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Mahotas – 2D Laplacian filter

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  • Last Updated : 09 Jun, 2021
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In this article we will see how we can apply 2D laplacian filter to the image in mahotas. A Laplacian filter is an edge detector used to compute the second derivatives of an image, measuring the rate at which the first derivatives change. This determines if a change in adjacent pixel values is from an edge or continuous progression.

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.laplacian_2D method
Syntax : mahotas.laplacian_2D(img)
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




# importing required libraries
import mahotas
import mahotas.demos
from pylab import gray, imshow, show
import numpy as np
import matplotlib.pyplot as plt
   
# loading image
img = mahotas.demos.load('lena')
 
 
   
# filtering image
img = img.max(2)
 
print("Image")
   
# showing image
imshow(img)
show()
 
# applying 2D Laplacian filter
new_img = mahotas.laplacian_2D(img)
  
 
# showing image
print("2D Laplacian filter")
imshow(new_img)
show()

Output :

Image

 

2D Laplacian filter
 

Another example 

Python3




# importing required libraries
import mahotas
import numpy as np
from pylab import gray, imshow, show
import os
import matplotlib.pyplot as plt
  
# loading image
img = mahotas.imread('dog_image.png')
 
 
# filtering image
img = img[:, :, 0]
   
print("Image")
   
# showing image
imshow(img)
show()
 
# applying 2D Laplacian filter
new_img = mahotas.laplacian_2D(img)
  
 
# showing image
print("2D Laplacian filter")
imshow(new_img)
show()

Output :

Image

 

2D Laplacian filter
 

 


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