modules/cudaimgproc/src/generalized

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This source file includes following definitions.
createGeneralizedHoughBallard
createGeneralizedHoughGuil
calcEdges
setTemplateImpl
setTemplateImpl
detectImpl
detectImpl
buildEdgePointList
filterMinDist
convertTo
setTemplate
setTemplate
detect
detect
setCannyLowThresh
getCannyLowThresh
setCannyHighThresh
getCannyHighThresh
setMinDist
getMinDist
setDp
getDp
setMaxBufferSize
getMaxBufferSize
setLevels
getLevels
setVotesThreshold
getVotesThreshold
processTempl
processImage
calcHist
findPosInHist
createGeneralizedHoughBallard
setTemplate
setTemplate
detect
detect
setCannyLowThresh
getCannyLowThresh
setCannyHighThresh
getCannyHighThresh
setMinDist
getMinDist
setDp
getDp
setMaxBufferSize
getMaxBufferSize
setXi
getXi
setLevels
getLevels
setAngleEpsilon
getAngleEpsilon
setMinAngle
getMinAngle
setMaxAngle
getMaxAngle
setAngleStep
getAngleStep
setAngleThresh
getAngleThresh
setMinScale
getMinScale
setMaxScale
getMaxScale
setScaleStep
getScaleStep
setScaleThresh
getScaleThresh
setPosThresh
getPosThresh
toRad
clampAngle
angleEq
processTempl
processImage
create
buildFeatureList
calcOrientation
calcScale
calcPosition
createGeneralizedHoughGuil
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#include "precomp.hpp"

using namespace cv;
using namespace cv::cuda;

#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) || !defined(HAVE_OPENCV_CUDAARITHM)

Ptr<GeneralizedHoughBallard> cv::cuda::createGeneralizedHoughBallard() { throw_no_cuda(); return Ptr<GeneralizedHoughBallard>(); }

Ptr<GeneralizedHoughGuil> cv::cuda::createGeneralizedHoughGuil() { throw_no_cuda(); return Ptr<GeneralizedHoughGuil>(); }

#else /* !defined (HAVE_CUDA) */

namespace cv { namespace cuda { namespace device
{
    namespace ght
    {
        template <typename T>
        int buildEdgePointList_gpu(PtrStepSzb edges, PtrStepSzb dx, PtrStepSzb dy, unsigned int* coordList, float* thetaList);
        void buildRTable_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
                             PtrStepSz<short2> r_table, int* r_sizes,
                             short2 templCenter, int levels);

        void Ballard_Pos_calcHist_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
                                      PtrStepSz<short2> r_table, const int* r_sizes,
                                      PtrStepSzi hist,
                                      float dp, int levels);
        int Ballard_Pos_findPosInHist_gpu(PtrStepSzi hist, float4* out, int3* votes, int maxSize, float dp, int threshold);

        void Guil_Full_setTemplFeatures(PtrStepb p1_pos, PtrStepb p1_theta, PtrStepb p2_pos, PtrStepb d12, PtrStepb r1, PtrStepb r2);
        void Guil_Full_setImageFeatures(PtrStepb p1_pos, PtrStepb p1_theta, PtrStepb p2_pos, PtrStepb d12, PtrStepb r1, PtrStepb r2);
        void Guil_Full_buildTemplFeatureList_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
                                                 int* sizes, int maxSize,
                                                 float xi, float angleEpsilon, int levels,
                                                 float2 center, float maxDist);
        void Guil_Full_buildImageFeatureList_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
                                                 int* sizes, int maxSize,
                                                 float xi, float angleEpsilon, int levels,
                                                 float2 center, float maxDist);
        void Guil_Full_calcOHist_gpu(const int* templSizes, const int* imageSizes, int* OHist,
                                     float minAngle, float maxAngle, float angleStep, int angleRange,
                                     int levels, int tMaxSize);
        void Guil_Full_calcSHist_gpu(const int* templSizes, const int* imageSizes, int* SHist,
                                     float angle, float angleEpsilon,
                                     float minScale, float maxScale, float iScaleStep, int scaleRange,
                                     int levels, int tMaxSize);
        void Guil_Full_calcPHist_gpu(const int* templSizes, const int* imageSizes, PtrStepSzi PHist,
                                     float angle, float angleEpsilon, float scale,
                                     float dp,
                                     int levels, int tMaxSize);
        int Guil_Full_findPosInHist_gpu(PtrStepSzi hist, float4* out, int3* votes, int curSize, int maxSize,
                                        float angle, int angleVotes, float scale, int scaleVotes,
                                        float dp, int threshold);
    }
}}}

