threshold __attribute__((aligned (4))); // for ordered features only
int left __attribute__((aligned (4)));
int right __attribute__((aligned (4)));
float left __attribute__((aligned (4)));
float right __attribute__((aligned (4)));
}
DTreeNode;
typedef struct __attribute__((aligned (4))) DTree
{
int nodeCount __attribute__((aligned (4)));
}
DTree;
Stump;
typedef struct __attribute__((aligned (4))) Stage
{
@ -78,25 +34,23 @@ __kernel void runHaarClassifierStump(
int nstages,
__global const Stage* stages,
__global const DTree* trees,
__global const DTreeNode* nodes,
__global const float* leaves,
__global const Stump* stumps,
volatile __global int* facepos,
int2 imgsize, int xyscale, float factor,
int4 normrect, int2 windowsize)
int4 normrect, int2 windowsize, int maxFaces)
{
int ix = get_global_id(0)*xyscale;
int ix = get_global_id(0)*xyscale*VECTOR_SIZE;
int iy = get_global_id(1)*xyscale;
sumstep /= sizeof(int);
sqsumstep /= sizeof(int);
if( ix < imgsize.x && iy < imgsize.y )
{
int ntrees, nodeOfs = 0, leafOfs = 0;
int ntrees;
int stageIdx, i;
float s = 0.f;
__global const DTreeNode* node;
__global const Stump* stump = stumps;
__global const OptFeature* f;
__global const int* psum = sum + mad24(iy, sumstep, ix);
@ -107,19 +61,17 @@ __kernel void runHaarClassifierStump(
pnsum[mad24(normrect.w, sumstep, normrect.z)])*invarea;
float sqval = (sqsum[mad24(iy + normrect.y, sqsumstep, ix + normrect.x)])*invarea;
float nf = (float)normarea * sqrt(max(sqval - sval * sval, 0.f));
float4 weight;
int4 ofs;
float4 weight, vsval;
int4 ofs, ofs0, ofs1, ofs2;
nf = nf > 0 ? nf : 1.f;
for( stageIdx = 0; stageIdx < nstages; stageIdx++ )
{
ntrees = stages[stageIdx].ntrees;
s = 0.f;
for( i = 0; i < ntrees; i++, nodeOfs++, leafOfs += 2 )
for( i = 0; i < ntrees; i++, stump++ )
{
node = nodes + nodeOfs;
f = optfeatures + node->featureIdx;
f = optfeatures + stump->featureIdx;
weight = f->weight;
ofs = f->ofs[0];
@ -131,7 +83,8 @@ __kernel void runHaarClassifierStump(
ofs = f->ofs[2];
sval += (psum[ofs.x] - psum[ofs.y] - psum[ofs.z] + psum[ofs.w])*weight.z;
}
s += leaves[ sval < node->threshold*nf ? leafOfs : leafOfs + 1 ];
s += (sval < stump->threshold*nf) ? stump->left : stump->right;
}
if( s < stages[stageIdx].threshold )
@ -142,7 +95,7 @@ __kernel void runHaarClassifierStump(
{
int nfaces = atomic_inc(facepos);
//printf("detected face #d!!!!\n", nfaces);
if( nfaces < MAX_FACES )
if( nfaces < maxFaces )
{
volatile __global int* face = facepos + 1 + nfaces*4;
face[0] = convert_int_rte(ix*factor);
@ -153,3 +106,82 @@ __kernel void runHaarClassifierStump(
}
}
}
#if 0
__kernel void runLBPClassifierStump(
__global const int* sum,
int sumstep, int sumoffset,
__global const int* sqsum,
int sqsumstep, int sqsumoffset,
__global const OptFeature* optfeatures,
int nstages,
__global const Stage* stages,
__global const Stump* stumps,
__global const int* bitsets,
int bitsetSize,
volatile __global int* facepos,
int2 imgsize, int xyscale, float factor,
int4 normrect, int2 windowsize, int maxFaces)
{
int ix = get_global_id(0)*xyscale*VECTOR_SIZE;
int iy = get_global_id(1)*xyscale;
sumstep /= sizeof(int);
sqsumstep /= sizeof(int);
if( ix < imgsize.x && iy < imgsize.y )
{
int ntrees;
int stageIdx, i;
float s = 0.f;
__global const Stump* stump = stumps;
__global const int* bitset = bitsets;
__global const OptFeature* f;
__global const int* psum = sum + mad24(iy, sumstep, ix);
__global const int* pnsum = psum + mad24(normrect.y, sumstep, normrect.x);
int normarea = normrect.z * normrect.w;
float invarea = 1.f/normarea;
float sval = (pnsum[0] - pnsum[normrect.z] - pnsum[mul24(normrect.w, sumstep)] +
pnsum[mad24(normrect.w, sumstep, normrect.z)])*invarea;
float sqval = (sqsum[mad24(iy + normrect.y, sqsumstep, ix + normrect.x)])*invarea;
float nf = (float)normarea * sqrt(max(sqval - sval * sval, 0.f));
float4 weight;
int4 ofs;
nf = nf > 0 ? nf : 1.f;
for( stageIdx = 0; stageIdx < nstages; stageIdx++ )
{
ntrees = stages[stageIdx].ntrees;
s = 0.f;
for( i = 0; i < ntrees; i++, stump++, bitset += bitsetSize )
{
f = optfeatures + stump->featureIdx;
weight = f->weight;
// compute LBP feature to val
s += (bitset[val >> 5] & (1 << (val & 31))) ? stump->left : stump->right;
}
if( s < stages[stageIdx].threshold )
break;
}
if( stageIdx == nstages )
{
int nfaces = atomic_inc(facepos);
if( nfaces < maxFaces )
{
volatile __global int* face = facepos + 1 + nfaces*4;
face[0] = convert_int_rte(ix*factor);
face[1] = convert_int_rte(iy*factor);
face[2] = convert_int_rte(windowsize.x*factor);
face[3] = convert_int_rte(windowsize.y*factor);
}
}
}
}
#endif