PlainsightAI Releases OpenFilter: Framework For Universal Vision Workloads

1 week ago 6

OpenFilter is an universal abstraction for building and running vision workloads in modular image/video processing pipelines. It simplifies communication between components (called filters) and supports synchronization, side-channel paths, metrics, and load balancing — all in pure Python.

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🔁 Easily pluggable filter components
🧪 Develop and test filters locally with Python
⚡ High-throughput synchronized pipelines
📡 MQTT/REST visualization and data publishing
🧵 Parallel processing via load-balanced filter branches
📊 Built-in telemetry and metrics streaming (coming soon)

Install OpenFilter with all utility filter dependencies:

pip install openfilter[all]

Install directly from GitHub:

pip install git+ssh://[email protected]/PlainsightAI/openfilter.git@main#egg=openfilter[all]

To install a specific version:

pip install git+ssh://[email protected]/PlainsightAI/[email protected]#egg=openfilter[all]

Editable install for development:

git clone [email protected]:PlainsightAI/openfilter.git cd openfilter make install

Here’s a minimal example that plays a video and visualizes it in the browser:

from openfilter.filter_runtime.filter import Filter from openfilter.filter_runtime.filters.video_in import VideoIn from openfilter.filter_runtime.filters.webvis import Webvis if __name__ == '__main__': Filter.run_multi([ (VideoIn, dict(sources='file://video.mp4', outputs='tcp://*')), (Webvis, dict(sources='tcp://localhost')), ])

Run it with:

Then open http://localhost:8000 to see your video stream.

Alternatively, simply use the CLI:

openfilter run - VideoIn --sources 'file://video.mp4!loop' - Webvis

Note: Ensure that a video.mp4 file exists. A simple example is available at examples/hello-world/video.mp4.

Explore real-world examples covering: