Tutorial: Classification Using MahotasΒΆ

New in version 0.8: Before version 0.8, texture was under mahotas, not under mahotas.features

Here is an example of using mahotas and milk for image classification (but most of the code can easily be adapted to use another machine learning package). I assume that there are three important directories: positives/ and negatives/ contain the manually labeled examples, and the rest of the data is in an unlabeled/ directory.

Here is the simple algorithm:

  1. Compute features for all of the images in positives and negatives
  2. learn a classifier
  3. use that classifier on the unlabeled images

In the code below I used jug to give you the possibility of running it on multiple processors, but the code also works if you remove every line which mentions TaskGenerator.

We start with a bunch of imports:

from glob import glob
import mahotas
import mahotas.features
import milk
from jug import TaskGenerator

Now, we define a function which computes features. In general, texture features are very fast and give very decent results:

@TaskGenerator
def features_for(imname):
    img = mahotas.imread(imname)
    return mahotas.features.haralick(img).mean(0)

mahotas.features.haralick returns features in 4 directions. We just take the mean (sometimes you use the spread ptp() too).

Now a pair of functions to learn a classifier and apply it. These are just milk functions:

@TaskGenerator
def learn_model(features, labels):
    learner = milk.defaultclassifier()
    return learner.train(features, labels)

@TaskGenerator
def classify(model, features):
     return model.apply(features)

We assume we have three pre-prepared directories with the images in jpeg format. This bit you will have to adapt for your own settings:

positives = glob('positives/*.jpg')
negatives = glob('negatives/*.jpg')
unlabeled = glob('unlabeled/*.jpg')

Finally, the actual computation. Get features for all training data and learn a model:

features = map(features_for, negatives + positives)
labels = [0] * len(negatives) + [1] * len(positives)

model = learn_model(features, labels)

labeled = [classify(model, features_for(u)) for u in unlabeled]

This uses texture features, which is probably good enough, but you can play with other features in mahotas.features if you’d like (or try mahotas.surf, but that gets more complicated).

(This was motivated by a question on Stackoverflow).