if __name__ == '__main__':
opt, _ = parseArguments()
if opt.directory is None:
print 'Need directory, use -d or --dir'
exit()
if opt.outfile is None:
print 'Need output file, use -o or --outfile'
exit()
print 'Loading data...'
files, filenames = or_util.getExtFiles('.npz', opt.directory)
data = map(lambda f: or_util.loadFile(f), files)
print 'Extracting features...'
features = map(lambda d: or_features.flattenFeatures(d, channels=(0,1)), data)
features = map(lambda d: or_ml.samplePerc(d, 0.005), features)
labels = or_ml.generateLabels(features)
X = np.concatenate(features)
y = np.concatenate(labels)
C = len(data)
N = len(y)
print 'Training... ( N =', N, ')'
shuffler = cross_validation.ShuffleSplit(N, 1, 0.1)
for train_idx, test_idx in shuffler:
X_train = X[train_idx,:]
X_test = X[test_idx,:]
y_train = y[train_idx]
y_test = y[test_idx]
def classifyPlastic(self, img): hs = or_features.flattenFeatures([img], channels=(0,1)) p = or_ml.mostFrequent(self.plasticTable[hs[:,1],hs[:,0]]) predictedClass = self.plasticClasses[p] return predictedClass
