C++ OutputArray::create方法代码示例

void FeatureExtractorLch3D::extractBlockHist(InputArray iBlock, OutputArray oFeature) {
    const static float MAX_H = 180.0f;
    const static float MAX_S = 255.0f;
    const static float MAX_V = 255.0f;

    Mat block = iBlock.getMat();
    int w = block.size().width;
    int h = block.size().height;

    float h_step = MAX_H / _h_bin;
    float s_step = MAX_S / _s_bin;
    float v_step = MAX_V / _v_bin;
    int count = 0;

    oFeature.create(_dim, 1, CV_32FC1);
    Mat feature = oFeature.getMat();
    feature.setTo(0);

    for (int y=0; y<h; ++y) {
        unsigned char* ptr = block.ptr<unsigned char>(y);
        for (int x=0; x<w; ++x) {
            int xx =  3 * x;
            unsigned char h = ptr[xx];
            unsigned char s = ptr[xx + 1];
            unsigned char v = ptr[xx + 2];

            int hi = min((int) floor(h/h_step), _h_bin - 1);
            int si = min((int) floor(s/s_step), _s_bin - 1);
            int vi = min((int) floor(v/v_step), _v_bin - 1);
            int i = (vi << (_h_bit + _s_bit)) + (si << _h_bit) + hi;
            ++feature.at<float>(i);
            ++count;
        }
    }
    for (int i=0; i < _dim; ++i) {
        feature.at<float>(i) /= count;
    }
}

void HDF5Impl::atread(OutputArray value, const String& atlabel)
{
    if (!atexists(atlabel))
        CV_Error_(Error::StsInternal, ("Attribute '%s' does not exist!", atlabel.c_str()));

    hid_t attr = H5Aopen(m_h5_file_id, atlabel.c_str(), H5P_DEFAULT);
    hid_t atype  = H5Aget_type(attr);
    hid_t aspace = H5Aget_space(attr);
    int rank = H5Sget_simple_extent_ndims(aspace);

    vector<hsize_t> dim_vec_(rank);
    H5Sget_simple_extent_dims(aspace, dim_vec_.data(), NULL);
    vector<int> dim_vec(dim_vec_.begin(), dim_vec_.end());

    int nchannels = 1;
    hid_t h5type;
    if (H5Tget_class(atype) == H5T_ARRAY)
    {
        hsize_t dims;
        H5Tget_array_dims(atype, &dims);
        nchannels = (int) dims;

        hid_t super_type = H5Tget_super(atype);
        h5type = H5Tget_native_type(super_type, H5T_DIR_ASCEND);
        H5Tclose(super_type);
    }
    else
        h5type = H5Tget_native_type(atype, H5T_DIR_ASCEND);

    int dtype = GetCVtype(h5type);

    value.create(rank, dim_vec.data(), CV_MAKETYPE(dtype, nchannels));
    H5Aread(attr, atype, value.getMat().data);

    H5Sclose(aspace);
    H5Tclose(atype);
    H5Aclose(attr);
}

void cv::fastNlMeansDenoisingMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
                                    int imgToDenoiseIndex, int temporalWindowSize,
                                    float h, int templateWindowSize, int searchWindowSize)
{
    vector<Mat> srcImgs;
    _srcImgs.getMatVector(srcImgs);

    fastNlMeansDenoisingMultiCheckPreconditions(
        srcImgs, imgToDenoiseIndex,
        temporalWindowSize, templateWindowSize, searchWindowSize
    );
    _dst.create(srcImgs[0].size(), srcImgs[0].type());
    Mat dst = _dst.getMat();

