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/_jit_kernels.py

@@ -246,22 +246,10 @@ def score_bitmap_rescored(
     return np.maximum(0.0, out).astype(np.float32)
 
 
-_HAS_NP_BITCOUNT = hasattr(np, "bitwise_count")
-
-
 def popcount_density(spread: np.ndarray) -> np.ndarray:
-    """Conta bit set per pixel.
-
-    Order:
-    1) Numba JIT parallel (preferito: piu veloce su 1080p, 0.5ms vs 1.6ms)
-    2) numpy.bitwise_count (NumPy 2.0+, SIMD ma single-thread)
-    3) Fallback numpy bit-shift puro
-    """
-    spread_c = np.ascontiguousarray(spread, dtype=np.uint8)
     if HAS_NUMBA:
-        return _jit_popcount_density(spread_c)
+        return _jit_popcount_density(np.ascontiguousarray(spread, dtype=np.uint8))
-    if _HAS_NP_BITCOUNT:
+    # Fallback
-        return np.bitwise_count(spread_c).astype(np.float32, copy=False)
     H, W = spread.shape
     out = np.zeros((H, W), dtype=np.float32)
     for b in range(8):

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 adattivo (Halcon-style): atan(2/max_side) deg, clampato.
-    angle_step = 5.0
+    # 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))
+        n = int(np.floor((a1 - a0) / step))
-        return [float(a0 + i * self.angle_step_deg) for i in range(n)]
+        return [float(a0 + i * step) for i in range(n)]
 
     # --- Training ------------------------------------------------------
 
@@ -415,7 +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)))
@@ -802,7 +821,7 @@ class LineShapeMatcher:
                 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: