from collections import deque import torch import numpy as np from utils.kalman_filter import KalmanFilter from dev.utils.log import logger from models import * from tracker import matching from .basetrack import BaseTrack, TrackState class STrack(BaseTrack): 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, kalman_filter): 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.kalman_filter.multi_predict(multi_mean, multi_covariance) multi_mean, multi_covariance = kalman_filter.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 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 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 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 def tlbr_to_tlwh(tlbr): ret = np.asarray(tlbr).copy() ret[2:] -= ret[:2] return ret @staticmethod 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 self.model = Darknet(opt.cfg, nID=14455) # load_darknet_weights(self.model, opt.weights) self.model.load_state_dict(torch.load(opt.weights, map_location='cpu')['model'], strict=False) self.model.cuda().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.init_thresh = self.det_thresh + 0.2 self.low_thresh = 0.4 self.buffer_size = int(frame_rate / 30.0 * opt.track_buffer) self.max_time_lost = self.buffer_size self.kalman_filter = KalmanFilter() def update(self, im_blob, img0): """ Processes the image frame and finds bounding box(detections). Associates the detection with corresponding tracklets and also handles lost, removed, refound and active tracklets Parameters ---------- im_blob : torch.float32 Tensor of shape depending upon the size of image. By default, shape of this tensor is [1, 3, 608, 1088] img0 : ndarray ndarray of shape depending on the input image sequence. By default, shape is [608, 1080, 3] Returns ------- output_stracks : list of Strack(instances) The list contains information regarding the online_tracklets for the recieved image tensor. """ self.frame_id += 1 activated_starcks = [] # for storing active tracks, for the current frame refind_stracks = [] # Lost Tracks whose detections are obtained in the current frame lost_stracks = [] # The tracks which are not obtained in the current frame but are not removed.(Lost for some time lesser than the threshold for removing) removed_stracks = [] t1 = time.time() ''' Step 1: Network forward, get detections & embeddings''' with torch.no_grad(): pred = self.model(im_blob) # pred is tensor of all the proposals (default number of proposals: 54264). Proposals have information associated with the bounding box and embeddings pred = pred[pred[:, :, 4] > self.low_thresh] # pred now has lesser number of proposals. Proposals rejected on basis of object confidence score if len(pred) > 0: dets = non_max_suppression(pred.unsqueeze(0), self.low_thresh, self.opt.nms_thres)[0].cpu() # Final proposals are obtained in dets. Information of bounding box and embeddings also included # Next step changes the detection scales scale_coords(self.opt.img_size, dets[:, :4], img0.shape).round() '''Detections is list of (x1, y1, x2, y2, object_conf, class_score, class_pred)''' # class_pred is the embeddings. dets = dets.numpy() remain_inds = dets[:, 4] > self.det_thresh inds_low = dets[:, 4] > self.low_thresh inds_high = dets[:, 4] < self.det_thresh inds_second = np.logical_and(inds_low, inds_high) dets_second = dets[inds_second] dets = dets[remain_inds] detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for (tlbrs, f) in zip(dets[:, :5], dets[:, 6:])] else: detections = [] dets_second = [] t2 = time.time() # print('Forward: {} s'.format(t2-t1)) ''' Add newly detected tracklets to tracked_stracks''' unconfirmed = [] tracked_stracks = [] # type: list[STrack] for track in self.tracked_stracks: if not track.is_activated: # previous tracks which are not active in the current frame are added in unconfirmed list unconfirmed.append(track) # print("Should not be here, in unconfirmed") else: # Active tracks are added to the local list 'tracked_stracks' tracked_stracks.append(track) ''' Step 2: First association, with embedding''' # Combining currently tracked_stracks and lost_stracks strack_pool = joint_stracks(tracked_stracks, self.lost_stracks) # Predict the current location with KF STrack.multi_predict(strack_pool, self.kalman_filter) 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) # The dists is the list of distances of the detection with the tracks in strack_pool matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7) # The matches is the array for corresponding matches of the detection with the corresponding strack_pool for itracked, idet in matches: # itracked is the id of the track and idet is the detection track = strack_pool[itracked] det = detections[idet] if track.state == TrackState.Tracked: # If the track is active, add the detection to the track track.update(detections[idet], self.frame_id) activated_starcks.append(track) else: # We have obtained a detection from a track which is not active, hence put the track in refind_stracks list track.re_activate(det, self.frame_id, new_id=False) refind_stracks.append(track) # None of the steps below happen if there are no undetected tracks. ''' Step 3: Second association, with IOU''' detections = [detections[i] for i in u_detection] # detections is now a list of the unmatched detections r_tracked_stracks = [] # This is container for stracks which were tracked till the # previous frame but no detection was found for it in the current frame for i in u_track: if strack_pool[i].state == TrackState.Tracked: r_tracked_stracks.append(strack_pool[i]) dists = matching.iou_distance(r_tracked_stracks, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5) # matches is the list of detections which matched with corresponding tracks by IOU distance method 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) # Same process done for some unmatched detections, but now considering IOU_distance as measure # 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], dets_second[:, 6:])] 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) # If no detections are obtained for tracks (u_track), the tracks are added to lost_tracks list and are marked lost '''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]) # The tracks which are yet not matched for it in u_unconfirmed: track = unconfirmed[it] track.mark_removed() removed_stracks.append(track) # after all these confirmation steps, if a new detection is found, it is initialized for a new track """ Step 4: Init new stracks""" for inew in u_detection: track = detections[inew] if track.score < self.init_thresh: continue track.activate(self.kalman_filter, self.frame_id) activated_starcks.append(track) """ Step 5: Update state""" # If the tracks are lost for more frames than the threshold number, the tracks are removed. 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('Remained match {} s'.format(t4-t3)) # Update the self.tracked_stracks and self.lost_stracks using the updates in this step. 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 = [t for t in self.lost_stracks if t.state == TrackState.Lost] # type: list[STrack] 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])) # print('Final {} s'.format(t5-t4)) 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