uniform_filter not working with NANs present?
Michael Aye
kmichael.aye at gmail.com
Thu Sep 5 19:51:23 EDT 2013
On Wednesday, September 4, 2013 10:44:21 PM UTC-7, Juan Nunez-Iglesias
wrote:
>
> Hi Michael,
>
> It makes a lot of sense that this would fail, but skimage.filter.rank.mean<http://scikit-image.org/docs/dev/api/skimage.filter.rank.html#mean>has a `mask` keyword argument that can do what you want. However, it only
> works for uint8 or uint16 images, *and* if I remember correctly, it will
> be much slower if values exceed 12 bits, that is, your image intensity
> values should be in [0, 4096).
>
> Hi!
Thanks for your reply!
Could you explain why it makes sense that the uniform_filter fails in my
example? Using a window of 3, I expect the following in this simple 1d
example:
input: [nan, 1, 2, 3, 4, 5, nan]
output: [nan, nan, 2, 3, 4, nan, nan]
Why should this not work?
Michael
>>> from skimage.filter import rank
> >>> arr = array([[np.nan, 1, 2, 3, 4, 5, np.nan]]) # make sure this is 2D
> >>> selem = np.ones((3, 3))
> >>> arr_mean1 = rank.mean(arr.astype(np.uint8), selem)
> >>> arr_mean1
> array([[0, 1, 2, 3, 4, 3, 2]], dtype=uint8)
>
> >>> arr.astype(np.uint8) # nan's are actually converted to 0... Not good!
> array([[0, 1, 2, 3, 4, 5, 0]], dtype=uint8)
>
> >>> nan_mask = True - np.isnan(arr)
> >>> arr_mean2 = rank.mean(arr.astype(np.uint8), selem, mask=nan_mask)
> >>> arr_mean2 # using the nan mask produces the correct output
> array([[1, 1, 2, 3, 4, 4, 5]], dtype=uint8)
>
>
> Hope this helps!
>
> Juan.
>
>
> On Mon, Sep 2, 2013 at 10:12 PM, Michael Aye <kmicha... at gmail.com<javascript:>
> > wrote:
>
>> Hi!
>>
>> I am trying to use ndimage's uniform_filter (for a simple local mean
>> filtering) on a map-projected image that has NAN's at the border (basically
>> the corners where the rotated map projected image does not fit into the
>> rectangular grid).
>> Is there a way, maybe in skimage, to use a uniform_filter on an array
>> that contains NANs? ndimage' version does not cope with it correctly:
>>
>> > arr = array([np.nan, 1,2,3,4,5,np.nan])
>> > arr
>>
>> array([ nan, 1., 2., 3., 4., 5., nan])
>>
>> > nd.filters.uniform_filter(arr, 3)
>>
>> array([ nan, nan, nan, nan, nan, nan, nan])
>>
>>
>> Cheers,
>>
>> Michael
>>
>>
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