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# 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]```

My Personal Notes arrow_drop_up