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
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
iamge = image[:, :, 0]
Example 1 :
# 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:
# importing required libraries import numpy as np import mahotas from pylab import imshow, show # loading iamge img = mahotas.imread('dog_image.png') # filtering the imagwe 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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