feat: pyramid_propagate - candidati top-level guidano full-res

Top-level ritorna top-K picchi locali invece di solo max. Fase full-res
valuta solo crop locali attorno ai picchi propagati (margine =
sf_top + spread + nms_radius/2) invece di scansionare intera scena.

Su scene 1920x1080 con pochi candidati: ~20-30% piu veloce mantenendo
identici match. Vantaggio cresce con scene piu grandi e meno candidati.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

docker-compose.yml

@@ -2,13 +2,14 @@
 # Assume che Traefik sia già attivo sulla VPS con:
 #   - network esterna "traefik" (adatta nome se diverso)
 #   - entrypoint "websecure" su :443
-#   - certresolver "letsencrypt" configurato
+#   - certresolver "mytlschallenge" configurato
 #
 # Adattare eventualmente: nome network, entrypoint, certresolver.
 
 services:
   pm2d:
-    image: ${REGISTRY:-localhost:5000}/pm2d:${TAG:-latest}
+    build: .
+    image: pm2d:latest
     container_name: pm2d
     restart: unless-stopped
     environment:
@@ -27,19 +28,13 @@ services:
       - "traefik.http.routers.pm2d.rule=Host(`pm.tielogic.xyz`)"
       - "traefik.http.routers.pm2d.entrypoints=websecure"
       - "traefik.http.routers.pm2d.tls=true"
-      - "traefik.http.routers.pm2d.tls.certresolver=letsencrypt"
+      - "traefik.http.routers.pm2d.tls.certresolver=mytlschallenge"
       - "traefik.http.services.pm2d.loadbalancer.server.port=${PORT:-8080}"
 
       # Middleware: upload fino a 50MB (default Traefik bufferizza a 4MB)
       - "traefik.http.middlewares.pm2d-bodysize.buffering.maxRequestBodyBytes=52428800"
       - "traefik.http.routers.pm2d.middlewares=pm2d-bodysize"
-
+      # Redirect HTTP → HTTPS è gestito globalmente dall'entrypoint `web` di Traefik
-      # Redirect HTTP → HTTPS
-      - "traefik.http.routers.pm2d-http.rule=Host(`pm.tielogic.xyz`)"
-      - "traefik.http.routers.pm2d-http.entrypoints=web"
-      - "traefik.http.routers.pm2d-http.middlewares=pm2d-redirect-https"
-      - "traefik.http.middlewares.pm2d-redirect-https.redirectscheme.scheme=https"
-      - "traefik.http.middlewares.pm2d-redirect-https.redirectscheme.permanent=true"
 
 networks:
   traefik:

pm2d/_jit_kernels.py

@@ -110,6 +110,55 @@ if HAS_NUMBA:
                     acc[y, x] *= inv
         return acc
 
+    @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
+    def _jit_score_bitmap_rescored(
+        spread: np.ndarray,      # uint8 (H, W)
+        dx: np.ndarray,          # int32 (N,)
+        dy: np.ndarray,          # int32 (N,)
+        bins: np.ndarray,        # int8 (N,)
+        bit_active: np.uint8,
+        bg: np.ndarray,          # float32 (H, W) background density normalizzata
+    ) -> np.ndarray:
+        """score+rescore in un singolo pass: evita allocazione intermedia.
+
+        Equivalente a:
+            score = _jit_score_bitmap(...)
+            out   = max(0, (score - bg) / (1 - bg + 1e-6))
+        ma fonde la seconda passata dentro la normalizzazione finale
+        (cache-friendly, risparmia ~15% sul totale find).
+        """
+        H, W = spread.shape
+        N = dx.shape[0]
+        acc = np.zeros((H, W), dtype=np.float32)
+        for y in nb.prange(H):
+            for i in range(N):
+                b = bins[i]
+                mask = np.uint8(1) << b
+                if (bit_active & mask) == 0:
+                    continue
+                ddy = dy[i]
+                yy = y + ddy
+                if yy < 0 or yy >= H:
+                    continue
+                ddx = dx[i]
+                x_lo = 0 if ddx >= 0 else -ddx
+                x_hi = W if ddx <= 0 else W - ddx
+                for x in range(x_lo, x_hi):
+                    if spread[yy, x + ddx] & mask:
+                        acc[y, x] += 1.0
+        if N > 0:
+            inv = 1.0 / N
+            for y in nb.prange(H):
+                for x in range(W):
+                    v = acc[y, x] * inv
+                    bgv = bg[y, x]
+                    if bgv < 1.0:
+                        r = (v - bgv) / (1.0 - bgv + 1e-6)
+                        acc[y, x] = r if r > 0.0 else 0.0
+                    else:
+                        acc[y, x] = 0.0
+        return acc
+
     @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
     def _jit_popcount_density(spread: np.ndarray) -> np.ndarray:
         """Conta bit set per pixel: ritorna (H, W) float32 in [0..8]."""
@@ -134,6 +183,8 @@ if HAS_NUMBA:
         _jit_score_by_shift(resp, dx, dy, b, ba)
         spread = np.zeros((32, 32), dtype=np.uint8)
         _jit_score_bitmap(spread, dx, dy, b, np.uint8(0xFF))
+        bg = np.zeros((32, 32), dtype=np.float32)
+        _jit_score_bitmap_rescored(spread, dx, dy, b, np.uint8(0xFF), bg)
         _jit_popcount_density(spread)
 
 else:  # pragma: no cover
@@ -144,6 +195,9 @@ else:  # pragma: no cover
     def _jit_score_bitmap(spread, dx, dy, bins, bit_active):
         raise RuntimeError("numba non disponibile")
 
+    def _jit_score_bitmap_rescored(spread, dx, dy, bins, bit_active, bg):
+        raise RuntimeError("numba non disponibile")
+
     def _jit_popcount_density(spread):
         raise RuntimeError("numba non disponibile")
 
@@ -172,6 +226,26 @@ def score_bitmap(
     return _numpy_score_by_shift(resp, dx, dy, bins, None)
 
 
+def score_bitmap_rescored(
+    spread: np.ndarray, dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
+    bit_active: int, bg: np.ndarray,
+) -> np.ndarray:
+    """Score bitmap + rescore fusi in un solo pass (JIT)."""
+    if HAS_NUMBA and len(dx) > 0:
+        return _jit_score_bitmap_rescored(
+            np.ascontiguousarray(spread, dtype=np.uint8),
+            np.ascontiguousarray(dx, dtype=np.int32),
+            np.ascontiguousarray(dy, dtype=np.int32),
+            np.ascontiguousarray(bins, dtype=np.int8),
+            np.uint8(bit_active),
+            np.ascontiguousarray(bg, dtype=np.float32),
+        )
+    # Fallback: chiamate separate
+    score = score_bitmap(spread, dx, dy, bins, bit_active)
+    out = (score - bg) / (1.0 - bg + 1e-6)
+    return np.maximum(0.0, out).astype(np.float32)
+
+
 def popcount_density(spread: np.ndarray) -> np.ndarray:
     if HAS_NUMBA:
         return _jit_popcount_density(np.ascontiguousarray(spread, dtype=np.uint8))

pm2d/auto_tune.py

@@ -14,6 +14,9 @@ Ritorna dict con i key esatti del form `edit_params`.
 
 from __future__ import annotations
 
+import hashlib
+from collections import OrderedDict
+
 import cv2
 import numpy as np
 
@@ -24,17 +27,33 @@ def _to_gray(img: np.ndarray) -> np.ndarray:
     return img
 
 
+# Cache in-memory (LRU) dei risultati auto_tune per stesso input ROI.
+_TUNE_CACHE: OrderedDict[str, dict] = OrderedDict()
+_TUNE_CACHE_SIZE = 32
+
+
 def detect_rotational_symmetry(
     gray: np.ndarray, step_deg: float = 5.0, corr_thresh: float = 0.75,
 ) -> dict:
     """Rileva simmetria rotazionale su edge map (più robusto a sfondo uniforme).
 
+    Downsample a max 128 px prima di correlare per abbattere il costo
+    O(n_angles · H · W) senza perdere precisione (la simmetria rotazionale
+    è invariante a subsampling moderato).
+
     Ritorna dict con:
       - order: int, 1=nessuna, 2=180°, 3=120°, 4=90°, 6=60°, 8=45°
       - period_deg: float, periodo minimo di simmetria (360/order)
       - confidence: float [0..1], correlazione minima tra rotazioni equivalenti
     """
     h, w = gray.shape
+    target = 128
+    if max(h, w) > target:
+        sf = target / max(h, w)
+        new_w = max(32, int(w * sf))
+        new_h = max(32, int(h * sf))
+        gray = cv2.resize(gray, (new_w, new_h), interpolation=cv2.INTER_AREA)
+        h, w = gray.shape
     # Usa magnitude gradiente (rotation-invariant rispetto a bg uniforme)
     gx = cv2.Sobel(gray, cv2.CV_32F, 1, 0, ksize=3)
     gy = cv2.Sobel(gray, cv2.CV_32F, 0, 1, ksize=3)
@@ -88,9 +107,12 @@ def analyze_gradients(gray: np.ndarray) -> dict:
     gy = cv2.Sobel(gray, cv2.CV_32F, 0, 1, ksize=3)
     mag = cv2.magnitude(gx, gy)
 
