C++ OutputArray::type方法代码示例

    float predict(InputArray _inputs, OutputArray _outputs, int) const
    {
        bool needprobs = _outputs.needed();
        Mat samples = _inputs.getMat(), probs, probsrow;
        int ptype = CV_64F;
        float firstres = 0.f;
        int i, nsamples = samples.rows;

        if( needprobs )
        {
            if( _outputs.fixedType() )
                ptype = _outputs.type();
            _outputs.create(samples.rows, nclusters, ptype);
        }
        else
            nsamples = std::min(nsamples, 1);

        for( i = 0; i < nsamples; i++ )
        {
            if( needprobs )
                probsrow = probs.row(i);
            Vec2d res = computeProbabilities(samples.row(i), needprobs ? &probsrow : 0, ptype);
            if( i == 0 )
                firstres = (float)res[1];
        }
        return firstres;
    }

void UMat::convertTo(OutputArray _dst, int _type, double alpha, double beta) const
{
    bool noScale = std::fabs(alpha - 1) < DBL_EPSILON && std::fabs(beta) < DBL_EPSILON;
    int stype = type(), cn = CV_MAT_CN(stype);

    if( _type < 0 )
        _type = _dst.fixedType() ? _dst.type() : stype;
    else
        _type = CV_MAKETYPE(CV_MAT_DEPTH(_type), cn);

    int sdepth = CV_MAT_DEPTH(stype), ddepth = CV_MAT_DEPTH(_type);
    if( sdepth == ddepth && noScale )
    {
        copyTo(_dst);
        return;
    }
#ifdef HAVE_OPENCL
    bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0;
    bool needDouble = sdepth == CV_64F || ddepth == CV_64F;
    if( dims <= 2 && cn && _dst.isUMat() && ocl::useOpenCL() &&
            ((needDouble && doubleSupport) || !needDouble) )
    {
        int wdepth = std::max(CV_32F, sdepth), rowsPerWI = 4;

        char cvt[2][40];
        ocl::Kernel k("convertTo", ocl::core::convert_oclsrc,
                      format("-D srcT=%s -D WT=%s -D dstT=%s -D convertToWT=%s -D convertToDT=%s%s%s",
                             ocl::typeToStr(sdepth), ocl::typeToStr(wdepth), ocl::typeToStr(ddepth),
                             ocl::convertTypeStr(sdepth, wdepth, 1, cvt[0]),
                             ocl::convertTypeStr(wdepth, ddepth, 1, cvt[1]),
                             doubleSupport ? " -D DOUBLE_SUPPORT" : "", noScale ? " -D NO_SCALE" : ""));
        if (!k.empty())
        {
            UMat src = *this;
            _dst.create( size(), _type );
            UMat dst = _dst.getUMat();

            float alphaf = (float)alpha, betaf = (float)beta;
            ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
                    dstarg = ocl::KernelArg::WriteOnly(dst, cn);

            if (noScale)
                k.args(srcarg, dstarg, rowsPerWI);
            else if (wdepth == CV_32F)
                k.args(srcarg, dstarg, alphaf, betaf, rowsPerWI);
            else
                k.args(srcarg, dstarg, alpha, beta, rowsPerWI);

            size_t globalsize[2] = { (size_t)dst.cols * cn, ((size_t)dst.rows + rowsPerWI - 1) / rowsPerWI };
            if (k.run(2, globalsize, NULL, false))
            {
                CV_IMPL_ADD(CV_IMPL_OCL);
                return;
            }
        }
    }
#endif
    Mat m = getMat(ACCESS_READ);
    m.convertTo(_dst, _type, alpha, beta);
}

    Vec2d predict2(InputArray _sample, OutputArray _probs) const
    {
        int ptype = CV_64F;
        Mat sample = _sample.getMat();
        CV_Assert(isTrained());

        CV_Assert(!sample.empty());
        if(sample.type() != CV_64FC1)
        {
            Mat tmp;
            sample.convertTo(tmp, CV_64FC1);
            sample = tmp;
        }
        sample = sample.reshape(1, 1);

