def associate_detections_to_trackers(bbox_points, features, track_boxs, tracker_features, iou_threshold=0.3):
result = bbox_points
iou_matrix = np.zeros((len(bbox_points), len(track_boxs)), dtype=np.float32)
# pose_matrix = np.zeros((len(bbox_points),len(track_boxs)),dtype=np.float32)
feat_matrix = np.zeros((len(bbox_points), len(track_boxs)), dtype=np.float32)
# dists = matching.iou_distance(bbox_points, track_boxs)
for d in range(len(bbox_points)):
for t in range(len(track_boxs)):
# pose_matrix[d,t] = get_pose_matching_score(bbox_points[d], track_boxs[t], trackers[t])
iou_matrix[d,t] = iou(bbox_points[d],track_boxs[t])
# feat_matrix[d,t] = euclidean_dist(features[d].unsqueeze(0), tracker_features[t].unsqueeze(0))
dists = -iou_matrix
matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7)
for cur, pre in matches:
# print(cur,pre)
result[cur] = track_boxs[pre]
return result
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]
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,
cat_spec_wh=self.opt.cat_spec_wh,
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]
# vis
'''
for i in range(0, dets.shape[0]):
bbox = dets[i][0:4]
cv2.rectangle(img0, (bbox[0], bbox[1]),
(bbox[2], bbox[3]),
(0, 255, 0), 2)
cv2.imshow('dets', img0)
cv2.waitKey(0)
id0 = id0-1
'''
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)
# 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.gate_cost_matrix(self.kalman_filter, dists, strack_pool, detections)
dists = matching.fuse_motion(self.kalman_filter, dists, strack_pool,
detections)
matches, u_track, u_detection = matching.linear_assignment(dists,
thresh=0.7)
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)
'''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
]
print('===========Frame {}=========='.format(self.frame_id))
print('Activated: {}'.format(
[track.track_id for track in activated_starcks]))
print('Refind: {}'.format([track.track_id
for track in refind_stracks]))
print('Lost: {}'.format([track.track_id for track in lost_stracks]))
print('Removed: {}'.format(
[track.track_id for track in removed_stracks]))
return output_stracks
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]
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'''
output = self.model.execute(im_blob)
# sigmoid
hm = 1 / (1 + np.exp(-output[0]))
wh = output[1]
reg = output[2]
id_feature = output[3]
'''
dets: n * 6 matrix where n is number of detections
6 elements:
bbox_top_left x, y; bbox_bottom_right x, y; conf_score; class
inds: indices of detection in flatten array (152*272)
'''
dets, inds = mot_decode(hm, wh, reg, conf_thres=self.opt.conf_thres)
# n * 128, where 128 is id feature vector size
id_feature = get_feat_from_idx(id_feature, inds)
# scale change and remove detections with small bbox
dets = self.post_process(dets, meta)
dets = self.merge_outputs([dets])[1]
if len(dets) > 0:
'''Detections'''
# dets[:, :5]: bboxes (4), score (1)
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)
# Predict the current location with KF
STrack.multi_predict(strack_pool)
# strack_pool: previous tracklets
# detections: current tracklets
dists = matching.embedding_distance(strack_pool, detections)
#dists = matching.iou_distance(strack_pool, detections)
dists = matching.fuse_motion(self.kalman_filter, dists, strack_pool,
detections)
# matches: [idx of tracked_stracks, idx of detections]
# u_track: [index of undefined track]
# u_detection: [index of undefined detection]
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)
'''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
def match(self, img, detections, features, frm_id):
# get track state
is_lost_arr = np.array([track.is_lost for track in self.tracks])
tracked_track_idx = np.where(is_lost_arr)[0]
lost_track_idx = np.where(is_lost_arr == False)[0]
tracked_stracks = map(lambda x: self.tracks[x], tracked_track_idx)
lost_stracks = map(lambda x: self.tracks[x], lost_track_idx)
print len(self.tracks)
self.tracks = []
# first match, we match active track with detection
## 1 匹配跟踪的轨迹
dists = matching.nearest_reid_distance(tracked_stracks,
detections,
features,
metric='euclidean')
