text: import cv2
  from cv2 import aruco
  from bach import darknet
  import bach.geometry
  
  
  class Detector:
      def __init__(self, configuration, meta, weights):
          """
          Default constructor.
          """
          self.configuration_file = configuration
          self.meta_file = meta
          self.weights_file = weights
          self.colors = dict()
          # Darknet
          self.network = None
          self.classes = None
          self.colors = None
          # ArUCO
          self.aruco_dictionary = None
          self.aruco_parameters = None
  
      def initialize(self):
          """
          Initialize the detector.
          """
          # Initialize Darknet
          if self.configuration_file and self.weights_file:
              self.network, self.classes, self.colors = darknet.load_network(self.configuration_file,
                                                                             self.meta_file,
                                                                             self.weights_file)
          else:
              return False
          # Initialize ArUco
          self.aruco_dictionary = aruco.Dictionary_get(aruco.DICT_4X4_50)
          self.aruco_parameters = aruco.DetectorParameters_create()
          self.aruco_parameters.adaptiveThreshWinSizeMin = 3
          self.aruco_parameters.adaptiveThreshWinSizeMax = 60
          self.aruco_parameters.adaptiveThreshWinSizeStep = 3
          self.aruco_parameters.minMarkerPerimeterRate = 0.004
          self.aruco_parameters.maxMarkerPerimeterRate = 0.032
          self.aruco_parameters.polygonalApproxAccuracyRate = 0.025
          self.aruco_parameters.markerBorderBits = 1
          self.aruco_parameters.maxErroneousBitsInBorderRate = 0.40
          self.aruco_parameters.errorCorrectionRate = 0.9
          self.aruco_parameters.detectInvertedMarker = False
          return True
  
      def detect_objects(self, frame, threshold=0.5):
          """
          Process a frame through the neural network and return detected objects.
          """
          processed_image = darknet.make_image(darknet.network_width(self.network),
                                               darknet.network_height(self.network),
                                               3)
          processed_frame = cv2.resize(frame.copy(),
                                       (darknet.lib.network_width(self.network),
                                        darknet.lib.network_height(self.network)),
                                       interpolation=cv2.INTER_NEAREST)
          darknet.copy_image_from_bytes(processed_image, processed_frame.tobytes())
          detections = darknet.detect_image(self.network,
                                            self.classes,
                                            processed_image,
                                            thresh=threshold,
                                            hier_thresh=threshold)
          return detections
  
      def detect_markers(self, frame):
          """
           Detect ArUco markers in a frame.
          """
          corners, ids, _ = aruco.detectMarkers(frame, self.aruco_dictionary, parameters=self.aruco_parameters)
          markers = dict()
          if ids is not None:
              for index in range(0, len(ids)):
                  x = 0
                  y = 0
                  for point in corners[index][0]:
                      x = x + point[0]
                      y = y + point[1]
                  point = bach.geometry.Point(x / 4, y / 4)
                  markers[ids[index][0]] = point
          return markers
  