glcm python

GLCM stands for Gray Level Co-occurrence Matrix, which is a statistical method used to analyze the texture of an image. In Python, the scikit-image library provides functions to compute GLCMs and extract features from them.

Here's an example of how to compute a GLCM in Python using scikit-image:

from skimage.feature import greycomatrix

# Load an image
image = ...

# Compute a GLCM with a distance of 1 and an angle of 0 degrees
glcm = greycomatrix(image, distances=[1], angles=[0], levels=256, symmetric=True, normed=True)

This will compute a GLCM for the input image with a distance of 1 pixel and an angle of 0 degrees. The `levels` parameter specifies the number of gray levels in the image (usually 256 for an 8-bit image). The `symmetric` parameter specifies whether the GLCM should be symmetric (i.e., whether the order of the pixel pairs matters). The `normed` parameter specifies whether the GLCM should be normalized (i.e., whether the values in the matrix should sum to 1.0).

Once you have a GLCM, you can extract features from it using the `skimage.feature.greycoprops` function. For example, here's how to compute the contrast, energy, and homogeneity of a GLCM:

from skimage.feature import greycoprops

contrast = greycoprops(glcm, 'contrast')
energy = greycoprops(glcm, 'energy')
homogeneity = greycoprops(glcm, 'homogeneity')

The `greycoprops` function accepts a GLCM and a feature name as input and returns the corresponding feature value. Other features that can be extracted from a GLCM include correlation, entropy, and dissimilarity.