key = cv2.waitKey(1) & 0xFF
if key == ord(' '):
if len(rects) > 0:
face = frame[rects[0][1]:rects[0][1] + rects[0][3],
rects[0][0]:rects[0][0] + rects[0][2]]
box = [(rects[0][1], rects[0][0] + rects[0][2],
rects[0][1] + rects[0][3], rects[0][0])]
encoding = face_recognition.face_encodings(rgb, box)[0]
elist = encoding.tolist()
faces = w.get(args['prefix'] + '/vectors/**')
counter = 0
for face in faces:
chunks = face.path.split('/')
name = chunks[-2]
if name == args['name']:
if counter <= int(chunks[-1]):
counter = int(chunks[-1]) + 1
uri = '{}/vectors/{}/{}'.format(args['prefix'], args['name'],
str(counter))
print('> Inserting face vector {}'.format(uri))
w.put(uri, Value.Json(json.dumps(elist)))
time.sleep(0.05)
if key == ord('q'):
exit(0)
z.close()
faces = json.load(f)
print('[INFO] Open zenoh session...')
zenoh.init_logger()
z = Zenoh(conf)
w = z.workspace()
time.sleep(0.5)
# If not yet existing, add a memory storage that will store the dataset
try:
storage_admin_path = '/@/router/local/plugin/storages/backend/memory/storage/facerecog-store'
if not w.get(storage_admin_path):
path_expr = '{}/vectors/**'.format(args['prefix'])
print('Add storage: on {}'.format(path_expr))
properties = {'path_expr': path_expr}
w.put(storage_admin_path, properties)
time.sleep(1)
except:
e = sys.exc_info()[0]
print('Error creating storage: {}'.format(e))
for k, vs in faces.items():
for j, v in enumerate(vs):
uri = '{}/vectors/{}/{}'.format(args['prefix'], k, j)
print('> Inserting face {}'.format(uri))
w.put(uri, Value.Json(json.dumps(v)))
z.close()
print('[INFO] Done.')