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/* Haar features calculation */

#include "precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/objdetect/objdetect_c.h"
#include <stdio.h>
#include "haar.hpp"
#if CV_HAAR_FEATURE_MAX_LOCAL != CV_HAAR_FEATURE_MAX
    #error CV_HAAR_FEATURE_MAX definition changed. Adjust CV_HAAR_FEATURE_MAX_LOCAL value please.
#endif

#if CV_SSE2
#   if 1 /*!CV_SSE4_1 && !CV_SSE4_2*/
#       define _mm_blendv_pd(a, b, m) _mm_xor_pd(a, _mm_and_pd(_mm_xor_pd(b, a), m))
#       define _mm_blendv_ps(a, b, m) _mm_xor_ps(a, _mm_and_ps(_mm_xor_ps(b, a), m))
#   endif
#endif

#if CV_HAAR_USE_AVX
#  if defined _MSC_VER
#    pragma warning( disable : 4752 )
#  endif
#else
#  if CV_SSE2
#    define CV_HAAR_USE_SSE 1
#  endif
#endif

/* these settings affect the quality of detection: change with care */
#define CV_ADJUST_FEATURES 1
#define CV_ADJUST_WEIGHTS  0

typedef struct CvHidHaarStageClassifier
{
    int  count;
    float threshold;
    CvHidHaarClassifier* classifier;
    int two_rects;

    struct CvHidHaarStageClassifier* next;
    struct CvHidHaarStageClassifier* child;
    struct CvHidHaarStageClassifier* parent;
} CvHidHaarStageClassifier;


typedef struct CvHidHaarClassifierCascade
{
    int  count;
    int  has_tilted_features;
    double inv_window_area;
    CvMat sum, sqsum, tilted;
    CvHidHaarStageClassifier* stage_classifier;
    sqsumtype *pq0, *pq1, *pq2, *pq3;
    sumtype *p0, *p1, *p2, *p3;

    bool  is_tree;
    bool  isStumpBased;
} CvHidHaarClassifierCascade;


const int icv_object_win_border = 1;
const float icv_stage_threshold_bias = 0.0001f;

static CvHaarClassifierCascade*
icvCreateHaarClassifierCascade( int stage_count )
{
    CvHaarClassifierCascade* cascade = 0;

    int block_size = sizeof(*cascade) + stage_count*sizeof(*cascade->stage_classifier);

    if( stage_count <= 0 )
        CV_Error( CV_StsOutOfRange, "Number of stages should be positive" );

    cascade = (CvHaarClassifierCascade*)cvAlloc( block_size );
    memset( cascade, 0, block_size );

    cascade->stage_classifier = (CvHaarStageClassifier*)(cascade + 1);
    cascade->flags = CV_HAAR_MAGIC_VAL;
    cascade->count = stage_count;

    return cascade;
}

static void
icvReleaseHidHaarClassifierCascade( CvHidHaarClassifierCascade** _cascade )
{
    if( _cascade && *_cascade )
    {
        cvFree( _cascade );
    }
}

/* create more efficient internal representation of haar classifier cascade */
static CvHidHaarClassifierCascade*
icvCreateHidHaarClassifierCascade( CvHaarClassifierCascade* cascade )
{
    CvHidHaarClassifierCascade* out = 0;

    int i, j, k, l;
    int datasize;
    int total_classifiers = 0;
    int total_nodes = 0;
    char errorstr[1000];
    CvHidHaarClassifier* haar_classifier_ptr;
    CvHidHaarTreeNode* haar_node_ptr;
    cv::Size orig_window_size;
    bool has_tilted_features = false;
    int max_count = 0;

    if( !CV_IS_HAAR_CLASSIFIER(cascade) )
        CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" );

    if( cascade->hid_cascade )
        CV_Error( CV_StsError, "hid_cascade has been already created" );

    if( !cascade->stage_classifier )
        CV_Error( CV_StsNullPtr, "" );

    if( cascade->count <= 0 )
        CV_Error( CV_StsOutOfRange, "Negative number of cascade stages" );

    orig_window_size = cascade->orig_window_size;

    /* check input structure correctness and calculate total memory size needed for
       internal representation of the classifier cascade */
    for( i = 0; i < cascade->count; i++ )
    {
        CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i;

        if( !stage_classifier->classifier ||
            stage_classifier->count <= 0 )
        {
            sprintf( errorstr, "header of the stage classifier #%d is invalid "
                     "(has null pointers or non-positive classfier count)", i );
            CV_Error( CV_StsError, errorstr );
        }

        max_count = MAX( max_count, stage_classifier->count );
        total_classifiers += stage_classifier->count;

        for( j = 0; j < stage_classifier->count; j++ )
        {
            CvHaarClassifier* classifier = stage_classifier->classifier + j;

            total_nodes += classifier->count;
            for( l = 0; l < classifier->count; l++ )
            {
                for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ )
                {
                    if( classifier->haar_feature[l].rect[k].r.width )
                    {
                        CvRect r = classifier->haar_feature[l].rect[k].r;
                        int tilted = classifier->haar_feature[l].tilted;
                        has_tilted_features = has_tilted_features | (tilted != 0);
                        if( r.width < 0 || r.height < 0 || r.y < 0 ||
                            r.x + r.width > orig_window_size.width
                            ||
                            (!tilted &&
                            (r.x < 0 || r.y + r.height > orig_window_size.height))
                            ||
                            (tilted && (r.x - r.height < 0 ||
                            r.y + r.width + r.height > orig_window_size.height)))
                        {
                            sprintf( errorstr, "rectangle #%d of the classifier #%d of "
                                     "the stage classifier #%d is not inside "
                                     "the reference (original) cascade window", k, j, i );
                            CV_Error( CV_StsNullPtr, errorstr );
                        }
                    }
                }
            }
        }
    }

    // this is an upper boundary for the whole hidden cascade size
    datasize = sizeof(CvHidHaarClassifierCascade) +
               sizeof(CvHidHaarStageClassifier)*cascade->count +
               sizeof(CvHidHaarClassifier) * total_classifiers +
               sizeof(CvHidHaarTreeNode) * total_nodes +
               sizeof(void*)*(total_nodes + total_classifiers);

    out = (CvHidHaarClassifierCascade*)cvAlloc( datasize );
    memset( out, 0, sizeof(*out) );

    /* init header */
    out->count = cascade->count;
    out->stage_classifier = (CvHidHaarStageClassifier*)(out + 1);
    haar_classifier_ptr = (CvHidHaarClassifier*)(out->stage_classifier + cascade->count);
    haar_node_ptr = (CvHidHaarTreeNode*)(haar_classifier_ptr + total_classifiers);

    out->isStumpBased = true;
    out->has_tilted_features = has_tilted_features;
    out->is_tree = false;

    /* initialize internal representation */
    for( i = 0; i < cascade->count; i++ )
    {
        CvHaarStageClassifier* stage_classifier = cascade->stage_classifier + i;
        CvHidHaarStageClassifier* hid_stage_classifier = out->stage_classifier + i;

        hid_stage_classifier->count = stage_classifier->count;
        hid_stage_classifier->threshold = stage_classifier->threshold - icv_stage_threshold_bias;
        hid_stage_classifier->classifier = haar_classifier_ptr;
        hid_stage_classifier->two_rects = 1;
        haar_classifier_ptr += stage_classifier->count;

        hid_stage_classifier->parent = (stage_classifier->parent == -1)
            ? NULL : out->stage_classifier + stage_classifier->parent;
        hid_stage_classifier->next = (stage_classifier->next == -1)
            ? NULL : out->stage_classifier + stage_classifier->next;
        hid_stage_classifier->child = (stage_classifier->child == -1)
            ? NULL : out->stage_classifier + stage_classifier->child;

        out->is_tree = out->is_tree || (hid_stage_classifier->next != NULL);

        for( j = 0; j < stage_classifier->count; j++ )
        {
            CvHaarClassifier* classifier = stage_classifier->classifier + j;
            CvHidHaarClassifier* hid_classifier = hid_stage_classifier->classifier + j;
            int node_count = classifier->count;
            float* alpha_ptr = (float*)(haar_node_ptr + node_count);

            hid_classifier->count = node_count;
            hid_classifier->node = haar_node_ptr;
            hid_classifier->alpha = alpha_ptr;

            for( l = 0; l < node_count; l++ )
            {
                CvHidHaarTreeNode* node = hid_classifier->node + l;
                CvHaarFeature* feature = classifier->haar_feature + l;
                memset( node, -1, sizeof(*node) );
                node->threshold = classifier->threshold[l];
                node->left = classifier->left[l];
                node->right = classifier->right[l];

                if( fabs(feature->rect[2].weight) < DBL_EPSILON ||
                    feature->rect[2].r.width == 0 ||
                    feature->rect[2].r.height == 0 )
                    memset( &(node->feature.rect[2]), 0, sizeof(node->feature.rect[2]) );
                else
                    hid_stage_classifier->two_rects = 0;
            }

            memcpy( alpha_ptr, classifier->alpha, (node_count+1)*sizeof(alpha_ptr[0]));
            haar_node_ptr =
                (CvHidHaarTreeNode*)cvAlignPtr(alpha_ptr+node_count+1, sizeof(void*));

            out->isStumpBased = out->isStumpBased && (node_count == 1);
        }
    }

    cascade->hid_cascade = out;
    assert( (char*)haar_node_ptr - (char*)out <= datasize );

    return out;
}


#define sum_elem_ptr(sum,row,col)  \
    ((sumtype*)CV_MAT_ELEM_PTR_FAST((sum),(row),(col),sizeof(sumtype)))

#define sqsum_elem_ptr(sqsum,row,col)  \
    ((sqsumtype*)CV_MAT_ELEM_PTR_FAST((sqsum),(row),(col),sizeof(sqsumtype)))

