some attempts to tune the performance

data/haarcascades/haarcascade_eye.xml

@@ -48,7 +48,6 @@
   <height>20</height>
   <width>20</width>
   <stageParams>
-    <maxDepth>0</maxDepth>
     <maxWeakCount>93</maxWeakCount></stageParams>
   <featureParams>
     <maxCatCount>0</maxCatCount></featureParams>

data/haarcascades/haarcascade_eye_tree_eyeglasses.xml

@@ -48,7 +48,6 @@
   <height>20</height>
   <width>20</width>
   <stageParams>
-    <maxDepth>0</maxDepth>
     <maxWeakCount>47</maxWeakCount></stageParams>
   <featureParams>
     <maxCatCount>0</maxCatCount></featureParams>

data/haarcascades/haarcascade_frontalface_alt.xml

@@ -48,7 +48,6 @@
   <height>20</height>
   <width>20</width>
   <stageParams>
-    <maxDepth>0</maxDepth>
     <maxWeakCount>213</maxWeakCount></stageParams>
   <featureParams>
     <maxCatCount>0</maxCatCount></featureParams>

data/haarcascades/haarcascade_frontalface_alt2.xml

@@ -48,7 +48,6 @@
   <height>20</height>
   <width>20</width>
   <stageParams>
-    <maxDepth>0</maxDepth>
     <maxWeakCount>109</maxWeakCount></stageParams>
   <featureParams>
     <maxCatCount>0</maxCatCount></featureParams>

data/haarcascades/haarcascade_frontalface_alt_tree.xml

@@ -49,7 +49,6 @@
   <height>20</height>
   <width>20</width>
   <stageParams>
-    <maxDepth>0</maxDepth>
     <maxWeakCount>406</maxWeakCount></stageParams>
   <featureParams>
     <maxCatCount>0</maxCatCount></featureParams>

data/haarcascades/haarcascade_frontalface_default.xml

@@ -48,7 +48,6 @@
   <height>24</height>
   <width>24</width>
   <stageParams>
-    <maxDepth>0</maxDepth>
     <maxWeakCount>211</maxWeakCount></stageParams>
   <featureParams>
     <maxCatCount>0</maxCatCount></featureParams>

data/haarcascades/haarcascade_fullbody.xml

@@ -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>

data/haarcascades/haarcascade_lefteye_2splits.xml

@@ -49,7 +49,6 @@
   <height>20</height>
   <width>20</width>
   <stageParams>
-    <maxDepth>0</maxDepth>
     <maxWeakCount>33</maxWeakCount></stageParams>
   <featureParams>
     <maxCatCount>0</maxCatCount></featureParams>

data/haarcascades/haarcascade_lowerbody.xml

@@ -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>

data/haarcascades/haarcascade_mcs_eyepair_big.xml

@@ -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>

data/haarcascades/haarcascade_mcs_eyepair_small.xml

@@ -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>

data/haarcascades/haarcascade_mcs_leftear.xml

@@ -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>

data/haarcascades/haarcascade_mcs_lefteye.xml

@@ -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>

data/haarcascades/haarcascade_mcs_mouth.xml

@@ -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>

data/haarcascades/haarcascade_mcs_nose.xml

@@ -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>

data/haarcascades/haarcascade_mcs_rightear.xml

@@ -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>

data/haarcascades/haarcascade_mcs_righteye.xml

@@ -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>

data/haarcascades/haarcascade_mcs_upperbody.xml

@@ -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>

data/haarcascades/haarcascade_profileface.xml

@@ -48,7 +48,6 @@
   <height>20</height>
   <width>20</width>
   <stageParams>
-    <maxDepth>0</maxDepth>
     <maxWeakCount>195</maxWeakCount></stageParams>
   <featureParams>
     <maxCatCount>0</maxCatCount></featureParams>

data/haarcascades/haarcascade_righteye_2splits.xml

@@ -49,7 +49,6 @@
   <height>20</height>
   <width>20</width>
   <stageParams>
-    <maxDepth>0</maxDepth>
     <maxWeakCount>34</maxWeakCount></stageParams>
   <featureParams>
     <maxCatCount>0</maxCatCount></featureParams>

data/haarcascades/haarcascade_smile.xml

@@ -50,7 +50,6 @@
   <height>36</height>
   <width>18</width>
   <stageParams>
-    <maxDepth>0</maxDepth>
     <maxWeakCount>53</maxWeakCount></stageParams>
   <featureParams>
     <maxCatCount>0</maxCatCount></featureParams>

data/haarcascades/haarcascade_upperbody.xml

@@ -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>

modules/objdetect/src/cascadedetect.cpp

@@ -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.stumps, ustumps);
-		copyVectorToUMat(data.nodes, unodes);
-		copyVectorToUMat(data.leaves, uleaves);
     }
     
