C++ OutputArray::isUMat方法代码示例

void cv::blendLinear( InputArray _src1, InputArray _src2, InputArray _weights1, InputArray _weights2, OutputArray _dst )
{
    int type = _src1.type(), depth = CV_MAT_DEPTH(type);
    Size size = _src1.size();

    CV_Assert(depth == CV_8U || depth == CV_32F);
    CV_Assert(size == _src2.size() && size == _weights1.size() && size == _weights2.size());
    CV_Assert(type == _src2.type() && _weights1.type() == CV_32FC1 && _weights2.type() == CV_32FC1);

    _dst.create(size, type);

    CV_OCL_RUN(_dst.isUMat(),
               ocl_blendLinear(_src1, _src2, _weights1, _weights2, _dst))

    Mat src1 = _src1.getMat(), src2 = _src2.getMat(), weights1 = _weights1.getMat(),
            weights2 = _weights2.getMat(), dst = _dst.getMat();

    if (depth == CV_8U)
    {
        BlendLinearInvoker<uchar> invoker(src1, src2, weights1, weights2, dst);
        parallel_for_(Range(0, src1.rows), invoker, dst.total()/(double)(1<<16));
    }
    else if (depth == CV_32F)
    {
        BlendLinearInvoker<float> invoker(src1, src2, weights1, weights2, dst);
        parallel_for_(Range(0, src1.rows), invoker, dst.total()/(double)(1<<16));
    }
}

void repeat(InputArray _src, int ny, int nx, OutputArray _dst)
{
    CV_Assert( _src.dims() <= 2 );
    CV_Assert( ny > 0 && nx > 0 );

    Size ssize = _src.size();
    _dst.create(ssize.height*ny, ssize.width*nx, _src.type());

    CV_OCL_RUN(_dst.isUMat(),
               ocl_repeat(_src, ny, nx, _dst))

    Mat src = _src.getMat(), dst = _dst.getMat();
    Size dsize = dst.size();
    int esz = (int)src.elemSize();
    int x, y;
    ssize.width *= esz; dsize.width *= esz;

    for( y = 0; y < ssize.height; y++ )
    {
        for( x = 0; x < dsize.width; x += ssize.width )
            memcpy( dst.data + y*dst.step + x, src.data + y*src.step, ssize.width );
    }

    for( ; y < dsize.height; y++ )
        memcpy( dst.data + y*dst.step, dst.data + (y - ssize.height)*dst.step, dsize.width );
}

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);
}

    void BTVL1::processImpl(Ptr<FrameSource>& frameSource, OutputArray _output)
    {
        if (outPos_ >= storePos_)
        {
            _output.release();
            return;
        }

        readNextFrame(frameSource);

        if (procPos_ < storePos_)
        {
            ++procPos_;
            processFrame(procPos_);
        }
        ++outPos_;

        CV_OCL_RUN(isUmat_,
                   ocl_processImpl(frameSource, _output))

        const Mat& curOutput = at(outPos_, outputs_);

        if (_output.kind() < _InputArray::OPENGL_BUFFER || _output.isUMat())
            curOutput.convertTo(_output, CV_8U);
        else
        {
            curOutput.convertTo(finalOutput_, CV_8U);
            arrCopy(finalOutput_, _output);
        }
    }

void cv::superres::arrCopy(InputArray src, OutputArray dst)
{
    if (dst.isUMat() || src.isUMat())
    {
        src.copyTo(dst);
        return;
    }

    typedef void (*func_t)(InputArray src, OutputArray dst);
    static const func_t funcs[10][10] =
    {
        { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 },
        { 0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0, mat2gpu },
        { 0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0, mat2gpu },
        { 0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0, mat2gpu },
        { 0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0, mat2gpu },
        { 0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0, mat2gpu },
        { 0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, 0, mat2gpu },
        { 0, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, 0, buf2arr },
        { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 },
        { 0, gpu2mat, gpu2mat, gpu2mat, gpu2mat, gpu2mat, gpu2mat, arr2buf, 0 , gpu2gpu },
    };

    const int src_kind = src.kind() >> _InputArray::KIND_SHIFT;
    const int dst_kind = dst.kind() >> _InputArray::KIND_SHIFT;

    CV_Assert( src_kind >= 0 && src_kind < 10 );
    CV_Assert( dst_kind >= 0 && dst_kind < 10 );

    const func_t func = funcs[src_kind][dst_kind];
    CV_Assert( func != 0 );

    func(src, dst);
}

void cv::superres::SuperResolution::nextFrame(OutputArray frame)
{
    isUmat_ = frame.isUMat();

    if (firstCall_)
    {
        initImpl(frameSource_);
        firstCall_ = false;
    }

    processImpl(frameSource_, frame);
}

    void calcBtvRegularization(InputArray _src, OutputArray _dst, int btvKernelSize,
                               const std::vector<float>& btvWeights, const UMat & ubtvWeights)
    {
        CV_OCL_RUN(_dst.isUMat(),
                   ocl_calcBtvRegularization(_src, _dst, btvKernelSize, ubtvWeights))
        (void)ubtvWeights;

        typedef void (*func_t)(InputArray _src, OutputArray _dst, int btvKernelSize, const std::vector<float>& btvWeights);
        static const func_t funcs[] =
        {
            0, calcBtvRegularizationImpl<float>, 0, calcBtvRegularizationImpl<Point3f>, 0
        };

        const func_t func = funcs[_src.channels()];
        CV_Assert(func != 0);
        func(_src, _dst, btvKernelSize, btvWeights);
    }

void UMat::copyTo(OutputArray _dst, InputArray _mask) const
{
    if( _mask.empty() )
    {
        copyTo(_dst);
        return;
    }
#ifdef HAVE_OPENCL
    int cn = channels(), mtype = _mask.type(), mdepth = CV_MAT_DEPTH(mtype), mcn = CV_MAT_CN(mtype);
    CV_Assert( mdepth == CV_8U && (mcn == 1 || mcn == cn) );

    if (ocl::useOpenCL() && _dst.isUMat() && dims <= 2)
    {
        UMatData * prevu = _dst.getUMat().u;
        _dst.create( dims, size, type() );

