C++ GpuMat::empty方法代码示例

void cv::cuda::BFMatcher_CUDA::knnMatchSingle(const GpuMat& query, const GpuMat& train,
    GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k,
    const GpuMat& mask, Stream& stream)
{
    if (query.empty() || train.empty())
        return;

    using namespace cv::cuda::device::bf_knnmatch;

    typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
                             const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
                             cudaStream_t stream);

    static const caller_t callersL1[] =
    {
        matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
        matchL1_gpu<unsigned short>, matchL1_gpu<short>,
        matchL1_gpu<int>, matchL1_gpu<float>
    };
    static const caller_t callersL2[] =
    {
        0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
        0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
        0/*matchL2_gpu<int>*/, matchL2_gpu<float>
    };
    static const caller_t callersHamming[] =
    {
        matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
        matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
        matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
    };

    CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
    CV_Assert(train.type() == query.type() && train.cols == query.cols);
    CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);

    const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;

    const int nQuery = query.rows;
    const int nTrain = train.rows;

    if (k == 2)
    {
        ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
        ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
    }
    else
    {
        ensureSizeIsEnough(nQuery, k, CV_32S, trainIdx);
        ensureSizeIsEnough(nQuery, k, CV_32F, distance);
        ensureSizeIsEnough(nQuery, nTrain, CV_32FC1, allDist);
    }

    trainIdx.setTo(Scalar::all(-1), stream);

    caller_t func = callers[query.depth()];
    CV_Assert(func != 0);

    func(query, train, k, mask, trainIdx, distance, allDist, StreamAccessor::getStream(stream));
}

void cv::gpu::BruteForceMatcher_GPU_base::knnMatchSingle(const GpuMat& query, const GpuMat& train,
        GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k,
        const GpuMat& mask, Stream& stream)
{
    if (query.empty() || train.empty())
        return;

    using namespace cv::gpu::device::bf_knnmatch;

    typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
                             const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
                             cudaStream_t stream);

    static const caller_t callers[3][6] =
    {
        {
            matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
            matchL1_gpu<unsigned short>, matchL1_gpu<short>,
            matchL1_gpu<int>, matchL1_gpu<float>
        },
        {
            0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
            0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
            0/*matchL2_gpu<int>*/, matchL2_gpu<float>
        },
        {
            matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
            matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
            matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
        }
    };

    CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
    CV_Assert(train.type() == query.type() && train.cols == query.cols);

    const int nQuery = query.rows;
    const int nTrain = train.rows;

    if (k == 2)
    {
        ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
        ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
    }
    else
    {
        ensureSizeIsEnough(nQuery, k, CV_32S, trainIdx);
        ensureSizeIsEnough(nQuery, k, CV_32F, distance);
        ensureSizeIsEnough(nQuery, nTrain, CV_32FC1, allDist);
    }

    if (stream)
        stream.enqueueMemSet(trainIdx, Scalar::all(-1));
    else
        trainIdx.setTo(Scalar::all(-1));

    caller_t func = callers[distType][query.depth()];
    CV_Assert(func != 0);

    func(query, train, k, mask, trainIdx, distance, allDist, StreamAccessor::getStream(stream));
}

void cv::gpu::BFMatcher_GPU::radiusMatchSingle(const GpuMat& query, const GpuMat& train,
    GpuMat& trainIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
    const GpuMat& mask, Stream& stream)
{
    if (query.empty() || train.empty())
        return;

    using namespace cv::gpu::device::bf_radius_match;

    typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, float maxDistance, const PtrStepSzb& mask,
                             const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
                             cudaStream_t stream);

    static const caller_t callersL1[] =
    {
        matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
        matchL1_gpu<unsigned short>, matchL1_gpu<short>,
        matchL1_gpu<int>, matchL1_gpu<float>
    };
    static const caller_t callersL2[] =
    {
        0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
        0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
        0/*matchL2_gpu<int>*/, matchL2_gpu<float>
    };
    static const caller_t callersHamming[] =
    {
        matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
        matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
        matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
    };

    const int nQuery = query.rows;
    const int nTrain = train.rows;

    CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
    CV_Assert(train.type() == query.type() && train.cols == query.cols);
    CV_Assert(trainIdx.empty() || (trainIdx.rows == nQuery && trainIdx.size() == distance.size()));
    CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);

    const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;

    ensureSizeIsEnough(1, nQuery, CV_32SC1, nMatches);
    if (trainIdx.empty())
    {
        ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32SC1, trainIdx);
        ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32FC1, distance);
    }

    if (stream)
        stream.enqueueMemSet(nMatches, Scalar::all(0));
    else
        nMatches.setTo(Scalar::all(0));

    caller_t func = callers[query.depth()];
    CV_Assert(func != 0);

    func(query, train, maxDistance, mask, trainIdx, distance, nMatches, StreamAccessor::getStream(stream));
}

void cv::gpu::ORB_GPU::buildScalePyramids(const GpuMat& image, const GpuMat& mask)
{
    CV_Assert(image.type() == CV_8UC1);
    CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()));

    imagePyr_.resize(nLevels_);
    maskPyr_.resize(nLevels_);

    for (int level = 0; level < nLevels_; ++level)
    {
        float scale = 1.0f / getScale(scaleFactor_, firstLevel_, level);

        Size sz(cvRound(image.cols * scale), cvRound(image.rows * scale));

        ensureSizeIsEnough(sz, image.type(), imagePyr_[level]);
        ensureSizeIsEnough(sz, CV_8UC1, maskPyr_[level]);
        maskPyr_[level].setTo(Scalar::all(255));

        // Compute the resized image
        if (level != firstLevel_)
        {
            if (level < firstLevel_)
            {
                resize(image, imagePyr_[level], sz, 0, 0, INTER_LINEAR);

                if (!mask.empty())
                    resize(mask, maskPyr_[level], sz, 0, 0, INTER_LINEAR);
            }
            else
            {
                resize(imagePyr_[level - 1], imagePyr_[level], sz, 0, 0, INTER_LINEAR);

                if (!mask.empty())
                {
                    resize(maskPyr_[level - 1], maskPyr_[level], sz, 0, 0, INTER_LINEAR);
                    threshold(maskPyr_[level], maskPyr_[level], 254, 0, THRESH_TOZERO);
                }
            }
        }
        else
        {
            image.copyTo(imagePyr_[level]);

            if (!mask.empty())
                mask.copyTo(maskPyr_[level]);
        }

        // Filter keypoints by image border
        ensureSizeIsEnough(sz, CV_8UC1, buf_);
        buf_.setTo(Scalar::all(0));
        Rect inner(edgeThreshold_, edgeThreshold_, sz.width - 2 * edgeThreshold_, sz.height - 2 * edgeThreshold_);
        buf_(inner).setTo(Scalar::all(255));

        bitwise_and(maskPyr_[level], buf_, maskPyr_[level]);
    }
}

void cv::gpu::BFMatcher_GPU::knnMatch2Collection(const GpuMat& query, const GpuMat& trainCollection,
    GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
    const GpuMat& maskCollection, Stream& stream)
{
    if (query.empty() || trainCollection.empty())
        return;

    using namespace cv::gpu::device::bf_knnmatch;

    typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
                             const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
                             int cc, cudaStream_t stream);

    static const caller_t callersL1[] =
    {
        match2L1_gpu<unsigned char>, 0/*match2L1_gpu<signed char>*/,
        match2L1_gpu<unsigned short>, match2L1_gpu<short>,
        match2L1_gpu<int>, match2L1_gpu<float>
    };
    static const caller_t callersL2[] =
    {
        0/*match2L2_gpu<unsigned char>*/, 0/*match2L2_gpu<signed char>*/,
        0/*match2L2_gpu<unsigned short>*/, 0/*match2L2_gpu<short>*/,
        0/*match2L2_gpu<int>*/, match2L2_gpu<float>
    };
    static const caller_t callersHamming[] =
    {
        match2Hamming_gpu<unsigned char>, 0/*match2Hamming_gpu<signed char>*/,
        match2Hamming_gpu<unsigned short>, 0/*match2Hamming_gpu<short>*/,
        match2Hamming_gpu<int>, 0/*match2Hamming_gpu<float>*/
    };

    CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
    CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);

    const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;

    const int nQuery = query.rows;

    ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
    ensureSizeIsEnough(1, nQuery, CV_32SC2, imgIdx);
    ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);

    if (stream)
        stream.enqueueMemSet(trainIdx, Scalar::all(-1));
    else
        trainIdx.setTo(Scalar::all(-1));

    caller_t func = callers[query.depth()];
    CV_Assert(func != 0);

    DeviceInfo info;
    int cc = info.majorVersion() * 10 + info.minorVersion();

    func(query, trainCollection, maskCollection, trainIdx, imgIdx, distance, cc, StreamAccessor::getStream(stream));
}

void _Flow::detect(void)
{
	Frame* pGray;
	Frame* pNextFrame;
	Frame* pPrevFrame;
	GpuMat* pPrev;
	GpuMat* pNext;
	GpuMat GMat;
	GpuMat pGMat[2];

	if(m_pStream==NULL)return;

	pGray = m_pStream->getGrayFrame();
	if(pGray->empty())return;

	pNextFrame = m_pGrayFrames->getLastFrame();
	if(pGray->getFrameID() <= pNextFrame->getFrameID())return;

	m_pGrayFrames->updateFrameIndex();
	pNextFrame = m_pGrayFrames->getLastFrame();
	pPrevFrame = m_pGrayFrames->getPrevFrame();

	pNextFrame->getResizedOf(pGray,m_width,m_height);
	pPrev = pPrevFrame->getGMat();
	pNext = pNextFrame->getGMat();

	if(pPrev->empty())return;
	if(pNext->empty())return;
	if(pPrev->size() != pNext->size())return;

	m_pFarn->calc(*pPrev, *pNext, m_GFlowMat);



	//Generate Depth Map from Flow
	if(m_bDepth==0)return;

	cuda::abs(m_GFlowMat, GMat);
	cuda::split(GMat, pGMat);
	cuda::add(pGMat[0],pGMat[1], GMat);
	cuda::multiply(GMat, Scalar(100), pGMat[1]);

	pGMat[1].convertTo(*(m_pDepth->getGMat()),CV_8UC1);
	m_pDepth->updatedGMat();

//	m_flowMax = cuda::sum(fGMat)[0] / (fGMat.cols*fGMat.rows);
//	fInterval = 1.0/m_flowMax;
//	fInterval *= 50.0;

//	cuda::min(fGMat,Scalar(m_flowMax),pGMat[0]);
//	cuda::multiply(pGMat[0],Scalar(fInterval),fGMat);

//	cv::cuda::cvtColor(depthGMat, idxGMat, CV_GRAY2BGR);
//	m_pGpuLUT->transform(idxGMat,segGMat);

}

void cv::cuda::BFMatcher_CUDA::matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches)
{
    if (trainIdx.empty() || distance.empty())
        return;

    Mat trainIdxCPU(trainIdx);
    Mat distanceCPU(distance);

    matchConvert(trainIdxCPU, distanceCPU, matches);
}

void cv::gpu::BruteForceMatcher_GPU_base::matchDownload(const GpuMat& trainIdx, const GpuMat& distance, vector<DMatch>& matches)
{
    if (trainIdx.empty() || distance.empty())
        return;

    Mat trainIdxCPU(trainIdx);
    Mat distanceCPU(distance);

    matchConvert(trainIdxCPU, distanceCPU, matches);
}

void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
                        const GpuMat& mask, GpuMat& valbuf, GpuMat& locbuf)
{
    using namespace mathfunc::minmaxloc;

    typedef void (*Caller)(const DevMem2D, double*, double*, int[2], int[2], PtrStep, PtrStep);
    typedef void (*MaskedCaller)(const DevMem2D, const PtrStep, double*, double*, int[2], int[2], PtrStep, PtrStep);

