[scikit-image] 回复：Re: my watershed

[Juan Nunez-Iglesias]

Actually this specific function has been on my mind for some time! Do you have a reference implementation, preferably in nD? =)

On 21 Sep 2017, 4:33 PM +1000, imagepy at sina.com, wrote:
> add one:
> if scikit-image can implements a ridge filter. just like watershed mixed with the skeleton. make all region a same label, then do level loop, just stop when one pixel left. It's usful in geography and hydrology analysis.
>
> ----- 原始邮件 -----
> 发件人：Juan Nunez-Iglesias <jni.soma at gmail.com>
> 收件人：scikit-image <scikit-image at python.org>, imagepy at sina.com, "Mailing_list_for_scikit-image_(http://scikit-image.org)" <scikit-image at python.org>
> 主题：Re: [scikit-image] my watershed
> 日期：2017年09月21日 13点48分
>
> Hi Yan,
>
> Thanks for this. Simple functions are very good for one’s own images, as well as to understand an algorithm, but for a widely used library like scikit-image, flexibility and robustness are least as important as speed. In skimage, we aim to support floating point input images, for which your code won’t work. There is a lot of thought into edge cases going into the skimage implementation, which unfortunately could have an effect on performance. The question we need to solve isn’t “can we make a super-fast watershed implementation”, but, “can we make a flexible and robust implementation that is also fast?” Using Numba is certainly not off-limits, but any candidate implementation should at a minimum pass the skimage test suite.
>
> I once tried a level-by-level implementation of watershed, by the way. It has a fatal flaw, which is that valleys with no markers will never get labeled. Here’s a test case that works with skimage but fails in your implementation. (Note also that your implementation overwrites the seeds image, which is pretty crazy. =)
>
> Thanks again!
>
> Juan.
>
> In [44]: import watershed
> In [45]: from skimage import morphology
> In [46]: import numpy as np
> In [47]: image = np.array([1, 0, 1, 0, 1, 0, 1], dtype=np.uint8)
> In [48]: seeds = np.array([0, 1, 0, 0, 0, 2, 0])
> In [49]: morphology.watershed(image, seeds)
> Out[49]: array([1, 1, 1, 1, 2, 2, 2], dtype=int32)
> In [50]: watershed.watershed(image, seeds)
> Out[50]: array([0, 0, 0, 0, 0, 0, 0])
> In [51]: seeds
> Out[51]: array([0, 0, 0, 0, 0, 0, 0])
>
> On 12 Sep 2017, 6:34 AM +1000, imagepy at sina.com, wrote:
> > Hi:
> > Now It supports nd. but less of test. this image, skimage cost 20s, mine less than 1s.
> > It is wrriten with numba in 90 lines. did not support compactness, but it's simple, and fast.
> >
> > YXDragon
> > _______________________________________________
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> > scikit-image at python.org
> > https://mail.python.org/mailman/listinfo/scikit-image
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