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, the combined center of mass of all labels greater than zero will be calculated. 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.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.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.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.center_of_mass(d)
(inf,)