debugging

SPIDER.py

@@ -142,7 +142,7 @@ class SPIDER(datasets.GeneratorBasedBuilder):
             "patient_id": datasets.Value("string"),
             "scan_type": datasets.Value("string"),
             # "raw_image": datasets.Image(),
-            # "numeric_array": datasets.Sequence(datasets.Value("int16")),
+            "numeric_array": datasets.Sequence(datasets.Value("int16")),
             "metadata": {
                 "num_vertebrae": datasets.Value(dtype="string"),
                 "num_discs": datasets.Value(dtype="string"),
@@ -224,7 +224,6 @@ class SPIDER(datasets.GeneratorBasedBuilder):
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
         paths_dict = dl_manager.download_and_extract(_URLS)
         scan_types = self.config.scan_types
-        # scan_types = ['t1'] #TODO: remove
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -443,10 +442,7 @@ class SPIDER(datasets.GeneratorBasedBuilder):
         # (note that only images need to be shuffled since masks and metadata
         # will be linked to the selected image)
         np.random.shuffle(image_files) 
-        
-        print(image_files)
-        print(mask_files)
-        print(len(image_files), len(mask_files))
+
         ## Generate next example
         # ----------------------
         for idx, example in enumerate(image_files):
@@ -463,6 +459,8 @@ class SPIDER(datasets.GeneratorBasedBuilder):
             # Convert .mha image to numeric array
             image_array = sitk.GetArrayFromImage(image)
             
+            #TODO: load mask file and numeric array
+            
             # Extract overview data corresponding to image
             image_overview = overview_dict[scan_id]
 
@@ -473,8 +471,8 @@ class SPIDER(datasets.GeneratorBasedBuilder):
             return_dict = {'patient_id':patient_id, 'scan_type':scan_type}
             # if raw_image:
             #     return_dict['raw_image'] = image
-            # if numeric_array:
-            #     return_dict['numeric_array'] = image_array
+            if numeric_array:
+                return_dict['numeric_array'] = image_array
             if metadata:
                 return_dict['metadata'] = image_overview
             if rad_gradings: