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Mahotas – Conditional Eroding Image

  • Last Updated : 27 Sep, 2021
Geek Week

In this article we will see how we can do conditional eroding of the image in mahotas. Erosion 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 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.cerode method
Syntax : mahotas.cerode(img, c_grey)
Argument : It takes image object, conditional image 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




# importing required libraries
import mahotas
import mahotas.demos
from pylab import gray, imshow, show
import numpy as np
   
# loading image
img = mahotas.demos.load('lena')
  
# grey image
g = img[:, :, 1]
  
# multiplying grey image values
g = g * 3
 
# filtering image
img = img.max(2)
   
# 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()
   
# eroding image using conditional grey image
new_img = mahotas.cerode(img, g)
   
# showing eroded 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
from pylab import gray, imshow, show
import os
  
# loading image
img = mahotas.imread('dog_image.png')
 
# getting grey image
g = img[:, :, 0]
 
  
# multiplying grey image values
g = g * 2
 
# filtering 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()
   
# eroding image using conditional grey image
new_img = mahotas.cerode(img, g)
   
# showing eroded image
print("Eroded Image")
imshow(new_img)
show()

Output : 

Image threshold using Otsu Method

Eroded Image

 

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