    switch (srcImgs[0].type()) {
        case CV_8U:
            parallel_for_(cv::Range(0, srcImgs[0].rows),
                FastNlMeansMultiDenoisingInvoker<uchar>(
                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
                    dst, templateWindowSize, searchWindowSize, h));
            break;
        case CV_8UC2:
            parallel_for_(cv::Range(0, srcImgs[0].rows),
                FastNlMeansMultiDenoisingInvoker<cv::Vec2b>(
                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
                    dst, templateWindowSize, searchWindowSize, h));
            break;
        case CV_8UC3:
            parallel_for_(cv::Range(0, srcImgs[0].rows),
                FastNlMeansMultiDenoisingInvoker<cv::Vec3b>(
                    srcImgs, imgToDenoiseIndex, temporalWindowSize,
                    dst, templateWindowSize, searchWindowSize, h));
            break;
        default:
            CV_Error(CV_StsBadArg,
                "Unsupported matrix format! Only uchar, Vec2b, Vec3b are supported");
    }
}

void Mat::copyTo( OutputArray _dst, InputArray _mask ) const
{
    Mat mask = _mask.getMat();
    if( !mask.data )
    {
        copyTo(_dst);
        return;
    }
    
    int cn = channels(), mcn = mask.channels();
    CV_Assert( mask.depth() == CV_8U && (mcn == 1 || mcn == cn) );
    bool colorMask = mcn > 1;
    
    size_t esz = colorMask ? elemSize1() : elemSize();
    BinaryFunc copymask = getCopyMaskFunc(esz);
    
    uchar* data0 = _dst.getMat().data;
    _dst.create( dims, size, type() );
    Mat dst = _dst.getMat();
    
    if( dst.data != data0 ) // do not leave dst uninitialized
        dst = Scalar(0);
    
    if( dims <= 2 )
    {
        Size sz = getContinuousSize(*this, dst, mask, mcn);
        copymask(data, step, mask.data, mask.step, dst.data, dst.step, sz, &esz);
        return;
    }
    
    const Mat* arrays[] = { this, &dst, &mask, 0 };
    uchar* ptrs[3];
    NAryMatIterator it(arrays, ptrs);
    Size sz((int)(it.size*mcn), 1);
    
    for( size_t i = 0; i < it.nplanes; i++, ++it )
        copymask(ptrs[0], 0, ptrs[2], 0, ptrs[1], 0, sz, &esz);
}

void cv::fastNlMeansDenoisingColored( InputArray _src, OutputArray _dst,
                                      float h, float hForColorComponents,
                                      int templateWindowSize, int searchWindowSize)
{
    int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);

    if (type != CV_8UC3 && type != CV_8UC4)
    {
        CV_Error(Error::StsBadArg, "Type of input image should be CV_8UC3!");
        return;
    }

    CV_OCL_RUN(_src.dims() <= 2 && (_dst.isUMat() || _src.isUMat()),
                ocl_fastNlMeansDenoisingColored(_src, _dst, h, hForColorComponents,
                                                templateWindowSize, searchWindowSize))

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

    Mat src_lab;
    cvtColor(src, src_lab, COLOR_LBGR2Lab);

    Mat l(src.size(), CV_8U);
    Mat ab(src.size(), CV_8UC2);
    Mat l_ab[] = { l, ab };
    int from_to[] = { 0,0, 1,1, 2,2 };
    mixChannels(&src_lab, 1, l_ab, 2, from_to, 3);

    fastNlMeansDenoising(l, l, h, templateWindowSize, searchWindowSize);
    fastNlMeansDenoising(ab, ab, hForColorComponents, templateWindowSize, searchWindowSize);

    Mat l_ab_denoised[] = { l, ab };
    Mat dst_lab(src.size(), CV_MAKE_TYPE(depth, 3));
    mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3);

    cvtColor(dst_lab, dst, COLOR_Lab2LBGR, cn);
}

static bool matchTemplateNaive_CCORR(InputArray _image, InputArray _templ, OutputArray _result)
{
    int type = _image.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
    int wdepth = CV_32F, wtype = CV_MAKE_TYPE(wdepth, cn);

    ocl::Device dev = ocl::Device::getDefault();
    int pxPerWIx = (cn==1 && dev.isIntel() && (dev.type() & ocl::Device::TYPE_GPU)) ? 4 : 1;
    int rated_cn = cn;
    int wtype1 = wtype;

