downscale_local_mean
-
skimage.transform.downscale_local_mean(image, factors, cval=0, clip=True)
[source] -
Down-sample N-dimensional image by local averaging.
The image is padded with
cval
if it is not perfectly divisible by the integer factors.In contrast to the 2-D interpolation in
skimage.transform.resize
andskimage.transform.rescale
this function may be applied to N-dimensional images and calculates the local mean of elements in each block of sizefactors
in the input image.Parameters: image : ndarray
N-dimensional input image.
factors : array_like
Array containing down-sampling integer factor along each axis.
cval : float, optional
Constant padding value if image is not perfectly divisible by the integer factors.
Returns: image : ndarray
Down-sampled image with same number of dimensions as input image.
Examples
12345678>>> a
=
np.arange(
15
).reshape(
3
,
5
)
>>> a
array([[
0
,
1
,
2
,
3
,
4
],
[
5
,
6
,
7
,
8
,
9
],
[
10
,
11
,
12
,
13
,
14
]])
>>> downscale_local_mean(a, (
2
,
3
))
array([[
3.5
,
4.
],
[
5.5
,
4.5
]])
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