// common

namespace
{
    class GeneralizedHoughBase
    {
    protected:
        GeneralizedHoughBase();
        virtual ~GeneralizedHoughBase() {}

        void setTemplateImpl(InputArray templ, Point templCenter);
        void setTemplateImpl(InputArray edges, InputArray dx, InputArray dy, Point templCenter);

        void detectImpl(InputArray image, OutputArray positions, OutputArray votes);
        void detectImpl(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes);

        void buildEdgePointList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy);

        virtual void processTempl() = 0;
        virtual void processImage() = 0;

        int cannyLowThresh_;
        int cannyHighThresh_;
        double minDist_;
        double dp_;
        int maxBufferSize_;

        Size templSize_;
        Point templCenter_;
        GpuMat templEdges_;
        GpuMat templDx_;
        GpuMat templDy_;

        Size imageSize_;
        GpuMat imageEdges_;
        GpuMat imageDx_;
        GpuMat imageDy_;

        GpuMat edgePointList_;

        GpuMat outBuf_;
        int posCount_;

    private:
#ifdef HAVE_OPENCV_CUDAFILTERS
        void calcEdges(InputArray src, GpuMat& edges, GpuMat& dx, GpuMat& dy);
#endif

        void filterMinDist();
        void convertTo(OutputArray positions, OutputArray votes);

#ifdef HAVE_OPENCV_CUDAFILTERS
        Ptr<cuda::CannyEdgeDetector> canny_;
        Ptr<cuda::Filter> filterDx_;
        Ptr<cuda::Filter> filterDy_;
#endif

        std::vector<float4> oldPosBuf_;
        std::vector<int3> oldVoteBuf_;
        std::vector<float4> newPosBuf_;
        std::vector<int3> newVoteBuf_;
        std::vector<int> indexies_;
    };

    GeneralizedHoughBase::GeneralizedHoughBase()
    {
        cannyLowThresh_ = 50;
        cannyHighThresh_ = 100;
        minDist_ = 1.0;
        dp_ = 1.0;

        maxBufferSize_ = 10000;

#ifdef HAVE_OPENCV_CUDAFILTERS
        canny_ = cuda::createCannyEdgeDetector(cannyLowThresh_, cannyHighThresh_);
        filterDx_ = cuda::createSobelFilter(CV_8UC1, CV_32S, 1, 0);
        filterDy_ = cuda::createSobelFilter(CV_8UC1, CV_32S, 0, 1);
#endif
    }

#ifdef HAVE_OPENCV_CUDAFILTERS
    void GeneralizedHoughBase::calcEdges(InputArray _src, GpuMat& edges, GpuMat& dx, GpuMat& dy)
    {
        GpuMat src = _src.getGpuMat();

        CV_Assert( src.type() == CV_8UC1 );
        CV_Assert( cannyLowThresh_ > 0 && cannyLowThresh_ < cannyHighThresh_ );

        ensureSizeIsEnough(src.size(), CV_32SC1, dx);
        ensureSizeIsEnough(src.size(), CV_32SC1, dy);

        filterDx_->apply(src, dx);
        filterDy_->apply(src, dy);

        ensureSizeIsEnough(src.size(), CV_8UC1, edges);

        canny_->setLowThreshold(cannyLowThresh_);
        canny_->setHighThreshold(cannyHighThresh_);
        canny_->detect(dx, dy, edges);
    }
#endif

    void GeneralizedHoughBase::setTemplateImpl(InputArray templ, Point templCenter)
    {
#ifndef HAVE_OPENCV_CUDAFILTERS
        (void) templ;
        (void) templCenter;
        throw_no_cuda();
#else
        calcEdges(templ, templEdges_, templDx_, templDy_);

        if (templCenter == Point(-1, -1))
            templCenter = Point(templEdges_.cols / 2, templEdges_.rows / 2);

        templSize_ = templEdges_.size();
        templCenter_ = templCenter;

        processTempl();
#endif
    }

    void GeneralizedHoughBase::setTemplateImpl(InputArray edges, InputArray dx, InputArray dy, Point templCenter)
    {
        edges.getGpuMat().copyTo(templEdges_);
        dx.getGpuMat().copyTo(templDx_);
        dy.getGpuMat().copyTo(templDy_);

