merge: greediness (kernel greedy alternativo a rescore strided)

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_greedy as _jit_score_bitmap_greedy,
     popcount_density as _jit_popcount,
     HAS_NUMBA,
 )
@@ -722,6 +723,7 @@ class LineShapeMatcher:
         pyramid_propagate: bool = True,
         propagate_topk: int = 8,
         refine_pose_joint: bool = False,
+        greediness: float = 0.0,
     ) -> list[Match]:
         """
         scale_penalty: se > 0, riduce lo score per match a scala diversa da 1.0:
@@ -813,23 +815,31 @@ class LineShapeMatcher:
                 neighbor_map[vi_c] = vi_sorted[start:end]
 
         # Pruning varianti via top-level (parallelizzato).
-        # coarse_stride > 1: valuta solo 1 pixel ogni stride, ~stride^2 speed-up.
-        # pyramid_propagate=True: ritorna top-K picchi per restringere full-res.
+        # coarse_stride > 1: 1 pixel ogni stride (~stride^2 speed-up).
+        # pyramid_propagate=True: top-K picchi per restringere full-res.
+        # greediness > 0: kernel greedy con early-exit (alternativo a rescore).
         cs = max(1, int(coarse_stride))
         peaks_by_vi: dict[int, list[tuple[int, int, float]]] = {}
+        use_greedy_top = greediness > 0.0
 
         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,
-            )
+            if use_greedy_top:
+                # Greedy non supporta stride né rescore bg
+                score = _jit_score_bitmap_greedy(
+                    spread_top, lvl.dx, lvl.dy, lvl.bin, bit_active_top,
+                    top_thresh, greediness,
+                )
+            else:
+                score = _jit_score_bitmap_rescored(
+                    spread_top, lvl.dx, lvl.dy, lvl.bin, bit_active_top,
+                    bg_cache_top[var.scale], stride=cs,
+                )
             if score.size == 0:
                 return vi, -1.0
             best = float(score.max())
             if pyramid_propagate and best > 0:
-                # Top-K posizioni > top_thresh (max propagate_topk)
                 flat = score.ravel()
                 k = min(propagate_topk, flat.size)
                 idx = np.argpartition(-flat, k - 1)[:k]