allows the user to use the secondary model when available

PolarBean · Apr 27, 2023 · 54e1ee1 · 54e1ee1
1 parent 3d0f302
commit 54e1ee1
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Showing 2 changed files with 20 additions and 4 deletions.
diff --git a/DeepSlice/coord_post_processing/angle_methods.py b/DeepSlice/coord_post_processing/angle_methods.py
@@ -85,6 +85,9 @@ def get_mean_angle(DV_list, ML_list, method, depths=None, species=None):
             weighted_accuracy = plane_alignment.make_gaussian_weights(max)
         else:
             weighted_accuracy = [1.0] * len(df_center)
+        df_center = np.array(df_center)
+        df_center[df_center < min] = min
+        df_center[df_center > max] = max-1
         weighted_accuracy = [weighted_accuracy[int(y)] for y in df_center]
         print(weighted_accuracy)
         DV_angle = np.average(DV_list, weights=weighted_accuracy)

diff --git a/DeepSlice/main.py b/DeepSlice/main.py
@@ -28,7 +28,8 @@ def predict(
         ensemble: bool = None,
         section_numbers: bool = True,
         legacy_section_numbers=False,
-        image_list = None
+        image_list = None,
+        use_secondary_model = False,
     ):
         """predicts the atlas position for a folder full of histological brain sections
 
@@ -66,10 +67,22 @@ def predict(
 
         if secondary_weights == "None":
             print(f"ensemble is not available for {self.species}")
+            if use_secondary_model:
+                print("WARNING: use_secondary_model is set but no secondary model is available. use_secondary_model will be ignored.")
+                use_secondary_model = False
             ensemble = False
-        predictions = neural_network.predictions_util(
-            self.model, image_generator, primary_weights, secondary_weights, ensemble
-        )
+        if use_secondary_model and ensemble:
+            print("WARNING: use_secondary_model is set but ensemble is also set. use_secondary_model will be ignored.")
+            use_secondary_model = False
+        if use_secondary_model:
+            print("Using secondary model")
+            predictions = neural_network.predictions_util(
+                self.model, image_generator, secondary_weights,None, ensemble
+            )
+        else:
+            predictions = neural_network.predictions_util(
+                self.model, image_generator, primary_weights, secondary_weights, ensemble
+            )
         predictions["width"] = width
         predictions["height"] = height
         if section_numbers: