opencv/modules/gpu/src/cascadeclassifier.cpp

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#include "precomp.hpp"
#include <vector>
#include <iostream>

using namespace cv;
using namespace cv::gpu;
using namespace std;

struct Stage
{
    int    first;
    int    ntrees;
    float  threshold;
    Stage(int f = 0, int n = 0, float t = 0.f) : first(f), ntrees(n), threshold(t) {}
};

struct DTreeNode
{
    int   featureIdx;
    int   left;
    int   right;
    DTreeNode(int f = 0, int l = 0, int r = 0) : featureIdx(f), left(l), right(r) {}
};

#if !defined (HAVE_CUDA)
// ============ old fashioned haar cascade ==============================================//
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU()               { throw_nogpu(); }
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const string&)  { throw_nogpu(); }
cv::gpu::CascadeClassifier_GPU::~CascadeClassifier_GPU()              { throw_nogpu(); }

bool cv::gpu::CascadeClassifier_GPU::empty() const { throw_nogpu(); return true; }
bool cv::gpu::CascadeClassifier_GPU::load(const string&)  { throw_nogpu(); return true; }
Size cv::gpu::CascadeClassifier_GPU::getClassifierSize() const { throw_nogpu(); return Size(); }

int cv::gpu::CascadeClassifier_GPU::detectMultiScale( const GpuMat& , GpuMat& , double , int , Size)  { throw_nogpu(); return 0; }

// ============ LBP cascade ==============================================//
cv::gpu::CascadeClassifier_GPU_LBP::CascadeClassifier_GPU_LBP()               { throw_nogpu(); }
cv::gpu::CascadeClassifier_GPU_LBP::~CascadeClassifier_GPU_LBP()              { throw_nogpu(); }

bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const                               { throw_nogpu(); return true; }
bool cv::gpu::CascadeClassifier_GPU_LBP::load(const string&)                         { throw_nogpu(); return true; }
Size cv::gpu::CascadeClassifier_GPU_LBP::getClassifierSize() const                   { throw_nogpu(); return Size(); }
void cv::gpu::CascadeClassifier_GPU_LBP::preallocateIntegralBuffer(cv::Size /*desired*/) { throw_nogpu();}

int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const cv::gpu::GpuMat&, cv::gpu::GpuMat&, cv::gpu::GpuMat&, double, int)  { throw_nogpu(); return 0; }

#else

cv::gpu::CascadeClassifier_GPU_LBP::CascadeClassifier_GPU_LBP()
{
}

cv::gpu::CascadeClassifier_GPU_LBP::~CascadeClassifier_GPU_LBP()
{
}

void cv::gpu::CascadeClassifier_GPU_LBP::preallocateIntegralBuffer(cv::Size desired)
{
    integral.create(desired.width + 1, desired.height + 1, CV_32SC1);
}

bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const
{
    return stage_mat.empty();
}

bool cv::gpu::CascadeClassifier_GPU_LBP::load(const string& classifierAsXml)
{
    FileStorage fs(classifierAsXml, FileStorage::READ);
    if (!fs.isOpened())
        return false;
    return read(fs.getFirstTopLevelNode());
}

#define GPU_CC_STAGE_TYPE           "stageType"
#define GPU_CC_FEATURE_TYPE         "featureType"
#define GPU_CC_BOOST                "BOOST"
#define GPU_CC_LBP                  "LBP"
#define GPU_CC_MAX_CAT_COUNT        "maxCatCount"
#define GPU_CC_HEIGHT               "height"
#define GPU_CC_WIDTH                "width"
#define GPU_CC_STAGE_PARAMS         "stageParams"
#define GPU_CC_MAX_DEPTH            "maxDepth"
#define GPU_CC_FEATURE_PARAMS       "featureParams"
#define GPU_CC_STAGES               "stages"
#define GPU_CC_STAGE_THRESHOLD      "stageThreshold"
#define GPU_THRESHOLD_EPS           1e-5f
#define GPU_CC_WEAK_CLASSIFIERS     "weakClassifiers"
#define GPU_CC_INTERNAL_NODES       "internalNodes"
#define GPU_CC_LEAF_VALUES          "leafValues"
#define GPU_CC_FEATURES             "features"
#define GPU_CC_RECT                 "rect"

bool CascadeClassifier_GPU_LBP::read(const FileNode &root)
{
    std::string stageTypeStr = (string)root[GPU_CC_STAGE_TYPE];
    CV_Assert(stageTypeStr == GPU_CC_BOOST);

    string featureTypeStr = (string)root[GPU_CC_FEATURE_TYPE];
    CV_Assert(featureTypeStr == GPU_CC_LBP);

