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Mahotas – Conditional Watershed of Image

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  • Last Updated : 19 Feb, 2022
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In this article, we will see how we can do a conditional watershed of the image in mahotas. In the study of image processing, a watershed is a transformation defined on a grayscale image. The name refers metaphorically to a geological watershed, or drainage divide, which separates adjacent drainage basins. 

In this tutorial, we will use the “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.cwatershed method

Syntax : mahotas.cwatershed(img, marker)

Argument : It takes image object and labeled marker 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')
 
 
   
# 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")
 
# creating a labelled image
marker, n_nucleus = mahotas.label(img)
   
# showing image
imshow(img)
show()
   
 
# watershed of image
new_img = mahotas.cwatershed(img, marker)
 
print("CWatershed Image")
 
# showing image
imshow(new_img)
show()

Output : 

Image threshold using Otsu Method

CWatershed 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')
 
 
# 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()
 
# creating a labelled image
marker, n_nucleus = mahotas.label(img)
   
# watershed of image
new_img = mahotas.cwatershed(img, marker)
 
print("CWatershed Image")
 
# showing image
imshow(new_img)
show()

Output: 

Image threshold using Otsu Method

CWatershed Image

 


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