text: from collections import deque
  import os
  import cv2
  import numpy as np
  import torch
  import torch.nn.functional as F
  from torchsummary import summary
  
  from core.mot.general import non_max_suppression_and_inds, non_max_suppression_jde, non_max_suppression, scale_coords
  from core.mot.torch_utils import intersect_dicts
  from models.mot.cstrack import Model
  
  from mot_online import matching
  from mot_online.kalman_filter import KalmanFilter
  from mot_online.log import logger
  from mot_online.utils import *
  
  from mot_online.basetrack import BaseTrack, TrackState
  
  
  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
          #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 int(opt.gpus[0]) >= 0:
              opt.device = torch.device('cuda')
          else:
              opt.device = torch.device('cpu')
          print('Creating model...')
  
          ckpt = torch.load(opt.weights, map_location=opt.device)  # load checkpoint
          self.model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=1).to(opt.device)  # create
          exclude = ['anchor'] if opt.cfg else []  # exclude keys
          if type(ckpt['model']).__name__ == "OrderedDict":
              state_dict = ckpt['model']
          else:
              state_dict = ckpt['model'].float().state_dict()  # to FP32
          state_dict = intersect_dicts(state_dict, self.model.state_dict(), exclude=exclude)  # intersect
          self.model.load_state_dict(state_dict, strict=False)  # load
          self.model.cuda().eval()
          total_params = sum(p.numel() for p in self.model.parameters())
          print(f'{total_params:,} total parameters.')
  
  
          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.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.low_thres = 0.1
          self.high_thres = self.opt.conf_thres + 0.1
  
      def update(self, im_blob, img0,seq_num, save_dir):
          self.frame_id += 1
          activated_starcks = []
          refind_stracks = []
          lost_stracks = []
          removed_stracks = []
          dets = []
  
          ''' Step 1: Network forward, get detections & embeddings'''
          with torch.no_grad():
              output = self.model(im_blob, augment=False)
              pred, train_out = output[1]
  
          pred = pred[pred[:, :, 4] > self.low_thres]
          detections = []
          if len(pred) > 0:
              dets,x_inds,y_inds = non_max_suppression_and_inds(pred[:,:6].unsqueeze(0), 0.1, self.opt.nms_thres,method='cluster_diou')
              dets = dets.numpy()
              if len(dets) != 0:
                  scale_coords(self.opt.img_size, dets[:, :4], img0.shape).round()
                  id_feature = output[0][0, y_inds, x_inds, :].cpu().numpy()
  
                  remain_inds = dets[:, 4] > self.opt.conf_thres
                  inds_low = dets[:, 4] > self.low_thres
                  inds_high = dets[:, 4] < self.opt.conf_thres
                  inds_second = np.logical_and(inds_low, inds_high)
                  dets_second = dets[inds_second]
                  if id_feature.shape[0] == 1:
                      id_feature_second = id_feature
                  else:
                      id_feature_second = id_feature[inds_second]
                  dets = dets[remain_inds]
                  id_feature = id_feature[remain_inds]
  
                  detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for
                                (tlbrs, f) in zip(dets[:, :5], id_feature)]
                  
              else:
                  detections = []
                  dets_second = []
                  id_feature_second = []
          else:
              detections = []
              dets_second = []
              id_feature_second = []
  
          ''' 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.fuse_motion(self.kalman_filter, dists, strack_pool, detections)
          #dists = matching.iou_distance(strack_pool, detections)
          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)
  
          # vis
          track_features, det_features, cost_matrix, cost_matrix_det, cost_matrix_track = [],[],[],[],[]
          if self.opt.vis_state == 1 and self.frame_id % 20 == 0:
              if len(dets) != 0:
                  for i in range(0, dets.shape[0]):
                      bbox = dets[i][0:4]
                      cv2.rectangle(img0, (int(bbox[0]), int(bbox[1])),(int(bbox[2]), int(bbox[3])),(0, 255, 0), 2)
                  track_features, det_features, cost_matrix, cost_matrix_det, cost_matrix_track = matching.vis_id_feature_A_distance(strack_pool, detections)
              vis_feature(self.frame_id,seq_num,img0,track_features,
                                    det_features, cost_matrix, cost_matrix_det, cost_matrix_track, max_num=5, out_path=save_dir)
  
          ''' 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)
  
          # association the untrack to the low score detections
          if len(dets_second) > 0:
              detections_second = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for
                                   (tlbrs, f) in zip(dets_second[:, :5], 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=0.4)
          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 = 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.high_thres:
                  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
  
  def vis_feature(frame_id,seq_num,img,track_features, det_features, cost_matrix, cost_matrix_det, cost_matrix_track,max_num=5, out_path='/home/XX/'):
      num_zero = ["0000","000","00","0"]
      img = cv2.resize(img, (778, 435))
  
