Rework the ANPR trigger to the real design: when a transient presses the button or a
subscriber passes QR/RFID, the entry/exit fires and takes its evidence snapshot — that
is the moment to recognize. snapshotAsync now takes the VisionClient and, after storing
each snapshot from an opt-in (config.anpr) camera, runs ANPR on the SAME image and
records the plate against the SAME session identity (device_events kind:"read" with
plate/confidence/region/snapshotId/source:"entry-exit-snapshot"). One image serves both
evidence and plate extraction; recognition fires only on a real entry/exit — no polling.
The entry/exit/subscription flows take an optional VisionClient and pass it through;
server.ts wires it. Removed the polling VisionReader and VISION_POLL_MS/VISION_DEDUPE_MS.
Advisory + fire-and-forget: a low-confidence/no-plate result records nothing, a vision
failure never delays or changes the open, and the plate does not feed the access
decision. Verified e2e: a simulated entry snapshot on an anpr camera (live fast_alpr)
stored the snapshot for the session and recorded {identity, plate:AA558EE, 0.999,
region:Albania, snapshotId}. Build + lint green.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
Make the vision service genuinely configurable (was env-only).
- SetupWizard: an "ANPR" checkbox on the camera form (writes config.anpr; persisted
only when on; sq+en) — opt-in is no longer raw JSON.
- DeviceMonitor optionally takes the VisionClient and probes /health each tick, emitting
a "vision" pseudo-device → a Vision chip (ready/degraded/offline + recognizer) in the
booth footer when VISION_ENABLED, no chip when off. Widened the DeviceStatus category
union (server + web) + footer maps + devices.catVision. Verified: ready/fast_alpr when
up, 0 chips when disabled.
- apps/vision/.env.example (Python service) + a VISION_* block in apps/server/.env.example
(Node side) + a Configuration section in opencv-anpr-service.md covering all four
layers and the caveats: the two processes share the VISION_ prefix but need SEPARATE
.env files; bind /analyze to 127.0.0.1; cache model weights at deploy; an unbound anpr
camera recognizes but every read is refused.
Build + lint green.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
Answers "is a recognized plate saved?" — now yes, for both transient and subscriber, as
an ANPR audit trail independent of whether it matched anything.
VisionReader now stores the snapshot bytes in `snapshots` keyed by identity=PLATE — the
same identity the flow signs its anomaly/event with — so GET /api/snapshots/by-identity/:plate
(the booth event-detail modal's snapshot strip) shows the car's photo against that
anomaly with no UI changes. It also records an unsigned device_events{kind:"read"}
breadcrumb (plate, confidence, region, model, snapshotId, and the dispatch outcome) as a
queryable recognition log. Switched from emitRead to calling ReadDispatcher.dispatch
directly (like qr-reader) to capture that outcome.
Non-blocking: a refused read (no session / unpaid / unknown plate) just returns
rejected — no barrier hold — and is logged with its snapshot for investigation. Plate
stays advisory (exit demands payment; subscription matches only a bound plate).
Verified e2e: a recognized AL plate with no open session signed exit.refused.noSession
(identity=plate), stored a 555KB snapshot under that plate, recorded the read breadcrumb
(accepted:false, reason "no open session"), and by-identity returned the image — the
refused read is fully investigable with its picture. Build + lint green.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
A VisionReader polls each opt-in camera (config.anpr===true, off by default) every
VISION_POLL_MS, captures a snapshot, recognizes via VisionClient, and on a confident
plate emits deviceEvents.emitRead({kind:"plate", value}) — the same event a physical
plate reader sends, so the existing ReadDispatcher routes it to the subscription/exit
flow unchanged (no flow rewrite).
The plate stays advisory by construction: the exit flow still demands a covering
payment, the subscription flow only matches a bound plate. Guards: low-confidence reads
dropped; debounce (VISION_DEDUPE_MS) so a parked car doesn't re-fire; per-camera
in-flight guard; idle when vision is off or no camera opts in. #recognizeOn is public
for a future on-demand (loop-edge/API) trigger.
