<jupyter_start><jupyter_text>Jupyter에서 Point cloud를 시각화 - Matplotlib 기반 시각화- k3d 기반 시각화> 코드는 [[여기에]](https://github.com/hunjung-lim/3D_People_Detection_Tracking/tree/master/include/visualization_helper.py)에 올려 두었습니다.<jupyter_code># -*- coding: utf-8 -*-
from __future__ import print_function

import os
os.chdir("/workspace/3D_People_Detection_Tracking") 
from include.visualization_helper import *

%matplotlib inline
import pcl
pc = pcl.load("/workspace/_pcd/test_pypcd_xyzrgb.pcd")  
pc_arr = pc.to_array()<jupyter_output><empty_output><jupyter_text>Matplotlib 기반 시각화<jupyter_code>visualization2D_xyz(pc_arr)
visualization3D_xyz(pc_arr)<jupyter_output>(x) : 18.9m
(y) : 12.5m
(z) : 1.8m<jupyter_text>k3d 기반 시각화설치 및 Jupyter 설정이 필요 합니다. [[참고]](https://github.com/K3D-tools/K3D-jupyter)배포하는 [Docker](https://hub.docker.com/r/adioshun/pcl_to_all/)에는 설정이 되어 있으니 바로 실행 하면 됩니다.<jupyter_code>visualization_inter3D_xyz(pc_arr) # import k3d<jupyter_output>2750
{'fraction_non_alphanumeric': 0.1311647429171039, 'fraction_numerical': 0.024134312696747113, 'mean_word_length': 6.693548387096774, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 12, 'https://': 3, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '8993', 'license': 'MIT', 'n_tokens_mistral': 484, 'n_tokens_neox': 523, 'n_words': 60, 'path': 'docs/900-system-setup/950_viz_jupyter.ipynb', 'repo_name': 'adioshun/just-the-docs', 'size': '953'}