Mahotas – Haar Transform

In this article we will see how we can do image haar transform in mahotas. The haar wavelet is a sequence of rescaled “square-shaped” functions which together form a wavelet family or basis. Wavelet analysis is similar to Fourier analysis in that it allows a target function over an interval to be represented in terms of an orthonormal basis. The Haar sequence is now recognised as the first known wavelet basis and extensively used as a teaching example.

In this tutorial we will use “luispedro” image, below is the command to load it.

mahotas.demos.load('luispedro')

Below is the luispedro image

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

Syntax : mahotas.haar(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]

Example 1:

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# importing various libraries
import numpy as np
import mahotas
import mahotas.demos
from mahotas.thresholding import soft_threshold
from pylab import imshow, show
from os import path
  
# loading image
f = mahotas.demos.load('luispedro', as_grey = True)
  
  
# showing image
print("Image")
  
imshow(f)
show()
  
# haar transform
h = mahotas.haar(f)
  
# showing image
print("Image with haar transform")
imshow(h)
show()

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Output :

Example 2:

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# importing required libraries
import mahotas
import numpy as np
from pylab import imshow, show
import os
  
  
# loading iamge
img = mahotas.imread('dog_image.png')
  
# filtering iamge
img = img[:, :, 0]
  
  
# showing image
print("Image")
  
imshow(img)
show()
  
# haar transform
h = mahotas.haar(img)
  
# showing image
print("Image with haar transform")
imshow(h)
show()

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Output :




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