# dist不参与运算 只是起到gating的作用 滤除过长的轨迹
dists = matching.gate_cost_matrix(self.kf, dists, tracked_stracks,
detections)
# match has format (track_id, det_id)
matches, u_track, u_detection = matching.linear_assignment(
dists, thresh=self.min_ap_dist)
for itracked, idet in matches:
print "first match is ", tracked_stracks[itracked].track_id
tracked_stracks[itracked].update(detections[idet], features[idet],
frm_id, self.kf)
self.tracks.append(tracked_stracks[itracked])
detections = [detections[idet] for idet in u_detection]
features = [features[idet] for idet in u_detection]
## 2 匹配消失的轨迹
dists = matching.nearest_reid_distance(lost_stracks,
detections,
features,
metric='euclidean')
dists = matching.gate_cost_matrix(self.kf, dists, lost_stracks,
detections)
matches, u_lost, u_detection = matching.linear_assignment(
dists, thresh=self.min_ap_dist)
for itracked, idet in matches:
print "second match is ", lost_stracks[itracked].track_id
lost_stracks[itracked].update(detections[idet], features[idet],
frm_id, self.kf)
self.tracks.append(lost_stracks[itracked])
## 3 1中未匹配的轨迹继续匹配
## 匹配方式改为IOU匹配
detections = [detections[i] for i in u_detection]
features = [features[idet] for idet in u_detection]
r_tracked_stracks = [tracked_stracks[idx] for idx in u_track]
dists = matching.iou_distance(r_tracked_stracks, detections)
matches, u_track, u_detection = matching.linear_assignment(dists,
thresh=0.7)
for itracked, idet in matches:
print "third match is ", r_tracked_stracks[itracked].track_id
r_tracked_stracks[itracked].update(detections[idet],
features[idet], frm_id, self.kf)
self.tracks.append(r_tracked_stracks[itracked])
# 未匹配到的更新
for idx in u_track:
r_tracked_stracks[idx].update(frm_id=frm_id, kalman_filter=self.kf)
self.tracks.append(r_tracked_stracks[idx])
detections = [detections[i] for i in u_detection]
features = [features[idet] for idet in u_detection]
## 4 2中未匹配的轨迹继续匹配
r_lost_stracks = [lost_stracks[idx] for idx in u_lost]
dists = matching.iou_distance(r_lost_stracks, detections)
matches, u_unconfirmed, u_detection = matching.linear_assignment(
dists, thresh=0.7)
for itracked, idet in matches:
print "forth match is ", r_lost_stracks[itracked].track_id
r_lost_stracks[itracked].update(detections[idet], features[idet],
frm_id, self.kf)
self.tracks.append(r_lost_stracks[itracked])
# check u_unconfirmed and delete it if satisfy
for idx in u_unconfirmed:
r_lost_stracks[idx].update(frm_id=frm_id, kalman_filter=self.kf)
if r_lost_stracks[idx].age >= P['max_lost_track_time']:
continue
self.tracks.append(r_lost_stracks[idx])
detections = [detections[i] for i in u_detection]
features = [features[idet] for idet in u_detection]
# 生成新的轨迹
for det, fea in zip(detections, features):
self.tracks.append(
BaseTrack(det, fea, frm_id, self.track_id, self.kf))
self.track_id += 1
def update(self, detections=None):
self.frame_id += 1
activated_tracks = []
refind_tracks = []
lost_tracks = []
removed_tracks = []
if not detections: # If detections None
detections = []
''' Add newly detected tracklets to tracked_tracks'''
unconfirmed = []
tracked_tracks = [] # type: list[Track]
for track in self.tracked_tracks:
if not track.is_activated:
unconfirmed.append(track)
else:
tracked_tracks.append(track)
''' Matching with embedding'''
track_pool = join_tracklists(tracked_tracks, self.lost_tracks)
# Predict location with Kalman Filter
# for track in track_pool:
# track.predict() #Predict Individually each track
Track.multi_predict(track_pool) # Predict Together all Tracks
# Get Embedding Distance
dists = embedding_distance(track_pool, detections)
# dists = gate_cost_matrix(self.kalman_filter, dists, track_pool, detections)
dists = fuse_motion(
self.kalman_filter, dists, track_pool, detections)
matches, u_track, u_detection = linear_assignment(
dists, thresh=0.7)
for track_idx, det_idx in matches:
track = track_pool[track_idx]
det = detections[det_idx]
if self.lsh:
self.lsh.index(det.curr_feat, track.track_id) # LSH Indexing
if track.state == TrackState.Tracked:
track.update(detections[det_idx], self.frame_id)
activated_tracks.append(track)
else:
track.re_activate(det, self.frame_id, new_id=False)
refind_tracks.append(track)
'''LSH Similarity - Higher Effect'''
