def train_api_with_members():
""" Trains the BetaFace API with all the faces of the existing
AmI lab members. """
client = BetaFaceAPI()
members = get_ami_lab_members()
for member in members:
member_headshots = get_headshots_for_member(member)
for headshot in member_headshots:
client.upload_face(headshot, member)
class UpgradeFaceSamples(PDU):
QUEUE = 'upgrade-face-samples'
def __init__(self, **kwargs):
super(UpgradeFaceSamples, self).__init__()
self.api = BetaFaceAPI()
def save_image_to_file(self, buffer, width, height, path):
image_buffer = array.array('B', buffer).tostring()
image_to_file = Image.frombuffer("RGB", (width, height),
image_buffer)
image_to_file.save(path)
def process_message(self, message):
""" Sends the face sample to BetaFaceAPI.
Right now, these samples are coming from the face recognition module,
whenever the recognition confidence is really high. """
person_name = message['person_name']
head_image = message['head_image']
image = head_image['image']
width = int(head_image['width'])
height = int(head_image['height'])
# save image to file
path = "/tmp/%s.jpg" % uuid.uuid4()
self.save_image_to_file(image, width, height, path)
self.logger.info("Saved face sample to file %s" % path)
# upload image to BetaFace API
self.api.upload_face(path, person_name)
self.logger.info("Fed face sample %s as an example for %s" %\
(path, person_name))
os.remove(str(path))
self.logger.info("Removed temporary file %s from disk" % str)
