text: import numpy as np
  from numba import jit
  from collections import deque
  import itertools
  import os
  import os.path as osp
  import time
  import torch
  import cv2
  import torch.nn.functional as F
  
  from models.model import create_model, load_model
  from models.decode import mot_decode, mot_decode2
  from tracking_utils.utils import *
  from tracking_utils.log import logger
  from tracking_utils.kalman_filter import KalmanFilter
  from models import *
  from tracker import matching
  from .basetrack import BaseTrack, TrackState
  from utils.post_process import ctdet_post_process
  from utils.image import get_affine_transform
  from models.utils import _tranpose_and_gather_feat
  from models.relation import RelModel
  
  import time
  
  global time_sync_rec
  time_sync_rec = {}
  
  def time_sync(tag, log=True):
      return
      global time_sync_rec
      torch.cuda.synchronize()
      stamp = time.time()
      if tag in time_sync_rec:
          count = stamp - time_sync_rec[tag]
          print('[%s] %.4f' % (tag, count))
      else:
          count = None
      time_sync_rec[tag] = stamp
      return count
  
  def time_clean():
      global time_sync_rec
      time_sync_rec = {}
  
  
  class STrack(BaseTrack):
      shared_kalman = KalmanFilter()
      def __init__(self, tlwh, score, temp_feat, buffer_size=30):
  
          # wait activate
          self._tlwh = np.asarray(tlwh, dtype=np.float)
          self.kalman_filter = None
          self.mean, self.covariance = None, None
          self.is_activated = False
  
          self.score = score
          self.tracklet_len = 0
  
          self.smooth_feat = None
          self.update_features(temp_feat)
          self.features = deque([], maxlen=buffer_size)
          self.alpha = 0.9
  
      def update_features(self, feat):
          feat /= np.linalg.norm(feat)
          self.curr_feat = feat
          if self.smooth_feat is None:
              self.smooth_feat = feat
          else:
              self.smooth_feat = self.alpha * self.smooth_feat + (1 - self.alpha) * feat
          self.features.append(feat)
          self.smooth_feat /= np.linalg.norm(self.smooth_feat)
  
      def predict(self):
          mean_state = self.mean.copy()
          if self.state != TrackState.Tracked:
              mean_state[7] = 0
          self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
  
      @staticmethod
      def multi_predict(stracks):
          if len(stracks) > 0:
              multi_mean = np.asarray([st.mean.copy() for st in stracks])
              multi_covariance = np.asarray([st.covariance for st in stracks])
              for i, st in enumerate(stracks):
                  if st.state != TrackState.Tracked:
                      multi_mean[i][7] = 0
              multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance)
              for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
                  stracks[i].mean = mean
                  stracks[i].covariance = cov
  
      def activate(self, kalman_filter, frame_id):
          """Start a new tracklet"""
          self.kalman_filter = kalman_filter
          self.track_id = self.next_id()
          self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
  
          self.tracklet_len = 0
          self.state = TrackState.Tracked
          if frame_id == 1:
              self.is_activated = True
          #self.is_activated = True
          self.frame_id = frame_id
          self.start_frame = frame_id
  
      def re_activate(self, new_track, frame_id, new_id=False):
          self.mean, self.covariance = self.kalman_filter.update(
              self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
          )
  
          self.update_features(new_track.curr_feat)
          self.tracklet_len = 0
          self.state = TrackState.Tracked
          self.is_activated = True
          self.frame_id = frame_id
          if new_id:
              self.track_id = self.next_id()
  
      def update(self, new_track, frame_id, update_feature=True):
          """
          Update a matched track
          :type new_track: STrack
          :type frame_id: int
          :type update_feature: bool
          :return:
          """
          self.frame_id = frame_id
          self.tracklet_len += 1
  
          new_tlwh = new_track.tlwh
          self.mean, self.covariance = self.kalman_filter.update(
              self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
          self.state = TrackState.Tracked
          self.is_activated = True
  
          self.score = new_track.score
          if update_feature:
              self.update_features(new_track.curr_feat)
  
      @property
      # @jit(nopython=True)
      def tlwh(self):
          """Get current position in bounding box format `(top left x, top left y,
                  width, height)`.
          """
          if self.mean is None:
              return self._tlwh.copy()
          ret = self.mean[:4].copy()
          ret[2] *= ret[3]
          ret[:2] -= ret[2:] / 2
          return ret
  
      @property
      # @jit(nopython=True)
      def tlbr(self):
          """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
          `(top left, bottom right)`.
          """
          ret = self.tlwh.copy()
          ret[2:] += ret[:2]
          return ret
  
      @staticmethod
      # @jit(nopython=True)
      def tlwh_to_xyah(tlwh):
          """Convert bounding box to format `(center x, center y, aspect ratio,
          height)`, where the aspect ratio is `width / height`.
          """
          ret = np.asarray(tlwh).copy()
          ret[:2] += ret[2:] / 2
          ret[2] /= ret[3]
          return ret
  
      def to_xyah(self):
          return self.tlwh_to_xyah(self.tlwh)
  
