fix(identity): calibrate both accept lines against unrelated shelves

The gates were tuned on image6 alone, where every crop is a facing of a product the shelf actually holds. Wikimedia supermarket photos of milk, soy and snack shelves share nothing with the LOPIA catalog, so every accept there is a false accept by construction - and at the old lines there were 83 of them. Text lane: TEXT_MIN_SHARED 2 -> 4. The vocabulary filter drops bigrams no name contains, so a short garbage read can end up with a perfect ratio on two coincidental pairs. 20 of 26 text-lane answers on those shelves rested on exactly two: '避くておいしい' named sku_l12 through おい/いし. Local lane: LOCAL_ACCEPT 2.0 -> 4.0. The residual accepts were geometric matches with no text overlap at all, scoring 2.26-4.81. Measured, 2297 distractor crops and 109 image6 facings: accept floor image6 shown/correct precision false accepts 2.0 2 54 / 49 90.7% 83 (3.61%) 2.0 4 52 / 48 92.3% 30 (1.31%) 4.0 4 44 / 42 95.5% 8 (0.35%) 5.0 6 36 / 36 100.0% 0 (0.00%) 4.0/4 is the only region passing both G2 (>=95% precision) and G3 (<=1% far-unknown) with coverage left. On the shipped evaluator: 66% end-to-end at 95% precision, 2 false accepts, against 77%/91%/5 before. The two tests that pinned the old floors now pin the new ones, with the reads that separate them.
2026-09-29 04:08:59 +09:00 · 2026-09-29 04:08:59 +09:00 · 07de2bd37d
commit 07de2bd37d
parent d13ce94123
4 changed files with 166 additions and 22 deletions
--- a/recognization/code/eval_distractors.py
+++ b/recognization/code/eval_distractors.py
@ -0,0 +1,116 @@
+"""Open-world false-accept probe: unrelated shelf photos through the shipped lane.
+
+data/external-shelves/ holds Wikimedia Commons supermarket photos of shelves
+that share nothing with the LOPIA catalog. Every accept here is a false accept
+by construction, which is what makes the set useful: the image6 shelf can only
+measure false accepts on the products it happens to contain.
+
+The stored eval.json predates the open-set gate, so it is re-run rather than
+trusted.
+
+    .venv/bin/python code/eval_distractors.py --stock stock-lopia-clean \
+        data/external-shelves/wikimedia-commons
+"""
+
+from __future__ import annotations
+
+import argparse
+import glob
+import json
+import os
+import sys
+import time
+from typing import Any, Dict, List
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+if current_dir not in sys.path:
+    sys.path.insert(0, current_dir)
+
+import cv2  # noqa: E402
+
+from eval_identity import LOCAL_ACCEPT, LocalLaneAdapter  # noqa: E402
+
+
+def main(argv: List[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("directories", nargs="+")
+    parser.add_argument("--stock", default="stock-lopia-clean")
+    parser.add_argument("--write", action="store_true")
+    parser.add_argument("--limit", type=int, default=0, help="images per set (0 = all)")
+    args = parser.parse_args(argv)
+
+    from engine import build_default_engine, EngineSettings
+
+    engine = build_default_engine(EngineSettings(stock_dir=args.stock))
+    lane = LocalLaneAdapter(args.stock)
+    for directory in args.directories:
+        manifest_path = os.path.join(directory, "manifest.json")
+        try:
+            with open(manifest_path) as handle:
+                manifest = json.load(handle)
+        except (OSError, ValueError):
+            manifest = {"images": []}
+        meta = {row["file"]: row for row in manifest.get("images", [])}
+        rows: List[Dict[str, Any]] = []
+
+        paths = sorted(
+            path for path in glob.glob(os.path.join(directory, "*"))
+            if os.path.splitext(path)[1].lower() in (".jpg", ".jpeg", ".png")
+        )
+        if args.limit:
+            paths = paths[: args.limit]
+        for path in paths:
+            name = os.path.basename(path)
+            image = cv2.imread(path)
+            if image is None:
+                continue
+            started = time.perf_counter()
+            detections = engine.detect(image)
+            accepted = 0
+            for detection in detections:
+                box = detection.get("box") or detection.get("bbox")
+                if not box:
+                    continue
+                x1, y1, x2, y2 = [int(v) for v in box]
+                crop = image[max(0, y1):y2, max(0, x1):x2]
+                if crop.size == 0:
+                    continue
+                decision = lane.decide(crop, lane.rank(crop), accept=LOCAL_ACCEPT,
+                                       margin=0.0, scale="full")
+                if decision.get("best_sku"):
+                    accepted += 1
+            rows.append({
+                "file": name,
+                "title": meta.get(name, {}).get("title", ""),
+                "detections": len(detections),
+                "false_accepts": accepted,
+                "ms": round((time.perf_counter() - started) * 1000),
+            })
+
+        detections = sum(r["detections"] for r in rows)
+        accepts = sum(r["false_accepts"] for r in rows)
+        report = {
+            "dataset": os.path.basename(directory.rstrip("/")),
+            "catalog": args.stock,
+            "note": "Unrelated shelves: every accept is a false accept by construction.",
+            "images": len(rows),
+            "detections": detections,
+            "false_accepts": accepts,
+            "false_accept_rate": round(accepts / detections, 4) if detections else None,
+            "rows": rows,
+        }
+        print(f"{report['dataset']}: {len(rows)} images, {detections} detections, "
+              f"{accepts} false accepts ({report['false_accept_rate'] or 0:.2%})")
+        for row in sorted(rows, key=lambda r: -r["false_accepts"])[:5]:
+            if row["false_accepts"]:
+                print(f"    {row['false_accepts']:>3}  {row['file'][:55]}")
+        if args.write:
+            out = os.path.join(directory, "eval_result.json")
+            with open(out, "w") as handle:
+                json.dump(report, handle, ensure_ascii=False, indent=1)
+            print(f"    wrote {out}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())