        CV_Assert( templEdges_.type() == CV_8UC1 );
        CV_Assert( templDx_.type() == CV_32FC1 && templDx_.size() == templEdges_.size() );
        CV_Assert( templDy_.type() == templDx_.type() && templDy_.size() == templEdges_.size() );

        if (templCenter == Point(-1, -1))
            templCenter = Point(templEdges_.cols / 2, templEdges_.rows / 2);

        templSize_ = templEdges_.size();
        templCenter_ = templCenter;

        processTempl();
    }

    void GeneralizedHoughBase::detectImpl(InputArray image, OutputArray positions, OutputArray votes)
    {
#ifndef HAVE_OPENCV_CUDAFILTERS
        (void) image;
        (void) positions;
        (void) votes;
        throw_no_cuda();
#else
        calcEdges(image, imageEdges_, imageDx_, imageDy_);

        imageSize_ = imageEdges_.size();

        posCount_ = 0;

        processImage();

        if (posCount_ == 0)
        {
            positions.release();
            if (votes.needed())
                votes.release();
        }
        else
        {
            if (minDist_ > 1)
                filterMinDist();
            convertTo(positions, votes);
        }
#endif
    }

    void GeneralizedHoughBase::detectImpl(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes)
    {
        edges.getGpuMat().copyTo(imageEdges_);
        dx.getGpuMat().copyTo(imageDx_);
        dy.getGpuMat().copyTo(imageDy_);

        CV_Assert( imageEdges_.type() == CV_8UC1 );
        CV_Assert( imageDx_.type() == CV_32FC1 && imageDx_.size() == imageEdges_.size() );
        CV_Assert( imageDy_.type() == imageDx_.type() && imageDy_.size() == imageEdges_.size() );

        imageSize_ = imageEdges_.size();

        posCount_ = 0;

        processImage();

        if (posCount_ == 0)
        {
            positions.release();
            if (votes.needed())
                votes.release();
        }
        else
        {
            if (minDist_ > 1)
                filterMinDist();
            convertTo(positions, votes);
        }
    }

    void GeneralizedHoughBase::buildEdgePointList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy)
    {
        using namespace cv::cuda::device::ght;

        typedef int (*func_t)(PtrStepSzb edges, PtrStepSzb dx, PtrStepSzb dy, unsigned int* coordList, float* thetaList);
        static const func_t funcs[] =
        {
            0,
            0,
            0,
            buildEdgePointList_gpu<short>,
            buildEdgePointList_gpu<int>,
            buildEdgePointList_gpu<float>,
            0
        };

        CV_Assert( edges.type() == CV_8UC1 );
        CV_Assert( dx.size() == edges.size() );
        CV_Assert( dy.type() == dx.type() && dy.size() == edges.size() );

        const func_t func = funcs[dx.depth()];
        CV_Assert( func != 0 );

        edgePointList_.cols = (int) (edgePointList_.step / sizeof(int));
        ensureSizeIsEnough(2, edges.size().area(), CV_32SC1, edgePointList_);

        edgePointList_.cols = func(edges, dx, dy, edgePointList_.ptr<unsigned int>(0), edgePointList_.ptr<float>(1));
    }

    struct IndexCmp
    {
        const int3* aux;

        explicit IndexCmp(const int3* _aux) : aux(_aux) {}

        bool operator ()(int l1, int l2) const
        {
            return aux[l1].x > aux[l2].x;
        }
    };

    void GeneralizedHoughBase::filterMinDist()
    {
        oldPosBuf_.resize(posCount_);
        oldVoteBuf_.resize(posCount_);

        cudaSafeCall( cudaMemcpy(&oldPosBuf_[0], outBuf_.ptr(0), posCount_ * sizeof(float4), cudaMemcpyDeviceToHost) );
        cudaSafeCall( cudaMemcpy(&oldVoteBuf_[0], outBuf_.ptr(1), posCount_ * sizeof(int3), cudaMemcpyDeviceToHost) );

