Building Machine Learning Systems with Python - Third Edition

Computing features from images

With mahotas, it is very easy to compute features from images. There is a submodule named mahotas.features, where feature computation functions are available.

A commonly used set of texture features is the Haralick set. As with many methods in image processing, the name honors its inventor. These features are texture-based; they distinguish between images that are smooth and those that are patterned, and between different patterns. With mahotas, it is very easy to compute them as follows:

haralick_features = mh.features.haralick(image) 
haralick_features_mean = np.mean(haralick_features, axis=0) 
haralick_features_all = np.ravel(haralick_features) 

The mh.features.haralick function returns a 4 x 13 array. The ...