    NxM.width =  (int)root[GPU_CC_WIDTH];
    NxM.height = (int)root[GPU_CC_HEIGHT];
    CV_Assert( NxM.height > 0 && NxM.width > 0 );

    isStumps = ((int)(root[GPU_CC_STAGE_PARAMS][GPU_CC_MAX_DEPTH]) == 1) ? true : false;
    CV_Assert(isStumps);

    // features
    FileNode fn = root[GPU_CC_FEATURE_PARAMS];
    if (fn.empty())
        return false;

    ncategories = fn[GPU_CC_MAX_CAT_COUNT];

    subsetSize = (ncategories + 31) / 32, nodeStep = 3 + ( ncategories > 0 ? subsetSize : 1 );

    fn = root[GPU_CC_STAGES];
    if (fn.empty())
        return false;

    std::vector<Stage> stages;
    stages.reserve(fn.size());

    std::vector<int> cl_trees;
    std::vector<DTreeNode> cl_nodes;
    std::vector<float> cl_leaves;
    std::vector<int> subsets;

    FileNodeIterator it = fn.begin(), it_end = fn.end();
    for (size_t si = 0; it != it_end; si++, ++it )
    {
        FileNode fns = *it;
        Stage st;
        st.threshold = (float)fns[GPU_CC_STAGE_THRESHOLD] - GPU_THRESHOLD_EPS;

        fns = fns[GPU_CC_WEAK_CLASSIFIERS];
        if (fns.empty())
            return false;

        st.ntrees = (int)fns.size();
        st.first = (int)cl_trees.size();

        stages.push_back(st);

        cl_trees.reserve(stages[si].first + stages[si].ntrees);

        // weak trees
        FileNodeIterator it1 = fns.begin(), it1_end = fns.end();
        for ( ; it1 != it1_end; ++it1 )
        {
            FileNode fnw = *it1;

            FileNode internalNodes = fnw[GPU_CC_INTERNAL_NODES];
            FileNode leafValues = fnw[GPU_CC_LEAF_VALUES];
            if ( internalNodes.empty() || leafValues.empty() )
                return false;
            int nodeCount = (int)internalNodes.size()/nodeStep;
            cl_trees.push_back(nodeCount);

            cl_nodes.reserve(cl_nodes.size() + nodeCount);
            cl_leaves.reserve(cl_leaves.size() + leafValues.size());

            if( subsetSize > 0 )
                subsets.reserve(subsets.size() + nodeCount * subsetSize);

            // nodes
            FileNodeIterator iIt = internalNodes.begin(), iEnd = internalNodes.end();

            for( ; iIt != iEnd; )
            {
                DTreeNode node((int)*(iIt++), (int)*(iIt++), (int)*(iIt++));
                cl_nodes.push_back(node);

                if( subsetSize > 0 )
                    for( int j = 0; j < subsetSize; j++, ++iIt )
                        subsets.push_back((int)*iIt);
            }

            // leaves
            iIt = leafValues.begin(), iEnd = leafValues.end();
            for( ; iIt != iEnd; ++iIt )
                cl_leaves.push_back((float)*iIt);
        }
    }
    fn = root[GPU_CC_FEATURES];
    if( fn.empty() )
        return false;
    std::vector<char> features;
    features.reserve(fn.size() * 4);
    FileNodeIterator f_it = fn.begin(), f_end = fn.end();
    for (; f_it != f_end; ++f_it)
    {
        FileNode rect = (*it)[GPU_CC_RECT];
        FileNodeIterator r_it = rect.begin();
        features.push_back(saturate_cast<uchar>((int)*(r_it++)));
        features.push_back(saturate_cast<uchar>((int)*(r_it++)));
        features.push_back(saturate_cast<uchar>((int)*(r_it++)));
        features.push_back(saturate_cast<uchar>((int)*(r_it++)));
    }

