# Mahotas – Parameter-Free Threshold Adjacency Statistics

• Last Updated : 11 Sep, 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'``)`  `# filtering 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]```

My Personal Notes arrow_drop_up