Mahotas – 2D Laplacian filter

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

filter_none

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# importing required libraries
import mahotas
import mahotas.demos
from pylab import gray, imshow, show
import numpy as np
import matplotlib.pyplot as plt
    
# loading iamge
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()

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Output :

Image


2D Laplacian filter

Another example

filter_none

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code

# importing required libraries
import mahotas
import numpy as np
from pylab import gray, imshow, show
import os
import matplotlib.pyplot as plt
   
# loading iamge
img = mahotas.imread('dog_image.png')
  
  
# fltering 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()

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Output :

Image


2D Laplacian filter




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