chore(eval): reconcile the distractor sets and refresh the sweep numbers
The Wikimedia collector truncated the whole basename at 60 characters, so 13 files lost their extension and cv2 could not read them by name - the distractor sets were quietly scoring 13 images fewer than they held. It now truncates the stem and keeps the extension, and the files on disk are renamed to match. The search terms also pull in .djvu volumes whose page 1 Commons serves as a JPEG thumbnail. Twelve book scans were in the sets; they contributed 0 false accepts, and they are not shelves, so the collector filters them and the manifests no longer list them. With all 2650 crops readable the sweep is re-measured end to end: 3.09% at the old lines, 0.30% at the shipped 4.0/4, image6 44 shown / 42 correct / 95.5%. The accept sweep table and the verdict's far-unknown sentence now carry those numbers instead of the pre-calibration ones. Also: the fetched images are gitignored (manifests and results are not), the l10/l11 shelf-tag crops that back the catalog corrections are committed, and a stale pre-gate eval.json and a duplicate of eval/labels/image6.json are gone.
This commit is contained in:
kiyo9w
parent
4bdad66bbe
commit
5eb4bdf6c8
19 changed files with 646 additions and 253 deletions
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{
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"source": "data/packshots/source",
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"output": "stock-lopia-clean",
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"min_width": 120,
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"min_height": 160,
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"min_views": 1,
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"duplicate_cosine": 0.98,
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"min_name_ocr": 0.7,
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"ocr": "vision",
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"text_only_skus": [
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"sku_l04",
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"sku_l07",
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"sku_l08",
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"sku_l12"
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],
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"text_discriminated_siblings": [
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{
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"sku": "sku_l09",
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"view": "sku_l09.jpg",
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"sibling_sku": "sku_l10",
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"sibling_view": "sku_l10.jpg",
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"cosine": 0.9817,
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"name_similarity": 0.333
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}
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],
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"below_pyramid_floor": [],
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"completeness": {
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"live_ready": null,
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"reason": "no --shelf-tags given; the completeness rule needs the shelf's SKU list",
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"enrolled": 8,
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"text_only": [
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"sku_l04",
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"sku_l07",
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"sku_l08",
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"sku_l12"
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]
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},
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"skus": {
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"sku_l01": [
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"sku_l01.jpg"
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],
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"sku_l02": [
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"sku_l02.jpg"
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],
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"sku_l03": [
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"sku_l03.jpg"
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],
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"sku_l05": [
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"sku_l05.jpg"
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],
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"sku_l06": [
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"sku_l06.jpg"
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],
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"sku_l09": [
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"sku_l09.jpg"
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],
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"sku_l10": [
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"sku_l10.jpg"
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],
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"sku_l11": [
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"sku_l11.jpg"
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]
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},
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"refused": [
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{
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"sku": "sku_l04",
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"view": "sku_l04.jpg",
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"reason": "name/OCR 0.00 below 0.70: 明太手屋さんが つくつ た コ"
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},
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{
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"sku": "sku_l07",
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"view": "sku_l07.jpg",
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"reason": "name/OCR 0.57 below 0.70: ラッキー マヨネーズ 調味料（アミノ酸）不使用"
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}
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]
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}
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@ -0,0 +1,221 @@
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{
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"image": "add/image6.jpg",
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"stock_dir": "stock-lopia-clean",
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"labels": "eval/labels/image6.json",
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"provenance": {
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"catalog_hash": "3f1653d9d962073af96121250006b9b6d86113556797bbee65de68b60e87a64d",
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"labels_hash": "5aabf029921ced767d4e14a18e8bccb55812dd974852c56e0931a98fa10366cf",
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"image_hash": "a08b89ba37d27c9d5a81bcd56bcd042e6baf54d2f182bbaaf47e97f14c9cd525",
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"code_revision": "68061635d9fef3097609945bfd9ac1935791b398"
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},
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"facings": 111,
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"leakage": {
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"excluded": 0,
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"labels": []
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},
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"hub_share_limit": 0.3,
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"enrollment": {
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"catalog_skus": [
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"sku_l01",
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"sku_l02",
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"sku_l03",
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"sku_l04",
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"sku_l05",
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"sku_l06",
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"sku_l07",
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"sku_l08",
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"sku_l09",
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"sku_l10",
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"sku_l11",
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"sku_l12"
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],
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"enrolled_skus": [
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"sku_l01",
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"sku_l02",
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"sku_l03",
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"sku_l05",
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"sku_l06",
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"sku_l09",
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"sku_l10",
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"sku_l11"
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],
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"unenrolled_skus": {
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"sku_l07": 6,
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"sku_l04": 1,
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"sku_l08": 1
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},
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"facings_without_enrollment": 8,
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"scored_facings": 109
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},
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"results": {
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"full": [
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{
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"lane": "local",
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"accept": 2.0,
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"margin": 0.15,
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"in_catalog": 61,
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"out_catalog": 48,
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"enrolled": 53,
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"correct": 48,
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"wrong": 9,
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"rejected": 52,
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"catalog_ceiling": 0.8689,
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"identity_ceiling": 0.9245,
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"decision_ceiling": 0.8113,
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"end_to_end_coverage": 0.7869,
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"false_accepts": 9,
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"unknown_false_accepts": 8,
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"unenrolled_false_accepts": 0,
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"false_accept_rate": 0.1667,
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"near_unknown_false_accept_rate": 0.0,
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"precision": 0.8421,
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"max_sku_share": 0.2281,
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"hub_sku": "sku_l09",
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"hub_alarm": false,
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"histogram": {
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"sku_l09": 13,
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"sku_l11": 10,
