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 __bbreg(boundingbox, reg):
      # calibrate bounding boxes
      if reg.shape[1] == 1:
          reg = np.reshape(reg, (reg.shape[2], reg.shape[3]))
  
      w = boundingbox[:, 2] - boundingbox[:, 0] + 1
      h = boundingbox[:, 3] - boundingbox[:, 1] + 1
      b1 = boundingbox[:, 0] + reg[:, 0] * w
      b2 = boundingbox[:, 1] + reg[:, 1] * h
      b3 = boundingbox[:, 2] + reg[:, 2] * w
      b4 = boundingbox[:, 3] + reg[:, 3] * h
      boundingbox[:, 0:4] = np.transpose(np.vstack([b1, b2, b3, b4]))
      return boundingbox
  
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 10 for calling the MTCNN obj's underlying member method code with example usage: obj.__bbreg(boundingbox, reg) and returns: boundingbox