def update(self, dets, img_size, root_dic, addtional_attribute_list, predict_num):
"""
Params:
dets - a numpy array of detections in the format [[x,y,w,h,score],[x,y,w,h,score],...]
Requires: this method must be called once for each frame even with empty detections.
Returns the a similar array, where the last column is the object ID.
NOTE:as in practical realtime MOT, the detector doesn't run on every single frame
"""
self.frame_count += 1
# get predicted locations from existing trackers.
trks = np.zeros((len(self.trackers), 5))
to_del = []
ret = []
for t, trk in enumerate(trks):
pos = self.trackers[t].predict() # kalman predict ,very fast ,<1ms
trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
if np.any(np.isnan(pos)):
to_del.append(t)
trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
for t in reversed(to_del):
self.trackers.pop(t)
if dets != []:
matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets, trks)
# update matched trackers with assigned detections
for t, trk in enumerate(self.trackers):
if t not in unmatched_trks:
d = matched[np.where(matched[:, 1] == t)[0], 0]
trk.update(dets[d, :][0])
trk.face_addtional_attribute.append(addtional_attribute_list[d[0]])
# create and initialise new trackers for unmatched detections
for i in unmatched_dets:
trk = KalmanBoxTracker(dets[i, :])
trk.face_addtional_attribute.append(addtional_attribute_list[i])
logger.info("new Tracker: {0}".format(trk.id + 1))
self.trackers.append(trk)
i = len(self.trackers)
for trk in reversed(self.trackers):
if dets == []:
trk.update([])
d = trk.get_state()
if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):
ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1)) # +1 as MOT benchmark requires positive
i -= 1
# remove dead tracklet
if trk.time_since_update >= self.max_age or trk.predict_num >= predict_num or d[2] < 0 or d[3] < 0 or d[0] > img_size[1] or d[1] > img_size[0]:
if len(trk.face_addtional_attribute) >= 5:
utils.save_to_file(root_dic, trk)
logger.info('remove tracker: {0}'.format(trk.id + 1))
self.trackers.pop(i)
if len(ret) > 0:
return np.concatenate(ret)
return np.empty((0, 5))
def update(self, dets, img=None):
"""
Params:
dets - a numpy array of detections in the format [[x,y,w,h,score],[x,y,w,h,score],...]
Requires: this method must be called once for each frame even with empty detections.
Returns the a similar array, where the last column is the object ID.
NOTE: The number of objects returned may differ from the number of detections provided.
"""
self.frame_count += 1
# get predicted locations from existing trackers.
trks = np.zeros((len(self.trackers), 5))
to_del = []
ret = []
for t, trk in enumerate(trks):
pos = self.trackers[t].predict() # for kal!
# print(pos)
trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
if (np.any(np.isnan(pos))):
to_del.append(t)
trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
for t in reversed(to_del):
self.trackers.pop(t)
if dets != []:
matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(
dets, trks)
# update matched trackers with assigned detections
for t, trk in enumerate(self.trackers):
if t not in unmatched_trks:
d = matched[np.where(matched[:, 1] == t)[0], 0]
trk.update(dets[d, :][0])
# create and initialise new trackers for unmatched detections
for i in unmatched_dets:
trk = KalmanBoxTracker(dets[i, :])
self.trackers.append(trk)
i = len(self.trackers)
for trk in reversed(self.trackers):
if dets == []:
trk.update([], img)
d = trk.get_state()
if (trk.time_since_update <
1) and (trk.hit_streak >= self.min_hits
or self.frame_count <= self.min_hits):
ret.append(np.concatenate((d, [trk.id + 1])).reshape(
1, -1)) # +1 as MOT benchmark requires positive
i -= 1
# remove dead tracklet
if trk.time_since_update > self.max_age:
self.trackers.pop(i)
if len(ret) > 0:
return np.concatenate(ret)
return np.empty((0, 5))
def update(self,dets):
"""
Params:
dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
Requires: this method must be called once for each frame even with empty detections.
Returns the a similar array, where the last column is the object ID.
NOTE: The number of objects returned may differ from the number of detections provided.
"""
if dets==[] and self.trackers==[]:
return []
self.frame_count += 1
#get predicted locations from existing trackers.
trks = np.zeros((len(self.trackers),5))
to_del = []
ret = []
for t,trk in enumerate(trks):
pos = self.trackers[t].predict()[0]
trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
if(np.any(np.isnan(pos))):
to_del.append(t)
trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
for t in reversed(to_del):
self.trackers.pop(t)
matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets,trks)
#update matched trackers with assigned detections
for t,trk in enumerate(self.trackers):
if(t not in unmatched_trks):
d = matched[np.where(matched[:,1]==t)[0],0]
trk.update(dets[d,:][0])
#create and initialise new trackers for unmatched detections
for i in unmatched_dets:
trk = KalmanBoxTracker(dets[i,:],self.classlabel)
self.trackers.append(trk)
i = len(self.trackers)
for trk in reversed(self.trackers):
d = trk.get_state()[0]
i -= 1
if((trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits )):
ret.append(d.tolist()+[trk.id])
#remove dead tracklet
elif(trk.time_since_update > self.max_age):
self.trackers.pop(i)
else:
ret.append(d.tolist()+[trk.id])
if(len(ret)>0):
return ret
return []
def update(self,dets):
"""
Params:
dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
Requires: this method must be called once for each frame even with empty detections.
Returns the a similar array, where the last column is the object ID.
NOTE: The number of objects returned may differ from the number of detections provided.
"""
self.frame_count += 1
"""
trks : array of trackers' position with the score of each tracker
to_del : array that contain indices of trackers need to be deleted as they are invalid
ret : array of returned trackers [pos,id]
colors : list of colors of the bounding boxes
"""
trks = np.zeros((len(self.trackers),5))
to_del = []
ret = []
colors=[]
#get predicted locations from existing trackers.
for t,trk in enumerate(trks):
pos = self.trackers[t].predict()[0]
trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
if(np.any(np.isnan(pos))):
to_del.append(t)
trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
for t in reversed(to_del):
self.trackers.pop(t)
#Compare detections to trackers and fil the matched, unmatched_dets, unmatched_trks lists
matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets,trks)
#update matched trackers with assigned detections
for t,trk in enumerate(self.trackers):
if(t not in unmatched_trks):
d = matched[np.where(matched[:,1]==t)[0],0]
trk.update(dets[d,:][0])
#create and initialise new trackers for unmatched detections
for i in unmatched_dets:
trk = KalmanBoxTracker(dets[i,:])
self.trackers.append(trk)
i = len(self.trackers)
for trk in reversed(self.trackers):
if dets == []:
trk.update([],img)
d = trk.get_state()[0]
i -= 1
if((trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits )):
ret.append(np.concatenate((d,[trk.id+1])).reshape(1,-1))
colors.append(trk.color)
#remove dead tracklet
elif(trk.time_since_update > self.max_age):
self.trackers.pop(i)
else:
ret.append(np.concatenate((d,[trk.id+1])).reshape(1,-1))
colors.append(trk.color)
if(len(ret)>0):
return np.concatenate(ret),colors
return np.empty((0,5)),[]