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 __pad(total_boxes, w, h):
      # compute the padding coordinates (pad the bounding boxes to square)
      tmpw = (total_boxes[:, 2] - total_boxes[:, 0] + 1).astype(
          np.int32
      )
      tmph = (total_boxes[:, 3] - total_boxes[:, 1] + 1).astype(
          np.int32
      )
      numbox = total_boxes.shape[0]
  
      dx = np.ones(numbox, dtype=np.int32)
      dy = np.ones(numbox, dtype=np.int32)
      edx = tmpw.copy().astype(np.int32)
      edy = tmph.copy().astype(np.int32)
  
      x = total_boxes[:, 0].copy().astype(np.int32)
      y = total_boxes[:, 1].copy().astype(np.int32)
      ex = total_boxes[:, 2].copy().astype(np.int32)
      ey = total_boxes[:, 3].copy().astype(np.int32)
  
      tmp = np.where(ex > w)
      edx.flat[tmp] = np.expand_dims(-ex[tmp] + w + tmpw[tmp], 1)
      ex[tmp] = w
  
      tmp = np.where(ey > h)
      edy.flat[tmp] = np.expand_dims(-ey[tmp] + h + tmph[tmp], 1)
      ey[tmp] = h
  
      tmp = np.where(x < 1)
      dx.flat[tmp] = np.expand_dims(2 - x[tmp], 1)
      x[tmp] = 1
  
      tmp = np.where(y < 1)
      dy.flat[tmp] = np.expand_dims(2 - y[tmp], 1)
      y[tmp] = 1
  
      return dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph
  
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 8 for calling the MTCNN obj's underlying member method code with example usage: obj.__pad(total_boxes, w, h) and returns: dy,  edy,  dx,  edx,  y,  ey,  x,  ex,  tmpw,  tmph