Product recognition demo for supermarket shelf images
cf7e8a5a88
Two false positives came out of running the rules against a real shelf, and both had the same shape: the rules were arguing with our own detector instead of with the shelf. 1. One tag per product, not per facing. Measured: a row carries 16-18 facings but only 4-5 tags, because four facings of a mayonnaise share a single tag. Counting facings invents missing tags on a shelf that is perfectly labelled. The rules now group a row's facings into products - by identity when the facings were recognised, by size and spacing otherwise - and count products. 2. A product's tag may sit anywhere along its run. Measured: a run of ピュアセレクト facings was split by geometry because one facing was detected narrower than its neighbours (54 px beside 150 px), and the fragment was then reported as missing the tag that was sitting further along its own run. A product now counts as missing only when no tag exists for its SKU anywhere in the row, and a fragment far narrower than the row's typical product is not judged at all. 3. A mismatch needs one trustworthy anchor. Measured noise: a wrong identity plus a garbled tag read (name match 0.11, no JAN) produced a confident "tag says 90, catalog says 188" that was really two bad reads disagreeing. Without the tag's JAN or a name that resembles the product, that is now a hint, not a violation. Measured on add/image6.jpg through the real still path: 0 false violations without identities (was 1), and with identities the only finding left is a genuine tag-versus-catalog price difference. Coverage hints now read "tags detected for 2 of 4 products in this row". 143 tests pass. Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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