Introduction
Mission statement: OpenImageIO is a toolset for reading, writing, and
manipulating image files of any image file format relevant to VFX / animation
via a format-agnostic API with a feature set, scalability, and robustness
needed for feature film production.
The primary target audience for OIIO is VFX studios and developers of
tools such as renderers, compositors, viewers, and other image-related
software you’d find in a production pipeline.
OpenImageIO consists of:
-
Simple but powerful ImageInput and ImageOutput APIs that provide
an abstraction for reading and writing image files of nearly any
format, without the calling application needing to know any of the
details of these file formats, and indeed without the calling
application needing to be aware of which formats are available.
-
A library that manages subclasses of ImageInput and ImageOutput that
implement I/O from specific file formats, with each file format’s
implementation stored as a plug-in. Therefore, an application using
OpenImageIO’s APIs can read and write any image file for which a
plugin can be found at runtime.
-
Plugins implementing I/O for several popular image file formats,
including TIFF, JPEG/JFIF, JPEG XL, OpenEXR, PNG, HDR/RGBE, ICO, BMP, Targa,
JPEG-2000, RMan Zfile, FITS, DDS, Softimage PIC, PNM, DPX, Cineon,
IFF, OpenVDB, Ptex, Photoshop PSD, Wavefront RLA, SGI, WebP,
GIF, DICOM, HEIF/HEIC/AVIF, many “RAW” digital camera formats, and a variety
of movie formats (readable as individual frames). More are being developed
all the time.
-
Several command line image tools based on these classes, including
oiiotool (command-line format conversion and image processing), iinfo
(print detailed info about images), iconvert (convert among formats,
data types, or modify metadata), idiff (compare images), igrep (search
images for matching metadata), and iv (an image viewer). Because these
tools are based on ImageInput/ImageOutput, they work with any image
formats for which ImageIO plugins are available.
-
An ImageCache class that transparently manages a cache so that it
can access truly vast amounts of image data (tens of thousands of
image files totaling multiple TB) very efficiently using only a tiny
amount (tens of megabytes at most) of runtime memory.
-
A TextureSystem class that provides filtered MIP-map texture
lookups, atop the nice caching behavior of ImageCache. This is used
in commercial renderers and has been used on many large VFX and
animated films.
-
ImageBuf and ImageBufAlgo functions – a simple class for storing
and manipulating whole images in memory, and a collection of the
most useful computations you might want to do involving those images,
including many image processing operations.
-
Python bindings for all of the major APIs.
🏢 Project administration and Licensing
OpenImageIO is © Copyright Contributors to the OpenImageIO project.
For original code, we use the Apache-2.0 license, and for
documentation, the Creative Commons Attribution 4.0 Unported
License. In 2023 we asked
historical users to relicense from the original BSD-3-clause
license to Apache-2.0, and over 99.86% of lines of code have been relicensed
to Apache-2.0. A small amount of code incorporated into this repository from
other projects are covered by compatible third-party open source
licenses.
The OpenImageIO project is part of the Academy Software
Foundation, a part of the Linux Foundation formed in
collaboration with the Academy of Motion Picture Arts and Sciences. The
technical charter and Project
Governance explain how the project is run, who makes
decisions, etc. Please be aware of our Code of Conduct.
💁 User Documentation
OpenImageIO Documentation
is the best place to start if you are interested in how to use OpenImageIO,
its APIs, its component programs (once they are built). There is also a PDF
version.
Additional resources:
- User quick start is a quick example of using
OpenImageIO in Python, C++, and the command line.
- Releases – see this for the latest release notes, also found in the CHANGES.md file.
👷 Building and installing OpenImageIO
Simple “how do I…”, “I’m having trouble”, or “is this a bug” questions are
best asked on the oiio-dev developer mail
list. That’s where the most people will see
it and potentially be able to answer your question quickly (more so than a GH
“issue”). For quick questions, you could also try the ASWF
Slack #openimageio channel.
A bug or build problem that you are relatively certain is a legit problem in
the code, and for which you can give clear instructions for how to
reproduce, should be reported as
issues.
To report a security vulnerability that is serious enough that it should not
be discussed publicly until a patch is ready, please file a GitHub security
advisory.
If confidentiality precludes a public question or issue, you may contact us
privately at [email protected], or for
security-related issues [email protected].
🔧 Contributing and developer documentation
OpenImageIO welcomes code contributions, and nearly 250 people
have done so over the years. We take code contributions via the usual GitHub
pull request (PR) mechanism.
- Architecture overview is a high-level
description of the major classes and their relationships.
- CONTRIBUTING has detailed instructions about the
development process.
- AI Policy decribes our policies on AI coding
assistance tools.
- ROADMAP is a high-level overview of the current
development priorities for the next annual release.
- RELEASING explains our policies and procedures for
making releases. We have a major, possibly-compatibility-breaking, release
annually in September/October, and minor bug fix and safe feature addition
release at the beginning of every month.
- Building the docs has instructions for
building the documentation locally, which may be helpful if you are editing
the documentation in nontrivial ways and want to preview the appearance.
- Other developer documentation is in the docs/dev directory.
- You may also have luck learning a bit about the organization and
architecture of the project by reading the DeepWiki Analysis of
OpenImageIO.
But take it with a grain of salt – like any LLM-generated summary, there
may be inaccuracies lurking.
☎️ Communications channels and additional resources
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