skimage2.measure.moments_normalized#

skimage2.measure.moments_normalized(mu, order=3, spacing=None)[source]#

Calculate all normalized central image moments up to a certain order.

Note that normalized central moments are translation and scale invariant but not rotation invariant.

Parameters:

mu(M[, …], M) array: Central image moments, where M must be greater than or equal to order.
orderint, optional: Maximum order of moments. Default is 3.
spacingtuple of float, shape (ndim,): The pixel spacing along each axis of the image.

Returns:

nu(order + 1``[, ...], ``order + 1) array: Normalized central image moments.

References

[1]

Wilhelm Burger, Mark Burge. Principles of Digital Image Processing: Core Algorithms. Springer-Verlag, London, 2009.

[2]

B. Jähne. Digital Image Processing. Springer-Verlag, Berlin-Heidelberg, 6. edition, 2005.

[3]

T. H. Reiss. Recognizing Planar Objects Using Invariant Image Features, from Lecture notes in computer science, p. 676. Springer, Berlin, 1993.

[4]

https://en.wikipedia.org/wiki/Image_moment

Examples

>>> from _skimage2.measure import moments, moments_central
>>> image = np.zeros((20, 20), dtype=np.float64)
>>> image[13:17, 13:17] = 1
>>> m = moments(image)
>>> centroid = (m[0, 1] / m[0, 0], m[1, 0] / m[0, 0])
>>> mu = moments_central(image, centroid)
>>> moments_normalized(mu)
array([[       nan,        nan, 0.078125  , 0.        ],
       [       nan, 0.        , 0.        , 0.        ],
       [0.078125  , 0.        , 0.00610352, 0.        ],
       [0.        , 0.        , 0.        , 0.        ]])