Recognizer v0.3.6: HDR-friendly empty + lower threshold + debug dump

User reports all 34 cells classified as 미인식 with score 0.00 even
when the grid was correctly cropped. Multiple compounding issues:

1. _is_empty required mean<60 (dark) AND std<14. HDR/bright captures
   produce pinkish empty slots with mean ~150-180, so even empty cells
   fell through to template matching. Drop the mean check; uniformity
   alone (std<18 grayscale, std<22 per-channel) is the real signal.

2. Score 0.00 across the board strongly suggests templates list was
   empty (only path that returns exactly 0.0). Track per-bucket load
   counts (slabs_ok/fail, artifacts_ok/fail) and surface them in the
   GUI status bar so a CDN failure is immediately visible. Currently
   no signal at all on download failure.

3. min_score 0.55 was tuned against simulator-clean renders. Real game
   captures have decorative cell borders, stack-count badges in
   corners, HDR shader effects. Lower to 0.35 and inset cell crops by
   16% on each side before matching to skip the decorative frame.

4. Add 디버그 저장 button + dump_debug() that saves screenshot.png,
   bbox_crop.png, cells/<row>-<col>.png, and report.txt with top-3
   matches per cell to %LOCALAPPDATA%/sephiria_inv/debug/<timestamp>/.
   Lets us iterate on tuning from real captures without round-tripping
   raw screenshots through chat each time.

sephiria_inv/gui.py

@@ -413,6 +413,7 @@ class ScreenshotFrame(ttk.Frame):
         ttk.Button(ctl, text="전체 화면 캡처", command=self._capture_screen).pack(side="left", padx=4)
         ttk.Button(ctl, text="파일 열기…", command=self._open_file).pack(side="left", padx=4)
         ttk.Button(ctl, text="영역 재지정", command=self._reselect_bbox).pack(side="left", padx=4)
+        ttk.Button(ctl, text="디버그 저장", command=self._save_debug).pack(side="left", padx=4)
         ttk.Button(ctl, text="이 구성으로 계산", command=self._confirm).pack(side="right")
 
         self.status = ttk.Label(
@@ -506,6 +507,8 @@ class ScreenshotFrame(ttk.Frame):
                 # First call may download a lot — keep artifacts on (user wants them)
                 warm_templates(include_artifacts=True)
                 self._templates_warmed = True
+            from .recognizer import load_stats
+            self._tpl_stats = load_stats()
             slot_num = int(round(self.slot_var.get()))
             cells = recognize_image(
                 self.image, self.bbox,
@@ -515,6 +518,27 @@ class ScreenshotFrame(ttk.Frame):
         except Exception as e:
             self.after(0, lambda: messagebox.showerror("인식 실패", str(e)))
 
+    def _save_debug(self) -> None:
+        if self.image is None or not self.bbox:
+            messagebox.showinfo("안내", "먼저 캡처 + 영역 지정을 해주세요.")
+            return
+        import os
+        from datetime import datetime
+        from .recognizer import dump_debug
+        base = os.environ.get("LOCALAPPDATA") or os.path.expanduser("~")
+        out_dir = os.path.join(base, "sephiria_inv", "debug",
+                               datetime.now().strftime("%Y%m%d-%H%M%S"))
+        try:
+            slot_num = int(round(self.slot_var.get()))
+            report = dump_debug(self.image, self.bbox, out_dir, slot_num=slot_num)
+            messagebox.showinfo(
+                "디버그 저장 완료",
+                f"폴더에 screenshot.png, bbox_crop.png, cells/, report.txt 가 저장됨.\n\n{out_dir}\n\n"
+                f"report: {report}",
+            )
+        except Exception as e:
+            messagebox.showerror("디버그 저장 실패", str(e))
+
     def _show_cells(self, cells: List[CellResult]) -> None:
         self.cells = cells
         for c in self.preview_inner.winfo_children():
@@ -541,10 +565,22 @@ class ScreenshotFrame(ttk.Frame):
                 kind_counts[effective["kind"]] = kind_counts.get(effective["kind"], 0) + 1
                 self._make_cell(y, x, slot_id, effective)
 
