product-recognition-demo/recognization/stock-lopia-clean/README.md

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
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 (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):

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.