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_size, root_dic, addtional_attribute_list,
predict_num):
"""
Params:
dets - 是所有检测物的numpy array, 为[[x1,y1,x2,y2,score],...]形式.
Requires: 每一检测的帧必须调用这个方法一次，即时没有检测到任何目标.
Returns：返回一个类似形式的numpy array,每一行的最后一个元素是目标的id.[[x1,y1,x2,y2,id],...]
NOTE:as in practical realtime MOT, the detector doesn't run on every single frame
"""
self.frame_count += 1
# 从已有的追踪器中预测位置.
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 != []:
# 使用匈牙利算法解决数据关联的问题
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])
try:
trk.face_addtional_attribute.append(
addtional_attribute_list[d[0]])
except Exception as e:
pass
# create and initialise new tr ackers for unmatched detections
# 为没有匹配的检测物创建和初始化一个新的追踪器
for i in unmatched_dets:
# 使用卡尔曼滤波解决动作预测的问题
trk = KalmanBoxTracker(dets[i, :])
trk.face_addtional_attribute.append(
addtional_attribute_list[i])
# 调用接口传送
logger.info("new Tracker: {0}".format(trk.id +
1)) # trk.id从零开始计数
utils.save_to_file(trk)
# 将新创建的追踪器添加到所有追踪器集合中
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]:
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))