Fix mypy complaints for already migrated code.

Signed-off-by: M Q <mingmelvinq@nvidia.com>

Author: MMelQin

examples/apps/ai_livertumor_seg_app/app.py

@@ -84,9 +84,9 @@ def compose(self):
         series_selector_op = DICOMSeriesSelectorOperator(self, rules=Sample_Rules_Text, name="series_selector_op")
         series_to_vol_op = DICOMSeriesToVolumeOperator(self, name="series_to_vol_op")
         # Model specific inference operator, supporting MONAI transforms.
-        liver_tumor_seg_op = LiverTumorSegOperator(
-            self, model_path=model_path, output_folder=app_output_path, name="seg_op"
-        )
+        liver_tumor_seg_op = LiverTumorSegOperator(self, model_path=model_path, name="seg_op")
+        #     self, model_path=model_path, output_folder=app_output_path, name="seg_op"
+        # )
 
         # Create the publisher operator
         stl_op = STLConversionOperator(self, output_file=app_output_path.joinpath("stl/mesh.stl"), name="stl_op")

examples/apps/ai_livertumor_seg_app/livertumor_seg_operator.py

@@ -11,6 +11,7 @@
 
 import logging
 from pathlib import Path
+from typing import Optional
 
 from numpy import uint8
 
@@ -60,13 +61,15 @@ class LiverTumorSegOperator(Operator):
 
     DEFAULT_OUTPUT_FOLDER = Path.cwd() / "saved_images_folder"
 
-    def __init__(self, frament: Fragment, *args, model_path: Path, output_folder: Path = None, **kwargs):
+    def __init__(
+        self, frament: Fragment, *args, model_path: Path, output_folder: Path = DEFAULT_OUTPUT_FOLDER, **kwargs
+    ):
         self.logger = logging.getLogger("{}.{}".format(__name__, type(self).__name__))
         self._input_dataset_key = "image"
         self._pred_dataset_key = "pred"
 
         self.model_path = model_path
-        self.output_folder = output_folder if output_folder else LiverTumorSegOperator.DEFAULT_OUTPUT_FOLDER
+        self.output_folder = output_folder
         self.output_folder.mkdir(parents=True, exist_ok=True)
         self.fragement = frament  # Cache and later pass the Fragment/Application to contained operator(s)
         self.input_name_image = "image"

monai/deploy/operators/dicom_seg_writer_operator.py

@@ -154,7 +154,7 @@ def to_segment_description(self, segment_number: int) -> hd.seg.SegmentDescripti
         )
 
 
-# @md.env(pip_packages=["pydicom >= 2.3.0", "highdicom >= 0.18.2"])
+# @md.env(pip_packages=["pydicom >= 2.3.0", "highdicom >= 0.18.2, "SimpleITK>=2.0.0"])
 class DICOMSegmentationWriterOperator(Operator):
     """
     This operator writes out a DICOM Segmentation Part 10 file to disk
@@ -279,7 +279,7 @@ def process_images(
     ):
         """ """
         # Get the seg image in numpy, and if the image is passed in as object, need to fake a input path.
-        seg_image_numpy = image
+        seg_image_numpy: np.ndarray = None
 
         if isinstance(image, Image):
             seg_image_numpy = image.asnumpy()

monai/deploy/operators/monai_bundle_inference_operator.py

@@ -670,13 +670,13 @@ def _receive_input(self, name: str, op_input, context):
             if not value.exists():
                 raise ValueError(f"Input path, {value}, does not exist.")
 
-            file_path = value.path / name
+            file_path = value / name
             # The named input can only be a folder as of now, but just in case things change.
             if value.is_file():
                 file_path = value
             elif not file_path.exists() and value.is_dir():
                 # Expect one and only one file exists for use.
-                files = [f for f in value.path.glob("*") if f.is_file()]
+                files = [f for f in value.glob("*") if f.is_file()]
                 if len(files) != 1:
                     raise ValueError(f"Input path, {value}, should have one and only one file.")