        indexies_.resize(posCount_);
        for (int i = 0; i < posCount_; ++i)
            indexies_[i] = i;
        std::sort(indexies_.begin(), indexies_.end(), IndexCmp(&oldVoteBuf_[0]));

        newPosBuf_.clear();
        newVoteBuf_.clear();
        newPosBuf_.reserve(posCount_);
        newVoteBuf_.reserve(posCount_);

        const int cellSize = cvRound(minDist_);
        const int gridWidth = (imageSize_.width + cellSize - 1) / cellSize;
        const int gridHeight = (imageSize_.height + cellSize - 1) / cellSize;

        std::vector< std::vector<Point2f> > grid(gridWidth * gridHeight);

        const double minDist2 = minDist_ * minDist_;

        for (int i = 0; i < posCount_; ++i)
        {
            const int ind = indexies_[i];

            Point2f p(oldPosBuf_[ind].x, oldPosBuf_[ind].y);

            bool good = true;

            const int xCell = static_cast<int>(p.x / cellSize);
            const int yCell = static_cast<int>(p.y / cellSize);

            int x1 = xCell - 1;
            int y1 = yCell - 1;
            int x2 = xCell + 1;
            int y2 = yCell + 1;

            // boundary check
            x1 = std::max(0, x1);
            y1 = std::max(0, y1);
            x2 = std::min(gridWidth - 1, x2);
            y2 = std::min(gridHeight - 1, y2);

            for (int yy = y1; yy <= y2; ++yy)
            {
                for (int xx = x1; xx <= x2; ++xx)
                {
                    const std::vector<Point2f>& m = grid[yy * gridWidth + xx];

                    for(size_t j = 0; j < m.size(); ++j)
                    {
                        const Point2f d = p - m[j];

                        if (d.ddot(d) < minDist2)
                        {
                            good = false;
                            goto break_out;
                        }
                    }
                }
            }

            break_out:

            if(good)
            {
                grid[yCell * gridWidth + xCell].push_back(p);

                newPosBuf_.push_back(oldPosBuf_[ind]);
                newVoteBuf_.push_back(oldVoteBuf_[ind]);
            }
        }

        posCount_ = static_cast<int>(newPosBuf_.size());
        cudaSafeCall( cudaMemcpy(outBuf_.ptr(0), &newPosBuf_[0], posCount_ * sizeof(float4), cudaMemcpyHostToDevice) );
        cudaSafeCall( cudaMemcpy(outBuf_.ptr(1), &newVoteBuf_[0], posCount_ * sizeof(int3), cudaMemcpyHostToDevice) );
    }

    void GeneralizedHoughBase::convertTo(OutputArray positions, OutputArray votes)
    {
        ensureSizeIsEnough(1, posCount_, CV_32FC4, positions);
        GpuMat(1, posCount_, CV_32FC4, outBuf_.ptr(0), outBuf_.step).copyTo(positions);

        if (votes.needed())
        {
            ensureSizeIsEnough(1, posCount_, CV_32FC3, votes);
            GpuMat(1, posCount_, CV_32FC4, outBuf_.ptr(1), outBuf_.step).copyTo(votes);
        }
    }
}

// GeneralizedHoughBallard

namespace
{
    class GeneralizedHoughBallardImpl : public GeneralizedHoughBallard, private GeneralizedHoughBase
    {
    public:
        GeneralizedHoughBallardImpl();

        void setTemplate(InputArray templ, Point templCenter) { setTemplateImpl(templ, templCenter); }
        void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter) { setTemplateImpl(edges, dx, dy, templCenter); }

        void detect(InputArray image, OutputArray positions, OutputArray votes) { detectImpl(image, positions, votes); }
        void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes) { detectImpl(edges, dx, dy, positions, votes); }

        void setCannyLowThresh(int cannyLowThresh) { cannyLowThresh_ = cannyLowThresh; }
        int getCannyLowThresh() const { return cannyLowThresh_; }

        void setCannyHighThresh(int cannyHighThresh) { cannyHighThresh_ = cannyHighThresh; }
        int getCannyHighThresh() const { return cannyHighThresh_; }

        void setMinDist(double minDist) { minDist_ = minDist; }
        double getMinDist() const { return minDist_; }

        void setDp(double dp) { dp_ = dp; }
        double getDp() const { return dp_; }

        void setMaxBufferSize(int maxBufferSize) { maxBufferSize_ = maxBufferSize; }
        int getMaxBufferSize() const { return maxBufferSize_; }

        void setLevels(int levels) { levels_ = levels; }
        int getLevels() const { return levels_; }

        void setVotesThreshold(int votesThreshold) { votesThreshold_ = votesThreshold; }
        int getVotesThreshold() const { return votesThreshold_; }

    private:
        void processTempl();
        void processImage();

        void calcHist();
        void findPosInHist();

        int levels_;
        int votesThreshold_;

