Check if image_shapes was initialized

utils/annotations.py

@@ -11,7 +11,7 @@
 
 
 class Annotations:
-    def __init__(self, annotation_store, global_store):
+    def __init__(self, annotation_store, image_shape):
         if annotation_store:
             slices = []
             for annotation_class in annotation_store:
@@ -49,7 +49,7 @@ def __init__(self, annotation_store, global_store):
         self.annotation_classes = annotation_classes
         self.annotations = annotations
         self.annotations_hash = self.get_annotations_hash()
-        self.image_shape = global_store["image_shapes"][0]
+        self.image_shape = image_shape
 
     def get_annotations(self):
         return self.annotations

utils/data_utils.py

@@ -168,7 +168,18 @@ def save_annotations_data(self, global_store, all_annotations, project_name):
         """
         Transforms annotations data to a pixelated mask and outputs to the Tiled server
         """
-        annotations = Annotations(all_annotations, global_store)
+        if "image_shapes" in global_store:
+            image_shape = global_store["image_shapes"][0]
+        else:
+            print("Global store was not filled.")
+            data_shape = (
+                tiled_datasets.get_data_shape_by_name(project_name)
+                if project_name
+                else None
+            )
+            image_shape = (data_shape[1], data_shape[2])
+
+        annotations = Annotations(all_annotations, image_shape)
         # TODO: Check sparse status, it may be worthwhile to store the mask as a sparse array
         # if our machine learning models can handle sparse arrays
         annotations.create_annotation_mask(sparse=False)