In this article we will see how we can get the appropriate structuring element of the image in mahotas. A structuring element is a matrix that identifies the pixel in the image being processed and defines the neighborhood used in the processing of each pixel.
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.get_structuring_elem method
Syntax : mahotas.get_structuring_elem(img, n)
Argument : It takes image object and integer as argument
Return : It returns numpy ndarray
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
# importing required libraries import mahotas
import mahotas.demos
from pylab import gray, imshow, show
import numpy as np
import matplotlib.pyplot as plt
# loading image img = mahotas.demos.load( 'lena' )
# filtering image img = img. max ( 2 )
print ("Image")
# showing image imshow(img) show() # getting structuring element value = mahotas.get_structuring_elem(img, 1 )
# showing value print (value)
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Output :
Image
[[0 1 0] [1 1 1] [0 1 0]]
Another example
# importing required libraries import mahotas
import numpy as np
from pylab import gray, imshow, show
import os
import matplotlib.pyplot as plt
# loading image img = mahotas.imread( 'dog_image.png' )
# filtering image img = img[:, :, 0 ]
print ("Image")
# showing image imshow(img) show() # getting structuring element value = mahotas.get_structuring_elem(img, 2 )
# showing value print (value)
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Output :
Image
[[1 1 1] [1 1 1] [1 1 1]]