KeepLargestConnectedComponent(d) example transforms (#3108)

Signed-off-by: Richard Brown <33289025+rijobro@users.noreply.github.com>
Project-MONAI · Oct 11, 2021 · 7b1b772 · 7b1b772
1 parent 31f9c1e
commit 7b1b772
Show file tree

Hide file tree

Showing 2 changed files with 12 additions and 2 deletions.
diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst
@@ -488,6 +488,8 @@ Post-processing
 
 `KeepLargestConnectedComponent`
 """""""""""""""""""""""""""""""
+.. image:: https://github.com/Project-MONAI/DocImages/raw/main/transforms/KeepLargestConnectedComponent.png
+    :alt: example of KeepLargestConnectedComponent
 .. autoclass:: KeepLargestConnectedComponent
     :members:
     :special-members: __call__
@@ -1249,6 +1251,8 @@ Post-processing (Dict)
 
 `KeepLargestConnectedComponentd`
 """"""""""""""""""""""""""""""""
+.. image:: https://github.com/Project-MONAI/DocImages/raw/main/transforms/KeepLargestConnectedComponentd.png
+    :alt: example of KeepLargestConnectedComponentd
 .. autoclass:: KeepLargestConnectedComponentd
     :members:
     :special-members: __call__

diff --git a/monai/transforms/utils_create_transform_ims.py b/monai/transforms/utils_create_transform_ims.py
@@ -139,8 +139,8 @@
     StdShiftIntensityd,
     ThresholdIntensityd,
 )
-from monai.transforms.post.array import LabelFilter, LabelToContour
-from monai.transforms.post.dictionary import AsDiscreted, LabelFilterd, LabelToContourd
+from monai.transforms.post.array import KeepLargestConnectedComponent, LabelFilter, LabelToContour
+from monai.transforms.post.dictionary import AsDiscreted, KeepLargestConnectedComponentd, LabelFilterd, LabelToContourd
 from monai.transforms.spatial.array import Rand2DElastic, RandAffine, RandAxisFlip, RandRotate90, Resize, Spacing
 from monai.transforms.spatial.dictionary import (
     Rand2DElasticd,
@@ -666,3 +666,9 @@ def create_transform_im(
     create_transform_im(RandAxisFlipd, dict(keys=keys, prob=1), data)
     create_transform_im(Resize, dict(spatial_size=(100, 100, 100)), data)
     create_transform_im(Resized, dict(keys=keys, spatial_size=(100, 100, 100), mode=["area", "nearest"]), data)
+    data_binary = deepcopy(data)
+    data_binary[CommonKeys.LABEL] = (data_binary[CommonKeys.LABEL] > 0).astype(np.float32)
+    create_transform_im(KeepLargestConnectedComponent, dict(applied_labels=1), data_binary, is_post=True, ndim=2)
+    create_transform_im(
+        KeepLargestConnectedComponentd, dict(keys=CommonKeys.LABEL, applied_labels=1), data_binary, is_post=True, ndim=2
+    )