    // copy data structures on gpu
    stage_mat = cv::gpu::GpuMat(1, (int)stages.size() * sizeof(Stage), CV_8UC1);
    stage_mat.upload(cv::Mat(1, stages.size() * sizeof(Stage), CV_8UC1, &(stages[0]) ));

    trees_mat = cv::gpu::GpuMat(1, (int)cl_trees.size(), CV_32SC1);
    stage_mat.upload(cv::Mat(cl_trees));

    nodes_mat = cv::gpu::GpuMat(1, (int)cl_nodes.size() * sizeof(DTreeNode), CV_8UC1);
    stage_mat.upload(cv::Mat(1, cl_nodes.size() * sizeof(DTreeNode), CV_8UC1, &(cl_nodes[0]) ));

    leaves_mat = cv::gpu::GpuMat(1, (int)cl_leaves.size(), CV_32FC1);
    stage_mat.upload(cv::Mat(cl_leaves));

    subsets_mat = cv::gpu::GpuMat(1, (int)subsets.size(), CV_32SC1);
    stage_mat.upload(cv::Mat(subsets));

    features_mat = cv::gpu::GpuMat(1, (int)features.size(), CV_8UC1);
    features_mat.upload(cv::Mat(features));

    return true;
}

#undef GPU_CC_STAGE_TYPE
#undef GPU_CC_BOOST
#undef GPU_CC_FEATURE_TYPE
#undef GPU_CC_LBP
#undef GPU_CC_MAX_CAT_COUNT
#undef GPU_CC_HEIGHT
#undef GPU_CC_WIDTH
#undef GPU_CC_STAGE_PARAMS
#undef GPU_CC_MAX_DEPTH
#undef GPU_CC_FEATURE_PARAMS
#undef GPU_CC_STAGES
#undef GPU_CC_STAGE_THRESHOLD
#undef GPU_THRESHOLD_EPS
#undef GPU_CC_WEAK_CLASSIFIERS
#undef GPU_CC_INTERNAL_NODES
#undef GPU_CC_LEAF_VALUES
#undef GPU_CC_FEATURES
#undef GPU_CC_RECT

Size cv::gpu::CascadeClassifier_GPU_LBP::getClassifierSize() const
{
    return NxM;
}

namespace cv { namespace gpu { namespace device
{
    namespace lbp
    {
        void cascadeClassify(const DevMem2Db stages, const DevMem2Di trees, const DevMem2Db nodes, const DevMem2Df leaves, const DevMem2Di subsets, const DevMem2Db features,
            const DevMem2Di integral, int workWidth, int workHeight, int clWidth, int clHeight, float scale, int step, int subsetSize, DevMem2D_<int4> objects, int minNeighbors = 4, cudaStream_t stream = 0);
    }
}}}

int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, GpuMat& scaledImageBuffer, GpuMat& objects, double scaleFactor, int minNeighbors /*, Size minSize=Size()*/)
{
    CV_Assert( scaleFactor > 1 && image.depth() == CV_8U );
    CV_Assert(!empty());

    const int defaultObjSearchNum = 100;

    // if( !objects.empty() && objects.depth() == CV_32S)
    //     objects.reshape(4, 1);
    // else
    //     objects.create(1 , defaultObjSearchNum, CV_32SC4);

    // temp solution
    objects.create(image.rows, image.cols, CV_32SC4);

    scaledImageBuffer.create(image.size(), image.type());

    // TODO: specify max objects size
    for( double factor = 1; ; factor *= scaleFactor )
    {
        cv::Size windowSize(cvRound(NxM.width * factor), cvRound(NxM.height * factor));
        cv::Size scaledImageSize(cvRound( image.cols / factor ), cvRound( image.rows / factor ));
        cv::Size processingRectSize( scaledImageSize.width - NxM.width + 1, scaledImageSize.height - NxM.height + 1 );

        // nothing to do
        if (processingRectSize.width <= 0 || processingRectSize.height <= 0 )
            break;
        // TODO: min max object sizes cheching
        cv::gpu::resize(image, scaledImageBuffer, scaledImageSize, 0, 0, INTER_NEAREST);
        //prepare image for evaluation
        integral.create(cv::Size(scaledImageSize.width + 1, scaledImageSize.height + 1), CV_32SC1);
        cv::gpu::integral(scaledImageBuffer, integral);

        int step = (factor <= 2.) + 1;

        cv::gpu::device::lbp::cascadeClassify(stage_mat, trees_mat, nodes_mat, leaves_mat, subsets_mat, features_mat,
         integral, processingRectSize.width, processingRectSize.height, windowSize.width, windowSize.height, scaleFactor, step, subsetSize, objects, minNeighbors);
    }
    // TODO: reject levels

    return 0;
}

// ============ old fashioned haar cascade ==============================================//
struct cv::gpu::CascadeClassifier_GPU::CascadeClassifierImpl
{
    CascadeClassifierImpl(const string& filename) : lastAllocatedFrameSize(-1, -1)
    {
        ncvSetDebugOutputHandler(NCVDebugOutputHandler);
        ncvSafeCall( load(filename) );
    }


