class IndexTrainer(object):
def __init__(self):
self.index = InvertedIndex()
self.bow = Bow()
self.extractor = Extractor('surf')
print self.index.author
print self.index.description
def load_feature(self, path='../models/feature.npy'):
self.features = np.load(path)
if len(self.features) > 200000:
self.features = self.features[:200000]
print "feature shape: ", self.features.shape
return self.features
def run(self, path):
self.bow.load()
self.index.reset(self.bow.centers)
images = imutil.get_list_image(path)
t = imutil.Timer(1)
t.tic()
for i,image in enumerate(images):
descriptors = self.extractor.extract(image)
self.index.append(image, descriptors)
if (i+1)%1000 == 0:
t.toc('finish 1000 images: ')
t.tic()
class IndexTrainer(object):
def __init__(self, centers):
self.bow = Bow(centers)
self.index = InvertedIndex()
def load_feature(self, path='../models/feature.npy'):
self.features = np.load(path)
if len(self.features) > 500000:
self.features = self.features[:500000]
print "feature shape: ", self.features.shape
return self.features
def train(self):
self.bow.load('../models/bow.pkl')
self.index.reset(self.bow.centers)
self.index.append('img1',self.features[:100])
self.index.append('img2',self.features[100:200])
self.index.append('img3',self.features[200:300])
print self.index