skimage2.feature.graycoprops#

skimage2.feature.graycoprops(P, prop='contrast')[source]#

Calculate texture properties of a GLCM.

Compute a feature of a gray level co-occurrence matrix to serve as a compact summary of the matrix. The properties are computed as follows:

  • ‘contrast’: \(\sum_{i,j=0}^{levels-1} P_{i,j}(i-j)^2\)

  • ‘dissimilarity’: \(\sum_{i,j=0}^{levels-1}P_{i,j}|i-j|\)

  • ‘homogeneity’: \(\sum_{i,j=0}^{levels-1}\frac{P_{i,j}}{1+(i-j)^2}\)

  • ‘ASM’: \(\sum_{i,j=0}^{levels-1} P_{i,j}^2\)

  • ‘energy’: \(\sqrt{ASM}\)

  • ‘correlation’:
    \[\sum_{i,j=0}^{levels-1} P_{i,j}\left[\frac{(i-\mu_i) \ (j-\mu_j)}{\sqrt{(\sigma_i^2)(\sigma_j^2)}}\right]\]

  • ‘mean’: \(\sum_{i=0}^{levels-1} i*P_{i}\)

  • ‘variance’: \(\sum_{i=0}^{levels-1} P_{i}*(i-mean)^2\)

  • ‘std’: \(\sqrt{variance}\)

  • ‘entropy’: \(\sum_{i,j=0}^{levels-1} -P_{i,j}*log(P_{i,j})\)

Each GLCM is normalized to have a sum of 1 before the computation of texture properties.

Changed in version 0.19: greycoprops was renamed to graycoprops in 0.19.

Parameters:
Pndarray

Input array. P is the gray-level co-occurrence histogram for which to compute the specified property. 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.

prop{‘contrast’, ‘dissimilarity’, ‘homogeneity’, ‘energy’, ‘correlation’, ‘ASM’, ‘mean’, ‘variance’, ‘std’, ‘entropy’}, optional

The property of the GLCM to compute. The default is ‘contrast’.

Returns:
results2-D ndarray

2-dimensional array. results[d, a] is the property ‘prop’ for the d’th distance and the a’th angle.

References

[1]

M. Hall-Beyer, 2007. GLCM Texture: A Tutorial v. 1.0 through 3.0. The GLCM Tutorial Home Page, https://prism.ucalgary.ca/handle/1880/51900 DOI:10.11575/PRISM/33280

Examples

Compute the contrast for GLCMs with distances [1, 2] and angles [0, pi/2] radians

>>> image = np.array([[0, 0, 1, 1],
...                   [0, 0, 1, 1],
...                   [0, 2, 2, 2],
...                   [2, 2, 3, 3]], dtype=np.uint8)
>>> from _skimage2.feature import graycomatrix
>>> g = graycomatrix(image, [1, 2], [0, np.pi/2], levels=4,
...                  normed=True, symmetric=True)
>>> contrast = graycoprops(g, 'contrast')
>>> contrast
array([[0.58333333, 1.        ],
       [1.25      , 2.75      ]])