dev

analyzer.py

@@ -147,17 +147,6 @@ def load_suit_templates():
             _suit_templates[s] = cv2.resize(img, (30, 30))
     return _suit_templates
 
-_rank_shape_templates = None
-def load_rank_shape_templates():
-    global _rank_shape_templates
-    if _rank_shape_templates is not None: return _rank_shape_templates
-    _rank_shape_templates = {}
-    for r in RANKS:
-        img = cv2.imread(f'{TEMPLATES_DIR}/rank_{r}.png', cv2.IMREAD_GRAYSCALE)
-        if img is not None:
-            _rank_shape_templates[r] = cv2.resize(img, (40, 50))
-    print(f'[+] Loaded {len(_rank_shape_templates)} rank templates')
-    return _rank_shape_templates
 
 def detect_suit_by_shape(img_bgr):
     """Match suit symbol area against 4 templates from card files."""
@@ -175,21 +164,6 @@ def detect_suit_by_shape(img_bgr):
             best_score, best_suit = score, s
     return best_suit if best_score > 0.15 else '?'
 
-def match_rank_by_shape(img_bgr, suit=None):
-    """Match rank — top 50% full width for better coverage of large digits."""
-    tpls = load_rank_shape_templates()
-    if not tpls: return '?'
-    h, w = img_bgr.shape[:2]
-    rank_area = img_bgr[:int(h*0.50), :]   # top half, full width
-    gray = cv2.cvtColor(rank_area, cv2.COLOR_BGR2GRAY)
-    _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-    binary_r = cv2.resize(binary, (40, 50))
-    best_rank, best_score = '?', -1.0
-    for r, tpl in tpls.items():
-        score = float(cv2.matchTemplate(binary_r, tpl, cv2.TM_CCOEFF_NORMED).max())
-        if score > best_score:
-            best_score, best_rank = score, r
-    return best_rank if best_score > 0.15 else '?'
 
 def detect_suit_ggpoker(img_bgr):
     """