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C++ PCL点云dscan密度分割三维
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前言

这篇博客针对《C++ PCL点云dscan密度分割三维》编写代码，代码整洁，规则，易读。 学习与应用推荐首选。

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运行结果

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文章目录

一、所需工具软件
二、使用步骤
       1. 主要代码
       2. 运行结果
三、在线协助

一、所需工具软件

       1. VS2019, Qt
       2. C++

二、使用步骤

代码如下（示例）：

#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/passthrough.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/ModelCoefficients.h>
// Visualization, [The CloudViewer](https://pcl.readthedocs.io/projects/tutorials/en/latest/cloud_viewer.html#cloud-viewer)
template <typename PointCloudPtrType>
void show_point_cloud(PointCloudPtrType cloud, std::string display_name) {
  pcl::visualization::CloudViewer viewer(display_name);
  viewer.showCloud(cloud);
  while (!viewer.wasStopped())
  {
  }
}

int main(int argc, char** argv) {
    // IO, [Reading Point Cloud data from PCD files](https://pcl.readthedocs.io/projects/tutorials/en/latest/reading_pcd.html#reading-pcd)
    pcl::PCDReader reader;
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>), cloud_f(new pcl::PointCloud<pcl::PointXYZ>);
    reader.read("10quyang2.pcd", *cloud);
    std::cout << "PointCloud before filtering has: " << cloud->points.size () << " data points." << std::endl;
    show_point_cloud(cloud, "original point cloud");
  
    // remove the biggest plane
    // Segmentation, Ransac, [Plane model segmentation](https://pcl.readthedocs.io/projects/tutorials/en/latest/planar_segmentation.html#planar-segmentation)
    pcl::SACSegmentation<pcl::PointXYZ> seg;
    pcl::PointIndices::Ptr inliers(new pcl::PointIndices);
    pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_plane(new pcl::PointCloud<pcl::PointXYZ>);
    seg.setOptimizeCoefficients(true);
    seg.setModelType(pcl::SACMODEL_PLANE);
    seg.setMethodType(pcl::SAC_RANSAC);
    seg.setMaxIterations(100);
    if (inliers->indices.size() == 0) {
        std::cout << "Could not estimate a planar anymore." << std::endl;
    } else {
        // Filter, [Extracting indices from a PointCloud](https://pcl.readthedocs.io/projects/tutorials/en/latest/extract_indices.html#extract-indices)
        pcl::ExtractIndices<pcl::PointXYZ> extract;
        extract.setInputCloud(cloud_filtered);
        extract.setIndices(inliers);
        extract.setNegative(false);
        extract.filter(*cloud_plane);
        pcl::PointXYZ min, max;
        pcl::getMinMax3D(*cloud_filtered, min, max);
        double min_z = min.z;
        std::cout << "ground plane size: " << cloud_plane->points.size()  << ", min_z:" << min_z << std::endl;
    }

    // KdTree, for more information, please ref [How to use a KdTree to search](https://pcl.readthedocs.io/projects/tutorials/en/latest/kdtree_search.html#kdtree-search)
    pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
    tree->setInputCloud(cloud_filtered);
    // Segmentation, [Euclidean Cluster Extraction](https://pcl.readthedocs.io/projects/tutorials/en/latest/cluster_extraction.html#cluster-extraction)
    std::vector<pcl::PointIndices> cluster_indices;
    clock_t start_ms = clock();
    
    // test 1. uncomment the following two lines to test the simple dbscan
    // DBSCANSimpleCluster<pcl::PointXYZ> ec;
    // ec.setCorePointMinPts(20);

    // test 2. uncomment the following two lines to test the precomputed dbscan
    // DBSCANPrecompCluster<pcl::PointXYZ>  ec;
    // ec.setCorePointMinPts(20);

    // test 3. uncomment the following two lines to test the dbscan with Kdtree for accelerating
    DBSCANKdtreeCluster<pcl::PointXYZ> ec;
    ec.setCorePointMinPts(20);

    // test 4. uncomment the following line to test the EuclideanClusterExtraction
    // pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
    clock_t end_ms = clock();
    std::cout << "cluster time cost:" << double(end_ms - start_ms) / CLOCKS_PER_SEC << " s" << std::endl;
    pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_clustered(new pcl::PointCloud<pcl::PointXYZI>);
    int j = 0;
    // visualization, use indensity to show different color for each cluster.
    for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin(); it != cluster_indices.end(); it++, j++) {
        for(std::vector<int>::const_iterator pit = it->indices.begin(); pit != it->indices.end(); ++pit) {
            pcl::PointXYZI tmp;
            tmp.x = cloud_filtered->points[*pit].x;
            tmp.y = cloud_filtered->points[*pit].y;
            tmp.z = cloud_filtered->points[*pit].z;
            tmp.intensity = j%8;
            cloud_clustered->points.push_back(tmp);
        }
    }
    show_point_cloud(cloud_clustered, "colored clusters of point cloud");
    // IO, [Writing Point Cloud data to PCD files](https://pcl.readthedocs.io/projects/tutorials/en/latest/writing_pcd.html#writing-pcd)
    pcl::PCDWriter writer;
    writer.write<pcl::PointXYZI>("cloud_clustered.pcd", *cloud_clustered, false);

    return 0;
}

运行结果

三、在线协助：

如需安装运行环境或远程调试，见文章底部个人 QQ 名片，由专业技术人员远程协助！

1）远程安装运行环境，代码调试
2）Visual Studio, Qt, C++, Python编程语言入门指导
3）界面美化
4）软件制作
5）云服务器申请
6）网站制作

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