image-processor

com.mbreissi.edgecommons.ImageProcessor is the EdgeCommons Python inference component for image models. It loads signed, content-addressed ONNX model bundles, runs inference on finalized image files from a spool it owns or on subscription-triggered images, and publishes one durably confirmed inference result per image.

  camera spool ─┐                        ┌─► app/inference/result   (confirmed, cleanup-gating)
                ├─► readiness ─► ledger ─┼─► data/<signal>          (decision mirror, best effort)
  trigger topic ┘        │        │      ├─► <image>.inference.json (evidence sidecar)
                         │        │      └─► processed/ | failed/   (the input, after confirmation)
                         │        └─ executor cell (ONNX Runtime, CUDA or CPU)
                         └─ model bundle cache (verified, warmed, atomically activated)

Doc	Start here when you want to…
Tutorial	learn by doing — run one image through the component and read every output it produced
How-to guides	accomplish a task — build and sign a bundle, add a route, deploy, repair a stuck job
Reference	look up an exact config option, topic, command, or metric
Explanation	understand how an image becomes a job, and why the walk is authoritative

Quick routing

• “I’m new here.” → Tutorial.
• “What config option does X?” → Reference — Configuration.
• “What message on which topic, and what does a command reply look like?” → Reference — Messaging interface.
• “What does this metric mean?” → Reference — Metrics.
• “What does a task family produce, and how do decision rules read it?” → Reference — Normalized output and decision rules.
• “What do I put in a config for a camera route, a trigger route, or a GPU device?” → Sample configurations.
• “Why does a lost camera announcement not lose an image?” → Explanation.

Audience

These pages are for the integrator wiring a camera to a model and the operator running the result. They describe what the component does; DESIGN.md and LLD.md in the repository root record why it does it that way and how the modules fit together.