merge: precisione rotazione + perf propagate + robustezza server

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

ROADMAP.md

@@ -2,6 +2,36 @@
 
 Lista ragionata di miglioramenti futuri. Priorità = impatto / effort, non urgenza temporale.
 
+## Fase 2 COMPLETATA (precisione rotazione + robustezza + perf)
+
+Root cause della rotazione imprecisa: lo score satura a 1.0 sulla spread
+bitmap dilatata (raggio 4-5) → il refine non vedeva gradiente né in angolo
+né in posizione, e `cv2.minMaxLoc` sul plateau saturo spostava il centro
+sull'angolo della finestra (errore sistematico 3·√2 ≈ 4.24 px).
+
+| Fix | Dettaglio |
+|---|---|
+| Refine su bitmap fine | `_refine_angle` ottimizza su spread raggio 1 (`spread_fine`, cached); score finale ricalcolato su spread coarse per mantenere semantica soglie |
+| Picco sub-pixel nel refine | centroide plateau / fit quadratico al posto di minMaxLoc (bias top-left) |
+| LM least-squares pos+angolo | `_subpixel_refine_lm` riscritto: snap edge ±2px lungo normale + LSQ 3x3 (dx, dy, dθ), ON di default |
+| Round feature offsets | troncamento `astype(int32)` → `np.round` (bias ~0.25 px) |
+| Centro rotazione coerente | `_prepare_padded_template`: rotazione attorno al centro reale del template nel padding (bias ≤0.5 px dipendente dall'angolo) |
+| `_angle_list` include estremo | range parziali ±tol ora testano anche +tol |
+| `_refine_pose_joint` rimosso | Nelder-Mead su funzione a gradini satura: terminava subito; param ora alias di refine_angle |
+| pyramid_propagate di default | kernel windowed (feature campionano l'intera scena: prima il crop troncava le feature → score 0); picchi = massimi locali (non top-K pixel); disattivato automaticamente per template elongati (>2:1) dove il picco top-level non localizza |
+| Piramide 3 livelli default | con clamp automatico sulla dimensione template (min 12 px al top) |
+| Cache scena: hash completo | prima hashava solo i primi 64KB → collisioni tra scene con stessa banda superiore → risultati della scena sbagliata |
+| Web server | lock matcher (race con threadpool FastAPI), LRU `_IMG_CACHE`, clamp ROI ovunque (400/422 invece di 500), `filtro_fp=off` disabilita davvero NCC, `_draw_matches` su crop locale |
+| GUI/legacy | centro overlay `(W-1)/2`→`W/2`, spread_radius default 5→4, EdgeShapeMatcher: angle list endpoint + cap candidati + save template_gray |
+
+Misure (GT sintetica 7 pose, scena 900x700, VPS 2 core):
+- Errore angolare mediano: **2.3° → 0.05°** (step 5°); a step 2° era 4.4° → **0.03°**
+- Errore posizione mediano: **4.24 px → 0.04 px**
+- find GT scene: 4.7s → 1.7s; scena reale 646x482: 1.14s → 0.81s
+- Benchmark suite 16 scenari: 96.5s → 84.2s, match count ≥ baseline
+  (eccezioni: dado_full -1 = match borderline su parte diversa;
+  lama_part_preciso 25→18 con baseline al cap max_matches)
+
 ## Fase 1 COMPLETATA (branch `speedFase1`)
 
 | ID | Voce | Status | Note |
@@ -84,9 +114,14 @@ Benchmark suite 16 scenari (4 immagini × full/part × fast/preciso):
 ## Target performance produzione
 
 Obiettivi da documento tecnico Vision Suite (Fase Beta):
-- [ ] **Precisione posizionale mediana**: <0.5 px → **raggiunto con subpixel (attualmente ~0.1-0.3 px atteso)**
+- [x] **Precisione posizionale mediana**: <0.5 px → **0.04 px misurato su GT sintetica (Fase 2)**
-- [ ] **Precisione angolare mediana**: <1.0° → **raggiunto con refinement (~0.5°)**
+- [x] **Precisione angolare mediana**: <1.0° → **0.05° misurato su GT sintetica (Fase 2)**
-- [ ] **Latency mediana**: <50 ms su 1920×1080 → **attuale ~1.7s su 830×822 (serve GPU o ulteriore CPU)**
+- [ ] **Latency mediana**: <50 ms su 1920×1080 → **~0.8s su 646×482 con 2 core; da misurare su hardware produzione**
 - [ ] **F1 score dataset sintetico**: >0.95 → **da misurare con dataset sintetico**
 
-Prossimo blocker per target: **latency**. Via più promettente: GPU (CuPy) o coarse-to-fine angolare.
+Prossimo blocker per target: **latency**. Nota: i kernel hot sono gia'
+Numba JIT (≈ velocita' C, prange parallelo): un port C++ dei kernel vale
+solo il margine SIMD esplicito (~2-4x con AVX2 su AND+popcount byte-wise).
+Prima di scriverlo conviene esaurire le vie algoritmiche rimaste:
+riduzione varianti al top-level (auto angle step per livello, stile
+Halcon), greediness di default, e GPU (CuPy/OpenCL) per scene 1080p.

benchmarks/test_suite.py

@@ -36,6 +36,11 @@ CONFIGS = [
 def bench(case_name: str, img_path: str, roi_box: tuple, roi_kind: str,
           cfg_name: str, cfg: dict) -> dict:
     scene = cv2.imread(str(TEST_DIR / img_path))
+    if scene is None:
+        # cv2.imread ritorna None silenzioso: senza check il crash arriva
+        # dopo, sullo slice, con un errore criptico.
+        raise FileNotFoundError(
+            f"Immagine di test non trovata o non leggibile: {TEST_DIR / img_path}")
     y0, y1, x0, x1 = roi_box
     roi = scene[y0:y1, x0:x1].copy()
     m = LineShapeMatcher(

pm2d/_jit_kernels.py

@@ -271,6 +271,108 @@ if HAS_NUMBA:
                         acc[y, x] = 0.0
         return acc
 
+    @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
+    def _jit_score_bitmap_rescored_window(
+        spread: np.ndarray,      # uint8 (H, W) - scena INTERA
+        dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
+        bit_active: np.uint8,
+        bg: np.ndarray,          # float32 (H, W) - scena intera
+        y0: nb.int64, x0: nb.int64,
+        wh: nb.int64, ww: nb.int64,
+    ) -> np.ndarray:
+        """Score rescored valutato SOLO nella finestra (y0, x0, wh, ww).
+
+        Le feature campionano lo spread dell'intera scena (bounds-checked
+        sui bordi scena): a differenza di chiamare il kernel su un crop,
+        le feature che escono dalla finestra NON contano come miss.
+        Usato dal path pyramid_propagate: costo ∝ area finestra.
+        """
+        H, W = spread.shape
+        N = dx.shape[0]
+        acc = np.zeros((wh, ww), dtype=np.float32)
+        for yi in nb.prange(wh):
+            y = y0 + yi
+            for i in range(N):
+                b = bins[i]
+                mask = np.uint8(1) << b
+                if (bit_active & mask) == 0:
+                    continue
+                yy = y + dy[i]
+                if yy < 0 or yy >= H:
+                    continue
+                ddx = dx[i]
+                xi_lo = 0
+                xi_hi = ww
+                lo = -(x0 + ddx)
+                if lo > xi_lo:
+                    xi_lo = lo
+                hi = W - (x0 + ddx)
+                if hi < xi_hi:
+                    xi_hi = hi
+                for xi in range(xi_lo, xi_hi):
+                    if spread[yy, x0 + xi + ddx] & mask:
+                        acc[yi, xi] += 1.0
+        if N > 0:
+            inv = 1.0 / N
+            for yi in nb.prange(wh):
+                for xi in range(ww):
+                    v = acc[yi, xi] * inv
+                    bgv = bg[y0 + yi, x0 + xi]
+                    if bgv < 1.0:
+                        r = (v - bgv) / (1.0 - bgv + 1e-6)
+                        acc[yi, xi] = r if r > 0.0 else 0.0
+                    else:
+                        acc[yi, xi] = 0.0
+        return acc
+
+    @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
+    def _jit_score_bitmap_rescored_window_u16(
+        spread: np.ndarray,      # uint16 (H, W)
+        dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
+        bit_active: np.uint16,
+        bg: np.ndarray,
+        y0: nb.int64, x0: nb.int64,
+        wh: nb.int64, ww: nb.int64,
+    ) -> np.ndarray:
+        """Versione uint16 (polarity 16-bin) del kernel windowed."""
+        H, W = spread.shape
+        N = dx.shape[0]
+        acc = np.zeros((wh, ww), dtype=np.float32)
+        for yi in nb.prange(wh):
+            y = y0 + yi
+            for i in range(N):
+                b = bins[i]
+                mask = np.uint16(1) << b
+                if (bit_active & mask) == 0:
+                    continue
+                yy = y + dy[i]
+                if yy < 0 or yy >= H:
+                    continue
+                ddx = dx[i]
+                xi_lo = 0
+                xi_hi = ww
+                lo = -(x0 + ddx)
+                if lo > xi_lo:
+                    xi_lo = lo
+                hi = W - (x0 + ddx)
+                if hi < xi_hi:
+                    xi_hi = hi
+                for xi in range(xi_lo, xi_hi):
+                    if spread[yy, x0 + xi + ddx] & mask:
+                        acc[yi, xi] += 1.0
+        if N > 0:
+            inv = 1.0 / N
+            for yi in nb.prange(wh):
+                for xi in range(ww):
+                    v = acc[yi, xi] * inv
+                    bgv = bg[y0 + yi, x0 + xi]
+                    if bgv < 1.0:
+                        r = (v - bgv) / (1.0 - bgv + 1e-6)
+                        acc[yi, xi] = r if r > 0.0 else 0.0
+                    else:
+                        acc[yi, xi] = 0.0
+        return acc
+
     @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
     def _jit_top_max_per_variant(
         spread: np.ndarray,            # uint8 (H, W)
@@ -426,6 +528,9 @@ if HAS_NUMBA:
         _jit_top_max_per_variant(
             spread, dx, dy, b, offsets, np.uint8(0xFF), bg_pv, scale_idx,
         )
+        _jit_score_bitmap_rescored_window(
+            spread, dx, dy, b, np.uint8(0xFF), bg, 4, 4, 8, 8,
+        )
         _jit_popcount_density(spread)
         spread16 = np.zeros((32, 32), dtype=np.uint16)
         _jit_score_bitmap_rescored_u16(
@@ -447,6 +552,12 @@ else:  # pragma: no cover
     def _jit_score_bitmap_rescored_strided(spread, dx, dy, bins, bit_active, bg, stride):
         raise RuntimeError("numba non disponibile")
 
+    def _jit_score_bitmap_rescored_window(spread, dx, dy, bins, bit_active, bg, y0, x0, wh, ww):
+        raise RuntimeError("numba non disponibile")
+
+    def _jit_score_bitmap_rescored_window_u16(spread, dx, dy, bins, bit_active, bg, y0, x0, wh, ww):
+        raise RuntimeError("numba non disponibile")
+
     def _jit_score_bitmap_greedy(spread, dx, dy, bins, bit_active, min_score, greediness):
         raise RuntimeError("numba non disponibile")
 
@@ -524,6 +635,39 @@ def score_bitmap_rescored(
     return np.maximum(0.0, out).astype(np.float32)
 
 
+def score_bitmap_rescored_window(
+    spread: np.ndarray, dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
+    bit_active: int, bg: np.ndarray,
+    y0: int, x0: int, wh: int, ww: int,
+) -> np.ndarray:
+    """Score rescored solo nella finestra (y0, x0, wh, ww) della scena.
+
+    Le feature campionano l'INTERA scena: feature fuori finestra ma dentro
+    scena contano correttamente (chiamare il kernel su un crop le tratta
+    come miss e azzera lo score — il bug che rendeva inutilizzabile il
+    path pyramid_propagate). Fallback no-numba: kernel pieno + slice.
+    """
+    if HAS_NUMBA and len(dx) > 0:
+        dx_c = np.ascontiguousarray(dx, dtype=np.int32)
+        dy_c = np.ascontiguousarray(dy, dtype=np.int32)
+        bins_c = np.ascontiguousarray(bins, dtype=np.int8)
+        bg_c = np.ascontiguousarray(bg, dtype=np.float32)
+        if spread.dtype == np.uint16:
+            return _jit_score_bitmap_rescored_window_u16(
+                np.ascontiguousarray(spread, dtype=np.uint16),
+                dx_c, dy_c, bins_c, np.uint16(bit_active), bg_c,
+                int(y0), int(x0), int(wh), int(ww),
+            )
+        return _jit_score_bitmap_rescored_window(
+            np.ascontiguousarray(spread, dtype=np.uint8),
+            dx_c, dy_c, bins_c, np.uint8(bit_active), bg_c,
+            int(y0), int(x0), int(wh), int(ww),
+        )
+    # Fallback (lento, solo senza numba): score full-frame + slice finestra
+    full = score_bitmap_rescored(spread, dx, dy, bins, bit_active, bg)
+    return full[y0:y0 + wh, x0:x0 + ww]
+
+
 def score_bitmap_greedy(
     spread: np.ndarray, dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
     bit_active: int, min_score: float, greediness: float,

pm2d/auto_tune.py

@@ -61,6 +61,8 @@ def detect_rotational_symmetry(
 
     center = (w / 2.0, h / 2.0)
     ref = mag
+    # ref è costante nel loop sugli angoli: centra una volta sola
+    rm = ref - ref.mean()
 
     correlations: list[tuple[float, float]] = []
     for ang in np.arange(step_deg, 360.0, step_deg):
@@ -68,7 +70,6 @@ def detect_rotational_symmetry(
         rot = cv2.warpAffine(
             mag, M, (w, h), borderValue=0.0,
         )
-        rm = ref - ref.mean()
         rs = rot - rot.mean()
         denom = np.sqrt((rm * rm).sum() * (rs * rs).sum()) + 1e-9
         c = float((rm * rs).sum() / denom)

pm2d/gui.py

@@ -196,8 +196,10 @@ def _warp_template_edges_to_scene(
     edge = cv2.Canny(template_gray, canny_low, canny_high)
     # Matrice affine: scala + rotazione attorno al centro template, poi traslazione
     Ht, Wt = h, w
-    cx_t = (Wt - 1) / 2.0
+    # Centro coerente con la convenzione train (center = w / 2.0, no -1):
-    cy_t = (Ht - 1) / 2.0
+    # (Wt-1)/2 introduceva uno shift di 0.5px per template di lato pari.
+    cx_t = Wt / 2.0
+    cy_t = Ht / 2.0
     M = cv2.getRotationMatrix2D((cx_t, cy_t), angle_deg, scale)
     # Traslazione per portare centro template a (cx, cy) della scena
     M[0, 2] += cx - cx_t
@@ -492,7 +494,9 @@ def run(
     num_features: int = 96,
     weak_grad: float = 30.0,
     strong_grad: float = 60.0,
-    spread_radius: int = 5,
+    # 4 allineato col default del matcher: raggio 5 peggiora la precisione
+    # di rotazione (spread troppo largo appiattisce il picco angolare).
+    spread_radius: int = 4,
     pyramid_levels: int = 3,
     min_score: float = 0.55,
     max_matches: int = 25,

pm2d/line_matcher.py

@@ -40,6 +40,7 @@ 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,
+    score_bitmap_rescored_window as _jit_score_bitmap_rescored_window,
     score_bitmap_greedy as _jit_score_bitmap_greedy,
     top_max_per_variant as _jit_top_max_per_variant,
     popcount_density as _jit_popcount,
@@ -172,7 +173,7 @@ class LineShapeMatcher:
         scale_step: float = 0.1,
         spread_radius: int = 4,
         min_feature_spacing: int = 3,
-        pyramid_levels: int = 2,
+        pyramid_levels: int = 3,
         top_score_factor: float = 0.5,
         n_threads: int | None = None,
         use_polarity: bool = False,
@@ -473,8 +474,46 @@ class LineShapeMatcher:
         step = self._effective_angle_step()
         if step <= 0 or a0 >= a1:
             return [float(a0)]
-        n = int(np.floor((a1 - a0) / step))
+        # Include l'estremo superiore: con range parziali (es. ±15°) il
-        return [float(a0 + i * step) for i in range(n)]
+        # +15° deve essere testato quanto il -15°. Se il range copre 360°
+        # interi l'estremo coincide con a0 (mod 360) e viene escluso per
+        # non duplicare la variante.
+        n = int(np.floor((a1 - a0) / step + 1e-9)) + 1
+        angles = [float(a0 + i * step) for i in range(n)]
+        if a1 - a0 >= 360.0:
+            angles = [a for a in angles if a - a0 < 360.0 - 1e-9]
+        return angles
+
+    def _prepare_padded_template(
+        self, template_gray: np.ndarray, mask_full: np.ndarray, scale: float,
+    ) -> tuple[np.ndarray, np.ndarray, tuple[float, float], int]:
+        """Scala + padda template e mask; ritorna (gray_p, mask_p, center, diag).
+
+        `center` e' il centro REALE del template dentro l'immagine paddata
+        (px + sw/2, py + sh/2): con padding floor differisce da diag/2 fino
+        a 0.5 px. Ruotare attorno a diag/2 (come si faceva prima) faceva
+        orbitare il centro-modello attorno al centro di rotazione, con un
+        bias di posizione dipendente dall'angolo. Tutti i percorsi che
+        ricostruiscono il template ruotato devono usare QUESTO helper.
+        """
+        h, w = template_gray.shape
+        sw = max(16, int(round(w * scale)))
+        sh = max(16, int(round(h * scale)))
+        gray_s = cv2.resize(template_gray, (sw, sh), interpolation=cv2.INTER_LINEAR)
+        mask_s = cv2.resize(mask_full, (sw, sh), interpolation=cv2.INTER_NEAREST)
+        diag = int(np.ceil(np.hypot(sh, sw))) + 6
+        py = (diag - sh) // 2
+        px = (diag - sw) // 2
+        gray_p = cv2.copyMakeBorder(
+            gray_s, py, diag - sh - py, px, diag - sw - px,
+            cv2.BORDER_REPLICATE,
+        )
+        mask_p = cv2.copyMakeBorder(
+            mask_s, py, diag - sh - py, px, diag - sw - px,
+            cv2.BORDER_CONSTANT, value=0,
+        )
+        center = (px + sw / 2.0, py + sh / 2.0)
+        return gray_p, mask_p, center, diag
 
     # --- Training ------------------------------------------------------
 
@@ -504,6 +543,13 @@ class LineShapeMatcher:
         h, w = gray.shape
         self.template_size = (w, h)
         self.template_gray = gray.copy()
+        # Clamp livelli piramide alla dimensione template: al top-level il
+        # lato minimo deve restare >= 12 px, sotto le feature collassano
+        # tutte negli stessi (dx,dy) e lo score top diventa rumore.
+        max_lv = 1
+        while min(w, h) / (2 ** max_lv) >= 12 and max_lv < 4:
+            max_lv += 1
+        self.pyramid_levels = max(1, min(self.pyramid_levels, max_lv))
         if mask is None:
             mask_full = np.full((h, w), 255, dtype=np.uint8)
         else:
@@ -566,24 +612,10 @@ class LineShapeMatcher:
         Estrazione algorithm identica al train() originale, separato per
         riuso da add_template_view (multi-template ensemble).
         """
-        h, w = gray.shape
         for s in self._scale_list():
-            sw = max(16, int(round(w * s)))
+            gray_p, mask_p, center, diag = self._prepare_padded_template(
-            sh = max(16, int(round(h * s)))
+                gray, mask_full, s,
-            gray_s = cv2.resize(gray, (sw, sh), interpolation=cv2.INTER_LINEAR)
-            mask_s = cv2.resize(mask_full, (sw, sh), interpolation=cv2.INTER_NEAREST)
-            diag = int(np.ceil(np.hypot(sh, sw))) + 6
-            py = (diag - sh) // 2
-            px = (diag - sw) // 2
-            gray_p = cv2.copyMakeBorder(
-                gray_s, py, diag - sh - py, px, diag - sw - px,
-                cv2.BORDER_REPLICATE,
             )
-            mask_p = cv2.copyMakeBorder(
-                mask_s, py, diag - sh - py, px, diag - sw - px,
-                cv2.BORDER_CONSTANT, value=0,
-            )
-            center = (diag / 2.0, diag / 2.0)
 
             for ang in self._angle_list():
                 M = cv2.getRotationMatrix2D(center, ang, 1.0)
@@ -600,10 +632,10 @@ class LineShapeMatcher:
                 if len(fx) < 8:
                     continue
 
-                cx_c = diag / 2.0
+                # round (non truncation): astype(int32) tronca verso zero
-                cy_c = diag / 2.0
+                # e introduceva un bias sistematico ~0.25 px verso il centro.
-                dx = (fx - cx_c).astype(np.int32)
+                dx = np.round(fx - center[0]).astype(np.int32)
-                dy = (fy - cy_c).astype(np.int32)
+                dy = np.round(fy - center[1]).astype(np.int32)
 
                 x0 = int(dx.min()); x1 = int(dx.max())
                 y0 = int(dy.min()); y1 = int(dy.max())
@@ -687,8 +719,13 @@ class LineShapeMatcher:
         try:
             import hashlib
             h = hashlib.md5()
-            sample = gray.tobytes()[:65536]
+            # Hash dell'INTERA scena: hashare solo i primi 64KB (prime
-            h.update(sample)
+            # ~80 righe a 830px) faceva collidere scene con la stessa
+            # banda superiore (es. sfondo uniforme da camera fissa) →
+            # find() ritornava i risultati della scena sbagliata.
+            # tobytes() copiava gia' tutto il buffer, il costo extra
+            # dell'md5 completo e' ~1ms.
+            h.update(gray.tobytes())
             h.update(f"|{gray.shape}|{gray.dtype}".encode())
             h.update(
                 f"|{self.weak_grad}|{self.strong_grad}"
@@ -717,11 +754,17 @@ class LineShapeMatcher:
         while len(self._scene_cache) > self._SCENE_CACHE_SIZE:
             self._scene_cache.popitem(last=False)
 
