tngo
aaed5d388f
Replaces the DINO gallery as the recognition path with the measured design from docs/research/2026-09-28-recognition-architecture/DECISION.md: RootSIFT over a per-SKU reference pyramid, similarity-constrained RANSAC and a hue check, fused with an OCR text lane that vetoes a match whose package text names a different catalog row and answers on its own when geometry finds nothing. - identity_lanes.py: LocalLane, TextLane, DecisionPolicy. The text lane builds each row's signature from the catalog name plus its packshot OCR, so a row with no enrolled image is still answerable. - engine.py: every backend groups scores by canonical SKU, so no view-suffixed id reaches the wire and a name and price always resolve. decide_crop fuses the lanes; score_crop_lanes feeds the live path. Both lanes are built in warm_up (non-fatally) so the first frame is not the slow one, and the text lane shares the engine's OCR lock. - live.py: decides per crop on the lane's own rank order. The lane's order comes from its rank key and its value is only the gate, so re-sorting by value named a different SKU than the lane chose; averaging in an empty observation also diluted a good confidence below the accept line. - identity.py: a `local` score scale whose gate is one absolute line, because the value is already a margin over the runner-up. - app.py, run-demo-server.sh: `--identity local` is the default; the demo server no longer starts on the leaked `stock-lopia` catalog. - eval_live.py: the replay vocabulary accepts `local`, so a recording from the shipped server can be scored. - README.md: documents the three backends and what each is for. Measured on the corrected image6 labels at live resolution: 36/61 in-catalog facings named at 95% precision, 0 of 48 unknowns accepted, 11 labels sourced from the text lane alone. Through the real server: /v1/recognize 90% precision, /v1/stream 100% precision, no view-suffixed ids on either.
3.1 KiB
Shelf label files
Ground truth for the identity evaluators (code/eval_identity.py,
code/eval_live.py) and for the evidence scripts in
docs/research/2026-09-28-recognition-architecture/evidence/.
{"image": "add/image6.jpg", "version": 2, "facings": [ ... ]}
image is the photo the boxes index into. facings is one row per detected
product facing, in that photo's own pixel coordinates.
Row schema
| Field | Required | Meaning |
|---|---|---|
index |
yes | Row identity. Stable across label revisions, so a run's per-facing log joins to truth by index, never by array position. |
box |
yes | [x1, y1, x2, y2] in image pixels. |
label |
yes | Canonical SKU id (sku_l01), unknown for a facing that is not in the catalog, or exclude for a facing no lane can be scored on. |
source |
yes | How the label was decided. v1-confirmed-visual = the v1 label was checked against the package and kept; visual-audit-2026-09-28 = the label was re-read from the package in the 2026-09-28 audit. |
v1_label |
yes | The label this row carried in v1. Kept so claims computed on v1 stay reproducible and the 26 corrections stay auditable. |
note |
when label != v1_label |
Why the label changed. Also on a few unchanged rows that need a remark (e.g. #46-48, the tare bottles that may be what stock.csv calls l12). |
match |
no | v1 provenance only: the field DECISION.md §3 reads as a match IoU, and as evidence that v1 labels were propagated from tag or box matching rather than read off packages. Values here are 0.0, 0.911, 1.0. No scorer reads it; it is carried through unchanged for traceability. |
label takes a canonical SKU id, unknown, or exclude. This file uses
sku_l01-sku_l11: l12 has no real facing on the shelf, so no row carries it.
exclude rows stay in the file so the facing inventory is complete; every
scorer drops them before computing a rate. The evaluators read index, box,
and label; the provenance fields are additive.
image6.json is the corrected v2 set: 111 rows, 109 scored (2 exclude), 61
in-catalog across 11 SKUs, 48 unknown, 26 labels changed from v1. It is a copy
of docs/research/2026-09-28-recognition-architecture/evidence/image6_v2.json;
edit the evidence copy and re-copy, so the two cannot drift.
Schema check
DECISION.md §7 sets the rule for every label file:
Every label row carries
source,confidence, andlabeler. A label file without them fails a schema check.
The current file does not meet it. All 111 rows carry source and v1_label,
but none carries confidence or labeler: the file records where each label
came from and what it replaced, not a per-row certainty or a labeler name.
Inventing confidence values after the fact would be worse than the gap, so they
are absent rather than guessed.
Tier-2 labels must add both. confidence is the labeler's own certainty for
that row; labeler names the person or pass that produced it, because Tier 2 is
double-labelled and a disagreement has to be traceable to a pass. Tier 1
(image6_v2.json) is dev-only and its numbers never ship, so it stays as it is.