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]

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_height):
output[r] = (convolve(input[r-1], filter[0])
+ convolve(input[r], filter[1])
+ convolve(input[r+1], filter[2])

Jeff