def __init__(self):
self.Helpers = Helpers()
self.Helpers = Helpers()
self.confs = self.Helpers.loadConfigs()
self.LogFile = self.Helpers.setLogFile(self.confs["aiCore"]["Logs"] +
"Client/")
self.apiUrl = "http://" + self.confs["aiCore"]["IP"] + ":" + str(
self.confs["aiCore"]["Port"]) + "/infer"
self.headers = {"content-type": 'application/json'}
self.Helpers.logMessage(self.LogFile, "CLIENT", "INFO", "Client Ready")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Model")
self.Data = Data()
self.Helpers.logger.info("Model class initialized.")
def __init__(self):
""" Initializes the Movidius NCS1 Classifier Trainer Class """
self.Helpers = Helpers("Evaluator")
self.confs = self.Helpers.confs
self.labelsToName = {}
self.checkpoint_file = tf.train.latest_checkpoint(
self.confs["Classifier"]["LogDir"])
# Open the labels file
self.labels = open(
self.confs["Classifier"]["DatasetDir"] + "/" + self.confs["Classifier"]["Labels"], 'r')
# Create a dictionary to refer each label to their string name
for line in self.labels:
label, string_name = line.split(':')
string_name = string_name[:-1] # Remove newline
self.labelsToName[int(label)] = string_name
# Create a dictionary that will help people understand your dataset better. This is required by the Dataset class later.
self.items_to_descriptions = {
'image': 'A 3-channel RGB coloured image that is ex: office, people',
'label': 'A label that ,start from zero'
}
self.Helpers.logger.info(
"Evaluator class initialization complete.")
def __init__(self):
""" Initializes the Server class. """
# Server setup
self.Helpers = Helpers()
self.LogFile = self.Helpers.setLogFile(
self.Helpers.confs["Server"]["Logs"] + "/Server")
# Movidius setup
self.Movidius = Movidius(self.Helpers.confs["Server"]["Logs"])
self.Movidius.checkNCS()
self.ValidDir = self.Helpers.confs["Classifier"]["ValidPath"]
self.TestingDir = self.Helpers.confs["Classifier"]["TestingPath"]
self.Helpers.logMessage(self.LogFile, "Facial Recognition Server",
"STATUS", "Movidius configured")
# Facenet setup
self.Facenet = Facenet(self.Helpers.confs["Server"]["Logs"])
self.Movidius.allocateGraph(self.Facenet.LoadGraph())
self.Facenet.PreprocessKnown(self.ValidDir, self.Movidius.Graph)
self.Helpers.logMessage(self.LogFile, "Facial Recognition Server",
"STATUS", "Facenet configured")
def __init__(self):
""" Initializes the class. """
super(CamStream, self).__init__()
self.Helpers = Helpers("CamStream")
self.Helpers.logger.info(
"CamStream Helper Class initialization complete.")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Core")
self.QModel = QModel()
self.Helpers.logger.info("IdcQnn QNN initialization complete.")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("FoscamRead")
super(FoscamRead, self).__init__()
self.Helpers.logger.info("FoscamRead class initialized.")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Core")
self.Helpers.logger.info(
"COVID-19 Tensorflow DenseNet Classifier initialization complete.")
def __init__(self):
""" Initializes the class. """
super(RealsenseRead, self).__init__()
self.Helpers = Helpers("Realsense D415 Reader")
self.colorizer = rs.colorizer()
# OpenCV fonts
self.font = cv2.FONT_HERSHEY_SIMPLEX
self.black = (0, 0, 0)
self.green = (0, 255, 0)
self.white = (255, 255, 255)
# Starts the Realsense module
self.Realsense = Realsense()
# Connects to the Realsense camera
self.profile = self.Realsense.connect()
# Starts the socket module
self.Socket = Socket("Realsense D415 Reader")
# Sets up the object detection model
self.Model = Model()
self.Helpers.logger.info(
"Realsense D415 Reader Class initialization complete.")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("GeniSysAI")
# Initiates the iotJumpWay connection class
self.iotJumpWay = iotJumpWay({
"host":
self.Helpers.confs["iotJumpWay"]["host"],
"port":
self.Helpers.confs["iotJumpWay"]["port"],
"lid":
self.Helpers.confs["iotJumpWay"]["lid"],
"aid":
self.Helpers.confs["iotJumpWay"]["aid"],
"an":
self.Helpers.confs["iotJumpWay"]["an"],
"un":
self.Helpers.confs["iotJumpWay"]["un"],
"pw":
self.Helpers.confs["iotJumpWay"]["pw"]
})
self.iotJumpWay.appConnect()
self.iotJumpWay.appChannelSub(
self.Helpers.confs["iotJumpWay"]["channels"]["commands"])
self.iotJumpWay.deviceCommandsCallback = self.commands
self.Helpers.logger.info("GeniSysAI Class initialization complete.")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("OpenVINO", False)
os.environ["KMP_BLOCKTIME"] = "1"
os.environ["KMP_SETTINGS"] = "1"
os.environ["KMP_AFFINITY"] = "granularity=fine,verbose,compact,1,0"
os.environ["OMP_NUM_THREADS"] = str(
self.Helpers.confs["cnn"]["system"]["cores"])
self.testing_dir = self.Helpers.confs["cnn"]["data"]["test"]
self.valid = self.Helpers.confs["cnn"]["data"]["valid_types"]
mxml = self.Helpers.confs["cnn"]["model"]["ir"]
mbin = os.path.splitext(mxml)[0] + ".bin"
ie = IECore()
self.net = ie.read_network(model=mxml, weights=mbin)
self.input_blob = next(iter(self.net.inputs))
self.net = ie.load_network(
network=self.net,
device_name=self.Helpers.confs["cnn"]["model"]["device"])
self.Helpers.logger.info("Class initialization complete.")
