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. |
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| .. | ||
| references/rejected | ||
| README.md | ||
| sku_l01.jpg | ||
| sku_l02.jpg | ||
| sku_l03.jpg | ||
| sku_l05.jpg | ||
| sku_l06.jpg | ||
| sku_l09.jpg | ||
| sku_l11.jpg | ||
| stock.csv | ||
LOPIA clean catalog
Official manufacturer pack shots, one front view per SKU. This replaces the
views in ../stock-lopia/, which were 71-119 px crops cut from the test shelf
add/image6.jpg itself: one 18 px sliver, duplicate crops, a wrong product on
sku_l09, and five SKUs with no view at all. Enrolling from the test shelf is
why every dark package collapsed onto one SKU and why the identity head was
trained on its own exam.
Rebuild or extend with:
python code/rebuild_catalog.py --source stock-lopia-clean --output stock-lopia-clean \
--replace --min-views 1 --csv stock-lopia/stock.csv
--min-views 1 because official pages publish a single front view per JAN. The
default floor of 2 exists to catch accidental single-view enrollment from shelf
crops, where several views are available.
Provenance
| SKU | Product | Source | Image |
|---|---|---|---|
| sku_l01 | キユーピー エッグケア 305g | kewpie.co.jp | 4901577090301L.jpg |
| sku_l02 | キユーピー 血圧ケア アマニ油マヨネーズ 200g | kewpie.co.jp | 4901577060052L.jpg |
| sku_l03 | キユーピー からしマヨネーズ 200g | kewpie.co.jp | 4901577338779L.jpg |
| sku_l05 | キユーピー ゼロ ノンコレステロール 310g | kewpie.co.jp | 4901577092381L.jpg |
| sku_l06 | キユーピー 内臓脂肪ケア フィッテ 210g | kewpie.co.jp (discontinued 2026秋) | 4901577083600L.jpg |
| sku_l09 | ピュアセレクト コクうま カロリーカット65% 360g | ajinomoto.co.jp | A140600321 |
| sku_l11 | キユーピー ハーフ 400g | kewpie.co.jp | 4901577031083L.jpg |
Kewpie images follow https://img.kewpie.co.jp/products_src/g3/imgs/<JAN13>L.jpg;
Ajinomoto images follow
https://www.ajinomoto.co.jp/products/Image.aspx?PicType=0&PIMNO=<PIMNO>.
Known gaps
Five SKUs in ../stock-lopia/stock.csv have no official pack shot here, so no
image lane can match them. They stay in stock.csv as text-only rows: the text
lane builds a signature from the printed name, which is how it can name or veto
a near-unknown the matcher cannot see. They are the current image-coverage
ceiling, not a recognition failure:
| SKU | Catalog name | Why it is missing |
|---|---|---|
| sku_l04 | やまや めんたいマヨネーズタイプ 200g | Yamaya's current lineup has no 200 g mentaiko mayo |
| sku_l07 | 丸和油脂 ラッキーマヨネーズ 200g | Maruwa's current catalog has no 200 g Lucky Mayonnaise |
| sku_l08 | スパイシーストーム ペリペリミックス 30g | No official 30 g Peri-Peri Mix found |
| sku_l10 | ピュアセレクト マヨネーズ 450g | Ajinomoto's current lineup is 400/250/600 g/1 kg; no 450 g |
| sku_l12 | 焼肉のたれ 中辛 | Brand not recorded in the catalog; ambiguous |
The catalog names for these five disagree with the manufacturers' current lineups, so the shelf tags and the CSV need reconciling before enrollment.
Keep the text-only rows when rebuilding: the CSV carry-over must preserve every source row, not just the SKUs with a kept view. Dropping these five costs the text lane the signatures that cover them.
sku_l09.jpg is enrolled but only 305 px tall, below the 800 px source floor
the enrollment protocol wants (§8 of the architecture decision). Its matcher
scores dwarf the other references (85-129 inliers against 10-40), so it wins
facings it should not; re-source it before trusting l09 precision.
Rejected
references/rejected/kewpie-mayonnaise-450g.jpg is Kewpie Mayonnaise 450g (JAN
4901577042072). It was briefly filed as sku_l09 and then as sku_l10 because
the catalog's names pointed there, but it is neither product: l09 is Ajinomoto
Pure Select コクうま カロリーカット 65% 360g and l10 is Pure Select マヨネーズ
450g. It is kept as provenance for that mix-up and is never enrolled.