feat: recognition server API, reference stream client, and live label memory

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app.py becomes the recognition server: /v1 REST (health, catalog + template
images, samples, recognize, classify, inspect, stock) plus /v1/stream over
WebSocket, with token auth, size limits, one consistent error shape, and the
legacy /api/sessions routes kept but now backed by the real engine instead of
the fake adapter. Default bind is 127.0.0.1; LAN and TLS are opt-in.

stream_client.py is the reference non-browser client (camera, video file, or a
pan across a still) and prints results/s, RTT, detect time and label stability.

Live quality work driven by measurements:
- Labels are remembered per place: a fresh track covering a recently recognized
  product inherits the label instead of queueing for classification again. On a
  continuous pan this raised recognized products in view from 9/66 to 17-21/66
  and cut time-to-first-label to ~1.6 s.
- Classification priority is newest-first, because sorting by area spends the
  budget on products that are already leaving the frame.
- Per-session classification concurrency raised to 12.

hybrid_fast (ORB shortlist then hybrid) is implemented but NOT the default: it
is 3.4x faster (13 ms vs 44 ms p50) yet agreed with hybrid on only 47% of
accepted crops across 553 crops, far below the 98% adoption rule. It stays
available for experiments; the numbers are in the commit and the benchmark.

Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>

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kiyo9w 2026-09-24 12:31:17 +09:00

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