skimage2.feature.graycomatrix#

skimage2.feature.graycomatrix(image, distances, angles, levels=None, symmetric=False, normed=False)[source]#

Calculate the gray-level co-occurrence matrix.

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

Changed in version 0.19: greymatrix was renamed to graymatrix in 0.19.

Parameters:

imagearray_like: Integer typed input image. Only positive valued images are supported. If type is other than uint8, the argument levels needs to be set.
distancesarray_like: List of pixel pair distance offsets.
anglesarray_like: List of pixel pair angles in radians.
levelsint, optional: The input image should contain integers in [0, levels-1], where levels indicate the number of gray-levels counted (typically 256 for an 8-bit image). This argument is required for 16-bit images or higher and is typically the maximum of the image. As the output matrix is at least levels x levels, it might be preferable to use binning of the input image rather than large values for levels.
symmetricbool, 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.
normedbool, 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:

P4-D ndarray: The gray-level co-occurrence histogram. The value P[i,j,d,theta] is the number of times that gray-level j occurs at a distance d and at an angle theta from gray-level i. If normed is False, the output is of type uint32, otherwise it is float64. The dimensions are: levels x levels x number of distances x number of angles.

References

[1]

M. Hall-Beyer, 2007. GLCM Texture: A Tutorial https://prism.ucalgary.ca/handle/1880/51900 DOI:10.11575/PRISM/33280

[2]

R.M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification”, IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, no. 6, pp. 610-621, Nov. 1973. DOI:10.1109/TSMC.1973.4309314

[3]

M. Nadler and E.P. Smith, Pattern Recognition Engineering, Wiley-Interscience, 1993.

[4]

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

Examples

Compute 4 GLCMs using 1-pixel distance and 4 different angles. For example, an angle of 0 radians refers to the neighboring pixel to the right; pi/4 radians to the top-right diagonal neighbor; pi/2 radians to the pixel above, and so forth.

>>> image = np.array([[0, 0, 1, 1],
...                   [0, 0, 1, 1],
...                   [0, 2, 2, 2],
...                   [2, 2, 3, 3]], dtype=np.uint8)
>>> result = graycomatrix(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)