306ea9b3e8
Eight images for sku_l04, sku_l07 and sku_l09 from six source domains, scored through the shipped LocalLaneAdapter and DecisionPolicy against stock-lopia-clean: 4 correct accepts, 4 abstains, no false accepts. Leak-tested first: the highest template match of any image against add/image6.jpg is 0.675, so none is a crop of the evaluation photo. The result that matters is sku_l07. It has no enrolled image, and both its images accept through the text lane alone - one reads ラッキー フヨネーズ, the other the rear label's 製造者：丸和油脂株式会社 with 内容量：200g, which together name the row 丸和油脂 ラッキーマヨネーズ 200g. The text lane's coverage claim holds on data that was not used to build it. The four l04 abstentions are correct behaviour: l04 is also unenrolled and its reads are weak (one reads only コと旨 タ 味, another scores 0.379 against a 0.45 accept line). Also records the l12 reconciliation, the Kewpie 450 g research, and two reusable pan clips with 11 rejected candidates for a future Tier-3 replay. Nothing here is Tier 2 or Tier 3 and every manifest says so.
37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
"""Wikimedia Commons search helper for the kewpie external-proxy collection."""
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import json
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import sys
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import urllib.parse
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import _fetch
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def csearch(q, limit=30):
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u = (
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"https://commons.wikimedia.org/w/api.php?action=query&generator=search"
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f"&gsrsearch={urllib.parse.quote(q)}&gsrnamespace=6&gsrlimit={limit}"
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"&prop=imageinfo&iiprop=url|size|extmetadata&format=json"
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)
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d = json.loads(_fetch.text(u))
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out = []
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for p in d.get("query", {}).get("pages", {}).values():
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ii = (p.get("imageinfo") or [{}])[0]
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em = ii.get("extmetadata", {})
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out.append(
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(
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p["title"],
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ii.get("width"),
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ii.get("height"),
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em.get("LicenseShortName", {}).get("value"),
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em.get("Artist", {}).get("value", "")[:60],
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ii.get("url"),
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)
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)
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return out
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if __name__ == "__main__":
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for q in sys.argv[1:]:
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print("###", q)
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for t in csearch(q):
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print(" ", t[0], "|", t[1], "x", t[2], "|", t[3], "|", t[4])
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