In this article we will see how we can dilate 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.
In this tutorial we will use “luispedro” image, below is the command to load it.
mahotas.demos.load('luispedro')
Below is the luispedro image

In order to do this we will use mahotas.morph.dilatemethod
Syntax : mahotas.morph.dilate(image)
Argument :It takes image object 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
luispedro = mahotas.demos.load( 'luispedro' )
luispedro = luispedro. max ( 2 )
T_otsu = mahotas.otsu(luispedro)
img = luispedro > T_otsu
print ( "Image threshold using Otsu Method" )
imshow(img)
show()
dilate_img = mahotas.morph.dilate(img)
print ( "Dilated Image" )
imshow(dilate_img)
show()
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Output :
Image threshold using Otsu Method

Dilated Image

Another example
Python3
import mahotas
import numpy as np
import matplotlib.pyplot as plt
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()
dilate_img = mahotas.morph.dilate(img)
print ( "Dilated Image" )
imshow(dilate_img)
show()
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Output :
Image threshold using Otsu Method

Dilated Image
