PyWavefront
Python library for importing Wavefront .obj files
PyWavefront reads Wavefront 3D object files (`something.obj`, `something.obj.gz` and `something.mtl`) and generates interleaved vertex data for each material ready for rendering. The project is written primarily in Python, distributed under the BSD 3-Clause "New" or "Revised" License license, first published in 2013. Key topics include: 3d, loader, opengl-library, pyglet, python.
PyWavefront
PyWavefront reads Wavefront 3D object files (something.obj, something.obj.gz
and something.mtl) and generates interleaved vertex data for each material ready for rendering.
- Python 3.4+ is supported in 1.x versions
- Python 2.7 is supported in 0.x versions
A simple (optional) visualization module is also provided for
rendering the object(s). The interleaved data can also be used by
more modern renderers thought VBOs or VAOs.
Currently the most commonly used features in the specification has
been implemented:
- Positions
- Texture Coordinates
- Normals
- Vertex Color
- Material parsing
- Texture and texture parameters
We currently don't support parameter space vertices, line elements or smoothing groups.
Create an issue or pull request on github if needed features are missing.
The package is on pypi
or can be cloned on github.
bashpip install pywavefront
Also check out the roadmap for future plans.
Usage
Basic example loading an obj file:
pythonimport pywavefront scene = pywavefront.Wavefront('something.obj')
A more complex example
strict(Default:False) will raise an exception if unsupported features are found in the obj or mtl fileencoding(Default:utf-8) of the obj and mtl file(s)create_materials(Default:False) will create materials if mtl file is missing or obj file references non-existing materialscollect_faces(Default:False) will collect triangle face data for every mesh. In case faces with more than three vertices are specified they will be triangulated. See the documentation ofObjParser#consume_faces()inobj.py.parse(Default:True) decides if parsing should start immediately.cache(Default:False) writes the parsed geometry to a binary file for faster loading in the future
pythonimport pywavefront scene = pywavefront.Wavefront( 'something.obj', strict=True, encoding="iso-8859-1", parse=False, ) scene.parse() # Explicit call to parse() needed when parse=False # Iterate vertex data collected in each material for name, material in scene.materials.items(): # Contains the vertex format (string) such as "T2F_N3F_V3F" # T2F, C3F, N3F and V3F may appear in this string material.vertex_format # Contains the vertex list of floats in the format described above material.vertices # Material properties material.diffuse material.ambient material.texture # ..
Binary Cache
When cache=True the interleaved vertex data is written
as floats to a .bin file after the file is loaded. A json
file is also generated describing the contents of the binary file.
The binary file will be loaded the next time we attempt to load
the obj file reducing the loading time significantly.
Tests have shown loading time reduction by 10 to 100 times
depending on the size and structure of the original obj file.
Loading myfile.obj will generate the following files in the
same directory.
txtmyfile.obj.bin myfile.obj.json
Json file example:
json{ "created_at": "2018-07-16T14:28:43.451336", "version": "0.1", "materials": [ "lost_empire.mtl" ], "vertex_buffers": [ { "material": "Stone", "vertex_format": "T2F_N3F_V3F", "byte_offset": 0, "byte_length": 5637888 }, { "material": "Grass", "vertex_format": "T2F_N3F_V3F", "byte_offset": 5637888, "byte_length": 6494208 } ] }
These files will not be recreated until you delete them.
The bin file is also compressed with gzip to greatly reduce size.
Visualization
Pyglet is required to use the visualization module.
bashpip install pyglet
Example:
pythonimport pywavefront from pywavefront import visualization [create a window and set up your OpenGl context] obj = pywavefront.Wavefront('something.obj') [inside your drawing loop] visualization.draw(obj)
Logging
The default log level is ERROR. This is configurable including overriding the formatter.
pythonimport logging import pywavefront pywavefront.configure_logging( logging.DEBUG, formatter=logging.Formatter('%(name)s-%(levelname)s: %(message)s') )
Examples
The examples
directory contains some basic examples using the visualization module and further
instructions on how to run them.
Generating a Wavefront file with Blender
The following presumes you are using Blender to generate your mesh:
- Using Blender, create a mesh with a UV-mapped texture. The UV-mapping is important!
If it is working properly, you will see the texture applied within Blender's 3d view. - Export the mesh from Blender using the Wavefront format, including normals.
- Reference your
*.objfile as in the pywavefront example above.
Tests
All tests can be found in the tests directory. To run the tests:
bash# Install pywavefront in develop mode python setup.py develop # Install required packages for running tests pip install -r test-requirements.txt # Run all tests pytest # Optionally specific tests modules can be runned separately pytest tests/test_parser.py
Community
PyWavefront Discord server : https://discord.gg/h3Rh4QN
Owners & Maintainers
- Einar Forselv (@einarf) - Main Contact
- Kurt Yoder (@greenmoss) - Backup
Contributors
In alphabetical order:
- ComFreek
- Daniel Coelho 1danielcoelho
- @dav92lee
- Jerek Shoemaker (intrepid94)
- Marxlp
- Mathieu Lamarre
- Oliv4945
- Patrik Huber (patrikhuber)
- Sérgio Agostinho (SergioRAgostinho)
- Zohar Jackson
- hkarrson (hkarrson)
Project History
PyWavefront was originally started by @greenmoss (Kurt Yoder) in 2013.
He was the sole maintainer of the project until February 2019 when
the PyWavefront Maintainers organization was created adding @einarf
(Einar Forselv) as an additional owner and maintainer of the project.
License
PyWavefront is BSD-licensed
Contributors
Showing top 12 contributors by commit count.

