> Please do that! The plateau handling in our peak finding seems
> sub-optimal and confusing.
>
Stephan: it's done here :)
https://github.com/scikit-image/scikit-image/issues/3016
Best
--
François Boulogne.
http://www.sciunto.org
GPG: 32D5F22F
___
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.],
>
On Wed, 11 Apr 2018 16:07:55 +0200, François Boulogne wrote:
> Another point I would like to make: recently, a scipy contributor added
> a beautiful function to detect peaks. I'm already using this feature in
> my code. The first PR is here: https://github.com/scipy/scipy/pull/8264
> Others are for
Hi,
I haven't tested the difference between both functions, but I can say
that peak_local_max and _prominent_peaks are used in several places
internally.
Another point I would like to make: recently, a scipy contributor added
a beautiful function to detect peaks. I'm already using this feature in
Thank you Juan,
all my researches from a well-known web search engine gave me
peak_local_max as a solution, in particular, the example attached to the
watershed function.
Sorry about this. Maybe this function should be marked as deprecated in
favor of local_maxima.
About peak_local_max, th
Hi Yann, and thanks for the interest!
We actually already have this algorithm implemented in
skimage.morphology.local_maxima.
peak_local_max is a bit different and I must admit I don’t understand the logic
in it. I *particularly* don’t understand the following result:
In [1]: def rmax(I):
.
First mistake, this should work, but the discretization of the
'continuous' values should be handled with care.
def rmax(I):
"""
Own version of regional maximum
This avoids plateaus problems of peak_local_max
I: original image, int values
returns: binary array, with 1 for the maxima
"""
I
