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

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# importing required libraries
import mahotas
import mahotas.demos
from pylab import gray, imshow, show
import numpy as np
import matplotlib.pyplot as plt
    
# loading iamge
img = mahotas.demos.load('lena')
  
  
    
# filtering image
img = img.max(2)
  
print("Image")
    
# showing image
imshow(img)
show()
  
# getting structring element
value = mahotas.get_structuring_elem(img, 1)
   
# showing value
print(value)

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Output :

Image

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

Another example

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code

# importing required libraries
import mahotas
import numpy as np
from pylab import gray, imshow, show
import os
import matplotlib.pyplot as plt
   
# loading iamge
img = mahotas.imread('dog_image.png')
  
  
# fltering image
img = img[:, :, 0]
    
print("Image")
    
# showing image
imshow(img)
show()
  
# getting structring element
value = mahotas.get_structuring_elem(img, 2)
   
# showing value
print(value)

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Output :

Image

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



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