In this article we will see how we can get the zernike feature of the given image in mahotas. Zernike polynomials are an orthogonal basis set (a set of functions for which the integral of the product of any pair of functions is zero)
For this tutorial we will use ‘lena’ image, below is the command to load the lena image
mahotas.demos.load('lena')
Below is the lena image
In order to do this we will use mahotas.features.zernike method
Syntax : mahotas.features.zernike(img, degree, radius)
Argument : It takes image object and two integer as argument
Return : It returns 1-D array
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 librariesimport mahotasimport mahotas.demosfrom pylab import gray, imshow, showimport numpy as npimport matplotlib.pyplot as plt # loading imageimg = mahotas.demos.load('lena') # filtering imageimg = img.max(2)print("Image") # showing imageimshow(img)show()# degreedegree = 10# radiusradius = 10# computing zernike featurevalue = mahotas.features.zernike(img, degree, radius) # printing valueprint(value) |
Output :
Image
[0.31830989 0.01261485 0.00614926 0.00769591 0.0097145 0.01757332 0.00617458 0.01008905 0.01415304 0.01099679 0.02894761 0.01838737 0.0074247 0.01333135 0.01958184 0.00431827 0.00540781 0.01675913 0.03511082 0.00699177 0.00357231 0.01593838 0.01621848 0.0240565 0.0154929 0.01631347 0.03239474 0.02506811 0.00796528 0.01291179 0.01198231 0.01916542 0.0165929 0.01032658 0.02028499 0.02506003]
Another example
Python3
# importing required librariesimport mahotasimport numpy as npfrom pylab import gray, imshow, showimport osimport matplotlib.pyplot as plt # loading imageimg = mahotas.imread('dog_image.png')# filtering imageimg = img[:, :, 0] print("Image") # showing imageimshow(img)show()# degreedegree = 10# radiusradius = 10# computing zernike featurevalue = mahotas.features.zernike(img, degree, radius) # printing valueprint(value) |
Output :
Image
[0.31830989 0.00985427 0.00714652 0.00171408 0.00442245 0.01796711 0.00716781 0.00179965 0.0039829 0.0031081 0.02447476 0.0011686 0.009291 0.00174885 0.00357579 0.00692029 0.0043969 0.03528869 0.00264739 0.01381883 0.00750501 0.0036528 0.00867514 0.01298398 0.0129556 0.00602334 0.04108562 0.00377269 0.01859098 0.01109795 0.00178511 0.0082474 0.01928068 0.01873102 0.00882483 0.04558572]
