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Mahotas – Conditional Dilating Image

  • Last Updated : 30 Jul, 2021

In this article we will see how we can do conditional dilating of the image in mahotas. Dilation adds pixels to the boundaries of objects in an image, while erosion removes pixels on object boundaries. The number of pixels added or removed from the objects in an image depends on the size and shape of the structuring element used to process the image. We use mahotas.morph.dilate method to do normal dilating.

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

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mahotas.demos.load('lena')

Below is the lena image  



In order to do this we will use mahotas.cdilate method
Syntax : mahotas.cdilate(img, c_grey, Bc={3×3 cross}, n=1)
Argument : It takes image object, conditional image as compulsory argument, element structure and iteration number are optional 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
# 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 * 100
  
# 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()
  
# dilating image using conditional grey image
dilate_img = mahotas.cdilate(img, g)
  
# showing dilated image
print("Dilated Image")
imshow(dilate_img)
show()

Output : 

Image threshold using Otsu Method



Dilated 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')
     
# grey image
g = img[:, :, 2]
 
# multiplying grey image values
g = g * 100
  
# 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()
  
# dilating image using conditional grey image
dilate_img = mahotas.cdilate(img, g)
  
# showing dilated image
print("Dilated Image")
imshow(dilate_img)
show()

Output : 

Image threshold using Otsu Method 

Dilated Image

 




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