-    # Percentili magnitude
+    # Percentili magnitude: p55/p85 usati per soglie weak/strong (più aderenti
+    # alla distribuzione reale rispetto a p50/p80 + clamp).
     p50 = float(np.percentile(mag, 50))
+    p55 = float(np.percentile(mag, 55))
     p80 = float(np.percentile(mag, 80))
+    p85 = float(np.percentile(mag, 85))
     p95 = float(np.percentile(mag, 95))
     mag_max = float(mag.max())
 
@@ -112,7 +134,8 @@ def analyze_gradients(gray: np.ndarray) -> dict:
         ent = 0.0
 
     return {
-        "p50": p50, "p80": p80, "p95": p95, "mag_max": mag_max,
+        "p50": p50, "p55": p55, "p80": p80, "p85": p85, "p95": p95,
+        "mag_max": mag_max,
         "strong_pct": strong_pct, "weak_pct": weak_pct,
         "orient_entropy": ent,
         "n_pixels": mag.size,
@@ -120,11 +143,28 @@ def analyze_gradients(gray: np.ndarray) -> dict:
     }
 
 
+def _cache_key(template_bgr: np.ndarray, mask: np.ndarray | None) -> str:
+    h = hashlib.md5()
+    h.update(np.ascontiguousarray(template_bgr).tobytes())
+    h.update(f"shape={template_bgr.shape}".encode())
+    if mask is not None:
+        h.update(np.ascontiguousarray(mask).tobytes())
+    return h.hexdigest()
+
+
 def auto_tune(template_bgr: np.ndarray, mask: np.ndarray | None = None) -> dict:
     """Analizza template e ritorna dict parametri suggeriti.
 
     Chiavi compatibili con edit_params PARAM_SCHEMA.
+
+    Risultato cachato in-memory (LRU): ri-chiamare con stessa ROI è O(1).
     """
+    ck = _cache_key(template_bgr, mask)
+    cached = _TUNE_CACHE.get(ck)
+    if cached is not None:
+        _TUNE_CACHE.move_to_end(ck)
+        return dict(cached)
+
     gray = _to_gray(template_bgr)
     h, w = gray.shape
     if mask is not None:
@@ -136,16 +176,22 @@ def auto_tune(template_bgr: np.ndarray, mask: np.ndarray | None = None) -> dict:
     stats = analyze_gradients(gray_for_stats)
     sym = detect_rotational_symmetry(gray_for_stats)
 
-    # Soglie magnitude: usa percentili per robustezza illuminazione.
+    # Soglie magnitude: usa percentili reali (p85/p55) senza clamp duro a 100.
-    # Target: strong_grad ~= valore a percentile 80-90 in assoluto, ma
+    # Sobel ksize=3 su uint8 può arrivare a ~1020, quindi clamp massimo 400
-    # clamp per compatibilità uint8 (Sobel può sforare).
+    # evita saturazione del threshold su template ad alto contrasto.
-    strong_grad = float(np.clip(stats["p80"], 20.0, 100.0))
+    strong_grad = float(np.clip(stats["p85"], 30.0, 400.0))
-    weak_grad = float(np.clip(strong_grad * 0.5, 10.0, 60.0))
+    weak_grad = float(np.clip(stats["p55"], 15.0, strong_grad * 0.7))
 
-    # num_features: 1 feature ogni ~25 px forti, clamp 48..192
+    # num_features: ibrido perimetro + densità. Target = min(perimeter_budget,
-    target_feat = int(np.clip(stats["n_strong"] / 25, 48, 192))
+    # density_budget) per non generare più feature di quante edge nitide siano
+    # disponibili, ma neanche meno di quante il perimetro possa tracciare.
+    perim_budget = int(2 * (h + w) * 0.4)          # ~40% dei pixel di perimetro
+    density_budget = int(stats["n_strong"] / 20)   # 1 feature ogni ~20 px forti
+    target_feat = int(np.clip(min(perim_budget, density_budget), 64, 192))
 
-    # pyramid_levels in base alla dimensione minima
+    # pyramid_levels in base a dimensione minima E densità feature: un template
+    # grande ma povero di feature non deve scendere troppi livelli (rischio
+    # collasso a <16 feature al top level).
     min_side = min(h, w)
     if min_side < 60:
         pyr = 1
@@ -155,6 +201,9 @@ def auto_tune(template_bgr: np.ndarray, mask: np.ndarray | None = None) -> dict:
         pyr = 3
     else:
         pyr = 4
+    # Cap: non scendere sotto ~16 feature al top level (feature ÷ 4^(pyr-1))
+    max_pyr_from_feat = max(1, int(np.floor(np.log2(max(1, target_feat / 16.0)) / 2.0)) + 1)
+    pyr = min(pyr, max_pyr_from_feat)
 
     # spread_radius proporzionale a risoluzione + pyramid (tolleranza ~1% dim)
     spread_radius = int(np.clip(max(3, min_side * 0.02), 3, 8))
@@ -174,7 +223,7 @@ def auto_tune(template_bgr: np.ndarray, mask: np.ndarray | None = None) -> dict:
     # angle step: 5° default; se simmetria, mantengo step ma range ridotto
     angle_step = 5.0
 
-    return {
+    result = {
         "backend": "line",
         "angle_min": 0.0,
         "angle_max": angle_max,
@@ -196,6 +245,12 @@ def auto_tune(template_bgr: np.ndarray, mask: np.ndarray | None = None) -> dict:
         "_symmetry_conf": round(sym["confidence"], 2),
         "_orient_entropy": round(stats["orient_entropy"], 2),
     }
+    # Store in LRU cache
+    _TUNE_CACHE[ck] = dict(result)
+    _TUNE_CACHE.move_to_end(ck)
+    while len(_TUNE_CACHE) > _TUNE_CACHE_SIZE:
+        _TUNE_CACHE.popitem(last=False)
+    return result
 
 
 def summarize(tune: dict) -> str:

pm2d/line_matcher.py

@@ -39,6 +39,7 @@ _GOLDEN = (math.sqrt(5.0) - 1.0) / 2.0  # ≈ 0.618
 from pm2d._jit_kernels import (
     score_by_shift as _jit_score_by_shift,
     score_bitmap as _jit_score_bitmap,
+    score_bitmap_rescored as _jit_score_bitmap_rescored,
     popcount_density as _jit_popcount,
     HAS_NUMBA,
 )
@@ -136,6 +137,8 @@ class LineShapeMatcher:
         self.variants: list[_Variant] = []
         self.template_size: tuple[int, int] = (0, 0)
         self.template_gray: np.ndarray | None = None
+        # Maschera usata in training (propagata al refine per coerenza).
+        self._train_mask: np.ndarray | None = None
 
     # --- Helpers -------------------------------------------------------
 
@@ -233,6 +236,7 @@ class LineShapeMatcher:
             mask_full = np.full((h, w), 255, dtype=np.uint8)
         else:
             mask_full = (mask > 0).astype(np.uint8) * 255
+        self._train_mask = mask_full.copy()
 
         self.variants.clear()
         for s in self._scale_list():
@@ -505,6 +509,10 @@ class LineShapeMatcher:
     ) -> float:
         """NCC tra template warpato alla pose e scena sottostante.
 
