The deep review's first recommendation was real data, because every accuracy number
so far comes from one or two photographs - the identity benchmark even takes its
gallery and its queries from the same photo. This is the mechanism for replacing
that.
`--record-frames DIR` writes each processed frame, sampled every Nth, with a JSON
line describing what the server saw in it: the boxes, the identity channels and the
tag reading. The lines are the labelling input - `labelling_candidates()` returns
the facings the tag channel named with a corroborated price, which are seeds for the
confirmation screen rather than guesses. Nothing here is a training set on its own.
Sampling keeps each frame's own number in the index, so a sampled recording says so
instead of hiding it, and `/v1/health` reports how much has been written. Measured
on a 163-frame walk: 32 frames and their lines.
`docs/DATA.md` is the runbook: what to capture (a slow walk per rail, a close pass
for the barcode, and the same shelf on a second visit), how to turn it into labels,
and how to freeze a benchmark with a cross-visit split.
167 tests pass.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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