Skip to main content

Octree structure containing 3D triangular mesh model

Project description

Octree structure containing a 3D triangular mesh model. To be used for ray tracing / shadow casting.

Written in C++ for speed, but exposed to Python using Cython.

Latest PyPI version Number of PyPI downloads

Details

Pyoctree uses an adaptive structure, so it will automatically divide branches to ensure that there are no more than 200 objects per leaf.

Intersection testing uses parallel processing via OpenMP. To use more than a single processor, set value of environment variable OMP_NUM_THREADS to number of desired processors.

Requirements

  • Python 2.7 or Python >= 3.5

  • vtk >= v6.2.0 or >= v7.0 (optional, for outputting a vtk file for viewing octree structure in Paraview)

  • Cython >= v0.20 and a C++ compiler for building the extension module. Suggested compilers are:

    • The Microsoft C++ Compiler for Python 2.7 if using Python 2

    • Microsoft Visual C++ 2015 (14.0) if using Python 3

    • gcc on Linux

    • Mingw32 on Windows or Linux

Note that a compiler is not required if installing using the provided Python wheel.

Installation

1. Building from source

In a command prompt, browse to the base directory containing the setup.py file and type:

python setup.py install

2. Installation using Python wheel

Download the python wheel from releases i.e. pyoctree-0.2.0-cp27-cp27m-win_amd64.whl for Python 2.7 on Windows 64-bit. Then, open a command prompt, browse to the download directory and type:

pip install pyoctree-0.2.0-cp27-cp27m-win_amd64.whl

Usage

1. Creating the octree representation of a 3D triangular mesh model

from pyoctree import pyoctree as ot
tree = ot.PyOctree(pointCoords,connectivity)

where:

  • pointCoords is a Nx3 numpy array of floats (dtype=float) representing the 3D coordinates of the mesh points

  • connectivity is a Nx3 numpy array of integers (dtype=np.int32) representing the point connectivity of each tri element in the mesh

2. Finding intersection between mesh object and ray

The octree can be used to quickly find intersections between the object and a ray. For example:

import numpy as np
startPoint = [0.0,0.0,0.0]
endPoint   = [0.0,0.0,1.0]
rayList    = np.array([[startPoint,endPoint]],dtype=np.float32)
intersectionFound  = tree.rayIntersection(rayList)

Examples

Some Jupyter notebooks are provided in the Examples directory on how to use pyoctree.

Help

If help is required, please create an issue on Github.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyoctree-0.2.0.tar.gz (2.0 MB view details)

Uploaded Source

Built Distributions

pyoctree-0.2.0-cp36-cp36m-win_amd64.whl (128.5 kB view details)

Uploaded CPython 3.6m Windows x86-64

pyoctree-0.2.0-cp27-cp27m-win_amd64.whl (148.6 kB view details)

Uploaded CPython 2.7m Windows x86-64

File details

Details for the file pyoctree-0.2.0.tar.gz.

File metadata

  • Download URL: pyoctree-0.2.0.tar.gz
  • Upload date:
  • Size: 2.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for pyoctree-0.2.0.tar.gz
Algorithm Hash digest
SHA256 e23779b816aa8cd0bf62cb040cd0e372355b44e233222b89ffa8b8e09b27c2d2
MD5 8e8ab75428b990200109bc383d54e71e
BLAKE2b-256 a3be3e092ed2bacc7cda6e345ab01703df2e1e0fd744e2a5152d08f1c29b065a

See more details on using hashes here.

Provenance

File details

Details for the file pyoctree-0.2.0-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for pyoctree-0.2.0-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 79443368ef823954767f54db817f991e1996193a6b8110073f2f4bf5f4902e7f
MD5 77c976d64b348663b1623e15e5b29909
BLAKE2b-256 2c78a23b355b50c4e4b6d67fee20f72b2dd201055c34011a2a4e0e0c326ce501

See more details on using hashes here.

Provenance

File details

Details for the file pyoctree-0.2.0-cp27-cp27m-win_amd64.whl.

File metadata

File hashes

Hashes for pyoctree-0.2.0-cp27-cp27m-win_amd64.whl
Algorithm Hash digest
SHA256 9786365c4b4d52ad92d0ba80d3ca05dd83d85d9028c749c5003d23eef5c9bdfb
MD5 31a4d71bd97b61d67fecfeef3f107550
BLAKE2b-256 be34a8b3d0896e7cb5a8cbdfa767a3d589fe0051269fb54fdf2f76ed43e664c0

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page