113-gaussian-noise

monai/transforms/transforms.py

@@ -64,6 +64,24 @@ def __call__(self, img):
         return rescale_array(img, self.minv, self.maxv, self.dtype)
 
 
+@export
+class GaussianNoise(Randomizable):
+    """Add gaussian noise to image.
+
+    Args:
+        mean (float or array of floats): Mean or “centre” of the distribution.
+        scale (float): Standard deviation (spread) of distribution.
+        size (int or tuple of ints): Output shape. Default: None (single value is returned).
+    """
+
+    def __init__(self, mean=0.0, std=0.1):
+        self.mean = mean
+        self.std = std
+
+    def __call__(self, img):
+        return img + self.R.normal(self.mean, self.R.uniform(0, self.std), size=img.shape)
+
+
 @export
 class Flip:
     """Reverses the order of elements along the given axis. Preserves shape.

tests/test_gaussian_noise.py

@@ -0,0 +1,38 @@
+# 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 parameterized import parameterized
+
+from monai.transforms import GaussianNoise
+from tests.utils import NumpyImageTestCase2D
+
+
+class GaussianNoiseTest(NumpyImageTestCase2D):
+
+    @parameterized.expand([
+        ("test_zero_mean", 0, 0.1),
+        ("test_non_zero_mean", 1, 0.5)
+    ])
+    def test_correct_results(self, _, mean, std):
+        seed = 42
+        gaussian_fn = GaussianNoise(mean=mean, std=std)
+        gaussian_fn.set_random_state(seed)
+        noised = gaussian_fn(self.imt)
+        np.random.seed(seed)
+        expected = self.imt + np.random.normal(mean, np.random.uniform(0, std), size=self.imt.shape)
+        assert np.allclose(expected, noised)
+
+
+if __name__ == '__main__':
+    unittest.main()