feat: angle_step auto adattivo a dimensione template

Halcon-style: angle_step_deg=0 attiva derivazione automatica
step = atan(2/max_side) deg, clampato [0.5, 10]. Template grande
ottiene step fine, piccolo step grosso. auto_tune emette il valore
calcolato direttamente.

_refine_angle ora usa _effective_angle_step() per coerenza con
training quando la modalita auto e attiva.

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

pm2d/auto_tune.py

@@ -220,8 +220,11 @@ def auto_tune(template_bgr: np.ndarray, mask: np.ndarray | None = None) -> dict:
     else:
         min_score = 0.45
 
-    # angle step: 5° default; se simmetria, mantengo step ma range ridotto
-    angle_step = 5.0
+    # angle step adattivo (Halcon-style): atan(2/max_side) deg, clampato.
+    # Template grande → step fine (rotazione minima visibile su perimetro).
+    # Template piccolo → step grosso (over-sampling = sprecato).
+    max_side = max(h, w)
+    angle_step = float(np.clip(np.degrees(np.arctan2(2.0, max_side)), 1.0, 8.0))
 
     result = {
         "backend": "line",

pm2d/line_matcher.py

@@ -197,12 +197,31 @@ class LineShapeMatcher:
         n = int(np.floor((s1 - s0) / self.scale_step)) + 1
         return [float(s0 + i * self.scale_step) for i in range(n)]
 
+    def _auto_angle_step(self) -> float:
+        """Step angolare derivato da dimensione template (Halcon-style).
+
+        Formula: step ≈ atan(2 / max_side) gradi. Garantisce che la
+        rotazione minima produca uno spostamento di ≥2 px sul perimetro
+        del template (sotto sample il matching coarse perde candidati).
+        Clampato in [0.5°, 10°].
+        """
+        max_side = max(self.template_size) if self.template_size != (0, 0) else 64
+        step = math.degrees(math.atan2(2.0, float(max_side)))
+        return float(np.clip(step, 0.5, 10.0))
+
+    def _effective_angle_step(self) -> float:
+        """Risolve angle_step_deg gestendo modalità auto (<=0)."""
+        if self.angle_step_deg <= 0:
+            return self._auto_angle_step()
+        return self.angle_step_deg
+
     def _angle_list(self) -> list[float]:
         a0, a1 = self.angle_range_deg
-        if self.angle_step_deg <= 0 or a0 >= a1:
+        step = self._effective_angle_step()
+        if step <= 0 or a0 >= a1:
             return [float(a0)]
-        n = int(np.floor((a1 - a0) / self.angle_step_deg))
-        return [float(a0 + i * self.angle_step_deg) for i in range(n)]
+        n = int(np.floor((a1 - a0) / step))
+        return [float(a0 + i * step) for i in range(n)]
 
     # --- Training ------------------------------------------------------
 
@@ -393,108 +412,6 @@ 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)
@@ -517,7 +434,7 @@ class LineShapeMatcher:
         if original_score is not None and original_score >= 0.99:
             return (angle_deg, original_score, cx, cy)
         if search_radius is None:
-            search_radius = self.angle_step_deg / 2.0
+            search_radius = self._effective_angle_step() / 2.0
 
         h, w = template_gray.shape
         sw = max(16, int(round(w * scale)))
@@ -676,7 +593,6 @@ class LineShapeMatcher:
         verify_threshold: float = 0.4,
         coarse_angle_factor: int = 2,
         scale_penalty: float = 0.0,
-        refine_pose_joint: bool = False,
     ) -> list[Match]:
         """
         scale_penalty: se > 0, riduce lo score per match a scala diversa da 1.0:
@@ -901,16 +817,11 @@ 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:
-                ang_f, score_f, cx_f, cy_f = self._refine_pose_joint(
-                    spread0, self.template_gray, cx_f, cy_f,
-                    var.angle_deg, var.scale, mask_full,
-                )
-            elif refine_angle and self.template_gray is not None:
+            if 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,
-                    search_radius=self.angle_step_deg / 2.0,
+                    search_radius=self._effective_angle_step() / 2.0,
                     original_score=score,
                 )
             if verify_ncc: