Product recognition demo for supermarket shelf images

kiyo9w 0f1d46f9b3 refactor: make the matcher and detector safe for concurrent clients ... 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>		2026-09-24 11:43:24 +09:00
recognization	refactor: make the matcher and detector safe for concurrent clients	2026-09-24 11:43:24 +09:00
.gitignore	Initial commit for push folder	2026-09-18 09:13:53 +09:00