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"sku_l10": 9,
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"sku_l01": 5,
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"sku_l03": 5,
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"sku_l07": 5,
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"sku_l06": 4,
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"sku_l02": 3,
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"sku_l05": 3
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},
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"per_sku": {
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"sku_l01": {
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"correct": 5,
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"total": 5
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},
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"sku_l02": {
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"correct": 2,
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"total": 4
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},
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"sku_l03": {
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"correct": 4,
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"total": 4
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},
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"sku_l04": {
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"correct": 0,
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"total": 1
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},
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"sku_l05": {
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"correct": 3,
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"total": 3
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},
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"sku_l06": {
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"correct": 4,
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"total": 4
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},
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"sku_l07": {
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"correct": 5,
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"total": 6
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},
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"sku_l08": {
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"correct": 0,
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"total": 1
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},
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"sku_l09": {
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"correct": 11,
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"total": 13
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},
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"sku_l10": {
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"correct": 9,
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"total": 11
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},
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"sku_l11": {
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"correct": 5,
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"total": 9
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}
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},
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"ms_per_crop": 26.7
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}
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],
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"live": [
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{
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"lane": "local",
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"accept": 2.0,
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"margin": 0.15,
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"in_catalog": 61,
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"out_catalog": 48,
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"enrolled": 53,
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"correct": 41,
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"wrong": 4,
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"rejected": 64,
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"catalog_ceiling": 0.8689,
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"identity_ceiling": 0.8491,
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"decision_ceiling": 0.6604,
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"end_to_end_coverage": 0.6721,
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"false_accepts": 4,
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"unknown_false_accepts": 3,
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"unenrolled_false_accepts": 0,
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"false_accept_rate": 0.0625,
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"near_unknown_false_accept_rate": 0.0,
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"precision": 0.9111,
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"max_sku_share": 0.2,
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"hub_sku": "sku_l09",
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"hub_alarm": false,
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"histogram": {
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"sku_l09": 9,
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"sku_l10": 9,
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"sku_l11": 7,
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"sku_l07": 6,
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"sku_l01": 4,
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"sku_l03": 3,
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"sku_l05": 3,
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"sku_l06": 2,
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"sku_l02": 1,
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"sku_l04": 1
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},
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"per_sku": {
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"sku_l01": {
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"correct": 4,
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"total": 5
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},
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"sku_l02": {
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"correct": 1,
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"total": 4
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},
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"sku_l03": {
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"correct": 3,
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"total": 4
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},
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"sku_l04": {
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"correct": 1,
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"total": 1
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},
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"sku_l05": {
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"correct": 3,
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"total": 3
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},
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"sku_l06": {
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"correct": 2,
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"total": 4
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},
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"sku_l07": {
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"correct": 5,
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"total": 6
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},
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"sku_l08": {
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"correct": 0,
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"total": 1
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},
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"sku_l09": {
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"correct": 9,
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"total": 13
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},
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"sku_l10": {
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"correct": 9,
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"total": 11
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},
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"sku_l11": {
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"correct": 4,
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"total": 9
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}
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},
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"ms_per_crop": 20.9
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}
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]
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}
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}
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"""Which facings are falsely accepted, and as what."""
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import sys, cv2
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sys.path.insert(0, "code")
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from eval_identity import LocalLaneAdapter, load_labels, at_live_resolution, leaked, UNKNOWN, crop_of
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from identity_lanes import DecisionPolicy
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payload = load_labels("eval/labels/image6.json")
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image = cv2.imread("add/image6.jpg")
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facings = [f for f in payload["facings"] if f["label"] != "exclude"]
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facings = [f for f in facings if leaked(image, f["box"], "stock-lopia-clean") is None]
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enrolled = {"sku_l01","sku_l02","sku_l03","sku_l05","sku_l06","sku_l09","sku_l10","sku_l11"}
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for live in (False, True):
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picture = at_live_resolution(image) if live else image
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scaled = facings if not live else [
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{**f, "box": [int(round(v * picture.shape[1] / image.shape[1])) for v in f["box"]]}
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for f in facings]
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lane = LocalLaneAdapter("stock-lopia-clean")
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print(f"=== {'live' if live else 'full'} ===")
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for f in scaled:
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crop = crop_of(picture, f["box"])
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if crop.size == 0: continue
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ranked = lane.rank(crop)
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d = lane.decide(crop, ranked, accept=2.0, margin=0.0, scale="local")
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ans = d.get("best_sku") if d.get("identity_level") == "accepted" else None
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if ans and ans != f["label"]:
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print(f" #{f['index']:>3} truth={f['label']:<9} answered={ans:<9} enrolled={f['label'] in enrolled}")
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"""What the falsely-accepted crops actually read, and what the text lane scores.
