def initOut(self):
print ' Loading Data...'
self.train_set = DatasetFromFolder(sequence_dir, 'MOT16/test/MOT16-%02d'%self.seq_index)
detection_dir = self.out_dir +'res_training_det.txt'
res_training = self.out_dir + 'res_training.txt' # the result of the training data
self.createTxt(detection_dir)
self.createTxt(res_training)
self.copyLines(self.seq_index, 1, detection_dir, self.tt, 1)
self.evaluation(1, self.tt, detection_dir, res_training)
def initOut(self):
print(' Loading Data...')
self.train_set = DatasetFromFolder(sequence_dir, mot_dataset_dir + 'MOT16/test/MOT16-%02d' % self.seq_index,
tau_conf_score)
detection_dir = self.out_dir + 'res_det.txt'
res_training = self.out_dir + 'res.txt' # the tracking results
self.createTxt(detection_dir)
self.createTxt(res_training)
self.copyLines(self.seq_index, 1, detection_dir, self.seq_len, 1)
self.evaluation(1, self.seq_len, detection_dir, res_training)
def initOut(self):
start = time.time()
print ' Loading Data...'
print ' Training'
self.train_set = DatasetFromFolder(sequence_dir)
gt_training = self.out_dir + 'gt_training.txt' # the gt of the training data
self.copyLines(self.seq_index, 1, gt_training, self.tt)
detection_dir = self.out_dir + 'res_training_det.txt'
res_training = self.out_dir + 'res_training.txt' # the result of the training data
self.createTxt(detection_dir)
self.createTxt(res_training)
self.copyLines(self.seq_index, 1, detection_dir, self.tt, 1)
# Evaluating on the training data
# motmetrics = open(metrics_dir, 'a')
# print >> motmetrics, '*'*30, self.tt, '*'*30
# print >> motmetrics, 'Training'
self.evaluation(1, self.tt, detection_dir, res_training)
print ' Time consuming:', (time.time() - start) / 60.0
# cmd = 'python3 evaluation.py %s %s'%(gt_training, res_training)
# (status, output) = commands.getstatusoutput(cmd)
# print >> motmetrics, output
# print >> motmetrics, 'The time consuming:{}\n\n'.format((time.time()-start)/60)
# motmetrics.close()
if self.tt < self.length:
# Evaluating on the validation data
start = time.time()
print ' Validation'
# The distant sequence
head = self.length - self.tt + 1
tail = self.length
# The sequence nearby
# head = self.tt
# tail = 2*self.tt-1
gt_valiadation = self.out_dir + 'gt_validation.txt' # the gt of the validation data
self.copyLines(self.seq_index, head, gt_valiadation, tail)
detection_dir = self.out_dir + 'res_validation_det.txt'
res_validation = self.out_dir + 'res_validation.txt' # the result of the validation data
self.createTxt(detection_dir)
self.createTxt(res_validation)
self.copyLines(self.seq_index, head, detection_dir, tail, 1)
# motmetrics = open(metrics_dir, 'a')
# print >> motmetrics, 'Validation'
self.evaluation(head, tail, detection_dir, res_validation)
print ' Time consuming:', (time.time() - start) / 60.0
# cmd = 'python3 evaluation.py %s %s'%(gt_valiadation, res_validation)
# (status, output) = commands.getstatusoutput(cmd)
# print >> motmetrics, output
# print >> motmetrics, 'The time consuming:{}\n\n'.format((time.time()-start)/60)
# motmetrics.close()
else:
# Evaluating on the validation data
for seq in seqs:
if seq == self.seq_index:
continue
print ' %02d_Validation' % seq
start = time.time()
seq_dir = 'MOT16/train/MOT%d-%02d' % (year, seq)
self.train_set = DatasetFromFolder(seq_dir)
gt_seq = self.out_dir + 'gt_%02d.txt' % seq
seqL = self.copyLines(seq, 1, gt_seq)
detection_dir = self.out_dir + 'res_%02d_det.txt' % seq
c_validation = self.out_dir + 'res_%02d.txt' % seq
self.createTxt(detection_dir)
self.createTxt(c_validation)
self.copyLines(seq, 1, detection_dir, tag=1)
# motmetrics = open(metrics_dir, 'a')
# print >> motmetrics, '%02d_validation'%seq
self.evaluation(1, seqL, detection_dir, c_validation)
print ' Time consuming:', (time.time() - start) / 60.0