Wavelet Transforms
New in version 0.9.1: Wavelet functions were only added in version 0.9.1
We are going to use wavelets to transform an image so that most of its values
are 0 (and otherwise small), but most of the signal is preserved.
The code for this tutorial is avalailable from the source distribution as
mahotas/demos/wavelet_compression.py.
We start by importing and loading our input image
import numpy as np
import mahotas
from mahotas.thresholding import soft_threshold
from matplotlib import pyplot as plt
from os import path
luispedro_image = '../../mahotas/demos/data/luispedro.jpg'
f = mahotas.imread(luispedro_image, as_grey=True)
f = f[:256,:256]
plt.gray()
# Show the data:
print("Fraction of zeros in original image: {0}".format(np.mean(f==0)))
plt.imshow(f)
plt.show()
(Source code)
There are no zeros in the original image. We now try a baseline compression
method: save every other pixel and only highorder bits.
direct = f[::2,::2].copy()
direct /= 8
direct = direct.astype(np.uint8)
print("Fraction of zeros in original image (after division by 8): {0}".format(np.mean(direct==0)))
plt.imshow(direct)
plt.show()
(Source code)
There are only a few zeros, though. We have, however, thrown away 75% of the
values. Can we get a better image, using the same number of values, though?
We will transform the image using a Daubechies wavelet (D8) and then discard
the highorder bits.
# Transform using D8 Wavelet to obtain transformed image t:
t = mahotas.daubechies(f,'D8')
# Discard loworder bits:
t /= 8
t = t.astype(np.int8)
print("Fraction of zeros in transform (after division by 8): {0}".format(np.mean(t==0)))
plt.imshow(t)
plt.show()
(Source code)
This has 60% zeros! What does the reconstructed image look like?
# Let us look at what this looks like
r = mahotas.idaubechies(t, 'D8')
plt.imshow(r)
plt.show()
(Source code)
This is a pretty good reduction without much quality loss. We can go further and
discard small values in the transformed space. Also, let’s make the remaining
values even smaller in magnitude.
Now, this will be 77% of zeros, with the remaining being small values. This
image would compress very well as a lossless image and we could reconstruct the
full image after transmission. The quality is certainly higher than just
keeping every fourth pixel and loworder bits.
tt = soft_threshold(t, 12)
print("Fraction of zeros in transform (after division by 8 & soft thresholding): {0}".format(np.mean(tt==0)))
# Let us look again at what we have:
rt = mahotas.idaubechies(tt, 'D8')
plt.imshow(rt)
(Source code)
What About the Borders?
In this example, we can see some artifacts at the border. We can use
wavelet_center and wavelet_decenter to handle borders to correctly:
fc = mahotas.wavelet_center(f)
t = mahotas.daubechies(fc, 'D8')
r = mahotas.idaubechies(fc, 'D8')
rd = mahotas.wavelet_decenter(r, fc.shape)
Now, rd is equal (except for rounding) to fc without any border effects.
API Documentation
A package for computer vision in Python.
Main Features
 features
 Compute global and local features (several submodules, include SURF and Haralick features)
 convolve
 Convolution and wavelets
 morph
 Morphological features. Most are available at the mahotas level, include erode(), dilate()...
 watershed
 Seeded watershed implementation
 imread/imsave
 read/write image
Citation:
Coelho, Luis Pedro, 2013. Mahotas: Open source software for scriptable
computer vision. Journal of Open Research Software, 1:e3, DOI:
http://dx.doi.org/10.5334/jors.ac

mahotas.haar(f, preserve_energy=True, inline=False)
Haar transform
Parameters :  f : 2D ndarray
preserve_energy : bool, optional
Whether to normalise the result so that energy is preserved (the
default).
inline : bool, optional
Whether to write the results to the input image. By default, a new
image is returned. Integer images are always converted to floating
point and copied.

See also
 ihaar
 function Reverse Haar transform

mahotas.ihaar(f, preserve_energy=True, inline=False)
Reverse Haar transform
ihaar(haar(f)) is more or less equal to f (equal, except for
possible rounding issues).
Parameters :  f : 2D ndarray
Input image. If it is an integer image, it is converted to floating
point (double).
preserve_energy : bool, optional
Whether to normalise the result so that energy is preserved (the
default).
inline : bool, optional
Whether to write the results to the input image. By default, a new
image is returned. Integer images are always converted to floating
point and copied.

Returns :  f : ndarray

See also
 haar
 function Forward Haar transform

mahotas.daubechies(f, code, inline=False)
Daubechies wavelet transform
This function works best if the image sizes are powers of 2!
Parameters :  f : ndarray
code : str
One of ‘D2’, ‘D4’, ... ‘D20’
inline : bool, optional
Whether to write the results to the input image. By default, a new
image is returned. Integer images are always converted to floating
point and copied.

See also
 haar
 function Haar transform (equivalent to D2)

mahotas.idaubechies(f, code, inline=False)
Daubechies wavelet inverse transform
Parameters :  f : ndarray
code : str
One of ‘D2’, ‘D4’, ... ‘D20’
inline : bool, optional
Whether to write the results to the input image. By default, a new
image is returned. Integer images are always converted to floating
point and copied.

See also
 haar
 function Haar transform (equivalent to D2)