C++ OutputArray::setTo方法代码示例

    void calcBtvRegularizationImpl(InputArray _src, OutputArray _dst, int btvKernelSize, const std::vector<float>& btvWeights)
    {
        Mat src = _src.getMat();
        _dst.create(src.size(), src.type());
        _dst.setTo(Scalar::all(0));
        Mat dst = _dst.getMat();

        const int ksize = (btvKernelSize - 1) / 2;

        BtvRegularizationBody<T> body;

        body.src = src;
        body.dst = dst;
        body.ksize = ksize;
        body.btvWeights = &btvWeights[0];

        parallel_for_(Range(ksize, src.rows - ksize), body);
    }

    static bool ocl_calcBtvRegularization(InputArray _src, OutputArray _dst, int btvKernelSize, const UMat & ubtvWeights)
    {
        int cn = _src.channels();
        ocl::Kernel k("calcBtvRegularization", ocl::superres::superres_btvl1_oclsrc,
                      format("-D cn=%d", cn));
        if (k.empty())
            return false;

        UMat src = _src.getUMat();
        _dst.create(src.size(), src.type());
        _dst.setTo(Scalar::all(0));
        UMat dst = _dst.getUMat();

        const int ksize = (btvKernelSize - 1) / 2;

        k.args(ocl::KernelArg::ReadOnlyNoSize(src), ocl::KernelArg::WriteOnly(dst),
              ksize, ocl::KernelArg::PtrReadOnly(ubtvWeights));

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

void BriefDescriptorExtractor::computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const
{
    // Construct integral image for fast smoothing (box filter)
    Mat sum;

    Mat grayImage = image.getMat();
    if( image.type() != CV_8U ) cvtColor( image, grayImage, COLOR_BGR2GRAY );

    ///TODO allow the user to pass in a precomputed integral image
    //if(image.type() == CV_32S)
    //  sum = image;
    //else

    integral( grayImage, sum, CV_32S);

    //Remove keypoints very close to the border
    KeyPointsFilter::runByImageBorder(keypoints, image.size(), PATCH_SIZE/2 + KERNEL_SIZE/2);

    descriptors.create((int)keypoints.size(), bytes_, CV_8U);
    descriptors.setTo(Scalar::all(0));
    test_fn_(sum, keypoints, descriptors);
}

void FREAK::computeDescriptors( InputArray _image, std::vector<KeyPoint>& keypoints, OutputArray _descriptors ) const {

    Mat image = _image.getMat();
    Mat imgIntegral;
    integral(image, imgIntegral, DataType<iiMatType>::type);
    std::vector<int> kpScaleIdx(keypoints.size()); // used to save pattern scale index corresponding to each keypoints
    const std::vector<int>::iterator ScaleIdxBegin = kpScaleIdx.begin(); // used in std::vector erase function
    const std::vector<cv::KeyPoint>::iterator kpBegin = keypoints.begin(); // used in std::vector erase function
    const float sizeCst = static_cast<float>(FREAK_NB_SCALES/(FREAK_LOG2* nOctaves));
    srcMatType pointsValue[FREAK_NB_POINTS];
    int thetaIdx = 0;
    int direction0;
    int direction1;

    // compute the scale index corresponding to the keypoint size and remove keypoints close to the border
    if( scaleNormalized )
    {
        for( size_t k = keypoints.size(); k--; )
        {
            //Is k non-zero? If so, decrement it and continue"
            kpScaleIdx[k] = std::max( (int)(std::log(keypoints[k].size/FREAK_SMALLEST_KP_SIZE)*sizeCst+0.5) ,0);
            if( kpScaleIdx[k] >= FREAK_NB_SCALES )
                kpScaleIdx[k] = FREAK_NB_SCALES-1;

            if( keypoints[k].pt.x <= patternSizes[kpScaleIdx[k]] || //check if the description at this specific position and scale fits inside the image
                 keypoints[k].pt.y <= patternSizes[kpScaleIdx[k]] ||
                 keypoints[k].pt.x >= image.cols-patternSizes[kpScaleIdx[k]] ||
                 keypoints[k].pt.y >= image.rows-patternSizes[kpScaleIdx[k]]
               )
            {
                keypoints.erase(kpBegin+k);
                kpScaleIdx.erase(ScaleIdxBegin+k);
            }
        }
    }
    else
    {
        const int scIdx = std::max( (int)(1.0986122886681*sizeCst+0.5) ,0);
        for( size_t k = keypoints.size(); k--; )
        {
            kpScaleIdx[k] = scIdx; // equivalent to the formule when the scale is normalized with a constant size of keypoints[k].size=3*SMALLEST_KP_SIZE
            if( kpScaleIdx[k] >= FREAK_NB_SCALES )
            {
                kpScaleIdx[k] = FREAK_NB_SCALES-1;
            }
            if( keypoints[k].pt.x <= patternSizes[kpScaleIdx[k]] ||
                keypoints[k].pt.y <= patternSizes[kpScaleIdx[k]] ||
                keypoints[k].pt.x >= image.cols-patternSizes[kpScaleIdx[k]] ||
                keypoints[k].pt.y >= image.rows-patternSizes[kpScaleIdx[k]]
               )
            {
                keypoints.erase(kpBegin+k);
                kpScaleIdx.erase(ScaleIdxBegin+k);
            }
        }
    }

    // allocate descriptor memory, estimate orientations, extract descriptors
    if( !extAll )
    {
        // extract the best comparisons only
        _descriptors.create((int)keypoints.size(), FREAK_NB_PAIRS/8, CV_8U);
        _descriptors.setTo(Scalar::all(0));
        Mat descriptors = _descriptors.getMat();

        void *ptr = descriptors.data+(keypoints.size()-1)*descriptors.step[0];

        for( size_t k = keypoints.size(); k--; ) {
            // estimate orientation (gradient)
            if( !orientationNormalized )
            {
                thetaIdx = 0; // assign 0° to all keypoints
                keypoints[k].angle = 0.0;
            }
            else
            {
                // get the points intensity value in the un-rotated pattern
                for( int i = FREAK_NB_POINTS; i--; ) {
                    pointsValue[i] = meanIntensity<srcMatType, iiMatType>(image, imgIntegral,
                                                                          keypoints[k].pt.x, keypoints[k].pt.y,
                                                                          kpScaleIdx[k], 0, i);
                }
                direction0 = 0;
                direction1 = 0;
                for( int m = 45; m--; )
                {
                    //iterate through the orientation pairs
                    const int delta = (pointsValue[ orientationPairs[m].i ]-pointsValue[ orientationPairs[m].j ]);
                    direction0 += delta*(orientationPairs[m].weight_dx)/2048;
                    direction1 += delta*(orientationPairs[m].weight_dy)/2048;
                }

                keypoints[k].angle = static_cast<float>(atan2((float)direction1,(float)direction0)*(180.0/CV_PI));//estimate orientation
                thetaIdx = int(FREAK_NB_ORIENTATION*keypoints[k].angle*(1/360.0)+0.5);
                if( thetaIdx < 0 )
                    thetaIdx += FREAK_NB_ORIENTATION;

                if( thetaIdx >= FREAK_NB_ORIENTATION )
                    thetaIdx -= FREAK_NB_ORIENTATION;
            }
//.........这里部分代码省略.........