Settle WHERE the host-side ANPR service lives and how it joins the build: in this
monorepo at apps/vision/ (not a separate repo), still a separate OS process called
over localhost HTTP, wired into the Turbo graph via a thin package.json shim whose
scripts shell to Python tooling (uv/uvicorn/ruff/pytest). Co-located source honors the
vision-service runtime+license isolation decision (AGPL reach is a linking boundary,
not a folder); the fast-alpr MIT baseline removes most of the split-repo pressure
anyway. New page vision-service-packaging; updates vision-service, opencv-anpr-service,
the CLAUDE.md layout, index, log. Not built yet — packaging decision only.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
Research note from the recognizer-options query. fast-alpr v0.4.0 (MIT) — a swappable
YOLOv9-detector + CCT-OCR pipeline on ONNX Runtime, CPU-only and offline — fits the
decided vision-service architecture and is MIT end-to-end (code + published weights),
so the ANPR path may not need the scoped AGPL exception. Flags the open caveats:
verify model-weight provenance, and benchmark AL-plate accuracy (default global vs.
the 40+ country EU model). fast-alpr is plate-only, so the vehicle-verification job
stays ours to build. Decision kept open. Updates opencv-anpr-service (new "Recognizer
evaluation" section + licensing nuance), vision-service (open/next), index, log.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
julian