        UMat dst = _dst.getUMat();

        bool haveDstUninit = false;
        if( prevu != dst.u ) // do not leave dst uninitialized
            haveDstUninit = true;

        String opts = format("-D COPY_TO_MASK -D T1=%s -D scn=%d -D mcn=%d%s",
                             ocl::memopTypeToStr(depth()), cn, mcn,
                             haveDstUninit ? " -D HAVE_DST_UNINIT" : "");

        ocl::Kernel k("copyToMask", ocl::core::copyset_oclsrc, opts);
        if (!k.empty())
        {
            k.args(ocl::KernelArg::ReadOnlyNoSize(*this),
                   ocl::KernelArg::ReadOnlyNoSize(_mask.getUMat()),
                   haveDstUninit ? ocl::KernelArg::WriteOnly(dst) :
                                   ocl::KernelArg::ReadWrite(dst));

            size_t globalsize[2] = { cols, rows };
            if (k.run(2, globalsize, NULL, false))
            {
                CV_IMPL_ADD(CV_IMPL_OCL);
                return;
            }
        }
    }
#endif
    Mat src = getMat(ACCESS_READ);
    src.copyTo(_dst, _mask);
}

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 upscale(InputArray _src, OutputArray _dst, int scale)
    {
        int cn = _src.channels();
        CV_Assert( cn == 1 || cn == 3 || cn == 4 );

        CV_OCL_RUN(_dst.isUMat(),
                   ocl_upscale(_src, _dst, scale))

        typedef void (*func_t)(InputArray src, OutputArray dst, int scale);
        static const func_t funcs[] =
        {
            0, upscaleImpl<float>, 0, upscaleImpl<Point3f>, upscaleImpl<Point4f>
        };

        const func_t func = funcs[cn];
        CV_Assert(func != 0);
        func(_src, _dst, scale);
    }

 void calcBtvRegularization(InputArray _src, OutputArray _dst, int btvKernelSize,
                            const std::vector<float>& btvWeights, const UMat & ubtvWeights)
 {
     CV_OCL_RUN(_dst.isUMat(),
                ocl_calcBtvRegularization(_src, _dst, btvKernelSize, ubtvWeights))
     CV_UNUSED(ubtvWeights);
     if (_src.channels() == 1)
     {
         calcBtvRegularizationImpl<float>(_src, _dst, btvKernelSize, btvWeights);
     }
     else if (_src.channels() == 3)
     {
         calcBtvRegularizationImpl<Point3f>(_src, _dst, btvKernelSize, btvWeights);
     }
     else
     {
         CV_Error(Error::StsBadArg, "Unsupported number of channels in _src");
     }
 }

void flip( InputArray _src, OutputArray _dst, int flip_mode )
{
    CV_Assert( _src.dims() <= 2 );

    CV_OCL_RUN( _dst.isUMat(), ocl_flip(_src,_dst, flip_mode))

    Mat src = _src.getMat();
    _dst.create( src.size(), src.type() );
    Mat dst = _dst.getMat();
    size_t esz = src.elemSize();

    if( flip_mode <= 0 )
        flipVert( src.data, src.step, dst.data, dst.step, src.size(), esz );
    else
        flipHoriz( src.data, src.step, dst.data, dst.step, src.size(), esz );

    if( flip_mode < 0 )
        flipHoriz( dst.data, dst.step, dst.data, dst.step, dst.size(), esz );
}

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 diffSign(InputArray _src1, OutputArray _src2, OutputArray _dst)
    {
        CV_OCL_RUN(_dst.isUMat(),
                   ocl_diffSign(_src1, _src2, _dst))

        Mat src1 = _src1.getMat(), src2 = _src2.getMat();
        _dst.create(src1.size(), src1.type());
        Mat dst = _dst.getMat();

        const int count = src1.cols * src1.channels();

        for (int y = 0; y < src1.rows; ++y)
        {
            const float * const src1Ptr = src1.ptr<float>(y);
            const float * const src2Ptr = src2.ptr<float>(y);
            float* dstPtr = dst.ptr<float>(y);

            for (int x = 0; x < count; ++x)
                dstPtr[x] = diffSign(src1Ptr[x], src2Ptr[x]);
        }
    }

//static
void QualityBRISQUE::computeFeatures(InputArray img, OutputArray features)
{
    CV_Assert(features.needed());
    CV_Assert(img.isMat());
    CV_Assert(!img.getMat().empty());

    auto mat = mat_convert(img.getMat());

    const auto vals = ComputeBrisqueFeature(mat);
    cv::Mat valmat( cv::Size( (int)vals.size(), 1 ), CV_32FC1, (void*)vals.data()); // create row vector, type depends on brisque_calc_element_type

    if (features.isUMat())
        valmat.copyTo(features.getUMatRef());
    else if (features.isMat())
        // how to move data instead?
        // if calling this:
        //      features.getMatRef() = valmat;
        //  then shared data is erased when valmat is released, corrupting the data in the outputarray for the caller
        valmat.copyTo(features.getMatRef());
    else
        CV_Error(cv::Error::StsNotImplemented, "Unsupported output type");
}