code: import os
  import cv2
  import time
  import argparse
  import torch
  import warnings
  import numpy as np
  
  from detector import build_detector
  from deep_sort import build_tracker
  from utils.draw import draw_boxes
  from utils.parser import get_config
  from utils.log import get_logger
  from utils.io import write_results
  
  
  class VideoTracker(object):
      def __init__(self, cfg, args, video_path):
          self.cfg = cfg
          self.args = args
          self.video_path = video_path
          self.logger = get_logger("root")
          self.video_name = video_path.split("/")[-1].split(".")[0]
          use_cuda = args.use_cuda and torch.cuda.is_available()
          if not use_cuda:
              warnings.warn("Running in cpu mode which maybe very slow!", UserWarning)
  
          if args.display:
              cv2.namedWindow("test", cv2.WINDOW_NORMAL)
              cv2.resizeWindow("test", args.display_width, args.display_height)
  
          if args.cam != -1:
              print("Using webcam " + str(args.cam))
              self.vdo = cv2.VideoCapture(args.cam)
          else:
              self.vdo = cv2.VideoCapture()
          self.detector = build_detector(cfg, use_cuda=use_cuda)
          self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
          self.class_names = self.detector.class_names
          print("Class name: ", self.class_names)
  
      def __enter__(self):
          if self.args.cam != -1:
              ret, frame = self.vdo.read()
              assert ret, "Error: Camera error"
              self.im_width = frame.shape[0]
              self.im_height = frame.shape[1]
  
          else:
              assert os.path.isfile(self.video_path), "Path error"
              self.vdo.open(self.video_path)
              self.im_width = int(self.vdo.get(cv2.CAP_PROP_FRAME_WIDTH))
              self.im_height = int(self.vdo.get(cv2.CAP_PROP_FRAME_HEIGHT))
              assert self.vdo.isOpened()
  
          if self.args.save_path:
              os.makedirs(self.args.save_path, exist_ok=True)
  
              # path of saved video and results
              self.save_video_path = os.path.join(self.args.save_path, self.video_name + "_results.avi")
              self.save_results_path = os.path.join(self.args.save_path, self.video_name + "_results.txt")
  
              # create video writer
              fourcc = cv2.VideoWriter_fourcc(*'MJPG')
              self.writer = cv2.VideoWriter(self.save_video_path, fourcc, 20, (self.im_width, self.im_height))
  
              # logging
              self.logger.info("Save results to {}".format(self.args.save_path))
  
          return self
  
      def __exit__(self, exc_type, exc_value, exc_traceback):
          if exc_type:
              print(exc_type, exc_value, exc_traceback)
  
      def run(self):
          results = []
          idx_frame = 0
          while self.vdo.grab():
              idx_frame += 1
              if idx_frame % self.args.frame_interval:
                  continue
  
              start = time.time()
              _, ori_im = self.vdo.retrieve()
              im = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB)
  
              # do detection
              bbox_xywh, cls_conf, cls_ids = self.detector(im)
  
              # select person class
              mask = cls_ids < 7
  
              bbox_xywh = bbox_xywh[mask]
              # bbox dilation just in case bbox too small, delete this line if using a better pedestrian detector
              bbox_xywh[:, 3:] *= 1.2
              cls_conf = cls_conf[mask]
  
              # do tracking
              outputs = self.deepsort.update(bbox_xywh, cls_conf, im)
  
              # draw boxes for visualization
              if len(outputs) > 0:
                  bbox_tlwh = []
                  bbox_xyxy = outputs[:, :4]
                  identities = outputs[:, -1]
                  ori_im = draw_boxes(ori_im, bbox_xyxy, identities)
  
                  for bb_xyxy in bbox_xyxy:
                      bbox_tlwh.append(self.deepsort._xyxy_to_tlwh(bb_xyxy))
  
                  results.append((idx_frame - 1, bbox_tlwh, identities))
  
              end = time.time()
  
              if self.args.display:
                  cv2.imshow("test", ori_im)
                  cv2.waitKey(1)
  
              if self.args.save_path:
                  self.writer.write(ori_im)
  
              # save results
              write_results(self.save_results_path, results, 'mot')
  
              # logging
              self.logger.info("time: {:.03f}s, fps: {:.03f}, detection numbers: {}, tracking numbers: {}" \
                               .format(end - start, 1 / (end - start), bbox_xywh.shape[0], len(outputs)))
  
  
  def parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument("VIDEO_PATH", type=str)
      parser.add_argument("--config_detection", type=str, default="./configs/yolov3.yaml")
      parser.add_argument("--config_deepsort", type=str, default="./configs/deep_sort.yaml")
      # parser.add_argument("--ignore_display", dest="display", action="store_false", default=True)
      parser.add_argument("--display", action="store_true")
      parser.add_argument("--frame_interval", type=int, default=1)
      parser.add_argument("--display_width", type=int, default=800)
      parser.add_argument("--display_height", type=int, default=600)
      parser.add_argument("--save_path", type=str, default="./output/")
      parser.add_argument("--cpu", dest="use_cuda", action="store_false", default=True)
      parser.add_argument("--camera", action="store", dest="cam", type=int, default="-1")
      return parser.parse_args()
  
  
  if __name__ == "__main__":
      args = parse_args()
      cfg = get_config()
      cfg.merge_from_file(args.config_detection)
      cfg.merge_from_file(args.config_deepsort)
  
      with VideoTracker(cfg, args, video_path=args.VIDEO_PATH) as vdo_trk:
          vdo_trk.run()
  
apis: torch.cuda.is_available