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;

#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(cv::Size /*frameSize*/){ 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::allocateBuffers(cv::Size /*frame*/)       { throw_nogpu();}

int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const cv::gpu::GpuMat& /*image*/, cv::gpu::GpuMat& /*objectsBuf*/,
double /*scaleFactor*/, int /*minNeighbors*/, cv::Size /*maxObjectSize*/){ throw_nogpu(); return 0;}

#else

cv::gpu::CascadeClassifier_GPU_LBP::CascadeClassifier_GPU_LBP(cv::Size detectionFrameSize) { allocateBuffers(detectionFrameSize); }
cv::gpu::CascadeClassifier_GPU_LBP::~CascadeClassifier_GPU_LBP(){}

void cv::gpu::CascadeClassifier_GPU_LBP::allocateBuffers(cv::Size frame)
{
    if (frame == cv::Size())
        return;

    if (resuzeBuffer.empty() || frame.width > resuzeBuffer.cols || frame.height > resuzeBuffer.rows)
    {
        resuzeBuffer.create(frame, CV_8UC1);

        integral.create(frame.height + 1, frame.width + 1, CV_32SC1);
        NcvSize32u roiSize;
        roiSize.width = frame.width;
        roiSize.height = frame.height;

        cudaDeviceProp prop;
        cudaSafeCall( cudaGetDeviceProperties(&prop, cv::gpu::getDevice()) );

        Ncv32u bufSize;
        ncvSafeCall( nppiStIntegralGetSize_8u32u(roiSize, &bufSize, prop) );
        integralBuffer.create(1, bufSize, CV_8UC1);
    }

    candidates.create(1 , frame.width >> 1, CV_32SC4);
}

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

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

struct Stage
{
    int    first;
    int    ntrees;
    float  threshold;
};

// currently only stump based boost classifiers are supported
bool CascadeClassifier_GPU_LBP::read(const FileNode &root)
{
    const char *GPU_CC_STAGE_TYPE       = "stageType";
    const char *GPU_CC_FEATURE_TYPE     = "featureType";
    const char *GPU_CC_BOOST            = "BOOST";
    const char *GPU_CC_LBP              = "LBP";
    const char *GPU_CC_MAX_CAT_COUNT    = "maxCatCount";
    const char *GPU_CC_HEIGHT           = "height";
    const char *GPU_CC_WIDTH            = "width";
    const char *GPU_CC_STAGE_PARAMS     = "stageParams";
    const char *GPU_CC_MAX_DEPTH        = "maxDepth";
    const char *GPU_CC_FEATURE_PARAMS   = "featureParams";
    const char *GPU_CC_STAGES           = "stages";
    const char *GPU_CC_STAGE_THRESHOLD  = "stageThreshold";
    const float GPU_THRESHOLD_EPS       = 1e-5f;
    const char *GPU_CC_WEAK_CLASSIFIERS = "weakClassifiers";
    const char *GPU_CC_INTERNAL_NODES   = "internalNodes";
    const char *GPU_CC_LEAF_VALUES      = "leafValues";
    const char *GPU_CC_FEATURES         = "features";
    const char *GPU_CC_RECT             = "rect";

    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);

    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<int> 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);// (int, int, float)

        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) * 3);
            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; )
            {
                cl_nodes.push_back((int)*(iIt++));
                cl_nodes.push_back((int)*(iIt++));
                cl_nodes.push_back((int)*(iIt++));

                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<uchar> features;
    features.reserve(fn.size() * 4);
    FileNodeIterator f_it = fn.begin(), f_end = fn.end();
    for (; f_it != f_end; ++f_it)
    {
        FileNode rect = (*f_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.upload(cv::Mat(1, stages.size() * sizeof(Stage), CV_8UC1, (uchar*)&(stages[0]) ));
    trees_mat.upload(cv::Mat(cl_trees).reshape(1,1));
    nodes_mat.upload(cv::Mat(cl_nodes).reshape(1,1));
    leaves_mat.upload(cv::Mat(cl_leaves).reshape(1,1));
    subsets_mat.upload(cv::Mat(subsets).reshape(1,1));
    features_mat.upload(cv::Mat(features).reshape(4,1));

    return true;
}

namespace cv { namespace gpu { namespace device
{
    namespace lbp
    {
        void classifyStumpFixed(const DevMem2Di& integral,
                                const int integralPitch,
                                const DevMem2Db& mstages,
                                const int nstages,
                                const DevMem2Di& mnodes,
                                const DevMem2Df& mleaves,
                                const DevMem2Di& msubsets,
                                const DevMem2Db& mfeatures,
                                const int workWidth,
                                const int workHeight,
                                const int clWidth,
                                const int clHeight,
                                float scale,
                                int step,
                                int subsetSize,
                                DevMem2D_<int4> objects,
                                unsigned int* classified);

        void connectedConmonents(DevMem2D_<int4>  candidates, int ncandidates, DevMem2D_<int4> objects,int groupThreshold, float grouping_eps, unsigned int* nclasses);
        void bindIntegral(DevMem2Di integral);
        void unbindIntegral();
    }
}}}

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

    const int defaultObjSearchNum = 100;
    const float grouping_eps = 0.2f;

    if( !objects.empty() && objects.depth() == CV_32S)
        objects.reshape(4, 1);
    else
        objects.create(1 , image.cols >> 4, CV_32SC4);

    // GpuMat candidates(1 , defaultObjSearchNum, CV_32SC4);
    // used for debug
    // candidates.setTo(cv::Scalar::all(0));
    // objects.setTo(cv::Scalar::all(0));
    if (maxObjectSize == cv::Size())
        maxObjectSize = image.size();

    allocateBuffers(image.size());

    unsigned int classified = 0;
    GpuMat dclassified(1, 1, CV_32S);
    cudaSafeCall( cudaMemcpy(dclassified.ptr(), &classified, sizeof(int), cudaMemcpyHostToDevice) );

    //int step = 2;
    // cv::gpu::device::lbp::bindIntegral(integral);

    Size scaledImageSize(image.cols, image.rows);
    Size processingRectSize( scaledImageSize.width - NxM.width + 1, scaledImageSize.height - NxM.height + 1 );
    Size windowSize(NxM.width, NxM.height);

    float factor = 1;

    for (;;)
    {
        if (processingRectSize.width <= 0 || processingRectSize.height <= 0 )
            break;

        if( windowSize.width > maxObjectSize.width || windowSize.height > maxObjectSize.height )
            break;

        // if( windowSize.width < minObjectSize.width || windowSize.height < minObjectSize.height )
        //     continue;

        GpuMat scaledImg      = resuzeBuffer(cv::Rect(0, 0, scaledImageSize.width, scaledImageSize.height));
        GpuMat scaledIntegral = integral(cv::Rect(0, 0, scaledImageSize.width + 1, scaledImageSize.height + 1));
        GpuMat currBuff = integralBuffer;

        gpu::resize(image, scaledImg, scaledImageSize, 0, 0, CV_INTER_LINEAR);
        gpu::integralBuffered(scaledImg, scaledIntegral, currBuff);

        int step = factor <= 2.f ? 2 : 1;

        device::lbp::classifyStumpFixed(integral, integral.step1(), stage_mat, stage_mat.cols / sizeof(Stage), nodes_mat, leaves_mat, subsets_mat, features_mat,
            processingRectSize.width, processingRectSize.height, windowSize.width, windowSize.height, factor, step, subsetSize, candidates, dclassified.ptr<unsigned int>());

        factor *= scaleFactor;
        windowSize = cv::Size(cvRound(NxM.width * factor), cvRound(NxM.height * factor));
        scaledImageSize = cv::Size(cvRound( image.cols / factor ), cvRound( image.rows / factor ));
        processingRectSize = cv::Size(scaledImageSize.width - NxM.width + 1, scaledImageSize.height - NxM.height + 1 );
    }

    // cv::gpu::device::lbp::unbindIntegral();
    if (groupThreshold <= 0  || objects.empty())
        return 0;

    cudaSafeCall( cudaMemcpy(&classified, dclassified.ptr(), sizeof(int), cudaMemcpyDeviceToHost) );
    device::lbp::connectedConmonents(candidates, classified, objects, groupThreshold, grouping_eps, dclassified.ptr<unsigned int>());

    cudaSafeCall( cudaMemcpy(&classified, dclassified.ptr(), sizeof(int), cudaMemcpyDeviceToHost) );
    cudaSafeCall( cudaDeviceSynchronize() );

    return classified;
}

// ============ 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 */