PCL学习Day7

提取关键点

• 计算分辨率‘

#include <pcl/io/pcd_io.h>
#include <pcl/search/kdtree.h>

// This function by Tommaso Cavallari and Federico Tombari, taken from the tutorial
// http://pointclouds.org/documentation/tutorials/correspondence_grouping.php
double computeCloudResolution(const pcl::PointCloud<pcl::PointXYZ>::ConstPtr& cloud)
{
    
    
	double resolution = 0.0;
	int numberOfPoints = 0;
	int nres;
	std::vector<int> indices(2);
	std::vector<float> squaredDistances(2);
	pcl::search::KdTree<pcl::PointXYZ> tree;
	tree.setInputCloud(cloud);

	for (size_t i = 0; i < cloud->size(); ++i)
	{
    
    
		if (!std::isfinite((*cloud)[i].x))
			continue;

		// Considering the second neighbor since the first is the point itself.
		nres = tree.nearestKSearch(i, 2, indices, squaredDistances);
		if (nres == 2)
		{
    
    
			resolution += sqrt(squaredDistances[1]);
			++numberOfPoints;
		}
	}
	if (numberOfPoints != 0)
		resolution /= numberOfPoints;

	return resolution;
}
#pragma once

• 计算关键点并且显示

#include<pcl/io/pcd_io.h>
#include <pcl/point_types.h>
// 包含相关头文件
#include<pcl/keypoints/iss_3d.h>
#include<pcl/visualization/pcl_visualizer.h>
#include "resolution.h"// 计算模型的分辨率

using namespace std;
typedef pcl::PointXYZ PointT;

int main() {
    
    
	// 读取点云
	pcl::PointCloud<PointT>::Ptr cloud(new pcl::PointCloud<PointT>);
	pcl::io::loadPCDFile("rabbit.pcd", *cloud);
	std::cout << "original cloud size : " << cloud->size() << std::endl;
	double resolution = computeCloudResolution(cloud);// 计算模型分辨率
	cout << "模型分辨率为" << resolution << endl;

	pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>());

	pcl::ISSKeypoint3D<PointT, PointT> iss_dector;
	pcl::ISSKeypoint3D<PointT, PointT> iss_detector;
	iss_detector.setSearchMethod(tree);
	iss_detector.setSalientRadius(6 * resolution);
	iss_detector.setNonMaxRadius(4 * resolution);
	iss_detector.setThreshold21(0.975);
	iss_detector.setThreshold32(0.975);
	iss_detector.setMinNeighbors(5);
	iss_detector.setNumberOfThreads(4);
	iss_detector.setInputCloud(cloud);
	pcl::PointCloud<PointT>::Ptr keys(new pcl::PointCloud<PointT>);
	iss_detector.compute(*keys);
	pcl::io::savePCDFile("keypoints.pcd", *keys, true);
	std::cout << "key points size : " << keys->size() << std::endl;
	//显示
	pcl::visualization::PCLVisualizer viewer;
	pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> fildColor(cloud, 255, 222, 173);//设置点云颜色
	viewer.addPointCloud<pcl::PointXYZ>(cloud, fildColor, "orginal cloud");
	viewer.addPointCloud<pcl::PointXYZ>(keys, "keyPoints");
	viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 5, "keyPoints");//设置点的大小
	viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 255, 0, 0, "keyPoints");.//设置点的颜色
	viewer.spin();
	system("pause");
	return 0;
}

参考链接：https://github.com/MNewBie/PCL-Notes/blob/master/chapter2.md

https://zhuanlan.zhihu.com/p/268524083