# scipy.ndimage.center_of_mass¶

scipy.ndimage.center_of_mass(input, labels=None, index=None)[source]

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

Parameters
inputndarray

Data from which to calculate center-of-mass. The masses can either be positive or negative.

labelsndarray, optional

Labels for objects in input, as generated by ndimage.label. Only used with index. Dimensions must be the same as input.

indexint or sequence of ints, optional

Labels for which to calculate centers-of-mass. If not specified, all labels greater than zero are used. Only used with labels.

Returns
center_of_masstuple, or list of tuples

Coordinates of centers-of-mass.

Examples

>>> a = np.array(([0,0,0,0],
...               [0,1,1,0],
...               [0,1,1,0],
...               [0,1,1,0]))
>>> from scipy import ndimage
>>> ndimage.measurements.center_of_mass(a)
(2.0, 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.measurements.center_of_mass(b, lbl, [1,2])
[(0.33333333333333331, 1.3333333333333333), (3.5, 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.measurements.center_of_mass(c)
(-4.0, 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.measurements.center_of_mass(d)
(inf,)


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