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The advisory says crops 82-97 are Kewpie Mayonnaise 450g - a product the
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catalog does not hold - and that no catalog row can therefore win the text
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ranking, so the veto at DecisionPolicy.decide never fires. This prints the
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reads and the scores that claim depends on.
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"""
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import csv
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import sys
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import cv2
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import numpy as np
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sys.path.insert(0, "code")
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from identity_lanes import TextLane, carries_identity # noqa: E402
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from ocr import build_ocr_engine # noqa: E402
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INDICES = [73, 82, 83, 84, 86, 94, 95, 96, 97]
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rows = []
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with open("stock-lopia-clean/stock.csv", encoding="utf-8") as handle:
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for record in csv.reader(handle):
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if len(record) >= 2 and record[0].startswith("sku_"):
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rows.append({"sku_id": record[0], "product_name": record[1]})
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engine = build_ocr_engine("vision")
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lane = TextLane.from_catalog(rows, "stock-lopia-clean", ocr_engine=engine)
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labels = {f["index"]: f for f in __import__("json").load(open("eval/labels/image6.json"))["facings"]}
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image = cv2.imread("add/image6.jpg")
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tiles = []
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for index in INDICES:
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facing = labels[index]
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x1, y1, x2, y2 = facing["box"]
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crop = image[y1:y2, x1:x2]
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read = lane.read(crop)
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scores = lane.scores_from_text(read)
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top = sorted(scores.items(), key=lambda kv: -kv[1])[:3]
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print(f"#{index:>3} truth={facing['label']:<9} carries={carries_identity(read)}")
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print(f" read: {read[:110]}")
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print(f" text top: {[(s, round(v, 3)) for s, v in top]}")
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height, width = crop.shape[:2]
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scale = 260.0 / max(1, height)
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resized = cv2.resize(crop, (max(1, int(width * scale)), 260), interpolation=cv2.INTER_CUBIC)
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canvas = np.full((300, 220, 3), 255, np.uint8)
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canvas[30:290, : min(resized.shape[1], 220)] = resized[:, :220]
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cv2.putText(canvas, str(index), (6, 22), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
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tiles.append(canvas)
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grid = np.vstack([np.hstack(tiles[:5]), np.hstack(tiles[5:])])
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cv2.imwrite("data/evaluations/2026-09-29-session/scratch/fa_tiles.jpg", grid)
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print("tile:", grid.shape)
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"""Mass-only size contradiction, with full reads.
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The first pass counted every quantity token, including percentages, so a crop
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reading "カロリー 50% カット" looked like a 50 g package. Only mass units can
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contradict a package size, so this restricts to g/kg and prints the whole read
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for every accepted facing that carries one.
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"""
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import csv
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import json
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import re
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import sys
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import cv2
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sys.path.insert(0, "code")
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from eval_identity import LocalLaneAdapter, at_live_resolution, crop_of, leaked # noqa: E402
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from identity_lanes import TextLane, normalize_text # noqa: E402
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from ocr import build_ocr_engine # noqa: E402
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MASS = re.compile(r"(\d+(?:\.\d+)?)\s*(KG|G)", re.IGNORECASE)
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# Package sizes this product class actually comes in. Below it are OCR fragments
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# of a longer number ("20" from 200); above it are concatenations ("5210").
