C++ OutputArray::release方法代码示例

void myConvexityDefects( InputArray _points, InputArray _hull, OutputArray _defects ) {
    Mat points = _points.getMat();
    int ptnum = points.checkVector(2, CV_32S);
    CV_Assert( ptnum > 3 );
    Mat hull = _hull.getMat();
    CV_Assert( hull.checkVector(1, CV_32S) > 2 );

    std::vector<CvConvexityDefect> seq;
    convexityDefects(_points, _hull, seq);

    if( seq.size() == 0 ) {
        _defects.release();
        return;
    }

    _defects.create(seq.size(), 1, CV_32SC4);
    Mat defects = _defects.getMat();

    auto it = seq.begin();
    CvPoint* ptorg = (CvPoint*)points.data;

    for( unsigned i = 0;  i < seq.size();  ++i, ++it ) {
        CvConvexityDefect& d = *it;
        int idx0 = (int)(d.start - ptorg);
        int idx1 = (int)(d.end - ptorg);
        int idx2 = (int)(d.depth_point - ptorg);
        CV_Assert( 0 <= idx0 && idx0 < ptnum );
        CV_Assert( 0 <= idx1 && idx1 < ptnum );
        CV_Assert( 0 <= idx2 && idx2 < ptnum );
        CV_Assert( d.depth >= 0 );
        int idepth = cvRound(d.depth*256);
        defects.at<Vec4i>(i) = Vec4i(idx0, idx1, idx2, idepth);
    }
}

    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::hconcat(const Mat* src, size_t nsrc, OutputArray _dst)
{
    CV_INSTRUMENT_REGION();

    if( nsrc == 0 || !src )
    {
        _dst.release();
        return;
    }

    int totalCols = 0, cols = 0;
    for( size_t i = 0; i < nsrc; i++ )
    {
        CV_Assert( src[i].dims <= 2 &&
                   src[i].rows == src[0].rows &&
                   src[i].type() == src[0].type());
        totalCols += src[i].cols;
    }
    _dst.create( src[0].rows, totalCols, src[0].type());
    Mat dst = _dst.getMat();
    for( size_t i = 0; i < nsrc; i++ )
    {
        Mat dpart = dst(Rect(cols, 0, src[i].cols, src[i].rows));
        src[i].copyTo(dpart);
        cols += src[i].cols;
    }
}

bool VideoCapture::read(OutputArray image)
{
    if(grab())
        retrieve(image);
    else
        image.release();
    return !image.empty();
}

/* 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);
    }
}

bool VideoCapture::read(OutputArray image)
{
    CV_INSTRUMENT_REGION();

    if(grab())
        retrieve(image);
    else
        image.release();
    return !image.empty();
}

void DescriptorExtractor::compute( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const
{
    if( image.empty() || keypoints.empty() )
    {
        descriptors.release();
        return;
    }

    KeyPointsFilter::runByImageBorder( keypoints, image.size(), 0 );
    KeyPointsFilter::runByKeypointSize( keypoints, std::numeric_limits<float>::epsilon() );

    computeImpl( image, keypoints, descriptors );
}

/*
 * Compute the descriptors for a set of keypoints in an image.
 * image        The image.
 * keypoints    The input keypoints. Keypoints for which a descriptor cannot be computed are removed.
 * descriptors  Copmputed descriptors. Row i is the descriptor for keypoint i.
 */
void Feature2D::compute( InputArray image,
                         std::vector<KeyPoint>& keypoints,
                         OutputArray descriptors )
{
    CV_INSTRUMENT_REGION();

    if( image.empty() )
    {
        descriptors.release();
        return;
    }
    detectAndCompute(image, noArray(), keypoints, descriptors, true);
}

void cv::gpu::GeneralizedHough_GPU::download(const GpuMat& d_positions, OutputArray h_positions_, OutputArray h_votes_)
{
    if (d_positions.empty())
    {
        h_positions_.release();
        if (h_votes_.needed())
            h_votes_.release();
        return;
    }

