feat(carwash): review outbox, booth side — plate-blurred vehicle crop + the operator's choice, queued for a trusted remote reviewer

The operator's category choice is a hypothesis, not truth (user, 2026-09-06): each wash
order with a vehicle read queues a package for a trusted reviewer over the private overlay
(Netbird); the verdict becomes the phase-B training label and the per-operator error rate.
wiki/concepts/vision-review-outbox.md.

- Boxes: the vision service returns the vehicle bbox; snapshot.ts stores the vehicle and
  plate boxes on the read as FRACTIONS of the analysed frame (the stored snapshot is a
  downscaled copy); vehicleForIdentity() returns them.
- carwash_review_outbox (migration 0031) + review-outbox.ts: crop = detector box + 8 %
  margin, ≤ 640 px, plate blurred in place from the plate box; payload carries a
  pseudonymous booth id and a keyed operator hash — no site name, no plate, no OSD, no
  bystanders; multipart POST with a per-booth bearer; 2xx → sent (image dropped);
  400/404/413/415/422 → abandoned; anything else → backoff 1 min·2^n capped 6 h; voided
  orders and items older than 14 days abandoned unsent. Nothing queued while unconfigured.
- Enqueue is fire-and-forget off the intake path in createOrder; the loop runs every
  CARWASH_REVIEW_INTERVAL_SEC (60) and stops on close.
- GET /api/carwash/review/status (site:read) + a "Remote review" line in Setup → Car wash.
- Env CARWASH_REVIEW_URL / _TOKEN / _BOOTH_ID (all three or off) documented in
  .env.example and forwarded by compose.
- Tests: review-outbox.test.ts (crop + blur on a synthetic frame, config/pseudonyms,
  queue/drain/backoff/abandon, through the app). Wiki: new concept page, index,
  venue-modules As built, log. The collector is not built.

Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU

apps/vision/vision_service/vehicle.py

@@ -235,7 +235,11 @@ class YoloxVehicleDetector:
         best = pick_vehicle(found, plate)
         if best is None:
             return None
-        return VehicleResult(body_type=best.body_type, confidence=round(best.confidence, 4))
+        h, w = frame.shape[:2]
+        box = BBox(
+            x1=max(0, int(best.x1)), y1=max(0, int(best.y1)), x2=min(w, int(best.x2)), y2=min(h, int(best.y2))
+        )
+        return VehicleResult(body_type=best.body_type, confidence=round(best.confidence, 4), bbox=box)
 
 
 def time_detect(