def _testImageSet(self,imageset,className,dataset,subset,disp,verbose):
        count = 0
        correct = 0
        badFeat = False
        if(subset > 0):
            imageset = imageset[0:subset]
        for img in imageset:
            if verbose:
                print "Opening file: " + img.filename
            featureVector = []
            for extractor in self.mFeatureExtractors:
                feats = extractor.extract(img)
                if( feats is not None ):
                    featureVector.extend(feats)
                else:
                    badFeat = True
            if( badFeat ):
                del img
                badFeat = False
                continue 
            featureVector.extend([className])
            dataset.append(featureVector)
            test = orange.ExampleTable(self.mOrangeDomain,[featureVector])
            c = self.mClassifier(test[0])
            testClass = test[0].getclass()
            if(testClass==c):
                text =  "Classified as " + str(c)
                self._WriteText(disp,img,text, Color.GREEN)
                correct = correct + 1
            else:   
                text =  "Mislassified as " + str(c)
                self._WriteText(disp,img,text, Color.RED)
            count = count + 1
            del img
            
        return([dataset,count,correct])
dataset/ETHPy150Open sightmachine/SimpleCV/SimpleCV/MachineLearning/NaiveBayesClassifier.py/NaiveBayesClassifier._testImageSet/original