class Engine:
def __init__(self):
self.hashnet = Hashnet()
self.hashnet.load()
self.db = DBManager(**db_params)
def match(self, target, candidates, threshold=10):
hash_matrix = get_hash_matrix(candidates)
hashcode, features = target
hamming_dists = hamming_dist(hashcode, hash_matrix)
indices = np.argpartition(hamming_dists, threshold)[:threshold]
shortlist = [candidates[i] for i in indices]
feature_matrix = get_feature_matrix(shortlist)
dists = cosine_dist(features, feature_matrix)
winner = shortlist[dists.argmin()]
distance = dists.min()
return winner, distance
def search(self, target_image, page_size):
tick = datetime.now()
target_features = self.hashnet.extract_features(target_image)
session = self.db.Session()
current_page = 0
queryset = session.query(Image).filter(Image.hash != None)
optimal_dist = None
winner = None
while True:
query = queryset.limit(page_size).offset(current_page * page_size)
candidates = query.all()
if not len(candidates):
break
current_page += 1
matched, distance = self.match(target_features, candidates)
if optimal_dist is None or distance < optimal_dist:
optimal_dist = distance
winner = matched
session.close()
tock = datetime.now()
print("\nSearching completed in {}".format(tock - tick), flush=True)
if winner is None:
print(
"There is nothing found during the search, most likely because there's not images in the database",
flush=True)
return None, None
print("Most similar image is {}\nwith cosine distance={}".format(
winner, optimal_dist),
flush=True)
return winner, 1 - optimal_dist
ssh_pkey=ssh_private_key,
remote_bind_address=(
db_params['host'],
3306
)
)
server.start()
db_params['host'] = server.local_bind_host
db_params['port'] = server.local_bind_port
db = DBManager(**db_params)
storage = Storage(**oss_params)
hashnet = Hashnet()
hashnet.load()
print("Scanning database for images not being processed...")
session = db.Session()
results = session.query(Image).filter(
or_(
Image.features == None,
Image.hash == None
)
).all()
n = len(results)
print("Scanning complete! {} in total.\n".format(n))
print("Processing images...")
batch_size = 32
widgets = [progressbar.Percentage(), " ", progressbar.Bar(), " ", progressbar.ETA()]
pbar = progressbar.ProgressBar(maxval=n, widgets=widgets, term_width=50).start()
for i in range(0, n, batch_size):