      @staticmethod
      # @jit(nopython=True)
      def tlbr_to_tlwh(tlbr):
          ret = np.asarray(tlbr).copy()
          ret[2:] -= ret[:2]
          return ret
  
      @staticmethod
      # @jit(nopython=True)
      def tlwh_to_tlbr(tlwh):
          ret = np.asarray(tlwh).copy()
          ret[2:] += ret[:2]
          return ret
  
      def __repr__(self):
          return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)
  
  
  class JDETracker(object):
      def __init__(self, opt, frame_rate=30):
          self.opt = opt
          if opt.gpus[0] >= 0:
              opt.device = torch.device('cuda')
          else:
              opt.device = torch.device('cpu')
          print('Creating model...')
          self.model = create_model(opt.arch, opt.heads, opt.head_conv)
          self.model = RelModel(self.model, rel_type=opt.task)
          self.model = load_model(self.model, opt.load_model)
          self.model = self.model.to(opt.device)
          self.model.eval()
  
          self.tracked_stracks = []  # type: list[STrack]
          self.lost_stracks = []  # type: list[STrack]
          self.removed_stracks = []  # type: list[STrack]
  
          self.frame_id = 0
          self.det_thresh = opt.conf_thres
          self.buffer_size = int(frame_rate / 30.0 * opt.track_buffer)
          self.max_time_lost = self.buffer_size
          self.max_per_image = opt.K
          self.mean = np.array(opt.mean, dtype=np.float32).reshape(1, 1, 3)
          self.std = np.array(opt.std, dtype=np.float32).reshape(1, 1, 3)
  
          self.kalman_filter = KalmanFilter()
          self.raw_dets = []
  
      def post_process(self, dets, meta):
          dets = dets.detach().cpu().numpy()
          dets = dets.reshape(1, -1, dets.shape[2])
          dets = ctdet_post_process(
              dets.copy(), [meta['c']], [meta['s']],
              meta['out_height'], meta['out_width'], self.opt.num_classes)
          for j in range(1, self.opt.num_classes + 1):
              dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape(-1, 5)
          return dets[0]
  
      def merge_outputs(self, detections):
          results = {}
          for j in range(1, self.opt.num_classes + 1):
              results[j] = np.concatenate(
                  [detection[j] for detection in detections], axis=0).astype(np.float32)
  
          scores = np.hstack(
              [results[j][:, 4] for j in range(1, self.opt.num_classes + 1)])
          if len(scores) > self.max_per_image:
              kth = len(scores) - self.max_per_image
              thresh = np.partition(scores, kth)[kth]
              for j in range(1, self.opt.num_classes + 1):
                  keep_inds = (results[j][:, 4] >= thresh)
                  results[j] = results[j][keep_inds]
          return results
  
      def update(self, im_blob, img0, output):
          time_clean()
          time_sync('prepare')
          self.frame_id += 1
          activated_starcks = []
          refind_stracks = []
          lost_stracks = []
          removed_stracks = []
  
          width = img0.shape[1]
          height = img0.shape[0]
          inp_height = im_blob.shape[2]
          inp_width = im_blob.shape[3]
          c = np.array([width / 2., height / 2.], dtype=np.float32)
          s = max(float(inp_width) / float(inp_height) * height, width) * 1.0
          meta = {'c': c, 's': s,
                  'out_height': inp_height // self.opt.down_ratio,
                  'out_width': inp_width // self.opt.down_ratio}
  
          ''' Step 1: Network forward, get detections & embeddings'''
          time_sync('prepare')
          with torch.no_grad():
              hm = output['hm'].sigmoid_()
              wh = output['wh']
              rois = output['rois']
              id_feature = output['id']
              id_feature = F.normalize(id_feature, dim=1)
  
              reg = output['reg'] if self.opt.reg_offset else None
              time_sync('decode')
              dets, inds = mot_decode2(hm, rois, wh, output['rpn_map'].shape[-2:], reg=reg, ltrb=self.opt.ltrb, K=self.opt.K, thr=self.opt.nms_thres)
              time_sync('decode')
              id_feature = id_feature[inds]
              # id_feature = id_feature.squeeze(0)
              id_feature = id_feature.cpu().numpy()
  
          time_sync('post')
          dets = dets.unsqueeze(0)
          dets = self.post_process(dets, meta)
          dets = self.merge_outputs([dets])[1]
  
          self.raw_dets.append(dets)
          remain_inds = dets[:, 4] > self.opt.conf_thres
          dets = dets[remain_inds]
          id_feature = id_feature[remain_inds]
          time_sync('post')
          # print(id_feature.shape)
  
          # vis
          '''
          for i in range(0, dets.shape[0]):
              bbox = dets[i][0:4]
              cv2.rectangle(img0, (bbox[0], bbox[1]),
                            (bbox[2], bbox[3]),
                            (0, 255, 0), 2)
          cv2.imshow('dets', img0)
          cv2.waitKey(0)
          id0 = id0-1
          '''
  
