Thank you, that makes sense.
Your link does not explain why float images are restricted to [-1; 1] though?
So with my data I can't just do:
image = np.array(data.coins(), dtype=np.float)
I guess skimage makes an assumption that images are within a fixed range?
Best,
Victor Poughon
________________________________
De : scikit-image [[email protected]] de
la part de Juan Nunez-Iglesias [[email protected]]
Envoyé : vendredi 3 novembre 2017 14:56
À : Mailing list for scikit-image (http://scikit-image.org)
Objet : Re: [scikit-image] Observed difference between
skimage.filters.rank.mean and scipy.signal.convolve2d
Hi Victor,
The biggest problem is that you’re getting bitten by datatypes. Please read the
following document:
http://scikit-image.org/docs/dev/user_guide/data_types.html
Specifically, rank_mean is a uint8 image, so only contains integers between 0
and 255. naive_convolve is a float image, with continuous values between 0 and
255. Try this:
In [17]: naive_convolve_float = naive_convolve / 255
In [18]: rank_mean_float = rank_mean / 255
In [19]: plt.imshow(np.abs(naive_convolve_float - rank_mean_float),
cmap='magma')
Out[19]: <matplotlib.image.AxesImage at 0x10d098fd0>
Result:
[cid:47EBAC9317A4470AA3DEF2C02C973463]
There is a smaller difference also. Internally, filters.rank uses a fancy
rolling histogram algorithm with integer data values. This means that the
result of the rank_mean is only approximately accurate, essentially to within
integer rounding (good enough for most real-world uses), while the convolve2d
code gives you an exact value (to within floating point error).
Hope this helps!
Juan.
On 2 Nov 2017, 7:54 PM +1100, Poughon Victor <[email protected]>, wrote:
Hello,
I looks like skimage.filters.rank.mean and scipy.signal.convolve2d don't output
exactly the same images. When doing:
image = data.coins()
K = np.ones((11, 11))
rank_mean = rank.mean(image, selem=K)
naive_convolve = convolve2d(image, K, mode="same") / K.sum()
All output pixel are different, with an absolute difference varying randomly
between 0 and 1. Of course there's also a massive difference at the border, but
that's expected because convolve2d treats image boundaries differently. But
even in the center of the image all pixels are different. I've made a test
script with an illustrated output image, you can check it out in this gist:
https://gist.github.com/vpoughon/b4afc76ce5dc681fda9d0550d41359d3
Am I doing something wrong?
Thanks,
Victor Poughon
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