def main():
model_dir_path = './demo/models'
image_dir_path = "./demo/data/images"
fnet = FaceNet()
fnet.load_model(model_dir_path)
database = {}
# database["danielle"] = fnet.img_to_encoding(image_dir_path + "/danielle.png")
# fnet.img_to_encoding(image_dir_path + "/lyz.jpg")
database["ybg"] = fnet.img_to_encoding(image_dir_path + "/ybg.jpg")
database["tian"] = fnet.img_to_encoding(image_dir_path + "/tian.jpg")
# verifies whether a particular camera image is a person in the image database
dist, is_valid = fnet.verify(image_dir_path + "/camera_6.jpg", "ybg",
database)
print('camera_6.jpg is' + (' ' if is_valid else ' not ') + 'ybg')
# whether a particular camera image is which person in the image database (or not at all)
dist, identity = fnet.who_is_it(image_dir_path + "/camera_6.jpg", database)
if identity is None:
print('camera_6.jpg is not found in database')
else:
print('camera_6.jpg is ' + str(identity))
def main():
if len(sys.argv) < 3:
print("usage: face_recognition_launcher <path> <id>")
return 1
image_path = sys.argv[1]
person_id = sys.argv[2]
model_dir_path = './demo/models'
image_dir_path = "./demo/data/images/"
fnet = FaceNet()
fnet.load_model(model_dir_path)
database = {}
for root, dirs, files in os.walk(image_dir_path):
for file in files:
# print(image_dir_path + file)
database[file.split('.')[0]] = fnet.img_to_encoding(
image_dir_path + file)
dist, is_valid = fnet.verify(image_path, person_id, database)
is_found = False
identity = None
if is_valid:
is_found = True
else:
dist, identity = fnet.who_is_it(image_path, database)
print(str(is_found) + " : " + identity)
return is_found, identity
def main():
model_dir_path = './models_1'
image_dir_path = "./data/images"
fnet = FaceNet()
fnet.load_model(model_dir_path)
database = dict()
database["danielle"] = fnet.img_to_encoding(image_dir_path + "/danielle.png")
database["younes"] = fnet.img_to_encoding(image_dir_path + "/younes.jpg")
database["tian"] = fnet.img_to_encoding(image_dir_path + "/tian.jpg")
database["andrew"] = fnet.img_to_encoding(image_dir_path + "/andrew.jpg")
database["kian"] = fnet.img_to_encoding(image_dir_path + "/kian.jpg")
database["dan"] = fnet.img_to_encoding(image_dir_path + "/dan.jpg")
database["sebastiano"] = fnet.img_to_encoding(image_dir_path + "/sebastiano.jpg")
database["bertrand"] = fnet.img_to_encoding(image_dir_path + "/bertrand.jpg")
database["kevin"] = fnet.img_to_encoding(image_dir_path + "/kevin.jpg")
database["felix"] = fnet.img_to_encoding(image_dir_path + "/felix.jpg")
database["benoit"] = fnet.img_to_encoding(image_dir_path + "/benoit.jpg")
database["arnaud"] = fnet.img_to_encoding(image_dir_path + "/arnaud.jpg")
dist, is_valid = fnet.verify(image_dir_path + "/camera_0.jpg", "younes", database)
print('camera_0.jpg is' + (' ' if is_valid else ' not ') + 'yournes')
dist, is_valid = fnet.verify(image_dir_path + "/camera_2.jpg", "kian", database)
print('camera_0.jpg is' + (' ' if is_valid else ' not ') + 'yournes')
dist, identity = fnet.who_is_it(image_dir_path + "/camera_0.jpg", database)
if identity is None:
print('camera_0.jpg is not found in database')
else:
print('camera_0.jpg is ' + str(identity))
def main():
model_dir_path = './models'
image_dir_path = "./data/images"
fnet = FaceNet()
fnet.load_model(model_dir_path)
database = dict()
database["danielle"] = fnet.img_to_encoding(image_dir_path + "/danielle.png")
database["younes"] = fnet.img_to_encoding(image_dir_path + "/younes.jpg")
database["tian"] = fnet.img_to_encoding(image_dir_path + "/tian.jpg")
database["andrew"] = fnet.img_to_encoding(image_dir_path + "/andrew.jpg")
database["kian"] = fnet.img_to_encoding(image_dir_path + "/kian.jpg")
database["dan"] = fnet.img_to_encoding(image_dir_path + "/dan.jpg")
database["sebastiano"] = fnet.img_to_encoding(image_dir_path + "/sebastiano.jpg")
database["bertrand"] = fnet.img_to_encoding(image_dir_path + "/bertrand.jpg")
database["kevin"] = fnet.img_to_encoding(image_dir_path + "/kevin.jpg")
database["felix"] = fnet.img_to_encoding(image_dir_path + "/felix.jpg")
database["benoit"] = fnet.img_to_encoding(image_dir_path + "/benoit.jpg")
database["arnaud"] = fnet.img_to_encoding(image_dir_path + "/arnaud.jpg")
dist, is_valid = fnet.verify(image_dir_path + "/camera_0.jpg", "younes", database)
print('camera_0.jpg is' + (' ' if is_valid else ' not ') + 'yournes')
dist, is_valid = fnet.verify(image_dir_path + "/camera_2.jpg", "kian", database)
print('camera_0.jpg is' + (' ' if is_valid else ' not ') + 'yournes')
dist, identity = fnet.who_is_it(image_dir_path + "/camera_0.jpg", database)
if identity is None:
print('camera_0.jpg is not found in database')
else:
print('camera_0.jpg is ' + str(identity))
def load_obj(name):
with open(name + '.pkl', 'rb') as f:
return pickle.load(f)
#iniciate id counter
id = 0
# names related to ids: example ==> Marcelo: id=1, etc
model_dir_path = './models/fnet_w.h5'
image_dir_path = "./images"
names = os.listdir(image_dir_path)
fnet = FaceNet()
fnet.load_model(model_dir_path)
database = load_obj('database')
face_detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
fa = FaceAligner(predictor, desiredFaceWidth=96)
cam = cv2.VideoCapture(1)
cam.set(3, 640) # set video width
cam.set(4, 480) # set video height
# Frontal faces pattern
check_dir = "./check"
pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
def load_obj(name):
with open(name + '.pkl', 'rb') as f:
return pickle.load(f)
cam = cv2.VideoCapture(1)
cam.set(3, 640) # set video width
cam.set(4, 480) # set video height
model_dir_path = './models/fnet_w.h5'
image_dir_path = "./images"
fnet = FaceNet()
fnet.load_model(model_dir_path)
database = load_obj('database')
print(database)
clr = int(input("Would you like to clear the database?"))
if clr:
database = dict()
print("Database Cleared!")
else:
pass
# Frontal faces pattern
#face_detector = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
face_detector = dlib.get_frontal_face_detector()