        Mat probs;
        if( _probs.needed() )
        {
            if( _probs.fixedType() )
                ptype = _probs.type();
            _probs.create(1, nclusters, ptype);
            probs = _probs.getMat();
        }

        return computeProbabilities(sample, !probs.empty() ? &probs : 0, ptype);
    }

void addNoise(InputArray src_, OutputArray dest_, const double sigma, const double sprate, const int seed)
{
	if(seed!=0) cv::theRNG().state = seed;
	if (dest_.empty() || dest_.size() != src_.size() || dest_.type() != src_.type()) dest_.create(src_.size(), src_.type());
	Mat src = src_.getMat();
	Mat dest = dest_.getMat();
	if (src.channels() == 1)
	{
		addNoiseMono(src, dest, sigma);
		if (sprate != 0)addNoiseSoltPepperMono(dest, dest, sprate, seed);
		return;
	}
	else
	{
		vector<Mat> s(src.channels());
		vector<Mat> d(src.channels());
		split(src, s);
		for (int i = 0; i < src.channels(); i++)
		{
			addNoiseMono(s[i], d[i], sigma);
			if (sprate != 0)addNoiseSoltPepperMono(d[i], d[i], sprate, seed);
		}
		cv::merge(d, dest);
	}
	if (seed != 0) cv::theRNG().state = cv::getTickCount();
}

int fixedType_handler(OutputArray dst)
{
    int type = CV_32FC2; // return points only {x, y}
    if (dst.fixedType())
    {
        type = dst.type();
        CV_Assert(type == CV_32FC2 || type == CV_32FC3); // allow points + confidence level: {x, y, confidence}
    }
    const int N = 100;
    dst.create(Size(1, N), type);
    Mat m = dst.getMat();
    if (m.type() == CV_32FC2)
    {
        for (int i = 0; i < N; i++)
            m.at<Vec2f>(i) = Vec2f((float)i, (float)(i*2));
    }
    else if (m.type() == CV_32FC3)
    {
        for (int i = 0; i < N; i++)
            m.at<Vec3f>(i) = Vec3f((float)i, (float)(i*2), 1.0f / (i + 1));
    }
    else
    {
        CV_Assert(0 && "Internal error");
    }
    return CV_MAT_CN(type);
}

/* dst = src */
void Mat::copyTo( OutputArray _dst ) const
{
    int dtype = _dst.type();
    if( _dst.fixedType() && dtype != type() )
    {
        CV_Assert( channels() == CV_MAT_CN(dtype) );
        convertTo( _dst, dtype );
        return;
    }

    if( empty() )
    {
        _dst.release();
        return;
    }

    if( dims <= 2 )
    {
        _dst.create( rows, cols, type() );
        Mat dst = _dst.getMat();
        if( data == dst.data )
            return;

        if( rows > 0 && cols > 0 )
        {
            const uchar* sptr = data;
            uchar* dptr = dst.data;

            // to handle the copying 1xn matrix => nx1 std vector.
            Size sz = size() == dst.size() ?
                getContinuousSize(*this, dst) :
                getContinuousSize(*this);
            size_t len = sz.width*elemSize();

            for( ; sz.height--; sptr += step, dptr += dst.step )
                memcpy( dptr, sptr, len );
        }
        return;
    }

    _dst.create( dims, size, type() );
    Mat dst = _dst.getMat();
    if( data == dst.data )
        return;

    if( total() != 0 )
    {
        const Mat* arrays[] = { this, &dst };
        uchar* ptrs[2];
        NAryMatIterator it(arrays, ptrs, 2);
        size_t sz = it.size*elemSize();

        for( size_t i = 0; i < it.nplanes; i++, ++it )
            memcpy(ptrs[1], ptrs[0], sz);
    }
}

void cv::convertPointsHomogeneous( InputArray _src, OutputArray _dst )
{
    int stype = _src.type(), dtype = _dst.type();
    CV_Assert( _dst.fixedType() );
    
    if( CV_MAT_CN(stype) > CV_MAT_CN(dtype) )
        convertPointsFromHomogeneous(_src, _dst);
    else
        convertPointsToHomogeneous(_src, _dst);
}

void warpShift(InputArray src_, OutputArray dest_, int shiftx, int shifty, int borderType)
{
	Mat src = src_.getMat();
	if(dest_.empty() ||dest_.size()!=src_.size() || dest_.type() != src_.type()) dest_.create( src.size(), src.type() );
	Mat dest = dest_.getMat();

	if(borderType<0)
		warpShift_(src,dest,shiftx,shifty);
	else
		warpShift_(src,dest,shiftx,shifty,borderType);
}

void UMat::convertTo(OutputArray _dst, int _type, double alpha, double beta) const
{
    bool noScale = std::fabs(alpha - 1) < DBL_EPSILON && std::fabs(beta) < DBL_EPSILON;
    int stype = type(), cn = CV_MAT_CN(stype);

