code: def detect_center(
      self, img, threshold=0.5, scales=[1.0], do_flip=False
  ):
      det, landmarks = self.detect(img, threshold, scales, do_flip)
      if det.shape[0] == 0:
          return None, None
      bindex = 0
      if det.shape[0] > 1:
          img_size = np.asarray(img.shape)[0:2]
          bounding_box_size = (det[:, 2] - det[:, 0]) * (
              det[:, 3] - det[:, 1]
          )
          img_center = img_size / 2
          offsets = np.vstack(
              [
                  (det[:, 0] + det[:, 2]) / 2 - img_center[1],
                  (det[:, 1] + det[:, 3]) / 2 - img_center[0],
              ]
          )
          offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
          bindex = np.argmax(
              bounding_box_size - offset_dist_squared * 2.0
          )  # some extra weight on the centering
      bbox = det[bindex, :]
      landmark = landmarks[bindex, :, :]
      return bbox, landmark
  
imports: from __future__ import print_function
  import sys
  import os
  import datetime
  import time
  import numpy
  import mxnet
  from mxnet import ndarray
  import cv2
  from rcnn.logger import logger
  from rcnn.processing.bbox_transform import clip_boxes
  from rcnn.processing.generate_anchor import generate_anchors_fpn, anchors_plane
  from rcnn.processing.nms import gpu_nms_wrapper, cpu_nms_wrapper
  from rcnn.processing.bbox_transform import bbox_overlaps
label_desc: Use image node_id 4 for calling the RetinaFace obj's underlying member method code with example usage: obj.detect_center(img, threshold, scales, do_flip) and returns: bbox,  landmark, None,  None