2025-08-01 00:18 🐛

app.py

@@ -418,19 +418,19 @@ def predict(image: Image.Image, variant_dataset_metric: str):
     complete_zero_map = strucrual_zero_map + sampling_zero_map
 
     # Normalize maps for display purposes
-    def normalize_map(x: np.ndarray) -> np.ndarray:
-        """ Normalize the map to [0, 1] range for visualization. """
-        x_min = np.min(x)
-        x_max = np.max(x)
-        if x_max - x_min < EPS:
-            return np.zeros_like(x)
-        return (x - x_min) / (x_max - x_min + EPS)
+    # def normalize_map(x: np.ndarray) -> np.ndarray:
+    #     """ Normalize the map to [0, 1] range for visualization. """
+    #     x_min = np.min(x)
+    #     x_max = np.max(x)
+    #     if x_max - x_min < EPS:
+    #         return np.zeros_like(x)
+    #     return (x - x_min) / (x_max - x_min + EPS)
     
-    strucrual_zero_map = normalize_map(strucrual_zero_map)
-    sampling_zero_map = normalize_map(sampling_zero_map)
-    lambda_map = normalize_map(lambda_map)
-    den_map = normalize_map(den_map)
-    complete_zero_map = normalize_map(complete_zero_map)
+    # strucrual_zero_map = normalize_map(strucrual_zero_map)
+    # sampling_zero_map = normalize_map(sampling_zero_map)
+    # lambda_map = normalize_map(lambda_map)
+    # den_map = normalize_map(den_map)
+    # complete_zero_map = normalize_map(complete_zero_map)
     
     # Apply a colormap (e.g., 'jet') to get an RGBA image
     colormap = cm.get_cmap("jet")