#define calc_sum(rect,offset) \
    ((rect).p0[offset] - (rect).p1[offset] - (rect).p2[offset] + (rect).p3[offset])

CV_IMPL void
cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* _cascade,
                                     const CvArr* _sum,
                                     const CvArr* _sqsum,
                                     const CvArr* _tilted_sum,
                                     double scale )
{
    CvMat sum_stub, *sum = (CvMat*)_sum;
    CvMat sqsum_stub, *sqsum = (CvMat*)_sqsum;
    CvMat tilted_stub, *tilted = (CvMat*)_tilted_sum;
    CvHidHaarClassifierCascade* cascade;
    int coi0 = 0, coi1 = 0;
    int i;
    cv::Rect equRect;
    double weight_scale;

    if( !CV_IS_HAAR_CLASSIFIER(_cascade) )
        CV_Error( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier pointer" );

    if( scale <= 0 )
        CV_Error( CV_StsOutOfRange, "Scale must be positive" );

    sum = cvGetMat( sum, &sum_stub, &coi0 );
    sqsum = cvGetMat( sqsum, &sqsum_stub, &coi1 );

    if( coi0 || coi1 )
        CV_Error( CV_BadCOI, "COI is not supported" );

    if( !CV_ARE_SIZES_EQ( sum, sqsum ))
        CV_Error( CV_StsUnmatchedSizes, "All integral images must have the same size" );

    if( CV_MAT_TYPE(sqsum->type) != CV_64FC1 ||
        CV_MAT_TYPE(sum->type) != CV_32SC1 )
        CV_Error( CV_StsUnsupportedFormat,
        "Only (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowed" );

    if( !_cascade->hid_cascade )
        icvCreateHidHaarClassifierCascade(_cascade);

    cascade = _cascade->hid_cascade;

    if( cascade->has_tilted_features )
    {
        tilted = cvGetMat( tilted, &tilted_stub, &coi1 );

        if( CV_MAT_TYPE(tilted->type) != CV_32SC1 )
            CV_Error( CV_StsUnsupportedFormat,
            "Only (32s, 64f, 32s) combination of (sum,sqsum,tilted_sum) formats is allowed" );

        if( sum->step != tilted->step )
            CV_Error( CV_StsUnmatchedSizes,
            "Sum and tilted_sum must have the same stride (step, widthStep)" );

        if( !CV_ARE_SIZES_EQ( sum, tilted ))
            CV_Error( CV_StsUnmatchedSizes, "All integral images must have the same size" );
        cascade->tilted = *tilted;
    }

    _cascade->scale = scale;
    _cascade->real_window_size.width = cvRound( _cascade->orig_window_size.width * scale );
    _cascade->real_window_size.height = cvRound( _cascade->orig_window_size.height * scale );

    cascade->sum = *sum;
    cascade->sqsum = *sqsum;

    equRect.x = equRect.y = cvRound(scale);
    equRect.width = cvRound((_cascade->orig_window_size.width-2)*scale);
    equRect.height = cvRound((_cascade->orig_window_size.height-2)*scale);
    weight_scale = 1./(equRect.width*equRect.height);
    cascade->inv_window_area = weight_scale;

    cascade->p0 = sum_elem_ptr(*sum, equRect.y, equRect.x);
    cascade->p1 = sum_elem_ptr(*sum, equRect.y, equRect.x + equRect.width );
    cascade->p2 = sum_elem_ptr(*sum, equRect.y + equRect.height, equRect.x );
    cascade->p3 = sum_elem_ptr(*sum, equRect.y + equRect.height,
                                     equRect.x + equRect.width );

    cascade->pq0 = sqsum_elem_ptr(*sqsum, equRect.y, equRect.x);
    cascade->pq1 = sqsum_elem_ptr(*sqsum, equRect.y, equRect.x + equRect.width );
    cascade->pq2 = sqsum_elem_ptr(*sqsum, equRect.y + equRect.height, equRect.x );
    cascade->pq3 = sqsum_elem_ptr(*sqsum, equRect.y + equRect.height,
                                          equRect.x + equRect.width );

    /* init pointers in haar features according to real window size and
       given image pointers */
    for( i = 0; i < _cascade->count; i++ )
    {
        int j, k, l;
        for( j = 0; j < cascade->stage_classifier[i].count; j++ )
        {
            for( l = 0; l < cascade->stage_classifier[i].classifier[j].count; l++ )
            {
                CvHaarFeature* feature =
                    &_cascade->stage_classifier[i].classifier[j].haar_feature[l];
                /* CvHidHaarClassifier* classifier =
                    cascade->stage_classifier[i].classifier + j; */
                CvHidHaarFeature* hidfeature =
                    &cascade->stage_classifier[i].classifier[j].node[l].feature;
                double sum0 = 0, area0 = 0;
                cv::Rect r[3];

                int base_w = -1, base_h = -1;
                int new_base_w = 0, new_base_h = 0;
                int kx, ky;
                int flagx = 0, flagy = 0;
                int x0 = 0, y0 = 0;
                int nr;

                /* align blocks */
                for( k = 0; k < CV_HAAR_FEATURE_MAX; k++ )
                {
                    if( !hidfeature->rect[k].p0 )
                        break;
                    r[k] = feature->rect[k].r;
                    base_w = (int)CV_IMIN( (unsigned)base_w, (unsigned)(r[k].width-1) );
                    base_w = (int)CV_IMIN( (unsigned)base_w, (unsigned)(r[k].x - r[0].x-1) );
                    base_h = (int)CV_IMIN( (unsigned)base_h, (unsigned)(r[k].height-1) );
                    base_h = (int)CV_IMIN( (unsigned)base_h, (unsigned)(r[k].y - r[0].y-1) );
                }

                nr = k;

                base_w += 1;
                base_h += 1;
                kx = r[0].width / base_w;
                ky = r[0].height / base_h;

                if( kx <= 0 )
                {
                    flagx = 1;
                    new_base_w = cvRound( r[0].width * scale ) / kx;
                    x0 = cvRound( r[0].x * scale );
                }

                if( ky <= 0 )
                {
                    flagy = 1;
                    new_base_h = cvRound( r[0].height * scale ) / ky;
                    y0 = cvRound( r[0].y * scale );
                }

                for( k = 0; k < nr; k++ )
                {
                    cv::Rect tr;
                    double correction_ratio;

                    if( flagx )
                    {
                        tr.x = (r[k].x - r[0].x) * new_base_w / base_w + x0;
                        tr.width = r[k].width * new_base_w / base_w;
                    }
                    else
                    {
                        tr.x = cvRound( r[k].x * scale );
                        tr.width = cvRound( r[k].width * scale );
                    }

                    if( flagy )
                    {
                        tr.y = (r[k].y - r[0].y) * new_base_h / base_h + y0;
                        tr.height = r[k].height * new_base_h / base_h;
                    }
                    else
                    {
                        tr.y = cvRound( r[k].y * scale );
                        tr.height = cvRound( r[k].height * scale );
                    }

#if CV_ADJUST_WEIGHTS
                    {
                    // RAINER START
                    const float orig_feature_size =  (float)(feature->rect[k].r.width)*feature->rect[k].r.height;
                    const float orig_norm_size = (float)(_cascade->orig_window_size.width)*(_cascade->orig_window_size.height);
                    const float feature_size = float(tr.width*tr.height);
                    //const float normSize    = float(equRect.width*equRect.height);
                    float target_ratio = orig_feature_size / orig_norm_size;
                    //float isRatio = featureSize / normSize;
                    //correctionRatio = targetRatio / isRatio / normSize;
                    correction_ratio = target_ratio / feature_size;
                    // RAINER END
                    }
#else
                    correction_ratio = weight_scale * (!feature->tilted ? 1 : 0.5);
#endif

                    if( !feature->tilted )
                    {
                        hidfeature->rect[k].p0 = sum_elem_ptr(*sum, tr.y, tr.x);
                        hidfeature->rect[k].p1 = sum_elem_ptr(*sum, tr.y, tr.x + tr.width);
                        hidfeature->rect[k].p2 = sum_elem_ptr(*sum, tr.y + tr.height, tr.x);
                        hidfeature->rect[k].p3 = sum_elem_ptr(*sum, tr.y + tr.height, tr.x + tr.width);
                    }
                    else
                    {
                        hidfeature->rect[k].p2 = sum_elem_ptr(*tilted, tr.y + tr.width, tr.x + tr.width);
                        hidfeature->rect[k].p3 = sum_elem_ptr(*tilted, tr.y + tr.width + tr.height,
                                                              tr.x + tr.width - tr.height);
                        hidfeature->rect[k].p0 = sum_elem_ptr(*tilted, tr.y, tr.x);
                        hidfeature->rect[k].p1 = sum_elem_ptr(*tilted, tr.y + tr.height, tr.x - tr.height);
                    }

                    hidfeature->rect[k].weight = (float)(feature->rect[k].weight * correction_ratio);

                    if( k == 0 )
                        area0 = tr.width * tr.height;
                    else
                        sum0 += hidfeature->rect[k].weight * tr.width * tr.height;
                }
                CV_Assert(area0 > 0);
                hidfeature->rect[0].weight = (float)(-sum0/area0);
            } /* l */
        } /* j */
    }
}


CV_INLINE
double icvEvalHidHaarClassifier( CvHidHaarClassifier* classifier,
                                 double variance_norm_factor,
                                 size_t p_offset )
{
    int idx = 0;
    /*#if CV_HAAR_USE_SSE && !CV_HAAR_USE_AVX
        if(cv::checkHardwareSupport(CV_CPU_SSE2))//based on old SSE variant. Works slow
        {
            double CV_DECL_ALIGNED(16) temp[2];
            __m128d zero = _mm_setzero_pd();
            do
            {
                CvHidHaarTreeNode* node = classifier->node + idx;
                __m128d t = _mm_set1_pd((node->threshold)*variance_norm_factor);
                __m128d left = _mm_set1_pd(node->left);
                __m128d right = _mm_set1_pd(node->right);

                double _sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
                _sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
                if( node->feature.rect[2].p0 )
                    _sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;

                __m128d sum = _mm_set1_pd(_sum);
                t = _mm_cmplt_sd(sum, t);
                sum = _mm_blendv_pd(right, left, t);

                _mm_store_pd(temp, sum);
                idx = (int)temp[0];
            }
            while(idx > 0 );

        }
        else
    #endif*/
    {
        do
        {
            CvHidHaarTreeNode* node = classifier->node + idx;
            double t = node->threshold * variance_norm_factor;

            double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
            sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;

            if( node->feature.rect[2].p0 )
                sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;

            idx = sum < t ? node->left : node->right;
        }
        while( idx > 0 );
    }
    return classifier->alpha[-idx];
}



static int
cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
                               CvPoint pt, double& stage_sum, int start_stage )
{
#if CV_HAAR_USE_AVX
    bool haveAVX = CV_CPU_HAS_SUPPORT_AVX;
#else
#  ifdef CV_HAAR_USE_SSE
    bool haveSSE2 = cv::checkHardwareSupport(CV_CPU_SSE2);
#  endif
#endif

    int p_offset, pq_offset;
    int i, j;
    double mean, variance_norm_factor;
    CvHidHaarClassifierCascade* cascade;

    if( !CV_IS_HAAR_CLASSIFIER(_cascade) )
        CV_Error( !_cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid cascade pointer" );

    cascade = _cascade->hid_cascade;
    if( !cascade )
        CV_Error( CV_StsNullPtr, "Hidden cascade has not been created.\n"
            "Use cvSetImagesForHaarClassifierCascade" );

    if( pt.x < 0 || pt.y < 0 ||
        pt.x + _cascade->real_window_size.width >= cascade->sum.width ||
        pt.y + _cascade->real_window_size.height >= cascade->sum.height )
        return -1;

    p_offset = pt.y * (cascade->sum.step/sizeof(sumtype)) + pt.x;
    pq_offset = pt.y * (cascade->sqsum.step/sizeof(sqsumtype)) + pt.x;
    mean = calc_sum(*cascade,p_offset)*cascade->inv_window_area;
    variance_norm_factor = cascade->pq0[pq_offset] - cascade->pq1[pq_offset] -
                           cascade->pq2[pq_offset] + cascade->pq3[pq_offset];
    variance_norm_factor = variance_norm_factor*cascade->inv_window_area - mean*mean;
    if( variance_norm_factor >= 0. )
        variance_norm_factor = std::sqrt(variance_norm_factor);
    else
        variance_norm_factor = 1.;

    if( cascade->is_tree )
    {
        CvHidHaarStageClassifier* ptr = cascade->stage_classifier;
        assert( start_stage == 0 );

        while( ptr )
        {
            stage_sum = 0.0;
            j = 0;

#if CV_HAAR_USE_AVX
            if(haveAVX)
            {
                for( ; j <= ptr->count - 8; j += 8 )
                {
                    stage_sum += cv_haar_avx::icvEvalHidHaarClassifierAVX(
                                                     ptr->classifier + j,
                                                     variance_norm_factor, p_offset );
                }
            }
#endif
            for( ; j < ptr->count; j++ )
            {
                stage_sum += icvEvalHidHaarClassifier( ptr->classifier + j, variance_norm_factor, p_offset );
            }

            if( stage_sum >= ptr->threshold )
            {
                ptr = ptr->child;
            }
            else
            {
                while( ptr && ptr->next == NULL ) ptr = ptr->parent;
                if( ptr == NULL )
                    return 0;
                ptr = ptr->next;
            }
        }
    }
    else if( cascade->isStumpBased )
    {
#if CV_HAAR_USE_AVX
        if(haveAVX)
        {
            for( i = start_stage; i < cascade->count; i++ )
            {
                stage_sum = 0.0;
                j = 0;
                if( cascade->stage_classifier[i].two_rects )
                {
                    for( ; j <= cascade->stage_classifier[i].count - 8; j += 8 )
                    {
                        stage_sum += cv_haar_avx::icvEvalHidHaarStumpClassifierTwoRectAVX(
                                                         cascade->stage_classifier[i].classifier + j,
                                                         variance_norm_factor, p_offset);
                    }

                    for( ; j < cascade->stage_classifier[i].count; j++ )
                    {
                        CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
                        CvHidHaarTreeNode* node = classifier->node;

                        double t = node->threshold*variance_norm_factor;
                        double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
                        sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
                        stage_sum += classifier->alpha[sum >= t];
                    }
                }
                else
                {
                    for( ; j <= (cascade->stage_classifier[i].count)-8; j+=8 )
                    {
                        stage_sum += cv_haar_avx::icvEvalHidHaarStumpClassifierAVX(
                                                         cascade->stage_classifier[i].classifier + j,
                                                         variance_norm_factor, p_offset);
                    }

                    for( ; j < cascade->stage_classifier[i].count; j++ )
                    {
                        CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
                        CvHidHaarTreeNode* node = classifier->node;

                        double t = node->threshold*variance_norm_factor;
                        double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
                        sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
                        if( node->feature.rect[2].p0 )
                            sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;
                        stage_sum += classifier->alpha[sum >= t];
                    }
                }
                if( stage_sum < cascade->stage_classifier[i].threshold )
                    return -i;
            }
        }
        else
#elif defined CV_HAAR_USE_SSE //old SSE optimization
        if(haveSSE2)
        {
            for( i = start_stage; i < cascade->count; i++ )
            {
                __m128d vstage_sum = _mm_setzero_pd();
                if( cascade->stage_classifier[i].two_rects )
                {
                    for( j = 0; j < cascade->stage_classifier[i].count; j++ )
                    {
                        CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
                        CvHidHaarTreeNode* node = classifier->node;

                        // ayasin - NHM perf optim. Avoid use of costly flaky jcc
                        __m128d t = _mm_set_sd(node->threshold*variance_norm_factor);
                        __m128d a = _mm_set_sd(classifier->alpha[0]);
                        __m128d b = _mm_set_sd(classifier->alpha[1]);
                        __m128d sum = _mm_set_sd(calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight +
                                                 calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight);
                        t = _mm_cmpgt_sd(t, sum);
                        vstage_sum = _mm_add_sd(vstage_sum, _mm_blendv_pd(b, a, t));
                    }
                }
                else
                {
                    for( j = 0; j < cascade->stage_classifier[i].count; j++ )
                    {
                        CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
                        CvHidHaarTreeNode* node = classifier->node;
                        // ayasin - NHM perf optim. Avoid use of costly flaky jcc
                        __m128d t = _mm_set_sd(node->threshold*variance_norm_factor);
                        __m128d a = _mm_set_sd(classifier->alpha[0]);
                        __m128d b = _mm_set_sd(classifier->alpha[1]);
                        double _sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
                        _sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
                        if( node->feature.rect[2].p0 )
                            _sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;
                        __m128d sum = _mm_set_sd(_sum);

                        t = _mm_cmpgt_sd(t, sum);
                        vstage_sum = _mm_add_sd(vstage_sum, _mm_blendv_pd(b, a, t));
                    }
                }
                __m128d i_threshold = _mm_set1_pd(cascade->stage_classifier[i].threshold);
                if( _mm_comilt_sd(vstage_sum, i_threshold) )
                    return -i;
            }
        }
        else
#endif // AVX or SSE
        {
            for( i = start_stage; i < cascade->count; i++ )
            {
                stage_sum = 0.0;
                if( cascade->stage_classifier[i].two_rects )
                {
                    for( j = 0; j < cascade->stage_classifier[i].count; j++ )
                    {
                        CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
                        CvHidHaarTreeNode* node = classifier->node;
                        double t = node->threshold*variance_norm_factor;
                        double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
                        sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
                        stage_sum += classifier->alpha[sum >= t];
                    }
                }
                else
                {
                    for( j = 0; j < cascade->stage_classifier[i].count; j++ )
                    {
                        CvHidHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
                        CvHidHaarTreeNode* node = classifier->node;
                        double t = node->threshold*variance_norm_factor;
                        double sum = calc_sum(node->feature.rect[0],p_offset) * node->feature.rect[0].weight;
                        sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
                        if( node->feature.rect[2].p0 )
                            sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;
                        stage_sum += classifier->alpha[sum >= t];
                    }
                }
                if( stage_sum < cascade->stage_classifier[i].threshold )
                    return -i;
            }
        }
    }
    else
    {
        for( i = start_stage; i < cascade->count; i++ )
        {
            stage_sum = 0.0;
            int k = 0;

#if CV_HAAR_USE_AVX
            if(haveAVX)
            {
                for( ; k < cascade->stage_classifier[i].count - 8; k += 8 )
                {
                    stage_sum += cv_haar_avx::icvEvalHidHaarClassifierAVX(
                                                     cascade->stage_classifier[i].classifier + k,
                                                     variance_norm_factor, p_offset );
                }
            }
#endif
            for(; k < cascade->stage_classifier[i].count; k++ )
            {

                stage_sum += icvEvalHidHaarClassifier(
                    cascade->stage_classifier[i].classifier + k,
                    variance_norm_factor, p_offset );
            }

            if( stage_sum < cascade->stage_classifier[i].threshold )
                return -i;
        }
    }
    return 1;
}


CV_IMPL int
cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
                            CvPoint pt, int start_stage )
{
    CV_INSTRUMENT_REGION();

    double stage_sum;
    return cvRunHaarClassifierCascadeSum(_cascade, pt, stage_sum, start_stage);
}

namespace cv
{

const size_t PARALLEL_LOOP_BATCH_SIZE = 100;

class HaarDetectObjects_ScaleImage_Invoker : public ParallelLoopBody
{
public:
    HaarDetectObjects_ScaleImage_Invoker( const CvHaarClassifierCascade* _cascade,
                                          int _stripSize, double _factor,
                                          const Mat& _sum1, const Mat& _sqsum1, Mat* _norm1,
                                          Mat* _mask1, Rect _equRect, std::vector<Rect>& _vec,
                                          std::vector<int>& _levels, std::vector<double>& _weights,
                                          bool _outputLevels, Mutex *_mtx )
    {
        cascade = _cascade;
        stripSize = _stripSize;
        factor = _factor;
        sum1 = _sum1;
        sqsum1 = _sqsum1;
        norm1 = _norm1;
        mask1 = _mask1;
        equRect = _equRect;
        vec = &_vec;
        rejectLevels = _outputLevels ? &_levels : 0;
        levelWeights = _outputLevels ? &_weights : 0;
        mtx = _mtx;
    }

    void operator()(const Range& range) const CV_OVERRIDE
    {
        CV_INSTRUMENT_REGION();

        Size winSize0 = cascade->orig_window_size;
        Size winSize(cvRound(winSize0.width*factor), cvRound(winSize0.height*factor));
        int y1 = range.start*stripSize, y2 = std::min(range.end*stripSize, sum1.rows - 1 - winSize0.height);

        if (y2 <= y1 || sum1.cols <= 1 + winSize0.width)
            return;

        Size ssz(sum1.cols - 1 - winSize0.width, y2 - y1);
        int x, y, ystep = factor > 2 ? 1 : 2;

        std::vector<Rect> vecLocal;
        std::vector<int> rejectLevelsLocal;
        std::vector<double> levelWeightsLocal;

        for( y = y1; y < y2; y += ystep )
            for( x = 0; x < ssz.width; x += ystep )
            {
                double gypWeight;
                int result = cvRunHaarClassifierCascadeSum( cascade, cvPoint(x,y), gypWeight, 0 );
                if( rejectLevels )
                {
                    if( result == 1 )
                        result = -1*cascade->count;
                    if( cascade->count + result < 4 )
                    {
                        vecLocal.push_back(Rect(cvRound(x*factor), cvRound(y*factor),
                                            winSize.width, winSize.height));
                        rejectLevelsLocal.push_back(-result);
                        levelWeightsLocal.push_back(gypWeight);

                        if (vecLocal.size() >= PARALLEL_LOOP_BATCH_SIZE)
                        {
                            mtx->lock();
                            vec->insert(vec->end(), vecLocal.begin(), vecLocal.end());
                            rejectLevels->insert(rejectLevels->end(), rejectLevelsLocal.begin(), rejectLevelsLocal.end());
                            levelWeights->insert(levelWeights->end(), levelWeightsLocal.begin(), levelWeightsLocal.end());
                            mtx->unlock();

                            vecLocal.clear();
                            rejectLevelsLocal.clear();
                            levelWeightsLocal.clear();
                        }
                    }
                }
                else
                {
                    if( result > 0 )
                    {
                        vecLocal.push_back(Rect(cvRound(x*factor), cvRound(y*factor),
                                            winSize.width, winSize.height));

                        if (vecLocal.size() >= PARALLEL_LOOP_BATCH_SIZE)
                        {
                            mtx->lock();
                            vec->insert(vec->end(), vecLocal.begin(), vecLocal.end());
                            mtx->unlock();

                            vecLocal.clear();
                        }
                    }
                }
            }

        if (rejectLevelsLocal.size())
        {
            mtx->lock();
            vec->insert(vec->end(), vecLocal.begin(), vecLocal.end());
            rejectLevels->insert(rejectLevels->end(), rejectLevelsLocal.begin(), rejectLevelsLocal.end());
            levelWeights->insert(levelWeights->end(), levelWeightsLocal.begin(), levelWeightsLocal.end());
            mtx->unlock();
        }
        else
            if (vecLocal.size())
            {
                mtx->lock();
                vec->insert(vec->end(), vecLocal.begin(), vecLocal.end());
                mtx->unlock();
            }
    }

    const CvHaarClassifierCascade* cascade;
    int stripSize;
    double factor;
    Mat sum1, sqsum1, *norm1, *mask1;
    Rect equRect;
    std::vector<Rect>* vec;
    std::vector<int>* rejectLevels;
    std::vector<double>* levelWeights;
    Mutex* mtx;
};


class HaarDetectObjects_ScaleCascade_Invoker : public ParallelLoopBody
{
public:
    HaarDetectObjects_ScaleCascade_Invoker( const CvHaarClassifierCascade* _cascade,
                                            Size _winsize, const Range& _xrange, double _ystep,
                                            size_t _sumstep, const int** _p, const int** _pq,
                                            std::vector<Rect>& _vec, Mutex* _mtx )
    {
        cascade = _cascade;
        winsize = _winsize;
        xrange = _xrange;
        ystep = _ystep;
        sumstep = _sumstep;
        p = _p; pq = _pq;
        vec = &_vec;
        mtx = _mtx;
    }

    void operator()(const Range& range) const CV_OVERRIDE
    {
        CV_INSTRUMENT_REGION();

        int iy, startY = range.start, endY = range.end;
        const int *p0 = p[0], *p1 = p[1], *p2 = p[2], *p3 = p[3];
        const int *pq0 = pq[0], *pq1 = pq[1], *pq2 = pq[2], *pq3 = pq[3];
        bool doCannyPruning = p0 != 0;
        int sstep = (int)(sumstep/sizeof(p0[0]));

        std::vector<Rect> vecLocal;

        for( iy = startY; iy < endY; iy++ )
        {
            int ix, y = cvRound(iy*ystep), ixstep = 1;
            for( ix = xrange.start; ix < xrange.end; ix += ixstep )
            {
                int x = cvRound(ix*ystep); // it should really be ystep, not ixstep

                if( doCannyPruning )
                {
                    int offset = y*sstep + x;
                    int s = p0[offset] - p1[offset] - p2[offset] + p3[offset];
                    int sq = pq0[offset] - pq1[offset] - pq2[offset] + pq3[offset];
                    if( s < 100 || sq < 20 )
                    {
                        ixstep = 2;
                        continue;
                    }
                }

                int result = cvRunHaarClassifierCascade( cascade, cvPoint(x, y), 0 );
                if( result > 0 )
                {
                    vecLocal.push_back(Rect(x, y, winsize.width, winsize.height));

                    if (vecLocal.size() >= PARALLEL_LOOP_BATCH_SIZE)
                    {
                        mtx->lock();
                        vec->insert(vec->end(), vecLocal.begin(), vecLocal.end());
                        mtx->unlock();

                        vecLocal.clear();
                    }
                }
                ixstep = result != 0 ? 1 : 2;
            }
        }

        if (vecLocal.size())
        {
            mtx->lock();
            vec->insert(vec->end(), vecLocal.begin(), vecLocal.end());
            mtx->unlock();
        }
    }

    const CvHaarClassifierCascade* cascade;
    double ystep;
    size_t sumstep;
    Size winsize;
    Range xrange;
    const int** p;
    const int** pq;
    std::vector<Rect>* vec;
    Mutex* mtx;
};


}


CvSeq*
cvHaarDetectObjectsForROC( const CvArr* _img,
                     CvHaarClassifierCascade* cascade, CvMemStorage* storage,
                     std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
                     double scaleFactor, int minNeighbors, int flags,
                     CvSize minSize, CvSize maxSize, bool outputRejectLevels )
{
    CV_INSTRUMENT_REGION();

    const double GROUP_EPS = 0.2;
    CvMat stub, *img = (CvMat*)_img;
    cv::Ptr<CvMat> temp, sum, tilted, sqsum, normImg, sumcanny, imgSmall;
    CvSeq* result_seq = 0;
    cv::Ptr<CvMemStorage> temp_storage;

    std::vector<cv::Rect> allCandidates;
    std::vector<cv::Rect> rectList;
    std::vector<int> rweights;
    double factor;
    int coi;
    bool doCannyPruning = (flags & CV_HAAR_DO_CANNY_PRUNING) != 0;
    bool findBiggestObject = (flags & CV_HAAR_FIND_BIGGEST_OBJECT) != 0;
    bool roughSearch = (flags & CV_HAAR_DO_ROUGH_SEARCH) != 0;
    cv::Mutex mtx;

    if( !CV_IS_HAAR_CLASSIFIER(cascade) )
        CV_Error( !cascade ? CV_StsNullPtr : CV_StsBadArg, "Invalid classifier cascade" );

    if( !storage )
        CV_Error( CV_StsNullPtr, "Null storage pointer" );

    img = cvGetMat( img, &stub, &coi );
    if( coi )
        CV_Error( CV_BadCOI, "COI is not supported" );

    if( CV_MAT_DEPTH(img->type) != CV_8U )
        CV_Error( CV_StsUnsupportedFormat, "Only 8-bit images are supported" );

    if( scaleFactor <= 1 )
        CV_Error( CV_StsOutOfRange, "scale factor must be > 1" );

    if( findBiggestObject )
        flags &= ~CV_HAAR_SCALE_IMAGE;

    if( maxSize.height == 0 || maxSize.width == 0 )
    {
        maxSize.height = img->rows;
        maxSize.width = img->cols;
    }

    temp.reset(cvCreateMat( img->rows, img->cols, CV_8UC1 ));
    sum.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ));
    sqsum.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_64FC1 ));

    if( !cascade->hid_cascade )
        icvCreateHidHaarClassifierCascade(cascade);

    if( cascade->hid_cascade->has_tilted_features )
        tilted.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ));

    result_seq = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvAvgComp), storage );

    if( CV_MAT_CN(img->type) > 1 )
    {
        cvCvtColor( img, temp, CV_BGR2GRAY );
        img = temp;
    }

    if( findBiggestObject )
        flags &= ~(CV_HAAR_SCALE_IMAGE|CV_HAAR_DO_CANNY_PRUNING);

    if( flags & CV_HAAR_SCALE_IMAGE )
    {
        CvSize winSize0 = cascade->orig_window_size;
        imgSmall.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_8UC1 ));

        for( factor = 1; ; factor *= scaleFactor )
        {
            CvSize winSize = { cvRound(winSize0.width*factor),
                               cvRound(winSize0.height*factor) };
            CvSize sz = { cvRound(img->cols/factor), cvRound(img->rows/factor) };
            CvSize sz1 = { sz.width - winSize0.width + 1, sz.height - winSize0.height + 1 };

