[SciPy-user] PIL and gaussian_filter?

[Johannes Strömberg]

Thanks a lot, it works like a charm.

Does anyone know of an efficient way of implementing a threshold
filter, i.e. where the resulting value is either the difference
between the current value and the threshold (if the value is above the
threshold) or otherwise 0?

/Johannes

On Wed, May 21, 2008 at 4:26 PM, Zachary Pincus <zachary.pincus at yale.edu> wrote:
> Hello,
>
>> When I use the asarray() method on my PIL image I get a 3-dimensional
>> array, shape is  (w, h, 3 [rgb-values]).
>>
>> I am wondering how I can transform this to something that is
>> compatible with f.e. ndimage.gaussian_filter? (When I run it directly
>> it simply removes color from the image).
>
> ndimage works as the name implies: it is a library for dealing with n-
> dimensional images. So in this case, it treated your input as a [w,h,
> 3] array of voxels (a 3D image), and then applied the gaussian
> smoothing of the requested variance across all three dimensions. The
> result of which was probably (a) some degree of smoothing in the x and
> y dimensions (as requested), and (b) the same degree of smoothing
> across the "z" (color channel) dimension, which (as it is only "3
> voxels high" translates into quite a bit of mixing of the pixels).
> This "z smoothing" of course translates into mixing the color
> channels, probably by quite a large degree if your gaussian variance
> was anything above one pixel. Which would give the effect of "removing
> the color" from the image.
>
> So, you need to apply the gaussian filter to each channel
> independently. You could either do this with a for loop, or even
> easier, pass a tuple to the sigma parameter to describe the requested
> variance in each dimension. To smooth by a 2-pixel stdev gaussian in
> w, a 4-pixel gaussian in h, and to do no smoothing across color
> channels, just pass 'sigma=[2,4,0]' to gaussian_filter.
>
> Zach
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