        GpuMat r_table_;
        GpuMat r_sizes_;

        GpuMat hist_;
    };

    GeneralizedHoughBallardImpl::GeneralizedHoughBallardImpl()
    {
        levels_ = 360;
        votesThreshold_ = 100;
    }

    void GeneralizedHoughBallardImpl::processTempl()
    {
        using namespace cv::cuda::device::ght;

        CV_Assert( levels_ > 0 );

        buildEdgePointList(templEdges_, templDx_, templDy_);

        ensureSizeIsEnough(levels_ + 1, maxBufferSize_, CV_16SC2, r_table_);
        ensureSizeIsEnough(1, levels_ + 1, CV_32SC1, r_sizes_);
        r_sizes_.setTo(Scalar::all(0));

        if (edgePointList_.cols > 0)
        {
            buildRTable_gpu(edgePointList_.ptr<unsigned int>(0), edgePointList_.ptr<float>(1), edgePointList_.cols,
                            r_table_, r_sizes_.ptr<int>(), make_short2(templCenter_.x, templCenter_.y), levels_);
            cuda::min(r_sizes_, maxBufferSize_, r_sizes_);
        }
    }

    void GeneralizedHoughBallardImpl::processImage()
    {
        calcHist();
        findPosInHist();
    }

    void GeneralizedHoughBallardImpl::calcHist()
    {
        using namespace cv::cuda::device::ght;

        CV_Assert( levels_ > 0 && r_table_.rows == (levels_ + 1) && r_sizes_.cols == (levels_ + 1) );
        CV_Assert( dp_ > 0.0);

        const double idp = 1.0 / dp_;

        buildEdgePointList(imageEdges_, imageDx_, imageDy_);

        ensureSizeIsEnough(cvCeil(imageSize_.height * idp) + 2, cvCeil(imageSize_.width * idp) + 2, CV_32SC1, hist_);
        hist_.setTo(Scalar::all(0));

        if (edgePointList_.cols > 0)
        {
            Ballard_Pos_calcHist_gpu(edgePointList_.ptr<unsigned int>(0), edgePointList_.ptr<float>(1), edgePointList_.cols,
                                     r_table_, r_sizes_.ptr<int>(),
                                     hist_,
                                     (float)dp_, levels_);
        }
    }

    void GeneralizedHoughBallardImpl::findPosInHist()
    {
        using namespace cv::cuda::device::ght;

        CV_Assert( votesThreshold_ > 0 );

        ensureSizeIsEnough(2, maxBufferSize_, CV_32FC4, outBuf_);

        posCount_ = Ballard_Pos_findPosInHist_gpu(hist_, outBuf_.ptr<float4>(0), outBuf_.ptr<int3>(1), maxBufferSize_, (float)dp_, votesThreshold_);
    }
}

Ptr<GeneralizedHoughBallard> cv::cuda::createGeneralizedHoughBallard()
{
    return makePtr<GeneralizedHoughBallardImpl>();
}

// GeneralizedHoughGuil

namespace
{
    class GeneralizedHoughGuilImpl : public GeneralizedHoughGuil, private GeneralizedHoughBase
    {
    public:
        GeneralizedHoughGuilImpl();

        void setTemplate(InputArray templ, Point templCenter) { setTemplateImpl(templ, templCenter); }
        void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter) { setTemplateImpl(edges, dx, dy, templCenter); }

        void detect(InputArray image, OutputArray positions, OutputArray votes) { detectImpl(image, positions, votes); }
        void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes) { detectImpl(edges, dx, dy, positions, votes); }

        void setCannyLowThresh(int cannyLowThresh) { cannyLowThresh_ = cannyLowThresh; }
        int getCannyLowThresh() const { return cannyLowThresh_; }

