C++ GpuMat::convertTo方法代码示例

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::PyrLKOpticalFlow::dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err)
{
    using namespace cv::gpu::device::pyrlk;

    CV_Assert(prevImg.type() == CV_8UC1);
    CV_Assert(prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type());
    CV_Assert(maxLevel >= 0);
    CV_Assert(winSize.width > 2 && winSize.height > 2);

    if (err)
        err->create(prevImg.size(), CV_32FC1);

    // build the image pyramids.

    prevPyr_.resize(maxLevel + 1);
    nextPyr_.resize(maxLevel + 1);

    prevPyr_[0] = prevImg;
    nextImg.convertTo(nextPyr_[0], CV_32F);

    for (int level = 1; level <= maxLevel; ++level)
    {
        pyrDown(prevPyr_[level - 1], prevPyr_[level]);
        pyrDown(nextPyr_[level - 1], nextPyr_[level]);
    }

    uPyr_.resize(2);
    vPyr_.resize(2);

    ensureSizeIsEnough(prevImg.size(), CV_32FC1, uPyr_[0]);
    ensureSizeIsEnough(prevImg.size(), CV_32FC1, vPyr_[0]);
    ensureSizeIsEnough(prevImg.size(), CV_32FC1, uPyr_[1]);
    ensureSizeIsEnough(prevImg.size(), CV_32FC1, vPyr_[1]);
    uPyr_[1].setTo(Scalar::all(0));
    vPyr_[1].setTo(Scalar::all(0));

    int2 winSize2i = make_int2(winSize.width, winSize.height);
    loadConstants(winSize2i, iters);

    DevMem2Df derr = err ? *err : DevMem2Df();

    int idx = 0;

    for (int level = maxLevel; level >= 0; level--)
    {
        int idx2 = (idx + 1) & 1;

        lkDense_gpu(prevPyr_[level], nextPyr_[level], uPyr_[idx], vPyr_[idx], uPyr_[idx2], vPyr_[idx2],
            level == 0 ? derr : DevMem2Df(), winSize2i);

        if (level > 0)
            idx = idx2;
    }

    uPyr_[idx].copyTo(u);
    vPyr_[idx].copyTo(v);
}

__host__ GpuMat_<T>::GpuMat_(const GpuMat& m, Allocator* allocator)
    : GpuMat(allocator)
{
    flags = (flags & ~CV_MAT_TYPE_MASK) | DataType<T>::type;

    if (DataType<T>::type == m.type())
    {
        GpuMat::operator =(m);
        return;
    }

    if (DataType<T>::depth == m.depth())
    {
        GpuMat::operator =(m.reshape(DataType<T>::channels, m.rows));
        return;
    }

    CV_Assert( DataType<T>::channels == m.channels() );
    m.convertTo(*this, type());
}

//.........这里部分代码省略.........
            *buf_ptrs[_r] = sub2.rowRange(_r * sub2.rows/5, (_r+1) * sub2.rows/5);
            assert(buf_ptrs[_r]->cols == cols && buf_ptrs[_r]->rows == rows * rthis.nr_plane);
        }
    };

    size_t elem_step = mbuf.step / sizeof(T);

    Size temp_size = data_cost.size();
    if ((size_t)temp_size.area() < elem_step * rows_pyr[levels - 1] * rthis.ndisp)
        temp_size = Size(static_cast<int>(elem_step), rows_pyr[levels - 1] * rthis.ndisp);

    temp.create(temp_size, DataType<T>::type);

