code: def mot_evaluate(
      self,
      data_root,
      seqs,
      output_dir,
      data_type="mot",
      model_type="JDE",
      save_images=False,
      save_videos=False,
      show_image=False,
      scaled=False,
      det_results_dir="",
  ):
      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_DATA_TYPE
      ), "data_type should be 'mot', 'mcmot' or 'kitti'"
      assert (
          model_type in MOT_ARCH
      ), "model_type should be 'JDE', 'DeepSORT', 'FairMOT' or 'ByteTrack'"
  
      # run tracking
      n_frame = 0
      timer_avgs, timer_calls = [], []
      for seq in seqs:
          infer_dir = os.path.join(data_root, seq)
          if not os.path.exists(infer_dir) or not os.path.isdir(
              infer_dir
          ):
              logger.warning(
                  "Seq {} error, {} has no images.".format(
                      seq, infer_dir
                  )
              )
              continue
          if os.path.exists(os.path.join(infer_dir, "img1")):
              infer_dir = os.path.join(infer_dir, "img1")
  
          frame_rate = 30
          seqinfo = os.path.join(data_root, seq, "seqinfo.ini")
          if os.path.exists(seqinfo):
              meta_info = open(seqinfo).read()
              frame_rate = int(
                  meta_info[
                      meta_info.find("frameRate")
                      + 10 : meta_info.find("\nseqLength")
                  ]
              )
  
          save_dir = (
              os.path.join(output_dir, "mot_outputs", seq)
              if save_images or save_videos
              else None
          )
          logger.info("Evaluate seq: {}".format(seq))
  
          self.dataset.set_images(self.get_infer_images(infer_dir))
          dataloader = create("EvalMOTReader")(self.dataset, 0)
  
          result_filename = os.path.join(
              result_root, "{}.txt".format(seq)
          )
  
          with paddle.no_grad():
              if model_type in MOT_ARCH_JDE:
                  results, nf, ta, tc = self._eval_seq_jde(
                      dataloader,
                      save_dir=save_dir,
                      show_image=show_image,
                      frame_rate=frame_rate,
                  )
              elif model_type in MOT_ARCH_SDE:
                  results, nf, ta, tc = self._eval_seq_sde(
                      dataloader,
                      save_dir=save_dir,
                      show_image=show_image,
                      frame_rate=frame_rate,
                      seq_name=seq,
                      scaled=scaled,
                      det_file=os.path.join(
                          det_results_dir, "{}.txt".format(seq)
                      ),
                  )
              elif model_type == "CenterTrack":
                  results, nf, ta, tc = self._eval_seq_centertrack(
                      dataloader,
                      save_dir=save_dir,
                      show_image=show_image,
                      frame_rate=frame_rate,
                  )
              else:
                  raise ValueError(model_type)
  
          write_mot_results(
              result_filename, results, data_type, self.cfg.num_classes
          )
          n_frame += nf
          timer_avgs.append(ta)
          timer_calls.append(tc)
  
          if save_videos:
              output_video_path = os.path.join(
                  save_dir, "..", "{}_vis.mp4".format(seq)
              )
              cmd_str = "ffmpeg -f image2 -i {}/%05d.jpg {}".format(
                  save_dir, output_video_path
              )
              os.system(cmd_str)
              logger.info("Save video in {}.".format(output_video_path))
  
          # update metrics
          for metric in self._metrics:
              metric.update(
                  data_root,
                  seq,
                  data_type,
                  result_root,
                  result_filename,
              )
  
      timer_avgs = np.asarray(timer_avgs)
      timer_calls = np.asarray(timer_calls)
      all_time = np.dot(timer_avgs, timer_calls)
      avg_time = all_time / np.sum(timer_calls)
      logger.info(
          "Time elapsed: {:.2f} seconds, FPS: {:.2f}".format(
              all_time, 1.0 / avg_time
          )
      )
  
      # accumulate metric to log out
      for metric in self._metrics:
          metric.accumulate()
          metric.log()
      # reset metric states for metric may performed multiple times
      self._reset_metrics()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import glob
  import re
  import paddle
  import paddle.nn
  import numpy
  from tqdm import tqdm
  from collections import defaultdict
  from ppdet.core.workspace import create
  from ppdet.utils.checkpoint import load_weight, load_pretrain_weight
  from ppdet.modeling.mot.utils import Detection, get_crops, scale_coords, clip_box
  from ppdet.modeling.mot.utils import MOTTimer, load_det_results, write_mot_results, save_vis_results
  from ppdet.modeling.mot.tracker import JDETracker, CenterTracker
  from ppdet.modeling.mot.tracker import DeepSORTTracker, OCSORTTracker, BOTSORTTracker
  from ppdet.modeling.architectures import YOLOX
  from ppdet.metrics import Metric, MOTMetric, KITTIMOTMetric, MCMOTMetric
  from ppdet.data.source.category import get_categories
  import ppdet.utils.stats
  from .callbacks import Callback, ComposeCallback
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 12 for calling the Tracker obj's underlying member method code with example usage: obj.mot_evaluate(data_root, seqs, output_dir, data_type, model_type, save_images, save_videos, show_image, scaled, det_results_dir) without return types