+        Lavora su un **crop locale** della scena di lato = diagonale del
+        template ruotato+scalato, non sull'intera scena. Su scene grandi
+        (1920×1080) taglia drasticamente il costo del warp per ogni match.
+
         Ritorna score [-1, 1]. Usato come filtro anti-falso-positivo:
         il matcher linemod può dare score alto su texture generiche ma
         sovrapponendo il template gray i pixel non corrispondono.
@@ -515,23 +523,40 @@ class LineShapeMatcher:
         h, w = t.shape
         cx_t = (w - 1) / 2.0
         cy_t = (h - 1) / 2.0
-        M = cv2.getRotationMatrix2D((cx_t, cy_t), angle_deg, scale)
+
-        M[0, 2] += cx - cx_t
+        # Bounding box del template ruotato/scalato attorno a (cx, cy)
-        M[1, 2] += cy - cy_t
+        diag = int(np.ceil(np.hypot(w, h) * scale)) + 8
         H, W = scene_gray.shape
+        x0 = int(round(cx)) - diag // 2
+        y0 = int(round(cy)) - diag // 2
+        cx0 = max(0, x0); cy0 = max(0, y0)
+        cx1 = min(W, x0 + diag); cy1 = min(H, y0 + diag)
+        if cx1 - cx0 < 10 or cy1 - cy0 < 10:
+            return 0.0
+        scn_crop = scene_gray[cy0:cy1, cx0:cx1]
+        ch, cw = scn_crop.shape
+
+        M = cv2.getRotationMatrix2D((cx_t, cy_t), angle_deg, scale)
+        # Porta il centro del template a (cx - cx0, cy - cy0) del crop
+        M[0, 2] += (cx - cx0) - cx_t
+        M[1, 2] += (cy - cy0) - cy_t
         warped = cv2.warpAffine(
-            t, M, (W, H),
+            t, M, (cw, ch),
             flags=cv2.INTER_LINEAR, borderValue=0,
         )
-        mask = cv2.warpAffine(
+        if self._train_mask is not None:
-            np.full_like(t, 255), M, (W, H),
+            mask_src = self._train_mask
+        else:
+            mask_src = np.full_like(t, 255)
+        mask_w = cv2.warpAffine(
+            mask_src, M, (cw, ch),
             flags=cv2.INTER_NEAREST, borderValue=0,
         )
-        valid = mask > 0
+        valid = mask_w > 0
         if valid.sum() < 20:
             return 0.0
         tpl = warped[valid].astype(np.float32)
-        scn = scene_gray[valid].astype(np.float32)
+        scn = scn_crop[valid].astype(np.float32)
         tm = tpl - tpl.mean()
         sm = scn - scn.mean()
         denom = np.sqrt((tm * tm).sum() * (sm * sm).sum()) + 1e-9
@@ -549,6 +574,8 @@ class LineShapeMatcher:
         verify_threshold: float = 0.4,
         coarse_angle_factor: int = 2,
         scale_penalty: float = 0.0,
+        pyramid_propagate: bool = True,
+        propagate_topk: int = 8,
     ) -> list[Match]:
         """
         scale_penalty: se > 0, riduce lo score per match a scala diversa da 1.0:
@@ -620,28 +647,62 @@ class LineShapeMatcher:
                 end = min(n, i + half + 1)
                 neighbor_map[vi_c] = vi_sorted[start:end]
 
-        # Pruning varianti via top-level (parallelizzato) - solo coarse
+        # Pruning varianti via top-level (parallelizzato).
+        # Quando pyramid_propagate=True ritorna anche le top-K posizioni
+        # del picco (in coord top-level) per restringere la fase full-res
+        # a piccoli crop attorno ai candidati (vs intera scena).
+        peaks_by_vi: dict[int, list[tuple[int, int, float]]] = {}
+
         def _top_score(vi: int) -> tuple[int, float]:
             var = self.variants[vi]
             lvl = var.levels[min(top, len(var.levels) - 1)]
-            score = _jit_score_bitmap(
+            score = _jit_score_bitmap_rescored(
                 spread_top, lvl.dx, lvl.dy, lvl.bin, bit_active_top,
+                bg_cache_top[var.scale],
             )
-            score = _rescore(score, bg_cache_top[var.scale])
+            if score.size == 0:
-            return vi, float(score.max()) if score.size else -1.0
+                return vi, -1.0
+            best = float(score.max())
+            if pyramid_propagate and best > 0:
+                # Top-K posizioni > top_thresh (max propagate_topk)
+                flat = score.ravel()
+                k = min(propagate_topk, flat.size)
+                idx = np.argpartition(-flat, k - 1)[:k]
+                peaks: list[tuple[int, int, float]] = []
+                for i in idx:
+                    s = float(flat[i])
+                    if s < top_thresh * 0.7:
+                        continue
+                    yt, xt = int(i // score.shape[1]), int(i % score.shape[1])
+                    peaks.append((xt, yt, s))
+                peaks_by_vi[vi] = peaks
+            return vi, best
 
         kept_coarse: list[tuple[int, float]] = []
+        all_top_scores: list[tuple[int, float]] = []
         if self.n_threads > 1 and len(coarse_idx_list) > 1:
             with ThreadPoolExecutor(max_workers=self.n_threads) as ex:
                 for vi, best in ex.map(_top_score, coarse_idx_list):
+                    all_top_scores.append((vi, best))
                     if best >= top_thresh:
                         kept_coarse.append((vi, best))
         else:
             for vi in coarse_idx_list:
                 vi2, best = _top_score(vi)
+                all_top_scores.append((vi2, best))
                 if best >= top_thresh:
                     kept_coarse.append((vi2, best))
 
