def betaface_recognition(image_dict):
""" Given a message contained a cropped head, send it to BetaFace
API for face recognition. This can take anywhere from 1 to 10 seconds.
"""
try:
api = BetaFaceAPI()
# Get image from message, save it to disk and
# pass it on to BetaFace API
image = base64_to_image(image_dict['head_image']['image'],
int(image_dict['head_image']['width']),
int(image_dict['head_image']['height']))
temp_path = random_file_name('jpg', "FR_")
logger.info("Generated random path to save image: %s" % temp_path)
image = image.rotate(180)
image.save(temp_path)
logger.info("Saved image to disk at path %s" % temp_path)
matches = api.recognize_faces(temp_path, 'amilab.ro')
logger.info("Received recognized faces from BetaFace API %r" % matches)
#os.remove(str(temp_path))
#logger.info("Removed image from disk (%s)" % str(temp_path))
return matches
except:
logger.exception("Failed to recognize faces via BetaFace API")
return {}
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)
def __init__(self, **kwargs):
super(UpgradeFaceSamples, self).__init__()
self.api = BetaFaceAPI()
import re
from pybetaface.api import BetaFaceAPI
default_name = "fakemeuser"
namespace = "fakeme"
client = BetaFaceAPI()
def get_info_from_pic(path, name=None):
if name is None:
name = default_name
info = client.upload_face(path, name + '@' + namespace)
img_uid = info['img_uid']
person_info = client.get_image_info(img_uid)
raw = person_info['raw_content']
m = re.search(
".*<name>age</name><value>([0-9]+)</value>.*<name>gender</name><value>(female|male)</value>.*<name>race</name><value>(.*)(</value>)",
raw)
age = m.group(1)
gender = m.group(2)
race = m.group(3).split('<')[0]
return {'url': path, 'age': age, 'gender': gender, 'race': race}
def generate_picture(path):
max_pic = 6
matches = client.recognize_faces(path, namespace)
similar_pictures = {}
for i, (same_pic_name, confidence) in enumerate(matches.items()):
if similar_pictures.__len__() < max_pic:
if not same_pic_name.__contains__(default_name):