Update rule_based_quality.py

rule_based_quality.py

@@ -1,59 +1,45 @@
 import numpy as np
 
 class RuleBasedQualityEvaluator:
-    def __init__(self):
-        self.thresholds = {
-            "perfect": {
-                "circularity": 0.90,
-                "angle_error": 5.0,
-                "side_var": 0.05
-            },
-            "medium": {
-                "circularity": 0.70,
-                "angle_error": 15.0,
-                "side_var": 0.15
-            }
-        }
-
-    def evaluate(self, features: dict, shape: str):
-        score = 1.0
-
-        # circle
-        if shape.lower() == "circle":
-            circ = features.get("circularity", 0.0)
-            score = np.clip((circ - 0.7) / 0.3, 0, 1)
-
-            if circ >= self.thresholds["perfect"]["circularity"]:
+
+    def evaluate(self, f: dict, shape: str):
+        shape = shape.lower()
+
+        if shape == "circle":
+            circ = f["circularity"]
+            ecc = f["eccentricity"]
+
+            score = np.clip((circ - 0.8) / 0.2, 0, 1)
+
+            if circ >= 0.95 and ecc < 0.3:
                 return "perfect", float(score)
-            elif circ >= self.thresholds["medium"]["circularity"]:
+            elif circ >= 0.85:
                 return "medium", float(score * 0.8)
             else:
                 return "irregular", float(score * 0.6)
 
-        # square/rectangle
-        if shape.lower() in ["square", "rectangle"]:
-            angle_err = features.get("angle_error", 90.0)
-            side_var = features.get("side_length_variance", 1.0)
+        if shape in ["square", "rectangle"]:
+            ang = f["angle_error"]
+            side_var = f["side_length_variance"]
 
-            score = 1 - np.clip((angle_err / 30 + side_var) / 2, 0, 1)
+            score = 1 - np.clip((ang/30 + side_var) / 2, 0, 1)
 
-            if angle_err <= 5 and side_var <= 0.05:
+            if ang <= 5 and side_var <= 0.05:
                 return "perfect", float(score)
-            elif angle_err <= 15 and side_var <= 0.15:
+            elif ang <= 15 and side_var <= 0.15:
                 return "medium", float(score * 0.8)
             else:
                 return "irregular", float(score * 0.6)
 
-        # triangle
-        if shape.lower() == "triangle":
-            side_var = features.get("side_length_variance", 1.0)
-            angle_err = features.get("angle_error", 60.0)
+        if shape == "triangle":
+            ang = f["angle_error"]
+            side_var = f["side_length_variance"]
 
-            score = 1 - np.clip((side_var + angle_err / 60) / 2, 0, 1)
+            score = 1 - np.clip((ang/25 + side_var) / 2, 0, 1)
 
-            if side_var <= 0.05 and angle_err <= 5:
+            if ang <= 5 and side_var <= 0.05:
                 return "perfect", float(score)
-            elif side_var <= 0.15 and angle_err <= 15:
+            elif ang <= 15 and side_var <= 0.15:
                 return "medium", float(score * 0.8)
             else:
                 return "irregular", float(score * 0.6)