    NCVStatus process(const GpuMat& src, GpuMat& objects, float scaleStep, int minNeighbors,
                      bool findLargestObject, bool visualizeInPlace, NcvSize32u ncvMinSize,
                      /*out*/unsigned int& numDetections)
    {
        calculateMemReqsAndAllocate(src.size());

        NCVMemPtr src_beg;
        src_beg.ptr = (void*)src.ptr<Ncv8u>();
        src_beg.memtype = NCVMemoryTypeDevice;

        NCVMemSegment src_seg;
        src_seg.begin = src_beg;
        src_seg.size  = src.step * src.rows;

        NCVMatrixReuse<Ncv8u> d_src(src_seg, static_cast<int>(devProp.textureAlignment), src.cols, src.rows, static_cast<int>(src.step), true);
        ncvAssertReturn(d_src.isMemReused(), NCV_ALLOCATOR_BAD_REUSE);

        CV_Assert(objects.rows == 1);

        NCVMemPtr objects_beg;
        objects_beg.ptr = (void*)objects.ptr<NcvRect32u>();
        objects_beg.memtype = NCVMemoryTypeDevice;

        NCVMemSegment objects_seg;
        objects_seg.begin = objects_beg;
        objects_seg.size = objects.step * objects.rows;
        NCVVectorReuse<NcvRect32u> d_rects(objects_seg, objects.cols);
        ncvAssertReturn(d_rects.isMemReused(), NCV_ALLOCATOR_BAD_REUSE);

        NcvSize32u roi;
        roi.width = d_src.width();
        roi.height = d_src.height();

        Ncv32u flags = 0;
        flags |= findLargestObject? NCVPipeObjDet_FindLargestObject : 0;
        flags |= visualizeInPlace ? NCVPipeObjDet_VisualizeInPlace  : 0;

        ncvStat = ncvDetectObjectsMultiScale_device(
            d_src, roi, d_rects, numDetections, haar, *h_haarStages,
            *d_haarStages, *d_haarNodes, *d_haarFeatures,
            ncvMinSize,
            minNeighbors,
            scaleStep, 1,
            flags,
            *gpuAllocator, *cpuAllocator, devProp, 0);
        ncvAssertReturnNcvStat(ncvStat);
        ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);

        return NCV_SUCCESS;
    }


    NcvSize32u getClassifierSize() const  { return haar.ClassifierSize; }
    cv::Size getClassifierCvSize() const { return cv::Size(haar.ClassifierSize.width, haar.ClassifierSize.height); }


private:


    static void NCVDebugOutputHandler(const std::string &msg) { CV_Error(CV_GpuApiCallError, msg.c_str()); }


    NCVStatus load(const string& classifierFile)
    {
        int devId = cv::gpu::getDevice();
        ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), NCV_CUDA_ERROR);

        // Load the classifier from file (assuming its size is about 1 mb) using a simple allocator
        gpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeDevice, static_cast<int>(devProp.textureAlignment));
        cpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeHostPinned, static_cast<int>(devProp.textureAlignment));

        ncvAssertPrintReturn(gpuCascadeAllocator->isInitialized(), "Error creating cascade GPU allocator", NCV_CUDA_ERROR);
        ncvAssertPrintReturn(cpuCascadeAllocator->isInitialized(), "Error creating cascade CPU allocator", NCV_CUDA_ERROR);