-    def _spread_bitmap(self, gray: np.ndarray) -> np.ndarray:
+    def _spread_bitmap(
+        self, gray: np.ndarray, radius: int | None = None,
+    ) -> np.ndarray:
         """Spread bitmap: bit b acceso dove bin b è presente nel raggio.
 
         dtype: uint8 per N_BINS=8, uint16 per N_BINS_POL=16 (use_polarity).
         Se use_gpu=True: Sobel + dilate via cv2.UMat (OpenCL kernel GPU).
+        radius: override del raggio di spread (default self.spread_radius).
+        radius=0/1 produce una bitmap "fine" senza tolleranza, usata nel
+        refine finale: sulla bitmap dilatata lo score satura e il refine
+        non distingue pose entro ±spread_radius px / ±atan(spread/R) gradi.
         """
         if self.use_gpu and not isinstance(gray, cv2.UMat):
             gray_in = cv2.UMat(np.ascontiguousarray(gray))
@@ -729,7 +772,8 @@ class LineShapeMatcher:
             gray_in = gray
         mag, bins = self._gradient(gray_in)
         valid = mag >= self.weak_grad
-        k = 2 * self.spread_radius + 1
+        r = self.spread_radius if radius is None else max(0, int(radius))
+        k = 2 * r + 1
         kernel = np.ones((k, k), dtype=np.uint8)
         H, W = (gray.shape if isinstance(gray, np.ndarray)
                 else (gray.get().shape[0], gray.get().shape[1]))
@@ -755,7 +799,9 @@ class LineShapeMatcher:
             if not bin_present[b]:
                 continue  # XX: nessun pixel di questo bin sopra weak_grad
             mask_b = ((bins == b) & valid).astype(np.uint8)
-            if self.use_gpu:
+            if r == 0:
+                d_np = mask_b
+            elif self.use_gpu:
                 d = cv2.dilate(cv2.UMat(mask_b), kernel)
                 d_np = d.get()
             else:
@@ -828,111 +874,9 @@ class LineShapeMatcher:
         oy = float(np.clip(oy, -0.5, 0.5))
         return x + ox, y + oy
 
-    def _refine_pose_joint(
-        self,
-        spread0: np.ndarray,
-        template_gray: np.ndarray,
-        cx: float, cy: float,
-        angle_deg: float, scale: float,
-        mask_full: np.ndarray,
-        max_iter: int = 24,
-        tol: float = 1e-3,
-    ) -> tuple[float, float, float, float]:
-        """Refine congiunto (cx, cy, angle) via Nelder-Mead 3D.
-
-        Ottimizza simultaneamente posizione e angolo (vs golden search 1D
-        sull'angolo poi quadratico 2D su xy che alterna assi). Halcon-style:
-        un singolo iter LM stila il match a precisione sub-pixel + sub-step.
-        Ritorna (angle, score, cx, cy) dove score e quello calcolato sulla
-        scena spread (no template gray).
-        """
-        h, w = template_gray.shape
-        sw = max(16, int(round(w * scale)))
-        sh = max(16, int(round(h * scale)))
-        gray_s = cv2.resize(template_gray, (sw, sh), interpolation=cv2.INTER_LINEAR)
-        mask_s = cv2.resize(mask_full, (sw, sh), interpolation=cv2.INTER_NEAREST)
-        diag = int(np.ceil(np.hypot(sh, sw))) + 6
-        py = (diag - sh) // 2; px = (diag - sw) // 2
-        gray_p = cv2.copyMakeBorder(gray_s, py, diag - sh - py, px, diag - sw - px,
-                                     cv2.BORDER_REPLICATE)
-        mask_p = cv2.copyMakeBorder(mask_s, py, diag - sh - py, px, diag - sw - px,
-                                     cv2.BORDER_CONSTANT, value=0)
-        center = (diag / 2.0, diag / 2.0)
-        H, W = spread0.shape
-
-        def _score(params: tuple[float, float, float]) -> float:
-            ddx, ddy, dang = params
-            ang = angle_deg + dang
-            M = cv2.getRotationMatrix2D(center, ang, 1.0)
-            gray_r = cv2.warpAffine(gray_p, M, (diag, diag),
-                                     flags=cv2.INTER_LINEAR,
-                                     borderMode=cv2.BORDER_REPLICATE)
-            mask_r = cv2.warpAffine(mask_p, M, (diag, diag),
-                                     flags=cv2.INTER_NEAREST, borderValue=0)
-            mag, bins = self._gradient(gray_r)
-            fx, fy, fb = self._extract_features(mag, bins, mask_r)
-            if len(fx) < 8:
-                return 0.0
-            cxe = cx + ddx; cye = cy + ddy
-            ix = int(round(cxe)); iy = int(round(cye))
-            tot = 0
-            valid = 0
-            for i in range(len(fx)):
-                xs = ix + int(fx[i] - center[0])
-                ys = iy + int(fy[i] - center[1])
-                if 0 <= xs < W and 0 <= ys < H:
-                    bit = np.uint8(1 << int(fb[i]))
-                    if spread0[ys, xs] & bit:
-                        tot += 1
-                    valid += 1
-            return -float(tot) / max(1, valid)  # minimize -score
-
-        # Nelder-Mead 3D inline (no scipy). Simplex iniziale: vertice + offset
-        # dx=±0.5px, dy=±0.5px, dθ=±step/2.
-        step_a = self.angle_step_deg / 2.0 if self.angle_step_deg > 0 else 1.0
-        x0 = np.array([0.0, 0.0, 0.0])
-        simplex = np.array([
-            x0,
-            x0 + [0.5, 0.0, 0.0],
-            x0 + [0.0, 0.5, 0.0],
-            x0 + [0.0, 0.0, step_a],
-        ])
-        fvals = np.array([_score(tuple(s)) for s in simplex])
-        for _ in range(max_iter):
-            order = np.argsort(fvals)
-            simplex = simplex[order]; fvals = fvals[order]
-            if abs(fvals[-1] - fvals[0]) < tol:
-                break
-            centroid = simplex[:-1].mean(axis=0)
-            xr = centroid + 1.0 * (centroid - simplex[-1])
-            fr = _score(tuple(xr))
-            if fvals[0] <= fr < fvals[-2]:
-                simplex[-1] = xr; fvals[-1] = fr
-                continue
-            if fr < fvals[0]:
-                xe = centroid + 2.0 * (centroid - simplex[-1])
-                fe = _score(tuple(xe))
-                if fe < fr:
-                    simplex[-1] = xe; fvals[-1] = fe
-                else:
-                    simplex[-1] = xr; fvals[-1] = fr
-                continue
-            xc = centroid + 0.5 * (simplex[-1] - centroid)
-            fc = _score(tuple(xc))
-            if fc < fvals[-1]:
-                simplex[-1] = xc; fvals[-1] = fc
-                continue
-            for k in range(1, 4):
-                simplex[k] = simplex[0] + 0.5 * (simplex[k] - simplex[0])
-                fvals[k] = _score(tuple(simplex[k]))
-        best_i = int(np.argmin(fvals))
-        ddx, ddy, dang = simplex[best_i]
-        return (angle_deg + float(dang), -float(fvals[best_i]),
-                cx + float(ddx), cy + float(ddy))
-
     def _refine_angle(
         self,
-        spread0: np.ndarray,       # bitmap uint8 (H, W)
+        spread0: np.ndarray,       # bitmap uint8/uint16 (H, W) - spread pieno
         bit_active: int,
         template_gray: np.ndarray,
         cx: float, cy: float,
@@ -941,33 +885,31 @@ class LineShapeMatcher:
         angle_fine_step: float = 0.5,
         search_radius: float | None = None,
         original_score: float | None = None,
+        spread_fine: np.ndarray | None = None,
     ) -> tuple[float, float, float, float]:
         """Ricerca angolare fine (sub-step) attorno al match grezzo.
 
-        Genera 5 template temporanei a angle ± {0.5, 1.0} * step e sceglie
+        Golden-section sull'angolo + argmax posizione in finestra ±3 px.
-        l'angolo con score massimo (parabolic fit sulle 3 score centrali).
         Ritorna (angle_refined, score, cx_refined, cy_refined).
+
+        L'ottimizzazione gira sulla bitmap FINE (spread_fine, raggio 1):
+        sulla bitmap dilatata (spread0, raggio 4-5) lo score satura a 1.0
+        per qualunque posa entro ±spread px / ±atan(spread/R) gradi e il
+        refine non vede alcun gradiente (l'angolo restava quello grezzo
+        quantizzato e cv2.minMaxLoc sul plateau saturo spostava il centro
+        sull'angolo in alto a sinistra della finestra: errore misurato
+        3·sqrt(2) ≈ 4.24 px). Lo score RITORNATO e' ricalcolato alla posa
+        raffinata su spread0, per mantenere la semantica precedente
+        (tolleranza spread_radius) su soglie/min_score.
         """
-        # NB: rimosso early-skip su score >= 0.99. Lo score linemod/shape
-        # satura facilmente a 1.0 (specie con pyramid_propagate o spread
-        # ampio) ma NON garantisce angolo preciso: l'angolo grezzo della
-        # variante e' quantizzato a multipli di angle_step (5 deg default).
-        # Refine angolare e' essenziale per orientamento sub-step.
         if search_radius is None:
             search_radius = self._effective_angle_step()
+        # Bitmap su cui ottimizzare: fine se disponibile, altrimenti spread0.
+        opt_map = spread_fine if spread_fine is not None else spread0
 
-        h, w = template_gray.shape
+        gray_p, mask_p, center, diag = self._prepare_padded_template(
-        sw = max(16, int(round(w * scale)))
+            template_gray, mask_full, scale,
-        sh = max(16, int(round(h * scale)))
+        )
-        gray_s = cv2.resize(template_gray, (sw, sh), interpolation=cv2.INTER_LINEAR)
-        mask_s = cv2.resize(mask_full, (sw, sh), interpolation=cv2.INTER_NEAREST)
-        diag = int(np.ceil(np.hypot(sh, sw))) + 6
-        py = (diag - sh) // 2; px = (diag - sw) // 2
-        gray_p = cv2.copyMakeBorder(gray_s, py, diag - sh - py, px, diag - sw - px,
-                                     cv2.BORDER_REPLICATE)
-        mask_p = cv2.copyMakeBorder(mask_s, py, diag - sh - py, px, diag - sw - px,
-                                     cv2.BORDER_CONSTANT, value=0)
-        center = (diag / 2.0, diag / 2.0)
 
