C++ PointViewSet::erase方法代码示例

本文整理汇总了C++中PointViewSet::erase方法的典型用法代码示例。如果您正苦于以下问题：C++ PointViewSet::erase方法的具体用法？C++ PointViewSet::erase怎么用？C++ PointViewSet::erase使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类PointViewSet的用法示例。

在下文中一共展示了PointViewSet::erase方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。

示例1: run

PointViewSet PMFFilter::run(PointViewPtr input)
{
    bool logOutput = log()->getLevel() > LogLevel::Debug1;
    if (logOutput)
        log()->floatPrecision(8);
    log()->get(LogLevel::Debug2) << "Process PMFFilter...\n";

    auto idx = processGround(input);

    PointViewSet viewSet;
    if (!idx.empty() && (m_classify || m_extract))
    {

        if (m_classify)
        {
            log()->get(LogLevel::Debug2) << "Labeled " << idx.size() << " ground returns!\n";

            // set the classification label of ground returns as 2
            // (corresponding to ASPRS LAS specification)
            for (const auto& i : idx)
            {
                input->setField(Dimension::Id::Classification, i, 2);
            }

            viewSet.insert(input);
        }

        if (m_extract)
        {
            log()->get(LogLevel::Debug2) << "Extracted " << idx.size() << " ground returns!\n";

            // create new PointView containing only ground returns
            PointViewPtr output = input->makeNew();
            for (const auto& i : idx)
            {
                output->appendPoint(*input, i);
            }

            viewSet.erase(input);
            viewSet.insert(output);
        }
    }
    else
    {
        if (idx.empty())
            log()->get(LogLevel::Debug2) << "Filtered cloud has no ground returns!\n";

        if (!(m_classify || m_extract))
            log()->get(LogLevel::Debug2) << "Must choose --classify or --extract\n";

        // return the input buffer unchanged
        viewSet.insert(input);
    }

    return viewSet;
}

开发者ID:EricAlex，项目名称:PDAL，代码行数:56，代码来源:PMFFilter.cpp

示例2: run

PointViewSet SMRFilter::run(PointViewPtr view)
{
    log()->get(LogLevel::Info) << "run: Process SMRFilter...\n";

    std::vector<PointId> idx = processGround(view);

    PointViewSet viewSet;

    if (!idx.empty() && (m_classify || m_extract))
    {

        if (m_classify)
        {
            log()->get(LogLevel::Info) << "run: Labeled " << idx.size() << " ground returns!\n";

            // set the classification label of ground returns as 2
            // (corresponding to ASPRS LAS specification)
            for (const auto& i : idx)
            {
                view->setField(Dimension::Id::Classification, i, 2);
            }

            viewSet.insert(view);
        }

        if (m_extract)
        {
            log()->get(LogLevel::Info) << "run: Extracted " << idx.size() << " ground returns!\n";

            // create new PointView containing only ground returns
            PointViewPtr output = view->makeNew();
            for (const auto& i : idx)
            {
                output->appendPoint(*view, i);
            }

            viewSet.erase(view);
            viewSet.insert(output);
        }
    }
    else
    {
        if (idx.empty())
            log()->get(LogLevel::Info) << "run: Filtered cloud has no ground returns!\n";

        if (!(m_classify || m_extract))
            log()->get(LogLevel::Info) << "run: Must choose --classify or --extract\n";

        // return the view buffer unchanged
        viewSet.insert(view);
    }

    return viewSet;
}

开发者ID:PDAL，项目名称:PDAL，代码行数:54，代码来源:SMRFilter.cpp

示例3: run

PointViewSet RadiusOutlierFilter::run(PointViewPtr input)
{
    bool logOutput = log()->getLevel() > LogLevel::Debug1;
    if (logOutput)
        log()->floatPrecision(8);
    log()->get(LogLevel::Debug2) << "Process RadiusOutlierFilter...\n";

    // convert PointView to PointXYZ
    typedef pcl::PointCloud<pcl::PointXYZ> Cloud;
    Cloud::Ptr cloud(new Cloud);
    BOX3D bounds;
    input->calculateBounds(bounds);
    pclsupport::PDALtoPCD(input, *cloud, bounds);

    pclsupport::setLogLevel(log()->getLevel());

    // setup the outlier filter
    pcl::RadiusOutlierRemoval<pcl::PointXYZ> ror(true);
    ror.setInputCloud(cloud);
    ror.setMinNeighborsInRadius(m_min_neighbors);
    ror.setRadiusSearch(m_radius);

    pcl::PointCloud<pcl::PointXYZ> output;
    ror.setNegative(true);
    ror.filter(output);

    // filtered to return inliers
    pcl::PointIndicesPtr inliers(new pcl::PointIndices);
    ror.getRemovedIndices(*inliers);

    PointViewSet viewSet;
    if (inliers->indices.empty())
    {
        log()->get(LogLevel::Warning) << "Requested filter would remove all points. Try a larger radius/smaller minimum neighbors.\n";
        viewSet.insert(input);
        return viewSet;
    }

    // inverse are the outliers
    std::vector<int> outliers(input->size()-inliers->indices.size());
    for (PointId i = 0, j = 0, k = 0; i < input->size(); ++i)
    {
        if (i == (PointId)inliers->indices[j])
        {
            j++;
            continue;
        }
        outliers[k++] = i;
    }

    if (!outliers.empty() && (m_classify || m_extract))
    {

        if (m_classify)
        {
            log()->get(LogLevel::Debug2) << "Labeled " << outliers.size() << " outliers as noise!\n";

            // set the classification label of outlier returns as 18
            // (corresponding to ASPRS LAS specification for high noise)
            for (const auto& i : outliers)
            {
                input->setField(Dimension::Id::Classification, i, 18);
            }

            viewSet.insert(input);
        }

        if (m_extract)
        {
            log()->get(LogLevel::Debug2) << "Extracted " << inliers->indices.size() << " inliers!\n";

            // create new PointView containing only outliers
            PointViewPtr output = input->makeNew();
            for (const auto& i : inliers->indices)
            {
                output->appendPoint(*input, i);
            }

            viewSet.erase(input);
            viewSet.insert(output);
        }
    }
    else
    {
        if (outliers.empty())
            log()->get(LogLevel::Warning) << "Filtered cloud has no outliers!\n";

        if (!(m_classify || m_extract))
            log()->get(LogLevel::Warning) << "Must choose --classify or --extract\n";

        // return the input buffer unchanged
        viewSet.insert(input);
    }

    return viewSet;
}

开发者ID:Rafaelaniemann，项目名称:PDAL，代码行数:96，代码来源:RadiusOutlierFilter.cpp

示例4: run


//.........这里部分代码省略.........
            break;
        case 1:
            pcl::console::setVerbosityLevel(pcl::console::L_ERROR);
            break;
        case 2:
            pcl::console::setVerbosityLevel(pcl::console::L_WARN);
            break;
        case 3:
            pcl::console::setVerbosityLevel(pcl::console::L_INFO);
            break;
        case 4:
            pcl::console::setVerbosityLevel(pcl::console::L_DEBUG);
            break;
        default:
            pcl::console::setVerbosityLevel(pcl::console::L_VERBOSE);
            break;
    }

    // setup the outlier filter
    pcl::StatisticalOutlierRemoval<pcl::PointXYZ> sor(true);
    sor.setInputCloud(cloud);
    sor.setMeanK(m_meanK);
    sor.setStddevMulThresh(m_multiplier);

    pcl::PointCloud<pcl::PointXYZ> output;
    sor.setNegative(true);
    sor.filter(output);

    // filtered to return inliers
    pcl::PointIndicesPtr inliers(new pcl::PointIndices);
    sor.getRemovedIndices(*inliers);

    log()->get(LogLevel::Debug2) << inliers->indices.size() << std::endl;

    PointViewSet viewSet;
    if (inliers->indices.empty())
    {
        log()->get(LogLevel::Warning) << "Requested filter would remove all points. Try increasing the multiplier.\n";
        viewSet.insert(input);
        return viewSet;
    }

    // inverse are the outliers
    std::vector<int> outliers(input->size()-inliers->indices.size());
    for (PointId i = 0, j = 0, k = 0; i < input->size(); ++i)
    {
        if (i == (PointId)inliers->indices[j])
        {
            j++;
            continue;
        }
        outliers[k++] = i;
    }

    if (!outliers.empty() && (m_classify || m_extract))
    {

        if (m_classify)
        {
            log()->get(LogLevel::Debug2) << "Labeled " << outliers.size() << " outliers as noise!\n";

            // set the classification label of outlier returns as 18
            // (corresponding to ASPRS LAS specification for high noise)
            for (const auto& i : outliers)
            {
                input->setField(Dimension::Id::Classification, i, 18);
            }

            viewSet.insert(input);
        }

        if (m_extract)
        {
            log()->get(LogLevel::Debug2) << "Extracted " << inliers->indices.size() << " inliers!\n";

            // create new PointView containing only outliers
            PointViewPtr output = input->makeNew();
            for (const auto& i : inliers->indices)
            {
                output->appendPoint(*input, i);
            }

            viewSet.erase(input);
            viewSet.insert(output);
        }
    }
    else
    {
        if (outliers.empty())
            log()->get(LogLevel::Warning) << "Filtered cloud has no outliers!\n";

        if (!(m_classify || m_extract))
            log()->get(LogLevel::Warning) << "Must choose --classify or --extract\n";

        // return the input buffer unchanged
        viewSet.insert(input);
    }

    return viewSet;
}

开发者ID:EricAlex，项目名称:PDAL，代码行数:101，代码来源:StatisticalOutlierFilter.cpp

注：本文中的PointViewSet::erase方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。