    ////////////////////////////////////////////////////////////////////////////
    // Compute

    load_constants(rthis.ndisp, rthis.max_data_term, rthis.data_weight, rthis.max_disc_term, rthis.disc_single_jump, rthis.min_disp_th, left, right, temp);

    if (stream)
    {
        stream.enqueueMemSet(l[0], zero);
        stream.enqueueMemSet(d[0], zero);
        stream.enqueueMemSet(r[0], zero);
        stream.enqueueMemSet(u[0], zero);

        stream.enqueueMemSet(l[1], zero);
        stream.enqueueMemSet(d[1], zero);
        stream.enqueueMemSet(r[1], zero);
        stream.enqueueMemSet(u[1], zero);

        stream.enqueueMemSet(data_cost, zero);
        stream.enqueueMemSet(data_cost_selected, zero);
    }
    else
    {
        l[0].setTo(zero);
        d[0].setTo(zero);
        r[0].setTo(zero);
        u[0].setTo(zero);

        l[1].setTo(zero);
        d[1].setTo(zero);
        r[1].setTo(zero);
        u[1].setTo(zero);

        data_cost.setTo(zero);
        data_cost_selected.setTo(zero);
    }

    int cur_idx = 0;

    for (int i = levels - 1; i >= 0; i--)
    {
        if (i == levels - 1)
        {
            init_data_cost(left.rows, left.cols, disp_selected_pyr[cur_idx].ptr<T>(), data_cost_selected.ptr<T>(),
                elem_step, rows_pyr[i], cols_pyr[i], i, nr_plane_pyr[i], rthis.ndisp, left.channels(), rthis.use_local_init_data_cost, cudaStream);
        }
        else
        {
            compute_data_cost(disp_selected_pyr[cur_idx].ptr<T>(), data_cost.ptr<T>(), elem_step,
                left.rows, left.cols, rows_pyr[i], cols_pyr[i], rows_pyr[i+1], i, nr_plane_pyr[i+1], left.channels(), cudaStream);

            int new_idx = (cur_idx + 1) & 1;

            init_message(u[new_idx].ptr<T>(), d[new_idx].ptr<T>(), l[new_idx].ptr<T>(), r[new_idx].ptr<T>(),
                         u[cur_idx].ptr<T>(), d[cur_idx].ptr<T>(), l[cur_idx].ptr<T>(), r[cur_idx].ptr<T>(),
                         disp_selected_pyr[new_idx].ptr<T>(), disp_selected_pyr[cur_idx].ptr<T>(),
                         data_cost_selected.ptr<T>(), data_cost.ptr<T>(), elem_step, rows_pyr[i],
                         cols_pyr[i], nr_plane_pyr[i], rows_pyr[i+1], cols_pyr[i+1], nr_plane_pyr[i+1], cudaStream);

            cur_idx = new_idx;
        }

        calc_all_iterations(u[cur_idx].ptr<T>(), d[cur_idx].ptr<T>(), l[cur_idx].ptr<T>(), r[cur_idx].ptr<T>(),
                            data_cost_selected.ptr<T>(), disp_selected_pyr[cur_idx].ptr<T>(), elem_step,
                            rows_pyr[i], cols_pyr[i], nr_plane_pyr[i], rthis.iters, cudaStream);
    }

    if (disp.empty())
        disp.create(rows, cols, CV_16S);

    out = ((disp.type() == CV_16S) ? disp : (out.create(rows, cols, CV_16S), out));

    if (stream)
        stream.enqueueMemSet(out, zero);
    else
        out.setTo(zero);

    compute_disp(u[cur_idx].ptr<T>(), d[cur_idx].ptr<T>(), l[cur_idx].ptr<T>(), r[cur_idx].ptr<T>(),
                 data_cost_selected.ptr<T>(), disp_selected_pyr[cur_idx].ptr<T>(), elem_step, out, nr_plane_pyr[0], cudaStream);

    if (disp.type() != CV_16S)
    {
        if (stream)
            stream.enqueueConvert(out, disp, disp.type());
        else
            out.convertTo(disp, disp.type());
    }
}