    static const Caller callers[2][7] = 
    { { min_max_loc_multipass_caller<unsigned char>, min_max_loc_multipass_caller<char>, 
        min_max_loc_multipass_caller<unsigned short>, min_max_loc_multipass_caller<short>, 
        min_max_loc_multipass_caller<int>, min_max_loc_multipass_caller<float>, 0 },
      { min_max_loc_caller<unsigned char>, min_max_loc_caller<char>, 
        min_max_loc_caller<unsigned short>, min_max_loc_caller<short>, 
        min_max_loc_caller<int>, min_max_loc_caller<float>, min_max_loc_caller<double> } };

    static const MaskedCaller masked_callers[2][7] = 
    { { min_max_loc_mask_multipass_caller<unsigned char>, min_max_loc_mask_multipass_caller<char>, 
        min_max_loc_mask_multipass_caller<unsigned short>, min_max_loc_mask_multipass_caller<short>, 
        min_max_loc_mask_multipass_caller<int>, min_max_loc_mask_multipass_caller<float>, 0 },
      { min_max_loc_mask_caller<unsigned char>, min_max_loc_mask_caller<char>, 
        min_max_loc_mask_caller<unsigned short>, min_max_loc_mask_caller<short>, 
        min_max_loc_mask_caller<int>, min_max_loc_mask_caller<float>, min_max_loc_mask_caller<double> } };

    CV_Assert(src.channels() == 1);
    CV_Assert(mask.empty() || (mask.type() == CV_8U && src.size() == mask.size()));
    CV_Assert(src.type() != CV_64F || hasNativeDoubleSupport(getDevice()));

    double minVal_; if (!minVal) minVal = &minVal_;
    double maxVal_; if (!maxVal) maxVal = &maxVal_;
    int minLoc_[2];
    int maxLoc_[2];

    Size valbuf_size, locbuf_size;
    get_buf_size_required(src.cols, src.rows, src.elemSize(), valbuf_size.width, 
                          valbuf_size.height, locbuf_size.width, locbuf_size.height);
    valbuf.create(valbuf_size, CV_8U);
    locbuf.create(locbuf_size, CV_8U);

    if (mask.empty())
    {
        Caller caller = callers[hasAtomicsSupport(getDevice())][src.type()];
        if (!caller) CV_Error(CV_StsBadArg, "minMaxLoc: unsupported type");
        caller(src, minVal, maxVal, minLoc_, maxLoc_, valbuf, locbuf);
    }
    else
    {
        MaskedCaller caller = masked_callers[hasAtomicsSupport(getDevice())][src.type()];
        if (!caller) CV_Error(CV_StsBadArg, "minMaxLoc: unsupported type");
        caller(src, mask, minVal, maxVal, minLoc_, maxLoc_, valbuf, locbuf);
    }

    if (minLoc) { minLoc->x = minLoc_[0]; minLoc->y = minLoc_[1]; }
    if (maxLoc) { maxLoc->x = maxLoc_[0]; maxLoc->y = maxLoc_[1]; }
}

void cv::gpu::BFMatcher_GPU::radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, const GpuMat& nMatches,
    vector< vector<DMatch> >& matches, bool compactResult)
{
    if (trainIdx.empty() || distance.empty() || nMatches.empty())
        return;

    Mat trainIdxCPU(trainIdx);
    Mat distanceCPU(distance);
    Mat nMatchesCPU(nMatches);

    radiusMatchConvert(trainIdxCPU, distanceCPU, nMatchesCPU, matches, compactResult);
}

void cv::gpu::BruteForceMatcher_GPU_base::matchCollection(const GpuMat& query, const GpuMat& trainCollection,
    GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
    const GpuMat& masks, Stream& stream)
{
    if (query.empty() || trainCollection.empty())
        return;

    using namespace ::cv::gpu::device::bf_match;

    typedef void (*caller_t)(const DevMem2Db& query, const DevMem2Db& trains, const DevMem2D_<PtrStepb>& masks,
                             const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
                             int cc, cudaStream_t stream);

    static const caller_t callers[3][6] =
    {
        {
            matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
            matchL1_gpu<unsigned short>, matchL1_gpu<short>,
            matchL1_gpu<int>, matchL1_gpu<float>
        },
        {
            0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
            0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
            0/*matchL2_gpu<int>*/, matchL2_gpu<float>
        },
        {
            matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
            matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
            matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
        }
    };