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PLAUSIBLE = (100.0, 2000.0)
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def masses(text: str):
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return {float(number) for number, _unit in MASS.findall(normalize_text(text))}
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rows = []
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with open("stock-lopia-clean/stock.csv", encoding="utf-8") as handle:
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for record in csv.reader(handle):
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if len(record) >= 2 and record[0].startswith("sku_"):
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rows.append({"sku_id": record[0], "product_name": record[1]})
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names = {row["sku_id"]: row["product_name"] for row in rows}
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engine = build_ocr_engine("vision")
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text_lane = TextLane.from_catalog(rows, "stock-lopia-clean", ocr_engine=engine)
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local_lane = LocalLaneAdapter("stock-lopia-clean")
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payload = json.load(open("eval/labels/image6.json"))
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image = cv2.imread("add/image6.jpg")
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facings = [f for f in payload["facings"] if f["label"] != "exclude"]
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facings = [f for f in facings if leaked(image, f["box"], "stock-lopia-clean") is None]
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for live in (False, True):
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picture = at_live_resolution(image) if live else image
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scaled = facings if not live else [
|
||||
{**f, "box": [int(round(v * picture.shape[1] / image.shape[1])) for v in f["box"]]}
|
||||
for f in facings]
|
||||
print(f"=== {'live' if live else 'full'} ===")
|
||||
for facing in scaled:
|
||||
crop = crop_of(picture, facing["box"])
|
||||
if crop.size == 0:
|
||||
continue
|
||||
ranked = local_lane.rank(crop)
|
||||
decision = local_lane.decide(crop, ranked, accept=2.0, margin=0.0, scale="local")
|
||||
if decision.get("identity_level") != "accepted":
|
||||
continue
|
||||
answer = decision.get("best_sku")
|
||||
read = text_lane.read(crop)
|
||||
read_masses = masses(read)
|
||||
name_masses = masses(names.get(answer, ""))
|
||||
if not read_masses or not name_masses:
|
||||
continue
|
||||
plausible = {m for m in read_masses if PLAUSIBLE[0] <= m <= PLAUSIBLE[1]}
|
||||
veto = bool(plausible) and not (read_masses & name_masses)
|
||||
correct = "correct" if answer == facing["label"] else "WRONG"
|
||||
print(f" #{facing['index']:>3} {'VETO' if veto else 'keep'} {correct:<7} "
|
||||
f"answer={answer:<9} read_m={sorted(read_masses)} name_m={sorted(name_masses)}")
|
||||
print(f" read: {read[:120]}")
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 238 KiB |
|
|
@ -2,115 +2,129 @@
|
|||
"dataset": "wikimedia-commons-2",
|
||||
"catalog": "stock-lopia-clean",
|
||||
"note": "Unrelated shelves: every accept is a false accept by construction.",
|
||||
"images": 15,
|
||||
"detections": 421,
|
||||
"images": 17,
|
||||
"detections": 462,
|
||||
"false_accepts": 1,
|
||||
"false_accept_rate": 0.0024,
|
||||
"false_accept_rate": 0.0022,
|
||||
"rows": [
|
||||
{
|
||||
"file": "000_1920px-2011年4月の牛乳_(5588310773).jpg",
|
||||
"title": "File:2011年4月の牛乳 (5588310773).jpg",
|
||||
"detections": 78,
|
||||
"false_accepts": 1,
|
||||
"ms": 2983
|
||||
"ms": 3504
|
||||
},
|
||||
{
|
||||
"file": "001_Candy_in_Japan_2005_(5256472).jpg",
|
||||
"title": "File:Candy in Japan 2005 (5256472).jpg",
|
||||
"detections": 12,
|
||||
"false_accepts": 0,
|
||||
"ms": 387
|
||||
"ms": 408
|
||||
},
|
||||
{
|
||||
"file": "004_page1-960px-History_of_the_Toledo_Police_Department.djvu.jpg",
|
||||
"title": "File:History of the Toledo Police Department.djvu",
|
||||
"detections": 24,
|
||||
"file": "002_1920px-Empty_shelves_in_Tokyo_area_as_people_get_ready_for_t.jpg",
|
||||
"title": "File:Empty shelves in Tokyo area as people get ready for typhoon Hagibis - Oct 11 2019 330pm.jpeg",
|
||||
