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Mahotas – Parameter-Free Threshold Adjacency Statistics
  • Last Updated : 28 Apr, 2021

In this article we will see how we can get the image’s parameter-free threshold adjacency statistics in mahotas. TAS were presented by Hamilton et al. in “Fast automated cell phenotype image classification” 
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.pftas method
Syntax : mahotas.features.pftas(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 pftas
value = mahotas.features.pftas(img)
  
 
# printing value
print(value)

Output :
 

Image

 

 

[8.40466496e-01 3.96107929e-02 3.32482230e-02 4.78710924e-02
 1.99986198e-02 9.29542475e-03 4.81678283e-03 3.41591333e-03
 1.27665448e-03 8.74954977e-01 3.30841335e-02 2.54587942e-02
 3.93565900e-02 1.67089809e-02 5.66629477e-03 2.56520631e-03
 1.63400128e-03 5.71021954e-04 8.94910256e-01 2.94171187e-02
 2.18929382e-02 3.09704979e-02 1.29246004e-02 5.15770440e-03
 2.69414206e-03 1.49270033e-03 5.40041990e-04 7.95067984e-01
 5.76368630e-02 4.24876742e-02 5.77221625e-02 2.45406623e-02
 1.12339424e-02 7.21633656e-03 3.25844038e-03 8.35934968e-04
 9.01310067e-01 2.80622737e-02 1.99915045e-02 3.05637402e-02
 1.27837749e-02 4.03875587e-03 1.90138423e-03 1.03160208e-03
 3.16897372e-04 8.28594029e-01 4.43179717e-02 3.44044708e-02
 5.11290091e-02 2.25801812e-02 1.03552423e-02 4.92079472e-03
 2.92782150e-03 7.70479341e-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')
 
 
# fltering image
img = img[:, :, 0]
   
print("Image")
   
# showing image
imshow(img)
show()
 
# computing pftas
value = mahotas.features.pftas(img)
  
 
# printing value
print(value)

Output :
 

Image

 

 

[9.09810233e-01 2.60317846e-02 1.97574078e-02 2.77915537e-02
 1.31694722e-02 2.52446879e-03 6.36716463e-04 2.17571455e-04
 6.07920241e-05 9.15640448e-01 2.48822727e-02 1.86702013e-02
 2.63437145e-02 1.18992323e-02 2.02411568e-03 4.07844204e-04
 1.09513721e-04 2.26580113e-05 9.71165298e-01 9.19026798e-03
 6.63816594e-03 8.62583483e-03 3.68366898e-03 5.02318497e-04
 1.13426757e-04 5.40127416e-05 2.70063708e-05 8.33778879e-01
 4.29548185e-02 3.26013800e-02 5.29056931e-02 2.73491801e-02
 7.36566005e-03 1.98765890e-03 8.80608375e-04 1.76121675e-04
 9.00955422e-01 2.52231333e-02 1.89294439e-02 3.21553830e-02
 1.65154923e-02 4.43605931e-03 1.16101879e-03 5.12783301e-04
 1.11264301e-04 9.08750580e-01 2.31333775e-02 1.64857417e-02
 2.92278667e-02 1.50633649e-02 4.92893055e-03 1.33347821e-03
 7.40821225e-04 3.35838955e-04]

 

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