    if( _type < 0 )
        _type = _dst.fixedType() ? _dst.type() : stype;
    else
        _type = CV_MAKETYPE(CV_MAT_DEPTH(_type), cn);

    int sdepth = CV_MAT_DEPTH(stype), ddepth = CV_MAT_DEPTH(_type);
    if( sdepth == ddepth && noScale )
    {
        copyTo(_dst);
        return;
    }

    bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0;
    bool needDouble = sdepth == CV_64F || ddepth == CV_64F;
    if( dims <= 2 && cn && _dst.isUMat() && ocl::useOpenCL() &&
            ((needDouble && doubleSupport) || !needDouble) )
    {
        char cvt[40];
        ocl::Kernel k("convertTo", ocl::core::convert_oclsrc,
                      format("-D srcT=%s -D dstT=%s -D convertToDT=%s%s", ocl::typeToStr(sdepth),
                             ocl::typeToStr(ddepth), ocl::convertTypeStr(CV_32F, ddepth, 1, cvt),
                             doubleSupport ? " -D DOUBLE_SUPPORT" : ""));
        if (!k.empty())
        {
            UMat src = *this;
            _dst.create( size(), _type );
            UMat dst = _dst.getUMat();

            float alphaf = (float)alpha, betaf = (float)beta;
            k.args(ocl::KernelArg::ReadOnlyNoSize(src), ocl::KernelArg::WriteOnly(dst, cn), alphaf, betaf);

            size_t globalsize[2] = { dst.cols * cn, dst.rows };
            if (k.run(2, globalsize, NULL, false))
                return;
        }
    }

    Mat m = getMat(ACCESS_READ);
    m.convertTo(_dst, _type, alpha, beta);
}

void UMat::copyTo(OutputArray _dst) const
{
    int dtype = _dst.type();
    if( _dst.fixedType() && dtype != type() )
    {
        CV_Assert( channels() == CV_MAT_CN(dtype) );
        convertTo( _dst, dtype );
        return;
    }

    if( empty() )
    {
        _dst.release();
        return;
    }

    size_t i, sz[CV_MAX_DIM], srcofs[CV_MAX_DIM], dstofs[CV_MAX_DIM], esz = elemSize();
    for( i = 0; i < (size_t)dims; i++ )
        sz[i] = size.p[i];
    sz[dims-1] *= esz;
    ndoffset(srcofs);
    srcofs[dims-1] *= esz;

    _dst.create( dims, size.p, type() );
    if( _dst.isUMat() )
    {
        UMat dst = _dst.getUMat();
        if( u == dst.u && dst.offset == offset )
            return;

        if (u->currAllocator == dst.u->currAllocator)
        {
            dst.ndoffset(dstofs);
            dstofs[dims-1] *= esz;
            u->currAllocator->copy(u, dst.u, dims, sz, srcofs, step.p, dstofs, dst.step.p, false);
            return;
        }
    }

    Mat dst = _dst.getMat();
    u->currAllocator->download(u, dst.data, dims, sz, srcofs, step.p, dst.step.p);
}

void cv::goodFeaturesToTrack( InputArray _image, OutputArray _corners,
                              int maxCorners, double qualityLevel, double minDistance,
                              InputArray _mask, int blockSize,
                              bool useHarrisDetector, double harrisK )
{
    Mat image = _image.getMat(), mask = _mask.getMat();
    
    CV_Assert( qualityLevel > 0 && minDistance >= 0 && maxCorners >= 0 );
    CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()) );

    Mat eig, tmp;
    if( useHarrisDetector )
        cornerHarris( image, eig, blockSize, 3, harrisK );
    else
        cornerMinEigenVal( image, eig, blockSize, 3 );

    double maxVal = 0;
    minMaxLoc( eig, 0, &maxVal, 0, 0, mask );
    threshold( eig, eig, maxVal*qualityLevel, 0, THRESH_TOZERO );
    dilate( eig, tmp, Mat());

    Size imgsize = image.size();

    vector<const float*> tmpCorners;