         H, W = spread0.shape
         margin = 3
@@ -982,14 +924,11 @@ class LineShapeMatcher:
         feat_cache = self._refine_feat_cache
         cache_scale_key = round(scale * 1000)
 
-        def _score_at_angle(off: float) -> tuple[float, float, float]:
+        def _feats_at_angle(ang: float):
-            """Ritorna (score, best_cx, best_cy) per angolo = angle_deg + off."""
-            ang = angle_deg + off
             ck = (round(ang * 20), cache_scale_key)
             cached = feat_cache.get(ck)
             if cached is not None:
-                fx, fy, fb = cached
+                return cached
-            else:
             M = cv2.getRotationMatrix2D(center, ang, 1.0)
             gray_r = cv2.warpAffine(gray_p, M, (diag, diag),
                                      flags=cv2.INTER_LINEAR,
@@ -1002,15 +941,27 @@ class LineShapeMatcher:
             if len(feat_cache) > 256:
                 feat_cache.pop(next(iter(feat_cache)))
             feat_cache[ck] = (fx, fy, fb)
+            return fx, fy, fb
+
+        def _score_at_angle(off: float) -> tuple[float, float, float]:
+            """Ritorna (score, best_cx, best_cy) per angolo = angle_deg + off.
+
+            Score = max su finestra ±margin px attorno a (cx, cy) sulla
+            bitmap di ottimizzazione; posizione = picco sub-pixel della
+            finestra (centroide plateau / fit quadratico, NON minMaxLoc
+            che sul plateau e' biased verso l'angolo top-left).
+            """
+            ang = angle_deg + off
+            fx, fy, fb = _feats_at_angle(ang)
             if len(fx) < 8:
                 return (0.0, cx, cy)
-            dx = (fx - center[0]).astype(np.int32)
+            dx = np.round(fx - center[0]).astype(np.int32)
-            dy = (fy - center[1]).astype(np.int32)
+            dy = np.round(fy - center[1]).astype(np.int32)
             y_lo = int(cy) - margin; y_hi = int(cy) + margin + 1
             x_lo = int(cx) - margin; x_hi = int(cx) + margin + 1
             sh_w = y_hi - y_lo; sw_w = x_hi - x_lo
             acc = np.zeros((sh_w, sw_w), dtype=np.float32)
-            spread_dtype = spread0.dtype.type
+            spread_dtype = opt_map.dtype.type
             for i in range(len(dx)):
                 ddx = int(dx[i]); ddy = int(dy[i]); b = int(fb[i])
                 bit = spread_dtype(1 << b)
@@ -1021,14 +972,19 @@ class LineShapeMatcher:
                 s_y0 = max(0, sy0); s_y1 = min(H, sy1)
                 s_x0 = max(0, sx0); s_x1 = min(W, sx1)
                 if s_y1 > s_y0 and s_x1 > s_x0:
-                    region = spread0[s_y0:s_y1, s_x0:s_x1]
+                    region = opt_map[s_y0:s_y1, s_x0:s_x1]
                     acc[a_y0:a_y1, a_x0:a_x1] += (
                         (region & bit) != 0
                     ).astype(np.float32)
             acc /= len(dx)
             _, max_val, _, max_loc = cv2.minMaxLoc(acc)
-            return (float(max_val),
+            if max_val <= 0.0:
-                    float(x_lo + max_loc[0]), float(y_lo + max_loc[1]))
+                return (0.0, cx, cy)
+            # Picco sub-pixel dentro la finestra (gestisce plateau e fit 3x3)
+            px_f, py_f = self._subpixel_peak(
+                acc, int(max_loc[0]), int(max_loc[1]), plateau_radius=margin,
+            )
+            return (float(max_val), float(x_lo + px_f), float(y_lo + py_f))
 
         # Golden-section search su [-search_radius, +search_radius]:
         # converge in log tempo a precisione ~0.1°, ~8 valutazioni vs 5
@@ -1064,7 +1020,25 @@ class LineShapeMatcher:
                 x1 = x2; s1 = s2; cx1 = cx2; cy1 = cy2
                 x2 = a_lo + _GOLDEN * (a_hi - a_lo)
                 s2, cx2, cy2 = _score_at_angle(x2)
+
+        ang_best, s_best, cx_best, cy_best = best
+        if spread_fine is None:
             return best
+        # Score finale alla posa raffinata sullo spread COARSE: stessa
+        # semantica dello score pre-refine (tolleranza spread_radius),
+        # cosi' min_score/verify mantengono il significato di prima.
+        fx, fy, fb = _feats_at_angle(ang_best)
+        if len(fx) < 8:
+            return best
+        xs = np.round(fx - center[0]).astype(np.int32) + int(round(cx_best))
+        ys = np.round(fy - center[1]).astype(np.int32) + int(round(cy_best))
+        ok = (xs >= 0) & (xs < W) & (ys >= 0) & (ys < H)
+        if not ok.any():
+            return (ang_best, 0.0, cx_best, cy_best)
+        bits = spread0[ys[ok], xs[ok]].astype(np.int32)
+        hit = (bits & np.left_shift(1, fb[ok].astype(np.int32))) != 0
+        score_coarse = float(hit.sum()) / len(fx)
+        return (ang_best, score_coarse, cx_best, cy_best)
 
     def _get_view_template(
         self, view_idx: int,
@@ -1089,26 +1063,13 @@ class LineShapeMatcher:
         """
         if self.template_gray is None:
             return 1.0
-        h, w = self.template_gray.shape
-        scale = variant.scale
-        sw = max(16, int(round(w * scale)))
-        sh = max(16, int(round(h * scale)))
-        gray_s = cv2.resize(self.template_gray, (sw, sh), interpolation=cv2.INTER_LINEAR)
         mask_src = (
             self._train_mask if self._train_mask is not None
             else np.full_like(self.template_gray, 255)
         )
-        mask_s = cv2.resize(mask_src, (sw, sh), interpolation=cv2.INTER_NEAREST)
+        gray_p, mask_p, center, diag = self._prepare_padded_template(
-        diag = int(np.ceil(np.hypot(sh, sw))) + 6
+            self.template_gray, mask_src, variant.scale,
-        py = (diag - sh) // 2; px = (diag - sw) // 2
-        gray_p = cv2.copyMakeBorder(
-            gray_s, py, diag - sh - py, px, diag - sw - px, cv2.BORDER_REPLICATE,
         )
-        mask_p = cv2.copyMakeBorder(
-            mask_s, py, diag - sh - py, px, diag - sw - px,
-            cv2.BORDER_CONSTANT, value=0,
-        )
-        center = (diag / 2.0, diag / 2.0)
         M = cv2.getRotationMatrix2D(center, angle_deg, 1.0)
         gray_r = cv2.warpAffine(gray_p, M, (diag, diag),
                                  flags=cv2.INTER_LINEAR,
@@ -1125,8 +1086,8 @@ class LineShapeMatcher:
         ix = int(round(cx)); iy = int(round(cy))
         hits = 0
         for i in range(n_feat):
-            xs = ix + int(fx[i] - center[0])
+            xs = ix + int(round(fx[i] - center[0]))
-            ys = iy + int(fy[i] - center[1])
+            ys = iy + int(round(fy[i] - center[1]))
             if 0 <= xs < W and 0 <= ys < H:
                 bit = spread_dtype(1 << int(fb[i]))
                 if spread0[ys, xs] & bit:
@@ -1140,26 +1101,13 @@ class LineShapeMatcher:
         """Soft-margin gradient similarity (Halcon Metric='use_polarity')."""
         if self.template_gray is None:
             return 0.0
-        h, w = self.template_gray.shape
-        scale = variant.scale
-        sw = max(16, int(round(w * scale)))
-        sh = max(16, int(round(h * scale)))
-        gray_s = cv2.resize(self.template_gray, (sw, sh), interpolation=cv2.INTER_LINEAR)
         mask_src = (
             self._train_mask if self._train_mask is not None
             else np.full_like(self.template_gray, 255)
         )
-        mask_s = cv2.resize(mask_src, (sw, sh), interpolation=cv2.INTER_NEAREST)
+        gray_p, mask_p, center, diag = self._prepare_padded_template(
-        diag = int(np.ceil(np.hypot(sh, sw))) + 6
+            self.template_gray, mask_src, variant.scale,
-        py = (diag - sh) // 2; px = (diag - sw) // 2
-        gray_p = cv2.copyMakeBorder(
-            gray_s, py, diag - sh - py, px, diag - sw - px, cv2.BORDER_REPLICATE,
         )
-        mask_p = cv2.copyMakeBorder(
-            mask_s, py, diag - sh - py, px, diag - sw - px,
-            cv2.BORDER_CONSTANT, value=0,
-        )
-        center = (diag / 2.0, diag / 2.0)
         M = cv2.getRotationMatrix2D(center, angle_deg, 1.0)
         gray_r = cv2.warpAffine(gray_p, M, (diag, diag),
                                  flags=cv2.INTER_LINEAR,
@@ -1179,8 +1127,8 @@ class LineShapeMatcher:
         ix = int(round(cx)); iy = int(round(cy))
         sims = []; weights = []
         for i in range(len(fx)):
-            xs = ix + int(fx[i] - center[0])
+            xs = ix + int(round(fx[i] - center[0]))
-            ys = iy + int(fy[i] - center[1])
+            ys = iy + int(round(fy[i] - center[1]))
             if not (0 <= xs < W and 0 <= ys < H):
                 continue
             tx = float(gx_t[int(fy[i]), int(fx[i])])
@@ -1201,35 +1149,34 @@ class LineShapeMatcher:
     def _subpixel_refine_lm(
         self, scene_gray: np.ndarray, variant: _Variant,
         cx: float, cy: float, angle_deg: float,
-        n_iters: int = 2,
+        n_iters: int = 4,
-    ) -> tuple[float, float]:
+        scene_grad: tuple[np.ndarray, np.ndarray] | None = None,
-        """Sub-pixel refinement iterativo via gradient-field least-squares.
+    ) -> tuple[float, float, float]:
+        """Refinement least-squares congiunto di posizione E angolo.
 
