Mahotas - Threshold Adjacency Statistics

Mahotas – Threshold Adjacency Statistics

Last Updated : 10 Jul, 2020

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 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

# 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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