          # img_to_draw = img0.copy()
          # for i in range(0, dets.shape[0]):
          #     bbox = dets[i][0:4]
          #     cv2.rectangle(img_to_draw, (int(bbox[0]), int(bbox[1])),
          #                   (int(bbox[2]), int(bbox[3])),
          #                   (0, 255, 0), 2)
          #     cv2.putText(img_to_draw, '%.2f'%dets[i][4], (int(bbox[0]), int(bbox[1]) - 2), cv2.FONT_HERSHEY_COMPLEX, 1, (10, 200, 200), thickness=2)
          # cv2.imwrite('./test.jpg', img_to_draw)
          # input()
  
  
          if len(dets) > 0:
              '''Detections'''
              detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for
                            (tlbrs, f) in zip(dets[:, :5], id_feature)]
          else:
              detections = []
  
          ''' Add newly detected tracklets to tracked_stracks'''
          unconfirmed = []
          tracked_stracks = []  # type: list[STrack]
          for track in self.tracked_stracks:
              if not track.is_activated:
                  unconfirmed.append(track)
              else:
                  tracked_stracks.append(track)
  
          ''' Step 2: First association, with embedding'''
          strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
          # Predict the current location with KF
          #for strack in strack_pool:
              #strack.predict()
          STrack.multi_predict(strack_pool)
          dists = matching.embedding_distance(strack_pool, detections)
          #dists = matching.iou_distance(strack_pool, detections)
          dists = matching.fuse_motion(self.kalman_filter, dists, strack_pool, detections)
          # print(dists.min(axis=1))
          # print(dists)
          # input()
          matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.4)
  
          for itracked, idet in matches:
              track = strack_pool[itracked]
              det = detections[idet]
              if track.state == TrackState.Tracked:
                  track.update(detections[idet], self.frame_id)
                  activated_starcks.append(track)
              else:
                  track.re_activate(det, self.frame_id, new_id=False)
                  refind_stracks.append(track)
  
          ''' Step 3: Second association, with IOU'''
          detections = [detections[i] for i in u_detection]
          r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]
          dists = matching.iou_distance(r_tracked_stracks, detections)
          matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5)
  
          for itracked, idet in matches:
              track = r_tracked_stracks[itracked]
              det = detections[idet]
              if track.state == TrackState.Tracked:
                  track.update(det, self.frame_id)
                  activated_starcks.append(track)
              else:
                  track.re_activate(det, self.frame_id, new_id=False)
                  refind_stracks.append(track)
                  
          for it in u_track:
              track = r_tracked_stracks[it]
              if not track.state == TrackState.Lost:
                  track.mark_lost()
                  lost_stracks.append(track)
  
          '''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''
          detections = [detections[i] for i in u_detection]
          dists = matching.iou_distance(unconfirmed, detections)
          matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
          for itracked, idet in matches:
              unconfirmed[itracked].update(detections[idet], self.frame_id)
              activated_starcks.append(unconfirmed[itracked])
          for it in u_unconfirmed:
              track = unconfirmed[it]
              track.mark_removed()
              removed_stracks.append(track)
  
          """ Step 4: Init new stracks"""
          for inew in u_detection:
              track = detections[inew]
              if track.score < self.det_thresh:
                  continue
              track.activate(self.kalman_filter, self.frame_id)
              activated_starcks.append(track)
          """ Step 5: Update state"""
          for track in self.lost_stracks:
              if self.frame_id - track.end_frame > self.max_time_lost:
                  track.mark_removed()
                  removed_stracks.append(track)
  
          # print('Ramained match {} s'.format(t4-t3))
  
          self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
          self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)
          self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)
          self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)
          self.lost_stracks.extend(lost_stracks)
          self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)
          self.removed_stracks.extend(removed_stracks)
          self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
          # get scores of lost tracks
          output_stracks = [track for track in self.tracked_stracks if track.is_activated]
  
          logger.debug('===========Frame {}=========='.format(self.frame_id))
          logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks]))
          logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks]))
          logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks]))
          logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks]))
  
          return output_stracks
  
  
  def joint_stracks(tlista, tlistb):
      exists = {}
      res = []
      for t in tlista:
          exists[t.track_id] = 1
          res.append(t)
      for t in tlistb:
          tid = t.track_id
          if not exists.get(tid, 0):
              exists[tid] = 1
              res.append(t)
      return res
  
  
  def sub_stracks(tlista, tlistb):
      stracks = {}
      for t in tlista:
          stracks[t.track_id] = t
      for t in tlistb:
          tid = t.track_id
          if stracks.get(tid, 0):
              del stracks[tid]
      return list(stracks.values())
  
  
  def remove_duplicate_stracks(stracksa, stracksb):
      pdist = matching.iou_distance(stracksa, stracksb)
      pairs = np.where(pdist < 0.15)
      dupa, dupb = list(), list()
      for p, q in zip(*pairs):
          timep = stracksa[p].frame_id - stracksa[p].start_frame
          timeq = stracksb[q].frame_id - stracksb[q].start_frame
          if timep > timeq:
              dupb.append(q)
          else:
              dupa.append(p)
      resa = [t for i, t in enumerate(stracksa) if not i in dupa]
      resb = [t for i, t in enumerate(stracksb) if not i in dupb]
      return resa, resb
  