def POST(self):
i = web.input()
uri = i['img']
data_to_64 = re.search(r'base64,(.*)', uri).group(1)
data = StringIO.StringIO()
# data.write(base64.decodestring(data_to_64))
data.write(data_to_64.decode('base64'))
data.seek(0)
im = resize_image(data)
print np.asarray(im)
X = np.asarray(im).flatten()
label = classifier.predict(normalize_features(X))
print label
return label[0]
import numpy as np
import mnist
from helper import normalize_features, random_sampling
from sklearn import datasets, svm, metrics
from sklearn.externals import joblib
training = mnist.DataReader('train')
testing = mnist.DataReader('t10k')
data, target = random_sampling(training.data, training.target, 3000)
# Gaussian Kernel, so far the best
classifier = svm.SVC(gamma=0.03)
classifier.fit(normalize_features(data), target.ravel())
joblib.dump(classifier, 'model.pkl')
print 'Training done, modek.pkl saved.'
predicted = classifier.predict(normalize_features(testing.data))
expected = testing.target.ravel()
print("Classification report for classifier %s:\n%s\n"
% (classifier, metrics.classification_report(expected, predicted)))
print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted))