Add in-place operation

skimage/segmentation/_clear_border.py

@@ -3,7 +3,7 @@ import numpy as np
 from ..measure import label
 
 
-def clear_border(labels, buffer_size=0, bgval=0):
+def clear_border(labels, buffer_size=0, bgval=0, in_place=False):
     """Clear objects connected to the label image border.
 
     The changes will be applied directly to the input.
@@ -17,11 +17,13 @@ def clear_border(labels, buffer_size=0, bgval=0):
         that touch the outside of the image are removed.
     bgval : float or int, optional
         Cleared objects are set to this value.
+    in_place : bool, optional
+        Whether or not to manipulate the labels array in-place.
 
     Returns
     -------
     labels : (N, M) array
-        Cleared binary image.  Note that the input label image is modified.
+        Cleared binary image.
 
     Examples
     --------
@@ -69,6 +71,9 @@ def clear_border(labels, buffer_size=0, bgval=0):
     # create mask for pixels to clear
     mask = label_mask[labels.ravel()].reshape(labels.shape)
 
+    if not in_place:
+        image = image.copy()
+
     # clear border pixels
     image[mask] = bgval
 

skimage/segmentation/tests/test_clear_border.py

@@ -1,5 +1,5 @@
 import numpy as np
-from numpy.testing import assert_array_equal
+from numpy.testing import assert_array_equal, assert_
 from skimage.segmentation import clear_border
 
 
@@ -34,14 +34,30 @@ def test_clear_border_non_binary():
                       [3, 4, 5, 4, 2],
                       [3, 3, 2, 1, 2]])
 
-    result = clear_border(image.copy())
+    result = clear_border(image)
     expected = np.array([[0, 0, 0, 0, 0],
                          [0, 0, 5, 4, 0],
                          [0, 4, 5, 4, 0],
                          [0, 0, 0, 0, 0]])
 
     assert_array_equal(result, expected)
+    assert_(not np.all(image == result))
 
 
+def test_clear_border_non_binary_inplace():
+    image = np.array([[1, 2, 3, 1, 2],
+                      [3, 3, 5, 4, 2],
+                      [3, 4, 5, 4, 2],
+                      [3, 3, 2, 1, 2]])
+
+    result = clear_border(image, in_place=True)
+    expected = np.array([[0, 0, 0, 0, 0],
+                         [0, 0, 5, 4, 0],
+                         [0, 4, 5, 4, 0],
+                         [0, 0, 0, 0, 0]])
+
+    assert_array_equal(result, expected)
+    assert_array_equal(image, result)
+
 if __name__ == "__main__":
     np.testing.run_module_suite()