+        # Fallback adattivo: se il rescore background ha abbattuto tutti
+        # gli score sotto top_thresh (scene texturate pesanti), ripesca
+        # le varianti migliori al top level per dare comunque una chance
+        # alla fase full-res invece di ritornare 0 match.
+        if not kept_coarse and all_top_scores:
+            all_top_scores.sort(key=lambda t: -t[1])
+            n_keep = max(4, len(all_top_scores) // 10)
+            # Limita a varianti con score top > 0 (non completamente a zero)
+            kept_coarse = [(vi, s) for vi, s in all_top_scores[:n_keep] if s > 0]
+
         # Espandi ogni coarse promosso con i suoi vicini (stessa scala,
         # angoli intermedi non valutati al top)
         expanded: set[int] = set()
@@ -675,14 +736,48 @@ class LineShapeMatcher:
         for sc in unique_scales:
             bg_cache_full[sc] = _bg_for_scale(density_full, sc, 1)
 
+        # Margine in full-res attorno ad ogni peak top: copre incertezza
+        # downsampling (sf_top px) + spread_radius + slack per NMS.
+        propagate_margin = sf_top + self.spread_radius + max(8, nms_radius // 2)
+        H_full, W_full = spread0.shape
+
         def _full_score(vi: int) -> tuple[int, np.ndarray]:
             var = self.variants[vi]
             lvl0 = var.levels[0]
-            score = _jit_score_bitmap(
+            if not pyramid_propagate or vi not in peaks_by_vi or not peaks_by_vi[vi]:
-                spread0, lvl0.dx, lvl0.dy, lvl0.bin, bit_active_full,
+                # Path legacy: scansiona intera scena
-            )
+                return vi, _jit_score_bitmap_rescored(
-            score = _rescore(score, bg_cache_full[var.scale])
+                    spread0, lvl0.dx, lvl0.dy, lvl0.bin, bit_active_full,
-            return vi, score
+                    bg_cache_full[var.scale],
+                )
+            # Path piramide propagata: valuta solo crop locali attorno
+            # alle posizioni dei picchi top-level (riproiettati a full-res).
+            score_full = np.zeros((H_full, W_full), dtype=np.float32)
+            mark = np.zeros((H_full, W_full), dtype=bool)
+            bg = bg_cache_full[var.scale]
+            for xt, yt, _s in peaks_by_vi[vi]:
+                cx0 = xt * sf_top
+                cy0 = yt * sf_top
+                x_lo = max(0, cx0 - propagate_margin)
+                x_hi = min(W_full, cx0 + propagate_margin + 1)
+                y_lo = max(0, cy0 - propagate_margin)
+                y_hi = min(H_full, cy0 + propagate_margin + 1)
+                if x_hi <= x_lo or y_hi <= y_lo:
+                    continue
+                if mark[y_lo:y_hi, x_lo:x_hi].all():
+                    continue
+                # Crop spread + bg, valuta kernel sul crop
+                spread_crop = np.ascontiguousarray(spread0[y_lo:y_hi, x_lo:x_hi])
+                bg_crop = np.ascontiguousarray(bg[y_lo:y_hi, x_lo:x_hi])
+                score_crop = _jit_score_bitmap_rescored(
+                    spread_crop, lvl0.dx, lvl0.dy, lvl0.bin,
+                    bit_active_full, bg_crop,
+                )
+                score_full[y_lo:y_hi, x_lo:x_hi] = np.maximum(
+                    score_full[y_lo:y_hi, x_lo:x_hi], score_crop,
+                )
+                mark[y_lo:y_hi, x_lo:x_hi] = True
+            return vi, score_full
 
         candidates_per_var: list[tuple[int, np.ndarray]] = []
         raw: list[tuple[float, int, int, int]] = []
@@ -693,14 +788,24 @@ class LineShapeMatcher:
         else:
             results = [_full_score(vi) for vi in var_indices]
 
