def test_something(self):
id_upper = 3900
sift = Sift()
kmeans = KmeansModel()
image_list = sorted([x for x in os.listdir(DATAPATH) if os.path.splitext(x)[1] == '.jpg' and int(os.path.splitext(x)[0]) < id_upper],key=lambda x: int(os.path.splitext(x)[0]))
print image_list
descriptors_list = sift.compute(image_list)
kmeans.fit(descriptors_list)
kmeans.save('kmeans_sift')
print 'ok'
def train_bow_sift(id_upper):
if not id_upper:
id_upper = 3900
sift = Sift()
kmeans = KmeansModel()
image_list = sorted([x for x in os.listdir(DATAPATH) if os.path.splitext(x)[1] == '.jpg' and int(os.path.splitext(x)[0]) < id_upper],key=lambda x: int(os.path.splitext(x)[0]))
sift.compute(image_list)
if not kmeans.load('kmeans_sift'):
kmeans.fit(sift.descriptors_list)
kmeans.save('kmeans_sift')
bow = Bow(kmeans)
# bag of words of samples
# label indicator1 indicator2 ...
# ... ... ... ...
bow.train_sift(sift.descriptors_list)
