Skip to content

README.md

Latest commit

 

History

History
25 lines (16 loc) · 1.34 KB

README.md

File metadata and controls

25 lines (16 loc) · 1.34 KB
Raw

Video Intelligence

Generate a three-scene video, independently inspect container metadata, scene boundaries, and audio, merge the measurements into a timeline, and publish a validated report. The supplied Docker image uses FFmpeg for the actual video analysis.

Run locally

Requires Go and Python 3.12+. Blender Render Farm also requires Docker. Run from a fresh checkout:

git clone https://github.com/UFFeScience/akoflow-examples.git
cd akoflow-examples/media/video-intelligence
./prepare.sh && ./run.sh

Preparation downloads the Go YAML module. Results are in outputs/manifest.json, outputs/dag-validation.json, and outputs/stage-<activity>.json. Independent branches run concurrently as soon as their own dependencies and a local execution slot are available.

Inspect the same DAG in Desktop

Start the local AkôFlow daemon with Docker available and configure AKOFLOW_API_TOKEN in your shell. From this example directory:

go run ../../tools/daemon-local -root ../.. -example media/video-intelligence

The command builds the declared image, validates Docker connectivity, registers local-only infrastructure, and executes the workflow through the daemon. Current verified screenshots are in screenshots/current/; their manifest identifies the exact run and image. The full daemon projection is in evidence/daemon-run.json.