Fix ImageFilter to allow Gaussian filter without filter_size (#8189)

Fixes #8127

Update `ImageFilter` to handle Gaussian filter without requiring
`filter_size`.

* Modify `monai/transforms/utility/array.py` to allow Gaussian filter
without `filter_size`.
- Adjust `_check_filter_format` method to skip `filter_size` check for
Gaussian filter. Indeed Gauss filter is the only one in the list that
doesn't require a filter_size.
* Add unit test in `tests/test_image_filter.py` for Gaussian filter
without `filter_size`.
  - Verify output shape matches input shape.

Note that this method is compliant with the dictionnary version since
this one load the fixed version.

Signed-off-by: Eloi <[email protected]>

---------

Signed-off-by: Eloi Navet <[email protected]>
Signed-off-by: Eloi <[email protected]>
Signed-off-by: Eloi [email protected]

monai/transforms/utility/array.py

@@ -1609,9 +1609,9 @@ def _check_all_values_uneven(self, x: tuple) -> None:
 
     def _check_filter_format(self, filter: str | NdarrayOrTensor | nn.Module, filter_size: int | None = None) -> None:
         if isinstance(filter, str):
-            if not filter_size:
+            if filter != "gauss" and not filter_size:  # Gauss is the only filter that does not require `filter_size`
                 raise ValueError("`filter_size` must be specified when specifying filters by string.")
-            if filter_size % 2 == 0:
+            if filter_size and filter_size % 2 == 0:
                 raise ValueError("`filter_size` should be a single uneven integer.")
             if filter not in self.supported_filters:
                 raise NotImplementedError(f"{filter}. Supported filters are {self.supported_filters}.")

tests/test_image_filter.py

@@ -134,6 +134,12 @@ def test_pass_empty_metadata_dict(self):
         out_tensor = filter(image)
         self.assertTrue(isinstance(out_tensor, MetaTensor))
 
+    def test_gaussian_filter_without_filter_size(self):
+        "Test Gaussian filter without specifying filter_size"
+        filter = ImageFilter("gauss", sigma=2)
+        out_tensor = filter(SAMPLE_IMAGE_2D)
+        self.assertEqual(out_tensor.shape[1:], SAMPLE_IMAGE_2D.shape[1:])
+
 
 class TestImageFilterDict(unittest.TestCase):