merge: dedup varianti

This commit is contained in:
2026-05-04 15:46:34 +02:00
+34
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@@ -316,8 +316,42 @@ class LineShapeMatcher:
kh=kh, kw=kw, kh=kh, kw=kw,
cx_local=float(cx_local), cy_local=float(cy_local), cx_local=float(cx_local), cy_local=float(cy_local),
)) ))
self._dedup_variants()
return len(self.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 ------------------------------------------------------ # --- Matching ------------------------------------------------------
def _response_map(self, gray: np.ndarray) -> np.ndarray: def _response_map(self, gray: np.ndarray) -> np.ndarray: