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Mahotas – Element Structure for Eroding Image

  • Last Updated : 29 Jul, 2021

In this article, we will see how we can set the element structure for erode of the image in mahotas. Erosion (usually represented by ?) is one of two fundamental operations (the other being dilation) in morphological image processing from which all other morphological operations are based. It was originally defined for binary images, later being extended to grayscale images, and subsequently to complete lattices. In order to erode the image we use mahotas.morph.erode method.

In this tutorial, we will use “luispedro” image, below is the command to load it.

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mahotas.demos.load('luispedro')

Below is the luispedro image  



Below is the default structure of the element for erosion, which a 1 cross  

np.array([
        [0, 1, 0],
        [1, 1, 1],
        [0, 1, 0]], 
        bool)

Below is the implementation 

Python3




# importing required libraries
import mahotas
import mahotas.demos
from pylab import gray, imshow, show
import numpy as np
 
# loading image
luispedro = mahotas.demos.load('luispedro')
 
# filtering image
luispedro = luispedro.max(2)
 
# otsu method
T_otsu = mahotas.otsu(luispedro)
  
 
# image values should be greater than otsu value
img = luispedro > T_otsu
 
print("Image threshold using Otsu Method")
 
# showing image
imshow(img)
show()
 
# erode structure
es = np.array([
        [1, 1, 1],
        [1, 1, 1],
        [1, 1, 1]], bool)
 
 
# eroding image using element structure
new_img = mahotas.morph.erode(img, es)
 
# showing dilated image
print("Eroded Image")
imshow(new_img)
show()

Output : 

Image threshold using Otsu Method

Eroded Image

Another example  

Python3




# importing required libraries
import mahotas
import numpy as np
import matplotlib.pyplot as plt
import os
  
# loading image
img = mahotas.imread('dog_image.png')
       
# setting filter to the image
img = img[:, :, 0]
 
# otsu method
T_otsu = mahotas.otsu(img)
  
 
# image values should be greater than otsu value
img = img > T_otsu
 
print("Image threshold using Otsu Method")
 
# showing image
imshow(img)
show()
 
# erode structure
es = np.array([
        [0, 0, 0],
        [0, 1, 0],
        [0, 0, 0]], bool)
 
 
# eroding image using element structure
new_img = mahotas.morph.erode(img, es)
 
# showing dilated image
print("Eroded Image")
imshow(new_img)
show()

Output : 

Image threshold using Otsu Method 

Eroded Image

 




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