In this article we will see how we can get the image’s threshold adjacency statistics feature in mahotas. TAS were presented by Hamilton et al. in “Fast automated cell phenotype image classification”. TAS give original parameters unlike PFTAS which gives a variation without any hardcoded parameters.
For this tutorial we will use ‘lena’ image, below is the command to load the lena image
mahotas.demos.load('lena')
Below is the lena image
In order to do this we will use
mahotas.features.tas
methodSyntax : mahotas.features.tas(img)
Argument : It takes image object as argument
Return : It returns 1-D array
Note : Input image should be filtered or should be loaded as grey
In order to filter the image we will take the image object which is numpy.ndarray and filter it with the help of indexing, below is the command to do this
image = image[:, :, 0]
Below is the implementation
# importing required libraries import mahotas import mahotas.demos from pylab import gray, imshow, show import numpy as np import matplotlib.pyplot as plt # loading iamge img = mahotas.demos.load( 'lena' ) # filtering image img = img. max ( 2 ) print ( "Image" ) # showing image imshow(img) show() # computing tas value = mahotas.features.tas(img) # printing value print (value) |
Output :
Image
[8.18235887e-01 4.96278071e-02 3.85778412e-02 5.42293510e-02 2.31141496e-02 8.96518478e-03 4.17582280e-03 2.30390223e-03 7.70054279e-04 8.11830699e-01 5.42434618e-02 3.79106870e-02 5.78859183e-02 2.54097764e-02 7.40147155e-03 2.98681431e-03 1.76294893e-03 5.68223210e-04 8.69779571e-01 3.56911714e-02 2.61354551e-02 4.12780295e-02 1.73316328e-02 5.09194046e-03 2.56976434e-03 1.52282331e-03 5.99611680e-04 7.43348142e-01 5.80286091e-02 4.97388078e-02 7.46472685e-02 3.83537568e-02 1.81614021e-02 1.17267978e-02 4.57940731e-03 1.41580823e-03 9.37920200e-01 1.55393289e-02 1.20666222e-02 1.87743206e-02 9.61712375e-03 3.05412151e-03 1.93789436e-03 8.37170364e-04 2.53218197e-04 9.13099391e-01 2.42303089e-02 1.70045074e-02 2.72925208e-02 1.13702921e-02 3.81980697e-03 1.62341796e-03 1.19050651e-03 3.69248007e-04]
Another example
# importing required libraries import mahotas import numpy as np from pylab import gray, imshow, show import os import matplotlib.pyplot as plt # loading iamge img = mahotas.imread( 'dog_image.png' ) # fltering image img = img[:, :, 0 ] print ( "Image" ) # showing image imshow(img) show() # computing tas value = mahotas.features.tas(img) # printing value print (value) |
Output :
Image
[8.92356868e-01 2.75272814e-02 2.05523535e-02 3.43358813e-02 1.80176597e-02 5.01153448e-03 1.33785553e-03 6.79775240e-04 1.80791287e-04 8.81674218e-01 3.13932157e-02 2.34006832e-02 3.69160363e-02 1.95048908e-02 5.11444295e-03 1.23809709e-03 6.09325269e-04 1.49090226e-04 9.06137850e-01 2.75823883e-02 2.03761048e-02 2.88661485e-02 1.36743022e-02 2.68646310e-03 4.75770564e-04 1.39449993e-04 6.15220557e-05 8.35720148e-01 4.69532212e-02 3.62894953e-02 5.08719737e-02 2.36920394e-02 4.84714813e-03 1.21050472e-03 2.87238408e-04 1.28231432e-04 9.38717680e-01 1.80549908e-02 1.33994005e-02 1.87263793e-02 8.80054720e-03 1.75569656e-03 3.62486722e-04 1.35538513e-04 4.72808768e-05 9.05435494e-01 2.48433294e-02 1.91342383e-02 2.97531477e-02 1.52476648e-02 4.03149662e-03 1.02763639e-03 4.30377634e-04 9.66153873e-05]
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