feat: two demo shelves, enrolled, plus the price-card filter they forced out

Two real store photographs, chosen for density and for products that look different
from each other, each with its own enrolled store:

  demo-images/demo-image-1-mediya-dairy.jpg   CC0        MEDIYA dairy case
      21 products kept (13 price cards dropped), 16 recognised, all 14 products named
  demo-images/demo-image-2-milk-case.jpg      CC BY 2.0  supermarket milk case
      56 detections, 49 recognised, all 13 products named

Both were enrolled the way a store would be: the products were read off a contact
sheet of the detections, their crops became the store's gallery (stock-mediya,
stock-milk150), and a head was trained on them. `build_store_from_shelf.py` is that
flow.

The filter came out of the first image. Every Japanese supermarket photo has the same
artifact: the detector boxes the shelf-edge price cards as products, because a card is
roughly the shape of a pack - 13 of 34 detections on image 1 were cards. Their text
separates them cleanly: a card's characters are 30-44% digits and it prints the
tax-excluded price (本体), while a product's are 3-19% digits. Cards are now dropped
before classification, and the count is reported as `price_cards_dropped` rather than
hidden.

demo-images/README.md records how to run both, what the demo shows, and the honest
caveat: both shelves are enrolled, so the recognition rate is in-distribution - what
the app does on a shelf it has been onboarded to, not on one it has never seen.

175 tests pass.

Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>

recognization/demo-images/ATTRIBUTION.json

@@ -0,0 +1,16 @@
+[
+ {
+  "file": "demo-image-1-mediya-dairy.jpg",
+  "title": "File:Azabudai-Hills---2025-04-05 015.jpg",
+  "license": "CC0",
+  "artist": "<a href=\"//commons.wikimedia.org/wiki/User:RuinDig\" title=\"User:RuinDig\">RuinDig/Yuki Uchida</a>",
+  "source": "https://commons.wikimedia.org/wiki/File%3AAzabudai-Hills---2025-04-05_015.jpg"
+ },
+ {
+  "file": "demo-image-2-milk-case.jpg",
+  "title": "File:Milk (2655574568).jpg",
+  "license": "CC BY 2.0",
+  "artist": "<a rel=\"nofollow\" class=\"external text\" href=\"https://www.flickr.com/people/36014533@N00\">yumtan</a>",
+  "source": "https://commons.wikimedia.org/wiki/File%3AMilk_%282655574568%29.jpg"
+ }
+]

recognization/demo-images/README.md

@@ -0,0 +1,63 @@
+# The two demo shelves
+
+Two real store photographs, chosen because they are dense and their products are
+visually distinct, with a store catalog enrolled for each. Both come from Wikimedia
+Commons under licences that allow reuse; `demo-images/ATTRIBUTION.json` records the
+author, licence and source of each.
+
+| | image 1 | image 2 |
+| --- | --- | --- |
+| file | `demo-images/demo-image-1-mediya-dairy.jpg` | `demo-images/demo-image-2-milk-case.jpg` |
+| scene | a MEDIYA dairy case | a supermarket milk case |
+| licence | **CC0** | **CC BY 2.0** |
+| detections | 21 products (13 price cards filtered out) | 56 products |
+| recognised | **16, covering all 14 products** | **49, covering all 13 products** |
+| store | `stock-mediya/` | `stock-milk150/` |
+
+## How to run them
+
+```bash
+cd recognization
+YOLO_OFFLINE=1 .venv/bin/python code/app.py \
+  --port 8090 --identity gallery --live-conf 0.25 \
+  --stock-dir stock-mediya          # or stock-milk150
+```
+
+Then POST the image, or open the web client and use **Photo** mode:
+
+```bash
+curl -s -X POST --data-binary @demo-images/demo-image-1-mediya-dairy.jpg \
+  -H 'content-type: image/jpeg' \
+  'http://127.0.0.1:8090/v1/recognize?classify=true&conf=0.25&tags=true' | python3 -m json.tool | head -30
+```
+
+## What the demo shows
+
+* **Every product on the shelf is boxed and named** - 小岩井 低脂肪牛乳,
+  荻山酪農 カフェ・オ・レ, 丹那 3.6牛乳, 木次 ミルクコーヒー, 木次 パスチャライズ牛乳,
+  キスキ ブラウン・スイス牛乳, 丹那 飲んでやさしいヨーグルト, 農場3.7牛乳【特選】,
+  MINOR FIGURES OAT, 北海道 低脂肪乳, タカナシ 低温殺菌牛乳, RUDE HEALTH Oat Organic,
+  小岩井 無脂肪牛乳, きすき ノンホモ牛乳 on the first; 明治 おいしい牛乳, ゆうパック
+  優良牛乳, 酪農家限定 低温殺菌牛乳, 北海道牛乳, MEGMILK, 毎日牛乳, タカナシ 低温殺菌牛乳,
+  タカナシ 殺菌低脂肪牛乳, 毎日骨太, カルシウム入り低脂肪乳, 無脂肪牛乳, 低脂肪牛乳, ミルク
+  on the second.
+* **Price cards are not boxed as products.** The detector finds them (a card is the
+  shape of a pack); their text gives them away - a card's characters are 30-44 %
+  digits and it prints the tax-excluded price (本体), a product's are 3-19 %. Measured
+  on image 1: 13 cards dropped, 21 products kept.
+* **What it cannot name, it does not invent.** Boxes on the dark shelf strips and on
+  cards come back as "check" (amber), not as a product name.
+
+## The honest caveat, for the demo
+
+Both shelves were **enrolled** - their products were read off the shelf and their
+crops became the store's catalog and its fine-tuned head, which is the product's own
+onboarding flow (a store is enrolled once, then scanned). So the recognition rate
+above is **in-distribution**: it is what the app does on a shelf it has been enrolled
+on, not on a shelf it has never seen. On shelves it has not been enrolled on, the
+measured behaviour is: detection transfers (46 of 54 unseen photos), identity does not
+(off-the-shelf embeddings reach ~25 % top-1 on fine-grained products; see
+`docs/EXTERNAL-EVAL.md`).
+
+The first image is CC0, so it can be used in any recording without attribution; the
+second is CC BY 2.0 and needs the credit in `ATTRIBUTION.json` if it appears on screen.