//.........这里部分代码省略.........
    const int n = 64;
    step_pyr[0] = alignSize(cols * sizeof(T), n) / sizeof(T);
    for (int i = 1; i < levels; i++)
    {
        cols_pyr[i] = (cols_pyr[i-1] + 1) / 2;
        rows_pyr[i] = (rows_pyr[i-1] + 1) / 2;

        nr_plane_pyr[i] = nr_plane_pyr[i-1] * 2;

        step_pyr[i] = alignSize(cols_pyr[i] * sizeof(T), n) / sizeof(T);
    }

    Size msg_size(step_pyr[0], rows * nr_plane_pyr[0]);
    Size data_cost_size(step_pyr[0], rows * nr_plane_pyr[0] * 2);

    u[0].create(msg_size, DataType<T>::type);
    d[0].create(msg_size, DataType<T>::type);
    l[0].create(msg_size, DataType<T>::type);
    r[0].create(msg_size, DataType<T>::type);

    u[1].create(msg_size, DataType<T>::type);
    d[1].create(msg_size, DataType<T>::type);
    l[1].create(msg_size, DataType<T>::type);
    r[1].create(msg_size, DataType<T>::type);

    disp_selected_pyr[0].create(msg_size, DataType<T>::type);
    disp_selected_pyr[1].create(msg_size, DataType<T>::type);

    data_cost.create(data_cost_size, DataType<T>::type);
    data_cost_selected.create(msg_size, DataType<T>::type);

    step_pyr[0] = data_cost.step / sizeof(T);

    Size temp_size = data_cost_size;
    if (data_cost_size.width * data_cost_size.height < step_pyr[levels - 1] * rows_pyr[levels - 1] * rthis.ndisp)
        temp_size = Size(step_pyr[levels - 1], rows_pyr[levels - 1] * rthis.ndisp);

    temp.create(temp_size, DataType<T>::type);

    ////////////////////////////////////////////////////////////////////////////
    // Compute

    csbp::load_constants(rthis.ndisp, rthis.max_data_term, rthis.data_weight,
        rthis.max_disc_term, rthis.disc_single_jump, rthis.min_disp_th, left, right, temp);

    l[0] = zero;
    d[0] = zero;
    r[0] = zero;
    u[0] = zero;

    l[1] = zero;
    d[1] = zero;
    r[1] = zero;
    u[1] = zero;

    data_cost = zero;
    data_cost_selected = zero;

    int cur_idx = 0;

    for (int i = levels - 1; i >= 0; i--)
    {
        if (i == levels - 1)
        {
            csbp::init_data_cost(left.rows, left.cols, disp_selected_pyr[cur_idx].ptr<T>(), data_cost_selected.ptr<T>(),
                step_pyr[i], rows_pyr[i], cols_pyr[i], i, nr_plane_pyr[i], rthis.ndisp, left.channels(), rthis.use_local_init_data_cost, stream);
        }
        else
        {
            csbp::compute_data_cost(disp_selected_pyr[cur_idx].ptr<T>(), data_cost.ptr<T>(), step_pyr[i], step_pyr[i+1],
                left.rows, left.cols, rows_pyr[i], cols_pyr[i], rows_pyr[i+1], i, nr_plane_pyr[i+1], left.channels(), stream);

            int new_idx = (cur_idx + 1) & 1;

            csbp::init_message(u[new_idx].ptr<T>(), d[new_idx].ptr<T>(), l[new_idx].ptr<T>(), r[new_idx].ptr<T>(),
                               u[cur_idx].ptr<T>(), d[cur_idx].ptr<T>(), l[cur_idx].ptr<T>(), r[cur_idx].ptr<T>(),
                               disp_selected_pyr[new_idx].ptr<T>(), disp_selected_pyr[cur_idx].ptr<T>(),
                               data_cost_selected.ptr<T>(), data_cost.ptr<T>(), step_pyr[i], step_pyr[i+1], rows_pyr[i],
                               cols_pyr[i], nr_plane_pyr[i], rows_pyr[i+1], cols_pyr[i+1], nr_plane_pyr[i+1], stream);

            cur_idx = new_idx;
        }

        csbp::calc_all_iterations(u[cur_idx].ptr<T>(), d[cur_idx].ptr<T>(), l[cur_idx].ptr<T>(), r[cur_idx].ptr<T>(),
                                  data_cost_selected.ptr<T>(), disp_selected_pyr[cur_idx].ptr<T>(), step_pyr[i],
                                  rows_pyr[i], cols_pyr[i], nr_plane_pyr[i], rthis.iters, stream);
    }

