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.![]()