In this article we will see how we can get the image roundness feature in mahotas. Roundness is dominated by the shape’s gross features rather than the definition of its edges and corners, or the surface roughness of a manufactured object. A smooth ellipse can have low roundness, if its eccentricity is large
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.roundness method
Syntax : mahotas.features.roundness(img)
Argument : It takes image object as argument
Return : It returns float value
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()# computing roundness in imagevalue = mahotas.features.roundness(img) # printing valueprint("Roundness : " + str(value)) |
Output :
Image
Roundness : 0.0
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()# computing roundness in imagevalue = mahotas.features.roundness(img) # printing valueprint("Roundness : " + str(value)) |
Output :
Image
Roundness : 0.04297201733896709
