Video Intelligence

The workflow creates a known three-scene video, inspects its streams, extracts scene thumbnails, measures audio, and assembles a searchable timeline. The known input makes scene and duration assertions deterministic while still exercising real FFmpeg processing in the OCI image.

Video Intelligence. Nodes in the same column can run in parallel once their dependencies complete.

Inputs

Generated three-scene audiovisual fixture

Outputs

Source video metadata
Scene thumbnails
Audio measurements
Timestamped timeline
Artifact manifest

Provenance to preserve

Stream codecs and dimensions
Observed duration
Scene count
Audio level method

Run this example locally

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.

Install Go and Python 3.12 or newer.

git clone https://github.com/UFFeScience/akoflow-examples.git &&
cd akoflow-examples &&
git checkout --detach d890c3171e3661890305b763b8fc3ede48bb61b4 &&
cd media/video-intelligence &&
./prepare.sh &&
./run.sh

Run the block from the directory where you keep your repositories. If you already cloned the repository, enter this example's directory and run ./prepare.sh && ./run.sh.

Preparation downloads the Go module and any declared Python packages once. The workload then runs locally with offline input data. Every meaningful step is a separate process; independent activities run in parallel after their dependencies complete.

Read outputs/manifest.json for the generated artifacts, outputs/dag-validation.json for dependency waves, and outputs/stage-<activity>.json for each activity's observed result. A failed stage stops the run and prevents a successful final manifest.

Expected outputs: source.mp4, probe.json, scenes.json, audio.json, timeline.json.

Run the same activities in Desktop

Start your local AkôFlow daemon with Docker available and configure AKOFLOW_API_TOKEN in your shell. From this example's directory, run:

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

This command validates the daemon connection, builds the declared image, registers the local environment and workflow, and submits a real run. The daemon owns execution and file transfers. Open the printed run ID in Desktop to inspect each activity.