def _testPath(self,path,className,dataset,subset,disp,verbose):
        count = 0
        correct = 0
        badFeat = False
        files = []
        for ext in IMAGE_FORMATS:
            files.extend(glob.glob( os.path.join(path, ext)))
        if(subset > 0):
            nfiles = min(subset,len(files))
        else:
            nfiles = len(files)
        for i in range(nfiles):
            infile = files[i]
            if verbose:
                print "Opening file: " + infile
            img = Image(infile)
            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/TreeClassifier.py/TreeClassifier._testPath/original