function to measure the (local) density of 1s in a binary image

[Egor Panfilov]

Hi Matteo!

The function you're looking for is as simple as convolution [1] with a
typical structuring element [2] (we have square, circle and many more). I
suppose, this can also be called as 'mean filter':

Here is an example:

> import numpy as np
> import scipy.ndimage as ndi
> from skimage.morphology import square
>
> img = np.random.uniform(size=(5, 5))
> img = (img > 0.5).astype(np.float)
> sem = square(3)/3**2   # Don't forget to normalize
> out = ndi.convolve(img, sem, mode='wrap')
>


print(img)
> print(sem)
> print(out)


Results to:

> [[ 1.  0.  0.  1.  0.]
>  [ 1.  0.  0.  0.  0.]
>  [ 0.  0.  1.  1.  0.]
>  [ 0.  0.  0.  0.  1.]
>  [ 1.  0.  1.  0.  0.]]
> [[ 0.11111111  0.11111111  0.11111111]
>  [ 0.11111111  0.11111111  0.11111111]
>  [ 0.11111111  0.11111111  0.11111111]]
> [[ 0.33333333  0.44444444  0.22222222  0.22222222  0.44444444]
>  [ 0.22222222  0.33333333  0.33333333  0.33333333  0.44444444]
>  [ 0.22222222  0.22222222  0.22222222  0.33333333  0.33333333]
>  [ 0.22222222  0.33333333  0.33333333  0.44444444  0.33333333]
>  [ 0.33333333  0.33333333  0.22222222  0.33333333  0.44444444]]


Cheers,
Egor

[1]
http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.convolve.html#scipy.ndimage.convolve

[2]
http://scikit-image.org/docs/dev/api/skimage.morphology.html#module-skimage.morphology

2016-05-26 18:11 GMT+03:00 Matteo <matteo.niccoli at gmail.com>:

> Is there a function in scikit-image to calculate the the local density of
> 1s in a binary image?
>
> By local density of 1s I mean:
> 1) count the number of pixels that have value of 1 in a running/sliding
> window (preferrably circular but square would work too)
> 2) divide by the total number of pixels in the running window
>
> Thanks
> Matteo
>
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