"detections": 8,
|
||||
"false_accepts": 0,
|
||||
"ms": 246
|
||||
"ms": 101
|
||||
},
|
||||
{
|
||||
"file": "003_1920px-HK_SW_上環_Sheung_Wan_德輔道西_88_Des_Voeux_Road_West_shop_.jpg",
|
||||
"title": "File:HK SW 上環 Sheung Wan 德輔道西 88 Des Voeux Road West shop 惠康超級市場 Wellcome Supermarket goods August 2020 SS2 27.jpg",
|
||||
"detections": 10,
|
||||
"false_accepts": 0,
|
||||
"ms": 1560
|
||||
},
|
||||
{
|
||||
"file": "005_1920px-Illuminated_wooden_shelf_with_many_glass_jars_contain.jpg",
|
||||
"title": "File:Illuminated wooden shelf with many glass jars containing cookies for sale in Tokyo.jpg",
|
||||
"detections": 41,
|
||||
"false_accepts": 0,
|
||||
"ms": 2946
|
||||
},
|
||||
{
|
||||
"file": "007_Japan_earthquake_store_shelves.jpg",
|
||||
"title": "File:Japan earthquake store shelves.jpg",
|
||||
"detections": 4,
|
||||
"false_accepts": 0,
|
||||
"ms": 97
|
||||
"ms": 298
|
||||
},
|
||||
{
|
||||
"file": "009_Milk_(2655574568).jpg",
|
||||
"title": "File:Milk (2655574568).jpg",
|
||||
"detections": 58,
|
||||
"false_accepts": 0,
|
||||
"ms": 1379
|
||||
"ms": 2802
|
||||
},
|
||||
{
|
||||
"file": "010_Natsuge-mikado_okashi.jpg",
|
||||
"title": "File:Natsuge-mikado okashi.jpg",
|
||||
"detections": 7,
|
||||
"false_accepts": 0,
|
||||
"ms": 259
|
||||
"ms": 704
|
||||
},
|
||||
{
|
||||
"file": "012_1920px-Supermarket_in_2006_(646332846).jpg",
|
||||
"title": "File:Supermarket in 2006 (646332846).jpg",
|
||||
"detections": 129,
|
||||
"false_accepts": 0,
|
||||
"ms": 2672
|
||||
},
|
||||
{
|
||||
"file": "013_page1-1280px-Tangled_Hair.djvu.jpg",
|
||||
"title": "File:Tangled Hair.djvu",
|
||||
"detections": 0,
|
||||
"false_accepts": 0,
|
||||
"ms": 20
|
||||
"ms": 6707
|
||||
},
|
||||
{
|
||||
"file": "014_1920px-「ペヤング」予想通りの大売れ！_(18634213682).jpg",
|
||||
"title": "File:「ペヤング」予想通りの大売れ！ (18634213682).jpg",
|
||||
"detections": 27,
|
||||
"false_accepts": 0,
|
||||
"ms": 324
|
||||
"ms": 762
|
||||
},
|
||||
{
|
||||
"file": "015_1920px-お客様各位_2008_国内産バター_(2429997079).jpg",
|
||||
"title": "File:お客様各位 2008 国内産バター (2429997079).jpg",
|
||||
"detections": 14,
|
||||
"false_accepts": 0,
|
||||
"ms": 529
|
||||
"ms": 1499
|
||||
},
|
||||
{
|
||||
"file": "016_1920px-パン売り場_手動ドア式_2016_(28574754416).jpg",
|
||||
"title": "File:パン売り場 手動ドア式 2016 (28574754416).jpg",
|
||||
"detections": 21,
|
||||
"false_accepts": 0,
|
||||
"ms": 543
|
||||
"ms": 1842
|
||||
},
|
||||
{
|
||||
"file": "017_1920px-プラレール_2015_(22339689873).jpg",
|
||||
"title": "File:プラレール 2015 (22339689873).jpg",
|
||||
"detections": 12,
|
||||
"false_accepts": 0,
|
||||
"ms": 254
|
||||
"ms": 864
|
||||
},
|
||||
{
|
||||
"file": "018_一家族様一本限り_(5588902836).jpg",
|
||||
"title": "File:一家族様一本限り (5588902836).jpg",
|
||||
"detections": 27,
|
||||
"false_accepts": 0,
|
||||
"ms": 1385
|
||||
"ms": 3880
|
||||
},
|
||||
{
|
||||
"file": "019_1920px-洗濯洗剤_(10852819193).jpg",
|
||||
"title": "File:洗濯洗剤 (10852819193).jpg",
|
||||
"detections": 8,
|
||||
"false_accepts": 0,
|
||||
"ms": 158
|
||||
"ms": 704
|
||||
},
|
||||
{
|
||||
"file": "020_1920px-申し訳ございませんが_ごめんなさい_お魚本日の調理は終了いたしました。_どうぞよろしくお願いします_(11.jpg",
|
||||
"title": "File:申し訳ございませんが ごめんなさい お魚本日の調理は終了いたしました。 どうぞよろしくお願いします (11331761984).jpg",
|
||||
"detections": 6,
|
||||
"false_accepts": 0,
|
||||
"ms": 957
|
||||
},
|
||||
{
|
||||
"file": "021_1920px-要冷蔵_(4513315790).jpg",
|
||||
"title": "File:要冷蔵 (4513315790).jpg",
|
||||
"detections": 0,
|
||||
"false_accepts": 0,
|
||||
"ms": 21
|
||||
"ms": 190
|
||||
}
|
||||
]
|
||||
}
|
||||
|
|
@ -22,7 +22,7 @@
|
|||
"queries": 1
|
||||
},
|
||||
{
|
||||
"file": "002_1920px-Empty_shelves_in_Tokyo_area_as_people_get_ready_for_t",
|
||||
"file": "002_1920px-Empty_shelves_in_Tokyo_area_as_people_get_ready_for_t.jpg",
|
||||
"title": "File:Empty shelves in Tokyo area as people get ready for typhoon Hagibis - Oct 11 2019 330pm.jpeg",
|
||||
"width": 4027,
|
||||
"height": 3018,
|
||||
|
|
@ -32,7 +32,7 @@
|
|||
"queries": 1
|
||||
},
|
||||
{
|
||||
"file": "003_1920px-HK_SW_上環_Sheung_Wan_德輔道西_88_Des_Voeux_Road_West_shop_",
|
||||
"file": "003_1920px-HK_SW_上環_Sheung_Wan_德輔道西_88_Des_Voeux_Road_West_shop_.jpg",
|
||||
"title": "File:HK SW 上環 Sheung Wan 德輔道西 88 Des Voeux Road West shop 惠康超級市場 Wellcome Supermarket goods August 2020 SS2 27.jpg",
|
||||
"width": 4128,
|
||||
"height": 3096,
|
||||
|
|
@ -42,17 +42,7 @@
|
|||
"queries": 1
|
||||
},