    // collect list of pointers to features - put them into temporary image
    for( int y = 1; y < imgsize.height - 1; y++ )
    {
        const float* eig_data = (const float*)eig.ptr(y);
        const float* tmp_data = (const float*)tmp.ptr(y);
        const uchar* mask_data = mask.data ? mask.ptr(y) : 0;

        for( int x = 1; x < imgsize.width - 1; x++ )
        {
            float val = eig_data[x];
            if( val != 0 && val == tmp_data[x] && (!mask_data || mask_data[x]) )
                tmpCorners.push_back(eig_data + x);
        }
    }

    sort( tmpCorners, greaterThanPtr<float>() );
    vector<Point2f> corners;
    size_t i, j, total = tmpCorners.size(), ncorners = 0;

    if(minDistance >= 1)
    {
         // Partition the image into larger grids
        int w = image.cols;
        int h = image.rows;

        const int cell_size = cvRound(minDistance);
        const int grid_width = (w + cell_size - 1) / cell_size;
        const int grid_height = (h + cell_size - 1) / cell_size;

        std::vector<std::vector<Point2f> > grid(grid_width*grid_height);

        minDistance *= minDistance;

        for( i = 0; i < total; i++ )
        {
            int ofs = (int)((const uchar*)tmpCorners[i] - eig.data);
            int y = (int)(ofs / eig.step);
            int x = (int)((ofs - y*eig.step)/sizeof(float));

	        bool good = true;

            int x_cell = x / cell_size;
            int y_cell = y / cell_size;

            int x1 = x_cell - 1;
            int y1 = y_cell - 1;
            int x2 = x_cell + 1;
            int y2 = y_cell + 1;

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

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

                    if( m.size() )
                    {
                        for(j = 0; j < m.size(); j++)
                        {
                            float dx = x - m[j].x;
                            float dy = y - m[j].y;

                            if( dx*dx + dy*dy < minDistance )
                            {
                                good = false;
                                goto break_out;
                            }
                        }
                    }                
//.........这里部分代码省略.........

bool solvePnP( InputArray _opoints, InputArray _ipoints,
               InputArray _cameraMatrix, InputArray _distCoeffs,
               OutputArray _rvec, OutputArray _tvec, bool useExtrinsicGuess, int flags )
{
    CV_INSTRUMENT_REGION()

    Mat opoints = _opoints.getMat(), ipoints = _ipoints.getMat();
    int npoints = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F));
    CV_Assert( npoints >= 0 && npoints == std::max(ipoints.checkVector(2, CV_32F), ipoints.checkVector(2, CV_64F)) );

    Mat rvec, tvec;
    if( flags != SOLVEPNP_ITERATIVE )
        useExtrinsicGuess = false;

    if( useExtrinsicGuess )
    {
        int rtype = _rvec.type(), ttype = _tvec.type();
        Size rsize = _rvec.size(), tsize = _tvec.size();
        CV_Assert( (rtype == CV_32F || rtype == CV_64F) &&
                   (ttype == CV_32F || ttype == CV_64F) );
        CV_Assert( (rsize == Size(1, 3) || rsize == Size(3, 1)) &&
                   (tsize == Size(1, 3) || tsize == Size(3, 1)) );
    }
    else
    {
        int mtype = CV_64F;
        // use CV_32F if all PnP inputs are CV_32F and outputs are empty
        if (_ipoints.depth() == _cameraMatrix.depth() && _ipoints.depth() == _opoints.depth() &&
            _rvec.empty() && _tvec.empty())
            mtype = _opoints.depth();

        _rvec.create(3, 1, mtype);
        _tvec.create(3, 1, mtype);
    }
    rvec = _rvec.getMat();
    tvec = _tvec.getMat();

    Mat cameraMatrix0 = _cameraMatrix.getMat();
    Mat distCoeffs0 = _distCoeffs.getMat();
    Mat cameraMatrix = Mat_<double>(cameraMatrix0);
    Mat distCoeffs = Mat_<double>(distCoeffs0);
    bool result = false;

    if (flags == SOLVEPNP_EPNP || flags == SOLVEPNP_DLS || flags == SOLVEPNP_UPNP)
    {
        Mat undistortedPoints;
        undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs);
        epnp PnP(cameraMatrix, opoints, undistortedPoints);

        Mat R;
        PnP.compute_pose(R, tvec);
        Rodrigues(R, rvec);
        result = true;
    }
    else if (flags == SOLVEPNP_P3P)
    {
        CV_Assert( npoints == 4);
        Mat undistortedPoints;
        undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs);
        p3p P3Psolver(cameraMatrix);

        Mat R;
        result = P3Psolver.solve(R, tvec, opoints, undistortedPoints);
        if (result)
            Rodrigues(R, rvec);
    }
    else if (flags == SOLVEPNP_AP3P)
    {
        CV_Assert( npoints == 4);
        Mat undistortedPoints;
        undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs);
        ap3p P3Psolver(cameraMatrix);

        Mat R;
        result = P3Psolver.solve(R, tvec, opoints, undistortedPoints);
        if (result)
            Rodrigues(R, rvec);
    }
    else if (flags == SOLVEPNP_ITERATIVE)
    {
        CvMat c_objectPoints = opoints, c_imagePoints = ipoints;
        CvMat c_cameraMatrix = cameraMatrix, c_distCoeffs = distCoeffs;
        CvMat c_rvec = rvec, c_tvec = tvec;
        cvFindExtrinsicCameraParams2(&c_objectPoints, &c_imagePoints, &c_cameraMatrix,
                                     c_distCoeffs.rows*c_distCoeffs.cols ? &c_distCoeffs : 0,
                                     &c_rvec, &c_tvec, useExtrinsicGuess );
        result = true;
    }
    /*else if (flags == SOLVEPNP_DLS)
    {
        Mat undistortedPoints;
        undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs);

        dls PnP(opoints, undistortedPoints);

        Mat R, rvec = _rvec.getMat(), tvec = _tvec.getMat();
        bool result = PnP.compute_pose(R, tvec);
        if (result)
            Rodrigues(R, rvec);
        return result;
//.........这里部分代码省略.........

static bool openvx_harris(Mat image, OutputArray _corners,
                          int _maxCorners, double _qualityLevel, double _minDistance,
                          int _blockSize, int _gradientSize, double _harrisK)
{
    using namespace ivx;

    if(image.type() != CV_8UC1) return false;

    //OpenVX implementations don't have to provide other sizes
    if(!(_blockSize == 3 || _blockSize == 5 || _blockSize == 7)) return false;

    try
    {
        Context context = ovx::getOpenVXContext();

        Image ovxImage = Image::createFromHandle(context, Image::matTypeToFormat(image.type()),
                                                 Image::createAddressing(image), image.data);
        //The minimum threshold which to eliminate Harris Corner scores (computed using the normalized Sobel kernel).
        //set to 0, we'll filter it later by threshold
        ivx::Scalar strengthThresh = ivx::Scalar::create<VX_TYPE_FLOAT32>(context, 0);

        //The gradient window size to use on the input.
        vx_int32 gradientSize = _gradientSize;

        //The block window size used to compute the harris corner score
        vx_int32 blockSize = _blockSize;

        //The scalar sensitivity threshold k from the Harris-Stephens equation
        ivx::Scalar sensivity = ivx::Scalar::create<VX_TYPE_FLOAT32>(context, _harrisK);

        //The radial Euclidean distance for non-maximum suppression
        ivx::Scalar minDistance = ivx::Scalar::create<VX_TYPE_FLOAT32>(context, _minDistance);

        vx_size capacity = image.cols * image.rows;
        Array corners = Array::create(context, VX_TYPE_KEYPOINT, capacity);
        ivx::Scalar numCorners = ivx::Scalar::create<VX_TYPE_SIZE>(context, 0);

        IVX_CHECK_STATUS(vxuHarrisCorners(context, ovxImage, strengthThresh, minDistance, sensivity,
                                          gradientSize, blockSize, corners, numCorners));

        std::vector<vx_keypoint_t> vxKeypoints;
        corners.copyTo(vxKeypoints);

        std::sort(vxKeypoints.begin(), vxKeypoints.end(), VxKeypointsComparator());

        vx_float32 maxStrength = 0.0f;
        if(vxKeypoints.size() > 0)
            maxStrength = vxKeypoints[0].strength;
        size_t maxKeypoints = min((size_t)_maxCorners, vxKeypoints.size());
        std::vector<Point2f> keypoints;
        keypoints.reserve(maxKeypoints);
        for(size_t i = 0; i < maxKeypoints; i++)
        {
            vx_keypoint_t kp = vxKeypoints[i];
            if(kp.strength < maxStrength*_qualityLevel) break;
            keypoints.push_back(Point2f((float)kp.x, (float)kp.y));
        }

        Mat(keypoints).convertTo(_corners, _corners.fixedType() ? _corners.type() : CV_32F);

