def load_data(self):
log.info("Loading {}'s data".format(self.name))
if not osp.exists(self.data_path):
log.info("No employee data was found")
else:
with open(self.data_path, 'r') as file:
self.data = json.load(file)
def load_faces(self):
for emp in self.employees:
if not emp.encoded_face.any():
log.info("{}'s face did not encoded yet. encoding and saving.".
format(emp.name))
img = self.load_img(emp.img_path)
emp.save_encoded_face(self.faces_encodings_from_img(img)[0])
def run(self):
log.info("Running!")
while True:
# Get a new frame (into self.np_output)
self.camera.capture(self.np_output, format="rgb")
face_loc_list = self.face_locations(self.np_output)
if face_loc_list:
self.analyze_frame(face_loc_list)
def __init__(self, name):
self.name = name
self.conf = Config()
self.img_path = self.conf.employee_img_path(name)
self.encoded_img_path = self.conf.employee_encoded_img_path(name)
self.data_path = self.conf.employee_data_path(name)
self.encoded_face = None
self.data = {}
self.in_timestamp = None
self.last_timestamp = None
self.load_encoding()
log.info("Employee named {} was loaded".format(self.name))
def load_employees(self):
log.info("Getting all existed employees")
# Check if there is an employees dir
if not osp.exists(self.employees_dir):
log.info("Creating employees directory")
os.makedirs(self.employees_dir)
# Get all saved emplyees directories
saved_employees = os.listdir(self.employees_dir)
if not len(saved_employees): return None
# Check if employees directories are valid. If so, add them
for emp in saved_employees:
img_path = osp.join(self.employees_dir, emp, "{}.jpg".format(emp))
if osp.exists(img_path):
self.employees.append(Employee(emp))
from src.employee import Employee, Employees_manager
from src.recognizer import Recognizer
from src.config import log
log.info("Begin!")
manager = Employees_manager()
employees = manager.get_employees()
recognizer = Recognizer(employees)
recognizer.run()
def save_img_to_archive(self, img_path, img):
log.info("Saving image: {}".format(img_path))
Image.fromarray(img).save(img_path)
def save_encoded_face(self, encoded_face):
log.info("Saving {}'s face encoding".format(self.name))
self.encoded_face = encoded_face
np.save(self.encoded_img_path, self.encoded_face)
def load_encoding(self):
log.info("Loading {}'s face encoding".format(self.name))
if not osp.exists(self.encoded_img_path):
log.info("No face encoding was found")
else:
self.encoded_face = np.load(self.encoded_img_path)
def save_data(self):
log.info("Saving {}'s data".format(self.name))
with open(self.data_path, 'w') as file:
json.dump(self.data, file)
del self.data
def analyze_frame(self, face_loc_list):
log.info("Found {} faces".format(len(face_loc_list)))
faces_encodings = self.faces_encodings_from_img(
self.np_output, face_loc_list)
for emp in self.employees:
self.compare_faces(emp, faces_encodings)
def compare_faces(self, employee, frame_faces, tolerance=0.6):
if True in list(
np.linalg.norm(frame_faces -
employee.encoded_face, axis=1) <= tolerance):
log.info("{} was found!".format(employee.name))
employee.add_timestamp(self.np_output, datetime.now())
