PCL滤波介绍(3)

#include <iostream>
#include <pcl/ModelCoefficients.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/extract_indices.h>
int main (int argc, char** argv)
{  
 /**从输入的.PCD 文件载入数据后，创建一个VOxelGrid滤波器对数据进行下采样，在这里进行下才样是为了加速处理过程，越少的点意味着分割循环中处理起来越快*/
 pcl::PCLPointCloud2::Ptr cloud_blob (new pcl::PCLPointCloud2), cloud_filtered_blob (new pcl::PCLPointCloud2);//申明滤波前后的点云
 pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>), cloud_p (new pcl::PointCloud<pcl::PointXYZ>), cloud_f (new pcl::PointCloud<pcl::PointXYZ>); 
 // 读取PCD文件  
 pcl::PCDReader reader;
reader.read ("table_scene_lms400.pcd", *cloud_blob);   //统计滤波前的点云个数
std::cerr << "PointCloud before filtering: " << cloud_blob->width * cloud_blob->height << " data points." << std::endl;  // 创建体素栅格下采样: 下采样的大小为1cm
 pcl::VoxelGrid<pcl::PCLPointCloud2> sor;  //体素栅格下采样对象
 sor.setInputCloud (cloud_blob);             //原始点云
 sor.setLeafSize (0.01f, 0.01f, 0.01f);    // 设置采样体素大小
 sor.filter (*cloud_filtered_blob);        //保存  // 转换为模板点云
 pcl::fromPCLPointCloud2 (*cloud_filtered_blob, *cloud_filtered); std::cerr << "PointCloud after filtering: " << cloud_filtered->width * cloud_filtered->height << " data points." << std::endl;  // 保存下采样后的点云  pcl::PCDWriter writer;
 writer.write<pcl::PointXYZ> ("table_scene_lms400_downsampled.pcd", *cloud_filtered, false);
 
 pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());
 pcl::PointIndices::Ptr inliers (new pcl::PointIndices ());
 pcl::SACSegmentation<pcl::PointXYZ> seg;               //创建分割对象
 seg.setOptimizeCoefficients (true);  //设置对估计模型参数进行优化处理
 seg.setModelType (pcl::SACMODEL_PLANE);                //设置分割模型类别
 seg.setMethodType (pcl::SAC_RANSAC);   //设置用哪个随机参数估计方法
 seg.setMaxIterations (1000);                            //设置最大迭代次数
 seg.setDistanceThreshold (0.01);  //判断是否为模型内点的距离阀值  
// 设置ExtractIndices的实际参数
 pcl::ExtractIndices<pcl::PointXYZ> extract;        //创建点云提取对象 int i = 0, nr_points = (int) cloud_filtered->points.size ();  // While 30% of the original cloud is still there
 while (cloud_filtered->points.size () > 0.3 * nr_points)
 {    // 为了处理点云包含的多个模型，在一个循环中执行该过程并在每次模型被提取后，保存剩余的点进行迭代    seg.setInputCloud (cloud_filtered);
   seg.segment (*inliers, *coefficients);  
  if (inliers->indices.size () == 0)
   {
     std::cerr << "Could not estimate a planar model for the given dataset." << std::endl;      break;
   }  
  // Extract the inliers  
  extract.setInputCloud (cloud_filtered);
   extract.setIndices (inliers);
   extract.setNegative (false);
   extract.filter (*cloud_p);
   std::cerr << "PointCloud representing the planar component: " << cloud_p->width * cloud_p->height << " data points." << std::endl;   std::stringstream ss;
   ss << "table_scene_lms400_plane_" << i << ".pcd";
   writer.write<pcl::PointXYZ> (ss.str (), *cloud_p, false);    
 // Create the filtering object
   extract.setNegative (true);
   extract.filter (*cloud_f);
   cloud_filtered.swap (cloud_f);
   i++;
 }  return (0);
}

#include <iostream>
#include <pcl/point_types.h>
#include <pcl/filters/radius_outlier_removal.h>
#include <pcl/filters/conditional_removal.h>
int main (int argc, char** argv)
{  if (argc != 2)  //确保输入的参数 
 {
   std::cerr << "please specify command line arg '-r' or '-c'" << std::endl;
   exit(0);
 }
 pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
 pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);  //填充点云
 cloud->width  = 5;
 cloud->height = 1;
 cloud->points.resize (cloud->width * cloud->height);  for (size_t i = 0; i < cloud->points.size (); ++i)
 {
   cloud->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
   cloud->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
   cloud->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
 }  if (strcmp(argv[1], "-r") == 0){
   pcl::RadiusOutlierRemoval<pcl::PointXYZ> outrem;  //创建滤波器    
   outrem.setInputCloud(cloud);    //设置输入点云
   outrem.setRadiusSearch(0.8);     //设置半径为0.8的范围内找临近点
   outrem.setMinNeighborsInRadius (2); //设置查询点的邻域点集数小于2的删除    // apply filter
   outrem.filter (*cloud_filtered);   //执行条件滤波   在半径为0.8 在此半径内必须要有两个邻居点，此点才会保存  }
    else if (strcmp(argv[1], "-c") == 0)
    {    //创建条件限定的下的滤波器
   pcl::ConditionAnd<pcl::PointXYZ>::Ptr range_cond (new
     pcl::ConditionAnd<pcl::PointXYZ> ());   //创建条件定义对象     
   //为条件定义对象添加比较算子
   range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (new
 pcl::FieldComparison<pcl::PointXYZ> ("z", pcl::ComparisonOps::GT, 0.0)));   //添加在Z字段上大于0的比较算子range_cond->addComparison (pcl::FieldComparison<pcl::PointXYZ>::ConstPtr (newpcl::FieldComparison<pcl::PointXYZ> ("z", pcl::ComparisonOps::LT, 0.8)));   //添加在Z字段上小于0.8的比较算子   
 // 创建滤波器并用条件定义对象初始化
   pcl::ConditionalRemoval<pcl::PointXYZ> condrem;
   condrem.setCondition (range_cond);              
   condrem.setInputCloud (cloud);                   //输入点云
   condrem.setKeepOrganized(true);               //设置保持点云的结构    // 执行滤波
   condrem.filter (*cloud_filtered);  //大于0.0小于0.8这两个条件用于建立滤波器  }  else{
   std::cerr << "please specify command line arg '-r' or '-c'" << std::endl;
   exit(0);
 }
 std::cerr << "Cloud before filtering: " << std::endl;  for (size_t i = 0; i < cloud->points.size (); ++i)
   std::cerr << "    " << cloud->points[i].x << " "
                       << cloud->points[i].y << " "
                       << cloud->points[i].z << std::endl;  
 std::cerr << "Cloud after filtering: " << std::endl;  for (size_t i = 0; i < cloud_filtered->points.size (); ++i)
   std::cerr << "    " << cloud_filtered->points[i].x << " "
                       << cloud_filtered->points[i].y << " "
                       << cloud_filtered->points[i].z << std::endl;  return (0);
}