1567 add RemoveRepeatedChannel

monai/transforms/__init__.py

@@ -250,6 +250,7 @@
     Identity,
     LabelToMask,
     Lambda,
+    RemoveRepeatedChannel,
     RepeatChannel,
     SimulateDelay,
     SplitChannel,
@@ -305,6 +306,9 @@
     RandLambdad,
     RandLambdaD,
     RandLambdaDict,
+    RemoveRepeatedChanneld,
+    RemoveRepeatedChannelD,
+    RemoveRepeatedChannelDict,
     RepeatChanneld,
     RepeatChannelD,
     RepeatChannelDict,

monai/transforms/utility/array.py

@@ -31,6 +31,7 @@
     "AsChannelLast",
     "AddChannel",
     "RepeatChannel",
+    "RemoveRepeatedChannel",
     "SplitChannel",
     "CastToType",
     "ToTensor",
@@ -161,6 +162,32 @@ def __call__(self, img: np.ndarray) -> np.ndarray:
         return np.repeat(img, self.repeats, 0)
 
 
+class RemoveRepeatedChannel(Transform):
+    """
+    RemoveRepeatedChannel data to undo RepeatChannel
+    The `repeats` count specifies the deletion of the origin data, for example:
+    ``RemoveRepeatedChannel(repeats=2)([[1, 2], [1, 2], [3, 4], [3, 4]])`` generates: ``[[1, 2], [3, 4]]``
+
+    Args:
+        repeats: the number of repetitions to be deleted for each element.
+    """
+
+    def __init__(self, repeats: int) -> None:
+        if repeats <= 0:
+            raise AssertionError("repeats count must be greater than 0.")
+
+        self.repeats = repeats
+
+    def __call__(self, img: np.ndarray) -> np.ndarray:
+        """
+        Apply the transform to `img`, assuming `img` is a "channel-first" array.
+        """
+        if np.shape(img)[0] < 2:
+            raise AssertionError("Image must have more than one channel")
+
+        return np.array(img[:: self.repeats, :])
+
+
 class SplitChannel(Transform):
     """
     Split Numpy array or PyTorch Tensor data according to the channel dim.

monai/transforms/utility/dictionary.py

@@ -35,6 +35,7 @@
     Identity,
     LabelToMask,
     Lambda,
+    RemoveRepeatedChannel,
     RepeatChannel,
     SimulateDelay,
     SplitChannel,
@@ -52,6 +53,7 @@
     "AsChannelLastd",
     "AddChanneld",
     "RepeatChanneld",
+    "RemoveRepeatedChanneld",
     "SplitChanneld",
     "CastToTyped",
     "ToTensord",
@@ -82,6 +84,8 @@
     "RandLambdaDict",
     "RepeatChannelD",
     "RepeatChannelDict",
+    "RemoveRepeatedChannelD",
+    "RemoveRepeatedChannelDict",
     "SplitChannelD",
     "SplitChannelDict",
     "CastToTypeD",
@@ -226,6 +230,28 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.nda
         return d
 
 
+class RemoveRepeatedChanneld(MapTransform):
+    """
+    Dictionary-based wrapper of :py:class:`monai.transforms.RemoveRepeatedChannel`.
+    """
+
+    def __init__(self, keys: KeysCollection, repeats: int) -> None:
+        """
+        Args:
+            keys: keys of the corresponding items to be transformed.
+                See also: :py:class:`monai.transforms.compose.MapTransform`
+            repeats: the number of repetitions for each element.
+        """
+        super().__init__(keys)
+        self.repeater = RemoveRepeatedChannel(repeats)
+
+    def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:
+        d = dict(data)
+        for key in self.keys:
+            d[key] = self.repeater(d[key])
+        return d
+
+
 class SplitChanneld(MapTransform):
     """
     Dictionary-based wrapper of :py:class:`monai.transforms.SplitChannel`.
@@ -836,6 +862,7 @@ def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> Dict[Hashable, torc
 AsChannelFirstD = AsChannelFirstDict = AsChannelFirstd
 AsChannelLastD = AsChannelLastDict = AsChannelLastd
 AddChannelD = AddChannelDict = AddChanneld
+RemoveRepeatedChannelD = RemoveRepeatedChannelDict = RemoveRepeatedChanneld
 RepeatChannelD = RepeatChannelDict = RepeatChanneld
 SplitChannelD = SplitChannelDict = SplitChanneld
 CastToTypeD = CastToTypeDict = CastToTyped

tests/test_remove_repeated_channel.py

@@ -0,0 +1,30 @@
+# Copyright 2020 - 2021 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+from parameterized import parameterized
+
+from monai.transforms import RemoveRepeatedChannel
+
+TEST_CASE_1 = [{"repeats": 2}, np.array([[1, 2], [1, 2], [3, 4], [3, 4]]), (2, 2)]
+
+
+class TestRemoveRepeatedChannel(unittest.TestCase):
+    @parameterized.expand([TEST_CASE_1])
+    def test_shape(self, input_param, input_data, expected_shape):
+        result = RemoveRepeatedChannel(**input_param)(input_data)
+        self.assertEqual(result.shape, expected_shape)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_remove_repeated_channeld.py

@@ -0,0 +1,34 @@
+# Copyright 2020 - 2021 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+from parameterized import parameterized
+
+from monai.transforms import RemoveRepeatedChanneld
+
+TEST_CASE_1 = [
+    {"keys": ["img"], "repeats": 2},
+    {"img": np.array([[1, 2], [1, 2], [3, 4], [3, 4]]), "seg": np.array([[1, 2], [1, 2], [3, 4], [3, 4]])},
+    (2, 2),
+]
+
+
+class TestRemoveRepeatedChanneld(unittest.TestCase):
+    @parameterized.expand([TEST_CASE_1])
+    def test_shape(self, input_param, input_data, expected_shape):
+        result = RemoveRepeatedChanneld(**input_param)(input_data)
+        self.assertEqual(result["img"].shape, expected_shape)
+
+
+if __name__ == "__main__":
+    unittest.main()