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Clarify that uint16 images are only faster up to a certain bit depth

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Johannes Schönberger
2014-10-07 08:38:26 -04:00
parent fdeeaa298a
commit c73d1c98e0
1 changed files with 7 additions and 6 deletions
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skimage/morphology/grey.py
+7 -6
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@@ -8,6 +8,7 @@ from . import cmorph
__all__ = ['erosion', 'dilation', 'opening', 'closing', 'white_tophat',
'black_tophat']
@default_fallback
def erosion(image, selem=None, out=None, shift_x=False, shift_y=False):
"""Return greyscale morphological erosion of an image.
@@ -37,9 +38,9 @@ def erosion(image, selem=None, out=None, shift_x=False, shift_y=False):
Notes
-----
For `uint8` and `uint16` data, the lower algorithm complexity makes the
`skimage.filter.rank.minimum` function more efficient for larger images
and structuring elements.
For `uint8` (and `uint16` up to a certain bit-depth) data, the lower
algorithm complexity makes the `skimage.filter.rank.minimum` function more
efficient for larger images and structuring elements.
Examples
--------
@@ -98,9 +99,9 @@ def dilation(image, selem=None, out=None, shift_x=False, shift_y=False):
Notes
-----
For `uint8` and `uint16` data, the lower algorithm complexity makes the
`skimage.filter.rank.maximum` function more efficient for larger images
and structuring elements.
For `uint8` (and `uint16` up to a certain bit-depth) data, the lower
algorithm complexity makes the `skimage.filter.rank.minimum` function more
efficient for larger images and structuring elements.
Examples
--------