+        stats = getattr(self, "_tpl_stats", None) or {}
+        tpl_line = ""
+        if stats:
+            total_loaded = stats.get("slabs_ok", 0) + stats.get("artifacts_ok", 0)
+            total_failed = stats.get("slabs_fail", 0) + stats.get("artifacts_fail", 0)
+            tpl_line = (
+                f"\n템플릿: 슬랩 {stats.get('slabs_ok',0)}/{stats.get('slabs_ok',0)+stats.get('slabs_fail',0)} · "
+                f"아티팩트 {stats.get('artifacts_ok',0)}/{stats.get('artifacts_ok',0)+stats.get('artifacts_fail',0)}"
+            )
+            if total_loaded == 0:
+                tpl_line += "  (CDN 다운로드 실패 — 인터넷 연결/방화벽 확인)"
         msg = (
             f"석판 {kind_counts.get('slab', 0)} · 아티팩트 {kind_counts.get('artifact', 0)} · "
             f"빈칸 {kind_counts.get('empty', 0)} · 합쳐진(?) {kind_counts.get('merged', 0)} · "
-            f"미인식 {kind_counts.get('unknown', 0)}\n"
+            f"미인식 {kind_counts.get('unknown', 0)}"
+            f"{tpl_line}\n"
             "셀을 클릭하면 종류/값을 교정할 수 있습니다. 끝나면 [이 구성으로 계산]."
         )
         self.status["text"] = msg

sephiria_inv/recognizer.py

@@ -80,17 +80,24 @@ _TEMPLATE_CACHE: List[_Template] = []
 _CACHE_BUILT = False
 
 
+_LAST_LOAD_STATS: Dict[str, int] = {"slabs_ok": 0, "slabs_fail": 0,
+                                    "artifacts_ok": 0, "artifacts_fail": 0}
+
+
 def _build_templates(*, include_artifacts: bool = True) -> List[_Template]:
     """Build (and cache) the full template list. Lazy because download is slow."""
     global _CACHE_BUILT
     if _CACHE_BUILT and _TEMPLATE_CACHE:
         return _TEMPLATE_CACHE
     out: List[_Template] = []
+    s_ok = s_fail = a_ok = a_fail = 0
     # Slabs: 4 rotations for rotatable, 1 otherwise
     for s in SLABS:
         img = fetch_slab_image(s.image)
         if img is None:
+            s_fail += 1
             continue
+        s_ok += 1
         base = _on_dark(img)
         rotations = (0, 1, 2, 3) if s.rotate else (0,)
         for r in rotations:
@@ -100,9 +107,13 @@ def _build_templates(*, include_artifacts: bool = True) -> List[_Template]:
         for a in ARTIFACTS:
             img = fetch_artifact_image(a.image)
             if img is None:
+                a_fail += 1
                 continue
+            a_ok += 1
             base = _on_dark(img)
             out.append(_Template("artifact", a.value, 0, _to_feat(base)))
+    _LAST_LOAD_STATS.update({"slabs_ok": s_ok, "slabs_fail": s_fail,
+                             "artifacts_ok": a_ok, "artifacts_fail": a_fail})
     _TEMPLATE_CACHE.clear()
     _TEMPLATE_CACHE.extend(out)
     _CACHE_BUILT = True
@@ -117,24 +128,51 @@ def warm_templates(*, include_artifacts: bool = True) -> int:
     return len(_build_templates(include_artifacts=include_artifacts))
 
 
+def load_stats() -> Dict[str, int]:
+    """Return last template load counts: slabs_ok, slabs_fail, artifacts_ok, artifacts_fail."""
+    return dict(_LAST_LOAD_STATS)
+
+
 # ---------- cell classification ----------
 
 def _is_empty(cell: Image.Image) -> bool:
-    """Heuristic: empty slots are dark and ~uniform."""
+    """Heuristic: empty slots are uniform color (any brightness).
+
+    Drops the dark-only assumption so HDR / bright-monitor captures with
+    pinkish slot backgrounds still detect as empty. Uniformity is the
+    actual invariant — empty slots have low std-dev whatever the hue.
+    """
     g = np.asarray(cell.convert("L"), dtype=np.float32)
-    return bool(g.mean() < 60.0 and g.std() < 14.0)
+    rgb = np.asarray(cell.convert("RGB"), dtype=np.float32)
+    chan_std = float(rgb.reshape(-1, 3).std(axis=0).mean())
+    return bool(g.std() < 18.0 and chan_std < 22.0)
+
+
+def _inset(cell: Image.Image, ratio: float = 0.16) -> Image.Image:
+    """Trim decorative borders / corner badges before template matching.
+
+    The in-game slot has chunky frame ornaments and a stack-count badge in
+    a corner. Templates are clean icons. Cropping ~16% off every side
+    aligns the comparable inner art and removes the badge area in most
+    games.
+    """
+    w, h = cell.size
+    dx = int(w * ratio)
+    dy = int(h * ratio)
+    return cell.crop((dx, dy, w - dx, h - dy))
 
