code: def videostream_predict(
      self,
      video_stream,
      output_dir,
      data_type="mot",
      model_type="JDE",
      visualization=True,
      draw_threshold=0.5,
  ):
      assert video_stream is not None, "--video_stream should be set."
  
      if not os.path.exists(output_dir):
          os.makedirs(output_dir)
      result_root = os.path.join(output_dir, "mot_results")
      if not os.path.exists(result_root):
          os.makedirs(result_root)
      assert data_type in [
          "mot",
          "kitti",
      ], "data_type should be 'mot' or 'kitti'"
      assert model_type in [
          "JDE",
          "FairMOT",
      ], "model_type should be 'JDE', or 'FairMOT'"
      seq = os.path.splitext(os.path.basename(video_stream))[0]
      self.dataset = MOTVideoStream(video_stream, keep_ori_im=True)
  
      save_dir = (
          os.path.join(output_dir, "mot_outputs", seq)
          if visualization
          else None
      )
  
      dataloader = create("MOTVideoStreamReader")(self.dataset, 0)
      result_filename = os.path.join(result_root, "{}.txt".format(seq))
  
      with paddle.no_grad():
          if model_type in ["JDE", "FairMOT"]:
              results, nf, ta, tc = self._eval_seq_jde(
                  dataloader,
                  save_dir=save_dir,
                  draw_threshold=draw_threshold,
              )
          else:
              raise ValueError(model_type)
  
      self.write_mot_results(result_filename, results, data_type)
  
      if visualization:
          #### Save using ffmpeg
          # output_video_path = os.path.join(save_dir, '..', '{}_vis.mp4'.format(seq))
          # cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -vf "scale=trunc(iw/2)*2:trunc(ih/2)*2" {}'.format(
          #    save_dir, output_video_path)
          # os.system(cmd_str)
          #### Save using opencv
          output_video_path = os.path.join(
              save_dir, "..", "{}_vis.avi".format(seq)
          )
          imgnames = glob.glob(os.path.join(save_dir, "*.jpg"))
          if len(imgnames) == 0:
              logger.info("No output images to save for video")
              return
          img = cv2.imread(os.path.join(save_dir, "00000.jpg"))
          video_writer = cv2.VideoWriter(
              output_video_path,
              apiPreference=0,
              fourcc=cv2.VideoWriter_fourcc("M", "J", "P", "G"),
              fps=30,
              frameSize=(img.shape[1], img.shape[0]),
          )
          for i in range(len(imgnames)):
              imgpath = os.path.join(save_dir, "{:05d}.jpg".format(i))
              img = cv2.imread(imgpath)
              video_writer.write(img)
          video_writer.release()
          logger.info("Save video in {}".format(output_video_path))
  
imports: import os
  import cv2
  import glob
  import paddle
  import numpy
  import collections
  from ppdet.utils.checkpoint import load_weight, load_pretrain_weight
  from ppdet.metrics import Metric, MOTMetric, KITTIMOTMetric
  import ppdet.utils.stats
  from ppdet.engine.callbacks import Callback, ComposeCallback
  from ppdet.core.workspace import create
  from ppdet.utils.logger import setup_logger
  from .dataset import MOTVideoStream, MOTImageStream
  from .modeling.mot.utils import Detection, get_crops, scale_coords, clip_box
  from .modeling.mot import visualization
  from .utils import Timer
label_desc: Use image node_id 6 for calling the StreamTracker obj's underlying member method code with example usage: obj.videostream_predict(video_stream, output_dir, data_type, model_type, visualization, draw_threshold) without return types