#ifdef VX_VERSION_1_1
        //we should take user memory back before release
        //(it's not done automatically according to standard)
        ovxImage.swapHandle();
#endif
    }
    catch (RuntimeError & e)
    {
        VX_DbgThrow(e.what());
    }
    catch (WrapperError & e)
    {
        VX_DbgThrow(e.what());
    }

    return true;
}

/* dst = src */
void Mat::copyTo( OutputArray _dst ) const
{
    int dtype = _dst.type();
    if( _dst.fixedType() && dtype != type() )
    {
        CV_Assert( channels() == CV_MAT_CN(dtype) );
        convertTo( _dst, dtype );
        return;
    }

    if( empty() )
    {
        _dst.release();
        return;
    }

    if( _dst.isUMat() )
    {
        _dst.create( dims, size.p, type() );
        UMat dst = _dst.getUMat();

        size_t i, sz[CV_MAX_DIM], dstofs[CV_MAX_DIM], esz = elemSize();
        for( i = 0; i < (size_t)dims; i++ )
            sz[i] = size.p[i];
        sz[dims-1] *= esz;
        dst.ndoffset(dstofs);
        dstofs[dims-1] *= esz;
        dst.u->currAllocator->upload(dst.u, data, dims, sz, dstofs, dst.step.p, step.p);
        return;
    }

    if( dims <= 2 )
    {
        _dst.create( rows, cols, type() );
        Mat dst = _dst.getMat();
        if( data == dst.data )
            return;

        if( rows > 0 && cols > 0 )
        {
            const uchar* sptr = data;
            uchar* dptr = dst.data;

            Size sz = getContinuousSize(*this, dst);
            size_t len = sz.width*elemSize();

            for( ; sz.height--; sptr += step, dptr += dst.step )
                memcpy( dptr, sptr, len );
        }
        return;
    }

    _dst.create( dims, size, type() );
    Mat dst = _dst.getMat();
    if( data == dst.data )
        return;

    if( total() != 0 )
    {
        const Mat* arrays[] = { this, &dst };
        uchar* ptrs[2];
        NAryMatIterator it(arrays, ptrs, 2);
        size_t sz = it.size*elemSize();

        for( size_t i = 0; i < it.nplanes; i++, ++it )
            memcpy(ptrs[1], ptrs[0], sz);
    }
}

    static bool openvx_sobel(InputArray _src, OutputArray _dst,
                             int dx, int dy, int ksize,
                             double scale, double delta, int borderType)
    {
        if (_src.type() != CV_8UC1 || _dst.type() != CV_16SC1 ||
            ksize != 3 || scale != 1.0 || delta != 0.0 ||
            (dx | dy) != 1 || (dx + dy) != 1 ||
            _src.cols() < ksize || _src.rows() < ksize ||
            ovx::skipSmallImages<VX_KERNEL_SOBEL_3x3>(_src.cols(), _src.rows())
            )
            return false;

        Mat src = _src.getMat();
        Mat dst = _dst.getMat();

        if ((borderType & BORDER_ISOLATED) == 0 && src.isSubmatrix())
            return false; //Process isolated borders only
        vx_enum border;
        switch (borderType & ~BORDER_ISOLATED)
        {
        case BORDER_CONSTANT:
            border = VX_BORDER_CONSTANT;
            break;
        case BORDER_REPLICATE:
//            border = VX_BORDER_REPLICATE;
//            break;
        default:
            return false;
        }

        try
        {
            ivx::Context ctx = ovx::getOpenVXContext();
            //if ((vx_size)ksize > ctx.convolutionMaxDimension())
            //    return false;

            Mat a;
            if (dst.data != src.data)
                a = src;
            else
                src.copyTo(a);

            ivx::Image
                ia = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,
                    ivx::Image::createAddressing(a.cols, a.rows, 1, (vx_int32)(a.step)), a.data),
                ib = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_S16,
                    ivx::Image::createAddressing(dst.cols, dst.rows, 2, (vx_int32)(dst.step)), dst.data);

            //ATTENTION: VX_CONTEXT_IMMEDIATE_BORDER attribute change could lead to strange issues in multi-threaded environments
            //since OpenVX standard says nothing about thread-safety for now
            ivx::border_t prevBorder = ctx.immediateBorder();
            ctx.setImmediateBorder(border, (vx_uint8)(0));
            if(dx)
                ivx::IVX_CHECK_STATUS(vxuSobel3x3(ctx, ia, ib, NULL));
            else
                ivx::IVX_CHECK_STATUS(vxuSobel3x3(ctx, ia, NULL, ib));
            ctx.setImmediateBorder(prevBorder);
        }
        catch (ivx::RuntimeError & e)
        {
            VX_DbgThrow(e.what());
        }
        catch (ivx::WrapperError & e)
        {
            VX_DbgThrow(e.what());
        }

        return true;
    }