code: def main(args):
      include_datasets = args.include.split(",")
      rec_list = []
      for ds in include_datasets:
          path_imgrec = os.path.join(ds, "train.rec")
          path_imgidx = os.path.join(ds, "train.idx")
          imgrec = mx.recordio.MXIndexedRecordIO(
              path_imgidx, path_imgrec, "r"
          )  # pylint: disable=redefined-variable-type
          rec_list.append(imgrec)
      if not os.path.exists(args.output):
          os.makedirs(args.output)
      for ds_id in range(len(rec_list)):
          id_list = []
          imgrec = rec_list[ds_id]
          s = imgrec.read_idx(0)
          header, _ = mx.recordio.unpack(s)
          assert header.flag > 0
          print("header0 label", header.label)
          header0 = (int(header.label[0]), int(header.label[1]))
          seq_identity = range(
              int(header.label[0]), int(header.label[1])
          )
          pp = 0
          for identity in seq_identity:
              id_dir = os.path.join(
                  args.output, "%d_%d" % (ds_id, identity)
              )
              os.makedirs(id_dir)
              pp += 1
              if pp % 10 == 0:
                  print("processing id", pp)
              s = imgrec.read_idx(identity)
              header, _ = mx.recordio.unpack(s)
              imgid = 0
              for _idx in range(
                  int(header.label[0]), int(header.label[1])
              ):
                  s = imgrec.read_idx(_idx)
                  _header, _img = mx.recordio.unpack(s)
                  _img = mx.image.imdecode(_img).asnumpy()[
                      :, :, ::-1
                  ]  # to bgr
                  image_path = os.path.join(id_dir, "%d.jpg" % imgid)
                  cv2.imwrite(image_path, _img)
                  imgid += 1
  
file_path: insightface/recognition/arcface_mxnet/common/rec2image.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import sys
  import mxnet
  from mxnet import ndarray
  import random
  import argparse
  import cv2
  import time
  import sklearn
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: main(args) without return types