    if (pxPerWIx!=1)
    {
        rated_cn = pxPerWIx;
        type = CV_MAKE_TYPE(depth, rated_cn);
        wtype1 = CV_MAKE_TYPE(wdepth, rated_cn);
    }

    char cvt[40];
    char cvt1[40];
    const char* convertToWT1 = ocl::convertTypeStr(depth, wdepth, cn, cvt);
    const char* convertToWT = ocl::convertTypeStr(depth, wdepth, rated_cn, cvt1);

    ocl::Kernel k("matchTemplate_Naive_CCORR", ocl::imgproc::match_template_oclsrc,
                  format("-D CCORR -D T=%s -D T1=%s -D WT=%s -D WT1=%s -D convertToWT=%s -D convertToWT1=%s -D cn=%d -D PIX_PER_WI_X=%d", ocl::typeToStr(type), ocl::typeToStr(depth), ocl::typeToStr(wtype1), ocl::typeToStr(wtype),
                         convertToWT, convertToWT1, cn, pxPerWIx));
    if (k.empty())
        return false;

    UMat image = _image.getUMat(), templ = _templ.getUMat();
    _result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32FC1);
    UMat result = _result.getUMat();

    k.args(ocl::KernelArg::ReadOnlyNoSize(image), ocl::KernelArg::ReadOnly(templ),
           ocl::KernelArg::WriteOnly(result));

    size_t globalsize[2] = { (result.cols+pxPerWIx-1)/pxPerWIx, result.rows};
    return k.run(2, globalsize, NULL, false);
}

static bool ocl_pyrDown( InputArray _src, OutputArray _dst, const Size& _dsz, int borderType)
{
    int type = _src.type(), depth = CV_MAT_DEPTH(type), channels = CV_MAT_CN(type);

    if ((channels != 1 && channels != 2 && channels != 4) || borderType != BORDER_DEFAULT)
        return false;

    bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0;
    if ((depth == CV_64F) && !(doubleSupport))
        return false;

    Size ssize = _src.size();
    Size dsize = _dsz.area() == 0 ? Size((ssize.width + 1) / 2, (ssize.height + 1) / 2) : _dsz;
    CV_Assert( ssize.width > 0 && ssize.height > 0 &&
            std::abs(dsize.width*2 - ssize.width) <= 2 &&
            std::abs(dsize.height*2 - ssize.height) <= 2 );

    UMat src = _src.getUMat();
    _dst.create( dsize, src.type() );
    UMat dst = _dst.getUMat();

    int float_depth = depth == CV_64F ? CV_64F : CV_32F;
    char cvt[2][50];
    ocl::Kernel k("pyrDown", ocl::imgproc::pyr_down_oclsrc,
                 format("-D T=%s -D FT=%s -D convertToT=%s -D convertToFT=%s%s",
                 ocl::typeToStr(type), ocl::typeToStr(CV_MAKETYPE(float_depth, channels)),
                 ocl::convertTypeStr(float_depth, depth, channels, cvt[0]),
                 ocl::convertTypeStr(depth, float_depth, channels, cvt[1]),
                 doubleSupport ? " -D DOUBLE_SUPPORT" : ""));
    if (k.empty())
        return false;

    k.args(ocl::KernelArg::ReadOnly(src), ocl::KernelArg::WriteOnly(dst));

    size_t localThreads[2]  = { 256, 1 };
    size_t globalThreads[2] = { src.cols, dst.rows };
    return k.run(2, globalThreads, localThreads, false);
}

void BackgroundSubtractorMOG::operator()(InputArray _image, OutputArray _fgmask, double learningRate)
{
    Mat image = _image.getMat();
    bool needToInitialize = nframes == 0 || learningRate >= 1 || image.size() != frameSize || image.type() != frameType;

    if( needToInitialize )
        initialize(image.size(), image.type());

    CV_Assert( image.depth() == CV_8U );
    _fgmask.create( image.size(), CV_8U );
    Mat fgmask = _fgmask.getMat();

    ++nframes;
    learningRate = learningRate >= 0 && nframes > 1 ? learningRate : 1./min( nframes, history );
    CV_Assert(learningRate >= 0);

    if( image.type() == CV_8UC1 )
        process8uC1( image, fgmask, learningRate, bgmodel, nmixtures, backgroundRatio, varThreshold, noiseSigma );
    else if( image.type() == CV_8UC3 )
        process8uC3( image, fgmask, learningRate, bgmodel, nmixtures, backgroundRatio, varThreshold, noiseSigma );
    else
        CV_Error( CV_StsUnsupportedFormat, "Only 1- and 3-channel 8-bit images are supported in BackgroundSubtractorMOG" );
}

void removeCols(InputArray _src, OutputArray _dst, cv::Range range)
{
    CV_Assert( range.start >= 0 && range.end < _src.getMat().cols );

    Mat src = _src.getMat();
    int type = src.type();

    _dst.create( src.rows, src.cols - range.size() - 1, type );
    Mat dst = _dst.getMat();

    if(src.data != dst.data || src.step != dst.step)
    {
        for(int i = range.start; i <= range.end; ++i)
        {
//            cout << i << endl;
            removeCol(src, src, range.start);
//            cout << "src: " << endl << src << endl;
        }

        src.copyTo(dst);
    }
    return;
}

    /* Post: fill _err with projection errors */
    void computeError( InputArray _m1, InputArray _m2, InputArray _model, OutputArray _err ) const
    {

        Mat opoints = _m1.getMat(), ipoints = _m2.getMat(), model = _model.getMat();

        int i, count = opoints.checkVector(3);
        Mat _rvec = model.col(0);
        Mat _tvec = model.col(1);


        Mat projpoints(count, 2, CV_32FC1);
        projectPoints(opoints, _rvec, _tvec, cameraMatrix, distCoeffs, projpoints);

        const Point2f* ipoints_ptr = ipoints.ptr<Point2f>();
        const Point2f* projpoints_ptr = projpoints.ptr<Point2f>();

        _err.create(count, 1, CV_32FC1);
        float* err = _err.getMat().ptr<float>();

        for ( i = 0; i < count; ++i)
            err[i] = (float)norm( Matx21f(ipoints_ptr[i] - projpoints_ptr[i]), NORM_L2SQR );

    }

void cv::gpu::flip(InputArray _src, OutputArray _dst, int flipCode, Stream& stream)
{
    typedef void (*func_t)(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream);
    static const func_t funcs[6][4] =
    {
        {NppMirror<CV_8U, nppiMirror_8u_C1R>::call, 0, NppMirror<CV_8U, nppiMirror_8u_C3R>::call, NppMirror<CV_8U, nppiMirror_8u_C4R>::call},
        {0,0,0,0},
        {NppMirror<CV_16U, nppiMirror_16u_C1R>::call, 0, NppMirror<CV_16U, nppiMirror_16u_C3R>::call, NppMirror<CV_16U, nppiMirror_16u_C4R>::call},
        {0,0,0,0},
        {NppMirror<CV_32S, nppiMirror_32s_C1R>::call, 0, NppMirror<CV_32S, nppiMirror_32s_C3R>::call, NppMirror<CV_32S, nppiMirror_32s_C4R>::call},
        {NppMirror<CV_32F, nppiMirror_32f_C1R>::call, 0, NppMirror<CV_32F, nppiMirror_32f_C3R>::call, NppMirror<CV_32F, nppiMirror_32f_C4R>::call}
    };

    GpuMat src = _src.getGpuMat();

    CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S || src.depth() == CV_32F);
    CV_Assert(src.channels() == 1 || src.channels() == 3 || src.channels() == 4);

    _dst.create(src.size(), src.type());
    GpuMat dst = _dst.getGpuMat();

    funcs[src.depth()][src.channels() - 1](src, dst, flipCode, StreamAccessor::getStream(stream));
}

Vec2d EM::predict(InputArray _sample, OutputArray _probs) const
{
    Mat sample = _sample.getMat();
    CV_Assert(isTrained());

