0f1d46f9b3
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>
14 lines
426 B
Text
14 lines
426 B
Text
torch>=2.0.0
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torchvision>=0.15.0
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# Exact: tracking.py relies on BYTETracker internals; the trained checkpoint was written by 8.4.152
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ultralytics==8.4.153
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# ByteTrack linear assignment; pinned so ultralytics never pip-installs it at runtime
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lap>=0.5.12
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# uvicorn WebSocket support for /v1/stream
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websockets>=13
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opencv-python>=4.8.0
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numpy>=1.23.0,<2.0.0
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fastapi>=0.100.0
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uvicorn>=0.20.0
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python-multipart>=0.0.6
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requests>=2.28.0
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