---
title: Vision review outbox — harvesting the operator's category choice for a trusted reviewer
type: concept
status: booth side built 2026-09-06; collector pending
related: [venue-modules, opencv-anpr-service, threat-model, append-only-event-chain, network-isolation]
---

# Vision review outbox

**The idea (user, 2026-09-06).** The Car Wash desk asks the operator for the vehicle's category,
and the entry camera now proposes one ([[venue-modules]] §Vehicle category from vision). The
operator's choice is what we would love to train the body-type classifier on — but the
operator **cannot be fully trusted** (mistake or intent; the [[threat-model]]). So the booth
hands each decision to a **trusted party** who reviews the picture and the label remotely,
and *that* verdict is the training label — and, per operator, the honest-mistake / fraud rate.
The booths sit on a private zero-trust overlay (**Netbird**), so the hand-off can go to a very
locked-down collector without exposing anything to the open internet.

## Rules (all enforced in `apps/server/src/modules/carwash/review-outbox.ts`)

1. **Offline-first, never on the intake path.** Creating a wash order *queues* a package (fire
   and forget — a failure is a log line); a background loop drains the queue when the overlay
   is up. The wash never waits on the network.
2. **One-way.** The booth POSTs; nothing ever comes back into the booth's decisions. The signed
   ledger ([[append-only-event-chain]]) stays the only record of what happened at the wash.
   Reviewer verdicts stay central and reach the owner as a report per site.
3. **Nothing that names the site leaves the booth.**
   - Only the vehicle **crop** (the detector's box + 8 % margin, ≤ 640 px) — no walls, no camera
     OSD (date / camera name burned into the frame), no bystanders.
   - The **plate is blurred inside the crop** on the booth, from the plate detector's own box.
   - The booth is a **pseudonymous id** set at deploy (`CARWASH_REVIEW_BOOTH_ID`); the operator
     is a **keyed hash** (`sha256(boothId:username)[:16]`). The mapping back to places and
     people is the reviewer's, held off the collector. The dataset export drops even those.
   - Boxes are stored as **fractions of the frame** on the vision read, so the crop is cut from
     the stored (downscaled) snapshot copy.
4. **The network is not the auth.** A per-booth bearer token on top of the overlay; the booth
   can do nothing at the collector but this one POST. Payloads are small (a crop ≈ 50–80 kB).
5. **Data minimisation.** Queued only when there is a vehicle box (no box = no sample); the
   image is dropped from the row once delivered; a voided order is abandoned unsent; anything
   older than 14 days is abandoned ("expired") rather than resurfacing a fortnight in a burst.

## The entry stream — the real accelerator (built 2026-09-07)

The wash stream is small; the **entry camera photographs every car**, in exactly the view the
classifier is trained on, with zero domain shift. So the booth can also queue **one in N entry
vehicle reads** as pure training material: the crop and the camera's class, *no* order, *no*
operator, *no* category — same crop-and-blur pipeline, same one-way path, same privacy
properties. `CARWASH_REVIEW_ENTRY_SAMPLE=N` = one in N entries; **`1` = every entry, and that is
the setting park-2 runs** (user, 2026-09-07: 4 TB on the collector host, bandwidth not an issue —
the only limit was ever the reviewer's time; the reviewer labels what they have time for, the
rest waits and stays useful once a first model exists, as the unlabelled pile it is measured on).
0/unset = off; needs the three upload settings. A washed car arrives twice, as an entry sample
and as the wash decision — intended, the `kind` keeps them apart.
Seam: the core announces every vehicle read (`deviceEvents.emitVehicleRead`, snapshot.ts, entry
and exit) and the Car Wash module decides — it samples entry reads in-process (`sampleEntry()`,
exactly one in N) and calls `enqueueEntry()`; the core never imports the module. Packages carry
`kind: "wash" | "entry"`; the collector stores the kind, the review screen shows an entry sample
as "entry stream — label the vehicle", the export carries a `kind` column, and **operator
agreement is computed from wash items only** (an entry sample has no operator decision).

An internet feed was considered the same day and kept OUT of the collector's ingest: licensed
sets only, in a separate folder with provenance, used as warm-up and weighted down, and never
the judge of accuracy — the evaluation set is gate crops only.

