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.
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
import mahotas
import mahotas.demos
from pylab import gray, imshow, show
import numpy as np
img = mahotas.demos.load( 'lena' )
g = img[:, :, 1 ]
g = g * 100
img = img. max ( 2 )
T_otsu = mahotas.otsu(img)
img = img > T_otsu
print ( "Image threshold using Otsu Method" )
imshow(img)
show()
dilate_img = mahotas.cdilate(img, g)
print ( "Dilated Image" )
imshow(dilate_img)
show()
|
Output :
Image threshold using Otsu Method
Dilated Image
Another example
Python3
import mahotas
import numpy as np
from pylab import gray, imshow, show
import os
img = mahotas.imread( 'dog_image.png' )
g = img[:, :, 2 ]
g = g * 100
img = img[:, :, 0 ]
T_otsu = mahotas.otsu(img)
img = img > T_otsu
print ( "Image threshold using Otsu Method" )
imshow(img)
show()
dilate_img = mahotas.cdilate(img, g)
print ( "Dilated Image" )
imshow(dilate_img)
show()
|
Output :
Image threshold using Otsu Method
Dilated Image
Share your thoughts in the comments
Please Login to comment...