 
 def _classify(
     cell: Image.Image,
     templates: List[_Template],
     *,
-    min_score: float = 0.55,
+    min_score: float = 0.35,
 ) -> Tuple[str, Optional[str], int, float]:
     """Return (kind, value, rotation, score)."""
     if _is_empty(cell):
         return "empty", None, 0, 1.0
-    feat = _to_feat(cell)
+    inner = _inset(cell)
+    feat = _to_feat(inner)
     # Stack template features into a matrix for one big dot-product
     if not templates:
         return "unknown", None, 0, 0.0
@@ -148,6 +186,27 @@ def _classify(
     return t.kind, t.value, t.rotation, best
 
 
+def _classify_with_top(
+    cell: Image.Image,
+    templates: List[_Template],
+    *,
+    top_k: int = 3,
+) -> Tuple[str, Optional[str], int, float, List[Tuple[str, str, int, float]]]:
+    """Like _classify but also returns the top-k matches for debug dumps."""
+    if _is_empty(cell):
+        return "empty", None, 0, 1.0, []
+    if not templates:
+        return "unknown", None, 0, 0.0, []
+    feat = _to_feat(_inset(cell))
+    M = np.stack([t.feat for t in templates], axis=0)
+    scores = M @ feat
+    order = np.argsort(-scores)[:top_k]
+    top = [(templates[i].kind, templates[i].value, templates[i].rotation,
+            float(scores[i])) for i in order]
+    kind, value, rot, score = _classify(cell, templates)
+    return kind, value, rot, score, top
+
+
 # ---------- public API ----------
 
 def recognize_image(
@@ -156,7 +215,7 @@ def recognize_image(
     *,
     slot_num: int = 34,
     include_artifacts: bool = True,
-    min_score: float = 0.55,
+    min_score: float = 0.35,
 ) -> List[CellResult]:
     """Slice img[bbox] into a 6-col grid and classify each cell.
 
@@ -184,6 +243,59 @@ def recognize_image(
     return out
 
 
+def dump_debug(
+    img: Image.Image,
+    bbox: Tuple[int, int, int, int],
+    out_dir: str,
+    *,
+    slot_num: int = 34,
+    include_artifacts: bool = True,
+) -> str:
+    """Save full screenshot, bbox crop, every cell crop and a top-3 match
+    report to out_dir. Returns the path to the report file. Used to iterate
+    on recognizer tuning from real captures.
+    """
+    import os
+    os.makedirs(out_dir, exist_ok=True)
+    img.save(os.path.join(out_dir, "screenshot.png"))
+    L, T, R, B = bbox
+    crop = img.crop((L, T, R, B)).convert("RGB")
+    crop.save(os.path.join(out_dir, "bbox_crop.png"))
+    grid = generate_grid_config(slot_num)
+    if not grid:
+        return out_dir
+    rows = len(grid)
+    cell_w = (R - L) // GRID_COLS
+    cell_h = (B - T) // rows
+    templates = _build_templates(include_artifacts=include_artifacts)
+    stats = load_stats()
+    lines = [
+        f"bbox: {bbox}",
+        f"grid: {len(grid)} rows x {GRID_COLS} cols, slot_num={slot_num}",
+        f"cell px: {cell_w} x {cell_h}",
+        f"templates loaded: total={len(templates)} stats={stats}",
+        "",
+    ]
+    cells_dir = os.path.join(out_dir, "cells")
+    os.makedirs(cells_dir, exist_ok=True)
+    for row in grid:
+        y = row["rows"]
+        for x in range(row["cols"]):
+            cx0 = x * cell_w
+            cy0 = y * cell_h
+            cell = crop.crop((cx0, cy0, cx0 + cell_w, cy0 + cell_h))
+            cell.save(os.path.join(cells_dir, f"{y}-{x}.png"))
+            kind, value, rot, score, top = _classify_with_top(cell, templates)
+            top_s = ", ".join(f"{k}:{v}@r{r}={s:.3f}" for k, v, r, s in top)
+            lines.append(
+                f"  {y}-{x}: kind={kind} value={value} rot={rot} score={score:.3f} | top: {top_s}"
+            )
+    report = os.path.join(out_dir, "report.txt")
+    with open(report, "w", encoding="utf-8") as fh:
+        fh.write("\n".join(lines))
+    return report
+
+
 def recognize_file(
     path: str,
     bbox: Tuple[int, int, int, int],