Classification Using Mahotas

Here is an example of using mahotas and milk for image classification. 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:

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


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

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

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

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


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). In general, I have found it hard to do classification with the sort of hard thresholds you are looking for unless the scanning is very controlled.

(This was motivated by a question on Stackoverflow).