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/fin.... you can see if it is useful. yxdragon----- 原始邮件 ----- 发件人：Stefan van der Walt <stefanv@berkeley.edu> 收件人："Mailing list for scikit-image (http://scikit-image.org)" <scikit-image@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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