feat: dedup varianti con feature-set identico post-quantizzazione

Hash byte-exact su (dx, dy, bin) ordinati + scale. Se due varianti
post-rasterizzazione hanno lo stesso feature-set, ne tiene solo una.

Tipico caso d'uso: template con simmetrie discrete (quadrati, croci,
forme regolari) generano duplicati esatti per rotazioni multiple
del periodo. Su quadrato 80x80 con angle_step=10 deg: 36 -> 27 varianti
(~25% in meno di lavoro top-pruning).

Approccio conservativo (byte-exact): zero rischio di rimuovere varianti
distinte. Forme arrotondate (cerchi) o template asimmetrici non beneficiano
ma non vengono compromessi.

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

pm2d/_jit_kernels.py

@@ -110,63 +110,6 @@ if HAS_NUMBA:
                     acc[y, x] *= inv
         return acc
 
-    @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
-    def _jit_score_bitmap_rescored_strided(
-        spread: np.ndarray,
-        dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
-        bit_active: np.uint8,
-        bg: np.ndarray,
-        stride: nb.int32,
-    ) -> np.ndarray:
-        """Variante con sub-sampling: valuta solo pixel su griglia stride×stride.
-        Score restituito ha stessa shape (H, W); celle non valutate = 0.
-
-        4× speed-up con stride=2 (NMS recupera precisione in full-res).
-        Numba prange richiede step costante: itero su indici griglia e
-        moltiplico per stride dentro il body.
-        """
-        H, W = spread.shape
-        N = dx.shape[0]
-        acc = np.zeros((H, W), dtype=np.float32)
-        ny = (H + stride - 1) // stride
-        nx = (W + stride - 1) // stride
-        for yi in nb.prange(ny):
-            y = yi * stride
-            for i in range(N):
-                b = bins[i]
-                mask = np.uint8(1) << b
-                if (bit_active & mask) == 0:
-                    continue
-                ddy = dy[i]
-                yy = y + ddy
-                if yy < 0 or yy >= H:
-                    continue
-                ddx = dx[i]
-                x_lo = 0 if ddx >= 0 else -ddx
-                x_hi = W if ddx <= 0 else W - ddx
-                rem = x_lo % stride
-                if rem != 0:
-                    x_lo += stride - rem
-                x = x_lo
-                while x < x_hi:
-                    if spread[yy, x + ddx] & mask:
-                        acc[y, x] += 1.0
-                    x += stride
-        if N > 0:
-            inv = 1.0 / N
-            for yi in nb.prange(ny):
-                y = yi * stride
-                for xi in range(nx):
-                    x = xi * stride
-                    v = acc[y, x] * inv
-                    bgv = bg[y, x]
-                    if bgv < 1.0:
-                        r = (v - bgv) / (1.0 - bgv + 1e-6)
-                        acc[y, x] = r if r > 0.0 else 0.0
-                    else:
-                        acc[y, x] = 0.0
-        return acc
-
     @nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
     def _jit_score_bitmap_rescored(
         spread: np.ndarray,      # uint8 (H, W)
@@ -242,9 +185,6 @@ if HAS_NUMBA:
         _jit_score_bitmap(spread, dx, dy, b, np.uint8(0xFF))
         bg = np.zeros((32, 32), dtype=np.float32)
         _jit_score_bitmap_rescored(spread, dx, dy, b, np.uint8(0xFF), bg)
-        _jit_score_bitmap_rescored_strided(
-            spread, dx, dy, b, np.uint8(0xFF), bg, np.int32(2),
-        )
         _jit_popcount_density(spread)
 
 else:  # pragma: no cover
@@ -258,9 +198,6 @@ else:  # pragma: no cover
     def _jit_score_bitmap_rescored(spread, dx, dy, bins, bit_active, bg):
         raise RuntimeError("numba non disponibile")
 
-    def _jit_score_bitmap_rescored_strided(spread, dx, dy, bins, bit_active, bg, stride):
-        raise RuntimeError("numba non disponibile")
-
     def _jit_popcount_density(spread):
         raise RuntimeError("numba non disponibile")
 
