# print(result)
# # retrieve by gabor
# method = Gabor()
# samples = method.make_samples(db)
# query = samples[query_idx]
# _, result = infer(query, samples=samples, depth=depth, d_type=d_type)
# print(result)
# # retrieve by HOG
# method = HOG()
# samples = method.make_samples(db)
# query = samples[query_idx]
# _, result = infer(query, samples=samples, depth=depth, d_type=d_type)
# print(result)
# # retrieve by VGG
# method = VGGNetFeat()
# samples = method.make_samples(db)
# query = samples[query_idx]
# _, result = infer(query, samples=samples, depth=depth, d_type=d_type)
# print(result)
# retrieve by resnet
method = ResNetFeat()
samples = method.make_samples(db)
query_idx = 1569
query = samples[query_idx]
_, result = infer(query, samples=samples, depth=depth, d_type=d_type)
print(result)
class NGT(object):
def __init__(self, db, f_class=None, d_type='L1'):
self.NGT_dir = 'NGT_{}_{}'.format(f_class,d_type)
self.NGT_path = b''
self.fearure = f_class
self.SQLdb = SQLite()
if f_class == 'daisy':
self.f_c = Daisy()
self.NGT_path = b'NGT/NGT_daisy_'+d_type.encode()
elif f_class == 'edge':
self.f_c = Edge()
self.NGT_path = b'NGT/NGT_edge_'+d_type.encode()
elif f_class == 'hog':
self.f_c = HOG()
self.NGT_path = b'NGT/NGT_hog_'+d_type.encode()
elif f_class == 'vgg':
self.f_c = VGGNetFeat()
self.NGT_path = b'NGT/NGT_vgg_'+d_type.encode()
elif f_class == 'res':
self.f_c = ResNetFeat()
self.NGT_path = b'NGT/NGT_res_'+d_type.encode()
if not os.path.exists(os.path.join(NGT_dir,self.NGT_dir)):
samples = self.f_c.make_samples(db, verbose=False)
dim = 0
try:
dim = samples[0]['hist'].shape[0]
except:
pass
images= []
objects = []
for i, row in enumerate(samples):
vector = row['hist']
link = row['img']
lable = row['cls']
data = {'index':i,'link':link,'lable':lable}
images.append(data)
objects.append(vector)
self.SQLdb.updateMuti(f_class,images)
# cPickle.dump(images, open(os.path.join(NGT_dir, sample_cache), "wb", True))
ngtpy.create(path=self.NGT_path, dimension=dim, distance_type=d_type)
self.index = ngtpy.Index(self.NGT_path)
self.index.batch_insert(objects)
self.index.save()
self.index = ngtpy.Index(self.NGT_path)
def search (self, link ,depth=5):
query=self.f_c.get_featInput(link)
r = self.index.search(query, depth)# result[index,square]
results = []
for item in r :
id = item[0]
results.append(self.SQLdb.select(self.fearure,id))
return results
def add (self, objects):
index = self.index
ids = index.insert(objects)
index.build_index()
index.save()
index.close()
return ids
def remove (self, id):
index = self.index
index.remove(id)
index.save()
index.close()
return 0