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(img) #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],
img) ## for dlib re-intialize the trackers ?!
#create and initialise new trackers for unmatched detections
for i in unmatched_dets:
if not self.use_dlib:
trk = KalmanBoxTracker(dets[i, :])
else:
# print(dets[i,:])
trk = CorrelationTracker(dets[i, :], img)
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, 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
trks = np.zeros((len(self.trackers), 5))
"""
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
"""
to_del = []
ret = []
colors = []
#get predicted locations from existing trackers.
for t, trk in enumerate(trks):
pos = self.trackers[t].predict(img)
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],
img) ## for dlib re-intialize the trackers ?!
#create and initialise new trackers for unmatched detections
for i in unmatched_dets:
trk = CorrelationTracker(dets[i, :], img)
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))
colors.append(trk.color)
i -= 1
#remove dead tracklet
if (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)), []
def update(self,
dets,
img_size,
root_dic,
addtional_attribute_list,
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: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(img) # for kal!
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],
img) ## for dlib re-intialize the trackers ?!
#print('update dets[d, :]: ' + str(dets[d, :][0]))
#logger.info('update trk: {0}'.format(trk.id + 1) + str(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:
if not self.use_dlib:
#print('dets[i, :] : ' + str(i) + str(dets[i, :]) + ' ' + str(dets[i, :][0]))
trk = KalmanBoxTracker(dets[i, :])
trk.face_addtional_attribute.append(
addtional_attribute_list[i])
logger.info("new Tracker: {0}".format(trk.id + 1))
else:
trk = CorrelationTracker(dets[i, :], img)
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
#ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1))
i -= 1
# remove dead tracklet
#if (trk.time_since_update >= self.max_age or d[0] < 0 or d[1] < 0 or d[2] > img_size[1] or d[3] > img_size[0]):
if (trk.time_since_update >= self.max_age 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) >= 15):
#utils.save_to_file(root_dic, trk)
utils._save_to_file(root_dic, trk)
logger.info('remove tracker: {0}'.format(trk.id + 1) +
' time_since_update:' +
str(trk.time_since_update) + str(d))
self.trackers.pop(i)
if (len(ret) > 0):
return np.concatenate(ret)
return np.empty((0, 5))
font = cv2.FONT_HERSHEY_PLAIN
# create sequence object
sequence = VOTSequence(dataset_path, sequence)
init_frame = 0
n_failures = 0
# create parameters and tracker objects
# parameters = NCCParams()
# tracker = NCCTracker(parameters)
# Meanshift
parameters = MSParams()
# tracker = MeanShiftTracker(parameters)
# Correlation
tracker = CorrelationTracker(parameters)
time_all = 0
# initialize visualization window
sequence.initialize_window(win_name)
# tracking loop - goes over all frames in the video sequence
frame_idx = 0
while frame_idx < sequence.length():
img = cv2.imread(sequence.frame(frame_idx))
# initialize or track
if frame_idx == init_frame:
# initialize tracker (at the beginning of the sequence or after tracking failure)
t_ = time.time()
tracker.initialize(
img, sequence.get_annotation(frame_idx, type='rectangle'))
def add(self, dets, img, cowids):
for i, det in enumerate(dets):
trk = CorrelationTracker(det, img, cowids[i])
self.trackers.append(trk)
def begin(self, dets, img, cowids):
self.trackers = []
# print(dets)
for i, det in enumerate(dets):
trk = CorrelationTracker(det, img, cowids[i])
self.trackers.append(trk)