Verified end-to-end: an in-memory anpr camera (AL plate image) + live fast_alpr service
→ VisionReader emitted exactly one {kind:"plate",value:"AA558EE"} onto the bus; debounce
held it to 1 emit over 7 polls. Build + lint green. Updates opencv-anpr-service
(trigger-wiring + per-camera opt-in marked done).
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
Node-side adapter to the apps/vision ANPR microservice (localhost HTTP: POST /analyze
with snapshot bytes, GET /health), returning a normalised VisionResult or null. Enforces
"advisory, never sole authority" at the boundary: opt-in (VISION_ENABLED, default off),
fail-soft (any error/timeout/unreachable → null, never throws into the lane → ticket
fallback), and re-applies the confidence floor (VISION_MIN_CONFIDENCE) on top of the
service's own low_confidence flag. Per-request AbortController timeout so a slow call
can't hang the barrier. Constructed in server.ts.
Verified: fail-soft (disabled/unreachable → null, no throw) and live end-to-end (Node
client → running fast_alpr service → AA558EE 0.999, region=Albania). NOT yet wired into
the read bus — the opt-in snapshot→DeviceReadEvent{kind:"plate"} trigger is the next
step. Build + lint green. Updates opencv-anpr-service (adapter gap marked done).
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
Record the verdict: the ANPR service is worthy to consume NOW as an advisory plate
IDENTITY source (Job 1) — the flows already treat a kind:"plate" read as first-class
(exit signs source:"lpr"; subscription matches read plate vs bound plates), so it feeds
an existing input with no flow rewrite. It is NOT worthy as the sole authority to open a
transient barrier (a plate is not a payment; spoofing needs Job 2 vehicle verification,
unbuilt) — gated by the confidence floor with ticket/manual fallback. Lists the four
gaps before consumption (VisionClient adapter, opt-in trigger, field accuracy,
weight-provenance). Next step is the adapter, not more model work.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
Benchmarked fast-alpr's four fast-plate-ocr models via the full pipeline on real AL
plates (AA558EE, AA687KE), CPU. All four read both correctly; the default
cct-xs-v2-global-model wins on confidence (0.999/1.000) AND speed (33-39ms) and returns
region=Albania. The "European 40+country" model is WORSE here (~0.77 confidence, one
synthetic misread) — overturning the "EU model better for AL" assumption from the prior
research. Decision: no config change. Resolves the AL-accuracy-benchmark open item
(results table + finding added to opencv-anpr-service); weight-provenance remains the
one open recognizer item. Re-benchmark on real on-site captures once cameras installed.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
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
The "permit/lejet" feature is really a subscription. Full rename of the
mutable master data, plus a recurring monthly price.
- DB (migration 0004, data-preserving ALTER RENAME): permits→subscriptions,
permit_credentials/_plates→subscription_*, sessions.permit_id→subscription_id.
- Pricing: per-subscription priceMinor + period(monthly) + currency, with a
site default (site_config.subscription_monthly_price_minor) pre-filling the form.
- Server: subscription-flow.ts (SubscriptionFlow), routes/subscriptions.ts
(/api/subscriptions). Web: SubscriptionManager, route, i18n (sq Abonimet/en).
- The signed ledger `permitId` payload is intentionally kept — immutable
hash-chained history; renaming it would break verification of past events.
Deferred (wiki notes): fee collection into the ledger/shift (a shift-attributed
payment), LPR/ANPR plate source, time-of-day access windows (overnight subscriber).
Also carries the device-footer UI surface (api DeviceStatus, router mount,
i18n devices) due to shared-file overlap with the preceding footer commit.
Verified end-to-end on a fresh DB and migration on a live-DB copy (sessions
preserved). Live DB migrated. Full monorepo builds clean.
Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
julian