            CvRect equRect = { icv_object_win_border, icv_object_win_border,
                winSize0.width - icv_object_win_border*2, winSize0.height - icv_object_win_border*2 };

            CvMat img1, sum1, sqsum1, norm1, tilted1, mask1;
            CvMat* _tilted = 0;

            if( sz1.width <= 0 || sz1.height <= 0 )
                break;
            if( winSize.width > maxSize.width || winSize.height > maxSize.height )
                break;
            if( winSize.width < minSize.width || winSize.height < minSize.height )
                continue;

            img1 = cvMat( sz.height, sz.width, CV_8UC1, imgSmall->data.ptr );
            sum1 = cvMat( sz.height+1, sz.width+1, CV_32SC1, sum->data.ptr );
            sqsum1 = cvMat( sz.height+1, sz.width+1, CV_64FC1, sqsum->data.ptr );
            if( tilted )
            {
                tilted1 = cvMat( sz.height+1, sz.width+1, CV_32SC1, tilted->data.ptr );
                _tilted = &tilted1;
            }
            norm1 = cvMat( sz1.height, sz1.width, CV_32FC1, normImg ? normImg->data.ptr : 0 );
            mask1 = cvMat( sz1.height, sz1.width, CV_8UC1, temp->data.ptr );

            cvResize( img, &img1, cv::INTER_LINEAR_EXACT );
            cvIntegral( &img1, &sum1, &sqsum1, _tilted );

            int ystep = factor > 2 ? 1 : 2;
            const int LOCS_PER_THREAD = 1000;
            int stripCount = ((sz1.width/ystep)*(sz1.height + ystep-1)/ystep + LOCS_PER_THREAD/2)/LOCS_PER_THREAD;
            stripCount = std::min(std::max(stripCount, 1), 100);

            cvSetImagesForHaarClassifierCascade( cascade, &sum1, &sqsum1, _tilted, 1. );

            cv::Mat _norm1 = cv::cvarrToMat(&norm1), _mask1 = cv::cvarrToMat(&mask1);
            cv::parallel_for_(cv::Range(0, stripCount),
                         cv::HaarDetectObjects_ScaleImage_Invoker(cascade,
                                (((sz1.height + stripCount - 1)/stripCount + ystep-1)/ystep)*ystep,
                                factor, cv::cvarrToMat(&sum1), cv::cvarrToMat(&sqsum1), &_norm1, &_mask1,
                                cv::Rect(equRect), allCandidates, rejectLevels, levelWeights, outputRejectLevels, &mtx));
        }
    }
    else
    {
        int n_factors = 0;
        cv::Rect scanROI;

        cvIntegral( img, sum, sqsum, tilted );

        if( doCannyPruning )
        {
            sumcanny.reset(cvCreateMat( img->rows + 1, img->cols + 1, CV_32SC1 ));
            cvCanny( img, temp, 0, 50, 3 );
            cvIntegral( temp, sumcanny );
        }

        for( n_factors = 0, factor = 1;
             factor*cascade->orig_window_size.width < img->cols - 10 &&
             factor*cascade->orig_window_size.height < img->rows - 10;
             n_factors++, factor *= scaleFactor )
            ;

        if( findBiggestObject )
        {
            scaleFactor = 1./scaleFactor;
            factor *= scaleFactor;
        }
        else
            factor = 1;

        for( ; n_factors-- > 0; factor *= scaleFactor )
        {
            const double ystep = std::max( 2., factor );
            cv::Size winSize(cvRound(cascade->orig_window_size.width * factor),
                             cvRound(cascade->orig_window_size.height * factor));
            cv::Rect equRect;
            int *p[4] = {0,0,0,0};
            int *pq[4] = {0,0,0,0};
            int startX = 0, startY = 0;
            int endX = cvRound((img->cols - winSize.width) / ystep);
            int endY = cvRound((img->rows - winSize.height) / ystep);

            if( winSize.width < minSize.width || winSize.height < minSize.height )
            {
                if( findBiggestObject )
                    break;
                continue;
            }

            if ( winSize.width > maxSize.width || winSize.height > maxSize.height )
            {
                if( !findBiggestObject )
                    break;
                continue;
            }

            cvSetImagesForHaarClassifierCascade( cascade, sum, sqsum, tilted, factor );
            cvZero( temp );

            if( doCannyPruning )
            {
                equRect.x = cvRound(winSize.width*0.15);
                equRect.y = cvRound(winSize.height*0.15);
                equRect.width = cvRound(winSize.width*0.7);
                equRect.height = cvRound(winSize.height*0.7);

                p[0] = (int*)(sumcanny->data.ptr + equRect.y*sumcanny->step) + equRect.x;
                p[1] = (int*)(sumcanny->data.ptr + equRect.y*sumcanny->step)
                            + equRect.x + equRect.width;
                p[2] = (int*)(sumcanny->data.ptr + (equRect.y + equRect.height)*sumcanny->step) + equRect.x;
                p[3] = (int*)(sumcanny->data.ptr + (equRect.y + equRect.height)*sumcanny->step)
                            + equRect.x + equRect.width;

                pq[0] = (int*)(sum->data.ptr + equRect.y*sum->step) + equRect.x;
                pq[1] = (int*)(sum->data.ptr + equRect.y*sum->step)
                            + equRect.x + equRect.width;
                pq[2] = (int*)(sum->data.ptr + (equRect.y + equRect.height)*sum->step) + equRect.x;
                pq[3] = (int*)(sum->data.ptr + (equRect.y + equRect.height)*sum->step)
                            + equRect.x + equRect.width;
            }

            if( !scanROI.empty() )
            {
                //adjust start_height and stop_height
                startY = cvRound(scanROI.y / ystep);
                endY = cvRound((scanROI.y + scanROI.height - winSize.height) / ystep);

                startX = cvRound(scanROI.x / ystep);
                endX = cvRound((scanROI.x + scanROI.width - winSize.width) / ystep);
            }

            cv::parallel_for_(cv::Range(startY, endY),
                cv::HaarDetectObjects_ScaleCascade_Invoker(cascade, winSize, cv::Range(startX, endX),
                                                           ystep, sum->step, (const int**)p,
                                                           (const int**)pq, allCandidates, &mtx ));

            if( findBiggestObject && !allCandidates.empty() && scanROI.empty() )
            {
                rectList.resize(allCandidates.size());
                std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());

                groupRectangles(rectList, std::max(minNeighbors, 1), GROUP_EPS);

                if( !rectList.empty() )
                {
                    size_t i, sz = rectList.size();
                    cv::Rect maxRect;

                    for( i = 0; i < sz; i++ )
                    {
                        if( rectList[i].area() > maxRect.area() )
                            maxRect = rectList[i];
                    }

                    allCandidates.push_back(maxRect);

                    scanROI = maxRect;
                    int dx = cvRound(maxRect.width*GROUP_EPS);
                    int dy = cvRound(maxRect.height*GROUP_EPS);
                    scanROI.x = std::max(scanROI.x - dx, 0);
                    scanROI.y = std::max(scanROI.y - dy, 0);
                    scanROI.width = std::min(scanROI.width + dx*2, img->cols-1-scanROI.x);
                    scanROI.height = std::min(scanROI.height + dy*2, img->rows-1-scanROI.y);

                    double minScale = roughSearch ? 0.6 : 0.4;
                    minSize.width = cvRound(maxRect.width*minScale);
                    minSize.height = cvRound(maxRect.height*minScale);
                }
            }
        }
    }

    rectList.resize(allCandidates.size());
    if(!allCandidates.empty())
        std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());

    if( minNeighbors != 0 || findBiggestObject )
    {
        if( outputRejectLevels )
        {
            groupRectangles(rectList, rejectLevels, levelWeights, minNeighbors, GROUP_EPS );
        }
        else
        {
            groupRectangles(rectList, rweights, std::max(minNeighbors, 1), GROUP_EPS);
        }
    }
    else
        rweights.resize(rectList.size(),0);

    if( findBiggestObject && rectList.size() )
    {
        CvAvgComp result_comp = {{0, 0, 0, 0},0};

        for( size_t i = 0; i < rectList.size(); i++ )
        {
            cv::Rect r = rectList[i];
            if( r.area() > cv::Rect(result_comp.rect).area() )
            {
                result_comp.rect = cvRect(r);
                result_comp.neighbors = rweights[i];
            }
        }
        cvSeqPush( result_seq, &result_comp );
    }
    else
    {
        for( size_t i = 0; i < rectList.size(); i++ )
        {
            CvAvgComp c;
            c.rect = cvRect(rectList[i]);
            c.neighbors = !rweights.empty() ? rweights[i] : 0;
            cvSeqPush( result_seq, &c );
        }
    }

    return result_seq;
}

CV_IMPL CvSeq*
cvHaarDetectObjects( const CvArr* _img,
                     CvHaarClassifierCascade* cascade, CvMemStorage* storage,
                     double scaleFactor,
                     int minNeighbors, int flags, CvSize minSize, CvSize maxSize )
{
    std::vector<int> fakeLevels;
    std::vector<double> fakeWeights;
    return cvHaarDetectObjectsForROC( _img, cascade, storage, fakeLevels, fakeWeights,
                                scaleFactor, minNeighbors, flags, minSize, maxSize, false );

}


static CvHaarClassifierCascade*
icvLoadCascadeCART( const char** input_cascade, int n, CvSize orig_window_size )
{
    int i;
    CvHaarClassifierCascade* cascade = icvCreateHaarClassifierCascade(n);
    cascade->orig_window_size = orig_window_size;

    for( i = 0; i < n; i++ )
    {
        int j, count, l;
        float threshold = 0;
        const char* stage = input_cascade[i];
        int dl = 0;

        /* tree links */
        int parent = -1;
        int next = -1;

        sscanf( stage, "%d%n", &count, &dl );
        stage += dl;

        CV_Assert( count > 0 && count < CV_HAAR_STAGE_MAX);
        cascade->stage_classifier[i].count = count;
        cascade->stage_classifier[i].classifier =
            (CvHaarClassifier*)cvAlloc( count*sizeof(cascade->stage_classifier[i].classifier[0]));

        for( j = 0; j < count; j++ )
        {
            CvHaarClassifier* classifier = cascade->stage_classifier[i].classifier + j;
            int k, rects = 0;
            char str[100];

            sscanf( stage, "%d%n", &classifier->count, &dl );
            stage += dl;

            CV_Assert( classifier->count > 0 && classifier->count< CV_HAAR_STAGE_MAX);
            classifier->haar_feature = (CvHaarFeature*) cvAlloc(
                classifier->count * ( sizeof( *classifier->haar_feature ) +
                                      sizeof( *classifier->threshold ) +
                                      sizeof( *classifier->left ) +
                                      sizeof( *classifier->right ) ) +
                (classifier->count + 1) * sizeof( *classifier->alpha ) );
            classifier->threshold = (float*) (classifier->haar_feature+classifier->count);
            classifier->left = (int*) (classifier->threshold + classifier->count);
            classifier->right = (int*) (classifier->left + classifier->count);
            classifier->alpha = (float*) (classifier->right + classifier->count);

            for( l = 0; l < classifier->count; l++ )
            {
                sscanf( stage, "%d%n", &rects, &dl );
                stage += dl;

                CV_Assert( rects >= 2 && rects <= CV_HAAR_FEATURE_MAX );

                for( k = 0; k < rects; k++ )
                {
                    cv::Rect r;
                    int band = 0;
                    sscanf( stage, "%d%d%d%d%d%f%n",
                            &r.x, &r.y, &r.width, &r.height, &band,
                            &(classifier->haar_feature[l].rect[k].weight), &dl );
                    stage += dl;
                    classifier->haar_feature[l].rect[k].r = cvRect(r);
                }
                sscanf( stage, "%99s%n", str, &dl );
                stage += dl;

                classifier->haar_feature[l].tilted = strncmp( str, "tilted", 6 ) == 0;

                for( k = rects; k < CV_HAAR_FEATURE_MAX; k++ )
                {
                    memset( classifier->haar_feature[l].rect + k, 0,
                            sizeof(classifier->haar_feature[l].rect[k]) );
                }

                sscanf( stage, "%f%d%d%n", &(classifier->threshold[l]),
                                       &(classifier->left[l]),
                                       &(classifier->right[l]), &dl );
                stage += dl;
            }
            for( l = 0; l <= classifier->count; l++ )
            {
                sscanf( stage, "%f%n", &(classifier->alpha[l]), &dl );
                stage += dl;
            }
        }

        sscanf( stage, "%f%n", &threshold, &dl );
        stage += dl;

        cascade->stage_classifier[i].threshold = threshold;

        /* load tree links */
        if( sscanf( stage, "%d%d%n", &parent, &next, &dl ) != 2 )
        {
            parent = i - 1;
            next = -1;
        }
        stage += dl;

        CV_Assert(parent >= 0 && parent < i);
        cascade->stage_classifier[i].parent = parent;
        cascade->stage_classifier[i].next = next;
        cascade->stage_classifier[i].child = -1;

        if( parent != -1 && cascade->stage_classifier[parent].child == -1 )
        {
            cascade->stage_classifier[parent].child = i;
        }
    }

    return cascade;
}

#ifndef _MAX_PATH
#define _MAX_PATH 1024
#endif

CV_IMPL CvHaarClassifierCascade*
cvLoadHaarClassifierCascade( const char* directory, CvSize orig_window_size )
{
    if( !directory )
        CV_Error( CV_StsNullPtr, "Null path is passed" );

    char name[_MAX_PATH];

    int n = (int)strlen(directory)-1;
    const char* slash = directory[n] == '\\' || directory[n] == '/' ? "" : "/";
    int size = 0;

    /* try to read the classifier from directory */
    for( n = 0; ; n++ )
    {
        sprintf( name, "%s%s%d/AdaBoostCARTHaarClassifier.txt", directory, slash, n );
        FILE* f = fopen( name, "rb" );
        if( !f )
            break;
        fseek( f, 0, SEEK_END );
        size += ftell( f ) + 1;
        fclose(f);
    }

    if( n == 0 && slash[0] )
        return (CvHaarClassifierCascade*)cvLoad( directory );

    if( n == 0 )
        CV_Error( CV_StsBadArg, "Invalid path" );

    size += (n+1)*sizeof(char*);
    const char** input_cascade = (const char**)cvAlloc( size );

    if( !input_cascade )
      CV_Error( CV_StsNoMem, "Could not allocate memory for input_cascade" );

    char* ptr = (char*)(input_cascade + n + 1);

    for( int i = 0; i < n; i++ )
    {
        sprintf( name, "%s/%d/AdaBoostCARTHaarClassifier.txt", directory, i );
        FILE* f = fopen( name, "rb" );
        if( !f )
            CV_Error( CV_StsError, "" );
        fseek( f, 0, SEEK_END );
        size = (int)ftell( f );
        fseek( f, 0, SEEK_SET );
        size_t elements_read = fread( ptr, 1, size, f );
        CV_Assert(elements_read == (size_t)(size));
        fclose(f);
        input_cascade[i] = ptr;
        ptr += size;
        *ptr++ = '\0';
    }

    input_cascade[n] = 0;

    CvHaarClassifierCascade* cascade = icvLoadCascadeCART( input_cascade, n, orig_window_size );

    if( input_cascade )
        cvFree( &input_cascade );

    return cascade;
}


CV_IMPL void
cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** _cascade )
{
    if( _cascade && *_cascade )
    {
        int i, j;
        CvHaarClassifierCascade* cascade = *_cascade;

        for( i = 0; i < cascade->count; i++ )
        {
            for( j = 0; j < cascade->stage_classifier[i].count; j++ )
                cvFree( &cascade->stage_classifier[i].classifier[j].haar_feature );
            cvFree( &cascade->stage_classifier[i].classifier );
        }
        icvReleaseHidHaarClassifierCascade( &cascade->hid_cascade );
        cvFree( _cascade );
    }
}


/****************************************************************************************\
*                                  Persistence functions                                 *
\****************************************************************************************/

/* field names */

#define ICV_HAAR_SIZE_NAME            "size"
#define ICV_HAAR_STAGES_NAME          "stages"
#define ICV_HAAR_TREES_NAME           "trees"
#define ICV_HAAR_FEATURE_NAME         "feature"
#define ICV_HAAR_RECTS_NAME           "rects"
#define ICV_HAAR_TILTED_NAME          "tilted"
#define ICV_HAAR_THRESHOLD_NAME       "threshold"
#define ICV_HAAR_LEFT_NODE_NAME       "left_node"
#define ICV_HAAR_LEFT_VAL_NAME        "left_val"
#define ICV_HAAR_RIGHT_NODE_NAME      "right_node"
#define ICV_HAAR_RIGHT_VAL_NAME       "right_val"
#define ICV_HAAR_STAGE_THRESHOLD_NAME "stage_threshold"
#define ICV_HAAR_PARENT_NAME          "parent"
#define ICV_HAAR_NEXT_NAME            "next"

static int
icvIsHaarClassifier( const void* struct_ptr )
{
    return CV_IS_HAAR_CLASSIFIER( struct_ptr );
}

static void*
icvReadHaarClassifier( CvFileStorage* fs, CvFileNode* node )
{
    CvHaarClassifierCascade* cascade = NULL;

    char buf[256];
    CvFileNode* seq_fn = NULL; /* sequence */
    CvFileNode* fn = NULL;
    CvFileNode* stages_fn = NULL;
    CvSeqReader stages_reader;
    int n;
    int i, j, k, l;
    int parent, next;

    stages_fn = cvGetFileNodeByName( fs, node, ICV_HAAR_STAGES_NAME );
    if( !stages_fn || !CV_NODE_IS_SEQ( stages_fn->tag) )
        CV_Error( CV_StsError, "Invalid stages node" );

    n = stages_fn->data.seq->total;
    cascade = icvCreateHaarClassifierCascade(n);