    CV_Assert(d_positions.rows == 2 && d_positions.type() == CV_32FC4);

    h_positions_.create(1, d_positions.cols, CV_32FC4);
    Mat h_positions = h_positions_.getMat();
    d_positions.row(0).download(h_positions);

    if (h_votes_.needed())
    {
        h_votes_.create(1, d_positions.cols, CV_32SC3);
        Mat h_votes = h_votes_.getMat();
        GpuMat d_votes(1, d_positions.cols, CV_32SC3, const_cast<int3*>(d_positions.ptr<int3>(1)));
        d_votes.download(h_votes);
    }
}

bool VideoCapture::retrieve(OutputArray image, int channel)
{
    IplImage* _img = cvRetrieveFrame(cap, channel);
    if( !_img )
    {
        image.release();
        return false;
    }
    if(_img->origin == IPL_ORIGIN_TL)
        cv::cvarrToMat(_img).copyTo(image);
    else
    {
        Mat temp = cv::cvarrToMat(_img);
        flip(temp, image, 0);
    }
    return true;
}

void cv::viz::computeNormals(const Mesh& mesh, OutputArray _normals)
{
    vtkSmartPointer<vtkPolyData> polydata = getPolyData(WMesh(mesh));
    vtkSmartPointer<vtkPolyData> with_normals = VtkUtils::ComputeNormals(polydata);

    vtkSmartPointer<vtkDataArray> generic_normals = with_normals->GetPointData()->GetNormals();
    if(generic_normals)
    {
        Mat normals(1, generic_normals->GetNumberOfTuples(), CV_64FC3);
        Vec3d *optr = normals.ptr<Vec3d>();

        for(int i = 0; i < generic_normals->GetNumberOfTuples(); ++i, ++optr)
            generic_normals->GetTuple(i, optr->val);

        normals.convertTo(_normals, mesh.cloud.type());
    }
    else
        _normals.release();
}

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);
}

bool solvePnPRansac(InputArray _opoints, InputArray _ipoints,
                        InputArray _cameraMatrix, InputArray _distCoeffs,
                        OutputArray _rvec, OutputArray _tvec, bool useExtrinsicGuess,
                        int iterationsCount, float reprojectionError, double confidence,
                        OutputArray _inliers, int flags)
{
    CV_INSTRUMENT_REGION()

    Mat opoints0 = _opoints.getMat(), ipoints0 = _ipoints.getMat();
    Mat opoints, ipoints;
    if( opoints0.depth() == CV_64F || !opoints0.isContinuous() )
        opoints0.convertTo(opoints, CV_32F);
    else
        opoints = opoints0;
    if( ipoints0.depth() == CV_64F || !ipoints0.isContinuous() )
        ipoints0.convertTo(ipoints, CV_32F);
    else
        ipoints = ipoints0;

    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)) );

    CV_Assert(opoints.isContinuous());
    CV_Assert(opoints.depth() == CV_32F || opoints.depth() == CV_64F);
    CV_Assert((opoints.rows == 1 && opoints.channels() == 3) || opoints.cols*opoints.channels() == 3);
    CV_Assert(ipoints.isContinuous());
    CV_Assert(ipoints.depth() == CV_32F || ipoints.depth() == CV_64F);
    CV_Assert((ipoints.rows == 1 && ipoints.channels() == 2) || ipoints.cols*ipoints.channels() == 2);

    _rvec.create(3, 1, CV_64FC1);
    _tvec.create(3, 1, CV_64FC1);

    Mat rvec = useExtrinsicGuess ? _rvec.getMat() : Mat(3, 1, CV_64FC1);
    Mat tvec = useExtrinsicGuess ? _tvec.getMat() : Mat(3, 1, CV_64FC1);
    Mat cameraMatrix = _cameraMatrix.getMat(), distCoeffs = _distCoeffs.getMat();

    int model_points = 5;
    int ransac_kernel_method = SOLVEPNP_EPNP;

    if( npoints == 4 )
    {
        model_points = 4;
        ransac_kernel_method = SOLVEPNP_P3P;
    }

    Ptr<PointSetRegistrator::Callback> cb; // pointer to callback
    cb = makePtr<PnPRansacCallback>( cameraMatrix, distCoeffs, ransac_kernel_method, useExtrinsicGuess, rvec, tvec);

    double param1 = reprojectionError;                // reprojection error
    double param2 = confidence;                       // confidence
    int param3 = iterationsCount;                     // number maximum iterations

    Mat _local_model(3, 2, CV_64FC1);
    Mat _mask_local_inliers(1, opoints.rows, CV_8UC1);

