In this article, we will see how we can get the euler number of the image in mahotas. The Euler number is a measure of the topology of an image. It is defined as the total number of objects in the image minus the number of holes in those objects. You can use either 4- or 8-connected neighborhoods.
In this tutorial, we will use “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.euler method
Syntax : mahotas.euler(img)
Argument : It takes image object as argument
Return : It returns integer
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 librariesimport mahotasimport mahotas.demosfrom pylab import gray, imshow, showimport numpy as np # loading imageimg = mahotas.demos.load('lena') # filtering imageimg = img.max(2)# otsu methodT_otsu = mahotas.otsu(img) # image values should be greater than otsu valueimg = img > T_otsu print("Image threshold using Otsu Method")# creating a labeled imagemarker, n_nucleus = mahotas.label(img) # showing imageimshow(img)show()# euler number of image of imageeuler = mahotas.euler(img)print("Euler Number of Image : " + str(euler)) |
Output :
Image threshold using Otsu Method
Euler Number of Image : 54.25
Another example
Python3
# importing required librariesimport mahotasimport numpy as npfrom pylab import gray, imshow, showimport os # loading imageimg = mahotas.imread('dog_image.png')# filtering imageimg = img[:, :, 0] # otsu methodT_otsu = mahotas.otsu(img) # image values should be greater than otsu valueimg = img > T_otsu print("Image threshold using Otsu Method") # showing imageimshow(img)show()# euler number of image of imageeuler = mahotas.euler(img)print("Euler Number of Image : " + str(euler)) |
Output :
Image threshold using Otsu Method
Euler Number of Image : 76.75