     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(ustumps),
-                       ocl::KernelArg::PtrReadOnly(unodes),
-                       ocl::KernelArg::PtrReadOnly(uleaves),
                        
                        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 )
+            maxNodesPerTree = std::max(maxNodesPerTree, tree.nodeCount);
-                isStumpBased = false;
+            
-                
             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();
+    ustumps.release();
-    unodes.release();
-    uleaves.release();
     if( !data.read(root) )
         return false;
 

modules/objdetect/src/cascadedetect.hpp

@@ -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];
+    const CascadeClassifierImpl::Data::Stump* cascadeStumps = &cascade.data.stumps[0];
-    CascadeClassifierImpl::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
+    const CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
-    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];
+        const CascadeClassifierImpl::Data::Stage& stage = cascadeStages[stageIdx];
-        sum = 0.0;
+        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];
+            const CascadeClassifierImpl::Data::Stump& stump = cascadeStumps[i];
-            double value = featureEvaluator(node.featureIdx);
+            double value = featureEvaluator(stump.featureIdx);
-            sum += cascadeLeaves[ value < node.threshold ? leafOfs : leafOfs + 1 ];
+            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];
+    const int* cascadeSubsets = &cascade.data.subsets[0];
-    float* cascadeLeaves = &cascade.data.leaves[0];
+    const CascadeClassifierImpl::Data::Stump* cascadeStumps = &cascade.data.stumps[0];
-    CascadeClassifierImpl::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
+    const CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
-    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];
+            const CascadeClassifierImpl::Data::Stump& stump = cascadeStumps[wi];
-            int c = featureEvaluator(node.featureIdx);
+            int c = featureEvaluator(stump.featureIdx);
-            const int* subset = &cascadeSubsets[nodeOfs*subsetSize];
+            const int* subset = &cascadeSubsets[wi*subsetSize];
-#ifdef HAVE_TEGRA_OPTIMIZATION
+            tmp += (subset[c>>5] & (1 << (c & 31))) ? stump.left : stump.right;
-            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;
         }
-#ifdef HAVE_TEGRA_OPTIMIZATION
+
-        if( tmp < stage.threshold ) {
+        if( tmp < stage.threshold )
+        {
             sum = (double)tmp;
             return -si;
         }
-#else
+        
-        if( sum < stage.threshold )
+        cascadeStumps += ntrees;
-            return -si;
+        cascadeSubsets += ntrees*subsetSize;
-#endif
     }
 
-#ifdef HAVE_TEGRA_OPTIMIZATION
     sum = (double)tmp;
-#endif
-
     return 1;
 }
 }

modules/objdetect/src/cascadedetect_convert.cpp

@@ -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" << "{"

modules/objdetect/src/opencl/haarobjectdetect.cl

@@ -1,43 +1,5 @@
-//                           License Agreement
+///////////////////////////// OpenCL kernels for face detection //////////////////////////////
-//                For Open Source Computer Vision Library
+////////////////////////////// see the opencv/doc/license.txt ///////////////////////////////
-//
-// 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.
-//
-//
 
 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)));
+    float left __attribute__((aligned (4)));
-    int right __attribute__((aligned (4)));
+    float right __attribute__((aligned (4)));
 }
-DTreeNode;
+Stump;
-
-typedef struct __attribute__((aligned (4))) DTree
-{
-    int nodeCount __attribute__((aligned (4)));
-}
-DTree;
 
 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 Stump* stumps,
-    __global const DTreeNode* nodes,
-    __global const float* leaves,
 
     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;
+        float4 weight, vsval;
-        int4 ofs;
+        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 + stump->featureIdx;
-                f = optfeatures + node->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
+