PCL点云变换与移除NaN

#include <iostream>

#include <pcl/io/pcd_io.h>
#include <pcl/io/ply_io.h>
#include <pcl/point_cloud.h>
#include <pcl/console/parse.h>
#include <pcl/common/transforms.h>
#include <pcl/visualization/pcl_visualizer.h>
// 命令行的帮助提示
void showHelp(char * program_name)
{
  std::cout << std::endl;
  std::cout << "Usage: " << program_name << " cloud_filename.[pcd|ply]" << std::endl;
  std::cout << "-h:  Show this help." << std::endl;
}

int main (int argc, char** argv)
{
  if (pcl::console::find_switch (argc, argv, "-h") || pcl::console::find_switch (argc, argv, "--help")) {
    showHelp (argv[0]);
    return 0;
  }
  // 读取文件
  std::vector<int> filenames;
  bool file_is_pcd = false;
  filenames = pcl::console::parse_file_extension_argument (argc, argv, ".ply");
  if (filenames.size () != 1)  {
    filenames = pcl::console::parse_file_extension_argument (argc, argv, ".pcd");
    if (filenames.size () != 1) {
      showHelp (argv[0]);
      return -1;
    } else {
      file_is_pcd = true;
    }
  }
 //载入文件
  pcl::PointCloud<pcl::PointXYZ>::Ptr source_cloud (new pcl::PointCloud<pcl::PointXYZ> ());

  if (file_is_pcd) {
    if (pcl::io::loadPCDFile (argv[filenames[0]], *source_cloud) < 0)  {
      std::cout << "Error loading point cloud " << argv[filenames[0]] << std::endl << std::endl;
      showHelp (argv[0]);
      return -1;
    }
  } else {
    if (pcl::io::loadPLYFile (argv[filenames[0]], *source_cloud) < 0)  {
      std::cout << "Error loading point cloud " << argv[filenames[0]] << std::endl << std::endl;
      showHelp (argv[0]);
      return -1;
    }
  }

  /* Reminder: how transformation matrices work :

           |-------> This column is the translation
    | 1 0 0 x |  \
    | 0 1 0 y |   }-> The identity 3x3 matrix (no rotation) on the left
    | 0 0 1 z |  /
    | 0 0 0 1 |    -> We do not use this line (and it has to stay 0,0,0,1)

    METHOD #1: Using a Matrix4f
    This is the "manual" method, perfect to understand but error prone !
  */
  Eigen::Matrix4f transform_1 = Eigen::Matrix4f::Identity();

  // Define a rotation matrix 定义旋转的角度  再有角度计算出旋转矩阵
  float theta = M_PI/4; // The angle of rotation in radians
  transform_1 (0,0) = cos (theta);
  transform_1 (0,1) = -sin(theta);
  transform_1 (1,0) = sin (theta);
  transform_1 (1,1) = cos (theta);
  //    (row, column)

  // Define a translation of 2.5 meters on the x axis.
  transform_1 (0,3) = 2.5;//意思就是在第一行第四个元素的值为2.5，也就是在x轴的平移为2.5

  // Print the transformation  打印出这个变换矩阵
  printf ("Method #1: using a Matrix4f\n");
  std::cout << transform_1 << std::endl;

  /*  METHOD #2: Using a Affine3f  第二种方案
    This method is easier and less error prone  更简单的方案
  */
  Eigen::Affine3f transform_2 = Eigen::Affine3f::Identity();

  // Define a translation of 2.5 meters on the x axis.
  transform_2.translation() << 2.5, 0.0, 0.0;

  // The same rotation matrix as before; theta radians arround Z axis
  transform_2.rotate (Eigen::AngleAxisf (theta, Eigen::Vector3f::UnitZ()));

  // Print the transformation
  printf ("\nMethod #2: using an Affine3f\n");
  std::cout << transform_2.matrix() << std::endl;

  // Executing the transformation
  pcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud (new pcl::PointCloud<pcl::PointXYZ> ());
  // 你可以使用 transform_1 或者 transform_2;效果都是一样的 
  pcl::transformPointCloud (*source_cloud, *transformed_cloud, transform_2);

  // 可视化的
  printf(  "\nPoint cloud colors :  white  = original point cloud\n"
      "                        red  = transformed point cloud\n");
  pcl::visualization::PCLVisualizer viewer ("Matrix transformation example");

   // 为点云设置RGB的值
  pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> source_cloud_color_handler (source_cloud, 255, 255, 255);
  // We add the point cloud to the viewer and pass the color handler
  viewer.addPointCloud (source_cloud, source_cloud_color_handler, "original_cloud");

  pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> transformed_cloud_color_handler (transformed_cloud, 230, 20, 20); // Red
  viewer.addPointCloud (transformed_cloud, transformed_cloud_color_handler, "transformed_cloud");

  viewer.addCoordinateSystem (1.0, 0);
  viewer.setBackgroundColor(0.05, 0.05, 0.05, 0); //设置背景颜色
  viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "original_cloud");
  viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "transformed_cloud");
  //viewer.setPosition(800, 400); // Setting visualiser window position

  while (!viewer.wasStopped ()) { // Display the visualiser until 'q' key is pressed
    viewer.spinOnce ();
  }

  return 0;
}

#include <pcl/io/pcd_io.h>
#include <pcl/filters/filter.h>
#include <iostream>
#include <pcl/visualization/cloud_viewer.h>

int main(int argc,char** argv)
{
if(argc !=3)
 {
  std::cout <<"\tUsage: "<<argv[0] <<"<input cloud> <output cloud>" <<std::endl;

  return  -1;
 }

//object for string the point cloud
pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZRGBA>);
//read a PCDfile from disk
if(pcl::io::loadPCDFile<pcl::PointXYZRGBA>(argv[1],*cloud) !=0)
{
 return -1;
}

//the mapping tells you to that points of the oldcloud the new ones correspond
//but we  will not use it
std::vector<int> mapping;
pcl::removeNaNFromPointCloud(*cloud, *cloud, mapping);
//pcl::removeNaNFromPointCloud(*cloud, *cloud, mapping);
//save it back
pcl::io::savePCDFileASCII(argv[2],*cloud);

pcl::visualization::CloudViewer viewer(argv[2]);
         viewer.showCloud(cloud);
        while (!viewer.wasStopped())
    {
        // Do nothing but wait.
    }

}