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Tutorial
  • INTRO
  • Part 0 (개요)
    • README
    • 3D 영상처리
    • [별첨] PCL & PCD란 (100%)
    • chapter02 : PCL 설치 (100%)
    • chapter03 : ROS 실습 준비(100%)
  • Part 1 (초급)
    • README
    • PCL 기반 로봇 비젼
    • [별첨] 파일 생성 및 입출력 (70%)
      • PCL-Cpp (70%)
      • PCL-Python (70%)
      • Open3D-Python (70%)
      • ROS 실습 (90%)
    • Filter
    • [별첨] 샘플링 (70%)
      • 다운샘플링-PCL-Cpp (70%)
      • 다운샘플링-PCL-Python (50%)
      • 업샘플링-PCL-Cpp (70%)
      • ROS 실습 (90%)
    • [별첨] 관심 영역 설정 (70%)
      • PCL-Cpp (70%)
      • PCL-Python (70%)
      • ROS 실습 (90%)
    • [별첨] 노이즈 제거 (70%)
      • PCL-Cpp (70%)
      • PCL-Python (50%)
      • ROS 실습 (90%)
  • Part 2 (중급)
    • README
    • Kd-Tree/Octree Search
    • Chapter03 : Sample Consensus
    • [별첨] 바닥제거 (RANSAC) (70%)
      • PCL-Cpp (70%)
      • PCL-Python (70%)
      • ROS 실습 (90%)
    • 군집화 (70%)
      • Euclidean-PCL-Cpp (70%)
      • Euclidean-PCL-Python (0%)
      • Conditional-Euclidean-PCL-Cpp (50%)
      • DBSCAN-PCL-Python (0%)
      • Region-Growing-PCL-Cpp (50%)
      • Region-Growing-RGB-PCL-Cpp (50%)
      • Min-Cut-PCL-Cpp (50%)
      • Model-Outlier-Removal-PCL-Cpp (50%)
      • Progressive-Morphological-Filter-PCL-Cpp (50%)
    • 포인트 탐색과 배경제거 (60%)
      • Search-Octree-PCL-Cpp (70%)
      • Search-Octree-PCL-Python (70%)
      • Search-Kdtree-PCL-Cpp (70%)
      • Search-Kdtree-PCL-Python (70%)
      • Compression-PCL-Cpp (70%)
      • DetectChanges-PCL-Cpp (50%)
      • DetectChanges-PCL-Python (50%)
    • 특징 찾기 (50%)
      • PFH-PCL-Cpp
      • FPFH-PCL-Cpp
      • Normal-PCL-Cpp (70%)
      • Normal-PCL-Python (80%)
      • Tmp
    • 분류/인식 (30%)
      • 인식-GeometricConsistencyGrouping
      • SVM-RGBD-PCL-Python (70%)
      • SVM-LIDAR-PCL-Python (0%)
      • SVM-ROS (0%)
    • 정합 (70%)
      • ICP-PCL-Cpp (70%)
      • ICP-ROS 실습 (10%)
    • 재구성 (30%)
      • Smoothig-PCL-Cpp (70%)
      • Smoothig-PCL-Python (70%)
      • Triangulation-PCL-Cpp (70%)
  • Part 3 (고급)
    • README
    • 딥러닝 기반 학습 데이터 생성 (0%)
      • PointGAN (90%)
      • AutoEncoder (0%)
    • 딥러닝 기반 샘플링 기법 (0%)
      • DenseLidarNet (50%)
      • Point Cloud Upsampling Network
      • Pseudo-LiDAR
    • 딥러닝 기반 자율주행 탐지 기술 (0%)
    • 딥러닝 기반 자율주행 분류 기술 (0%)
      • Multi3D
      • PointNet
      • VoxelNet (50%)
      • YOLO3D
      • SqueezeSeg
      • butNet
  • Snippets
    • PCL-Snippets
    • PCL-Python-Helper (10%)
    • Lidar Data Augmentation
  • Appendix
    • 시각화Code
    • 시각화툴
    • Annotation툴
    • Point Cloud Libraries (0%)
    • 데이터셋
    • Cling_PCL
    • 참고 자료
    • 작성 계획_Tips
    • 용어집
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On this page
  • 1. 읽기
  • 2. 생성
  • 3. 쓰기
  • 4. 변환
  • 5. 정보 출력

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  1. Part 1 (초급)
  2. [별첨] 파일 생성 및 입출력 (70%)

Open3D-Python (70%)

PreviousPCL-Python (70%)NextROS 실습 (90%)

Last updated 5 years ago

Was this helpful?

Jupyter 버젼은 에서 확인 가능 합니다.

1. 읽기

import open3d
import numpy as np


#PCD 파일 읽기
pc = open3d.read_point_cloud("./sample/lobby.pcd") 
print(pc)

#txt 파일 읽기
open3d.read_point_cloud("./sample/open3d_xyz.txt", format='xyz')

2. 생성

import open3d
import numpy as np

pc_array = np.array([[1, 2, 3], [3, 4, 5]], dtype=np.float32)
print(pc_array)

pc = open3d.PointCloud()
pc.points = open3d.Vector3dVector(pc_array)
print(pc)

3. 쓰기

지원 하는 확장자 : pcd, ply, xyz, xyzrgb, xyzn, pts

import open3d

open3d.write_point_cloud("pc2pcd.pcd", pc)

4. 변환

import open3d
import numpy as np

# Open3D to numpy
pcd_load = read_point_cloud("../../TestData/sync.pcd")
pc_array = np.asarray(pcd_load.points)
print("pc Type : {}".format(type(pc)))
print("pc_array Type : {}".format(type(pc_array)))

# numpy to Open3D
pc_new = open3d.PointCloud()
pc_new.points = open3d.Vector3dVector(pc_array)
open3d.write_point_cloud("pc2pcd.pcd", pc_new)

5. 정보 출력

import open3d
import numpy as np

print("포인트 수 : {}".format(pc.dimension))


#print ('Loaded ' + str(pc.width * pc.height) + ' data points from test_pcd.pcd with the following fields: ')

for i in range(0, 10):
    print ('x: ' + str(pc.points[i][0]) + ', y : ' + str(pc.points[i][1]) + ', z : ' + str(pc.points[i][2]))
[이곳]