#pragma once

namespace cv
{

class CascadeClassifierImpl : public BaseCascadeClassifier
{
public:
    CascadeClassifierImpl();
    virtual ~CascadeClassifierImpl();

    bool empty() const;
    bool load( const String& filename );
    void read( const FileNode& node );
    bool read_( const FileNode& node );
    void detectMultiScale( InputArray image,
                          CV_OUT std::vector<Rect>& objects,
                          double scaleFactor = 1.1,
                          int minNeighbors = 3, int flags = 0,
                          Size minSize = Size(),
                          Size maxSize = Size() );

    void detectMultiScale( InputArray image,
                          CV_OUT std::vector<Rect>& objects,
                          CV_OUT std::vector<int>& numDetections,
                          double scaleFactor=1.1,
                          int minNeighbors=3, int flags=0,
                          Size minSize=Size(),
                          Size maxSize=Size() );

    void detectMultiScale( InputArray image,
                          CV_OUT std::vector<Rect>& objects,
                          CV_OUT std::vector<int>& rejectLevels,
                          CV_OUT std::vector<double>& levelWeights,
                          double scaleFactor = 1.1,
                          int minNeighbors = 3, int flags = 0,
                          Size minSize = Size(),
                          Size maxSize = Size(),
                          bool outputRejectLevels = false );


    bool isOldFormatCascade() const;
    Size getOriginalWindowSize() const;
    int getFeatureType() const;
    void* getOldCascade();

    void setMaskGenerator(const Ptr<MaskGenerator>& maskGenerator);
    Ptr<MaskGenerator> getMaskGenerator();

protected:
    enum { SUM_ALIGN = 64 };

    bool detectSingleScale( InputArray image, Size processingRectSize,
                            int yStep, double factor, std::vector<Rect>& candidates,
                            std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
                            Size sumSize0, bool outputRejectLevels = false );
    bool ocl_detectSingleScale( InputArray image, Size processingRectSize,
                                int yStep, double factor, Size sumSize0 );


    void detectMultiScaleNoGrouping( InputArray image, std::vector<Rect>& candidates,
                                    std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
                                    double scaleFactor, Size minObjectSize, Size maxObjectSize,
                                    bool outputRejectLevels = false );

    enum { MAX_FACES = 10000 };
    enum { BOOST = 0 };
    enum { DO_CANNY_PRUNING    = CASCADE_DO_CANNY_PRUNING,
        SCALE_IMAGE         = CASCADE_SCALE_IMAGE,
        FIND_BIGGEST_OBJECT = CASCADE_FIND_BIGGEST_OBJECT,
        DO_ROUGH_SEARCH     = CASCADE_DO_ROUGH_SEARCH
    };

    friend class CascadeClassifierInvoker;

    template<class FEval>
    friend int predictOrdered( CascadeClassifierImpl& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);

    template<class FEval>
    friend int predictCategorical( CascadeClassifierImpl& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);

    template<class FEval>
    friend int predictOrderedStump( CascadeClassifierImpl& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);

    template<class FEval>
    friend int predictCategoricalStump( CascadeClassifierImpl& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);

    int runAt( Ptr<FeatureEvaluator>& feval, Point pt, double& weight );

    class Data
    {
    public:
        struct DTreeNode
        {
            int featureIdx;
            float threshold; // for ordered features only
            int left;
            int right;
        };

        struct DTree
        {
            int nodeCount;
        };

        struct Stage
        {
            int first;
            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() const { return maxNodesPerTree == 1; }

        int stageType;
        int featureType;
        int ncategories;
        int maxNodesPerTree;
        Size origWinSize;

        std::vector<Stage> stages;
        std::vector<DTree> classifiers;
        std::vector<DTreeNode> nodes;
        std::vector<float> leaves;
        std::vector<int> subsets;
        std::vector<Stump> stumps;
    };

    Data data;
    Ptr<FeatureEvaluator> featureEvaluator;
    Ptr<CvHaarClassifierCascade> oldCascade;