        Ncv32u haarNumStages, haarNumNodes, haarNumFeatures;
        ncvStat = ncvHaarGetClassifierSize(classifierFile, haarNumStages, haarNumNodes, haarNumFeatures);
        ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error reading classifier size (check the file)", NCV_FILE_ERROR);

        h_haarStages   = new NCVVectorAlloc<HaarStage64>(*cpuCascadeAllocator, haarNumStages);
        h_haarNodes    = new NCVVectorAlloc<HaarClassifierNode128>(*cpuCascadeAllocator, haarNumNodes);
        h_haarFeatures = new NCVVectorAlloc<HaarFeature64>(*cpuCascadeAllocator, haarNumFeatures);

        ncvAssertPrintReturn(h_haarStages->isMemAllocated(), "Error in cascade CPU allocator", NCV_CUDA_ERROR);
        ncvAssertPrintReturn(h_haarNodes->isMemAllocated(), "Error in cascade CPU allocator", NCV_CUDA_ERROR);
        ncvAssertPrintReturn(h_haarFeatures->isMemAllocated(), "Error in cascade CPU allocator", NCV_CUDA_ERROR);

        ncvStat = ncvHaarLoadFromFile_host(classifierFile, haar, *h_haarStages, *h_haarNodes, *h_haarFeatures);
        ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error loading classifier", NCV_FILE_ERROR);

        d_haarStages   = new NCVVectorAlloc<HaarStage64>(*gpuCascadeAllocator, haarNumStages);
        d_haarNodes    = new NCVVectorAlloc<HaarClassifierNode128>(*gpuCascadeAllocator, haarNumNodes);
        d_haarFeatures = new NCVVectorAlloc<HaarFeature64>(*gpuCascadeAllocator, haarNumFeatures);

        ncvAssertPrintReturn(d_haarStages->isMemAllocated(), "Error in cascade GPU allocator", NCV_CUDA_ERROR);
        ncvAssertPrintReturn(d_haarNodes->isMemAllocated(), "Error in cascade GPU allocator", NCV_CUDA_ERROR);
        ncvAssertPrintReturn(d_haarFeatures->isMemAllocated(), "Error in cascade GPU allocator", NCV_CUDA_ERROR);

        ncvStat = h_haarStages->copySolid(*d_haarStages, 0);
        ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", NCV_CUDA_ERROR);
        ncvStat = h_haarNodes->copySolid(*d_haarNodes, 0);
        ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", NCV_CUDA_ERROR);
        ncvStat = h_haarFeatures->copySolid(*d_haarFeatures, 0);
        ncvAssertPrintReturn(ncvStat == NCV_SUCCESS, "Error copying cascade to GPU", NCV_CUDA_ERROR);

        return NCV_SUCCESS;
    }


    NCVStatus calculateMemReqsAndAllocate(const Size& frameSize)
    {
        if (lastAllocatedFrameSize == frameSize)
        {
            return NCV_SUCCESS;
        }

        // Calculate memory requirements and create real allocators
        NCVMemStackAllocator gpuCounter(static_cast<int>(devProp.textureAlignment));
        NCVMemStackAllocator cpuCounter(static_cast<int>(devProp.textureAlignment));

        ncvAssertPrintReturn(gpuCounter.isInitialized(), "Error creating GPU memory counter", NCV_CUDA_ERROR);
        ncvAssertPrintReturn(cpuCounter.isInitialized(), "Error creating CPU memory counter", NCV_CUDA_ERROR);

        NCVMatrixAlloc<Ncv8u> d_src(gpuCounter, frameSize.width, frameSize.height);
        NCVMatrixAlloc<Ncv8u> h_src(cpuCounter, frameSize.width, frameSize.height);

        ncvAssertReturn(d_src.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
        ncvAssertReturn(h_src.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);

        NCVVectorAlloc<NcvRect32u> d_rects(gpuCounter, 100);
        ncvAssertReturn(d_rects.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);

        NcvSize32u roi;
        roi.width = d_src.width();
        roi.height = d_src.height();
        Ncv32u numDetections;
        ncvStat = ncvDetectObjectsMultiScale_device(d_src, roi, d_rects, numDetections, haar, *h_haarStages,
            *d_haarStages, *d_haarNodes, *d_haarFeatures, haar.ClassifierSize, 4, 1.2f, 1, 0, gpuCounter, cpuCounter, devProp, 0);

        ncvAssertReturnNcvStat(ncvStat);
        ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);

        gpuAllocator = new NCVMemStackAllocator(NCVMemoryTypeDevice, gpuCounter.maxSize(), static_cast<int>(devProp.textureAlignment));
        cpuAllocator = new NCVMemStackAllocator(NCVMemoryTypeHostPinned, cpuCounter.maxSize(), static_cast<int>(devProp.textureAlignment));

        ncvAssertPrintReturn(gpuAllocator->isInitialized(), "Error creating GPU memory allocator", NCV_CUDA_ERROR);
        ncvAssertPrintReturn(cpuAllocator->isInitialized(), "Error creating CPU memory allocator", NCV_CUDA_ERROR);
        return NCV_SUCCESS;
    }


    cudaDeviceProp devProp;
    NCVStatus ncvStat;

