Three defects, all found by scoring the live path against ground truth.
Inherited labels carried frame id 0. `_inherit_labels` set `label_box` but
never `label_frame_id`, so every inherited label was mapped back to the source
image with the first frame's offset. During a pan that puts the box a whole
pan-distance from the product it names, which is why most streamed tracks
matched no labelled facing. It now stamps the current frame and anchors to the
raw detection box rather than the smoothed track box, which lags a pan.
The reference lane upscaled views smaller than a pyramid level. `sku_l09.jpg`
is 407x305, so its 400 px level was a 305->400 blow-up: a blur with no
high-frequency detail to disagree with, which won 31 of 54 crops on the shelf
it was cut from. A view is now used only at the levels it actually has.
`score_stream.py` measured the wrong thing. It counted any facing touching the
window by one pixel (23 of 47 "in-catalog" facings were less than half
visible), scored a correct text-lane answer as a false accept because it
checked enrollment before correctness, and counted tracks rather than facings,
which reported 138% coverage of a 24-facing window. It now takes the union of
the window rects the client showed, keeps facings at least half inside, checks
correctness first, and reports facings named separately from labels correct.
Measured on add/image6.jpg, stock-lopia-clean, 20 s per run:
pan backend facings named precision
0 van 11/24 (46%) 52%
0 local 11/24 (46%) 100%
2 van 12/24 (50%) 45%
2 local 18/24 (75%) 100%
12 van 11/24 (46%) 28%
12 local 16/24 (67%) 83%
eval_identity on the same catalog is unchanged by the upscale fix (local 61%
end-to-end at 88% precision full, 59% at 95% live), and 1 of 602 distractor
crops from the 11 out-of-catalog shelf photos reaches 10 inliers, none reach 20.
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>
