(self, output_results, img_info, img_size)
| 157 | self.kalman_filter = KalmanFilter() |
| 158 | |
| 159 | def update(self, output_results, img_info, img_size): |
| 160 | self.frame_id += 1 |
| 161 | activated_starcks = [] |
| 162 | refind_stracks = [] |
| 163 | lost_stracks = [] |
| 164 | removed_stracks = [] |
| 165 | |
| 166 | if output_results.shape[1] == 5: |
| 167 | scores = output_results[:, 4] |
| 168 | bboxes = output_results[:, :4] |
| 169 | else: |
| 170 | output_results = output_results.cpu().numpy() |
| 171 | scores = output_results[:, 4] * output_results[:, 5] |
| 172 | bboxes = output_results[:, :4] # x1y1x2y2 |
| 173 | img_h, img_w = img_info[0], img_info[1] |
| 174 | scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w)) |
| 175 | bboxes /= scale |
| 176 | |
| 177 | remain_inds = scores > self.args.track_thresh |
| 178 | inds_low = scores > 0.1 |
| 179 | inds_high = scores < self.args.track_thresh |
| 180 | |
| 181 | inds_second = np.logical_and(inds_low, inds_high) |
| 182 | dets_second = bboxes[inds_second] |
| 183 | dets = bboxes[remain_inds] |
| 184 | scores_keep = scores[remain_inds] |
| 185 | scores_second = scores[inds_second] |
| 186 | |
| 187 | if len(dets) > 0: |
| 188 | '''Detections''' |
| 189 | detections = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for |
| 190 | (tlbr, s) in zip(dets, scores_keep)] |
| 191 | else: |
| 192 | detections = [] |
| 193 | |
| 194 | ''' Add newly detected tracklets to tracked_stracks''' |
| 195 | unconfirmed = [] |
| 196 | tracked_stracks = [] # type: list[STrack] |
| 197 | for track in self.tracked_stracks: |
| 198 | if not track.is_activated: |
| 199 | unconfirmed.append(track) |
| 200 | else: |
| 201 | tracked_stracks.append(track) |
| 202 | |
| 203 | ''' Step 2: First association, with high score detection boxes''' |
| 204 | strack_pool = joint_stracks(tracked_stracks, self.lost_stracks) |
| 205 | # Predict the current location with KF |
| 206 | STrack.multi_predict(strack_pool) |
| 207 | dists = matching.iou_distance(strack_pool, detections) |
| 208 | if not self.args.mot20: |
| 209 | dists = matching.fuse_score(dists, detections) |
| 210 | matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.args.match_thresh) |
| 211 | |
| 212 | for itracked, idet in matches: |
| 213 | track = strack_pool[itracked] |
| 214 | det = detections[idet] |
| 215 | if track.state == TrackState.Tracked: |
| 216 | track.update(detections[idet], self.frame_id) |
no test coverage detected