    /* read size */
    seq_fn = cvGetFileNodeByName( fs, node, ICV_HAAR_SIZE_NAME );
    if( !seq_fn || !CV_NODE_IS_SEQ( seq_fn->tag ) || seq_fn->data.seq->total != 2 )
        CV_Error( CV_StsError, "size node is not a valid sequence." );
    fn = (CvFileNode*) cvGetSeqElem( seq_fn->data.seq, 0 );
    if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0 )
        CV_Error( CV_StsError, "Invalid size node: width must be positive integer" );
    cascade->orig_window_size.width = fn->data.i;
    fn = (CvFileNode*) cvGetSeqElem( seq_fn->data.seq, 1 );
    if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0 )
        CV_Error( CV_StsError, "Invalid size node: height must be positive integer" );
    cascade->orig_window_size.height = fn->data.i;

    cvStartReadSeq( stages_fn->data.seq, &stages_reader );
    for( i = 0; i < n; ++i )
    {
        CvFileNode* stage_fn;
        CvFileNode* trees_fn;
        CvSeqReader trees_reader;

        stage_fn = (CvFileNode*) stages_reader.ptr;
        if( !CV_NODE_IS_MAP( stage_fn->tag ) )
        {
            sprintf( buf, "Invalid stage %d", i );
            CV_Error( CV_StsError, buf );
        }

        trees_fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_TREES_NAME );
        if( !trees_fn || !CV_NODE_IS_SEQ( trees_fn->tag )
            || trees_fn->data.seq->total <= 0 )
        {
            sprintf( buf, "Trees node is not a valid sequence. (stage %d)", i );
            CV_Error( CV_StsError, buf );
        }

        cascade->stage_classifier[i].classifier =
            (CvHaarClassifier*) cvAlloc( trees_fn->data.seq->total
                * sizeof( cascade->stage_classifier[i].classifier[0] ) );
        for( j = 0; j < trees_fn->data.seq->total; ++j )
        {
            cascade->stage_classifier[i].classifier[j].haar_feature = NULL;
        }
        cascade->stage_classifier[i].count = trees_fn->data.seq->total;

        cvStartReadSeq( trees_fn->data.seq, &trees_reader );
        for( j = 0; j < trees_fn->data.seq->total; ++j )
        {
            CvFileNode* tree_fn;
            CvSeqReader tree_reader;
            CvHaarClassifier* classifier;
            int last_idx;

            classifier = &cascade->stage_classifier[i].classifier[j];
            tree_fn = (CvFileNode*) trees_reader.ptr;
            if( !CV_NODE_IS_SEQ( tree_fn->tag ) || tree_fn->data.seq->total <= 0 )
            {
                sprintf( buf, "Tree node is not a valid sequence."
                         " (stage %d, tree %d)", i, j );
                CV_Error( CV_StsError, buf );
            }

            classifier->count = tree_fn->data.seq->total;
            classifier->haar_feature = (CvHaarFeature*) cvAlloc(
                classifier->count * ( sizeof( *classifier->haar_feature ) +
                                      sizeof( *classifier->threshold ) +
                                      sizeof( *classifier->left ) +
                                      sizeof( *classifier->right ) ) +
                (classifier->count + 1) * sizeof( *classifier->alpha ) );
            classifier->threshold = (float*) (classifier->haar_feature+classifier->count);
            classifier->left = (int*) (classifier->threshold + classifier->count);
            classifier->right = (int*) (classifier->left + classifier->count);
            classifier->alpha = (float*) (classifier->right + classifier->count);

            cvStartReadSeq( tree_fn->data.seq, &tree_reader );
            for( k = 0, last_idx = 0; k < tree_fn->data.seq->total; ++k )
            {
                CvFileNode* node_fn;
                CvFileNode* feature_fn;
                CvFileNode* rects_fn;
                CvSeqReader rects_reader;

                node_fn = (CvFileNode*) tree_reader.ptr;
                if( !CV_NODE_IS_MAP( node_fn->tag ) )
                {
                    sprintf( buf, "Tree node %d is not a valid map. (stage %d, tree %d)",
                             k, i, j );
                    CV_Error( CV_StsError, buf );
                }
                feature_fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_FEATURE_NAME );
                if( !feature_fn || !CV_NODE_IS_MAP( feature_fn->tag ) )
                {
                    sprintf( buf, "Feature node is not a valid map. "
                             "(stage %d, tree %d, node %d)", i, j, k );
                    CV_Error( CV_StsError, buf );
                }
                rects_fn = cvGetFileNodeByName( fs, feature_fn, ICV_HAAR_RECTS_NAME );
                if( !rects_fn || !CV_NODE_IS_SEQ( rects_fn->tag )
                    || rects_fn->data.seq->total < 1
                    || rects_fn->data.seq->total > CV_HAAR_FEATURE_MAX )
                {
                    sprintf( buf, "Rects node is not a valid sequence. "
                             "(stage %d, tree %d, node %d)", i, j, k );
                    CV_Error( CV_StsError, buf );
                }
                cvStartReadSeq( rects_fn->data.seq, &rects_reader );
                for( l = 0; l < rects_fn->data.seq->total; ++l )
                {
                    CvFileNode* rect_fn;
                    cv::Rect r;

                    rect_fn = (CvFileNode*) rects_reader.ptr;
                    if( !CV_NODE_IS_SEQ( rect_fn->tag ) || rect_fn->data.seq->total != 5 )
                    {
                        sprintf( buf, "Rect %d is not a valid sequence. "
                                 "(stage %d, tree %d, node %d)", l, i, j, k );
                        CV_Error( CV_StsError, buf );
                    }

                    fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 0 );
                    if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i < 0 )
                    {
                        sprintf( buf, "x coordinate must be non-negative integer. "
                                 "(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
                        CV_Error( CV_StsError, buf );
                    }
                    r.x = fn->data.i;
                    fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 1 );
                    if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i < 0 )
                    {
                        sprintf( buf, "y coordinate must be non-negative integer. "
                                 "(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
                        CV_Error( CV_StsError, buf );
                    }
                    r.y = fn->data.i;
                    fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 2 );
                    if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0
                        || r.x + fn->data.i > cascade->orig_window_size.width )
                    {
                        sprintf( buf, "width must be positive integer and "
                                 "(x + width) must not exceed window width. "
                                 "(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
                        CV_Error( CV_StsError, buf );
                    }
                    r.width = fn->data.i;
                    fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 3 );
                    if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= 0
                        || r.y + fn->data.i > cascade->orig_window_size.height )
                    {
                        sprintf( buf, "height must be positive integer and "
                                 "(y + height) must not exceed window height. "
                                 "(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
                        CV_Error( CV_StsError, buf );
                    }
                    r.height = fn->data.i;
                    fn = CV_SEQ_ELEM( rect_fn->data.seq, CvFileNode, 4 );
                    if( !CV_NODE_IS_REAL( fn->tag ) )
                    {
                        sprintf( buf, "weight must be real number. "
                                 "(stage %d, tree %d, node %d, rect %d)", i, j, k, l );
                        CV_Error( CV_StsError, buf );
                    }

                    classifier->haar_feature[k].rect[l].weight = (float) fn->data.f;
                    classifier->haar_feature[k].rect[l].r = cvRect(r);

                    CV_NEXT_SEQ_ELEM( sizeof( *rect_fn ), rects_reader );
                } /* for each rect */
                for( l = rects_fn->data.seq->total; l < CV_HAAR_FEATURE_MAX; ++l )
                {
                    classifier->haar_feature[k].rect[l].weight = 0;
                    classifier->haar_feature[k].rect[l].r = cvRect( 0, 0, 0, 0 );
                }

                fn = cvGetFileNodeByName( fs, feature_fn, ICV_HAAR_TILTED_NAME);
                if( !fn || !CV_NODE_IS_INT( fn->tag ) )
                {
                    sprintf( buf, "tilted must be 0 or 1. "
                             "(stage %d, tree %d, node %d)", i, j, k );
                    CV_Error( CV_StsError, buf );
                }
                classifier->haar_feature[k].tilted = ( fn->data.i != 0 );
                fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_THRESHOLD_NAME);
                if( !fn || !CV_NODE_IS_REAL( fn->tag ) )
                {
                    sprintf( buf, "threshold must be real number. "
                             "(stage %d, tree %d, node %d)", i, j, k );
                    CV_Error( CV_StsError, buf );
                }
                classifier->threshold[k] = (float) fn->data.f;
                fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_LEFT_NODE_NAME);
                if( fn )
                {
                    if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= k
                        || fn->data.i >= tree_fn->data.seq->total )
                    {
                        sprintf( buf, "left node must be valid node number. "
                                 "(stage %d, tree %d, node %d)", i, j, k );
                        CV_Error( CV_StsError, buf );
                    }
                    /* left node */
                    classifier->left[k] = fn->data.i;
                }
                else
                {
                    fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_LEFT_VAL_NAME );
                    if( !fn )
                    {
                        sprintf( buf, "left node or left value must be specified. "
                                 "(stage %d, tree %d, node %d)", i, j, k );
                        CV_Error( CV_StsError, buf );
                    }
                    if( !CV_NODE_IS_REAL( fn->tag ) )
                    {
                        sprintf( buf, "left value must be real number. "
                                 "(stage %d, tree %d, node %d)", i, j, k );
                        CV_Error( CV_StsError, buf );
                    }
                    /* left value */
                    if( last_idx >= classifier->count + 1 )
                    {
                        sprintf( buf, "Tree structure is broken: too many values. "
                                 "(stage %d, tree %d, node %d)", i, j, k );
                        CV_Error( CV_StsError, buf );
                    }
                    classifier->left[k] = -last_idx;
                    classifier->alpha[last_idx++] = (float) fn->data.f;
                }
                fn = cvGetFileNodeByName( fs, node_fn,ICV_HAAR_RIGHT_NODE_NAME);
                if( fn )
                {
                    if( !CV_NODE_IS_INT( fn->tag ) || fn->data.i <= k
                        || fn->data.i >= tree_fn->data.seq->total )
                    {
                        sprintf( buf, "right node must be valid node number. "
                                 "(stage %d, tree %d, node %d)", i, j, k );
                        CV_Error( CV_StsError, buf );
                    }
                    /* right node */
                    classifier->right[k] = fn->data.i;
                }
                else
                {
                    fn = cvGetFileNodeByName( fs, node_fn, ICV_HAAR_RIGHT_VAL_NAME );
                    if( !fn )
                    {
                        sprintf( buf, "right node or right value must be specified. "
                                 "(stage %d, tree %d, node %d)", i, j, k );
                        CV_Error( CV_StsError, buf );
                    }
                    if( !CV_NODE_IS_REAL( fn->tag ) )
                    {
                        sprintf( buf, "right value must be real number. "
                                 "(stage %d, tree %d, node %d)", i, j, k );
                        CV_Error( CV_StsError, buf );
                    }
                    /* right value */
                    if( last_idx >= classifier->count + 1 )
                    {
                        sprintf( buf, "Tree structure is broken: too many values. "
                                 "(stage %d, tree %d, node %d)", i, j, k );
                        CV_Error( CV_StsError, buf );
                    }
                    classifier->right[k] = -last_idx;
                    classifier->alpha[last_idx++] = (float) fn->data.f;
                }

