greycomatrix

greycomatrix

skimage.feature.greycomatrix(image, distances, angles, levels=256, symmetric=False, normed=False) [source]

Calculate the grey-level co-occurrence matrix.

A grey level co-occurrence matrix is a histogram of co-occurring greyscale values at a given offset over an image.

Parameters:

image : array_like of uint8 Integer typed input image. The image will be cast to uint8, so the maximum value must be less than 256. distances : array_like List of pixel pair distance offsets. angles : array_like List of pixel pair angles in radians. levels : int, optional The input image should contain integers in [0, levels-1], where levels indicate the number of grey-levels counted (typically 256 for an 8-bit image). The maximum value is 256. symmetric : bool, optional If True, the output matrix P[:, :, d, theta] is symmetric. This is accomplished by ignoring the order of value pairs, so both (i, j) and (j, i) are accumulated when (i, j) is encountered for a given offset. The default is False. normed : bool, optional If True, normalize each matrix P[:, :, d, theta] by dividing by the total number of accumulated co-occurrences for the given offset. The elements of the resulting matrix sum to 1. The default is False.

Returns:

P : 4-D ndarray The grey-level co-occurrence histogram. The value P[i,j,d,theta] is the number of times that grey-level j occurs at a distance d and at an angle theta from grey-level i. If normed is False, the output is of type uint32, otherwise it is float64.

References

[R147]

The GLCM Tutorial Home Page, http://www.fp.ucalgary.ca/mhallbey/tutorial.htm

[R148]

Pattern Recognition Engineering, Morton Nadler & Eric P. Smith

[R149]

Wikipedia, http://en.wikipedia.org/wiki/Co-occurrence_matrix

Examples

Compute 2 GLCMs: One for a 1-pixel offset to the right, and one for a 1-pixel offset upwards.

>>> image = np.array([[0, 0, 1, 1],
...                   [0, 0, 1, 1],
...                   [0, 2, 2, 2],
...                   [2, 2, 3, 3]], dtype=np.uint8)
>>> result = greycomatrix(image, [1], [0, np.pi/4, np.pi/2, 3*np.pi/4], levels=4)
>>> result[:, :, 0, 0]
array([[2, 2, 1, 0],
       [0, 2, 0, 0],
       [0, 0, 3, 1],
       [0, 0, 0, 1]], dtype=uint32)
>>> result[:, :, 0, 1]
array([[1, 1, 3, 0],
       [0, 1, 1, 0],
       [0, 0, 0, 2],
       [0, 0, 0, 0]], dtype=uint32)
>>> result[:, :, 0, 2]
array([[3, 0, 2, 0],
       [0, 2, 2, 0],
       [0, 0, 1, 2],
       [0, 0, 0, 0]], dtype=uint32)
>>> result[:, :, 0, 3]
array([[2, 0, 0, 0],
       [1, 1, 2, 0],
       [0, 0, 2, 1],
       [0, 0, 0, 0]], dtype=uint32)