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Mahotas – Gaussian filtering

  • Last Updated : 06 May, 2021

In this article we will see how we can do Gaussian filtering in mahotas. For this we are going to use the fluorescent microscopy image from a nuclear segmentation benchmark. We can get the image with the help of command given below 
 

mhotas.demos.nuclear_image()

A Gaussian filter is a linear filter. It’s usually used to blur the image or to reduce noise. If you use two of them and subtract, you can use them for “unsharp masking” (edge detection). The Gaussian filter alone will blur edges and reduce contrast.
Below is the nuclear_image 
 

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In order to do this we will use mahotas.gaussian_filter method 
 

Syntax : mahotas.gaussian_filter(nuclear, 20)
Argument : It takes numpy.ndarray object as argument and a integer
Return : It returns numpy.ndarray object 
 

Note : The input of the gaussian filter should be the filtered image object 
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]

Example 1 : 
 

Python3




# importing required libraries
import mahotas
import mahotas.demos
import numpy as np
from pylab import imshow, show
 
# getting nuclear image
nuclear = mh.demos.nuclear_image()
 
 
# filtering the image
nuclear = nuclear[:, :, 0]
 
print("Image with filter")
# showing the image
imshow(nuclear)
show()
 
# setting gaussian filter
nuclear = mahotas.gaussian_filter(nuclear, 35)
 
print("Image with gaussian filter")
# showing the gaussian filter
imshow(nuclear)
show()

Output : 
 

Example 2: 
 

Python3




# importing required libraries
import numpy as np
import mahotas
from pylab import imshow, show
 
# loading image
img = mahotas.imread('dog_image.png')
 
# filtering the image
img = img[:, :, 0]
   
print("Image with filter")
# showing the image
imshow(img)
show()
 
 
# setting gaussian filter
gaussian = mahotas.gaussian_filter(img, 15)
 
print("Image with gaussian filter")
# showing the gaussian filter
imshow(gaussian)
show()

Output : 
 

 




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