Product recognition demo for supermarket shelf images
00367a95c9
/v1/demo/plan + /v1/demo/frame give a demo the one thing a still image cannot: motion. The virtual camera pans a shelf row left to right, steps up, and pans back - a store employee sweeping a shelf - over a real shelf photo, ~1 ms per frame, deterministic, and wrapping so an unattended demo loops. The client drives only `step`, so the walk's speed follows whatever frame rate it can sustain. Three real defects came out of measuring the walk rather than a still: - Bands were keyed by on-screen position, so a walking camera minted a new key every few pixels of pan and re-read the same shelf continuously. Keyed by row now; OCR stops chasing the window. - The classification budget was consumed by retries of products already leaving the frame. Retries now skip anything near the edge, and a track gets 2 attempts. - The pan advance was 0.22 of the window per step: a facing was in view for one or two frames, so nothing could ever be classified (9 labelled tracks in 20 s). 0.06 keeps a product in view for about a second. Measured on the walk (20 s, 1280x720 frames, one client): orb labels 296 tracks across 21 SKUs with the first label at 0.2 s, while hybrid manages 13 tracks / 4 SKUs at 5.4 s - so the live default for a demo is the fast method, with hybrid kept for tap-to-inspect. Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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