    // call Ransac
    int result = createRANSACPointSetRegistrator(cb, model_points,
        param1, param2, param3)->run(opoints, ipoints, _local_model, _mask_local_inliers);

    if( result > 0 )
    {
        vector<Point3d> opoints_inliers;
        vector<Point2d> ipoints_inliers;
        opoints = opoints.reshape(3);
        ipoints = ipoints.reshape(2);
        opoints.convertTo(opoints_inliers, CV_64F);
        ipoints.convertTo(ipoints_inliers, CV_64F);

        const uchar* mask = _mask_local_inliers.ptr<uchar>();
        int npoints1 = compressElems(&opoints_inliers[0], mask, 1, npoints);
        compressElems(&ipoints_inliers[0], mask, 1, npoints);

        opoints_inliers.resize(npoints1);
        ipoints_inliers.resize(npoints1);
        result = solvePnP(opoints_inliers, ipoints_inliers, cameraMatrix,
                          distCoeffs, rvec, tvec, false,
                          (flags == SOLVEPNP_P3P || flags == SOLVEPNP_AP3P) ? SOLVEPNP_EPNP : flags) ? 1 : -1;
    }

    if( result <= 0 || _local_model.rows <= 0)
    {
        _rvec.assign(rvec);    // output rotation vector
        _tvec.assign(tvec);    // output translation vector

        if( _inliers.needed() )
            _inliers.release();

        return false;
    }
    else
    {
        _rvec.assign(_local_model.col(0));    // output rotation vector
        _tvec.assign(_local_model.col(1));    // output translation vector
    }

    if(_inliers.needed())
    {
        Mat _local_inliers;
        for (int i = 0; i < npoints; ++i)
        {
//.........这里部分代码省略.........

bool cv::solvePnPRansac(InputArray _opoints, InputArray _ipoints,
                        InputArray _cameraMatrix, InputArray _distCoeffs,
                        OutputArray _rvec, OutputArray _tvec, bool useExtrinsicGuess,
                        int iterationsCount, float reprojectionError, double confidence,
                        OutputArray _inliers, int flags)
{

    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)) );

    CV_Assert(opoints.isContinuous());
    CV_Assert(opoints.depth() == CV_32F || opoints.depth() == CV_64F);
    CV_Assert((opoints.rows == 1 && opoints.channels() == 3) || opoints.cols*opoints.channels() == 3);
    CV_Assert(ipoints.isContinuous());
    CV_Assert(ipoints.depth() == CV_32F || ipoints.depth() == CV_64F);
    CV_Assert((ipoints.rows == 1 && ipoints.channels() == 2) || ipoints.cols*ipoints.channels() == 2);

    _rvec.create(3, 1, CV_64FC1);
    _tvec.create(3, 1, CV_64FC1);

    Mat rvec = useExtrinsicGuess ? _rvec.getMat() : Mat(3, 1, CV_64FC1);
    Mat tvec = useExtrinsicGuess ? _tvec.getMat() : Mat(3, 1, CV_64FC1);
    Mat cameraMatrix = _cameraMatrix.getMat(), distCoeffs = _distCoeffs.getMat();

    Ptr<PointSetRegistrator::Callback> cb; // pointer to callback
    cb = makePtr<PnPRansacCallback>( cameraMatrix, distCoeffs, flags, useExtrinsicGuess, rvec, tvec);

    int model_points = 4;                             // minimum of number of model points
    if( flags == cv::SOLVEPNP_ITERATIVE ) model_points = 6;
    else if( flags == cv::SOLVEPNP_UPNP ) model_points = 6;
    else if( flags == cv::SOLVEPNP_EPNP ) model_points = 5;

    double param1 = reprojectionError;                // reprojection error
    double param2 = confidence;                       // confidence
    int param3 = iterationsCount;                     // number maximum iterations

    cv::Mat _local_model(3, 2, CV_64FC1);
    cv::Mat _mask_local_inliers(1, opoints.rows, CV_8UC1);

