Mahotas – Skeletonization by thinning of image
Last Updated :
19 Feb, 2022
In this article, we will see how we can do the skeletonization of images by thinning in mahotas. Skeletonization is a process for reducing foreground regions in a binary image to a skeletal remnant that largely preserves the extent and connectivity of the original region while throwing away most of the original foreground pixels. Thinning is a morphological operation that is used to remove selected foreground pixels from binary images, somewhat like erosion or opening.
In this tutorial, we will use the “Lena” image, below is the command to load it.
mahotas.demos.load('lena')
Below is the Lena image
In order to do this we will use mahotas.thin method
Syntax : mahotas.thin(img)
Argument : It takes image object as argument
Return : It returns image object
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
import mahotas
import mahotas.demos
from pylab import gray, imshow, show
import numpy as np
img = mahotas.demos.load( 'lena' )
img = img. max ( 2 )
T_otsu = mahotas.otsu(img)
img = img > T_otsu
print ( "Image threshold using Otsu Method" )
imshow(img)
show()
new_img = mahotas.thin(img)
print ( "Skeletonised Image" )
imshow(new_img)
show()
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Output :
Image threshold using Otsu Method
Skeletonised Image
Another example
Python3
import mahotas
import numpy as np
from pylab import gray, imshow, show
import os
img = mahotas.imread( 'dog_image.png' )
img = img[:, :, 0 ]
T_otsu = mahotas.otsu(img)
img = img > T_otsu
print ( "Image threshold using Otsu Method" )
imshow(img)
show()
new_img = mahotas.thin(img)
print ( "Skeletonised Image" )
imshow(new_img)
show()
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Output :
Image threshold using Otsu Method
Skeletonised Image
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