    Ptr<MaskGenerator> maskGenerator;
    UMat ugrayImage, uimageBuffer;
    UMat ufacepos, ustages, ustumps, usubsets;
    ocl::Kernel haarKernel, lbpKernel;
    bool tryOpenCL;

    Mutex mtx;
};

#define CC_CASCADE_PARAMS "cascadeParams"
#define CC_STAGE_TYPE     "stageType"
#define CC_FEATURE_TYPE   "featureType"
#define CC_HEIGHT         "height"
#define CC_WIDTH          "width"

#define CC_STAGE_NUM    "stageNum"
#define CC_STAGES       "stages"
#define CC_STAGE_PARAMS "stageParams"

#define CC_BOOST            "BOOST"
#define CC_MAX_DEPTH        "maxDepth"
#define CC_WEAK_COUNT       "maxWeakCount"
#define CC_STAGE_THRESHOLD  "stageThreshold"
#define CC_WEAK_CLASSIFIERS "weakClassifiers"
#define CC_INTERNAL_NODES   "internalNodes"
#define CC_LEAF_VALUES      "leafValues"

#define CC_FEATURES       "features"
#define CC_FEATURE_PARAMS "featureParams"
#define CC_MAX_CAT_COUNT  "maxCatCount"

#define CC_HAAR   "HAAR"
#define CC_RECTS  "rects"
#define CC_TILTED "tilted"

#define CC_LBP  "LBP"
#define CC_RECT "rect"

#define CC_HOG  "HOG"

#define CV_SUM_PTRS( p0, p1, p2, p3, sum, rect, step )                    \
    /* (x, y) */                                                          \
    (p0) = sum + (rect).x + (step) * (rect).y,                            \
    /* (x + w, y) */                                                      \
    (p1) = sum + (rect).x + (rect).width + (step) * (rect).y,             \
    /* (x + w, y) */                                                      \
    (p2) = sum + (rect).x + (step) * ((rect).y + (rect).height),          \
    /* (x + w, y + h) */                                                  \
    (p3) = sum + (rect).x + (rect).width + (step) * ((rect).y + (rect).height)

#define CV_TILTED_PTRS( p0, p1, p2, p3, tilted, rect, step )                        \
    /* (x, y) */                                                                    \
    (p0) = tilted + (rect).x + (step) * (rect).y,                                   \
    /* (x - h, y + h) */                                                            \
    (p1) = tilted + (rect).x - (rect).height + (step) * ((rect).y + (rect).height), \
    /* (x + w, y + w) */                                                            \
    (p2) = tilted + (rect).x + (rect).width + (step) * ((rect).y + (rect).width),   \
    /* (x + w - h, y + w + h) */                                                    \
    (p3) = tilted + (rect).x + (rect).width - (rect).height                         \
           + (step) * ((rect).y + (rect).width + (rect).height)

#define CALC_SUM_(p0, p1, p2, p3, offset) \
    ((p0)[offset] - (p1)[offset] - (p2)[offset] + (p3)[offset])

#define CALC_SUM(rect,offset) CALC_SUM_((rect)[0], (rect)[1], (rect)[2], (rect)[3], offset)

#define CV_SUM_OFS( p0, p1, p2, p3, sum, rect, step )                 \
/* (x, y) */                                                          \
(p0) = sum + (rect).x + (step) * (rect).y,                            \
/* (x + w, y) */                                                      \
(p1) = sum + (rect).x + (rect).width + (step) * (rect).y,             \
/* (x + w, y) */                                                      \
(p2) = sum + (rect).x + (step) * ((rect).y + (rect).height),          \
/* (x + w, y + h) */                                                  \
(p3) = sum + (rect).x + (rect).width + (step) * ((rect).y + (rect).height)

#define CV_TILTED_OFS( p0, p1, p2, p3, tilted, rect, step )                     \
/* (x, y) */                                                                    \
(p0) = tilted + (rect).x + (step) * (rect).y,                                   \
/* (x - h, y + h) */                                                            \
(p1) = tilted + (rect).x - (rect).height + (step) * ((rect).y + (rect).height), \
/* (x + w, y + w) */                                                            \
(p2) = tilted + (rect).x + (rect).width + (step) * ((rect).y + (rect).width),   \
/* (x + w - h, y + w + h) */                                                    \
(p3) = tilted + (rect).x + (rect).width - (rect).height                         \
+ (step) * ((rect).y + (rect).width + (rect).height)