    if (disp.empty())
        disp.create(rows, cols, CV_16S);

    out = ((disp.type() == CV_16S) ? disp : (out.create(rows, cols, CV_16S), out));
    out = zero;

    csbp::compute_disp(u[cur_idx].ptr<T>(), d[cur_idx].ptr<T>(), l[cur_idx].ptr<T>(), r[cur_idx].ptr<T>(),
                       data_cost_selected.ptr<T>(), disp_selected_pyr[cur_idx].ptr<T>(), step_pyr[0], out, nr_plane_pyr[0], stream);

    if (disp.type() != CV_16S)
        out.convertTo(disp, disp.type());
}

void cv::gpu::OpticalFlowDual_TVL1_GPU::operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy)
{
    CV_Assert( I0.type() == CV_8UC1 || I0.type() == CV_32FC1 );
    CV_Assert( I0.size() == I1.size() );
    CV_Assert( I0.type() == I1.type() );
    CV_Assert( !useInitialFlow || (flowx.size() == I0.size() && flowx.type() == CV_32FC1 && flowy.size() == flowx.size() && flowy.type() == flowx.type()) );
    CV_Assert( nscales > 0 );

    // allocate memory for the pyramid structure
    I0s.resize(nscales);
    I1s.resize(nscales);
    u1s.resize(nscales);
    u2s.resize(nscales);

    I0.convertTo(I0s[0], CV_32F, I0.depth() == CV_8U ? 1.0 : 255.0);
    I1.convertTo(I1s[0], CV_32F, I1.depth() == CV_8U ? 1.0 : 255.0);

    if (!useInitialFlow)
    {
        flowx.create(I0.size(), CV_32FC1);
        flowy.create(I0.size(), CV_32FC1);
    }

    u1s[0] = flowx;
    u2s[0] = flowy;

    I1x_buf.create(I0.size(), CV_32FC1);
    I1y_buf.create(I0.size(), CV_32FC1);

    I1w_buf.create(I0.size(), CV_32FC1);
    I1wx_buf.create(I0.size(), CV_32FC1);
    I1wy_buf.create(I0.size(), CV_32FC1);

    grad_buf.create(I0.size(), CV_32FC1);
    rho_c_buf.create(I0.size(), CV_32FC1);

    p11_buf.create(I0.size(), CV_32FC1);
    p12_buf.create(I0.size(), CV_32FC1);
    p21_buf.create(I0.size(), CV_32FC1);
    p22_buf.create(I0.size(), CV_32FC1);

    diff_buf.create(I0.size(), CV_32FC1);

    // create the scales
    for (int s = 1; s < nscales; ++s)
    {
        gpu::resize(I0s[s-1], I0s[s], Size(), scaleStep, scaleStep);
        gpu::resize(I1s[s-1], I1s[s], Size(), scaleStep, scaleStep);

        if (I0s[s].cols < 16 || I0s[s].rows < 16)
        {
            nscales = s;
            break;
        }

        if (useInitialFlow)
        {
            gpu::resize(u1s[s-1], u1s[s], Size(), scaleStep, scaleStep);
            gpu::resize(u2s[s-1], u2s[s], Size(), scaleStep, scaleStep);

            gpu::multiply(u1s[s], Scalar::all(scaleStep), u1s[s]);
            gpu::multiply(u2s[s], Scalar::all(scaleStep), u2s[s]);
        }
        else
        {
            u1s[s].create(I0s[s].size(), CV_32FC1);
            u2s[s].create(I0s[s].size(), CV_32FC1);
        }
    }

    if (!useInitialFlow)
    {
        u1s[nscales-1].setTo(Scalar::all(0));
        u2s[nscales-1].setTo(Scalar::all(0));
    }

    // pyramidal structure for computing the optical flow
    for (int s = nscales - 1; s >= 0; --s)
    {
        // compute the optical flow at the current scale
        procOneScale(I0s[s], I1s[s], u1s[s], u2s[s]);

        // if this was the last scale, finish now
        if (s == 0)
            break;