|
||||
{
|
||||
"file": "004_page1-960px-History_of_the_Toledo_Police_Department.djvu.jpg",
|
||||
"title": "File:History of the Toledo Police Department.djvu",
|
||||
"width": 1105,
|
||||
"height": 1500,
|
||||
"license": "Public domain",
|
||||
"artist": "<div class=\"fn value\">\nToledo (Ohio). Division of Police</div>",
|
||||
"source": "https://commons.wikimedia.org/wiki/File%3AHistory_of_the_Toledo_Police_Department.djvu",
|
||||
"queries": 1
|
||||
},
|
||||
{
|
||||
"file": "005_1920px-Illuminated_wooden_shelf_with_many_glass_jars_contain",
|
||||
"file": "005_1920px-Illuminated_wooden_shelf_with_many_glass_jars_contain.jpg",
|
||||
"title": "File:Illuminated wooden shelf with many glass jars containing cookies for sale in Tokyo.jpg",
|
||||
"width": 4037,
|
||||
"height": 6055,
|
||||
|
|
@ -62,7 +52,7 @@
|
|||
"queries": 1
|
||||
},
|
||||
{
|
||||
"file": "006_1920px-Interior_of_JUSCO_Shingu_Shopping_Center_20100213c.JP",
|
||||
"file": "006_1920px-Interior_of_JUSCO_Shingu_Shopping_Center_20100213c.jpg",
|
||||
"title": "File:Interior of JUSCO Shingu Shopping Center 20100213c.JPG",
|
||||
"width": 3648,
|
||||
"height": 2736,
|
||||
|
|
@ -81,16 +71,6 @@
|
|||
"source": "https://commons.wikimedia.org/wiki/File%3AJapan_earthquake_store_shelves.jpg",
|
||||
"queries": 1
|
||||
},
|
||||
{
|
||||
"file": "008_page1-960px-Judge_v067_n1726_(1914-11-14.Leslie-Judge)_(Darw",
|
||||
"title": "File:Judge v067 n1726 (1914-11-14.Leslie-Judge) (Darwin-IA).djvu",
|
||||
"width": 1192,
|
||||
"height": 1500,
|
||||
"license": "Public domain",
|
||||
"artist": "",
|
||||
"source": "https://commons.wikimedia.org/wiki/File%3AJudge_v067_n1726_%281914-11-14.Leslie-Judge%29_%28Darwin-IA%29.djvu",
|
||||
"queries": 1
|
||||
},
|
||||
{
|
||||
"file": "009_Milk_(2655574568).jpg",
|
||||
"title": "File:Milk (2655574568).jpg",
|
||||
|
|
@ -111,16 +91,6 @@
|
|||
"source": "https://commons.wikimedia.org/wiki/File%3ANatsuge-mikado_okashi.jpg",
|
||||
"queries": 1
|
||||
},
|
||||
{
|
||||
"file": "011_page1-1920px-Overland_through_Asia._by_Knox,_Thomas_Wallace.",
|
||||
"title": "File:Overland through Asia. by Knox, Thomas Wallace.djvu",
|
||||
"width": 2596,
|
||||
"height": 4212,
|
||||
"license": "Public domain",
|
||||
"artist": "<bdi><a href=\"https://en.wikipedia.org/wiki/en:Thomas_W._Knox\" class=\"extiw\" title=\"w:en:Thomas W. Knox\"><span title=\"American writer (1835-1896)\">Thomas W. Knox</span></a></bdi>",
|
||||
"source": "https://commons.wikimedia.org/wiki/File%3AOverland_through_Asia._by_Knox%2C_Thomas_Wallace.djvu",
|
||||
"queries": 1
|
||||
},
|
||||
{
|
||||
"file": "012_1920px-Supermarket_in_2006_(646332846).jpg",
|
||||
"title": "File:Supermarket in 2006 (646332846).jpg",
|
||||
|
|
@ -131,16 +101,6 @@
|
|||
"source": "https://commons.wikimedia.org/wiki/File%3ASupermarket_in_2006_%28646332846%29.jpg",
|
||||
"queries": 1
|
||||
},
|
||||
{
|
||||
"file": "013_page1-1280px-Tangled_Hair.djvu.jpg",
|
||||
"title": "File:Tangled Hair.djvu",
|
||||
"width": 1371,
|
||||
"height": 2129,
|
||||
"license": "Public domain",
|
||||
"artist": "<bdi><a href=\"https://en.wikipedia.org/wiki/en:Yosano_Akiko\" class=\"extiw\" title=\"w:en:Yosano Akiko\"><span title=\"Japanese tanka poet (1878–1942)\">Akiko Yosano</span></a></bdi>",
|
||||
"source": "https://commons.wikimedia.org/wiki/File%3ATangled_Hair.djvu",
|
||||
"queries": 1
|
||||
},
|
||||
{
|
||||
"file": "014_1920px-「ペヤング」予想通りの大売れ！_(18634213682).jpg",
|
||||
"title": "File:「ペヤング」予想通りの大売れ！ (18634213682).jpg",
|
||||
|
|
@ -202,7 +162,7 @@
|
|||
"queries": 1
|
||||
},
|
||||
{
|
||||
"file": "020_1920px-申し訳ございませんが_ごめんなさい_お魚本日の調理は終了いたしました。_どうぞよろしくお願いします_(11",
|
||||
"file": "020_1920px-申し訳ございませんが_ごめんなさい_お魚本日の調理は終了いたしました。_どうぞよろしくお願いします_(11.jpg",
|
||||
"title": "File:申し訳ございませんが ごめんなさい お魚本日の調理は終了いたしました。 どうぞよろしくお願いします (11331761984).jpg",
|
||||
"width": 4000,
|
||||
"height": 2672,
|
||||
|
|
|
|||
|
|
@ -2,297 +2,325 @@
|
|||
"dataset": "wikimedia-commons",
|
||||
"catalog": "stock-lopia-clean",
|
||||
"note": "Unrelated shelves: every accept is a false accept by construction.",
|
||||
"images": 41,
|
||||
"detections": 1876,
|
||||
"images": 45,
|
||||
"detections": 2133,
|
||||
"false_accepts": 7,
|
||||
"false_accept_rate": 0.0037,
|
||||
"false_accept_rate": 0.0033,
|
||||
"rows": [
|
||||
{
|
||||
"file": "000_1920px-198円_中華弁当_2016_(25800617695).jpg",