#define CALC_SUM_(p0, p1, p2, p3, offset) \
((p0)[offset] - (p1)[offset] - (p2)[offset] + (p3)[offset])

#define CALC_SUM(rect,offset) CALC_SUM_((rect)[0], (rect)[1], (rect)[2], (rect)[3], offset)

#define CALC_SUM_OFS_(p0, p1, p2, p3, ptr) \
((ptr)[p0] - (ptr)[p1] - (ptr)[p2] + (ptr)[p3])

#define CALC_SUM_OFS(rect, ptr) CALC_SUM_OFS_((rect)[0], (rect)[1], (rect)[2], (rect)[3], ptr)

//----------------------------------------------  HaarEvaluator ---------------------------------------
class HaarEvaluator : public FeatureEvaluator
{
public:
    struct Feature
    {
        Feature();
        bool read( const FileNode& node );

        bool tilted;

        enum { RECT_NUM = 3 };
        struct
        {
            Rect r;
            float weight;
        } rect[RECT_NUM];
    };

    struct OptFeature
    {
        OptFeature();

        enum { RECT_NUM = Feature::RECT_NUM };
        float calc( const int* pwin ) const;

        void setOffsets( const Feature& _f, int step, int tofs );

        int ofs[RECT_NUM][4];
        float weight[4];
    };

    HaarEvaluator();
    virtual ~HaarEvaluator();

    virtual bool read( const FileNode& node );
    virtual Ptr<FeatureEvaluator> clone() const;
    virtual int getFeatureType() const { return FeatureEvaluator::HAAR; }

    virtual bool setImage(InputArray, Size origWinSize, Size sumSize);
    virtual bool setWindow(Point pt);
    virtual Rect getNormRect() const;
    virtual void getUMats(std::vector<UMat>& bufs);

    double operator()(int featureIdx) const
    { return optfeaturesPtr[featureIdx].calc(pwin) * varianceNormFactor; }
    virtual double calcOrd(int featureIdx) const
    { return (*this)(featureIdx); }

protected:
    Size origWinSize, sumSize0;
    Ptr<std::vector<Feature> > features;
    Ptr<std::vector<OptFeature> > optfeatures;
    OptFeature* optfeaturesPtr; // optimization
    bool hasTiltedFeatures;

    Mat sum0, sum, sqsum0, sqsum;
    UMat usum0, usum, usqsum0, usqsum, ufbuf;

    Rect normrect;
    int nofs[4];

    const int* pwin;
    double varianceNormFactor;
};

inline HaarEvaluator::Feature :: Feature()
{
    tilted = false;
    rect[0].r = rect[1].r = rect[2].r = Rect();
    rect[0].weight = rect[1].weight = rect[2].weight = 0;
}

inline HaarEvaluator::OptFeature :: OptFeature()
{
    weight[0] = weight[1] = weight[2] = 0.f;

    ofs[0][0] = ofs[0][1] = ofs[0][2] = ofs[0][3] =
    ofs[1][0] = ofs[1][1] = ofs[1][2] = ofs[1][3] =
    ofs[2][0] = ofs[2][1] = ofs[2][2] = ofs[2][3] = 0;
}

inline float HaarEvaluator::OptFeature :: calc( const int* ptr ) const
{
    float ret = weight[0] * CALC_SUM_OFS(ofs[0], ptr) +
                weight[1] * CALC_SUM_OFS(ofs[1], ptr);

    if( weight[2] != 0.0f )
        ret += weight[2] * CALC_SUM_OFS(ofs[2], ptr);

    return ret;
}

inline void HaarEvaluator::OptFeature :: setOffsets( const Feature& _f, int step, int tofs )
{
    weight[0] = _f.rect[0].weight;
    weight[1] = _f.rect[1].weight;
    weight[2] = _f.rect[2].weight;

