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AcademySoftwareFoundation logoOpenTimelineIO

Open Source API and interchange format for editorial timeline information.

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In-memory dimensional time series database.

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Quick Overview

OpenTimelineIO (OTIO) is an open-source API and interchange format for editorial timeline information. Developed by the Academy Software Foundation, it aims to facilitate the exchange of timeline data between different software applications used in motion picture and media production.

Pros

  • Standardizes timeline data exchange across various tools and applications
  • Supports multiple file formats and provides easy conversion between them
  • Extensible architecture allows for custom adapters and plugins
  • Backed by major industry players, ensuring ongoing development and support

Cons

  • Learning curve for developers new to timeline concepts in media production
  • Limited support for some specialized timeline features in certain applications
  • Requires adoption by software vendors for maximum effectiveness
  • Documentation could be more comprehensive for advanced use cases

Code Examples

  1. Reading a timeline:
import opentimelineio as otio

timeline = otio.adapters.read_from_file("my_timeline.otio")
print(f"Timeline name: {timeline.name}")
print(f"Number of tracks: {len(timeline.tracks)}")
  1. Creating a simple timeline:
import opentimelineio as otio

timeline = otio.schema.Timeline("My Timeline")
track = otio.schema.Track("V1", kind=otio.schema.TrackKind.Video)
clip = otio.schema.Clip(name="My Clip", media_reference=otio.schema.ExternalReference(target_url="/path/to/media.mov"))
track.append(clip)
timeline.tracks.append(track)
  1. Converting between formats:
import opentimelineio as otio

timeline = otio.adapters.read_from_file("input.edl")
otio.adapters.write_to_file(timeline, "output.fcpxml")

Getting Started

To get started with OpenTimelineIO:

  1. Install the library:

    pip install opentimelineio
    
  2. Import the library in your Python script:

    import opentimelineio as otio
    
  3. Start working with timelines:

    # Read a timeline
    timeline = otio.adapters.read_from_file("my_timeline.otio")
    
    # Manipulate the timeline
    for track in timeline.tracks:
        print(f"Track: {track.name}")
        for clip in track:
            print(f"  Clip: {clip.name}")
    
    # Write the timeline to a new format
    otio.adapters.write_to_file(timeline, "output.fcpxml")
    

For more detailed information and advanced usage, refer to the official documentation at https://opentimelineio.readthedocs.io/.

Competitor Comparisons

3,442

In-memory dimensional time series database.

Pros of Atlas

  • Focused on time series data and metrics, providing powerful visualization and analysis tools
  • Highly scalable, designed to handle large-scale distributed systems
  • Integrates well with cloud environments, especially AWS

Cons of Atlas

  • More complex setup and configuration compared to OpenTimelineIO
  • Primarily tailored for metrics and monitoring, less versatile for general timeline management
  • Steeper learning curve for users not familiar with time series data concepts

Code Comparison

Atlas (Scala):

val ds = Atlas.timeSeriesDataset()
  .withName("my-metric")
  .withTags(Map("app" -> "web-server"))
  .withDatapoints(List(
    Datapoint(timestamp, value)
  ))

OpenTimelineIO (Python):

timeline = otio.Timeline()
track = otio.Track(name="my_track")
clip = otio.Clip(name="my_clip", media_reference=otio.ExternalReference(target_url="/path/to/media.mov"))
track.append(clip)
timeline.tracks.append(track)

Summary

Atlas excels in monitoring and analyzing time series data for large-scale systems, while OpenTimelineIO is better suited for managing and manipulating editorial timelines in media production workflows. Atlas offers robust scalability and cloud integration, but requires more setup and domain knowledge. OpenTimelineIO provides a more intuitive interface for timeline manipulation but may not be as suitable for metrics-focused applications.

12,835

Conductor is a microservices orchestration engine.