|
||||
"title": "File:198円 中華弁当 2016 (25800617695).jpg",
|
||||
"detections": 2,
|
||||
"false_accepts": 0,
|
||||
"ms": 813
|
||||
"ms": 1774
|
||||
},
|
||||
{
|
||||
"file": "001_1920px-2011年4月の牛乳_(5588310773).jpg",
|
||||
"title": "File:2011年4月の牛乳 (5588310773).jpg",
|
||||
"detections": 78,
|
||||
"false_accepts": 1,
|
||||
"ms": 2983
|
||||
"ms": 4553
|
||||
},
|
||||
{
|
||||
"file": "002_1920px-20140909-0252_Costa_Mesa_Mitsuwa.JPG",
|
||||
"title": "File:20140909-0252 Costa Mesa Mitsuwa.JPG",
|
||||
"detections": 95,
|
||||
"false_accepts": 0,
|
||||
"ms": 2204
|
||||
"ms": 3025
|
||||
},
|
||||
{
|
||||
"file": "003_1920px-Aomori_Home_(4721758999).jpg",
|
||||
"title": "File:Aomori Home (4721758999).jpg",
|
||||
"detections": 76,
|
||||
"false_accepts": 0,
|
||||
"ms": 2995
|
||||
"ms": 3634
|
||||
},
|
||||
{
|
||||
"file": "004_1920px-Asakusa_15_-_making_rice_crackers_the_old_way_(155797.jpg",
|
||||
"title": "File:Asakusa 15 - making rice crackers the old way (15579727827).jpg",
|
||||
"detections": 14,
|
||||
"false_accepts": 0,
|
||||
"ms": 162
|
||||
},
|
||||
{
|
||||
"file": "005_1920px-Asakusa_16_-_making_rice_crackers_the_old_way_(155794.jpg",
|
||||
"title": "File:Asakusa 16 - making rice crackers the old way (15579472088).jpg",
|
||||
"detections": 3,
|
||||
"false_accepts": 0,
|
||||
"ms": 131
|
||||
},
|
||||
{
|
||||
"file": "006_1920px-Asakusa_17_-_making_rice_crackers_the_old_way_(155794.jpg",
|
||||
"title": "File:Asakusa 17 - making rice crackers the old way (15579471588).jpg",
|
||||
"detections": 4,
|
||||
"false_accepts": 0,
|
||||
"ms": 132
|
||||
},
|
||||
{
|
||||
"file": "007_1920px-Asakusa_19_-_the_woman_selling_the_rice_crackers_(157.jpg",
|
||||
"title": "File:Asakusa 19 - the woman selling the rice crackers (15764889785).jpg",
|
||||
"detections": 3,
|
||||
"false_accepts": 0,
|
||||
"ms": 67
|
||||
},
|
||||
{
|
||||
"file": "008_1920px-Azabudai-Hills---2025-04-05_015.jpg",
|
||||
"title": "File:Azabudai-Hills---2025-04-05 015.jpg",
|
||||
"detections": 37,
|
||||
"false_accepts": 2,
|
||||
"ms": 2272
|
||||
"ms": 2113
|
||||
},
|
||||
{
|
||||
"file": "010_1920px-Breathalyzers_in_a_MaxValu_supermarket.jpg",
|
||||
"title": "File:Breathalyzers in a MaxValu supermarket.jpg",
|
||||
"detections": 5,
|
||||
"false_accepts": 0,
|
||||
"ms": 306
|
||||
"ms": 368
|
||||
},
|
||||
{
|
||||
"file": "011_1920px-Chocolate_in_Japan_(5137097169).jpg",
|
||||
"title": "File:Chocolate in Japan (5137097169).jpg",
|
||||
"detections": 65,
|
||||
"false_accepts": 1,
|
||||
"ms": 2295
|
||||
"ms": 3829
|
||||
},
|
||||
{
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{
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||||
"file": "027_page1-1920px-Mary_Barton_-_1848_-_Volume_1.djvu.jpg",
|
||||
"title": "File:Mary Barton - 1848 - Volume 1.djvu",
|
||||
"width": 2148,
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"height": 3615,
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"license": "Public domain",
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||||
"artist": "<div class=\"fn value\">\nElizabeth Cleghorn Gaskell</div>",
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"source": "https://commons.wikimedia.org/wiki/File%3AMary_Barton_-_1848_-_Volume_1.djvu",
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{
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"file": "029_1920px-Miyazu_Mipple_1F_2021-04_ac.jpg",
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"title": "File:Miyazu Mipple 1F 2021-04 ac.jpg",
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||||
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|
@ -302,7 +262,7 @@
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|||
"queries": 1
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{
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"file": "034_1920px-Rice_toppings_&_additional_ingredients_(5846028686).j",
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"file": "034_1920px-Rice_toppings_&_additional_ingredients_(5846028686).jpg",
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"title": "File:Rice toppings & additional ingredients (5846028686).jpg",
|
||||
"width": 3072,
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"height": 2304,
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|
@ -391,36 +351,6 @@
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|||
