def __init__(self):
self.queue_manager = QueueManager([
self.__class__.__name__,
"Interpreter",
"ColourDetection",
"PositionDetection",
])
self.models = [{
"name":
"coco",
"model_path":
config.directory.data.joinpath("coco_resnet"),
"category_index":
coco_category_index
}, {
"name":
"epic-kitchens",
"model_path":
config.directory.data.joinpath("epic_kitchens"),
"category_index":
epic_kitchens_category_index
}]
for model in self.models:
tf_model = tf.saved_model.load(str(model["model_path"]))
model["model"] = tf_model.signatures["serving_default"]
logger.info(f"{self.__class__.__name__} ready")
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "NaturalLanguageGenerator"]
)
self.memory = {}
logger.info(f"{self.__class__.__name__} ready")
def __init__(self):
self.queue_manager = QueueManager([
self.__class__.__name__, "WebCam", "WebCamBis", "ObjectDetection",
"OCR"
])
self.camera_names = ["WebCam"]
self.pictures = []
self.waiting_cameras = 0
self.save_body = None
logger.info(f"{self.__class__.__name__} ready")
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "ExternalInterface"])
self.answers = {}
self.description_types = [
"DESCRIPTION_NOTHING", "DESCRIPTION_ANSWER_S",
"DESCRIPTION_ANSWER_P", "DESCRIPTION_UNKNOWN"
]
self.build_generator()
logger.info(f"{self.__class__.__name__} ready")
def __init__(self):
self.queue_manager = QueueManager([self.__class__.__name__, "Manager"])
self.previous_query = None
model_path = str(config.directory.data.joinpath("rasa", "nlu"))
dirs = [f for f in listdir(model_path) if isdir(join(model_path, f))]
dirs.sort(reverse=True)
model = join(model_path, dirs[0])
self.interpreter = Interpreter.load(model)
logger.info(f"{self.__class__.__name__} ready")
def __init__(self):
self.queue_manager = QueueManager([
self.__class__.__name__,
"Interpreter",
"ColourDetection",
"PositionDetection",
])
self.category_index = coco_category_index
self.model_path = config.directory.data.joinpath("resnet")
model = tf.saved_model.load(str(self.model_path))
self.model = model.signatures["serving_default"]
logger.info(f"{self.__class__.__name__} ready")
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "Interpreter"])
data_file = config.directory.data.joinpath("colour", "lab.txt")
colour_list = pd.read_csv(data_file,
skiprows=28,
header=None,
names=["l", "a", "b", "name"])
colour_list = colour_list.values.tolist()[1:]
self.colour_list_names = [x[3] for x in colour_list]
self.colour_list_values = np.asarray(
[np.asarray(x[:3], dtype=np.float32) for x in colour_list])
logger.info(f"{self.__class__.__name__} ready")
def __init__(self, mode=COLOR_MODE):
self.kinect = PyKinectRuntime.PyKinectRuntime(mode)
if mode & DEPTH_MODE:
self.kinect_frame_size = (
self.kinect.depth_frame_desc.Height,
self.kinect.depth_frame_desc.Width,
)
if mode & COLOR_MODE:
self.kinect_frame_size = (
self.kinect.color_frame_desc.Height,
self.kinect.color_frame_desc.Width,
-1,
)
self.transform = mode & DEPTH_MODE and cv2.COLOR_GRAY2RGB or cv2.COLOR_RGBA2RGB
self.queue_manager = QueueManager(
[self.__class__.__name__, "CameraManager"])
logger.info(f"{self.__class__.__name__} ready")
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "CameraManager"])
# TODO: Missing intent for lateral position
self.intents_to_path = {
"read_text": [["CameraManager", "OCR", "Interpreter"]],
"detect_colour": [[
"CameraManager", "ObjectDetection", "ColourDetection",
"Interpreter"
]],
"identify": [
["CameraManager", "OCR", "Interpreter"],
["CameraManager", "ObjectDetection", "Interpreter"],
],
"recognise": [["CameraManager", "ObjectDetection", "Interpreter"]],
"locate": [["CameraManager", "ObjectDetection", "Interpreter"]],
}
logger.info(f"{self.__class__.__name__} ready")
def submit():
service_if_audio = "AutomaticSpeechRecognition"
service_if_text = "NaturalLanguageUnderstanding"
# Parse user request
user_request = UserRequest(
service_if_audio=service_if_audio, service_if_text=service_if_text
)
# Create queue for Ayesaac and send it
ayesaac_queue_manager = QueueManager([user_request.first_service])
ayesaac_queue_manager.publish(user_request.first_service, user_request.body)
status_url = url_for("submit_status", task_id=user_request.uid)
return (
status_url,
202,
{"Location": status_url},
)
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "CameraManager"])
logger.info(f"{self.__class__.__name__} ready")
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "Interpreter"])
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "Interpreter"])
self.pipeline = keras_ocr.pipeline.Pipeline()