    Rect r2 = weight[2] > 0 ? _f.rect[2].r : Rect(0,0,0,0);
    if (_f.tilted)
    {
        CV_TILTED_OFS( ofs[0][0], ofs[0][1], ofs[0][2], ofs[0][3], tofs, _f.rect[0].r, step );
        CV_TILTED_OFS( ofs[1][0], ofs[1][1], ofs[1][2], ofs[1][3], tofs, _f.rect[1].r, step );
        CV_TILTED_PTRS( ofs[2][0], ofs[2][1], ofs[2][2], ofs[2][3], tofs, r2, step );
    }
    else
    {
        CV_SUM_OFS( ofs[0][0], ofs[0][1], ofs[0][2], ofs[0][3], 0, _f.rect[0].r, step );
        CV_SUM_OFS( ofs[1][0], ofs[1][1], ofs[1][2], ofs[1][3], 0, _f.rect[1].r, step );
        CV_SUM_OFS( ofs[2][0], ofs[2][1], ofs[2][2], ofs[2][3], 0, r2, step );
    }
}


//----------------------------------------------  LBPEvaluator -------------------------------------

class LBPEvaluator : public FeatureEvaluator
{
public:
    struct Feature
    {
        Feature();
        Feature( int x, int y, int _block_w, int _block_h  ) :
            rect(x, y, _block_w, _block_h) {}

        bool read(const FileNode& node );

        Rect rect; // weight and height for block
    };

    struct OptFeature
    {
        OptFeature();

        int calc( const int* pwin ) const;
        void setOffsets( const Feature& _f, int step );
        int ofs[16];
    };

    LBPEvaluator();
    virtual ~LBPEvaluator();

    virtual bool read( const FileNode& node );
    virtual Ptr<FeatureEvaluator> clone() const;
    virtual int getFeatureType() const { return FeatureEvaluator::LBP; }

    virtual bool setImage(InputArray image, Size _origWinSize, Size);
    virtual bool setWindow(Point pt);
    virtual void getUMats(std::vector<UMat>& bufs);

    int operator()(int featureIdx) const
    { return optfeaturesPtr[featureIdx].calc(pwin); }
    virtual int calcCat(int featureIdx) const
    { return (*this)(featureIdx); }
protected:
    Size origWinSize, sumSize0;
    Ptr<std::vector<Feature> > features;
    Ptr<std::vector<OptFeature> > optfeatures;
    OptFeature* optfeaturesPtr; // optimization

    Mat sum0, sum;
    UMat usum0, usum, ufbuf;

    const int* pwin;
};


inline LBPEvaluator::Feature :: Feature()
{
    rect = Rect();
}

inline LBPEvaluator::OptFeature :: OptFeature()
{
    for( int i = 0; i < 16; i++ )
        ofs[i] = 0;
}

inline int LBPEvaluator::OptFeature :: calc( const int* p ) const
{
    int cval = CALC_SUM_OFS_( ofs[5], ofs[6], ofs[9], ofs[10], p );

    return (CALC_SUM_OFS_( ofs[0], ofs[1], ofs[4], ofs[5], p ) >= cval ? 128 : 0) |   // 0
           (CALC_SUM_OFS_( ofs[1], ofs[2], ofs[5], ofs[6], p ) >= cval ? 64 : 0) |    // 1
           (CALC_SUM_OFS_( ofs[2], ofs[3], ofs[6], ofs[7], p ) >= cval ? 32 : 0) |    // 2
           (CALC_SUM_OFS_( ofs[6], ofs[7], ofs[10], ofs[11], p ) >= cval ? 16 : 0) |  // 5
           (CALC_SUM_OFS_( ofs[10], ofs[11], ofs[14], ofs[15], p ) >= cval ? 8 : 0)|  // 8
           (CALC_SUM_OFS_( ofs[9], ofs[10], ofs[13], ofs[14], p ) >= cval ? 4 : 0)|   // 7
           (CALC_SUM_OFS_( ofs[8], ofs[9], ofs[12], ofs[13], p ) >= cval ? 2 : 0)|    // 6
           (CALC_SUM_OFS_( ofs[4], ofs[5], ofs[8], ofs[9], p ) >= cval ? 1 : 0);
}

