Adriano/Shape_Model_2D
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

merge: refine cache

Browse Source
This commit is contained in:
AdrianoDev
2026-05-04 15:43:23 +02:00
parent 11c5160385 f00cf9b621
commit 74e5a45a39
1 changed files with 29 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files
pm2d/line_matcher.py
+29 -8
View File
@@ -258,6 +258,8 @@ class LineShapeMatcher:
self._train_mask = mask_full.copy() self._train_mask = mask_full.copy()
self.variants.clear() self.variants.clear()
# Invalida cache feature di refine: il template e cambiato.
self._refine_feat_cache = {}
for s in self._scale_list(): for s in self._scale_list():
sw = max(16, int(round(w * s))) sw = max(16, int(round(w * s)))
sh = max(16, int(round(h * s))) sh = max(16, int(round(h * s)))
@@ -554,17 +556,36 @@ class LineShapeMatcher:
H, W = spread0.shape H, W = spread0.shape
margin = 3 margin = 3
# Cache template features per angolo (chiave: int(round(ang*20)) =
# bucket di 0.05°). Golden-search ricontratto puo richiedere lo
# stesso bucket piu volte; evita re-warp+gradient+extract (costoso).
# Cache a livello matcher per riusare tra chiamate find() su scene
# diverse: la rotazione del template non dipende dalla scena.
if not hasattr(self, '_refine_feat_cache'):
self._refine_feat_cache = {}
feat_cache = self._refine_feat_cache
cache_scale_key = round(scale * 1000)
def _score_at_angle(off: float) -> tuple[float, float, float]: def _score_at_angle(off: float) -> tuple[float, float, float]:
"""Ritorna (score, best_cx, best_cy) per angolo = angle_deg + off.""" """Ritorna (score, best_cx, best_cy) per angolo = angle_deg + off."""
ang = angle_deg + off ang = angle_deg + off
M = cv2.getRotationMatrix2D(center, ang, 1.0) ck = (round(ang * 20), cache_scale_key)
gray_r = cv2.warpAffine(gray_p, M, (diag, diag), cached = feat_cache.get(ck)
flags=cv2.INTER_LINEAR, if cached is not None:
borderMode=cv2.BORDER_REPLICATE) fx, fy, fb = cached
mask_r = cv2.warpAffine(mask_p, M, (diag, diag), else:
flags=cv2.INTER_NEAREST, borderValue=0) M = cv2.getRotationMatrix2D(center, ang, 1.0)
mag, bins = self._gradient(gray_r) gray_r = cv2.warpAffine(gray_p, M, (diag, diag),
fx, fy, fb = self._extract_features(mag, bins, mask_r) 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)
# LRU semplice: limita cache a ~256 angoli (8 angoli * 32 candidati)
if len(feat_cache) > 256:
feat_cache.pop(next(iter(feat_cache)))
feat_cache[ck] = (fx, fy, fb)
if len(fx) < 8: if len(fx) < 8:
return (0.0, cx, cy) return (0.0, cx, cy)
dx = (fx - center[0]).astype(np.int32) dx = (fx - center[0]).astype(np.int32)