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, imag...@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 > _______________________________________________ > scikit-image mailing list > scikit-image@python.org > https://mail.python.org/mailman/listinfo/scikit-image
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