        void setCannyHighThresh(int cannyHighThresh) { cannyHighThresh_ = cannyHighThresh; }
        int getCannyHighThresh() const { return cannyHighThresh_; }

        void setMinDist(double minDist) { minDist_ = minDist; }
        double getMinDist() const { return minDist_; }

        void setDp(double dp) { dp_ = dp; }
        double getDp() const { return dp_; }

        void setMaxBufferSize(int maxBufferSize) { maxBufferSize_ = maxBufferSize; }
        int getMaxBufferSize() const { return maxBufferSize_; }

        void setXi(double xi) { xi_ = xi; }
        double getXi() const { return xi_; }

        void setLevels(int levels) { levels_ = levels; }
        int getLevels() const { return levels_; }

        void setAngleEpsilon(double angleEpsilon) { angleEpsilon_ = angleEpsilon; }
        double getAngleEpsilon() const { return angleEpsilon_; }

        void setMinAngle(double minAngle) { minAngle_ = minAngle; }
        double getMinAngle() const { return minAngle_; }

        void setMaxAngle(double maxAngle) { maxAngle_ = maxAngle; }
        double getMaxAngle() const { return maxAngle_; }

        void setAngleStep(double angleStep) { angleStep_ = angleStep; }
        double getAngleStep() const { return angleStep_; }

        void setAngleThresh(int angleThresh) { angleThresh_ = angleThresh; }
        int getAngleThresh() const { return angleThresh_; }

        void setMinScale(double minScale) { minScale_ = minScale; }
        double getMinScale() const { return minScale_; }

        void setMaxScale(double maxScale) { maxScale_ = maxScale; }
        double getMaxScale() const { return maxScale_; }

        void setScaleStep(double scaleStep) { scaleStep_ = scaleStep; }
        double getScaleStep() const { return scaleStep_; }

        void setScaleThresh(int scaleThresh) { scaleThresh_ = scaleThresh; }
        int getScaleThresh() const { return scaleThresh_; }

        void setPosThresh(int posThresh) { posThresh_ = posThresh; }
        int getPosThresh() const { return posThresh_; }

    private:
        void processTempl();
        void processImage();

        double xi_;
        int levels_;
        double angleEpsilon_;

        double minAngle_;
        double maxAngle_;
        double angleStep_;
        int angleThresh_;

        double minScale_;
        double maxScale_;
        double scaleStep_;
        int scaleThresh_;

        int posThresh_;

        struct Feature
        {
            GpuMat p1_pos;
            GpuMat p1_theta;
            GpuMat p2_pos;

            GpuMat d12;

            GpuMat r1;
            GpuMat r2;

            GpuMat sizes;
            int maxSize;

            void create(int levels, int maxCapacity, bool isTempl);
        };

        typedef void (*set_func_t)(PtrStepb p1_pos, PtrStepb p1_theta, PtrStepb p2_pos, PtrStepb d12, PtrStepb r1, PtrStepb r2);
        typedef void (*build_func_t)(const unsigned int* coordList, const float* thetaList, int pointsCount,
                                     int* sizes, int maxSize,
                                     float xi, float angleEpsilon, int levels,
                                     float2 center, float maxDist);

        void buildFeatureList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Feature& features,
                              set_func_t set_func, build_func_t build_func, bool isTempl, Point2d center = Point2d());

        void calcOrientation();
        void calcScale(double angle);
        void calcPosition(double angle, int angleVotes, double scale, int scaleVotes);

        Feature templFeatures_;
        Feature imageFeatures_;

        std::vector< std::pair<double, int> > angles_;
        std::vector< std::pair<double, int> > scales_;

        GpuMat hist_;
        std::vector<int> h_buf_;
    };

    double toRad(double a)
    {
        return a * CV_PI / 180.0;
    }

    double clampAngle(double a)
    {
        double res = a;

        while (res > 360.0)
            res -= 360.0;
        while (res < 0)
            res += 360.0;

        return res;
    }

    bool angleEq(double a, double b, double eps = 1.0)
    {
        return (fabs(clampAngle(a - b)) <= eps);
    }