-        Halcon-equivalent SubPixel='least_squares_high'. Precisione attesa
+        Halcon-equivalent SubPixel='least_squares_high'. Per ogni feature
-        0.05 px (vs 0.5 px del fit quadratic 2D).
+        template cerca il picco sub-pixel del gradiente scena lungo la
+        normale dell'edge (snap ±2 px, fit parabolico su 5 campioni), poi
+        risolve ai minimi quadrati pesati il sistema 3x3 in (dx, dy, dθ):
+
+            n_i · (d + dθ·u_i) = t_i,    u_i = (r_y,i, -r_x,i)
+
+        dove r_i = offset feature dal centro, n_i = normale edge template,
+        t_i = offset del picco lungo n_i, u_i = derivata della rotazione
+        nella convenzione cv2.getRotationMatrix2D (R = [[c,s],[-s,c]]).
+        Tra le iterazioni offset e normali vengono ruotati analiticamente
+        (no re-warp del template). Precisione attesa <0.1 px / <0.1°.
+
+        scene_grad: (gx, gy) Sobel della scena precomputati (evita un
+        Sobel full-frame per ogni match). Ritorna (cx, cy, angle_deg).
         """
-        if self.template_gray is None:
+        t, train_mask = self._get_view_template(getattr(variant, "view_idx", 0))
-            return cx, cy
+        if t is None:
-        h, w = self.template_gray.shape
+            return cx, cy, angle_deg
-        scale = variant.scale
+        mask_src = train_mask if train_mask is not None else np.full_like(t, 255)
-        sw = max(16, int(round(w * scale)))
+        gray_p, mask_p, center, diag = self._prepare_padded_template(
-        sh = max(16, int(round(h * scale)))
+            t, mask_src, variant.scale,
-        gray_s = cv2.resize(self.template_gray, (sw, sh), interpolation=cv2.INTER_LINEAR)
-        mask_src = (
-            self._train_mask if self._train_mask is not None
-            else np.full_like(self.template_gray, 255)
         )
-        mask_s = cv2.resize(mask_src, (sw, sh), interpolation=cv2.INTER_NEAREST)
-        diag = int(np.ceil(np.hypot(sh, sw))) + 6
-        py = (diag - sh) // 2; px = (diag - sw) // 2
-        gray_p = cv2.copyMakeBorder(
-            gray_s, py, diag - sh - py, px, diag - sw - px, cv2.BORDER_REPLICATE,
-        )
-        mask_p = cv2.copyMakeBorder(
-            mask_s, py, diag - sh - py, px, diag - sw - px,
-            cv2.BORDER_CONSTANT, value=0,
-        )
-        center = (diag / 2.0, diag / 2.0)
         M = cv2.getRotationMatrix2D(center, angle_deg, 1.0)
         gray_r = cv2.warpAffine(gray_p, M, (diag, diag),
                                  flags=cv2.INTER_LINEAR,
@@ -1241,51 +1188,98 @@ class LineShapeMatcher:
         mag_t = cv2.magnitude(gx_t, gy_t)
         _, bins_t = self._gradient(gray_r)
         fx, fy, _ = self._extract_features(mag_t, bins_t, mask_r)
-        if len(fx) < 4:
+        if len(fx) < 8:
-            return cx, cy
+            return cx, cy, angle_deg
-        n = len(fx)
+        rx = (fx - center[0]).astype(np.float64)
-        ddx_t = (fx - center[0]).astype(np.float32)
+        ry = (fy - center[1]).astype(np.float64)
-        ddy_t = (fy - center[1]).astype(np.float32)
+        gxf = gx_t[fy, fx].astype(np.float64)
-        gx_tf = np.array([gx_t[int(fy[i]), int(fx[i])] for i in range(n)], dtype=np.float32)
+        gyf = gy_t[fy, fx].astype(np.float64)
-        gy_tf = np.array([gy_t[int(fy[i]), int(fx[i])] for i in range(n)], dtype=np.float32)
+        nm = np.hypot(gxf, gyf) + 1e-9
-        mag_tf = np.hypot(gx_tf, gy_tf) + 1e-6
+        nx = gxf / nm
-        nx_t = gx_tf / mag_tf
+        ny = gyf / nm
-        ny_t = gy_tf / mag_tf
+
+        if scene_grad is not None:
+            gx_s, gy_s = scene_grad
+        else:
             gx_s = cv2.Sobel(scene_gray, cv2.CV_32F, 1, 0, ksize=3)
             gy_s = cv2.Sobel(scene_gray, cv2.CV_32F, 0, 1, ksize=3)
         H, W = scene_gray.shape
-        cur_cx, cur_cy = float(cx), float(cy)
+
-        for _ in range(n_iters):
+        def _bilin(g: np.ndarray, xs: np.ndarray, ys: np.ndarray) -> np.ndarray:
-            xs = cur_cx + ddx_t
+            xs_c = np.clip(xs, 0.0, W - 1.001)
-            ys = cur_cy + ddy_t
+            ys_c = np.clip(ys, 0.0, H - 1.001)
-            xs_c = np.clip(xs, 0, W - 1.001)
-            ys_c = np.clip(ys, 0, H - 1.001)
             x0 = xs_c.astype(np.int32); y0 = ys_c.astype(np.int32)
             ax = xs_c - x0; ay = ys_c - y0
-            def _bilin(g):
+            return ((1 - ax) * (1 - ay) * g[y0, x0]
-                v00 = g[y0, x0]; v10 = g[y0, x0 + 1]
+                    + ax * (1 - ay) * g[y0, x0 + 1]
-                v01 = g[y0 + 1, x0]; v11 = g[y0 + 1, x0 + 1]
+                    + (1 - ax) * ay * g[y0 + 1, x0]
-                return ((1 - ax) * (1 - ay) * v00
+                    + ax * ay * g[y0 + 1, x0 + 1])
-                        + ax * (1 - ay) * v10
+
-                        + (1 - ax) * ay * v01
+        t_offsets = np.array([-2.0, -1.0, 0.0, 1.0, 2.0])
-                        + ax * ay * v11)
+        n_feat = len(rx)
-            sx_v = _bilin(gx_s)
+        idx = np.arange(n_feat)
-            sy_v = _bilin(gy_s)
+        cur_cx, cur_cy, cur_ang = float(cx), float(cy), float(angle_deg)
-            mag_s = np.hypot(sx_v, sy_v) + 1e-6
+        for _ in range(n_iters):
-            nx_s = sx_v / mag_s
+            px = cur_cx + rx
-            ny_s = sy_v / mag_s
+            py = cur_cy + ry
-            w = np.minimum(mag_s, 255.0).astype(np.float32)
+            # |grad| scena campionato a 5 offset lungo la normale di ogni
-            err_x = (nx_s - nx_t) * w
+            # feature; il picco sub-pixel lungo la normale e' la distanza
-            err_y = (ny_s - ny_t) * w
+            # firmata t_i dall'edge scena piu' vicino.
-            step_x = -float(err_x.sum()) / (w.sum() + 1e-6)
+            mags = np.empty((5, n_feat))
-            step_y = -float(err_y.sum()) / (w.sum() + 1e-6)
+            sxs = np.empty((5, n_feat))
-            step_x = max(-1.0, min(1.0, step_x))
+            sys_ = np.empty((5, n_feat))
-            step_y = max(-1.0, min(1.0, step_y))
+            for k, t_off in enumerate(t_offsets):
-            cur_cx += step_x
+                sx_v = _bilin(gx_s, px + t_off * nx, py + t_off * ny)
-            cur_cy += step_y
+                sy_v = _bilin(gy_s, px + t_off * nx, py + t_off * ny)
-            if abs(step_x) < 0.02 and abs(step_y) < 0.02:
+                sxs[k] = sx_v; sys_[k] = sy_v
+                mags[k] = np.hypot(sx_v, sy_v)
+            k_best = np.argmax(mags, axis=0)
+            m_pk = mags[k_best, idx]
+            t_i = t_offsets[k_best]
+            # Fit parabolico sui picchi interni (k in 1..3)
+            interior = (k_best >= 1) & (k_best <= 3)
+            if interior.any():
+                ki = k_best[interior]; ii = idx[interior]
+                m_m = mags[ki - 1, ii]; m_0 = mags[ki, ii]; m_p = mags[ki + 1, ii]
+                denom = (m_m - 2.0 * m_0 + m_p)
+                off = np.where(np.abs(denom) > 1e-9,
+                               0.5 * (m_m - m_p) / (denom - 1e-12), 0.0)
+                t_i = t_i.astype(np.float64)
+                t_i[interior] += np.clip(off, -0.5, 0.5)
+            # Peso: |grad| al picco * allineamento direzione (mod π se no
+            # polarity). Feature senza edge (sotto weak_grad) escluse;
+            # picco sul bordo finestra = snap inaffidabile → dimezzato.
+            sx_pk = sxs[k_best, idx]; sy_pk = sys_[k_best, idx]
+            cos_al = (nx * sx_pk + ny * sy_pk) / (m_pk + 1e-9)
+            align = np.maximum(0.0, cos_al) if self.use_polarity else np.abs(cos_al)
+            wgt = np.minimum(m_pk, 255.0) * align * align
+            wgt[m_pk < self.weak_grad] = 0.0
+            wgt[~interior] *= 0.5
+            if float(wgt.sum()) < 1e-6:
                 break
-        return cur_cx, cur_cy
+            # LSQ pesato 3x3: A_i = [n_x, n_y, n_x·r_y - n_y·r_x]
+            a3 = nx * ry - ny * rx
+            A = np.stack([nx, ny, a3], axis=1)
+            Aw = A * wgt[:, None]
+            AtA = Aw.T @ A
+            Atb = Aw.T @ t_i.astype(np.float64)
+            try:
+                sol = np.linalg.solve(AtA + 1e-6 * np.eye(3), Atb)
+            except np.linalg.LinAlgError:
+                break
+            ddx = float(np.clip(sol[0], -1.5, 1.5))
+            ddy = float(np.clip(sol[1], -1.5, 1.5))
+            dth = float(np.clip(sol[2], -math.radians(1.5), math.radians(1.5)))
+            cur_cx += ddx
+            cur_cy += ddy
+            cur_ang += math.degrees(dth)
+            # Ruota offset e normali di dθ (convenzione R = [[c,s],[-s,c]])
+            c = math.cos(dth); s = math.sin(dth)
+            rx, ry = c * rx + s * ry, -s * rx + c * ry
+            nx, ny = c * nx + s * ny, -s * nx + c * ny
+            if abs(ddx) < 0.01 and abs(ddy) < 0.01 and abs(dth) < 1.7e-4:
+                break
+        return cur_cx, cur_cy, cur_ang
 
