pcl::PointCloud<PointT>::Ptr cloud(new pcl::PointCloud<PointT>); //创建一个点云对象 ccHObject* entity = getSelectedEntities()[0]; ccPointCloud* ccCloud = ccHObjectCaster::ToPointCloud(entity); 续写这段代码，使用passthrough对点云中z值在(0 , 1.3)的点进行过滤，然后在z值为1.3处用ransac算法进行圆的拟合，计算圆的直径，并给出代码所需要的头文件

续写代码如下：

#include <pcl/filters/passthrough.h>
#include <pcl/sample_consensus/ransac.h>
#include <pcl/sample_consensus/sac_model_circle.h>

pcl::PointCloud<PointT>::Ptr cloud(new pcl::PointCloud<PointT>); //创建一个点云对象
ccHObject* entity = getSelectedEntities()[0];
ccPointCloud* ccCloud = ccHObjectCaster::ToPointCloud(entity);

//将ccPointCloud转换为pcl::PointCloud
pcl::copyPointCloud(*ccCloud, *cloud);

//使用Passthrough对点云进行z值过滤
pcl::PassThrough<PointT> pass;
pass.setInputCloud(cloud);
pass.setFilterFieldName("z");
pass.setFilterLimits(0.0, 1.3);
pcl::PointCloud<PointT>::Ptr filtered_cloud(new pcl::PointCloud<PointT>);
pass.filter(*filtered_cloud);

//使用RANSAC算法对点云中的圆进行拟合，并计算出圆的直径
pcl::SampleConsensusModelCircle2D<PointT>::Ptr model(new pcl::SampleConsensusModelCircle2D<PointT>(filtered_cloud));
pcl::RandomSampleConsensus<PointT> ransac(model);
ransac.setDistanceThreshold(0.01);
ransac.computeModel();
Eigen::VectorXf model_coefficients;
ransac.getModelCoefficients(model_coefficients);
float diameter = model_coefficients[3] * 2; //圆的直径为半径的两倍

//输出圆的直径
std::cout << "圆的直径为：" << diameter << std::endl;

代码中所需要的头文件为：

#include <pcl/filters/passthrough.h>
#include <pcl/sample_consensus/ransac.h>
#include <pcl/sample_consensus/sac_model_circle.h>