inline void LBPEvaluator::OptFeature :: setOffsets( const Feature& _f, int step )
{
    Rect tr = _f.rect;
    CV_SUM_OFS( ofs[0], ofs[1], ofs[4], ofs[5], 0, tr, step );
    tr.x += 2*_f.rect.width;
    CV_SUM_OFS( ofs[2], ofs[3], ofs[6], ofs[7], 0, tr, step );
    tr.y += 2*_f.rect.height;
    CV_SUM_OFS( ofs[10], ofs[11], ofs[14], ofs[15], 0, tr, step );
    tr.x -= 2*_f.rect.width;
    CV_SUM_OFS( ofs[8], ofs[9], ofs[12], ofs[13], 0, tr, step );
}

//---------------------------------------------- HOGEvaluator -------------------------------------------

class HOGEvaluator : public FeatureEvaluator
{
public:
    struct Feature
    {
        Feature();
        float calc( int offset ) const;
        void updatePtrs( const std::vector<Mat>& _hist, const Mat &_normSum );
        bool read( const FileNode& node );

        enum { CELL_NUM = 4, BIN_NUM = 9 };

        Rect rect[CELL_NUM];
        int featComponent; //component index from 0 to 35
        const float* pF[4]; //for feature calculation
        const float* pN[4]; //for normalization calculation
    };
    HOGEvaluator();
    virtual ~HOGEvaluator();
    virtual bool read( const FileNode& node );
    virtual Ptr<FeatureEvaluator> clone() const;
    virtual int getFeatureType() const { return FeatureEvaluator::HOG; }
    virtual bool setImage( InputArray image, Size winSize, Size );
    virtual bool setWindow( Point pt );
    double operator()(int featureIdx) const
    {
        return featuresPtr[featureIdx].calc(offset);
    }
    virtual double calcOrd( int featureIdx ) const
    {
        return (*this)(featureIdx);
    }

private:
    virtual void integralHistogram( const Mat& srcImage, std::vector<Mat> &histogram, Mat &norm, int nbins ) const;

    Size origWinSize;
    Ptr<std::vector<Feature> > features;
    Feature* featuresPtr;
    std::vector<Mat> hist;
    Mat normSum;
    int offset;
};

inline HOGEvaluator::Feature :: Feature()
{
    rect[0] = rect[1] = rect[2] = rect[3] = Rect();
    pF[0] = pF[1] = pF[2] = pF[3] = 0;
    pN[0] = pN[1] = pN[2] = pN[3] = 0;
    featComponent = 0;
}

inline float HOGEvaluator::Feature :: calc( int _offset ) const
{
    float res = CALC_SUM(pF, _offset);
    float normFactor = CALC_SUM(pN, _offset);
    res = (res > 0.001f) ? (res / ( normFactor + 0.001f) ) : 0.f;
    return res;
}

inline void HOGEvaluator::Feature :: updatePtrs( const std::vector<Mat> &_hist, const Mat &_normSum )
{
    int binIdx = featComponent % BIN_NUM;
    int cellIdx = featComponent / BIN_NUM;
    Rect normRect = Rect( rect[0].x, rect[0].y, 2*rect[0].width, 2*rect[0].height );

    const float* featBuf = (const float*)_hist[binIdx].data;
    size_t featStep = _hist[0].step / sizeof(featBuf[0]);

    const float* normBuf = (const float*)_normSum.data;
    size_t normStep = _normSum.step / sizeof(normBuf[0]);

    CV_SUM_PTRS( pF[0], pF[1], pF[2], pF[3], featBuf, rect[cellIdx], featStep );
    CV_SUM_PTRS( pN[0], pN[1], pN[2], pN[3], normBuf, normRect, normStep );
}




