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modules/omniverse/omniverse_integration.ipynb

@@ -245,13 +245,13 @@
     "    \"spacing_xy\": 1.5,\n",
     "    \"spacing_z\": 1.5,\n",
     "    \"autoencoder_def#num_splits\": 16,\n",
-    "    \"mask_generation_autoencoder_def#num_splits\": 16\n",
+    "    \"mask_generation_autoencoder_def#num_splits\": 16,\n",
     "}\n",
     "workflow = create_workflow(\n",
     "    config_file=os.path.join(bundle_root, \"configs/inference.json\"),\n",
     "    workflow_type=\"inference\",\n",
     "    bundle_root=bundle_root,\n",
-    "    **override\n",
+    "    **override,\n",
     ")\n",
     "\n",
     "# uncomment this line to run the inference workflow\n",
@@ -459,12 +459,13 @@
     "    },\n",
     "}\n",
     "\n",
+    "\n",
     "def nii_to_mesh(input_nii_path, output_nii_path, output_obj_path):\n",
     "    \"\"\"\n",
     "    This function converts each organ into a separate OBJ file and generates a GLTF file\n",
     "    containing all organs with hierarchical structure.\n",
     "    It processes the input NIfTI file and groups 140 labels into 17 categories.\n",
-    "    \n",
+    "\n",
     "    Args:\n",
     "        input_nii_path: path to the nii file\n",
     "        output_nii_path: path to save the obj files\n",
@@ -516,6 +517,7 @@
     "    )\n",
     "    print(f\"Saved whole segmentation {all_organ_filename}\")\n",
     "\n",
+    "\n",
     "input_nii_path = f\"{bundle_root}/datasets/IntegrationTest-AbdomenCT.nii.gz\"\n",
     "output_nii_path = f\"{bundle_root}/datasets/monai/nii\"\n",
     "output_obj_path = f\"{bundle_root}/datasets/monai/obj\"\n",