     def _verify_ncc(
         self, scene_gray: np.ndarray, cx: float, cy: float,
@@ -1370,15 +1364,21 @@ class LineShapeMatcher:
         coarse_stride: int = 1,
         scale_penalty: float = 0.0,
         search_roi: tuple[int, int, int, int] | None = None,
-        pyramid_propagate: bool = False,  # off di default: meno duplicati
+        # ON di default: full-res valutato solo in finestre locali attorno
-        propagate_topk: int = 4,
+        # ai picchi top-level (costo ∝ candidati, non varianti × W × H).
-        refine_pose_joint: bool = False,
+        # I duplicati che avevano fatto disattivare questa modalita' sono
+        # gestiti dalla NMS IoU poligonale post-refine.
+        pyramid_propagate: bool = True,
+        propagate_topk: int = 8,
+        refine_pose_joint: bool = False,  # deprecato: alias di refine_angle
         greediness: float = 0.0,
         batch_top: bool = False,
         nms_iou_threshold: float = 0.3,
         min_recall: float = 0.0,
         use_soft_score: bool = False,
-        subpixel_lm: bool = False,
+        # ON di default: least-squares finale (posizione + angolo) sui
+        # gradienti scena, precisione attesa <0.1 px / <0.1°.
+        subpixel_lm: bool = True,
         debug: bool = False,
         profile: bool = False,
     ) -> list[Match]:
@@ -1462,7 +1462,7 @@ class LineShapeMatcher:
         cached = self._scene_cache_get(cache_key) if cache_key else None
         if cached is not None:
             grays, spread_top, bit_active_top, density_top, spread0, \
-                bit_active_full, density_full, top = cached
+                bit_active_full, density_full, top, spread_fine = cached
         else:
             grays = [gray0]
             for _ in range(self.pyramid_levels - 1):
@@ -1478,6 +1478,7 @@ class LineShapeMatcher:
             spread0 = None
             bit_active_full = None
             density_full = None
+            spread_fine = None
         _checkpoint("spread_top")
         if nms_radius is None:
             nms_radius = max(8, min(self.template_size) // 2)
@@ -1488,6 +1489,18 @@ class LineShapeMatcher:
         # coarse_angle_factor (skip 1 ogni 2): con step fine non e' utile.
         # Risultato osservato: precisione "veloce" 10° dava risultati
         # migliori di "preciso" 2° proprio perche evitava il pruning.
+        # Il path windowed (pyramid_propagate) assume che il picco
+        # top-level localizzi la posizione entro il margine finestra.
+        # Su template ALLUNGATI (es. lama 40x280, o ROI parziale lungo un
+        # asse) lo score top-level ha un plateau lungo l'asse e il picco
+        # puo' essere lontano decine di px dal centro vero → le finestre
+        # tagliano fuori la posa giusta e il match muore in verify NCC.
+        # In quel caso si usa il full-scan esatto (costo maggiore ma
+        # nessuna perdita di recall).
+        if pyramid_propagate and self.template_size != (0, 0):
+            tw_t, th_t = self.template_size
+            if max(tw_t, th_t) / max(1, min(tw_t, th_t)) > 2.0:
+                pyramid_propagate = False
         eff_step = self._effective_angle_step()
         top_factor = self.top_score_factor
         cf_eff = max(1, coarse_angle_factor)
@@ -1598,16 +1611,25 @@ class LineShapeMatcher:
                 return vi, -1.0
             best = float(score.max())
             if pyramid_propagate and best > 0:
-                flat = score.ravel()
+                # Picchi = MASSIMI LOCALI sopra soglia, non top-K pixel:
-                k = min(propagate_topk, flat.size)
+                # su template allungati lo score top-level ha plateau
-                idx = np.argpartition(-flat, k - 1)[:k]
+                # estesi e i top-K pixel si concentrano tutti sulle 2-3
+                # istanze piu' forti, perdendo per sempre le altre.
+                # Soglia permissiva (0.5x): un picco scartato qui =
+                # istanza persa, un picco in piu' = solo una finestra
+                # extra di costo marginale (dedup via mark).
+                thr = top_thresh * 0.5
+                dil = cv2.dilate(score, np.ones((5, 5), np.uint8))
+                ys_l, xs_l = np.nonzero((score >= dil) & (score >= thr))
                 peaks: list[tuple[int, int, float]] = []
-                for i in idx:
+                if len(ys_l):
-                    s = float(flat[i])
+                    vals = score[ys_l, xs_l]
-                    if s < top_thresh * 0.7:
+                    k = min(max(propagate_topk, 2 * max_matches), len(vals))
-                        continue
+                    sel = np.argpartition(-vals, k - 1)[:k]
-                    yt, xt = int(i // score.shape[1]), int(i % score.shape[1])
+                    peaks = [
-                    peaks.append((xt, yt, s))
+                        (int(xs_l[i]), int(ys_l[i]), float(vals[i]))
+                        for i in sel
+                    ]
                 peaks_by_vi[vi] = peaks
             return vi, best
 
@@ -1664,6 +1686,13 @@ class LineShapeMatcher:
                 expanded.add(vi_n)
                 # Usa lo score del coarse come stima per il sort successivo
                 score_by_vi[vi_n] = max(score_by_vi.get(vi_n, 0.0), s_top)
+                # Propaga i picchi top-level del coarse anche ai vicini:
+                # l'oggetto e' nella stessa posizione (angolo ±step), quindi
+                # anche i vicini possono usare il path windowed invece del
+                # full-scan dell'intera scena (che dominava il costo full-res).
+                if (pyramid_propagate and vi_n != vi_c
+                        and peaks_by_vi.get(vi_c)):
+                    peaks_by_vi.setdefault(vi_n, []).extend(peaks_by_vi[vi_c])
         kept_variants: list[tuple[int, float]] = [
             (vi, score_by_vi[vi]) for vi in expanded
         ]
@@ -1690,54 +1719,63 @@ class LineShapeMatcher:
                     if (spread0 & (spread0.dtype.type(1) << b)).any())
             )
             density_full = _jit_popcount(spread0)
+            # Bitmap fine (raggio 1) per il refine: sulla bitmap dilatata
+            # lo score satura e il refine angolare/posizionale non vede
+            # alcun gradiente (vedi _refine_angle).
+            spread_fine = self._spread_bitmap(gray0, radius=1)
             # Salva cache scena complete
             if cache_key is not None:
                 self._scene_cache_put(cache_key, (
                     grays, spread_top, bit_active_top, density_top,
-                    spread0, bit_active_full, density_full, top,
+                    spread0, bit_active_full, density_full, top, spread_fine,
                 ))
         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.
+        # downsampling (2·sf_top px) + plateau radius del subpixel (10) +
-        propagate_margin = sf_top + self.spread_radius + max(8, nms_radius // 2)
+        # slack. NON serve includere nms_radius: la NMS lavora sui candidati
+        # estratti, non richiede score validi oltre il plateau del picco.
+        propagate_margin = 2 * sf_top + max(10, self.spread_radius) + 6
         H_full, W_full = spread0.shape
 
         def _full_score(vi: int) -> tuple[int, np.ndarray]:
             var = self.variants[vi]
             lvl0 = var.levels[0]
-            if not pyramid_propagate or vi not in peaks_by_vi or not peaks_by_vi[vi]:
+            peaks = peaks_by_vi.get(vi) if pyramid_propagate else None
+            margin = propagate_margin
+            if not peaks:
                 # Path legacy: scansiona intera scena
                 return vi, _jit_score_bitmap_rescored(
                     spread0, lvl0.dx, lvl0.dy, lvl0.bin, bit_active_full,
                     bg_cache_full[var.scale],
                 )
-            # Path piramide propagata: valuta solo crop locali attorno
+            # Path piramide propagata: valuta solo finestre locali attorno
-            # alle posizioni dei picchi top-level (riproiettati a full-res).
+            # ai picchi top-level (riproiettati a full-res). Il kernel
+            # windowed campiona lo spread dell'INTERA scena: chiamare il
+            # kernel su un crop trattava le feature fuori-crop come miss
+            # (template raggio > finestra → score ~0 ovunque, 0 match).
             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]:
+            for xt, yt, _s in peaks:
                 cx0 = xt * sf_top
                 cy0 = yt * sf_top
-                x_lo = max(0, cx0 - propagate_margin)
+                x_lo = max(0, cx0 - margin)
-                x_hi = min(W_full, cx0 + propagate_margin + 1)
+                x_hi = min(W_full, cx0 + margin + 1)
-                y_lo = max(0, cy0 - propagate_margin)
+                y_lo = max(0, cy0 - margin)
-                y_hi = min(H_full, cy0 + propagate_margin + 1)
+                y_hi = min(H_full, cy0 + 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
+                score_win = _jit_score_bitmap_rescored_window(
-                spread_crop = np.ascontiguousarray(spread0[y_lo:y_hi, x_lo:x_hi])
+                    spread0, lvl0.dx, lvl0.dy, lvl0.bin,
-                bg_crop = np.ascontiguousarray(bg[y_lo:y_hi, x_lo:x_hi])
+                    bit_active_full, bg,
-                score_crop = _jit_score_bitmap_rescored(
+                    y_lo, x_lo, y_hi - y_lo, x_hi - x_lo,
-                    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,
+                    score_full[y_lo:y_hi, x_lo:x_hi], score_win,
                 )
                 mark[y_lo:y_hi, x_lo:x_hi] = True
             return vi, score_full
@@ -1811,6 +1849,14 @@ class LineShapeMatcher:
         # Subpixel + refine + verify solo sui candidati pre-NMS (max pre_cap)
         kept: list[Match] = []
         tw, th = self.template_size
+        # Sobel scena precomputato una volta per il refine LM (prima era
+        # un Sobel full-frame per OGNI match).
+        scene_grad = None
+        if subpixel_lm and self.template_gray is not None and preliminary_int:
+            scene_grad = (
+                cv2.Sobel(gray0, cv2.CV_32F, 1, 0, ksize=3),
+                cv2.Sobel(gray0, cv2.CV_32F, 0, 1, ksize=3),
+            )
         for score, xi, yi, vi in preliminary_int:
             if subpixel and vi in score_maps:
                 cx_f, cy_f = self._subpixel_peak(
@@ -1821,12 +1867,10 @@ class LineShapeMatcher:
             var = self.variants[vi]
             ang_f = var.angle_deg
             score_f = score
-            if refine_pose_joint and self.template_gray is not None:
+            # refine_pose_joint (Nelder-Mead) rimosso: valutava lo score a
-                ang_f, score_f, cx_f, cy_f = self._refine_pose_joint(
+            # posizioni intere su bitmap satura (funzione a gradini piatta,
-                    spread0, self.template_gray, cx_f, cy_f,
+            # il simplex terminava subito). Ora e' alias del refine standard.
-                    var.angle_deg, var.scale, mask_full,
+            if (refine_angle or refine_pose_joint) and self.template_gray is not None:
-                )
-            elif refine_angle and self.template_gray is not None:
                 ang_f, score_f, cx_f, cy_f = self._refine_angle(
                     spread0, bit_active_full, self.template_gray, cx_f, cy_f,
                     var.angle_deg, var.scale, mask_full,
@@ -1835,14 +1879,15 @@ class LineShapeMatcher:
                     # del bin angolare della variante grezza.
                     search_radius=self._effective_angle_step(),
                     original_score=score,
+                    spread_fine=spread_fine,
                 )
-            # Halcon SubPixel='least_squares_high': refinement iterativo
+            # Halcon SubPixel='least_squares_high': least-squares finale
-            # gradient-field per precisione 0.05 px (vs 0.5 quadratic 2D).
+            # (posizione + angolo) sui gradienti scena, <0.1 px / <0.1°.
             if subpixel_lm and self.template_gray is not None:
-                cx_lm, cy_lm = self._subpixel_refine_lm(
+                cx_lm, cy_lm, ang_lm = self._subpixel_refine_lm(
-                    gray0, var, cx_f, cy_f, ang_f,
+                    gray0, var, cx_f, cy_f, ang_f, scene_grad=scene_grad,
                 )
-                cx_f, cy_f = float(cx_lm), float(cy_lm)
+                cx_f, cy_f, ang_f = float(cx_lm), float(cy_lm), float(ang_lm)
             # NCC verify (Halcon-style): se ncc_skip_above < 1.0 salta
             # il calcolo per shape-score gia alti. Default 1.01 = NCC sempre,
             # piu sicuro contro falsi positivi (lo shape-score satura facile).

pm2d/matcher.py

@@ -91,8 +91,16 @@ class EdgeShapeMatcher:
         a0, a1 = self.angle_range_deg
         if self.angle_step_deg <= 0 or a0 >= a1:
             return [float(a0)]
-        n = int(np.floor((a1 - a0) / self.angle_step_deg))
+        # n+1 valori per includere l'estremo superiore del range: con il
-        return [float(a0 + i * self.angle_step_deg) for i in range(n)]
+        # solo floor un range [0, 90] step 5 si fermava a 85° (off-by-one).
+        n = int(np.floor((a1 - a0) / self.angle_step_deg)) + 1
+        angles = [float(a0 + i * self.angle_step_deg) for i in range(n)]
+        if a1 - a0 >= 360.0:
+            # Range che copre il giro completo: a0+360° è la stessa pose di
+            # a0, escludi il duplicato (variante inutile in train/find).
+            eps = 1e-6
+            angles = [a for a in angles if a < a0 + 360.0 - eps]
+        return angles
 
     def train(self, template_bgr: np.ndarray) -> int:
         """Genera varianti per tutte le combinazioni (angolo, scala)."""
@@ -222,6 +230,14 @@ class EdgeShapeMatcher:
             for y, x in zip(ys, xs):
                 candidates.append((float(res[y, x]), int(x), int(y), ti))
 
+        # Cap candidati top-level: senza limite np.where con soglia bassa
+        # può generare migliaia di candidati, ognuno con un matchTemplate
+        # full-res nel refinement. Tieni solo i migliori per score.
+        max_candidates = max(1, max_matches * 10)
+        if len(candidates) > max_candidates:
+            candidates.sort(key=lambda c: -c[0])
+            candidates = candidates[:max_candidates]
+
         # Refinement a risoluzione piena: per ogni candidato top, finestra locale
         refined: list[tuple[float, int, int, int]] = []
         margin = sf + 4
@@ -294,6 +310,10 @@ class EdgeShapeMatcher:
         )
         arrays = {f"edge_{i}": t.edge for i, t in enumerate(self.templates)}
         arrays.update({f"mask_{i}": t.mask for i, t in enumerate(self.templates)})
+        # Persisti anche il grayscale originale: senza, l'overlay edge
+        # spariva dopo load() (template_gray restava None).
+        if self.template_gray is not None:
+            arrays["template_gray"] = self.template_gray
         np.savez_compressed(path, params=params, meta=meta, **arrays)
 
     @classmethod
@@ -312,6 +332,10 @@ class EdgeShapeMatcher:
             top_score_factor=float(p[12]) if len(p) > 12 else 0.6,
         )
         m.template_size = (int(p[8]), int(p[9]))
+        # Retrocompatibilità: modelli salvati prima non hanno template_gray
+        # (resta None: overlay edge non disponibile ma find() funziona).
+        if "template_gray" in z.files:
+            m.template_gray = z["template_gray"]
         meta = z["meta"]
         for i in range(len(meta)):
             m.templates.append(

pm2d/web/server.py

@@ -12,6 +12,7 @@ from __future__ import annotations
 import hashlib
 import os
 import tempfile
+import threading
 import time
 import uuid
 from collections import OrderedDict
@@ -64,14 +65,23 @@ STATIC_DIR.mkdir(exist_ok=True)
 CACHE_DIR = Path(tempfile.gettempdir()) / "pm2d_cache"
 CACHE_DIR.mkdir(exist_ok=True)
 
-# Cache in-memory (soft, ricaricata da disco se mancante)
+# Cache in-memory (soft, ricaricata da disco se mancante).
-_IMG_CACHE: dict[str, np.ndarray] = {}
+# LRU con capacità limitata: senza eviction le immagini si accumulavano
+# senza limite (leak di memoria su server long-running).
+_IMG_CACHE: OrderedDict[str, np.ndarray] = OrderedDict()
+_IMG_CACHE_SIZE = 64
 
 # Cache matcher addestrati: (roi_hash, params_hash) -> LineShapeMatcher
 # LRU con capacità limitata
 _MATCHER_CACHE: OrderedDict = OrderedDict()
 _MATCHER_CACHE_SIZE = 8
 
+# Lock globale matcher: gli endpoint girano nel threadpool FastAPI ma i
+# matcher condivisi (_MATCHER_CACHE, _RECIPE_MATCHERS) mutano stato interno
+# durante train()/find(). Serializzare il matching è la soluzione semplice
+# e corretta (un lock per-ricetta sarebbe over-engineering).
+_MATCHER_LOCK = threading.Lock()
+
 
 def _matcher_cache_key(roi: np.ndarray, tech: dict) -> str:
     h = hashlib.md5()
@@ -102,23 +112,32 @@ def _cache_put_matcher(key: str, matcher) -> None:
         _MATCHER_CACHE.popitem(last=False)
 
 
+def _img_cache_put(key: str, value: np.ndarray) -> None:
+    """Inserisce in _IMG_CACHE con eviction LRU (cap _IMG_CACHE_SIZE)."""
+    _IMG_CACHE[key] = value
+    _IMG_CACHE.move_to_end(key)
+    while len(_IMG_CACHE) > _IMG_CACHE_SIZE:
+        _IMG_CACHE.popitem(last=False)
+
+
 def _store_image(img: np.ndarray) -> str:
     iid = uuid.uuid4().hex[:12]
     cv2.imwrite(str(CACHE_DIR / f"{iid}.png"), img)
-    _IMG_CACHE[iid] = img
+    _img_cache_put(iid, img)
     return iid
 
 
 def _load_image(iid: str) -> np.ndarray | None:
     cached = _IMG_CACHE.get(iid)
     if cached is not None:
+        _IMG_CACHE.move_to_end(iid)  # LRU touch
         return cached
     p = CACHE_DIR / f"{iid}.png"
     if not p.exists():
         return None
     img = cv2.imread(str(p))
     if img is not None:
-        _IMG_CACHE[iid] = img
+        _img_cache_put(iid, img)
     return img
 
 app = FastAPI(title="PM2D Webapp", version="1.0.0")
@@ -131,6 +150,39 @@ def _encode_png(img: np.ndarray) -> bytes:
     return buf.tobytes()
 
 
+def _clamp_roi(x: int, y: int, w: int, h: int,
+               img_w: int, img_h: int) -> tuple[int, int, int, int]:
+    """Clampa la ROI dentro i limiti immagine.
+
+    Una ROI fuori immagine causava slice vuote → crash 500 negli endpoint
+    che non clampavano. Solleva 400 se la ROI risultante è degenere
+    (lato < 16 px: sotto questa soglia il train non estrae abbastanza
+    edge feature e produce 0 varianti → find() esplode con 500).
+    """
+    x = max(0, min(int(x), img_w - 1))
+    y = max(0, min(int(y), img_h - 1))
+    w = min(int(w), img_w - x)
+    h = min(int(h), img_h - y)
+    if w < 16 or h < 16:
+        raise HTTPException(
+            400, f"ROI fuori immagine o degenere: [{x}, {y}, {w}, {h}] "
+                 f"su immagine {img_w}x{img_h} (lato minimo 16 px)")
+    return x, y, w, h
+
+
+def _check_trained(m: "LineShapeMatcher", n_variants: int) -> None:
+    """Solleva 422 se il train non ha prodotto varianti.
+
+    Succede con ROI senza contrasto (sfondo uniforme) o troppo piccola:
+    senza questo check il find() successivo esplode con RuntimeError → 500.
+    """
+    if n_variants <= 0 or not m.variants:
+        raise HTTPException(
+            422, "La ROI non contiene abbastanza edge feature per il "
+                 "training (zona troppo uniforme o piccola): scegliere "
+                 "una regione con contorni netti")
+
+
 def _draw_matches(scene: np.ndarray, matches: list[Match],
                    template_gray: np.ndarray | None,
                    matcher: "LineShapeMatcher | None" = None) -> np.ndarray:
@@ -172,17 +224,29 @@ def _draw_matches(scene: np.ndarray, matches: list[Match],
             # `center = (diag / 2.0, diag / 2.0)` (no -1). Usare (tw-1)/2
             # introduceva uno shift di 0.5px per template di lato pari.
             cx_t = tw / 2.0; cy_t = th / 2.0
+            # Lavora su un CROP locale della scena di lato = diagonale del
+            # template ruotato+scalato (+margine), come _verify_ncc: warp
+            # + Sobel sull'INTERA scena per ogni match erano O(W·H) cadauno
+            # (costosissimo su scene grandi con molti match).
+            diag = int(np.ceil(np.hypot(tw, th) * m.scale)) + 8
+            x0 = int(round(m.cx)) - diag // 2
+            y0 = int(round(m.cy)) - diag // 2
+            gx0 = max(0, x0); gy0 = max(0, y0)
+            gx1 = min(W_scene, x0 + diag); gy1 = min(H_scene, y0 + diag)
+            cw, ch_ = gx1 - gx0, gy1 - gy0
+            if cw >= 3 and ch_ >= 3:
                 M = cv2.getRotationMatrix2D((cx_t, cy_t), m.angle_deg, m.scale)
-            M[0, 2] += m.cx - cx_t
+                # Porta il centro template a (m.cx - gx0, m.cy - gy0) del crop
-            M[1, 2] += m.cy - cy_t
+                M[0, 2] += (m.cx - gx0) - cx_t
+                M[1, 2] += (m.cy - gy0) - cy_t
                 warped_gray = cv2.warpAffine(
-                t, M, (W_scene, H_scene),
+                    t, M, (cw, ch_),
                     flags=cv2.INTER_LINEAR, borderValue=0)
                 # Maschera: train_mask se disponibile, altrimenti rettangolo pieno
                 mask_src = (matcher._train_mask if matcher._train_mask is not None
                             else np.full((th, tw), 255, dtype=np.uint8))
                 warped_mask = cv2.warpAffine(
-                mask_src, M, (W_scene, H_scene),
+                    mask_src, M, (cw, ch_),
                     flags=cv2.INTER_NEAREST, borderValue=0)
                 # Erode minimo (3x3) per togliere SOLO artefatti border-padding
                 # (~1px di bordo nero da warpAffine borderValue=0). Erode piu'
@@ -197,11 +261,16 @@ def _draw_matches(scene: np.ndarray, matches: list[Match],
                     edge_mask = mag >= matcher.strong_grad
                 edge_mask = edge_mask & (warped_mask > 0)
                 if edge_mask.any():
-                edge_overlay = np.zeros_like(out)
+                    # Edge ritraslati nel sistema scena: blend solo sul crop
+                    # (addWeighted lascia invariati i pixel con overlay nullo,
+                    # quindi l'output visivo è identico al full-frame).
+                    sub = out[gy0:gy1, gx0:gx1]
+                    edge_overlay = np.zeros_like(sub)
                     # Ciano (cambiato da verde): non collide col verde dell'asse
                     # Y dell'UCS che altrimenti scompariva nell'overlay edge.
                     edge_overlay[edge_mask] = (255, 200, 0)  # ciano (BGR)
-                out = cv2.addWeighted(out, 1.0, edge_overlay, 0.6, 0)
+                    out[gy0:gy1, gx0:gx1] = cv2.addWeighted(
+                        sub, 1.0, edge_overlay, 0.6, 0)
         L = max(20, int(L_base * m.scale))
         # X axis = rotazione di (1, 0) con cv2 matrix → (cos, -sin)
         x_end = (int(cx + L * ca), int(cy - L * sa))
@@ -246,7 +315,9 @@ class MatchParams(BaseModel):
     num_features: int = 96
     weak_grad: float = 30.0
     strong_grad: float = 60.0
-    spread_radius: int = 5
+    # 4 allineato col default del matcher: raggio 5 peggiora la precisione
+    # di rotazione (spread troppo largo appiattisce il picco angolare).
+    spread_radius: int = 4
     pyramid_levels: int = 3
     verify_threshold: float = 0.4
 
@@ -407,7 +478,13 @@ def _simple_to_technical(
         "min_score": p.min_score,
         "max_matches": p.max_matches,
         "nms_radius": 0,
-        "verify_threshold": FILTRO_FP_MAP.get(p.filtro_fp, 0.35),
+        # Fallback = livello "medio" della mappa (no valore hardcoded
+        # che divergerebbe se la mappa cambia).
+        "verify_threshold": FILTRO_FP_MAP.get(p.filtro_fp, FILTRO_FP_MAP["medio"]),
+        # "off" deve disabilitare DAVVERO il verify NCC: passare solo
+        # verify_threshold=0.0 lascerebbe attivo il calcolo NCC (che può
+        # comunque scartare match con score negativo / patch uniformi).
+        "verify_ncc": p.filtro_fp != "off",
         "scale_penalty": p.penalita_scala,
     }
 
@@ -541,9 +618,7 @@ def match(p: MatchParams):
     if model is None or scene is None:
         raise HTTPException(404, "Immagini non trovate")
     x, y, w, h = p.roi
-    x = max(0, x); y = max(0, y)
+    x, y, w, h = _clamp_roi(x, y, w, h, model.shape[1], model.shape[0])
-    w = max(1, min(w, model.shape[1] - x))
-    h = max(1, min(h, model.shape[0] - y))
     roi_img = model[y:y + h, x:x + w]
 
     tech_for_cache = {
@@ -557,6 +632,8 @@ def match(p: MatchParams):
         "pyramid_levels": p.pyramid_levels,
     }
     key = _matcher_cache_key(roi_img, tech_for_cache)
+    # Lock globale: matcher condivisi tra thread del pool FastAPI
+    with _MATCHER_LOCK:
         m = _cache_get_matcher(key)
         if m is None:
             m = LineShapeMatcher(
@@ -570,6 +647,7 @@ def match(p: MatchParams):
                 pyramid_levels=p.pyramid_levels,
             )
             t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
+            _check_trained(m, n)
             _cache_put_matcher(key, m)
         else:
             n = len(m.variants); t_train = 0.0
@@ -578,6 +656,8 @@ def match(p: MatchParams):
         matches = m.find(
             scene, min_score=p.min_score, max_matches=p.max_matches,
             nms_radius=nms, verify_threshold=p.verify_threshold,
+            # Soglia 0 = filtro FP disattivato: skippa proprio il calcolo NCC
+            verify_ncc=p.verify_threshold > 0.0,
         )
         t_find = time.time() - t0
 
@@ -610,9 +690,7 @@ def match_simple(p: SimpleMatchParams):
     if model is None or scene is None:
         raise HTTPException(404, "Immagini non trovate")
     x, y, w, h = p.roi
-    x = max(0, x); y = max(0, y)
+    x, y, w, h = _clamp_roi(x, y, w, h, model.shape[1], model.shape[0])
-    w = max(1, min(w, model.shape[1] - x))
-    h = max(1, min(h, model.shape[0] - y))
     roi_img = model[y:y + h, x:x + w]
 
     tech = _simple_to_technical(p, roi_img)
@@ -621,6 +699,8 @@ def match_simple(p: SimpleMatchParams):
     # Halcon-mode init params: incidono sul training, includere in cache key
     halcon_init_key = f"|pol={p.use_polarity}|gpu={p.use_gpu}"
     key = key + halcon_init_key
+    # Lock globale: matcher condivisi tra thread del pool FastAPI
+    with _MATCHER_LOCK:
         m = _cache_get_matcher(key)
         if m is None:
             m = LineShapeMatcher(
@@ -637,6 +717,7 @@ def match_simple(p: SimpleMatchParams):
                 use_gpu=p.use_gpu,
             )
             t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
+            _check_trained(m, n)
             _cache_put_matcher(key, m)
         else:
             n = len(m.variants); t_train = 0.0
@@ -646,6 +727,8 @@ def match_simple(p: SimpleMatchParams):
         matches = m.find(
             scene, min_score=tech["min_score"], max_matches=tech["max_matches"],
             nms_radius=nms, verify_threshold=tech["verify_threshold"],
+            # filtro_fp="off" → verify NCC davvero disabilitato
+            verify_ncc=tech.get("verify_ncc", True),
             scale_penalty=tech.get("scale_penalty", 0.0),
             # Halcon-mode flags
             min_recall=p.min_recall,
@@ -681,6 +764,7 @@ def tune(p: TuneParams):
     if model is None:
         raise HTTPException(404, "Immagine non trovata")
     x, y, w, h = p.roi
+    x, y, w, h = _clamp_roi(x, y, w, h, model.shape[1], model.shape[0])
     roi_img = model[y:y + h, x:x + w]
     t = auto_tune(roi_img)
     # Esponi parametri tecnici + meta diagnostica (_self_score, _validation,
@@ -814,6 +898,7 @@ def save_recipe(p: SaveRecipeParams):
     if model is None:
         raise HTTPException(404, "Modello non trovato")
     x, y, w, h = p.roi
+    x, y, w, h = _clamp_roi(x, y, w, h, model.shape[1], model.shape[0])
     roi_img = model[y:y + h, x:x + w]
     sp = SimpleMatchParams(
         model_id=p.model_id, scene_id=p.scene_id or p.model_id, roi=p.roi,
@@ -838,7 +923,10 @@ def save_recipe(p: SaveRecipeParams):
         use_polarity=p.use_polarity,
         use_gpu=p.use_gpu,
     )
-    m.train(roi_img)
+    # Lock globale: serializza il training pesante col matching in corso
+    with _MATCHER_LOCK:
+        n_var = m.train(roi_img)
+    _check_trained(m, n_var)
     safe_name = "".join(c for c in p.name if c.isalnum() or c in "._-")
     if not safe_name:
         raise HTTPException(400, "Nome ricetta non valido")
@@ -864,6 +952,14 @@ _RECIPE_MATCHERS: OrderedDict = OrderedDict()
 _RECIPE_MATCHERS_SIZE = 4
 
 
+def _recipe_matchers_put(name: str, matcher: LineShapeMatcher) -> None:
+    """Inserisce in _RECIPE_MATCHERS con eviction LRU (cap _RECIPE_MATCHERS_SIZE)."""
+    _RECIPE_MATCHERS[name] = matcher
+    _RECIPE_MATCHERS.move_to_end(name)
+    while len(_RECIPE_MATCHERS) > _RECIPE_MATCHERS_SIZE:
+        _RECIPE_MATCHERS.popitem(last=False)
+
+
 @app.post("/recipes/{name}/load")
 def load_recipe(name: str):
     """Carica ricetta .npz e popola cache matcher in memoria.
@@ -878,10 +974,8 @@ def load_recipe(name: str):
     if not path.is_file():
         raise HTTPException(404, f"Ricetta non trovata: {safe_name}")
     m = LineShapeMatcher.load_model(str(path))
-    _RECIPE_MATCHERS[safe_name] = m
+    with _MATCHER_LOCK:
-    _RECIPE_MATCHERS.move_to_end(safe_name)
+        _recipe_matchers_put(safe_name, m)
-    while len(_RECIPE_MATCHERS) > _RECIPE_MATCHERS_SIZE:
-        _RECIPE_MATCHERS.popitem(last=False)
     return {
         "name": safe_name,
         "n_variants": len(m.variants),
@@ -905,7 +999,9 @@ class RecipeMatchParams(BaseModel):
     greediness: float = 0.0
     refine_pose_joint: bool = False
     search_roi: list[int] | None = None
-    verify_threshold: float = 0.5
+    # Allineato a MatchParams.verify_threshold (0.4): valori divergenti
+    # davano risultati diversi tra /match e /match_recipe a parità di scena.
+    verify_threshold: float = 0.4
     scale_penalty: float = 0.0
 
 
@@ -913,23 +1009,30 @@ class RecipeMatchParams(BaseModel):
 def match_recipe(p: RecipeMatchParams):
     """Match con ricetta pre-trained: zero training, solo find."""
     safe_name = p.recipe if p.recipe.endswith(".npz") else f"{p.recipe}.npz"
-    m = _RECIPE_MATCHERS.get(safe_name)
-    if m is None:
-        # Auto-load on demand
-        path = RECIPES_DIR / safe_name
-        if not path.is_file():
-            raise HTTPException(404, f"Ricetta non trovata: {safe_name}")
-        m = LineShapeMatcher.load_model(str(path))
-        _RECIPE_MATCHERS[safe_name] = m
     scene = _load_image(p.scene_id)
     if scene is None:
         raise HTTPException(404, "Scena non trovata")
     search_roi_t = tuple(p.search_roi) if p.search_roi else None
+    # Lock globale: matcher condivisi tra thread del pool FastAPI
+    with _MATCHER_LOCK:
+        m = _RECIPE_MATCHERS.get(safe_name)
+        if m is not None:
+            _RECIPE_MATCHERS.move_to_end(safe_name)  # LRU touch
+        else:
+            # Auto-load on demand: stessa eviction LRU di load_recipe
+            # (senza cap la cache cresceva senza limite)
+            path = RECIPES_DIR / safe_name
+            if not path.is_file():
+                raise HTTPException(404, f"Ricetta non trovata: {safe_name}")
+            m = LineShapeMatcher.load_model(str(path))
+            _recipe_matchers_put(safe_name, m)
         t0 = time.time()
         matches = m.find(
             scene,
             min_score=p.min_score, max_matches=p.max_matches,
             verify_threshold=p.verify_threshold,
+            # Soglia 0 = filtro FP disattivato: skippa proprio il calcolo NCC
+            verify_ncc=p.verify_threshold > 0.0,
             scale_penalty=p.scale_penalty,
             min_recall=p.min_recall,
             use_soft_score=p.use_soft_score,