基于颜色的点云分割

保留一下这道程序

// Color_based_region_growth_segmentation.cpp : 定义控制台应用程序的入口点。
//
// Color - based region growth segmentation.cpp : 定义控制台应用程序的入口点。
//
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
using namespace pcl::console;
int main(int argc, char** argv)
{

	if (argc<2)
	{
		std::cout << ".exe xx.pcd -b_n 0 -kn 50 -st_n 30 -ct_n 0.05 -bc 0 -fc 10 -nc 0 -dt 10 -ct 6 -rt 5 -mt 600" << endl;

		return 0;
	}
	time_t start, end, diff[5], option;
	start = time(0);
	bool Bool_Cuting = false, b_n = false;
	int MinClusterSize = 600, KN_normal = 50;
	float far_cuting = 10, near_cuting = 0, DistanceThreshold = 10.0, ColorThreshold = 6, RegionColorThreshold = 5, SmoothnessThreshold = 30.0, CurvatureThreshold = 0.05;

	parse_argument(argc, argv, "-b_n", b_n);                 //是否使用法线信息辅助分割
	parse_argument(argc, argv, "-kn", KN_normal);            //搜索范围
	parse_argument(argc, argv, "-st_n", SmoothnessThreshold);//点与点归类时法线间的角度阈值
	parse_argument(argc, argv, "-ct_n", CurvatureThreshold); //点与点归类时曲率阈值

	parse_argument(argc, argv, "-bc", Bool_Cuting);          //是否进行范围滤波处理
	parse_argument(argc, argv, "-fc", far_cuting);           //滤波范围上限
	parse_argument(argc, argv, "-nc", near_cuting);          //滤波范围下限

	parse_argument(argc, argv, "-dt", DistanceThreshold);    //点与点归类时欧式距离阈值
	parse_argument(argc, argv, "-ct", ColorThreshold);       //点与点归类时颜色阈值
	parse_argument(argc, argv, "-rt", RegionColorThreshold); //聚类合并时颜色阈值
	parse_argument(argc, argv, "-mt", MinClusterSize);       //允许的最小聚类包含的点的个数，小于该值得聚类被合并到邻近聚类

															 //frist step load the point cloud
	pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZRGB>);
	if (pcl::io::loadPCDFile <pcl::PointXYZRGB>(argv[1], *cloud) == -1)
	{
		std::cout << "Cloud reading failed." << std::endl;
		return (-1);
	}
	end = time(0);
	diff[0] = difftime(end, start);
	PCL_INFO("\\Loading pcd file takes(seconds): %d\n", diff[0]);
	pcl::search::Search <pcl::PointXYZRGB>::Ptr tree = boost::shared_ptr<pcl::search::Search<pcl::PointXYZRGB> >(new pcl::search::KdTree<pcl::PointXYZRGB>);

	//Noraml estimation step(1 parameter)
	pcl::search::Search<pcl::PointXYZRGB>::Ptr tree1 = boost::shared_ptr<pcl::search::Search<pcl::PointXYZRGB> >(new pcl::search::KdTree<pcl::PointXYZRGB>);
	pcl::PointCloud <pcl::Normal>::Ptr normals(new pcl::PointCloud <pcl::Normal>);
	pcl::NormalEstimation<pcl::PointXYZRGB, pcl::Normal> normal_estimator;
	normal_estimator.setSearchMethod(tree);
	normal_estimator.setInputCloud(cloud);
	normal_estimator.setKSearch(KN_normal);
	normal_estimator.compute(*normals);
	end = time(0);
	diff[1] = difftime(end, start) - diff[0];
	PCL_INFO("\\Estimating normal takes(seconds): %d\n", diff[1]);
	//Optional step: cutting away the clutter scene too far away from camera
	pcl::IndicesPtr indices(new std::vector <int>);
	if (Bool_Cuting)//是否通过z轴范围对待处理数据进行裁剪
	{
		pcl::PassThrough<pcl::PointXYZRGB> pass;
		pass.setInputCloud(cloud);
		pass.setFilterFieldName("z");
		pass.setFilterLimits(near_cuting, far_cuting);
		pass.filter(*indices);
	}
	// Region growing RGB 
	pcl::RegionGrowingRGB<pcl::PointXYZRGB> reg;  //创建PointXYZRGB类型的区域生长分割对象
	reg.setInputCloud(cloud);                    //设置输入点云
	if (Bool_Cuting)reg.setIndices(indices);      //设置输入点云的索引
	reg.setSearchMethod(tree);                   //设置点云的搜索机制
	reg.setDistanceThreshold(DistanceThreshold); //设置距离阈值，用于判断两点是否是相邻点
	reg.setPointColorThreshold(ColorThreshold);  //设置两点颜色阈值，用于判断两点是否属于同一类
	reg.setRegionColorThreshold(RegionColorThreshold); //设置两类区域区域颜色阈值，用于判断两类区域是否聚类合并
	reg.setMinClusterSize(MinClusterSize);       //设置一个聚类的最少点数目为600
	if (b_n)
	{
		reg.setSmoothModeFlag(true);
		reg.setCurvatureTestFlag(true);
		reg.setInputNormals(normals);
		reg.setSmoothnessThreshold(SmoothnessThreshold / 180.0 * M_PI);
		reg.setCurvatureThreshold(CurvatureThreshold);
	}
	std::vector <pcl::PointIndices> clusters;
	reg.extract(clusters);
	end = time(0);
	diff[2] = difftime(end, start) - diff[0] - diff[1];
	PCL_INFO("\\RGB region growing takes(seconds): %d\n", diff[2]);
	pcl::PointCloud <pcl::PointXYZRGB>::Ptr colored_cloud = reg.getColoredCloud();
	pcl::visualization::CloudViewer viewer("基于颜色的区域生长算法实现点云分割");
	viewer.showCloud(colored_cloud);
	while (!viewer.wasStopped())
	{
		boost::this_thread::sleep(boost::posix_time::microseconds(100));
	}
	system("pause");
	return (0);
}