Mahotas – Center of Mass of given Image

In this article we will see how we can get the center of mass of the image in mahotas. Center of mass” (for binary images) is a bit convoluted way of saying “mean value across each dimension”. In other words – take all x coordinates and average them – and you got x coordinate of your “center of mass”, the same for y.
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.center_of_mass method
Syntax : mahotas.center_of_mass(img)
Argument : It takes image object as argument
Return : It returns center of mass co-ordinates 

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 libraries
# importing required libraries
import mahotas
import mahotas.demos
from pylab import gray, imshow, show
import numpy as np

# loading image
img = mahotas.demos.load('lena')

# grey image
g = img[:, :, 1]

# multiplying grey image values
g = g * 100

# filtering image
img = img.max(2)

# showing image
imshow(img)
show()

# getting center of mass
center = mahotas.center_of_mass(img)

# printing center of mass co-ordinate
print("Center of Mass : " + str(center))

Output :

Center of Mass : [246.64854256 259.45157125]

Another example 

Python3

# importing required libraries
import mahotas
import numpy as np
from pylab import gray, imshow, show
import os

# loading image
img = mahotas.imread('dog_image.png')


# filtering image
img = img[:, :, 0]

# showing image
imshow(img)
show()

# getting center of mass
center = mahotas.center_of_mass(img)

# printing center of mass co-ordinate
print("Center of Mass : " + str(center))

Output :

Center of Mass : [265.35619268 482.66701402]