Non-linear distance based filter
Eduardo Henrique Arnold
eduardoarnoldh at gmail.com
Wed Jul 16 12:56:59 EDT 2014
Yes, setting the vertical sampling value to a high value (i.e. 10000) did
the trick. It makes sense since it is going to sample the the next pixel in
a vertical position that does not exist in the image. With the right
distance matrix I could use Josh's idea, as he suggested on the first
reply. That was simpler than implementing something with several loops.
Thank you both, those were very quick and effective answers. :)
On Wednesday, July 16, 2014 1:16:34 AM UTC+1, Juan Nunez-Iglesias wrote:
>
> @JDWarner, actually, instead of iterating, setting one of the sampling
> dimensions to a very large number should produce the desired output, no? =)
>
>
> On Tue, Jul 15, 2014 at 11:13 AM, Josh Warner <silvertr... at gmail.com
> <javascript:>> wrote:
>
>> I don’t believe sampling works like this. The sampling= kwarg tells the
>> algorithm the spacing of each pixel/voxel in each spatial dimension. It
>> then calculates the distance transform. So if you put a zero in, it will
>> traverse in that dimension with a distance cost of zero for each step. This
>> will generate a result that has mostly spurious zero distances, rather than
>> select a particular axis to calculate the distance transform along.
>>
>> To calculate the correct distance function along a specific axis probably
>> requires repeated calls along that axis; since scipy.ndimage is by
>> definition n-dimensional it should be able to efficiently handle rank-1
>> arrays without issue.
>>
>> I use the super handy Chrome extension “Markdown Here” to write in
>> Markdown and then have my free entry fields converted to pretty HTML prior
>> to posting. At this point I pretty much think in Markdown…
>>
>> Josh
>>
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>
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