image processing style (2D kernel) convolutions?

[Kyler Laird]

It appears that Numeric only supports 1D convolutions. I need to
apply a 2D kernel such as
0 1 0
1 -4 1
0 1 0
to an image. Is there such a capability in Python/Numeric? It
appears that numarray has convolve2d(), but I don't have it
available where I'm working.

(I could express the problem better but I have a feeling that
people who know the answer know exactly what I mean. Tell me if
that's not true.)

Thank you.

--kyler

[Jeff Epler]

Re: image processing style (2D kernel) convolutions?

Well, it seems that you could convolve each row of the input image with
each row of the kernel, with the rows offset from each other. Then sum
the 3 convolved rows to get the output row

I know this isn't the right notation, and doesn't deal with r-1 or r+1
being out of range..
for r in range(input_hei ght):
output[r] = (convolve(input[r-1], filter[0])
+ convolve(input[r], filter[1])
+ convolve(input[r+1], filter[2])

Jeff