Update lighting_analyzer.py

lighting_analyzer.py

@@ -178,7 +178,7 @@ class LightingAnalyzer:
         horizontal_strength = np.mean(np.abs(gx))
         gradient_ratio = vertical_strength / max(horizontal_strength, 1e-5)
 
-        # -- 亮度均勻性 --
+        # 亮度的均勻性(分布) 
         brightness_uniformity = 1 - min(1, brightness_std / max(avg_brightness, 1e-5))
 
         # -- 高效的天花板分析 --
@@ -240,9 +240,9 @@ class LightingAnalyzer:
         top_gradient = np.mean(np.abs(cv2.Sobel(top_edge, cv2.CV_32F, 0, 1, ksize=3)))
 
         # 標準化
-        left_edge_density = min(1.0, left_gradient / 50)
-        right_edge_density = min(1.0, right_gradient / 50)
-        top_edge_density = min(1.0, top_gradient / 50)
+        left_edge_density = min(1, left_gradient / 50)
+        right_edge_density = min(1, right_gradient / 50)
+        top_edge_density = min(1, top_gradient / 50)
 
         # 封閉環境通常在圖像邊緣有較強的梯度
         boundary_edge_score = (left_edge_density + right_edge_density + top_edge_density) / 3
@@ -370,9 +370,9 @@ class LightingAnalyzer:
 
             # 加強對特定類型光源的檢測
             if circular_light_count >= 1:  # 即便只有一個圓形光源也很可能是室內
-                light_contribution = weights.get("light_features", 1.2) * 2.0
+                light_contribution = weights.get("light_features", 1.2) * 2
             elif indoor_light_score > 0.3:
-                light_contribution = weights.get("light_features", 1.2) * 1.0
+                light_contribution = weights.get("light_features", 1.2) * 1
 
             indoor_score += light_contribution
             feature_contributions["light_features"] = light_contribution
@@ -411,7 +411,7 @@ class LightingAnalyzer:
 
         # 6. 垂直/水平梯度比率
         gradient_contribution = 0
-        if features["gradient_ratio"] > 2.0:
+        if features["gradient_ratio"] > 2:
             combined_uniformity = (features["brightness_uniformity"] +
                                 features.get("ceiling_uniformity", 0)) / 2