PCLFiltering.cpp

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#ifdef LVR2_USE_PCL
 
#include "lvr2/reconstruction/PCLFiltering.hpp"
#include <pcl/pcl_config.h>
 
namespace lvr2
{
 
 
PCLFiltering::PCLFiltering( PointBufferPtr loader )
{
 
    // Check if we have RGB data
    size_t numPoints = loader->numPoints();
    m_useColors = loader->hasColors();
 
    m_pointCloud  = pcl::PointCloud<pcl::PointXYZRGB>::Ptr (new pcl::PointCloud<pcl::PointXYZRGB>);
 
 
    // Get data from loader object
    FloatChannelOptional points = loader->getFloatChannel("points");
    UCharChannelOptional colors = loader->getUCharChannel("colors");
 
    size_t numColors = 0;
    if(colors)
    {
        numColors = (*colors).numElements();
    }
 
    if(m_useColors)
    {
        assert(numColors == numPoints);
    }
 
 
    // Parse to PCL point cloud
    std::cout << timestamp << "Creating PCL point cloud for filtering" << std::endl;
    std::cout << timestamp << "Point cloud has " << numPoints << " points" << std::endl;
    m_pointCloud->resize(numPoints);
    float x, y, z;
    for(size_t i = 0; i < numPoints; i++)
    {
        x = (*points)[i][0];
        y = (*points)[i][1];
        z = (*points)[i][2];
 
        if(m_useColors)
        {
//            // Pack color information
            uint8_t r = (*colors)[i][0];
            uint8_t g = (*colors)[i][1];
            uint8_t b = (*colors)[i][2];
 
//            uint32_t rgb = ((uint32_t)r << 16 | (uint32_t)g << 8 | (uint32_t)b);
//            m_pointCloud->points[i].rgb = *reinterpret_cast<float*>(&rgb);
 
            m_pointCloud->points[i] = pcl::PointXYZRGB(r,g,b);
        }
 
        m_pointCloud->points[i].x = x;
        m_pointCloud->points[i].y = y;
        m_pointCloud->points[i].z = z;
    }
 
    m_pointCloud->width = numPoints;
    m_pointCloud->height = 1;
 
    // Create an empty kdtree representation, and pass it to the normal estimation object.
    // Its content will be filled inside the object, based on the given input dataset
    // (as no other search surface is given).
        m_kdTree = pcl::search::KdTree<pcl::PointXYZRGB>::Ptr( new pcl::search::KdTree<pcl::PointXYZRGB> );
    m_kdTree->setInputCloud (m_pointCloud);
}
 
void PCLFiltering::applyMLSProjection(float searchRadius)
{
    pcl::MovingLeastSquares<pcl::PointXYZRGB, pcl::PointNormal> mls;
    pcl::PointCloud<pcl::PointNormal> mls_points;
 
    // Set Parameters
    mls.setInputCloud(m_pointCloud);
    mls.setPolynomialFit(true);
    mls.setSearchMethod(m_kdTree);
    mls.setSearchRadius(searchRadius);
 
    std::cout << timestamp << "Applying MSL projection" << std::endl;
 
    // Reconstruct
    mls.process(mls_points);
 
    std::cout << timestamp << "Filtered cloud has " << mls_points.size() << " points" << std::endl;
    std::cout << timestamp << "Saving result" << std::endl;
 
    // Save filtered points
    m_pointCloud->resize(mls_points.size());
    float x, y, z;
    for(size_t i = 0; i < mls_points.size(); i++)
    {
        m_pointCloud->points[i].x = mls_points.points[i].x;
        m_pointCloud->points[i].y = mls_points.points[i].y;
        m_pointCloud->points[i].z = mls_points.points[i].z;
    }
 
    m_pointCloud->height = 1;
    m_pointCloud->width = mls_points.size();
 
}
 
void PCLFiltering::applyOutlierRemoval(int meank, float thresh)
{
    pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZRGB>);
 
    std::cout << timestamp << "Applying outlier removal" << std::endl;
 
    // Create the filtering object
    pcl::StatisticalOutlierRemoval<pcl::PointXYZRGB> sor;
    sor.setInputCloud (m_pointCloud);
    sor.setMeanK (meank);
    sor.setStddevMulThresh (thresh);
    sor.filter (*cloud_filtered);
 
    std::cout << timestamp << "Filtered cloud has " << cloud_filtered->size() << " points" << std::endl;
    std::cout << timestamp << "Saving result" << std::endl;
 
    m_pointCloud->width = cloud_filtered->width;
    m_pointCloud->height = cloud_filtered->height;
    m_pointCloud->points.swap (cloud_filtered->points);
}
 
PointBufferPtr PCLFiltering::getPointBuffer()
{
    PointBufferPtr p( new PointBuffer );
 
    floatArr points(new float[3 * m_pointCloud->size()]);
    ucharArr colors;
 
    if(m_useColors)
    {
       colors = ucharArr(new unsigned char[3 * m_pointCloud->size()]);
    }
 
    for(int i = 0; i < m_pointCloud->size(); i++)
    {
        size_t pos = 3 * i;
        points[pos    ] = m_pointCloud->points[i].x;
        points[pos + 1] = m_pointCloud->points[i].y;
        points[pos + 2] = m_pointCloud->points[i].z;
 
        if(m_useColors)
        {
            colors[pos    ] = m_pointCloud->points[i].r;
            colors[pos + 1] = m_pointCloud->points[i].g;
            colors[pos + 2] = m_pointCloud->points[i].b;
//            std::cout << (int)m_pointCloud->points[i].r << " "
//                 <<  (int)m_pointCloud->points[i].g << " "
//                 <<  (int)m_pointCloud->points[i].b << " " << std::endl;
        }
    }
 
    p->setPointArray(points, m_pointCloud->size());
 
    if(m_useColors)
    {
        p->setColorArray(colors, m_pointCloud->size());
    }
 
    return p;
}
 
PCLFiltering::~PCLFiltering()
{
    // TODO Auto-generated destructor stub
}
 
} /* namespace lvr2 */
 
#endif