def __init__(self, configs):
""" Initializes the class. """
self.Helpers = Helpers("iotJumpWay")
self.confs = configs
self.Helpers.logger.info("Initiating Local iotJumpWay Device.")
if self.confs['host'] == None:
raise ConfigurationException("** Host (host) property is required")
elif self.confs['port'] == None:
raise ConfigurationException("** Port (port) property is required")
elif self.confs['lid'] == None:
raise ConfigurationException("** Location ID (lid) property is required")
elif self.confs['zid'] == None:
raise ConfigurationException("** Zone ID (zid) property is required")
elif self.confs['did'] == None:
raise ConfigurationException("** Device ID (did) property is required")
elif self.confs['dn'] == None:
raise ConfigurationException("** Device Name (dn) property is required")
elif self.confs['un'] == None:
raise ConfigurationException("** MQTT UserName (un) property is required")
elif self.confs['pw'] == None:
raise ConfigurationException("** MQTT Password (pw) property is required")
self.mqttClient = None
self.mqttTLS = "/etc/ssl/certs/DST_Root_CA_X3.pem"
self.mqttHost = self.confs['host']
self.mqttPort = self.confs['port']
self.commandsCallback = None
self.Helpers.logger.info("Local iotJumpWay Device Initiated.")
def __init__(self):
""" Initializes the Data Class. """
self.Helpers = Helpers("DataProcessor")
self.confs = self.Helpers.confs
self.Helpers.logger.info("Data helper class initialization complete.")
def __init__(self, model, X_train, X_test, y_train,
y_test, optimizer, do_augmentation = False):
""" Initializes the Model class. """
self.Helpers = Helpers("Model", False)
self.model_type = model
self.optimizer = optimizer
self.colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
self.X_train = X_train
self.X_test = X_test
self.y_train = y_train
self.y_test = y_test
if do_augmentation == False:
self.seed = self.Helpers.confs[self.model_type]["data"]["seed_" + self.optimizer]
self.val_steps = self.Helpers.confs[self.model_type]["train"]["val_steps"]
self.batch_size = self.Helpers.confs[self.model_type]["train"]["batch"]
self.epochs = self.Helpers.confs[self.model_type]["train"]["epochs"]
self.weights_file = "Model/weights.h5"
self.model_json = "Model/model.json"
else:
self.seed = self.Helpers.confs[self.model_type]["data"]["seed_" + self.optimizer + "_augmentation"]
self.val_steps = self.Helpers.confs[self.model_type]["train"]["val_steps_augmentation"]
self.batch_size = self.Helpers.confs[self.model_type]["train"]["batch_augmentation"]
self.epochs = self.Helpers.confs[self.model_type]["train"]["epochs_augmentation"]
self.weights_file = "Model/weights_augmentation.h5"
self.model_json = "Model/model_augmentation.json"
random.seed(self.seed)
seed(self.seed)
tf.random.set_seed(self.seed)
self.Helpers.logger.info("Model class initialization complete.")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Core")
self.Core = OpenVINO()
self.Helpers.logger.info("Class initialization complete.")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Entities")
self.stemmer = LancasterStemmer()
self.Helpers.logger.info("Entities class initialized.")