if self.lsh and self.lsh_mode == 2:
unmatchedDetections = [(detections[i], i) for i in u_detection]
unmatchedTracks = [(track_pool[j], j)
for j in u_track if track_pool[j].state == TrackState.Tracked]
matches, u_track, u_detection = checklshash(
self.lsh, unmatchedDetections, unmatchedTracks)
for track_idx, det_idx in matches:
track = [t for t, i in unmatchedTracks if i == track_idx][0]
det = detections[det_idx]
self.lsh.index(det.curr_feat, track.track_id) # LSH Indexing
if track.state == TrackState.Tracked:
track.update(det, self.frame_id)
activated_tracks.append(track)
else:
track.re_activate(det, self.frame_id, new_id=False)
refind_tracks.append(track)
''' Matching with IOU'''
detections = [detections[i] for i in u_detection]
r_tracked_tracks = [track_pool[i]
for i in u_track if track_pool[i].state == TrackState.Tracked]
dists = iou_distance(r_tracked_tracks, detections)
matches, u_track, u_detection = linear_assignment(
dists, thresh=0.5)
for track_idx, det_idx in matches:
track = r_tracked_tracks[track_idx]
det = detections[det_idx]
if self.lsh:
self.lsh.index(det.curr_feat, track.track_id) # LSH Indexing
if track.state == TrackState.Tracked:
track.update(det, self.frame_id)
activated_tracks.append(track)
else:
track.re_activate(det, self.frame_id, new_id=False)
refind_tracks.append(track)
'''LSH Similarity - Reduced Effect'''
if self.lsh and self.lsh_mode == 1:
unmatchedDetections = [(detections[i], i) for i in u_detection]
unmatchedTracks = [(r_tracked_tracks[j], j) for j in u_track]
matches, u_unconfirmed, u_detection = checklshash(
self.lsh, unmatchedDetections, unmatchedTracks)
for track_idx, det_idx in matches:
track = [t for t, i in unmatchedTracks if i == track_idx][0]
det = detections[det_idx]
self.lsh.index(det.curr_feat, track.track_id) # LSH Indexing
if track.state == TrackState.Tracked:
track.update(det, self.frame_id)
activated_tracks.append(track)
else:
track.re_activate(det, self.frame_id, new_id=False)
refind_tracks.append(track)
# Mark unmatched tracks as lost
for it in u_track:
track = r_tracked_tracks[it]
if not track.state == TrackState.Lost:
track.mark_lost()
lost_tracks.append(track)
'''New unconfirmed tracks'''
detections = [detections[i] for i in u_detection]
dists = iou_distance(unconfirmed, detections)
matches, u_unconfirmed, u_detection = linear_assignment(
dists, thresh=0.7)
for track_idx, det_idx in matches:
unconfirmed[track_idx].update(detections[det_idx], self.frame_id)
activated_tracks.append(unconfirmed[track_idx])
'''Remove unconfirmed tracks'''
for it in u_unconfirmed:
track = unconfirmed[it]
track.mark_removed()
removed_tracks.append(track)
""" Initialise new tracks"""
for inew in u_detection:
track = detections[inew]
if track.score < self.det_thresh:
continue
track.activate(self.kalman_filter, self.frame_id)
activated_tracks.append(track)
""" Mark Track as lost"""
for track in self.lost_tracks:
if self.frame_id - track.end_frame > self.max_time_lost:
track.mark_removed()
removed_tracks.append(track)
# print('Ramained match {} s'.format(t4-t3))
self.tracked_tracks = [
t for t in self.tracked_tracks if t.state == TrackState.Tracked]
self.tracked_tracks = join_tracklists(
self.tracked_tracks, activated_tracks)
self.tracked_tracks = join_tracklists(
self.tracked_tracks, refind_tracks)
self.lost_tracks = remove_from_tracklists(
self.lost_tracks, self.tracked_tracks)
self.lost_tracks.extend(lost_tracks)
self.lost_tracks = remove_from_tracklists(
self.lost_tracks, self.removed_tracks)
self.removed_tracks.extend(removed_tracks)
self.tracked_tracks, self.lost_tracks = remove_duplicate_tracks(
self.tracked_tracks, self.lost_tracks)
# get scores of lost tracks
output_tracks = [
track for track in self.tracked_tracks if track.is_activated]
logger.debug(
'-----------Frame No. {}-----------'.format(self.frame_id))
logger.debug('Active: {}'.format(
[track.track_id for track in activated_tracks]))
logger.debug('ReFound: {}'.format(
[track.track_id for track in refind_tracks]))
logger.debug('Lost: {}'.format(
[track.track_id for track in lost_tracks]))
logger.debug('Deleted: {}'.format(
[track.track_id for track in removed_tracks]))
return output_tracks