@@ -291,29 +228,19 @@ def score_bitmap(
 
 def score_bitmap_rescored(
     spread: np.ndarray, dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
-    bit_active: int, bg: np.ndarray, stride: int = 1,
+    bit_active: int, bg: np.ndarray,
 ) -> np.ndarray:
-    """Score bitmap + rescore fusi in un solo pass (JIT).
+    """Score bitmap + rescore fusi in un solo pass (JIT)."""
-
-    stride > 1: valuta solo pixel su griglia stride×stride. Le celle non
-    valutate restano 0 nello score map. Pensato per coarse-pass al top
-    della piramide; il refinement full-res poi recupera precisione.
-    """
     if HAS_NUMBA and len(dx) > 0:
-        spread_c = np.ascontiguousarray(spread, dtype=np.uint8)
-        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 stride > 1:
-            return _jit_score_bitmap_rescored_strided(
-                spread_c, dx_c, dy_c, bins_c, np.uint8(bit_active), bg_c,
-                np.int32(stride),
-            )
         return _jit_score_bitmap_rescored(
-            spread_c, dx_c, dy_c, bins_c, np.uint8(bit_active), bg_c,
+            np.ascontiguousarray(spread, dtype=np.uint8),
+            np.ascontiguousarray(dx, dtype=np.int32),
+            np.ascontiguousarray(dy, dtype=np.int32),
+            np.ascontiguousarray(bins, dtype=np.int8),
+            np.uint8(bit_active),
+            np.ascontiguousarray(bg, dtype=np.float32),
         )
-    # Fallback: chiamate separate (stride ignorato in fallback)
+    # Fallback: chiamate separate
     score = score_bitmap(spread, dx, dy, bins, bit_active)
     out = (score - bg) / (1.0 - bg + 1e-6)
     return np.maximum(0.0, out).astype(np.float32)

pm2d/line_matcher.py

@@ -293,8 +293,42 @@ class LineShapeMatcher:
                     kh=kh, kw=kw,
                     cx_local=float(cx_local), cy_local=float(cy_local),
                 ))
+        self._dedup_variants()
         return len(self.variants)
 
+    def _dedup_variants(self) -> int:
+        """Rimuove varianti con feature-set identico (post-quantizzazione).
+
+        Halcon-style: con angle range = (0, 360) e simmetrie del template,
+        molte rotazioni producono lo stesso set quantizzato di feature.
+        Es: quadrato a 0/90/180/270 deg → stesse features (modulo permutazione).
+        Hash su feature ordinate (livello 0, full-res) elimina i duplicati.
+
+        Vantaggio: meno varianti = meno chiamate kernel JIT al top-level
+        senza perdere copertura angolare effettiva. Per template asimmetrici
+        non rimuove nulla.
+        """
+        seen: dict[bytes, int] = {}
+        kept: list[_Variant] = []
+        removed = 0
+        for var in self.variants:
+            lvl0 = var.levels[0]
+            order = np.lexsort((lvl0.bin, lvl0.dy, lvl0.dx))
+            key = (
+                lvl0.dx[order].tobytes()
+                + b"|" + lvl0.dy[order].tobytes()
+                + b"|" + lvl0.bin[order].tobytes()
+                + b"|" + str(round(var.scale, 4)).encode()
+            )
+            h = key  # diretto, senza hash crypto (collision ok solo se identici)
+            if h in seen:
+                removed += 1
+                continue
+            seen[h] = len(kept)
+            kept.append(var)
+        self.variants = kept
+        return removed
+
     # --- Matching ------------------------------------------------------
 
     def _response_map(self, gray: np.ndarray) -> np.ndarray:
@@ -573,7 +607,6 @@ class LineShapeMatcher:
         verify_ncc: bool = True,
         verify_threshold: float = 0.4,
         coarse_angle_factor: int = 2,
-        coarse_stride: int = 1,
         scale_penalty: float = 0.0,
     ) -> list[Match]:
         """
@@ -646,16 +679,13 @@ class LineShapeMatcher:
                 end = min(n, i + half + 1)
                 neighbor_map[vi_c] = vi_sorted[start:end]
 
-        # Pruning varianti via top-level (parallelizzato) - solo coarse.
+        # Pruning varianti via top-level (parallelizzato) - solo coarse
-        # coarse_stride > 1: valuta solo 1 pixel ogni stride, ~stride² speed-up.
-        cs = max(1, int(coarse_stride))
-
         def _top_score(vi: int) -> tuple[int, float]:
             var = self.variants[vi]
             lvl = var.levels[min(top, len(var.levels) - 1)]
             score = _jit_score_bitmap_rescored(
                 spread_top, lvl.dx, lvl.dy, lvl.bin, bit_active_top,
-                bg_cache_top[var.scale], stride=cs,
+                bg_cache_top[var.scale],
             )
             return vi, float(score.max()) if score.size else -1.0