> For the complete documentation index, see [llms.txt](https://pcl.gitbook.io/tutorial/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://pcl.gitbook.io/tutorial/part-2/part02-chapter02/part02-chapter02-search-octree-pcl-python.md).

# Search-Octree-PCL-Python  (70%)

> C++ 코드는 [\[이곳\]](https://github.com/adioshun/gitBook_Tutorial_PCL/blob/master/Intermediate/Part02-Chapter02-Search-Octree-PCL-Cpp.cpp)에서 다운로드 가능합니다. 원본 코드는 [\[이곳\]](https://github.com/strawlab/python-pcl/blob/master/examples/official/octree/octree_search.py)을 참고 하였습니다. 샘플파일은 [\[cloud\_cluster\_0.pcd\]](https://raw.githubusercontent.com/adioshun/gitBook_Tutorial_PCL/master/Intermediate/sample/cloud_cluster_0.pcd)을 사용하였습니다. Jupyter 버젼은 [\[이곳\]](https://github.com/adioshun/gitBook_Tutorial_PCL/blob/master/Intermediate/Part02-Chapter02-Search-Octree-PCL-Python.ipynb)에서 확인 가능 합니다.

```python
!python --version 
!pip freeze | grep pcl
```

```
Python 2.7.15rc1
python-pcl==0.3
```

```python
import pcl
import numpy as np
import random
```

```python
cloud = pcl.load("cloud_cluster_0.pcd")
```

```python
resolution = 0.2
octree = cloud.make_octreeSearch(resolution)
octree.add_points_from_input_cloud()
```

## SeartchPont 설정

* 3000번째 포인트

```python
searchPoint = pcl.PointCloud()
searchPoints = np.zeros((1, 3), dtype=np.float32)
searchPoints[0][0] = cloud[3000][0]
searchPoints[0][1] = cloud[3000][1]
searchPoints[0][2] = cloud[3000][2]
#searchPoints = (cloud[3000][0], cloud[3000][1], cloud[3000][2])

searchPoint.from_array(searchPoints)
```

## Neighbors within voxel search

```python
ind = octree.VoxelSearch(searchPoint)
```

```
VoxelSearch at (0.0346005521715 -1.46636068821 0.975462853909)
```

```python
print('Neighbors within voxel search at (' + str(searchPoint[0][0]) + ' ' + str(
    searchPoint[0][1]) + ' ' + str(searchPoint[0][2]) + ')')

for i in range(0, 5):#range(0, ind.size):
    print('index = ' + str(ind[i]))
    print('(' + str(cloud[ind[i]][0]) + ' ' +
          str(cloud[ind[i]][1]) + ' ' + str(cloud[ind[i]][2]))
```

```
Neighbors within voxel search at (0.0346005521715 -1.46636068821 0.975462853909)
index = 412
(-0.0524208694696 -1.53244829178 1.08694171906
index = 461
(-0.0523550510406 -1.5297921896 1.0849506855
index = 508
(-0.0461958646774 -1.51667225361 1.08676922321
index = 509
(-0.0491730645299 -1.52067470551 1.08531403542
index = 510
(-0.0522709041834 -1.52677381039 1.08309662342
```

## K nearest neighbor search

```python
K = 10
print('K nearest neighbor search at (' + str(searchPoint[0][0]) + ' ' + str(
        searchPoint[0][1]) + ' ' + str(searchPoint[0][2]) + ') with K=' + str(K))
```

```
K nearest neighbor search at (0.0346005521715 -1.46636068821 0.975462853909) with K=10
```

```python
[ind, sqdist] = octree.nearest_k_search_for_cloud(searchPoint, K)
```

```python
for i in range(0, ind.size):
    print('(' + str(cloud[ind[0][i]][0]) + ' ' + str(cloud[ind[0][i]][1]) + ' ' + str(
        cloud[ind[0][i]][2]) + ' (squared distance: ' + str(sqdist[0][i]) + ')')
```

```
(0.0346005521715 -1.46636068821 0.975462853909 (squared distance: 0.0)
(0.0317970663309 -1.46587443352 0.975684165955 (squared distance: 8.14496e-06)
(0.0374080836773 -1.46704232693 0.975152671337 (squared distance: 8.44308e-06)
(0.0345886982977 -1.46636962891 0.978524148464 (squared distance: 9.37174e-06)
(0.0346124246716 -1.46635174751 0.972395777702 (squared distance: 9.40718e-06)
(0.0373939499259 -1.46708440781 0.978200435638 (squared distance: 1.58212e-05)
(0.0318062528968 -1.46585941315 0.972620844841 (squared distance: 1.61364e-05)
(0.0317878909409 -1.46588945389 0.978741645813 (squared distance: 1.88836e-05)
(0.0374225899577 -1.46703207493 0.972084701061 (squared distance: 1.98266e-05)
(0.0289955306798 -1.4653942585 0.975902557373 (squared distance: 3.25436e-05)
```

## Neighbors within radius search

```python
radius = 0.02
print('Neighbors within radius search at (' + str(searchPoint[0][0]) + ' ' + str(
        searchPoint[0][1]) + ' ' + str(searchPoint[0][2]) + ') with radius=' + str(radius))
```

```
Neighbors within radius search at (0.0346005521715 -1.46636068821 0.975462853909) with radius=0.02
```

```python
[ind, sqdist] = octree.radius_search(searchPoints, radius, 10)
```

```
Exception TypeError: 'only length-1 arrays can be converted to Python scalars' in 'pcl._pcl.to_point_t' ignored
```

```python
for i in range(0, ind.size):
    print('(' + str(cloud[ind[0][i]][0]) + ' ' + str(cloud[ind[0][i]][1]) + ' ' + str(
        cloud[ind[0][i]][2]) + ' (squared distance: ' + str(sqdist[0][i]) + ')')
```


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## 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, and the optional `goal` query parameter:

```
GET https://pcl.gitbook.io/tutorial/part-2/part02-chapter02/part02-chapter02-search-octree-pcl-python.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.