# 다운샘플링-PCL-Cpp (70%)

> 코드는 [\[이곳\]](https://github.com/adioshun/gitBook_Tutorial_PCL/blob/master/Beginner/Part01-Chapter02-Downsampling-PCL-Cpp.cpp)에서 다운로드 가능합니다. 샘플파일은 [\[table\_scene\_lms400.pcd\]](<https://raw.githubusercontent.com/adioshun/gitBook_Tutorial_PCL/master/Beginner/sample/table_scene_lms400.pcd >)을 사용하였습니다.

```cpp
#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/voxel_grid.h>

//Downsampling a PointCloud using a VoxelGrid filter
//http://pointclouds.org/documentation/tutorials/voxel_grid.php#voxelgrid

int
main (int argc, char** argv)
{
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);

  // *.PCD 파일 읽기 (https://raw.github.com/PointCloudLibrary/data/master/tutorials/table_scene_lms400.pcd)
  pcl::io::loadPCDFile<pcl::PointXYZ> ("table_scene_lms400.pcd", *cloud);

 // 포인트수 출력
  std::cout << "Input : " << cloud->points.size () << " (" << pcl::getFieldsList (*cloud) <<")"<< std::endl;

  // 오브젝트 생성 
  //pcl::VoxelGrid<pcl::PCLPointCloud2> sor;
  pcl::VoxelGrid<pcl::PointXYZ> sor;
  sor.setInputCloud (cloud);              //입력
  sor.setLeafSize (0.01f, 0.01f, 0.01f); //leaf size  1cm 
  sor.filter (*cloud_filtered);          //출력 

  // 생성된 포인트클라우드 수 출력 
  std::cout << "Output : " << cloud_filtered->points.size () << " (" << pcl::getFieldsList (*cloud_filtered) <<")"<< std::endl;

  // 생성된 포인트클라우드 저장 
  pcl::io::savePCDFile<pcl::PointXYZ>("table_scene_lms400_downsampled.pcd", *cloud_filtered);
  return (0);
}
```

실행 & 결과

```
$ Input : 460400 (x y z)
$ Output : 41049 (x y z)
```

시각화 & 결과

```
$ pcl_viewer table_scene_lms400.pcd 
$ pcl_viewer table_scene_lms400_downsampled.pcd
```

| ![](https://i.imgur.com/yG5GYmm.png) | ![](https://i.imgur.com/l8urRKc.png) |
| ------------------------------------ | ------------------------------------ |
| 원본                                   | 원본 확대                                |
| ![](https://i.imgur.com/OB02KJu.png) | ![](https://i.imgur.com/RRGXu4O.png) |
| 결과                                   | 결과 확대                                |

## UniformSampling

Uniform sampling은 동작 방법은 동일하지만 결과값으로 인덱스(indices)를 출력하여 이후 식별자 정보등으로 활용 가능

> The UniformSampling class creates a 3D voxel grid (think about a voxel grid as a set of tiny 3D boxes in space) over the input point cloud data. Then, in each voxel (i.e., 3D box), all the points present will be approximated (i.e., downsampled) with their centroid. This approach is a bit slower than approximating them with the center of the voxel, but it represents the underlying surface more accurately.

```cpp
#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/uniform_sampling.h>


//https://github.com/PointCloudLibrary/pcl/blob/master/test/filters/test_uniform_sampling.cpp
//https://blog.csdn.net/qq_34839823/article/details/103843021
//The UniformSampling class creates a 3D voxel grid (think about a voxel grid as a set of tiny 3D boxes in space) over the input point cloud data. Then, in each voxel (i.e., 3D box), all the points present will be approximated (i.e., downsampled) with their centroid. This approach is a bit slower than approximating them with the center of the voxel, but it represents the underlying surface more accurately.
//http://docs.pointclouds.org/1.8.1/classpcl_1_1_uniform_sampling.html#details
//https://github.com/PointCloudLibrary/pcl/blob/master/tools/uniform_sampling.cpp

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

  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);

  // *.PCD 파일 읽기 (https://raw.github.com/PointCloudLibrary/data/master/tutorials/table_scene_lms400.pcd)
  pcl::io::loadPCDFile<pcl::PointXYZ> ("table_scene_lms400.pcd", *cloud);

  // 포인트수 출력
  std::cout << "Loaded :" << cloud->width * cloud->height  << std::endl;

  // 오프젝트 생성
  pcl::UniformSampling<pcl::PointXYZ> filter ; 
  filter.setInputCloud (cloud) ;     // 입력 
  filter.setRadiusSearch (0.01F) ;   // 탐색 범위 0.01F
  filter.filter (*cloud_filtered) ;  // 필터 적용 

  // 포인트수 출력  
  std::cout << "Filtered :" << cloud_filtered->width * cloud_filtered->height  << std::endl;  

  // 생성된 포인트클라우드 저장 
  pcl::PCDWriter writer;
  writer.write<pcl::PointXYZ> ("uniform_sampling.pcd", *cloud_filtered, false);

  return (0);
}
```

> 다운샘플링은 구조체(PCLPointCloud2)에서만 진행 하여야 하나??

Convert

```cpp
  pcl::PCLPointCloud2::Ptr cloud_blob (new pcl::PCLPointCloud2);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>)

  // Convert to the templated PointCloud
  pcl::fromPCLPointCloud2 (*cloud_filtered_blob, *cloud_filtered);
```


---

# 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-1/part01-chapter02/part01-chapter02-downsampling-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.