    CV_Assert(query.channels() == 1 && query.depth() < CV_64F);

    const int nQuery = query.rows;

    ensureSizeIsEnough(1, nQuery, CV_32S, trainIdx);
    ensureSizeIsEnough(1, nQuery, CV_32S, imgIdx);
    ensureSizeIsEnough(1, nQuery, CV_32F, distance);

    caller_t func = callers[distType][query.depth()];
    CV_Assert(func != 0);

    DeviceInfo info;
    int cc = info.majorVersion() * 10 + info.minorVersion();

    func(query, trainCollection, masks, trainIdx, imgIdx, distance, cc, StreamAccessor::getStream(stream));
}

void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
                        const GpuMat& mask, GpuMat& valBuf, GpuMat& locBuf)
{
    typedef void (*func_t)(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf);
    static const func_t funcs[] =
    {
        ::minMaxLoc::run<uchar>,
        ::minMaxLoc::run<schar>,
        ::minMaxLoc::run<ushort>,
        ::minMaxLoc::run<short>,
        ::minMaxLoc::run<int>,
        ::minMaxLoc::run<float>,
        ::minMaxLoc::run<double>
    };

    CV_Assert( src.channels() == 1 );
    CV_Assert( mask.empty() || (mask.size() == src.size() && mask.type() == CV_8U) );

    if (src.depth() == CV_64F)
    {
        if (!deviceSupports(NATIVE_DOUBLE))
            CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double");
    }

    Size valbuf_size, locbuf_size;
    ::minMaxLoc::getBufSize(src.cols, src.rows, src.elemSize(), valbuf_size.width, valbuf_size.height, locbuf_size.width, locbuf_size.height);
    ensureSizeIsEnough(valbuf_size, CV_8U, valBuf);
    ensureSizeIsEnough(locbuf_size, CV_8U, locBuf);

    const func_t func = funcs[src.depth()];

    double temp1, temp2;
    Point temp3, temp4;
    func(src, mask, minVal ? minVal : &temp1, maxVal ? maxVal : &temp2, minLoc ? &minLoc->x : &temp3.x, maxLoc ? &maxLoc->x : &temp4.x, valBuf, locBuf);
}

void cv::gpu::normalize(const GpuMat& src, GpuMat& dst, double a, double b, int norm_type, int dtype, const GpuMat& mask, GpuMat& norm_buf, GpuMat& cvt_buf)
{
    double scale = 1, shift = 0;
    if (norm_type == NORM_MINMAX)
    {
        double smin = 0, smax = 0;
        double dmin = std::min(a, b), dmax = std::max(a, b);
        minMax(src, &smin, &smax, mask, norm_buf);
        scale = (dmax - dmin) * (smax - smin > numeric_limits<double>::epsilon() ? 1.0 / (smax - smin) : 0.0);
        shift = dmin - smin * scale;
    }
    else if (norm_type == NORM_L2 || norm_type == NORM_L1 || norm_type == NORM_INF)
    {
        scale = norm(src, norm_type, mask, norm_buf);
        scale = scale > numeric_limits<double>::epsilon() ? a / scale : 0.0;
        shift = 0;
    }
    else
    {
        CV_Error(CV_StsBadArg, "Unknown/unsupported norm type");
    }

    if (mask.empty())
    {
        src.convertTo(dst, dtype, scale, shift);
    }
    else
    {
        src.convertTo(cvt_buf, dtype, scale, shift);
        cvt_buf.copyTo(dst, mask);
    }
}

void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask)
{
    if (mask.empty())
        ::bitwise_not_caller(src, dst, 0);
    else
        ::bitwise_not_caller(src, dst, mask, 0);
}

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;
    NCVStatus ncvStat = impl->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, ncvMinSize, numDetections);
    if (ncvStat != NCV_SUCCESS)
    {
        CV_Error(CV_GpuApiCallError, "Error in face detectioln");
    }

    return numDetections;
}