## The package

`multipart/form-data`: `meta` (JSON) + `image` (JPEG). Meta = `{ v, booth, item, order, at,
operator (hash), operatorCategory {id,name}, service, vision {class, confidence, categoryId},
downgraded, image {width, height, plateBlurred} }`. Headers: `Authorization: Bearer <token>`,
`X-Booth-Id`.

## Draining

Every `CARWASH_REVIEW_INTERVAL_SEC` (60): due items oldest-first, 20 per pass. `2xx` → sent
(image cleared). `400/404/413/415/422` → abandoned (the collector refused the package itself).
Anything else (auth not yet fixed, 429, 5xx, timeout, no route) → retry with backoff
`1 min · 2^attempts`, capped at 6 h. `GET /api/carwash/review/status` (site:read) and a line in
Setup → Car wash show queued / delivered / abandoned + the last error.

## Config

`CARWASH_REVIEW_URL`, `CARWASH_REVIEW_TOKEN`, `CARWASH_REVIEW_BOOTH_ID` — all three or the outbox
is off and **nothing is queued** (an unbounded queue nobody drains is worse than none). Set per
booth in the Komodo stack env; compose forwards them.

- **URL by Netbird DNS name** (`http://docker-station.nb.infra:8090/ingest`): the server container
  runs on the host network in prod, so it uses the booth's resolver and Netbird's DNS answers
  `*.nb.infra`; a collector that moves address costs no booth change. A failed lookup behaves like
  a collector outage (defer, backoff). The collector's own `COLLECTOR_BIND` must be the raw overlay
  **IP** — Docker port bindings take no hostname.
- **Secrets: one per booth, two consumers.** `wash_review_token_booth_2` is referenced by the
  booth's stack as its `CARWASH_REVIEW_TOKEN` *and* by the collector's stack inside
  `COLLECTOR_BOOTH_TOKENS=booth-2:[[wash_review_token_booth_2]],booth-3:[[…]]` — one value, nothing
  to keep in sync, rotating a booth touches one secret. (A first cut had one combined secret for
  the whole list; replaced the same day — rotation was all-or-nothing and the value lived twice.)
  Token format: opaque, `openssl rand -hex 32`; the collector only demands ≥ 16 chars and the list
  splits on commas/whitespace, which hex never contains. Never share a token between booths — it
  is what names the booth. Total Komodo secrets for one booth + the collector: two (the booth's
  token, the reviewer's password).
- **The operator hash needs no variable**: `sha256(boothId + ":" + username)[:16]`, computed on
  the booth from values already set; the owner recomputes it from the booth's usernames to map a
  hash back, the collector never can.

## The collector — skeleton built 2026-09-06 (`apps/collector`)

A deliberately small Fastify + SQLite service **in this monorepo** (so it imports the payload
contract and the class vocabulary from `@parking/shared` — the two ends cannot drift), delivered
to the reviewer's host by **its own Komodo stack** (`wash-collector` in `komodo/resources.toml`
→ `docker-compose.collector.yml` only; the booth stacks never see it and it never sees booth
services). Image `parking-collector:<branch>-<sha>` from the same workflow as the others.
Three surfaces, nothing else — it must not grow into a fleet console:

