text: import numpy as np
  from collections import deque
  import os
  import os.path as osp
  import copy
  import torch
  import torch.nn.functional as F
  
  from mot_online.kalman_filter import KalmanFilter
  from mot_online.basetrack import BaseTrack, TrackState
  from mot_online import matching
  
  
  
  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 BYTETracker(object):
      def __init__(self, frame_rate=30):
          self.tracked_stracks = []  # type: list[STrack]
          self.lost_stracks = []  # type: list[STrack]
          self.removed_stracks = []  # type: list[STrack]
  
          self.frame_id = 0
          
          self.low_thresh = 0.2
          self.track_thresh = 0.8
          self.det_thresh = self.track_thresh + 0.1
          
          
          self.buffer_size = int(frame_rate / 30.0 * 30)
          self.max_time_lost = self.buffer_size
          self.kalman_filter = KalmanFilter()
  
  #     def update(self, output_results):
      def update(self, det_bboxes, det_labels, frame_id, track_feats):
  
  #         self.frame_id += 1
          self.frame_id = frame_id + 1
          activated_starcks = []
          refind_stracks = []
          lost_stracks = []
          removed_stracks = []
          
  #         scores = output_results[:, 4]
  #         bboxes = output_results[:, :4]  # x1y1x2y2
          scores = det_bboxes[:, 4].cpu().numpy()
          bboxes = det_bboxes[:, :4].cpu().numpy()
  
          track_feature = F.normalize(track_feats).cpu().numpy()
  
          remain_inds = scores > self.track_thresh
          dets = bboxes[remain_inds]
          scores_keep = scores[remain_inds]
          id_feature = track_feature[remain_inds]
          
          
          inds_low = scores > self.low_thresh
          inds_high = scores < self.track_thresh
          inds_second = np.logical_and(inds_low, inds_high)
          dets_second = bboxes[inds_second]
          scores_second = scores[inds_second]
          id_feature_second = track_feature[inds_second]
          
          if len(dets) > 0:
              '''Detections'''
              detections = [STrack(STrack.tlbr_to_tlwh(tlbr), s, f) for
                            (tlbr, s, f) in zip(dets, scores_keep, 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 Kalman and IOU'''
          strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
          # Predict the current location with KF
          STrack.multi_predict(strack_pool)
          
          dists = matching.embedding_distance(strack_pool, detections)
          dists = matching.fuse_motion(self.kalman_filter, dists, strack_pool, detections)
          matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.6)
  #         dists = matching.iou_distance(strack_pool, detections)
  #         matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.8)
  
          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)
                  
          
          ''' Step 3.5: Second association, with IOU'''
          # association the untrack to the low score detections
          if len(dets_second) > 0:
              '''Detections'''
              detections_second = [STrack(STrack.tlbr_to_tlwh(tlbr), s, f) for
                            (tlbr, s, f) in zip(dets_second, scores_second, id_feature_second)]
          else:
              detections_second = []
          
          second_tracked_stracks = [r_tracked_stracks[i] for i in u_track if r_tracked_stracks[i].state == TrackState.Tracked]
          dists = matching.iou_distance(second_tracked_stracks, detections_second)
          matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.5)
          for itracked, idet in matches:
              track = second_tracked_stracks[itracked]
              det = detections_second[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]
              track = second_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]
  
  #         return output_stracks
  
          bboxes = []
          labels = [] 
          ids = []
          for track in output_stracks:
              if track.is_activated:
                  track_bbox = track.tlbr
                  bboxes.append([track_bbox[0], track_bbox[1], track_bbox[2], track_bbox[3], track.score])
                  labels.append(0)
                  ids.append(track.track_id)
          return torch.tensor(bboxes), torch.tensor(labels), torch.tensor(ids)
  
  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
  
  
  def remove_fp_stracks(stracksa, n_frame=10):
      remain = []
      for t in stracksa:
          score_5 = t.score_list[-n_frame:]
          score_5 = np.array(score_5, dtype=np.float32)
          index = score_5 < 0.45
          num = np.sum(index)
          if num < n_frame:
              remain.append(t)
      return remain
  