[Numpy-discussion] Sobel question
klimek at grc.nasa.gov
Fri Nov 12 13:23:02 CST 2004
Peter Verveer wrote:
> Thanks for your interest! I hope it is useful for you...
I think it will be, especially the segmentation and object measurements.
I'm apparently running into a learing curve so please bear with me.
> The Sobel filter is always a derivative along a single direction. A
> negative value is used to specifiy an axis relative to the rank of the
> array, so for a 2D array, axis=-1 is equivalent to axis=1, ...
In my tests axis=-1 and axis=1 produce different results. See code
below. And of course when I scale the results to a range of 0 to 255 and
convert to an image, the image is different also. Is this a bug or am I
import numarray.nd_image as ND
a = numarray.zeros([10, 10]) + 50
a = a.astype(numarray.UInt8)
for y in range(3,7):
for x in range(3,7):
a[y, x] = 200
a = a.astype(numarray.Float32) # convert to float
h1 = ND.sobel(a, 1)
h2 = ND.sobel(a, -1)
print 'min:', min(numarray.ravel(h1)), ' max:', max(numarray.ravel(h1))
print 'min:', min(numarray.ravel(h2)), ' max:', max(numarray.ravel(h2))
> If you want a gradient magnitude using Sobel derivatives, you can use
> the generic_gradient_magnitude() function.
This looks good except I can't get it to work. Can you show me a little
code fragment? I've gotten the following code to work but your way might
vert = ND.sobel(a, 0)
horz = ND.sobel(a, 1)
mag = numarray.sqrt((horz * horz) + (vert * vert))
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