def is_letter_input(letter):
if select.select([
sys.stdin,
], [], [], 0.0)[0]:
input_char = sys.stdin.read(1)
return input_char.lower() == letter.lower()
return False
if __name__ == '__main__':
counter = 0
cap = config.capturing()
# cap = cv2.VideoCapture(config.VIDEO_SOURCE)
if not os.path.exists(config.TRAINING_DIR + user_folder_prefix):
os.makedirs(config.TRAINING_DIR + user_folder_prefix)
person_name = os.listdir(config.CHECK_FACE_FOLDER)
known_encodings = []
names = []
for p_name in person_name:
person = face_recognition.load_image_file(
os.path.join(config.CHECK_FACE_FOLDER, p_name))
face_encoding = face_recognition.face_encodings(person)[0]
known_encodings.append(face_encoding)
names.append(os.path.splitext(p_name))
print('Loading Model...')
facenet.load_model(modeldir)
images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name(
"phase_train:0")
embedding_size = embeddings.get_shape()[1]
classifier_filename_exp = os.path.expanduser(classifier_filename)
with open(classifier_filename_exp, 'rb') as infile:
(model, class_names) = pickle.load(infile)
# video_capture = cv2.VideoCapture(config.VIDEO_SOURCE)
video_capture = config.capturing()
c = 0
print('Start Recognition')
prevTime = 0
while True:
ret, frame = video_capture.read()
frame = cv2.resize(frame, (0, 0), fx=0.50,
fy=0.50) # resize frame (optional)
curTime = time.time() + 1 # calc fps
timeF = frame_interval