[scikit-image] 回复：Re: local maxima improvements

[imagepy at sina.com]

Hi，everyone
I think we should not use peak_local_max for find watershed's seeds. why not use h_maxima? which can give a h tolerance.I think if we should replace it in the official demo? It would cause a misunderstanding.
And scikit-image's h_maxima, h_minima is very slow. here I implements one with numba, https://github.com/Image-Py/imagepy/blob/master/imagepy/ipyalg/hydrology/findmax.py. you can see if it is useful.
yxdragon----- 原始邮件 -----
发件人：Stefan van der Walt <stefanv at berkeley.edu>
收件人："Mailing list for scikit-image (http://scikit-image.org)" <scikit-image at python.org>
主题：Re: [scikit-image] local maxima improvements
日期：2018年04月12日 03点08分


On Wed, 11 Apr 2018 12:44:45 +1000, Juan Nunez-Iglesias wrote:
> In [7]: image
> Out[7]:
> array([[ 0.,  0.,  0.,  0.,  0.,  0.],
>        [ 0.,  1.,  0.,  0.,  0.,  0.],
>        [ 0.,  0.,  0.,  0.,  0.,  0.],
>        [ 2.,  2.,  2.,  4.,  4.,  2.],
>        [ 2.,  2.,  2.,  4.,  4.,  2.],
>        [ 2.,  2.,  2.,  2.,  2.,  2.]])
> 
> In [15]: feature.peak_local_max(image)
> In [17]: image_peak[tuple(feature.peak_local_max(image).T)] = 1
> 
> In [18]: image_peak
> Out[18]:
> array([[ 0.,  0.,  0.,  0.,  0.,  0.],
>        [ 0.,  1.,  0.,  0.,  0.,  0.],
>        [ 0.,  0.,  0.,  0.,  0.,  0.],
>        [ 0.,  1.,  0.,  1.,  1.,  0.],
>        [ 0.,  1.,  0.,  1.,  1.,  0.],
>        [ 0.,  0.,  0.,  0.,  0.,  0.]])
That output in column 1 looks highly suspect!  This is a great example
for a regression test, thanks Yann.
Stéfan
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