9 intensity normalisation transform

monai/data/transforms/intensity_normalizer.py

@@ -0,0 +1,49 @@
+# Copyright 2020 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 numpy as np
+import monai
+
+export = monai.utils.export("monai.data.transforms")
+
+
+@export
+class IntensityNormalizer:
+    """Normalize input based on provided args, using calculated mean and std if not provided
+    (shape of subtrahend and divisor must match. if 0, entire volume uses same subtrahend and
+     divisor, otherwise the shape can have dimension 1 for channels).
+     Current implementation can only support 'channel_last' format data.
+
+    Args:
+        subtrahend (ndarray): the amount to subtract by (usually the mean)
+        divisor (ndarray): the amount to divide by (usually the standard deviation)
+        dtype: output data format
+    """
+
+    def __init__(self, subtrahend=None, divisor=None, dtype=np.float32):
+        if subtrahend is not None or divisor is not None:
+            assert isinstance(subtrahend, np.ndarray) and isinstance(divisor, np.ndarray), \
+                'subtrahend and divisor must be set in pair and in numpy array.'
+        self.subtrahend = subtrahend
+        self.divisor = divisor
+        self.dtype = dtype
+
+    def __call__(self, img):
+        if self.subtrahend is not None and self.divisor is not None:
+            img -= self.subtrahend
+            img /= self.divisor
+        else:
+            img -= np.mean(img)
+            img /= np.std(img)
+
+        if self.dtype != img.dtype:
+            img = img.astype(self.dtype)
+        return img

tests/test_convolutions.py

@@ -9,13 +9,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
-from .utils import ImageTestCase
+from .utils import TorchImageTestCase2D
 
 from monai.networks.layers.convolutions import Convolution, ResidualUnit
 
 
-class TestConvolution2D(ImageTestCase):
+class TestConvolution2D(TorchImageTestCase2D):
     def test_conv1(self):
         conv = Convolution(2, self.input_channels, self.output_channels)
         out = conv(self.imt)
@@ -59,7 +58,7 @@ def test_transpose2(self):
         self.assertEqual(out.shape, expected_shape)
 
 
-class TestResidualUnit2D(ImageTestCase):
+class TestResidualUnit2D(TorchImageTestCase2D):
     def test_conv_only1(self):
         conv = ResidualUnit(2, 1, self.output_channels)
         out = conv(self.imt)

tests/test_intensity_normalizer.py

@@ -0,0 +1,30 @@
+# Copyright 2020 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 monai.data.transforms.intensity_normalizer import IntensityNormalizer
+from tests.utils import NumpyImageTestCase2D
+
+
+class IntensityNormTestCase(NumpyImageTestCase2D):
+
+    def test_image_normalizer_default(self):
+        normalizer = IntensityNormalizer()
+        normalised = normalizer(self.imt)
+        expected = (self.imt - np.mean(self.imt)) / np.std(self.imt)
+        self.assertTrue(np.allclose(normalised, expected))
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/utils.py

@@ -9,11 +9,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 import os
 import unittest
-import torch
+
 import numpy as np
+import torch
 
 from monai.utils.arrayutils import rescale_array
 
@@ -55,7 +55,7 @@ def create_test_image(width, height, num_objs=12, rad_max=30, noise_max=0.0, num
     return noisyimage, labels
 
 
-class ImageTestCase(unittest.TestCase):
+class NumpyImageTestCase2D(unittest.TestCase):
     im_shape = (128, 128)
     input_channels = 1
     output_channels = 4
@@ -64,7 +64,15 @@ class ImageTestCase(unittest.TestCase):
     def setUp(self):
         im, msk = create_test_image(self.im_shape[0], self.im_shape[1], 4, 20, 0, self.num_classes)
 
-        self.imt = torch.tensor(im[None, None])
+        self.imt = im[None, None]
+        self.seg1 = (msk[None, None] > 0).astype(np.float32)
+        self.segn = msk[None, None]
 
-        self.seg1 = torch.tensor((msk[None, None] > 0).astype(np.float32))
-        self.segn = torch.tensor(msk[None, None])
+
+class TorchImageTestCase2D(NumpyImageTestCase2D):
+
+    def setUp(self):
+        NumpyImageTestCase2D.setUp(self)
+        self.imt = torch.tensor(self.imt)
+        self.seg1 = torch.tensor(self.seg1)
+        self.segn = torch.tensor(self.segn)