    CV_Assert(!sample.empty());
    if(sample.type() != CV_64FC1)
    {
        Mat tmp;
        sample.convertTo(tmp, CV_64FC1);
        sample = tmp;
    }
    sample.reshape(1, 1);

    Mat probs;
    if( _probs.needed() )
    {
        _probs.create(1, nclusters, CV_64FC1);
        probs = _probs.getMat();
    }

    return computeProbabilities(sample, !probs.empty() ? &probs : 0);
}

void cv::textureFlattening(InputArray _src, InputArray _mask, OutputArray _dst,
                           double low_threshold, double high_threshold, int kernel_size)
{

    Mat src  = _src.getMat();
    Mat mask  = _mask.getMat();
    _dst.create(src.size(), src.type());
    Mat blend = _dst.getMat();

    Mat gray = Mat::zeros(mask.size(),CV_8UC1);

    if(mask.channels() == 3)
        cvtColor(mask, gray, COLOR_BGR2GRAY );
    else
        gray = mask;

    Mat cs_mask = Mat::zeros(src.size(),CV_8UC3);

    src.copyTo(cs_mask,gray);

    Cloning obj;
    obj.texture_flatten(src,cs_mask,gray,low_threshold,high_threshold,kernel_size,blend);
}

void cv::Scharr( InputArray _src, OutputArray _dst, int ddepth, int dx, int dy,
                 double scale, double delta, int borderType )
{
    int stype = _src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype);
    if (ddepth < 0)
        ddepth = sdepth;
    int dtype = CV_MAKETYPE(ddepth, cn);
    _dst.create( _src.size(), dtype );

#ifdef HAVE_TEGRA_OPTIMIZATION
    if (scale == 1.0 && delta == 0)
    {
        Mat src = _src.getMat(), dst = _dst.getMat();
        if (tegra::scharr(src, dst, dx, dy, borderType))
            return;
    }
#endif

#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
    if (IPPDerivScharr(_src, _dst, ddepth, dx, dy, scale, delta, borderType))
        return;
#endif
    int ktype = std::max(CV_32F, std::max(ddepth, sdepth));

    Mat kx, ky;
    getScharrKernels( kx, ky, dx, dy, false, ktype );
    if( scale != 1 )
    {
        // usually the smoothing part is the slowest to compute,
        // so try to scale it instead of the faster differenciating part
        if( dx == 0 )
            kx *= scale;
        else
            ky *= scale;
    }
    sepFilter2D( _src, _dst, ddepth, kx, ky, Point(-1, -1), delta, borderType );
}

void FeatureExtractorLch3D::extract(InputArray iFrame, OutputArray oFeature) {
    _h_bin = (int) powl(2, _h_bit);
    _s_bin = (int) powl(2, _s_bit);
    _v_bin = (int) powl(2, _v_bit);
    _dim = _h_bin * _s_bin * _v_bin;

    Mat rgbImg = iFrame.getMat();
    Mat hsvImg;
    cvtColor(rgbImg, hsvImg, CV_BGR2HSV);
    int w = hsvImg.size().width;
    int h = hsvImg.size().height;
    int ix = 0;
    int iy = 0;
    int sw = (int) ceil(w / (double) _num_block_x);
    int sh = (int) ceil(h / (double) _num_block_y);

    oFeature.create(_dim * _num_block_y * _num_block_x, 1, CV_32FC1);
    Mat hist = oFeature.getMat();

    int index = 0;
    while (iy < h) {
        int begin_y = iy;
        int end_y = min((iy + sh), h);
        Mat row_block = hsvImg.rowRange(begin_y, end_y);
        iy = end_y;
        ix = 0;
        while (ix < w) {
            int begin_x = ix;
            int end_x = min((ix + sw), w);
            Mat block = row_block.colRange(begin_x, end_x);
            ix = end_x;
            Mat hist_block = hist.rowRange(index, index + _dim);
            extractBlockHist(block, hist_block);
            index += _dim;
        }
    }
}