pyhum.texture module

Create a texture lengthscale map using the algorithm detailed by Buscombe et al. (forthcoming)

This textural lengthscale is not a direct measure of grain size. Rather, it is a statistical

representation that integrates over many attributes of bed texture, of which grain size is the most important.

The technique is a physically based means to identify regions of texture within a sidescan echogram,

and could provide a basis for objective, automated riverbed sediment classification.

Syntax

You call the function like this:

[] = PyHum.texture(humfile, sonpath, win, shift, doplot, density, numclasses, maxscale, notes)

Parameters

humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
win : int, optional [Default=100]
pixel in pixels of the moving window
shift : int, optional [Default=10]
shift in pixels for moving window operation
doplot : int, optional [Default=1]
if 1, make plots, otherwise do not make plots
density : int, optional [Default=win/2]
echogram will be sampled every ‘density’ pixels
numclasses : int, optional [Default=4]
number of ‘k means’ that the texture lengthscale will be segmented into
maxscale : int, optional [Default=20]
Max scale as inverse fraction of data length for wavelet analysis
notes : int, optional [Default=100]
notes per octave for wavelet analysis

Returns

sonpath+base+’_data_class.dat’: memory-mapped file
contains the texture lengthscale map
sonpath+base+’_data_kclass.dat’: memory-mapped file
contains the k-means segmented texture lengthscale map

References

[1] Buscombe, D., Grams, P.E., and Smith, S.M.C., Automated riverbed sediment classification using low-cost sidescan sonar. Journal of Hydraulic Engineering, 10.1061/(ASCE)HY.1943-7900.0001079, 06015019.
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