# 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 four 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.

Eight of the twelve rows are enrolled; `sku_l04`, `sku_l07`, `sku_l08` and
`sku_l12` stay text-only (see *Known gaps*). Every enrolled view is at least
1000 px tall.

Rebuild from the staged packshots with:

    python code/rebuild_catalog.py --source data/packshots/source \
        --output stock-lopia-clean --csv stock-lopia/stock.csv \
        --replace --min-views 1

`--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.

To re-run the audit without touching the catalog, point `--output` at a
scratch path inside the repo and keep the JSON:

    python code/rebuild_catalog.py --source data/packshots/source \
        --output data/packshots/audit-check --csv stock-lopia/stock.csv \
        --min-views 1 --json data/packshots/audit-check.json

## Provenance

Product names are the catalog's, verbatim from `stock.csv`; sizes are measured.

| SKU | Catalog name | View | Recorded source |
|---|---|---|---|
| sku_l01 | キューピー エッグケア 305g | 455x1000 | not recorded |
| sku_l02 | キューピー アマニ油マヨネーズ 200g | 468x1000 | not recorded |
| sku_l03 | キューピー からしマヨネーズ 200g | 450x1000 | not recorded |
| sku_l05 | キューピー ゼロ ノンコレステロール 310g | 461x1000 | not recorded |
| sku_l06 | キューピー フィッテ 210g | 654x1500 | [amazon.co.jp](https://www.amazon.co.jp/dp/B08V2676LK) |
| sku_l09 | ピュアセレクト コクうま カロリーカット65% 360g | 1000x1000 | not recorded |
| sku_l10 | ピュアセレクト マヨネーズ 400g | 1000x1000 | [rakuten.co.jp](https://item.rakuten.co.jp/matsukiyo/4901001114030/) (JAN 4901001114030) |
| sku_l11 | キューピー ハーフマヨネーズ 400g | 482x1000 | not recorded |

`data/packshots/source/ATTRIBUTION.json` records a URL for `sku_l06` and
`sku_l10` only. Its `_provenance_gap` note names the other six - l01, l02, l03,
l05, l09, l11 - as enrolled before any provenance was tracked, and the audit
reports them under `unattributed_skus` on every run. What *is* verified about
them, per that note: each is independent of the evaluation photo (template
match against `add/image6.jpg` peaks at 0.166, against 0.95 for a crop of it),
each is 1000 px or taller, and each one's own OCR matches its catalog row (see
*Name/OCR gate*). That is a traceability gap, not a correctness one.

`sku_l06` is the current package (`内臓脂肪を減らす`), which replaced the
superseded `内臓脂肪ケア` view on 2026-09-29. The catalog name was shortened to
match; the shelf tags were not part of that change.

## Name/OCR gate

A packshot's OCR has to explain its own catalog row's name, or it is refused.
Measured over the shipped packshots - every image read in all four rotations,
every read scored against all 12 catalog names:

| Class | n | Score |
|---|---|---|
| Correct packshot vs its own row | 8 | 0.9167 - 1.0000 |
| Wrong product vs any row | 1 image x 12 names | 0.0476 - 0.5833 |

`MIN_NAME_OCR = 0.75` is the midpoint of those two bands: 0.167 of headroom
above the worst correct packshot (l11, whose read omits `マヨネーズ`) and 0.167
below the wrong-product ceiling. The wrong-product image is
`references/rejected/kewpie-mayonnaise-450g.jpg`; its best score anywhere is
0.5833, tied against `キューピー からしマヨネーズ 200g` and
`キューピー ハーフマヨネーズ 400g`, and it bottoms out at 0.0476 against
`ピュアセレクト コクうま カロリーカット65% 360g`.

Reproduce the band with (run from the recognition root; unindented so it runs
verbatim):

```python
import csv, os, sys, cv2
sys.path.insert(0, 'code')
from ocr import build_ocr_engine
from rebuild_catalog import best_orientation_text, name_ocr_similarity

clean = 'stock-lopia-clean'
names = {r['SKU_ID']: r['product_name'] for r in csv.DictReader(open(clean + '/stock.csv'))}
engine = build_ocr_engine(None)
for n in sorted(os.listdir(clean)):
    if n.endswith('.jpg'):
        sku = n[:-4]
        read = best_orientation_text(engine, cv2.imread(os.path.join(clean, n)), names[sku])
        print(sku, round(name_ocr_similarity(names[sku], read), 4))

rej = 'references/rejected/kewpie-mayonnaise-450g.jpg'
read = best_orientation_text(engine, cv2.imread(os.path.join(clean, rej)), names['sku_l01'])
print('rejected max', max((round(name_ocr_similarity(v, read), 4), k) for k, v in names.items()))
```

The gate cannot separate siblings, and does not try: the `sku_l09` packshot
scores 0.8462 against the `sku_l10` name, because both are Ajinomoto
ピュアセレクト mayonnaise. That case is what `text_discriminated_siblings` flags
(l09 ~ l10, cosine 0.9817) - it is a cross-SKU geometry problem, not a gap a
threshold closes.

## Pyramid floor

Every enrolled view must be at least 800 px tall, or it is refused. This is a
hard gate, not a report. It exists because of `sku_l09`: the view enrolled at
305 px could not be stored as a 200/400/800 px pyramid, so its local lane
upscaled 305->400 px and scored 85-129 inliers against 10-40 for every other
reference, winning facings it should not. It was re-sourced at 1000x1000 on
2026-09-29. Reporting the defect instead of refusing it is what let it ship.

`data/packshots/source/sku_l07.jpg` is 600x600 and is refused on this rule
before OCR even runs.

## Known gaps

Four rows in `../stock-lopia/stock.csv` have no enrolled image, 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 there is no view |
|---|---|---|
| sku_l04 | やまや めんたいマヨネーズタイプ 200g | A packshot is staged and refused at name/OCR 0.00: the image reads only `明太手屋さんが つくつ た コ`, brand text that runs vertically. A correctly oriented or higher-resolution shot is what would clear it. |
| sku_l07 | 丸和油脂 ラッキーマヨネーズ 200g | A packshot is staged and refused twice: it is 600x600, under the pyramid floor, and scores name/OCR 0.5714. Every product bigram reads (`ラッキー マヨネーズ`); the six it misses are the maker prefix `丸和`/`和油`/`油脂` and the size `20`/`0G`/`00`, which the package does not print. |
| 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_l12 | ユーラス 野菜をおいしく食べる旨たれ 380g | No pack shot sourced |

`sku_l10` left this table on 2026-09-29. Its catalog name said 450 g, which
Ajinomoto does not make; the shelf tag in `add/image6.jpg` and the
manufacturer's lineup both say 400 g, so `stock.csv` was corrected and a
1000x1000 packshot (JAN 4901001114030) enrolled. See
`data/packshots/source/ATTRIBUTION.json`.

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 four costs the
text lane the signatures that cover them.

## Rejected

`references/rejected/kewpie-mayonnaise-450g.jpg` is Kewpie Mayonnaise 450g (JAN
4901577042072). It was filed as `sku_l09` and then as `sku_l10` because the
catalog's names pointed there, but it is neither: 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 400g. Both now
hold their own correct views. The rejected image is kept as provenance for that
mix-up and is never enrolled; the name/OCR gate scores it at most 0.5833
against any catalog name, below the 0.75 threshold.