code: def lmk2out(results, is_bbox_normalized=False):
      """
      Args:
          results: request a dict, should include: `landmark`, `im_id`,
                   if is_bbox_normalized=True, also need `im_shape`.
          is_bbox_normalized: whether or not landmark is normalized.
      """
      xywh_res = []
      for t in results:
          bboxes = t["bbox"][0]
          lengths = t["bbox"][1][0]
          im_ids = np.array(t["im_id"][0]).flatten()
          if bboxes.shape == (1, 1) or bboxes is None:
              continue
          face_index = t["face_index"][0]
          prior_box = t["prior_boxes"][0]
          predict_lmk = t["landmark"][0]
          prior = np.reshape(prior_box, (-1, 4))
          predictlmk = np.reshape(predict_lmk, (-1, 10))
  
          k = 0
          for a in range(len(lengths)):
              num = lengths[a]
              im_id = int(im_ids[a])
              for i in range(num):
                  score = bboxes[k][1]
                  theindex = face_index[i][0]
                  me_prior = prior[theindex, :]
                  lmk_pred = predictlmk[theindex, :]
                  prior_w = me_prior[2] - me_prior[0]
                  prior_h = me_prior[3] - me_prior[1]
                  prior_w_center = (me_prior[2] + me_prior[0]) / 2
                  prior_h_center = (me_prior[3] + me_prior[1]) / 2
                  lmk_decode = np.zeros((10))
                  for j in [0, 2, 4, 6, 8]:
                      lmk_decode[j] = (
                          lmk_pred[j] * 0.1 * prior_w + prior_w_center
                      )
                  for j in [1, 3, 5, 7, 9]:
                      lmk_decode[j] = (
                          lmk_pred[j] * 0.1 * prior_h + prior_h_center
                      )
                  im_shape = t["im_shape"][0][a].tolist()
                  image_h, image_w = int(im_shape[0]), int(im_shape[1])
                  if is_bbox_normalized:
                      lmk_decode = lmk_decode * np.array(
                          [
                              image_w,
                              image_h,
                              image_w,
                              image_h,
                              image_w,
                              image_h,
                              image_w,
                              image_h,
                              image_w,
                              image_h,
                          ]
                      )
                  lmk_res = {
                      "image_id": im_id,
                      "landmark": lmk_decode,
                      "score": score,
                  }
                  xywh_res.append(lmk_res)
                  k += 1
      return xywh_res
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import cv2
  import numpy
  from collections import OrderedDict
  import paddle
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 12 for calling a global function with example usage: lmk2out(results, is_bbox_normalized) and returns: xywh_res