# The implementation is adopted from FairMOT, # made publicly available under the MIT License at https://github.com/ifzhang/FairMOT from collections import deque import numpy as np import torch import torch.nn.functional as F from modelscope.models.cv.video_multi_object_tracking.models.decode import \ mot_decode from modelscope.models.cv.video_multi_object_tracking.models.model import ( create_model, load_model) from modelscope.models.cv.video_multi_object_tracking.tracker import matching from modelscope.models.cv.video_multi_object_tracking.tracker.basetrack import ( BaseTrack, TrackState) from modelscope.models.cv.video_multi_object_tracking.utils.kalman_filter import \ KalmanFilter from modelscope.models.cv.video_multi_object_tracking.utils.utils import ( _tranpose_and_gather_feat, ctdet_post_process) from modelscope.utils.logger import get_logger logger = get_logger() class STrack(BaseTrack): shared_kalman = KalmanFilter() def __init__(self, tlwh, score, temp_feat, buffer_size=30): # wait activate self._tlwh = np.asarray(tlwh, dtype=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): 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.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 Args: new_track: STrack frame_id: int update_feature: bool """ 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, model_path, device): self.opt = opt self.device = device self.model = create_model(opt.arch, opt.heads, opt.head_conv) self.model = load_model(self.model, model_path) self.model = self.model.to(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.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() def set_buffer_len(self, frame_rate): self.buffer_size = int(frame_rate / 30.0 * self.opt.track_buffer) self.max_time_lost = self.buffer_size 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): 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] if self.device.type == 'cuda': im_blob = im_blob.cuda() 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 with torch.no_grad(): output = self.model(im_blob)[-1] hm = output['hm'].sigmoid_() wh = output['wh'] id_feature = output['id'] id_feature = F.normalize(id_feature, dim=1) reg = output['reg'] if self.opt.reg_offset else None dets, inds = mot_decode( hm, wh, reg=reg, ltrb=self.opt.ltrb, K=self.opt.K) id_feature = _tranpose_and_gather_feat(id_feature, inds) id_feature = id_feature.squeeze(0) id_feature = id_feature.cpu().numpy() dets = self.post_process(dets, meta) dets = self.merge_outputs([dets])[1] remain_inds = dets[:, 4] > self.opt.conf_thres dets = dets[remain_inds] id_feature = id_feature[remain_inds] 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) 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=0.4) 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) 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) 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) output_stracks = [ track for track in self.tracked_stracks if track.is_activated ] 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 i not in dupa] resb = [t for i, t in enumerate(stracksb) if i not in dupb] return resa, resb