code: def imagestream_predict(
      self,
      output_dir,
      data_type="mot",
      model_type="JDE",
      visualization=True,
      draw_threshold=0.5,
  ):
      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 = "inputimages"
      self.dataset = MOTImageStream(keep_ori_im=True)
  
      save_dir = (
          os.path.join(output_dir, "mot_outputs", seq)
          if visualization
          else None
      )
  
      self.dataloader = create("MOTVideoStreamReader")(self.dataset, 0)
      self.dataloader_iter = iter(self.dataloader)
      result_filename = os.path.join(result_root, "{}.txt".format(seq))
  
      if model_type in ["JDE", "FairMOT"]:
          generator = self._eval_seq_jde_single_image(
              self.dataloader_iter,
              save_dir=save_dir,
              draw_threshold=draw_threshold,
          )
      else:
          raise ValueError(model_type)
      yield
      results = []
      while True:
          try:
              results, nf = next(generator)
              yield results
          except StopIteration as e:
              self.write_mot_results(
                  result_filename, results, data_type
              )
              return
  
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 5 for calling the StreamTracker obj's underlying member method code with example usage: obj.imagestream_predict(output_dir, data_type, model_type, visualization, draw_threshold) without return types