Mahotas – Conditional Watershed of Image
Last Updated :
19 Feb, 2022
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
import mahotas
import mahotas.demos
from pylab import gray, imshow, show
import numpy as np
img = mahotas.demos.load( 'lena' )
img = img. max ( 2 )
T_otsu = mahotas.otsu(img)
img = img > T_otsu
print ( "Image threshold using Otsu Method" )
marker, n_nucleus = mahotas.label(img)
imshow(img)
show()
new_img = mahotas.cwatershed(img, marker)
print ( "CWatershed Image" )
imshow(new_img)
show()
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Output :
Image threshold using Otsu Method
CWatershed Image
Another example
Python3
import mahotas
import numpy as np
from pylab import gray, imshow, show
import os
img = mahotas.imread( 'dog_image.png' )
img = img[:, :, 0 ]
T_otsu = mahotas.otsu(img)
img = img > T_otsu
print ( "Image threshold using Otsu Method" )
imshow(img)
show()
marker, n_nucleus = mahotas.label(img)
new_img = mahotas.cwatershed(img, marker)
print ( "CWatershed Image" )
imshow(new_img)
show()
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Output:
Image threshold using Otsu Method
CWatershed Image
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