Product recognition demo for supermarket shelf images
e52684c37e
The replayed walk and the harness that measures it disagreed with the phone in two ways, so every live number taken from them was measuring a different pipeline than the one that ships. The server cut a window `PAN_LONG_EDGE` tall, which on image6 shows one shelf row: a 720 px band reaches 52 of 66 enrolled facings and gives every other SKU 0% coverage, which reads as a matcher failure when it is only the crop. It now spans the sample's full height and pans horizontally, and `PAN_STEP` is expressed in sent-frame pixels so the pace survives the downscale. `stream_client.py` had the same window bug plus a box-mapping bug: it added the pan offset but never divided by the downscale, so a `--width` below the source size shrank every box and the evaluator matched the wrong facing. It now scales the window itself (so the frame it sends is byte-for-byte the app's 720x1280), records that scale per frame, and divides by it when mapping a label box back to source coordinates. The pan accumulator is clamped at the reversal: a negative `pan_x` satisfied `<= 0` on every later frame and flipped the direction each time, parking the window at the left edge. Measured through the real server at the app's geometry: 57 of 66 enrolled facings covered (86%) at 94% precision, 9 distinct SKUs. |
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| .github/workflows | ||
| recognization | ||
| .gitignore | ||