Mahotas – Threshold Adjacency Statistics
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
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In order to do this we will use mahotas.features.tas method
Syntax : 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
Python3
# importing required librariesimport mahotasimport mahotas.demosfrom pylab import gray, imshow, showimport numpy as npimport matplotlib.pyplot as plt # loading imageimg = mahotas.demos.load('lena') # filtering imageimg = img.max(2)print("Image") # showing imageimshow(img)show()# computing tasvalue = mahotas.features.tas(img) # printing valueprint(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
Python3
# importing required librariesimport mahotasimport numpy as npfrom pylab import gray, imshow, showimport osimport matplotlib.pyplot as plt # loading imageimg = mahotas.imread('dog_image.png')# filtering imageimg = img[:, :, 0] print("Image") # showing imageimshow(img)show()# computing tasvalue = mahotas.features.tas(img) # printing valueprint(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]
