Mahotas – Threshold Adjacency Statistics

In this article, we will see how we can get the image’s threshold adjacency statistics feature in mahotas. TAS was presented by Hamilton et al. in “Fast automated cell phenotype image classification”. TAS gives original parameters, unlike PFTAS which gives a variation without any hardcoded parameters.
For this tutorial, we will use the ‘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 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 libraries
import mahotas
import mahotas.demos
from pylab import gray, imshow, show
import numpy as np
import matplotlib.pyplot as plt

# loading image
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 

Python3

# importing required libraries
import mahotas
import numpy as np
from pylab import gray, imshow, show
import os
import matplotlib.pyplot as plt

# loading image
img = mahotas.imread('dog_image.png')


# filtering 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]