Product recognition demo for supermarket shelf images
929d77a802
engine.py is the process-wide, thread-safe core the server is built on: one YOLO instance behind a lock, thread-local matcher access, a classification pool so detection is never starved, the stock catalog under its own lock, empty-slot geometry normalized to a reference width (the detector's gap thresholds are absolute pixels), and a small LRU of recent still images for later classify/inspect calls. Detection output is normalized so every client sees the same box shape. tracking.py wraps ultralytics BYTETracker. The important part: ultralytics mints track ids from a process-global counter and resets it when a tracker is constructed, so with two live clients one client's ids would be re-issued while the other still held cached labels. SessionTracker gives each tracker its own counter and never touches the global one. Measured defaults (bench_engine.py, M5): detection p50 26-29 ms on CPU vs 112 ms on MPS with byte-identical boxes, so device defaults to cpu; hybrid classifies at ~51 ms/crop, 92 crops/s across 8 threads. Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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