Product recognition demo for supermarket shelf images
02c0fd1f4f
Until now a tag reached the rules only if its text could be read. A tag that was there but unreadable was indistinguishable from a missing one, so a missing-tag claim rested on the reader's failures rather than on the shelf - the false violation that made an earlier run report 106 of them. Two measurements shaped the detector: - The tags are printed ON the rail: a red brick-patterned strip running the width of the shelf. The rail is therefore findable without reading anything, and it is where tags must be looked for. - On the rail, a tag is a run of columns carrying paper (bright, unsaturated) against the brick. Projecting onto x finds 27 tags on add/image6.jpg where connected components found 9, because tag text fragments a component at every printed line. The pipeline now merges the two views: a tag whose text was read keeps its fields, a tag that was only seen is reported unreadable with its box. Measured on add/image6.jpg: unreadable tags 8 -> 17, "no tag" 94 -> 85, and the per-row hints became specific - "tags detected for 8 of 16 facings in this row". That last number is also a domain fact worth keeping: this store prints one tag per product, not per facing, so facings are the wrong denominator for a missing-tag rule. The coverage bar keeps the rules silent until that is fixed, which is the honest behaviour in the meantime. Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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