def __init__(self, optimizer, do_augmentation=False):
""" Initializes the Data class. """
self.Helpers = Helpers("Data", False)
self.optimizer = optimizer
self.do_augmentation = do_augmentation
if self.do_augmentation == False:
self.seed = self.Helpers.confs["cnn"]["data"]["seed_" +
self.optimizer]
self.dim = self.Helpers.confs["cnn"]["data"]["dim"]
else:
self.Augmentation = Augmentation("cnn", self.optimizer)
self.seed = self.Helpers.confs["cnn"]["data"]["seed_" +
self.optimizer +
"_augmentation"]
self.dim = self.Helpers.confs["cnn"]["data"]["dim_augmentation"]
seed(self.seed)
random.seed(self.seed)
self.data = []
self.labels = []
self.Helpers.logger.info("Data class initialization complete.")
def __init__(self):
""" Initializes the class. """
super(RealsenseStream, self).__init__()
self.Helpers = Helpers("Realsense D415 Streamer")
self.Helpers.logger.info(
"Realsense D415 Streamer Helper Class initialization complete.")
def __init__(self):
""" Initializes the AML/ALL Detection System Movidius NCS1 Classifier Data Class. """
self.Helpers = Helpers("DataProcessor")
self.confs = self.Helpers.confs
self.Helpers.logger.info("Data helper class initialization complete.")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("CamStream")
super(CamStream, self).__init__()
self.Helpers.logger.info("CamStream class initialized.")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("CamRead")
super(CamRead, self).__init__()
self.Helpers.logger.info("CamRead Class initialization complete.")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Sockets")
self.Helpers.logger.info(
"Socket Helper Class initialization complete.")
def __init__(self, optimizer, do_augmentation = False):
""" Initializes the Model class. """
self.Helpers = Helpers("Model", False)
self.optimizer = optimizer
self.do_augmentation = do_augmentation
self.testing_dir = self.Helpers.confs["cnn"]["data"]["test"]
self.valid = self.Helpers.confs["cnn"]["data"]["valid_types"]
self.colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
if self.do_augmentation == False:
self.seed = self.Helpers.confs["cnn"]["data"]["seed_" + self.optimizer]
self.weights_file = self.Helpers.confs["cnn"]["model"]["weights"]
self.model_json = self.Helpers.confs["cnn"]["model"]["model"]
else:
self.seed = self.Helpers.confs["cnn"]["data"]["seed_" + self.optimizer + "_augmentation"]
self.weights_file = self.Helpers.confs["cnn"]["model"]["weights_aug"]
self.model_json = self.Helpers.confs["cnn"]["model"]["model_aug"]
random.seed(self.seed)
seed(self.seed)
tf.random.set_seed(self.seed)
self.Helpers.logger.info("Model class initialization complete.")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Model", False)
os.environ["KMP_BLOCKTIME"] = "1"
os.environ["KMP_SETTINGS"] = "1"
os.environ["KMP_AFFINITY"] = "granularity=fine,verbose,compact,1,0"
os.environ["OMP_NUM_THREADS"] = str(
self.Helpers.confs["cnn"]["system"]["cores"])
tf.config.threading.set_inter_op_parallelism_threads(1)
tf.config.threading.set_intra_op_parallelism_threads(
self.Helpers.confs["cnn"]["system"]["cores"])
self.testing_dir = self.Helpers.confs["cnn"]["data"]["test"]
self.valid = self.Helpers.confs["cnn"]["data"]["valid_types"]
self.seed = self.Helpers.confs["cnn"]["data"]["seed"]
self.weights_file = self.Helpers.confs["cnn"]["model"]["weights"]
self.model_json = self.Helpers.confs["cnn"]["model"]["model"]
random.seed(self.seed)
seed(self.seed)
tf.random.set_seed(self.seed)
self.Helpers.logger.info("Class initialization complete.")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Model", False)
self.Helpers.logger.info(
"Model class initialization complete.")
def __init__(self):
""" Initializes the class. """
self.isTraining = False
self.Helpers = Helpers("NLU")
self.Helpers.logger.info("NLU class initialized.")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("LEDs", False)
self.Helpers.logger.info("LEDs Helper Class initialization complete.")
self.Helpers.logger.info("Using MRAA version " +
str(mraa.getVersion()))
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Core")
self.Core = Model()
self.Helpers.logger.info("AllDS2020 CNN initialization complete.")
def __init__(self, model):
""" Initializes the class. """
self.Helpers = Helpers("Server", False)
self.model = model
self.Helpers.logger.info("Class initialization complete.")
def __init__(self):
""" Initializes the class. """
self.Helpers = Helpers("Blockchain")
self.contractBalance = 5000
self.Helpers.logger.info("Blockchain Class initialization complete.")
