# Euclidean-PCL-Cpp  (70%)

> 코드는 [\[이곳\]](https://github.com/adioshun/gitBook_Tutorial_PCL/blob/master/Intermediate/Part02-Chapter01-Euclidean-PCL-Cpp.cpp)에서 다운로드 가능합니다. 샘플파일은 [\[RANSAC\_plane\_true.pcd\]](https://raw.githubusercontent.com/adioshun/gitBook_Tutorial_PCL/master/Intermediate/sample/RANSAC_plane_true.pcd)을 사용하였습니다.

```cpp
#include <pcl/ModelCoefficients.h>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/features/normal_3d.h>
#include <pcl/kdtree/kdtree.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/segmentation/extract_clusters.h>

// Euclidean Cluster Extraction
// http://pointclouds.org/documentation/tutorials/cluster_extraction.php#cluster-extraction

int 
main (int argc, char** argv)
{

  pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZRGB>);
  pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_f (new pcl::PointCloud<pcl::PointXYZRGB>);

  // *.PCD 파일 읽기 (https://raw.githubusercontent.com/adioshun/gitBook_Tutorial_PCL/master/Intermediate/sample/RANSAC_plane_true.pcd)
  pcl::io::loadPCDFile<pcl::PointXYZRGB> ("RANSAC_plane_true.pcd", *cloud);

  // 포인트수 출력
  std::cout << "PointCloud before filtering has: " << cloud->points.size () << " data points." << std::endl; //*

  // 탐색을 위한 KdTree 오브젝트 생성 //Creating the KdTree object for the search method of the extraction
  pcl::search::KdTree<pcl::PointXYZRGB>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZRGB>);
  tree->setInputCloud (cloud);  //KdTree 생성 


  std::vector<pcl::PointIndices> cluster_indices;       // 군집화된 결과물의 Index 저장, 다중 군집화 객체는 cluster_indices[0] 순으로 저장 
  // 군집화 오브젝트 생성  
  pcl::EuclideanClusterExtraction<pcl::PointXYZRGB> ec;
  ec.setInputCloud (cloud);       // 입력   
  ec.setClusterTolerance (0.02);  // 2cm  
  ec.setMinClusterSize (100);     // 최소 포인트 수 
  ec.setMaxClusterSize (25000);   // 최대 포인트 수
  ec.setSearchMethod (tree);      // 위에서 정의한 탐색 방법 지정 
  ec.extract (cluster_indices);   // 군집화 적용 

  // 클러스터별 정보 수집, 출력, 저장 
  int j = 0;
  for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it)
  {
    pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZRGB>);
    for (std::vector<int>::const_iterator pit = it->indices.begin (); pit != it->indices.end (); ++pit)
     cloud_cluster->points.push_back (cloud->points[*pit]); 
    cloud_cluster->width = cloud_cluster->points.size ();
    cloud_cluster->height = 1;
    cloud_cluster->is_dense = true;

    // 포인트수 출력
    std::cout << "PointCloud representing the Cluster: " << cloud_cluster->points.size () << " data points." << std::endl;

    // 클러스터별 이름 생성 및 저장 
    std::stringstream ss;
    ss << "cloud_cluster_" << j << ".pcd";
    pcl::PCDWriter writer;
    writer.write<pcl::PointXYZRGB> (ss.str (), *cloud_cluster, false); //*
    j++;
  }

  return (0);
}
```

실행 & 결과

```
$ PointCloud before filtering has: 23330 data points.
$ PointCloud representing the Cluster: 5981 data points.
$ PointCloud representing the Cluster: 5111 data points.
$ PointCloud representing the Cluster: 4431 data points.
$ PointCloud representing the Cluster: 2768 data points.
$ PointCloud representing the Cluster: 2513 data points.
$ PointCloud representing the Cluster: 1552 data points.
$ PointCloud representing the Cluster: 934 data points.
```

시각화 & 결과

```
$ pcl_viewer cloud_cluster_0.pcd 
$ pcl_viewer cloud_cluster_6.pcd
```

| ![](https://i.imgur.com/j6HoJBy.png) | ![](https://i.imgur.com/4ZXd2eu.png) | ![](https://i.imgur.com/6WVsQwo.png) |
| ------------------------------------ | ------------------------------------ | ------------------------------------ |
| 원본(`RANSAC_plane_true.pcd`)          | 결과(`cloud_cluster_0.pcd`)            | 결과(`cloud_cluster_6.pcd`)            |

* [Adaptive clustering](https://github.com/yzrobot/adaptive_clustering): `Online learning for human classification in 3D LiDAR-based tracking(2017)` 에서 활용한 Euclidean clustering 기법&#x20;


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://pcl.gitbook.io/tutorial/part-2/part02-chapter01/part02-chapter01-euclidean-pcl-cpp.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.