C++ OutputArray::assign方法代码示例

 virtual void evaluateAll(OutputArray feature_values) const
 {
     Mat_<int> feature_vals(1, (int)features_.size());
     for( int i = 0; i < (int)features_.size(); ++i )
     {
         feature_vals(0, i) = evaluate(i);
     }
     feature_values.assign(feature_vals);
 }

// Apply the thinning procedure to a given image
void thinning(InputArray input, OutputArray output){
    Mat processed = input.getMat().clone();
    // Enforce the range of the input image to be in between 0 - 255
    processed /= 255;

    Mat prev = Mat::zeros(processed.size(), CV_8UC1);
    Mat diff;

    do {
        thinningIteration(processed, 0);
        thinningIteration(processed, 1);
        absdiff(processed, prev, diff);
        processed.copyTo(prev);
    }
    while (countNonZero(diff) > 0);

    processed *= 255;

    output.assign(processed);
}

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;
}