Pros of Conductor

  • Designed for orchestrating microservices and distributed systems
  • Supports complex workflows with branching, looping, and dynamic task execution
  • Provides a web-based UI for workflow management and monitoring

Cons of Conductor

  • Steeper learning curve due to its complexity and distributed nature
  • Requires additional infrastructure setup and maintenance
  • May be overkill for simpler timeline-based projects

Code Comparison

OpenTimelineIO example:

from opentimelineio import Timeline, Track, Clip

timeline = Timeline()
track = Track()
clip = Clip(name="My Clip", media_reference=None)
track.append(clip)
timeline.tracks.append(track)

Conductor example:

WorkflowDef workflowDef = new WorkflowDef();
workflowDef.setName("myWorkflow");
workflowDef.setDescription("Sample workflow");
workflowDef.setVersion(1);
workflowDef.setTasks(Arrays.asList(task1, task2, task3));

Key Differences

  • OpenTimelineIO focuses on timeline representation for media and entertainment
  • Conductor is geared towards workflow orchestration in distributed systems
  • OpenTimelineIO has a simpler API for timeline manipulation
  • Conductor offers more advanced features for complex workflow management
1,604

Pros of Metacat

  • Designed for large-scale metadata management across diverse data sources
  • Supports a wide range of data platforms and formats
  • Offers advanced search and discovery features for big data ecosystems

Cons of Metacat

  • More complex setup and configuration compared to OpenTimelineIO
  • Primarily focused on data catalog management, less versatile for timeline-based workflows
  • Steeper learning curve for users not familiar with big data ecosystems

Code Comparison

OpenTimelineIO example:

from opentimelineio import timeline
tl = timeline.Timeline()
tl.name = "My Timeline"
clip = timeline.Clip(name="My Clip")
tl.tracks.append(timeline.Track().append(clip))

Metacat example:

import com.netflix.metacat.common.dto.TableDto;
import com.netflix.metacat.common.dto.PartitionDto;

TableDto table = new TableDto();
table.setName("my_table");
PartitionDto partition = new PartitionDto();
partition.setName("part1");
table.setPartitions(Arrays.asList(partition));

While OpenTimelineIO focuses on timeline manipulation for media production, Metacat is geared towards metadata management for large-scale data systems. OpenTimelineIO offers a more straightforward API for timeline-related tasks, while Metacat provides robust features for cataloging and managing metadata across diverse data sources.

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README

OpenTimelineIO

OpenTimelineIO

Supported VFX Platform Versions Supported Versions Build Status codecov docs CII Best Practices

Links

PUBLIC BETA NOTICE

OpenTimelineIO is currently in Public Beta. That means that it may be missing some essential features and there are large changes planned. During this phase we actively encourage you to provide feedback, requests, comments, and/or contributions.

Overview

OpenTimelineIO is an interchange format and API for editorial cut information. OTIO contains information about the order and length of cuts and references to external media. It is not however, a container format for media.

For integration with applications, the core OTIO library is implemented in C++ and provides an in-memory data model, as well as library functions for interpreting, manipulating, and serializing that data model. Within the core is a dependency-less library for dealing strictly with time, opentime.

The project also supports an official python binding, which is intended to be an idiomatic and ergonomic binding for python developers. The python binding includes a plugin system which supports a number of different types of plugins, most notably adapters, which can be used to read and write legacy formats into the OTIO data model.

Documentation

Documentation, including quick start, architecture, use cases, API docs, and much more, is available on ReadTheDocs

Supported VFX Platforms

The current release supports:

  • VFX platform 2023, 2022, 2021, 2020
  • Python 3.7 - 3.10

For more information on our vfxplatform support policy: Contribution Guidelines Documentation Page For more information on the vfxplatform: VFX Platform Homepage

Adapter Plugins

To provide interoperability with other file formats or applications lacking a native integration, the opentimelineio community has built a number of python adapter plugins. This includes Final Cut Pro XML, AAF, CMX 3600 EDL, and more.

Note: for releases after v0.16, the OpenTimelineIO PyPI package will only include the core libraries and file formats. Users that need the full set of adapter plugins should use the OpenTimelineIO-Plugins PyPI Package. Each OpenTimelineIO release will have a matching OpenTimelineIO-Plugins release.

For more information: https://github.com/AcademySoftwareFoundation/OpenTimelineIO/issues/1386

For more information about this, including supported formats, see: https://opentimelineio.readthedocs.io/en/latest/tutorials/adapters.html

All adapters except the native .otio, .otioz and .otiod have been relocated to separate repositories under the OpenTimelineIO organization located here: https://github.com/OpenTimelineIO The OTIO python bindings also support several other kinds of plugins, for more information see:

  • Media Linkers - Generate media references to local media according to your local conventions.
  • HookScripts - Scripts that can run at various points during OTIO execution (ie before the media linker)
  • SchemaDefs - Define OTIO schemas.

Installing / Quick-Start

The Python-wrapped version of OpenTimelineIO is publicly available via PyPI. You can install OpenTimelineIO via:

python -m pip install opentimelineio

For detailed installation instructions and notes on how to run the included viewer program, see: https://opentimelineio.readthedocs.io/en/latest/tutorials/quickstart.html

Example Usage

C++:

#include <iostream>

#include "opentimelineio/timeline.h"

namespace otio = opentimelineio::OPENTIMELINEIO_VERSION;

void
main()
{
    otio::ErrorStatus err;
    otio::SerializableObject::Retainer<otio::Timeline> tl(
            dynamic_cast<otio::Timeline*>(
                otio::Timeline::from_json_file("taco.otio", &err)
        )
    );
    const std::vector<otio::SerializableObject::Retainer<otio::Clip>> clips = (
            tl->find_clips()
    );
    for (const auto& cl : clips)
    {
        otio::RationalTime dur = cl->duration();
        std::cout << "Name: " << cl->name() << " [";
        std::cout << dur.value() << "/" << dur.rate() << "]" << std::endl;
    }
}

Python:

import opentimelineio as otio

timeline = otio.adapters.read_from_file("foo.aaf")
for clip in timeline.find_clips():
  print(clip.name, clip.duration())

There are more code examples here: https://github.com/AcademySoftwareFoundation/OpenTimelineIO/tree/main/examples

Also, looking through the unit tests is a great way to see what OTIO can do: https://github.com/AcademySoftwareFoundation/OpenTimelineIO/tree/main/tests

OTIO includes a viewer program as well (see the quickstart section for instructions on installing it):

OTIO View Screenshot

Developing

If you want to contribute to the project, please see: https://opentimelineio.readthedocs.io/en/latest/tutorials/contributing.html

You can get the latest development version via:

git clone git@github.com:AcademySoftwareFoundation/OpenTimelineIO.git --recursive

You can install development dependencies with python -m pip install .[dev]

You can also install the PySide2 dependency with python -m pip install .[view]

You may need to escape the [ depending on your shell, \[view\] .

Currently the code base is written against python 3.7, 3.8, 3.9, 3.10 and 3.11, in keeping with the pep8 style. We ask that before developers submit pull request, they:

  • run make test -- to ensure that none of the unit tests were broken
  • run make lint -- to ensure that coding conventions conform to pep8
  • run make coverage -- to detect code which isn't covered

PEP8: https://www.python.org/dev/peps/pep-0008/

For advanced developers, arguments can be passed to CMake through the pip commandline by using the CMAKE_ARGS environment variable.

*nix Example:

env CMAKE_ARGS="-DCMAKE_VAR=VALUE1 -DCMAKE_VAR_2=VALUE2" pip install .

Additionaly, to reproduce CI failures regarding the file manifest, run: make manifest locally to run the python check-manifest program.

C++ Coverage Builds

To enable C++ code coverage reporting via gcov/lcov for builds, set the following environment variables:

  • OTIO_CXX_COVERAGE_BUILD=ON
  • OTIO_CXX_BUILD_TMP_DIR=path/to/build/dir

When building/installing through pip/setup.py, these variables must be set before running the install command (python -m pip install . for example).

License

OpenTimelineIO is open source software. Please see the LICENSE.txt for details.

Nothing in the license file or this project grants any right to use Pixar or any other contributor’s trade names, trademarks, service marks, or product names.

Contact

For more information, please visit http://opentimeline.io/ or https://github.com/AcademySoftwareFoundation/OpenTimelineIO or join our discussion forum: https://lists.aswf.io/g/otio-discussion