    Ptr<NCVMemNativeAllocator> gpuCascadeAllocator;
    Ptr<NCVMemNativeAllocator> cpuCascadeAllocator;

    Ptr<NCVVectorAlloc<HaarStage64> >           h_haarStages;
    Ptr<NCVVectorAlloc<HaarClassifierNode128> > h_haarNodes;
    Ptr<NCVVectorAlloc<HaarFeature64> >         h_haarFeatures;

    HaarClassifierCascadeDescriptor haar;

    Ptr<NCVVectorAlloc<HaarStage64> >           d_haarStages;
    Ptr<NCVVectorAlloc<HaarClassifierNode128> > d_haarNodes;
    Ptr<NCVVectorAlloc<HaarFeature64> >         d_haarFeatures;

    Size lastAllocatedFrameSize;

    Ptr<NCVMemStackAllocator> gpuAllocator;
    Ptr<NCVMemStackAllocator> cpuAllocator;
};


cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU() : findLargestObject(false), visualizeInPlace(false), impl(0) {}
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const string& filename) : findLargestObject(false), visualizeInPlace(false), impl(0) { load(filename); }
cv::gpu::CascadeClassifier_GPU::~CascadeClassifier_GPU() { release(); }
bool cv::gpu::CascadeClassifier_GPU::empty() const { return impl == 0; }
void cv::gpu::CascadeClassifier_GPU::release() { if (impl) { delete impl; impl = 0; } }


bool cv::gpu::CascadeClassifier_GPU::load(const string& filename)
{
    release();
    impl = new CascadeClassifierImpl(filename);
    return !this->empty();
}


Size cv::gpu::CascadeClassifier_GPU::getClassifierSize() const
{
    return this->empty() ? Size() : impl->getClassifierCvSize();
}


int cv::gpu::CascadeClassifier_GPU::detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor, int minNeighbors, Size minSize)
{
    CV_Assert( scaleFactor > 1 && image.depth() == CV_8U);
    CV_Assert( !this->empty());

    const int defaultObjSearchNum = 100;
    if (objectsBuf.empty())
    {
        objectsBuf.create(1, defaultObjSearchNum, DataType<Rect>::type);
    }

    NcvSize32u ncvMinSize = impl->getClassifierSize();

    if (ncvMinSize.width < (unsigned)minSize.width && ncvMinSize.height < (unsigned)minSize.height)
    {
        ncvMinSize.width = minSize.width;
        ncvMinSize.height = minSize.height;
    }

    unsigned int numDetections;
    ncvSafeCall( impl->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, ncvMinSize, numDetections) );

    return numDetections;
}


struct RectConvert
{
    Rect operator()(const NcvRect32u& nr) const { return Rect(nr.x, nr.y, nr.width, nr.height); }
    NcvRect32u operator()(const Rect& nr) const
    {
        NcvRect32u rect;
        rect.x = nr.x;
        rect.y = nr.y;
        rect.width = nr.width;
        rect.height = nr.height;
        return rect;
    }
};


void groupRectangles(std::vector<NcvRect32u> &hypotheses, int groupThreshold, double eps, std::vector<Ncv32u> *weights)
{
    vector<Rect> rects(hypotheses.size());
    std::transform(hypotheses.begin(), hypotheses.end(), rects.begin(), RectConvert());

    if (weights)
    {
        vector<int> weights_int;
        weights_int.assign(weights->begin(), weights->end());
        cv::groupRectangles(rects, weights_int, groupThreshold, eps);
    }
    else
    {
        cv::groupRectangles(rects, groupThreshold, eps);
    }
    std::transform(rects.begin(), rects.end(), hypotheses.begin(), RectConvert());
    hypotheses.resize(rects.size());
}

