snow_partitioning¶

snow_partitioning
(im, dt=None, r_max=4, sigma=0.4)[source]¶ Partition the void space into pore regions using a markerbased watershed algorithm, with specially filtered peaks as markers.
 Parameters:
im (array_like) – A boolean image of the domain, with
True
indicating the pore space andFalse
elsewhere.dt (array_like, optional) – The distance transform of the pore space. This is done automatically if not provided, but if the distance transform has already been computed then supplying it can save some time.
r_max (int) – The radius of the spherical structuring element to use in the Maximum filter stage that is used to find peaks. The default is 4.
sigma (float) – The standard deviation of the Gaussian filter used in step 1. The default is 0.4. If 0 is given then the filter is not applied, which is useful if a distance transform is supplied as the
im
argument that has already been processed.
 Returns:
results – A custom object with the follow data as attributes:
 ’im’
The binary image of the void space
 ’dt’
The distance transform of the image
 ’peaks’
The peaks of the distance transform after applying the steps of the SNOW algorithm
 ’regions’
The void space partitioned into pores using a marker based watershed with the peaks found by the SNOW algorithm
 Return type:
Results object
Notes
The SNOW network extraction algorithm (SubNetwork of an Oversegmented Watershed) was designed to handle to perculiarities of high porosity materials, but it applies well to other materials as well.
References
[1] Gostick, J. “A versatile and efficient network extraction algorithm using markerbased watershed segmenation”. Physical Review E. (2017)