    GeneralizedHoughGuilImpl::GeneralizedHoughGuilImpl()
    {
        maxBufferSize_ = 1000;

        xi_ = 90.0;
        levels_ = 360;
        angleEpsilon_ = 1.0;

        minAngle_ = 0.0;
        maxAngle_ = 360.0;
        angleStep_ = 1.0;
        angleThresh_ = 15000;

        minScale_ = 0.5;
        maxScale_ = 2.0;
        scaleStep_ = 0.05;
        scaleThresh_ = 1000;

        posThresh_ = 100;
    }

    void GeneralizedHoughGuilImpl::processTempl()
    {
        using namespace cv::cuda::device::ght;

        buildFeatureList(templEdges_, templDx_, templDy_, templFeatures_,
            Guil_Full_setTemplFeatures, Guil_Full_buildTemplFeatureList_gpu,
            true, templCenter_);

        h_buf_.resize(templFeatures_.sizes.cols);
        cudaSafeCall( cudaMemcpy(&h_buf_[0], templFeatures_.sizes.data, h_buf_.size() * sizeof(int), cudaMemcpyDeviceToHost) );
        templFeatures_.maxSize = *std::max_element(h_buf_.begin(), h_buf_.end());
    }

    void GeneralizedHoughGuilImpl::processImage()
    {
        using namespace cv::cuda::device::ght;

        CV_Assert( levels_ > 0 );
        CV_Assert( templFeatures_.sizes.cols == levels_ + 1 );
        CV_Assert( minAngle_ >= 0.0 && minAngle_ < maxAngle_ && maxAngle_ <= 360.0 );
        CV_Assert( angleStep_ > 0.0 && angleStep_ < 360.0 );
        CV_Assert( angleThresh_ > 0 );
        CV_Assert( minScale_ > 0.0 && minScale_ < maxScale_ );
        CV_Assert( scaleStep_ > 0.0 );
        CV_Assert( scaleThresh_ > 0 );
        CV_Assert( dp_ > 0.0 );
        CV_Assert( posThresh_ > 0 );

        const double iAngleStep = 1.0 / angleStep_;
        const int angleRange = cvCeil((maxAngle_ - minAngle_) * iAngleStep);

        const double iScaleStep = 1.0 / scaleStep_;
        const int scaleRange = cvCeil((maxScale_ - minScale_) * iScaleStep);

        const double idp = 1.0 / dp_;
        const int histRows = cvCeil(imageSize_.height * idp);
        const int histCols = cvCeil(imageSize_.width * idp);

        ensureSizeIsEnough(histRows + 2, std::max(angleRange + 1, std::max(scaleRange + 1, histCols + 2)), CV_32SC1, hist_);
        h_buf_.resize(std::max(angleRange + 1, scaleRange + 1));

        ensureSizeIsEnough(2, maxBufferSize_, CV_32FC4, outBuf_);

        buildFeatureList(imageEdges_, imageDx_, imageDy_, imageFeatures_,
            Guil_Full_setImageFeatures, Guil_Full_buildImageFeatureList_gpu,
            false);

        calcOrientation();

        for (size_t i = 0; i < angles_.size(); ++i)
        {
            const double angle = angles_[i].first;
            const int angleVotes = angles_[i].second;

            calcScale(angle);

            for (size_t j = 0; j < scales_.size(); ++j)
            {
                const double scale = scales_[j].first;
                const int scaleVotes = scales_[j].second;

                calcPosition(angle, angleVotes, scale, scaleVotes);
            }
        }
    }

    void GeneralizedHoughGuilImpl::Feature::create(int levels, int maxCapacity, bool isTempl)
    {
        if (!isTempl)
        {
            ensureSizeIsEnough(levels + 1, maxCapacity, CV_32FC2, p1_pos);
            ensureSizeIsEnough(levels + 1, maxCapacity, CV_32FC2, p2_pos);
        }

        ensureSizeIsEnough(levels + 1, maxCapacity, CV_32FC1, p1_theta);

        ensureSizeIsEnough(levels + 1, maxCapacity, CV_32FC1, d12);

        if (isTempl)
        {
            ensureSizeIsEnough(levels + 1, maxCapacity, CV_32FC2, r1);
            ensureSizeIsEnough(levels + 1, maxCapacity, CV_32FC2, r2);
        }

        ensureSizeIsEnough(1, levels + 1, CV_32SC1, sizes);
        sizes.setTo(Scalar::all(0));

