class_docstr: Allows to perform MTCNN Detection ->
      a) Detection of faces (with the confidence probability)
      b) Detection of keypoints (left eye, right eye, nose, mouth_left, mouth_right)
class_docstr_tok: ["Allows","to","perform","MTCNN","Detection","-",">","a",")","Detection","of","faces","(","with","the","confidence","probability",")","b",")","Detection","of","keypoints","(","left","eye",",","right","eye",",","nose",",","mouth_left",",","mouth_right",")"]
docstr: Initializes the MTCNN.
  :param weights_file: file uri with the weights of the P, R and O networks from MTCNN. By default it will load
  the ones bundled with the package.
  :param min_face_size: minimum size of the face to detect
  :param steps_threshold: step's thresholds values
  :param scale_factor: scale factor
docstr_tok: ["Initializes","the","MTCNN",".",":","param","weights_file",":","file","uri","with","the","weights","of","the","P",",","R","and","O","networks","from","MTCNN",".","By","default","it","will","load","the","ones","bundled","with","the","package",".",":","param","min_face_size",":","minimum","size","of","the","face","to","detect",":","param","steps_threshold",":","step","'s","thresholds","values",":","param","scale_factor",":","scale","factor"]
code: def __init__(
      self,
      weights_file: str = None,
      min_face_size: int = 20,
      steps_threshold: list = None,
      scale_factor: float = 0.709,
  ):
      """
      Initializes the MTCNN.
      :param weights_file: file uri with the weights of the P, R and O networks from MTCNN. By default it will load
      the ones bundled with the package.
      :param min_face_size: minimum size of the face to detect
      :param steps_threshold: step's thresholds values
      :param scale_factor: scale factor
      """
      if steps_threshold is None:
          steps_threshold = [0.6, 0.7, 0.7]
  
      if weights_file is None:
          weights_file = pkg_resources.resource_stream(
              "mtcnn", "data/mtcnn_weights.npy"
          )
  
      self._min_face_size = min_face_size
      self._steps_threshold = steps_threshold
      self._scale_factor = scale_factor
  
      (
          self._pnet,
          self._rnet,
          self._onet,
      ) = NetworkFactory().build_P_R_O_nets_from_file(weights_file)
  
imports: import cv2
  import numpy
  import pkg_resources
  from mtcnn.exceptions import InvalidImage
  from mtcnn.network.factory import NetworkFactory
label_desc: Use image node_id 1 to create a new MTCNN object from inherited base classes: object with example: obj = MTCNN(weights_file, min_face_size, steps_threshold, scale_factor)