reconcile() compares one accepted identity with its tag and the catalog entry and
publishes a state plus the individual signals (JAN exact, name by longest common
substring, price against either the sale or the regular price). It never changes
the product identity: a disagreement is a second opinion shown next to the first,
and only a checksummed barcode outranks the matcher.
evaluate_compliance() is the first rule family: missing_price_tag, the owner's
missing_tag_group (>= 3 consecutive facings without a tag), orphan_tag,
price_mismatch, plus an unreadable_tags hint. Every rule fails closed on evidence
quality - a row whose tags could not be read produces "move closer", never a
violation. Findings must appear in 3 of the last 5 frames before they publish, so
the UI never flickers.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The recognition server will classify crops from several clients at once. Two
things blocked that:
- OpenCV Feature2D objects were shared across calls, so parallel
classify_crop() calls could corrupt each other. Each thread now gets its own
extractor set; the template cache is untouched.
- score_crop() is extracted from classify_crop() so callers that need every SKU
score (live track aggregation) do not have to re-derive it from the top-5
list. classify_crop() output is byte-identical to before (verified against the
pre-change module on 82 real crops x 4 methods).
- ShelfProductDetector accepts device/imgsz so the server can pin inference.
Dependencies: pin ultralytics to the version that wrote the checkpoint and that
the session tracker targets (8.4.153), add lap so ultralytics never
pip-installs it mid-request, and add websockets for the streaming endpoint.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