//----------------------------------------------  predictor functions -------------------------------------

template<class FEval>
inline int predictOrdered( CascadeClassifierImpl& cascade,
                           Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
    int nstages = (int)cascade.data.stages.size();
    int nodeOfs = 0, leafOfs = 0;
    FEval& featureEvaluator = (FEval&)*_featureEvaluator;
    float* cascadeLeaves = &cascade.data.leaves[0];
    CascadeClassifierImpl::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
    CascadeClassifierImpl::Data::DTree* cascadeWeaks = &cascade.data.classifiers[0];
    CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];

    for( int si = 0; si < nstages; si++ )
    {
        CascadeClassifierImpl::Data::Stage& stage = cascadeStages[si];
        int wi, ntrees = stage.ntrees;
        sum = 0;

        for( wi = 0; wi < ntrees; wi++ )
        {
            CascadeClassifierImpl::Data::DTree& weak = cascadeWeaks[stage.first + wi];
            int idx = 0, root = nodeOfs;

            do
            {
                CascadeClassifierImpl::Data::DTreeNode& node = cascadeNodes[root + idx];
                double val = featureEvaluator(node.featureIdx);
                idx = val < node.threshold ? node.left : node.right;
            }
            while( idx > 0 );
            sum += cascadeLeaves[leafOfs - idx];
            nodeOfs += weak.nodeCount;
            leafOfs += weak.nodeCount + 1;
        }
        if( sum < stage.threshold )
            return -si;
    }
    return 1;
}

template<class FEval>
inline int predictCategorical( CascadeClassifierImpl& cascade,
                               Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
    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::DTree* cascadeWeaks = &cascade.data.classifiers[0];
    CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];

    for(int si = 0; si < nstages; si++ )
    {
        CascadeClassifierImpl::Data::Stage& stage = cascadeStages[si];
        int wi, ntrees = stage.ntrees;
        sum = 0;

        for( wi = 0; wi < ntrees; wi++ )
        {
            CascadeClassifierImpl::Data::DTree& weak = cascadeWeaks[stage.first + wi];
            int idx = 0, root = nodeOfs;
            do
            {
                CascadeClassifierImpl::Data::DTreeNode& node = cascadeNodes[root + idx];
                int c = featureEvaluator(node.featureIdx);
                const int* subset = &cascadeSubsets[(root + idx)*subsetSize];
                idx = (subset[c>>5] & (1 << (c & 31))) ? node.left : node.right;
            }
            while( idx > 0 );
            sum += cascadeLeaves[leafOfs - idx];
            nodeOfs += weak.nodeCount;
            leafOfs += weak.nodeCount + 1;
        }
        if( sum < stage.threshold )
            return -si;
    }
    return 1;
}

template<class FEval>
inline int predictOrderedStump( CascadeClassifierImpl& cascade,
                                Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
    CV_Assert(!cascade.data.stumps.empty());
    FEval& featureEvaluator = (FEval&)*_featureEvaluator;
    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++ )
    {
        const CascadeClassifierImpl::Data::Stage& stage = cascadeStages[stageIdx];
        tmp = 0;

        int ntrees = stage.ntrees;
        for( int i = 0; i < ntrees; i++ )
        {
            const CascadeClassifierImpl::Data::Stump& stump = cascadeStumps[i];
            double value = featureEvaluator(stump.featureIdx);
            tmp += value < stump.threshold ? stump.left : stump.right;
        }

        if( tmp < stage.threshold )
        {
            sum = (double)tmp;
            return -stageIdx;
        }
        cascadeStumps += ntrees;
    }

    sum = (double)tmp;
    return 1;
}

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();
    FEval& featureEvaluator = (FEval&)*_featureEvaluator;
    size_t subsetSize = (cascade.data.ncategories + 31)/32;
    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++ )
    {
        const CascadeClassifierImpl::Data::Stage& stage = cascadeStages[si];
        int wi, ntrees = stage.ntrees;
        tmp = 0;

        for( wi = 0; wi < ntrees; wi++ )
        {
            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;
        }

        if( tmp < stage.threshold )
        {
            sum = (double)tmp;
            return -si;
        }

        cascadeStumps += ntrees;
        cascadeSubsets += ntrees*subsetSize;
    }

    sum = (double)tmp;
    return 1;
}
}