Mahotas – Zernike Moments
In this article we will see how we can get the zernike moments 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). In image processing, computer vision and related fields, an image moment is a certain particular weighted average of the image pixels’ intensities, or a function of such moments, usually chosen to have some attractive property or interpretation. Image moments are useful to describe objects after segmentation.
For this tutorial we will use ‘lena’ image, below is the command to load the lena image
Below is the lena image
In order to do this we will use mahotas.features.zernike_moments method
Syntax : mahotas.features.zernike_moments(img, radius)
Argument : It takes image object and 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
[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 ]
[0.31830989 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 ]
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