002：pcl 点云投影

1.首先包含的对应的ModelCoefficients.h以及filter中向平面投影的project_inlier.h

#include <iostream>

#include <pcl/io/pcd_io.h>

#include <pcl/point_types.h>

#include <pcl/ModelCoefficients.h>

#include <pcl/filters/project_inliers.h>

2.增加可视化显示的代码

int user_data;

void

viewerOneOff(pcl::visualization::PCLVisualizer& viewer)

{

       viewer.setBackgroundColor(1.0, 0.5, 1.0);

       pcl::PointXYZ o;

       o.x = 1.0;

       o.y = 0;

       o.z = 0;

       viewer.addSphere(o, 0.25, "sphere", 0);

       std::cout << "i only run once" << std::endl;
}

void

viewerPsycho(pcl::visualization::PCLVisualizer& viewer)

{

       static unsigned count = 0;

       std::stringstream ss;

       ss << "Once per viewer loop: " << count++;

       viewer.removeShape("text", 0);

       viewer.addText(ss.str(), 200, 300, "text", 0);

       //FIXME: possible race condition here:

       user_data++;

}

3.创建点云对象指针并初始化，输出到屏幕

/2.初始化该对象

  cloud->width  = 5;//对于未组织的点云的相当于points个数

  cloud->height = 1; //对未组织的点云指定为1

  cloud->points.resize (cloud->width * cloud->height); //修剪或追加值初始化的元素

  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);

  }

  // 3.cerr 输出对象放置刷屏

  std::cerr << "Cloud before projection: " << 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;

//投影前点
`Cloud before projection:
    1.28125 577.094 197.938
    828.125 599.031 491.375
    358.688 917.438 842.563
    764.5 178.281 879.531
    727.531 525.844 311.281

4.设置ModelCoefficients值。在这种情况下，我们使用一个平面模型，其中ax + by + cz + d = 0，其中a = b = d = 0，c = 1，或者换句话说，XY平面

  // 4.创建一个系数为X=Y=0,Z=1的平面

  pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients ());

  coefficients->values.resize (4);

  coefficients->values[0] = coefficients->values[1] = 0;

  coefficients->values[2] = 1.0;

  coefficients->values[3] = 0;

5.通过该滤波将所有的点投影到创建的平面上，并输出结果
** 注意这里在使用的时候再创建滤波后对象不规范，应该放在程序开始的时候**

  //5.创建滤波后对象，并通过滤波投影，并显示结果

  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_projected(new 
pcl::PointCloud<pcl::PointXYZ>);

  // 创建滤波器对象

  pcl::ProjectInliers<pcl::PointXYZ> proj;

  proj.setModelType (pcl::SACMODEL_PLANE);

  proj.setInputCloud (cloud);

  proj.setModelCoefficients (coefficients);

  proj.filter (*cloud_projected);
  //可视化显示
   pcl::visualization::CloudViewer viewer("Cloud Viewer");

  //showCloud函数是同步的，在此处等待直到渲染显示为止

  viewer.showCloud(cloud);

  //该注册函数在可视化时只调用一次

  viewer.runOnVisualizationThreadOnce(viewerOneOff);

  //该注册函数在渲染输出时每次都调用

  viewer.runOnVisualizationThread(viewerPsycho);

  while (!viewer.wasStopped())

  {

         //在此处可以添加其他处理

         user_data++;

  }
  

  std::cerr << "Cloud after projection: " << std::endl;

  for (size_t i = 0; i < cloud_projected->points.size (); ++i)

    std::cerr << "    " << cloud_projected->points[i].x << " " 

                        << cloud_projected->points[i].y << " " 

                        << cloud_projected->points[i].z << std::endl;
  return (0);

//投影后点
Cloud before projection:
    1.28125 577.094 197.938
    828.125 599.031 491.375
    358.688 917.438 842.563
    764.5 178.281 879.531
    727.531 525.844 311.281
Cloud after projection:
    1.28125 577.094 0
    828.125 599.031 0
    358.688 917.438 0
    764.5 178.281 0
    727.531 525.844 0

6.参考网址
pcl官网例程
all-in_one 中的有api 以及例子，但是具体理论说明还是参考官网吧！
...PCL-1.8.1-AllInOne-msvc2017-win64(1)sharedocpcl-1.8 utorialssources中例子要比pcl入门精通要全