                CV_NEXT_SEQ_ELEM( sizeof( *node_fn ), tree_reader );
            } /* for each node */
            if( last_idx != classifier->count + 1 )
            {
                sprintf( buf, "Tree structure is broken: too few values. "
                         "(stage %d, tree %d)", i, j );
                CV_Error( CV_StsError, buf );
            }

            CV_NEXT_SEQ_ELEM( sizeof( *tree_fn ), trees_reader );
        } /* for each tree */

        fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_STAGE_THRESHOLD_NAME);
        if( !fn || !CV_NODE_IS_REAL( fn->tag ) )
        {
            sprintf( buf, "stage threshold must be real number. (stage %d)", i );
            CV_Error( CV_StsError, buf );
        }
        cascade->stage_classifier[i].threshold = (float) fn->data.f;

        parent = i - 1;
        next = -1;

        fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_PARENT_NAME );
        if( !fn || !CV_NODE_IS_INT( fn->tag )
            || fn->data.i < -1 || fn->data.i >= cascade->count )
        {
            sprintf( buf, "parent must be integer number. (stage %d)", i );
            CV_Error( CV_StsError, buf );
        }
        parent = fn->data.i;
        fn = cvGetFileNodeByName( fs, stage_fn, ICV_HAAR_NEXT_NAME );
        if( !fn || !CV_NODE_IS_INT( fn->tag )
            || fn->data.i < -1 || fn->data.i >= cascade->count )
        {
            sprintf( buf, "next must be integer number. (stage %d)", i );
            CV_Error( CV_StsError, buf );
        }
        next = fn->data.i;

        cascade->stage_classifier[i].parent = parent;
        cascade->stage_classifier[i].next = next;
        cascade->stage_classifier[i].child = -1;

        if( parent != -1 && cascade->stage_classifier[parent].child == -1 )
        {
            cascade->stage_classifier[parent].child = i;
        }

        CV_NEXT_SEQ_ELEM( sizeof( *stage_fn ), stages_reader );
    } /* for each stage */

    return cascade;
}

static void
icvWriteHaarClassifier( CvFileStorage* fs, const char* name, const void* struct_ptr,
                        CvAttrList attributes )
{
    int i, j, k, l;
    char buf[256];
    const CvHaarClassifierCascade* cascade = (const CvHaarClassifierCascade*) struct_ptr;

    /* TODO: parameters check */

    cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_HAAR, attributes );

    cvStartWriteStruct( fs, ICV_HAAR_SIZE_NAME, CV_NODE_SEQ | CV_NODE_FLOW );
    cvWriteInt( fs, NULL, cascade->orig_window_size.width );
    cvWriteInt( fs, NULL, cascade->orig_window_size.height );
    cvEndWriteStruct( fs ); /* size */

    cvStartWriteStruct( fs, ICV_HAAR_STAGES_NAME, CV_NODE_SEQ );
    for( i = 0; i < cascade->count; ++i )
    {
        cvStartWriteStruct( fs, NULL, CV_NODE_MAP );
        sprintf( buf, "stage %d", i );
        cvWriteComment( fs, buf, 1 );

        cvStartWriteStruct( fs, ICV_HAAR_TREES_NAME, CV_NODE_SEQ );

        for( j = 0; j < cascade->stage_classifier[i].count; ++j )
        {
            CvHaarClassifier* tree = &cascade->stage_classifier[i].classifier[j];

            cvStartWriteStruct( fs, NULL, CV_NODE_SEQ );
            sprintf( buf, "tree %d", j );
            cvWriteComment( fs, buf, 1 );

            for( k = 0; k < tree->count; ++k )
            {
                CvHaarFeature* feature = &tree->haar_feature[k];

                cvStartWriteStruct( fs, NULL, CV_NODE_MAP );
                if( k )
                {
                    sprintf( buf, "node %d", k );
                }
                else
                {
                    sprintf( buf, "root node" );
                }
                cvWriteComment( fs, buf, 1 );

                cvStartWriteStruct( fs, ICV_HAAR_FEATURE_NAME, CV_NODE_MAP );

                cvStartWriteStruct( fs, ICV_HAAR_RECTS_NAME, CV_NODE_SEQ );
                for( l = 0; l < CV_HAAR_FEATURE_MAX && feature->rect[l].r.width != 0; ++l )
                {
                    cvStartWriteStruct( fs, NULL, CV_NODE_SEQ | CV_NODE_FLOW );
                    cvWriteInt(  fs, NULL, feature->rect[l].r.x );
                    cvWriteInt(  fs, NULL, feature->rect[l].r.y );
                    cvWriteInt(  fs, NULL, feature->rect[l].r.width );
                    cvWriteInt(  fs, NULL, feature->rect[l].r.height );
                    cvWriteReal( fs, NULL, feature->rect[l].weight );
                    cvEndWriteStruct( fs ); /* rect */
                }
                cvEndWriteStruct( fs ); /* rects */
                cvWriteInt( fs, ICV_HAAR_TILTED_NAME, feature->tilted );
                cvEndWriteStruct( fs ); /* feature */

                cvWriteReal( fs, ICV_HAAR_THRESHOLD_NAME, tree->threshold[k]);

                if( tree->left[k] > 0 )
                {
                    cvWriteInt( fs, ICV_HAAR_LEFT_NODE_NAME, tree->left[k] );
                }
                else
                {
                    cvWriteReal( fs, ICV_HAAR_LEFT_VAL_NAME,
                        tree->alpha[-tree->left[k]] );
                }

                if( tree->right[k] > 0 )
                {
                    cvWriteInt( fs, ICV_HAAR_RIGHT_NODE_NAME, tree->right[k] );
                }
                else
                {
                    cvWriteReal( fs, ICV_HAAR_RIGHT_VAL_NAME,
                        tree->alpha[-tree->right[k]] );
                }

                cvEndWriteStruct( fs ); /* split */
            }

            cvEndWriteStruct( fs ); /* tree */
        }

        cvEndWriteStruct( fs ); /* trees */

        cvWriteReal( fs, ICV_HAAR_STAGE_THRESHOLD_NAME, cascade->stage_classifier[i].threshold);
        cvWriteInt( fs, ICV_HAAR_PARENT_NAME, cascade->stage_classifier[i].parent );
        cvWriteInt( fs, ICV_HAAR_NEXT_NAME, cascade->stage_classifier[i].next );

        cvEndWriteStruct( fs ); /* stage */
    } /* for each stage */

    cvEndWriteStruct( fs ); /* stages */
    cvEndWriteStruct( fs ); /* root */
}

static void*
icvCloneHaarClassifier( const void* struct_ptr )
{
    CvHaarClassifierCascade* cascade = NULL;

    int i, j, k, n;
    const CvHaarClassifierCascade* cascade_src =
        (const CvHaarClassifierCascade*) struct_ptr;

    n = cascade_src->count;
    cascade = icvCreateHaarClassifierCascade(n);
    cascade->orig_window_size = cascade_src->orig_window_size;

    for( i = 0; i < n; ++i )
    {
        cascade->stage_classifier[i].parent = cascade_src->stage_classifier[i].parent;
        cascade->stage_classifier[i].next = cascade_src->stage_classifier[i].next;
        cascade->stage_classifier[i].child = cascade_src->stage_classifier[i].child;
        cascade->stage_classifier[i].threshold = cascade_src->stage_classifier[i].threshold;

        cascade->stage_classifier[i].count = 0;
        cascade->stage_classifier[i].classifier =
            (CvHaarClassifier*) cvAlloc( cascade_src->stage_classifier[i].count
                * sizeof( cascade->stage_classifier[i].classifier[0] ) );

        cascade->stage_classifier[i].count = cascade_src->stage_classifier[i].count;

        for( j = 0; j < cascade->stage_classifier[i].count; ++j )
            cascade->stage_classifier[i].classifier[j].haar_feature = NULL;

        for( j = 0; j < cascade->stage_classifier[i].count; ++j )
        {
            const CvHaarClassifier* classifier_src =
                &cascade_src->stage_classifier[i].classifier[j];
            CvHaarClassifier* classifier =
                &cascade->stage_classifier[i].classifier[j];

            classifier->count = classifier_src->count;
            classifier->haar_feature = (CvHaarFeature*) cvAlloc(
                classifier->count * ( sizeof( *classifier->haar_feature ) +
                                      sizeof( *classifier->threshold ) +
                                      sizeof( *classifier->left ) +
                                      sizeof( *classifier->right ) ) +
                (classifier->count + 1) * sizeof( *classifier->alpha ) );
            classifier->threshold = (float*) (classifier->haar_feature+classifier->count);
            classifier->left = (int*) (classifier->threshold + classifier->count);
            classifier->right = (int*) (classifier->left + classifier->count);
            classifier->alpha = (float*) (classifier->right + classifier->count);
            for( k = 0; k < classifier->count; ++k )
            {
                classifier->haar_feature[k] = classifier_src->haar_feature[k];
                classifier->threshold[k] = classifier_src->threshold[k];
                classifier->left[k] = classifier_src->left[k];
                classifier->right[k] = classifier_src->right[k];
                classifier->alpha[k] = classifier_src->alpha[k];
            }
            classifier->alpha[classifier->count] =
                classifier_src->alpha[classifier->count];
        }
    }

    return cascade;
}


CvType haar_type( CV_TYPE_NAME_HAAR, icvIsHaarClassifier,
                  (CvReleaseFunc)cvReleaseHaarClassifierCascade,
                  icvReadHaarClassifier, icvWriteHaarClassifier,
                  icvCloneHaarClassifier );

/* End of file. */
