Product recognition demo for supermarket shelf images
d38d863873
macOS Vision holds the GIL for the ~124 ms it takes to read a rail band, so the tag pass decides the live frame rate: with 2 bands every 15 frames a walking session fell to 1.7 results/s, and 1 band every 30 frames brings it back to 7.2 with tags still settling in a second or two. Measured, not guessed - the same walk, same method (orb), only the OCR duty cycle changed. The walk also gained a `pan` parameter (default 0.06, clamped 0.005-0.5) so a demo can choose its pace: pan=0.02 sweeps a row over ~8 s, which reads like a person rather than a time-lapse. Demo configuration measured end to end on the walk (20 s, 1280x720, one client): orb, pan=0.02, 1 band/30 frames -> 7.2 results/s, 178 labelled tracks, 21 distinct SKUs, first label 0.7 s, 3 readable price tags. Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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