Automatic image classification

[schollz]

Install simplecv + orange

Dependencies:

sudo apt-get install ipython python-opencv python-scipy \
 python-numpy python-pygame python-setuptools python-pip

Install:

sudo pip install svgwrite
sudo pip install https://github.com/sightmachine/SimpleCV/zipball/develop
sudo pip install orange   # takes awhile

Some test code:

from SimpleCV import *
import glob

class Trainer():

    def __init__(self,classes, trainPaths):
        self.classes = classes
        self.trainPaths = trainPaths


    def getExtractors(self):
        hhfe = HueHistogramFeatureExtractor(10)
        ehfe = EdgeHistogramFeatureExtractor(10)
        haarfe = HaarLikeFeatureExtractor(fname='haar.txt')
        return [hhfe,ehfe,haarfe]

    def getClassifiers(self,extractors):
        svm = SVMClassifier(extractors)
        tree = TreeClassifier(extractors)
        bayes = NaiveBayesClassifier(extractors)
        knn = KNNClassifier(extractors)
        return [svm,tree,bayes,knn]

    def train(self):
        self.classifiers = self.getClassifiers(self.getExtractors())
        for classifier in self.classifiers:
            classifier.train(self.trainPaths,self.classes,verbose=False)

    def test(self,testPaths):
        for classifier in self.classifiers:
            print classifier.test(testPaths,self.classes,verbose=False)

    def visualizeResults(self,classifier,imgs):
        for img in imgs:
            className = classifier.classify(img)
            img.drawText(className,10,10,fontsize=60,color=Color.BLUE)         
        imgs.show()

classes = ['chicken','nochicken',]

def main():
    trainPaths = ['./'+c+'/train/' for c in classes ]
    testPaths =  ['./'+c+'/test/'   for c in classes ]

    trainer = Trainer(classes,trainPaths)
    trainer.train()
    tree = trainer.classifiers[1]

    imgs = ImageSet()
    for p in testPaths:
        imgs += ImageSet(p)
    random.shuffle(imgs)

    print "Result test"
    trainer.test(testPaths)

    print(trainer.visualizeResults(tree,imgs))

    tree.save("tree.dat")
    classifierFile = 'tree.dat'
    classifier = TreeClassifier.load(classifierFile)
    for path in glob.glob("*/*/*jpg"):
        print(path,classifier.classify(Image(path)))

main()