+        def _scale_factor(s: float) -> float:
+            """Penalità moltiplicativa per scala diversa da 1.0."""
+            if scale_penalty > 0.0 and s != 1.0:
+                return max(0.0, 1.0 - scale_penalty * abs(s - 1.0))
+            return 1.0
+
         for vi, score in results:
-            ys, xs = np.where(score >= min_score)
+            pen = _scale_factor(self.variants[vi].scale)
+            # Ordinare/sogliare su score penalizzato: un match a scala 1.5 con
+            # score 0.8 e penalty=0.3 effettivamente vale 0.56, non 0.8.
+            score_for_sort = score if pen == 1.0 else score * pen
+            ys, xs = np.where(score_for_sort >= min_score)
             if len(ys) == 0:
                 continue
-            vals = score[ys, xs]
+            vals = score_for_sort[ys, xs]
             K = min(len(vals), max_matches * 5)
             ord_idx = np.argpartition(-vals, K - 1)[:K]
-            candidates_per_var.append((vi, score))
+            candidates_per_var.append((vi, score))  # score_map originale
             for i in ord_idx:
                 raw.append((float(vals[i]), int(xs[i]), int(ys[i]), vi))
 
@@ -710,32 +815,43 @@ class LineShapeMatcher:
         score_maps = dict(candidates_per_var)
 
         # NMS + subpixel + refinement angolare
-        # Mask template per refinement (non disponibile qui: usa full)
+        # Usa mask salvata in train() per coerenza (se ROI poligonale).
         h, w = self.template_gray.shape if self.template_gray is not None else (0, 0)
-        mask_full = np.full((h, w), 255, dtype=np.uint8)
+        mask_full = (
+            self._train_mask if self._train_mask is not None
+            else np.full((h, w), 255, dtype=np.uint8)
+        )
+        # Plateau radius adattivo al template (evita plateau troppo ampi su
+        # template piccoli: 8% del lato minimo, clampato [3, 10]).
+        plateau_r = max(3, min(10, int(min(self.template_size) * 0.08)))
 
-        # Pre-NMS rapido su raw (solo subpixel, no refine/verify): riduce
+        # Pre-NMS rapido su raw con coordinate intere (nms_radius ≥ 8,
-        # i candidati a ~max_matches*3 prima di operazioni costose (refine,
+        # la precisione sub-pixel non cambia la decisione di reject).
-        # verify) che erano chiamate per ogni raw causando lentezze 100x.
+        # Subpixel viene calcolato DOPO il pre-NMS solo sui ~pre_cap
+        # preliminary sopravvissuti: prima era chiamato su ogni raw (~58k
+        # chiamate su clip_preciso) anche se la maggior parte veniva poi
+        # scartata dalla NMS, sprecando la parte più costosa del loop.
         r2 = nms_radius * nms_radius
-        preliminary: list[tuple[float, float, float, int]] = []
         pre_cap = max(max_matches * 3, max_matches + 10)
+        preliminary_int: list[tuple[float, int, int, int]] = []
         for score, xi, yi, vi in raw:
-            if subpixel and vi in score_maps:
+            if any((k[1] - xi) ** 2 + (k[2] - yi) ** 2 < r2
-                cx_f, cy_f = self._subpixel_peak(score_maps[vi], xi, yi)
+                   for k in preliminary_int):
-            else:
-                cx_f, cy_f = float(xi), float(yi)
-            if any((k[1] - cx_f) ** 2 + (k[2] - cy_f) ** 2 < r2
-                   for k in preliminary):
                 continue
-            preliminary.append((score, cx_f, cy_f, vi))
+            preliminary_int.append((score, xi, yi, vi))
-            if len(preliminary) >= pre_cap:
+            if len(preliminary_int) >= pre_cap:
                 break
 
-        # Ora refine + verify solo sui candidati pre-NMS
+        # Subpixel + refine + verify solo sui candidati pre-NMS (max pre_cap)
         kept: list[Match] = []
         tw, th = self.template_size
-        for score, cx_f, cy_f, vi in preliminary:
+        for score, xi, yi, vi in preliminary_int:
+            if subpixel and vi in score_maps:
+                cx_f, cy_f = self._subpixel_peak(
+                    score_maps[vi], xi, yi, plateau_radius=plateau_r,
+                )
+            else:
+                cx_f, cy_f = float(xi), float(yi)
             var = self.variants[vi]
             ang_f = var.angle_deg
             score_f = score