`scipy.ndimage._measurements:center_of_mass`

source: build-install/usr/lib/python3.14/site-packages/scipy/ndimage/_measurements.py:1485

Signature

def center_of_mass(input, labels = None, index = None)

Summary

Calculate the center of mass of the values of an array at labels.

Parameters

input : ndarray: Data from which to calculate center-of-mass. The masses can either be positive or negative.
labels : ndarray, optional: Labels for objects in input, as generated by ndimage.label. Only used with index. Dimensions must be the same as input.
index : int or sequence of ints, optional: Labels for which to calculate centers-of-mass. If not specified, the combined center of mass of all labels greater than zero will be calculated. Only used with labels.

Returns

center_of_mass : tuple, or list of tuples: Coordinates of centers-of-mass.

Examples

import numpy as np
a = np.array(([0,0,0,0],
              [0,1,1,0],
              [0,1,1,0],
              [0,1,1,0]))
from scipy import ndimage

✓

ndimage.center_of_mass(a)

✗

(np.float64(2.0), np.float64(1.5))

Calculation of multiple objects in an image

b = np.array(([0,1,1,0],
              [0,1,0,0],
              [0,0,0,0],
              [0,0,1,1],
              [0,0,1,1]))
lbl = ndimage.label(b)[0]

✓

ndimage.center_of_mass(b, lbl, [1,2])

✗

[(np.float64(0.3333333333333333), np.float64(1.3333333333333333)), (np.float64(3.5), np.float64(2.5))]

Negative masses are also accepted, which can occur for example when bias is removed from measured data due to random noise.

c = np.array(([-1,0,0,0],
              [0,-1,-1,0],
              [0,1,-1,0],
              [0,1,1,0]))

✓

ndimage.center_of_mass(c)

✗

(np.float64(-4.0), np.float64(1.0))

If there are division by zero issues, the function does not raise an error but rather issues a RuntimeWarning before returning inf and/or NaN.

d = np.array([-1, 1])

✓

ndimage.center_of_mass(d)

✗

(np.float64(inf),)

Aliases

scipy.ndimage.center_of_mass