      if len(det_features) != 0:
          max_f = det_features.max()
          min_f = det_features.min()
          det_features = np.round((det_features - min_f) / (max_f - min_f) * 255)
          det_features = det_features.astype(np.uint8)
          d_F_M = []
          cutpff_line = [40]*512
          for d_f in det_features:
              for row in range(45):
                  d_F_M += [[40]*3+d_f.tolist()+[40]*3]
              for row in range(3):
                  d_F_M += [[40]*3+cutpff_line+[40]*3]
          d_F_M = np.array(d_F_M)
          d_F_M = d_F_M.astype(np.uint8)
          det_features_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET)
          feature_img2 = cv2.resize(det_features_img, (435, 435))
          #cv2.putText(feature_img2, "det_features", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
      else:
          feature_img2 = np.zeros((435, 435))
          feature_img2 = feature_img2.astype(np.uint8)
          feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET)
          #cv2.putText(feature_img2, "det_features", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
      feature_img = np.concatenate((img, feature_img2), axis=1)
  
      if len(cost_matrix_det) != 0 and len(cost_matrix_det[0]) != 0:
          max_f = cost_matrix_det.max()
          min_f = cost_matrix_det.min()
          cost_matrix_det = np.round((cost_matrix_det - min_f) / (max_f - min_f) * 255)
          d_F_M = []
          cutpff_line = [40]*len(cost_matrix_det)*10
          for c_m in cost_matrix_det:
              add = []
              for row in range(len(c_m)):
                  add += [255-c_m[row]]*10
              for row in range(10):
                  d_F_M += [[40]+add+[40]]
          d_F_M = np.array(d_F_M)
          d_F_M = d_F_M.astype(np.uint8)
          cost_matrix_det_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET)
          feature_img2 = cv2.resize(cost_matrix_det_img, (435, 435))
          #cv2.putText(feature_img2, "cost_matrix_det", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
      else:
          feature_img2 = np.zeros((435, 435))
          feature_img2 = feature_img2.astype(np.uint8)
          feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET)
          #cv2.putText(feature_img2, "cost_matrix_det", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
      feature_img = np.concatenate((feature_img, feature_img2), axis=1)
  
      if len(track_features) != 0:
          max_f = track_features.max()
          min_f = track_features.min()
          track_features = np.round((track_features - min_f) / (max_f - min_f) * 255)
          track_features = track_features.astype(np.uint8)
          d_F_M = []
          cutpff_line = [40]*512
          for d_f in track_features:
              for row in range(45):
                  d_F_M += [[40]*3+d_f.tolist()+[40]*3]
              for row in range(3):
                  d_F_M += [[40]*3+cutpff_line+[40]*3]
          d_F_M = np.array(d_F_M)
          d_F_M = d_F_M.astype(np.uint8)
          track_features_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET)
          feature_img2 = cv2.resize(track_features_img, (435, 435))
          #cv2.putText(feature_img2, "track_features", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
      else:
          feature_img2 = np.zeros((435, 435))
          feature_img2 = feature_img2.astype(np.uint8)
          feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET)
          #cv2.putText(feature_img2, "track_features", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
      feature_img = np.concatenate((feature_img, feature_img2), axis=1)
  
      if len(cost_matrix_track) != 0 and len(cost_matrix_track[0]) != 0:
          max_f = cost_matrix_track.max()
          min_f = cost_matrix_track.min()
          cost_matrix_track = np.round((cost_matrix_track - min_f) / (max_f - min_f) * 255)
          d_F_M = []
          cutpff_line = [40]*len(cost_matrix_track)*10
          for c_m in cost_matrix_track:
              add = []
              for row in range(len(c_m)):
                  add += [255-c_m[row]]*10
              for row in range(10):
                  d_F_M += [[40]+add+[40]]
          d_F_M = np.array(d_F_M)
          d_F_M = d_F_M.astype(np.uint8)
          cost_matrix_track_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET)
          feature_img2 = cv2.resize(cost_matrix_track_img, (435, 435))
          #cv2.putText(feature_img2, "cost_matrix_track", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
      else:
          feature_img2 = np.zeros((435, 435))
          feature_img2 = feature_img2.astype(np.uint8)
          feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET)
          #cv2.putText(feature_img2, "cost_matrix_track", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
      feature_img = np.concatenate((feature_img, feature_img2), axis=1)
  
      if len(cost_matrix) != 0 and len(cost_matrix[0]) != 0:
          max_f = cost_matrix.max()
          min_f = cost_matrix.min()
          cost_matrix = np.round((cost_matrix - min_f) / (max_f - min_f) * 255)
          d_F_M = []
          cutpff_line = [40]*len(cost_matrix[0])*10
          for c_m in cost_matrix:
              add = []
              for row in range(len(c_m)):
                  add += [255-c_m[row]]*10
              for row in range(10):
                  d_F_M += [[40]+add+[40]]
          d_F_M = np.array(d_F_M)
          d_F_M = d_F_M.astype(np.uint8)
          cost_matrix_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET)
          feature_img2 = cv2.resize(cost_matrix_img, (435, 435))
          #cv2.putText(feature_img2, "cost_matrix", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
      else:
          feature_img2 = np.zeros((435, 435))
          feature_img2 = feature_img2.astype(np.uint8)
          feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET)
          #cv2.putText(feature_img2, "cost_matrix", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
      feature_img = np.concatenate((feature_img, feature_img2), axis=1)
  
      dst_path = out_path + "/" + seq_num + "_" + num_zero[len(str(frame_id))-1] + str(frame_id) + '.png'
      cv2.imwrite(dst_path, feature_img)
  