Mahotas – Appropriate structuring element of image

In this article we will see how we can get the appropriate structuring element of the image in mahotas. A structuring element is a matrix that identifies the pixel in the image being processed and defines the neighborhood used in the processing of each pixel. 
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.get_structuring_elem method
Syntax : mahotas.get_structuring_elem(img, n)
Argument : It takes image object and integer as argument
Return : It returns numpy ndarray 

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()

# getting structuring element
value = mahotas.get_structuring_elem(img, 1)

# showing value
print(value)

Output :

Image

[[0 1 0]
 [1 1 1]
 [0 1 0]]

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()

# getting structuring element
value = mahotas.get_structuring_elem(img, 2)

# showing value
print(value)

Output :

Image

[[1 1 1]
 [1 1 1]
 [1 1 1]]