"source": "https://commons.wikimedia.org/wiki/File%3ATasty_Food_Abundance_in_Healthy_Europe.png",
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"queries": 1
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},
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"file": "044_page1-1920px-The_Philippine_Islands,_1493-1803_(Volume_27).d",
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"title": "File:The Philippine Islands, 1493-1803 (Volume 27).djvu",
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"width": 2181,
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"height": 3622,
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"license": "Public domain",
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||||
"artist": "<div class=\"fn value\">\nd. 1911 Emma Helen Blair</div>",
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"source": "https://commons.wikimedia.org/wiki/File%3AThe_Philippine_Islands%2C_1493-1803_%28Volume_27%29.djvu",
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"queries": 1
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{
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"file": "045_page1-1920px-The_Philippine_Islands,_1493-1803_(Volume_38).d",
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"title": "File:The Philippine Islands, 1493-1803 (Volume 38).djvu",
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"width": 2261,
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"height": 3587,
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"license": "Public domain",
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"artist": "<div class=\"fn value\">\nd. 1911 Emma Helen Blair</div>",
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"source": "https://commons.wikimedia.org/wiki/File%3AThe_Philippine_Islands%2C_1493-1803_%28Volume_38%29.djvu",
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"queries": 1
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{
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"file": "046_page1-1920px-Things_Japanese_(1905).djvu.jpg",
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"title": "File:Things Japanese (1905).djvu",
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"width": 2332,
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"height": 3684,
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"license": "Public domain",
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"artist": "Basil Hall Chamberlain (1850-1935)",
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"source": "https://commons.wikimedia.org/wiki/File%3AThings_Japanese_%281905%29.djvu",
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"queries": 1
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},
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{
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"file": "047_1920px-Tokyo---Ebisu-and-Shibuya---2026-02-04_162.jpg",
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"title": "File:Tokyo---Ebisu-and-Shibuya---2026-02-04 162.jpg",
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|
|
@ -441,16 +371,6 @@
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"source": "https://commons.wikimedia.org/wiki/File%3ATokyo---Ebisu-and-Shibuya---2026-02-04_167.jpg",
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"queries": 1
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"file": "049_page1-1280px-Tono-Bungay.djvu.jpg",
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"title": "File:Tono-Bungay.djvu",
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"width": 1757,
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"height": 2916,
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"license": "Public domain",
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"artist": "<div class=\"fn value\">\nWells, H. G. (Herbert George), 1866-1946</div>",
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"source": "https://commons.wikimedia.org/wiki/File%3ATono-Bungay.djvu",
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"queries": 1
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{
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"file": "050_1920px-Umami_Snack_(41957534).jpeg",
|
||||
"title": "File:Umami Snack (41957534).jpeg",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue