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",")"]
code: def __compute_scale_pyramid(self, m, min_layer):
      scales = []
      factor_count = 0
  
      while min_layer >= 12:
          scales += [m * np.power(self._scale_factor, factor_count)]
          min_layer = min_layer * self._scale_factor
          factor_count += 1
  
      return scales
  
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 4 for calling the MTCNN obj's underlying member method code with example usage: obj.__compute_scale_pyramid(m, min_layer) and returns: scales