5432 convert metadict types

.github/workflows/pythonapp.yml

@@ -88,6 +88,10 @@ jobs:
       name: Install torch cpu from pytorch.org (Windows only)
       run: |
         python -m pip install torch==1.12.1+cpu torchvision==0.13.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
+    - if: runner.os == 'Linux'
+      name: Install itk pre-release (Linux only)
+      run: |
+        python -m pip install --pre -U itk
     - name: Install the dependencies
       run: |
         python -m pip install torch==1.12.1 torchvision==0.13.1

monai/data/image_reader.py

@@ -318,7 +318,12 @@ def _get_meta_dict(self, img) -> Dict:
 
         """
         img_meta_dict = img.GetMetaDataDictionary()
-        meta_dict = {key: img_meta_dict[key] for key in img_meta_dict.GetKeys() if not key.startswith("ITK_")}
+        meta_dict = {}
+        for key in img_meta_dict.GetKeys():
+            if key.startswith("ITK_"):
+                continue
+            val = img_meta_dict[key]
+            meta_dict[key] = np.asarray(val) if type(val).__name__.startswith("itk") else val
 
         meta_dict["spacing"] = np.asarray(img.GetSpacing())
         return meta_dict

tests/test_load_image.py

@@ -315,7 +315,7 @@ def test_channel_dim(self, input_param, filename, expected_shape):
         with tempfile.TemporaryDirectory() as tempdir:
             filename = os.path.join(tempdir, filename)
             nib.save(nib.Nifti1Image(test_image, np.eye(4)), filename)
-            result = LoadImage(image_only=True, **input_param)(filename)
+            result = LoadImage(image_only=True, **input_param)(filename)  # with itk, meta has 'qto_xyz': itkMatrixF44
 
         self.assertTupleEqual(
             result.shape, (3, 128, 128, 128) if input_param.get("ensure_channel_first", False) else expected_shape