        maxSize = 0;
    }

    void GeneralizedHoughGuilImpl::buildFeatureList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Feature& features,
                                                    set_func_t set_func, build_func_t build_func, bool isTempl, Point2d center)
    {
        CV_Assert( levels_ > 0 );

        const double maxDist = sqrt((double) templSize_.width * templSize_.width + templSize_.height * templSize_.height) * maxScale_;

        features.create(levels_, maxBufferSize_, isTempl);
        set_func(features.p1_pos, features.p1_theta, features.p2_pos, features.d12, features.r1, features.r2);

        buildEdgePointList(edges, dx, dy);

        if (edgePointList_.cols > 0)
        {
            build_func(edgePointList_.ptr<unsigned int>(0), edgePointList_.ptr<float>(1), edgePointList_.cols,
                features.sizes.ptr<int>(), maxBufferSize_, (float)xi_, (float)angleEpsilon_, levels_, make_float2((float)center.x, (float)center.y), (float)maxDist);
        }
    }

    void GeneralizedHoughGuilImpl::calcOrientation()
    {
        using namespace cv::cuda::device::ght;

        const double iAngleStep = 1.0 / angleStep_;
        const int angleRange = cvCeil((maxAngle_ - minAngle_) * iAngleStep);

        hist_.setTo(Scalar::all(0));
        Guil_Full_calcOHist_gpu(templFeatures_.sizes.ptr<int>(), imageFeatures_.sizes.ptr<int>(0), hist_.ptr<int>(),
                                (float)minAngle_, (float)maxAngle_, (float)angleStep_, angleRange, levels_, templFeatures_.maxSize);
        cudaSafeCall( cudaMemcpy(&h_buf_[0], hist_.data, h_buf_.size() * sizeof(int), cudaMemcpyDeviceToHost) );

        angles_.clear();

        for (int n = 0; n < angleRange; ++n)
        {
            if (h_buf_[n] >= angleThresh_)
            {
                const double angle = minAngle_ + n * angleStep_;
                angles_.push_back(std::make_pair(angle, h_buf_[n]));
            }
        }
    }

    void GeneralizedHoughGuilImpl::calcScale(double angle)
    {
        using namespace cv::cuda::device::ght;

        const double iScaleStep = 1.0 / scaleStep_;
        const int scaleRange = cvCeil((maxScale_ - minScale_) * iScaleStep);

        hist_.setTo(Scalar::all(0));
        Guil_Full_calcSHist_gpu(templFeatures_.sizes.ptr<int>(), imageFeatures_.sizes.ptr<int>(0), hist_.ptr<int>(),
                                (float)angle, (float)angleEpsilon_, (float)minScale_, (float)maxScale_,
                                (float)iScaleStep, scaleRange, levels_, templFeatures_.maxSize);
        cudaSafeCall( cudaMemcpy(&h_buf_[0], hist_.data, h_buf_.size() * sizeof(int), cudaMemcpyDeviceToHost) );

        scales_.clear();

        for (int s = 0; s < scaleRange; ++s)
        {
            if (h_buf_[s] >= scaleThresh_)
            {
                const double scale = minScale_ + s * scaleStep_;
                scales_.push_back(std::make_pair(scale, h_buf_[s]));
            }
        }
    }

    void GeneralizedHoughGuilImpl::calcPosition(double angle, int angleVotes, double scale, int scaleVotes)
    {
        using namespace cv::cuda::device::ght;

        hist_.setTo(Scalar::all(0));
        Guil_Full_calcPHist_gpu(templFeatures_.sizes.ptr<int>(), imageFeatures_.sizes.ptr<int>(0), hist_,
                                (float)angle, (float)angleEpsilon_, (float)scale, (float)dp_, levels_, templFeatures_.maxSize);

        posCount_ = Guil_Full_findPosInHist_gpu(hist_, outBuf_.ptr<float4>(0), outBuf_.ptr<int3>(1),
                                                posCount_, maxBufferSize_, (float)angle, angleVotes,
                                                (float)scale, scaleVotes, (float)dp_, posThresh_);
    }
}

Ptr<GeneralizedHoughGuil> cv::cuda::createGeneralizedHoughGuil()
{
    return makePtr<GeneralizedHoughGuilImpl>();
}

#endif /* !defined (HAVE_CUDA) */
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root/modules/cudaimgproc/src/generalized_hough.cpp

DEFINITIONS