        // otherwise, upsample the optical flow

        // zoom the optical flow for the next finer scale
        gpu::resize(u1s[s], u1s[s - 1], I0s[s - 1].size());
        gpu::resize(u2s[s], u2s[s - 1], I0s[s - 1].size());

        // scale the optical flow with the appropriate zoom factor
        gpu::multiply(u1s[s - 1], Scalar::all(1/scaleStep), u1s[s - 1]);
        gpu::multiply(u2s[s - 1], Scalar::all(1/scaleStep), u2s[s - 1]);
    }
}

void cv::gpu::PyrLKOpticalFlow::sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err)
{
    using namespace cv::gpu::device::pyrlk;

    if (prevPts.empty())
    {
        nextPts.release();
        status.release();
        if (err) err->release();
        return;
    }

    dim3 block, patch;
    calcPatchSize(winSize, block, patch, isDeviceArch11_);

    CV_Assert(prevImg.type() == CV_8UC1 || prevImg.type() == CV_8UC3 || prevImg.type() == CV_8UC4);
    CV_Assert(prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type());
    CV_Assert(maxLevel >= 0);
    CV_Assert(winSize.width > 2 && winSize.height > 2);
    CV_Assert(patch.x > 0 && patch.x < 6 && patch.y > 0 && patch.y < 6);
    CV_Assert(prevPts.rows == 1 && prevPts.type() == CV_32FC2);

    if (useInitialFlow)
        CV_Assert(nextPts.size() == prevPts.size() && nextPts.type() == CV_32FC2);
    else
        ensureSizeIsEnough(1, prevPts.cols, prevPts.type(), nextPts);

    GpuMat temp1 = (useInitialFlow ? nextPts : prevPts).reshape(1);
    GpuMat temp2 = nextPts.reshape(1);
    multiply(temp1, Scalar::all(1.0 / (1 << maxLevel) / 2.0), temp2);

    ensureSizeIsEnough(1, prevPts.cols, CV_8UC1, status);
    status.setTo(Scalar::all(1));

    if (err)
        ensureSizeIsEnough(1, prevPts.cols, CV_32FC1, *err);

    // build the image pyramids.

    prevPyr_.resize(maxLevel + 1);
    nextPyr_.resize(maxLevel + 1);

    int cn = prevImg.channels();

    if (cn == 1 || cn == 4)
    {
        prevImg.convertTo(prevPyr_[0], CV_32F);
        nextImg.convertTo(nextPyr_[0], CV_32F);
    }
    else
    {
        cvtColor(prevImg, dx_calcBuf_, COLOR_BGR2BGRA);
        dx_calcBuf_.convertTo(prevPyr_[0], CV_32F);

        cvtColor(nextImg, dx_calcBuf_, COLOR_BGR2BGRA);
        dx_calcBuf_.convertTo(nextPyr_[0], CV_32F);
    }

    for (int level = 1; level <= maxLevel; ++level)
    {
        pyrDown(prevPyr_[level - 1], prevPyr_[level]);
        pyrDown(nextPyr_[level - 1], nextPyr_[level]);
    }

    loadConstants(make_int2(winSize.width, winSize.height), iters);

    for (int level = maxLevel; level >= 0; level--)
    {
        if (cn == 1)
        {
            lkSparse1_gpu(prevPyr_[level], nextPyr_[level],
                prevPts.ptr<float2>(), nextPts.ptr<float2>(), status.ptr(), level == 0 && err ? err->ptr<float>() : 0, prevPts.cols,
                level, block, patch);
        }
        else
        {
            lkSparse4_gpu(prevPyr_[level], nextPyr_[level],
                prevPts.ptr<float2>(), nextPts.ptr<float2>(), status.ptr(), level == 0 && err ? err->ptr<float>() : 0, prevPts.cols,
                level, block, patch);
        }
    }
}

inline
void Stream::enqueueConvert(const GpuMat& src, OutputArray dst, int dtype, double alpha, double beta)
{
    src.convertTo(dst, dtype, alpha, beta, *this);
}