- **`POST /ingest`** — bearer token **per booth** (`COLLECTOR_BOOTH_TOKENS`, `boothId:token`
  pairs; constant-time compare), `X-Booth-Id` must match the token's booth, multipart `meta` +
  `image` (JPEG magic checked, 2 MB cap), `meta` validated field by field against the contract
  above (unknown vision class, non-id item, wrong booth → 422), **idempotent on the item id**
  (a retry after a lost 2xx → 200 `duplicate`). Stored: `crops/<booth>/<item>.jpg` on the
  volume + one `items` row. The booth now also sends `operatorCategory.classes` (the classes
  the chosen category covers at that site) so a reviewer's CLASS can be judged against the
  operator's CATEGORY without the site's setup.
- **`/review`** (+ `/api/items`, `/api/items/:id/image`, `/api/items/:id/review`, `/api/stats`)
  — the reviewer's screen, served by the process itself (no build, no framework): one pending
  crop at a time, the operator's pick and the camera's pick beside it, one button (and one
  key) per vocabulary class + *unusable* + *skip*. HTTP Basic, one login
  (`COLLECTOR_REVIEWER_USER/PASS`), over the overlay. Stats: per booth received / pending /
  reviewed; per operator (booth + hash) **agree / disagree / unusable** — disagree = the
  reviewer's class is outside the operator's chosen category. That column is the honest-mistake
  / fraud rate.
- **`GET /export/labels.csv`** — reviewed, usable rows: item, booth, crop path, the reviewer's
  label, the operator's category + classes, the camera's class + confidence, downgraded, at.
  Crops are not packaged: the phase-B trainer runs **on the same host** and reads the SQLite
  + crops straight off the volume, read-only ([[bodytype-classifier-training]]: CPU-only, the
  Xeon is enough) — the `trainer` service beside the collector in
  `docker-compose.collector.yml` (the CSV export stays for a human with a spreadsheet).
- **Training section on `/review`** (+ `/api/training/status|jobs|jobs/:id|versions/:v/report`)
  — a thin proxy, behind the same reviewer login, to the trainer's job API on the compose
  network (`COLLECTOR_TRAINER_URL`, unset = hidden): labels per class vs the minimum, Train
  (mode / backbone / floor), the running job's log, the versions with Report / Evaluate /
  Publish. The collector forwards only a fixed set of paths and knobs; the trainer validates
  values and answers 409 while a job runs.

**Where the data lives.** The collector writes to `/data` in its container: `collector.sqlite`
and one JPEG per item at `crops/<booth-id>/<item-id>.jpg`. `/data` is the named Docker volume
`collector-data` (compose), on the host under Docker's volume directory — normally
`/var/lib/docker/volumes/wash-collector_collector-data/_data/` (`docker volume inspect
wash-collector_collector-data` confirms). The trainer mounts the same volume read-only at its
own `/data`; nothing is copied or exported for training.

**Deploy notes.** Bind the published port to the host's **Netbird address** (`COLLECTOR_BIND`),
never `0.0.0.0` on a host with a public interface; Netbird policy: booths → this host:8090 and
nothing else. The host must be onboarded as a Komodo server like the booths. `TAG` is pinned
and promoted with the booths (one sha for all stacks) — fine while the collector stays small;
its own repo the day it needs its own cadence. Deploy the collector BEFORE a booth that sends a
package kind it does not know (a 422 is abandoned, not retried). The export neutralises cells
that start like a spreadsheet formula (category/service names are booth-supplied text).

> **Incident 2026-09-16 — the collector's DB predated the `kind` column; nothing worked for 9
> days and nothing said so.** The reviewer opened /review: *Training — trainer not reachable:
> fetch failed*. On the host: collector `stage-2d9bb15` **unhealthy** (`/health` → 500 *no such
> column: kind*), trainer healthy but every `/readiness` a Python traceback; the volume's
> `collector.sqlite` (created 2026-09-07 by the previous build, **0 items**) had the original
> column set. `CREATE TABLE IF NOT EXISTS` shapes only a NEW database — an existing volume keeps
> its old columns, so every query naming `kind` failed: the collector's health, **every ingest**
> (booths would have got 500s and kept retrying — the log shows none ever arrived, a separate
> question), and the trainer's readiness. The trainer's stdlib server printed the traceback and
> dropped the socket, which the collector could only render as "fetch failed".
>
> Fixes (same day): `CollectorDb` now **migrates on open** — `PRAGMA table_info` vs a list of the
> columns added since the first deploy, `ALTER TABLE … ADD COLUMN` for each missing one (all
> nullable or defaulted; **append to that list whenever a column joins the CREATE**); the trainer's
> handlers are guarded — an unexpected exception is a **500 JSON** naming the error, never a
> dropped connection; the collector's status proxy surfaces the trainer's error text. Rule going
> forward: the collector owns the schema; the trainer only reads; a deploy that changes the table
> must be accompanied by a migration entry, and the Training section is the first place a
> schema/DB mismatch shows — read its error text before suspecting the network.

**Status (2026-09-07).** Live: the collector runs on `art-docker-station` and park-2 is wired to
it (`stage-dbbb051` on both stacks, every entry sampled). The review screen at
`http://docker-station.nb.infra:8090/review` is filling; no labels reviewed yet.