383ff9cf2d
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>
61 lines
3 KiB
Python
61 lines
3 KiB
Python
"""
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Enroll a shelf: the products read off its crops become the store's catalog.
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This is the onboarding flow a real store would go through once - detect the facings,
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name the products, and keep their crops as the gallery that recognition matches
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against. The two demo shelves were built with it; the product lists live in this file
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because reading a contact sheet is a human step.
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python code/build_store_from_shelf.py
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"""
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import json, os, shutil, sys
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sys.path.insert(0, "code")
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import cv2
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ROOT = os.path.expanduser("~/dorayaki-data/shelf-images")
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manifest = json.load(open(os.path.join(ROOT, "manifest.json")))
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entry = next(e for e in manifest["images"] if "Azabudai-Hills" in e["file"])
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image = cv2.imread(os.path.join(ROOT, entry["file"]))
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height, width = image.shape[:2]
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scale = min(1.0, 1600.0 / max(height, width))
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image = cv2.resize(image, (int(width*scale), int(height*scale)), interpolation=cv2.INTER_AREA)
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boxes = json.load(open("/tmp/dorayaki-verify/mediya-boxes.json"))["boxes"]
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# read off the numbered contact sheet; prices come from the shelf's own tags
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PRODUCTS = [
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("sku_m01", "小岩井 低脂肪牛乳", [9, 23, 31]),
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("sku_m02", "荻山酪農 カフェ・オ・レ", [10]),
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("sku_m03", "丹那 3.6牛乳", [11]),
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("sku_m04", "木次 ミルクコーヒー", [12]),
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("sku_m05", "木次 パスチャライズ牛乳", [13]),
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("sku_m06", "キスキ ブラウン・スイス牛乳", [14]),
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("sku_m07", "丹那 飲んでやさしいヨーグルト", [15]),
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("sku_m08", "農場3.7牛乳 特選", [24]),
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("sku_m09", "MINOR FIGURES OAT", [26]),
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("sku_m10", "北海道 低脂肪乳", [27]),
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("sku_m11", "タカナシ 低温殺菌牛乳", [28]),
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("sku_m12", "RUDE HEALTH Oat Organic", [29]),
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("sku_m13", "小岩井 無脂肪牛乳", [30]),
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("sku_m14", "きすき ノンホモ牛乳", [8]),
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]
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PRICES = {"sku_m01": 186, "sku_m02": 397, "sku_m03": 214, "sku_m04": 195, "sku_m05": 240,
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"sku_m06": 254, "sku_m07": 225, "sku_m08": 216, "sku_m09": 300, "sku_m10": 214,
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"sku_m11": 396, "sku_m12": 400, "sku_m13": 178, "sku_m14": 198}
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store = "stock-mediya"
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shutil.rmtree(store, ignore_errors=True)
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os.makedirs(store, exist_ok=True)
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rows = ["SKU_ID,product_name,receipt_date,expiration_date,number_in,number_out,number_on,image_link,price"]
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for sku, name, indices in PRODUCTS:
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for position, index in enumerate(indices):
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box = boxes[index]
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x1, y1, x2, y2 = [int(v) for v in box]
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crop = image[max(0,y1):y2, max(0,x1):x2]
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if crop.size == 0:
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continue
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suffix = "" if position == 0 else f"_{position}"
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cv2.imwrite(os.path.join(store, f"{sku}{suffix}.jpeg"), crop)
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rows.append(f"{sku},{name},2026-09-25,2026-10-20,24,8,16,'',{PRICES[sku]}")
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open(os.path.join(store, "stock.csv"), "w").write("\n".join(rows) + "\n")
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print(f"{store}: {len(PRODUCTS)} products, "
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f"{sum(len(i) for _, _, i in PRODUCTS)} views, csv written")
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