NCVStatus loadFromXML(const std::string &filename,
                      HaarClassifierCascadeDescriptor &haar,
                      std::vector<HaarStage64> &haarStages,
                      std::vector<HaarClassifierNode128> &haarClassifierNodes,
                      std::vector<HaarFeature64> &haarFeatures)
{
    NCVStatus ncvStat;

    haar.NumStages = 0;
    haar.NumClassifierRootNodes = 0;
    haar.NumClassifierTotalNodes = 0;
    haar.NumFeatures = 0;
    haar.ClassifierSize.width = 0;
    haar.ClassifierSize.height = 0;
    haar.bHasStumpsOnly = true;
    haar.bNeedsTiltedII = false;
    Ncv32u curMaxTreeDepth;

    std::vector<char> xmlFileCont;

    std::vector<HaarClassifierNode128> h_TmpClassifierNotRootNodes;
    haarStages.resize(0);
    haarClassifierNodes.resize(0);
    haarFeatures.resize(0);

    Ptr<CvHaarClassifierCascade> oldCascade = (CvHaarClassifierCascade*)cvLoad(filename.c_str(), 0, 0, 0);
    if (oldCascade.empty())
    {
        return NCV_HAAR_XML_LOADING_EXCEPTION;
    }

    haar.ClassifierSize.width = oldCascade->orig_window_size.width;
    haar.ClassifierSize.height = oldCascade->orig_window_size.height;

    int stagesCound = oldCascade->count;
    for(int s = 0; s < stagesCound; ++s) // by stages
    {
        HaarStage64 curStage;
        curStage.setStartClassifierRootNodeOffset(static_cast<Ncv32u>(haarClassifierNodes.size()));

        curStage.setStageThreshold(oldCascade->stage_classifier[s].threshold);

        int treesCount = oldCascade->stage_classifier[s].count;
        for(int t = 0; t < treesCount; ++t) // by trees
        {
            Ncv32u nodeId = 0;
            CvHaarClassifier* tree = &oldCascade->stage_classifier[s].classifier[t];

            int nodesCount = tree->count;
            for(int n = 0; n < nodesCount; ++n)  //by features
            {
                CvHaarFeature* feature = &tree->haar_feature[n];

                HaarClassifierNode128 curNode;
                curNode.setThreshold(tree->threshold[n]);

                NcvBool bIsLeftNodeLeaf = false;
                NcvBool bIsRightNodeLeaf = false;

                HaarClassifierNodeDescriptor32 nodeLeft;
                if ( tree->left[n] <= 0 )
                {
                    Ncv32f leftVal = tree->alpha[-tree->left[n]];
                    ncvStat = nodeLeft.create(leftVal);
                    ncvAssertReturn(ncvStat == NCV_SUCCESS, ncvStat);
                    bIsLeftNodeLeaf = true;
                }
                else
                {
                    Ncv32u leftNodeOffset = tree->left[n];
                    nodeLeft.create((Ncv32u)(h_TmpClassifierNotRootNodes.size() + leftNodeOffset - 1));
                    haar.bHasStumpsOnly = false;
                }
                curNode.setLeftNodeDesc(nodeLeft);

                HaarClassifierNodeDescriptor32 nodeRight;
                if ( tree->right[n] <= 0 )
                {
                    Ncv32f rightVal = tree->alpha[-tree->right[n]];
                    ncvStat = nodeRight.create(rightVal);
                    ncvAssertReturn(ncvStat == NCV_SUCCESS, ncvStat);
                    bIsRightNodeLeaf = true;
                }
                else
                {
                    Ncv32u rightNodeOffset = tree->right[n];
                    nodeRight.create((Ncv32u)(h_TmpClassifierNotRootNodes.size() + rightNodeOffset - 1));
                    haar.bHasStumpsOnly = false;
                }
                curNode.setRightNodeDesc(nodeRight);

                Ncv32u tiltedVal = feature->tilted;
                haar.bNeedsTiltedII = (tiltedVal != 0);

                Ncv32u featureId = 0;
                for(int l = 0; l < CV_HAAR_FEATURE_MAX; ++l) //by rects
                {
                    Ncv32u rectX = feature->rect[l].r.x;
                    Ncv32u rectY = feature->rect[l].r.y;
                    Ncv32u rectWidth = feature->rect[l].r.width;
                    Ncv32u rectHeight = feature->rect[l].r.height;

                    Ncv32f rectWeight = feature->rect[l].weight;

                    if (rectWeight == 0/* && rectX == 0 &&rectY == 0 && rectWidth == 0 && rectHeight == 0*/)
                        break;

                    HaarFeature64 curFeature;
                    ncvStat = curFeature.setRect(rectX, rectY, rectWidth, rectHeight, haar.ClassifierSize.width, haar.ClassifierSize.height);
                    curFeature.setWeight(rectWeight);
                    ncvAssertReturn(NCV_SUCCESS == ncvStat, ncvStat);
                    haarFeatures.push_back(curFeature);

                    featureId++;
                }

                HaarFeatureDescriptor32 tmpFeatureDesc;
                ncvStat = tmpFeatureDesc.create(haar.bNeedsTiltedII, bIsLeftNodeLeaf, bIsRightNodeLeaf,
                    featureId, static_cast<Ncv32u>(haarFeatures.size()) - featureId);
                ncvAssertReturn(NCV_SUCCESS == ncvStat, ncvStat);
                curNode.setFeatureDesc(tmpFeatureDesc);

                if (!nodeId)
                {
                    //root node
                    haarClassifierNodes.push_back(curNode);
                    curMaxTreeDepth = 1;
                }
                else
                {
                    //other node
                    h_TmpClassifierNotRootNodes.push_back(curNode);
                    curMaxTreeDepth++;
                }

                nodeId++;
            }
        }

        curStage.setNumClassifierRootNodes(treesCount);
        haarStages.push_back(curStage);
    }

    //fill in cascade stats
    haar.NumStages = static_cast<Ncv32u>(haarStages.size());
    haar.NumClassifierRootNodes = static_cast<Ncv32u>(haarClassifierNodes.size());
    haar.NumClassifierTotalNodes = static_cast<Ncv32u>(haar.NumClassifierRootNodes + h_TmpClassifierNotRootNodes.size());
    haar.NumFeatures = static_cast<Ncv32u>(haarFeatures.size());

    //merge root and leaf nodes in one classifiers array
    Ncv32u offsetRoot = static_cast<Ncv32u>(haarClassifierNodes.size());
    for (Ncv32u i=0; i<haarClassifierNodes.size(); i++)
    {
        HaarFeatureDescriptor32 featureDesc = haarClassifierNodes[i].getFeatureDesc();

        HaarClassifierNodeDescriptor32 nodeLeft = haarClassifierNodes[i].getLeftNodeDesc();
        if (!featureDesc.isLeftNodeLeaf())
        {
            Ncv32u newOffset = nodeLeft.getNextNodeOffset() + offsetRoot;
            nodeLeft.create(newOffset);
        }
        haarClassifierNodes[i].setLeftNodeDesc(nodeLeft);

        HaarClassifierNodeDescriptor32 nodeRight = haarClassifierNodes[i].getRightNodeDesc();
        if (!featureDesc.isRightNodeLeaf())
        {
            Ncv32u newOffset = nodeRight.getNextNodeOffset() + offsetRoot;
            nodeRight.create(newOffset);
        }
        haarClassifierNodes[i].setRightNodeDesc(nodeRight);
    }

    for (Ncv32u i=0; i<h_TmpClassifierNotRootNodes.size(); i++)
    {
        HaarFeatureDescriptor32 featureDesc = h_TmpClassifierNotRootNodes[i].getFeatureDesc();

        HaarClassifierNodeDescriptor32 nodeLeft = h_TmpClassifierNotRootNodes[i].getLeftNodeDesc();
        if (!featureDesc.isLeftNodeLeaf())
        {
            Ncv32u newOffset = nodeLeft.getNextNodeOffset() + offsetRoot;
            nodeLeft.create(newOffset);
        }
        h_TmpClassifierNotRootNodes[i].setLeftNodeDesc(nodeLeft);

        HaarClassifierNodeDescriptor32 nodeRight = h_TmpClassifierNotRootNodes[i].getRightNodeDesc();
        if (!featureDesc.isRightNodeLeaf())
        {
            Ncv32u newOffset = nodeRight.getNextNodeOffset() + offsetRoot;
            nodeRight.create(newOffset);
        }
        h_TmpClassifierNotRootNodes[i].setRightNodeDesc(nodeRight);

        haarClassifierNodes.push_back(h_TmpClassifierNotRootNodes[i]);
    }

    return NCV_SUCCESS;
}

#endif /* HAVE_CUDA */