    // call Ransac
    int result = createRANSACPointSetRegistrator(cb, model_points, param1, param2, param3)->run(opoints, ipoints, _local_model, _mask_local_inliers);

    if( result <= 0 || _local_model.rows <= 0)
    {
        _rvec.assign(rvec);    // output rotation vector
        _tvec.assign(tvec);    // output translation vector

        if( _inliers.needed() )
            _inliers.release();

        return false;
    }
    else
    {
        _rvec.assign(_local_model.col(0));    // output rotation vector
        _tvec.assign(_local_model.col(1));    // output translation vector
    }

    if(_inliers.needed())
    {
        Mat _local_inliers;
        int count = 0;
        for (int i = 0; i < _mask_local_inliers.rows; ++i)
        {
            if((int)_mask_local_inliers.at<uchar>(i) == 1) // inliers mask
            {
                _local_inliers.push_back(count);    // output inliers vector
                count++;
            }
        }
        _local_inliers.copyTo(_inliers);
    }
    return true;
}

void convexHull( InputArray _points, OutputArray _hull, bool clockwise, bool returnPoints )
{
    CV_INSTRUMENT_REGION()

    CV_Assert(_points.getObj() != _hull.getObj());
    Mat points = _points.getMat();
    int i, total = points.checkVector(2), depth = points.depth(), nout = 0;
    int miny_ind = 0, maxy_ind = 0;
    CV_Assert(total >= 0 && (depth == CV_32F || depth == CV_32S));

    if( total == 0 )
    {
        _hull.release();
        return;
    }

    returnPoints = !_hull.fixedType() ? returnPoints : _hull.type() != CV_32S;

    bool is_float = depth == CV_32F;
    AutoBuffer<Point*> _pointer(total);
    AutoBuffer<int> _stack(total + 2), _hullbuf(total);
    Point** pointer = _pointer;
    Point2f** pointerf = (Point2f**)pointer;
    Point* data0 = points.ptr<Point>();
    int* stack = _stack;
    int* hullbuf = _hullbuf;

    CV_Assert(points.isContinuous());

    for( i = 0; i < total; i++ )
        pointer[i] = &data0[i];

    // sort the point set by x-coordinate, find min and max y
    if( !is_float )
    {
        std::sort(pointer, pointer + total, CHullCmpPoints<int>());
        for( i = 1; i < total; i++ )
        {
            int y = pointer[i]->y;
            if( pointer[miny_ind]->y > y )
                miny_ind = i;
            if( pointer[maxy_ind]->y < y )
                maxy_ind = i;
        }
    }
    else
    {
        std::sort(pointerf, pointerf + total, CHullCmpPoints<float>());
        for( i = 1; i < total; i++ )
        {
            float y = pointerf[i]->y;
            if( pointerf[miny_ind]->y > y )
                miny_ind = i;
            if( pointerf[maxy_ind]->y < y )
                maxy_ind = i;
        }
    }

    if( pointer[0]->x == pointer[total-1]->x &&
        pointer[0]->y == pointer[total-1]->y )
    {
        hullbuf[nout++] = 0;
    }
    else
    {
        // upper half
        int *tl_stack = stack;
        int tl_count = !is_float ?
            Sklansky_( pointer, 0, maxy_ind, tl_stack, -1, 1) :
            Sklansky_( pointerf, 0, maxy_ind, tl_stack, -1, 1);
        int *tr_stack = stack + tl_count;
        int tr_count = !is_float ?
            Sklansky_( pointer, total-1, maxy_ind, tr_stack, -1, -1) :
            Sklansky_( pointerf, total-1, maxy_ind, tr_stack, -1, -1);

        // gather upper part of convex hull to output
        if( !clockwise )
        {
            std::swap( tl_stack, tr_stack );
            std::swap( tl_count, tr_count );
        }

        for( i = 0; i < tl_count-1; i++ )
            hullbuf[nout++] = int(pointer[tl_stack[i]] - data0);
        for( i = tr_count - 1; i > 0; i-- )
            hullbuf[nout++] = int(pointer[tr_stack[i]] - data0);
        int stop_idx = tr_count > 2 ? tr_stack[1] : tl_count > 2 ? tl_stack[tl_count - 2] : -1;

        // lower half
        int *bl_stack = stack;
        int bl_count = !is_float ?
            Sklansky_( pointer, 0, miny_ind, bl_stack, 1, -1) :
            Sklansky_( pointerf, 0, miny_ind, bl_stack, 1, -1);
        int *br_stack = stack + bl_count;
        int br_count = !is_float ?
            Sklansky_( pointer, total-1, miny_ind, br_stack, 1, 1) :
            Sklansky_( pointerf, total-1, miny_ind, br_stack, 1, 1);

        if( clockwise )
        {
//.........这里部分代码省略.........