def run_trackers(tracker, seqs, evalType):
numSeq = len(seqs)
seqResults = []
##################################################
# chose sequence to run from below
##################################################
for idxSeq in range(0, numSeq):
subS = seqs[idxSeq]
print('{0}:{1}, total frame: {2}'.format(
idxSeq + 1, subS.name, subS.endFrame - subS.startFrame))
if not OVERWRITE_RESULT:
trk_src = os.path.join(RESULT_SRC.format(evalType), tracker.name)
result_src = os.path.join(trk_src, subS.name + '.json')
if os.path.exists(result_src):
r = UAV_script.load_seq_result(result_src)
seqResults.append(r)
continue
####################
# we only re-run tracker with target size small than a threshold:
tracker, res = run_KCF_variant(tracker, subS, debug=False)
r = Result(tracker.name, subS.name, subS.startFrame, subS.endFrame,
res['type'], evalType, res['res'], res['fps'], None)
r.refresh_dict()
seqResults.append(r)
# end for subseqs
if SAVE_RESULT:
UAV_script.save_seq_result(RESULT_SRC, r)
return seqResults
def main():
if OVERWRITE_RESULT:
# tracker = KCFTracker(feature_type='multi_cnn', sub_feature_type='dsst',
# sub_sub_feature_type='adapted_lr', load_model=True, vgglayer='',
# model_path='./trained_models/CNN_Model_OBT100_multi_cnn_best_cifar_big_valid.h5',
# name_suffix='_best_valid_CNN')
tracker = KCFTracker(feature_type='multi_cnn', sub_feature_type='dnn_scale',
load_model=True,
model_path='./trained_models/CNN_Model_OBT100_multi_cnn_best_cifar_big_valid.h5',
name_suffix='_best_valid_CNN')
else:
tracker = Tracker(name='KCFmulti_cnn_dsst_adapted_lr_best_valid_CNN')
evalTypes = ['OPE']
if SETUP_SEQ:
print('Setup sequences ...')
Temple_color_script.setup_seqs(SRC_DIR)
print('Starting benchmark for trackers: {0}'.format(tracker.name))
for evalType in evalTypes:
seqNames = Temple_color_script.get_seq_names(SRC_DIR)
seqs = Temple_color_script.load_seq_configs(seqNames, SRC_DIR)
######################################################################
results = run_trackers(tracker, seqs, evalType)
######################################################################
if len(results) > 0:
######################################################################
evalResults, attrList = butil.calc_result(tracker, seqs, results, evalType, ANNO_DIR)
######################################################################
print ("Result of Sequences\t -- '{0}'".format(tracker.name))
for i, seq in enumerate(seqs):
try:
print('\t{0}:\'{1}\'{2}\taveCoverage : {3:.3f}%\taveErrCenter : {4:.3f}'.format(
i,
seq.name,
" " * (12 - len(seq.name)),
sum(seq.aveCoverage) / len(seq.aveCoverage) * 100,
sum(seq.aveErrCenter) / len(seq.aveErrCenter)))
except:
print('\t\'{0}\' ERROR!!'.format(seq.name))
print("Result of attributes\t -- '{0}'".format(tracker.name))
for attr in attrList:
print("\t\'{}\'\t overlap : {:04.2f}% \t\t failures : {:04.2f}".format(attr.name, attr.overlap, attr.error))
if SAVE_RESULT:
UAV_script.save_scores(attrList, RESULT_SRC)
def main():
tracker = Tracker(name='KCFmulti_cnn_dsst_adapted_lr_best_valid_CNN')
evalTypes = ['OPE']
if SETUP_SEQ:
print('Setup sequences ...')
Temple_color_script.setup_seqs(SRC_DIR)
print('Starting benchmark for trackers: {0}'.format(tracker.name))
for evalType in evalTypes:
seqNames = Temple_color_script.get_seq_names(SRC_DIR)
seqs = Temple_color_script.load_seq_configs(seqNames, SRC_DIR)
######################################################################
results = run_trackers(tracker, seqs, evalType)
######################################################################
if len(results) > 0:
######################################################################
evalResults, attrList = butil.calc_result(tracker, seqs, results,
evalType, ANNO_DIR)
######################################################################
print("Result of Sequences\t -- '{0}'".format(tracker.name))
for i, seq in enumerate(seqs):
try:
print(
'\t{0}:\'{1}\'{2}\taveCoverage : {3:.3f}%\taveErrCenter : {4:.3f}'
.format(
i, seq.name, " " * (12 - len(seq.name)),
sum(seq.aveCoverage) / len(seq.aveCoverage) * 100,
sum(seq.aveErrCenter) / len(seq.aveErrCenter)))
except:
print('\t\'{0}\' ERROR!!'.format(seq.name))
print("Result of attributes\t -- '{0}'".format(tracker.name))
for attr in attrList:
print(
"\t\'{}\'\t overlap : {:04.2f}% \t\t failures : {:04.2f}".
format(attr.name, attr.overlap, attr.error))
if SAVE_RESULT:
UAV_script.save_scores(attrList, RESULT_SRC)
def run_trackers(tracker, seqs, evalType):
numSeq = len(seqs)
seqResults = []
##################################################
# chose sequence to run from below
##################################################
for idxSeq in range(0, numSeq):
subS = seqs[idxSeq]
print('{0}:{1}, total frame: {2}'.format(idxSeq + 1, subS.name, subS.endFrame - subS.startFrame))
trk_src = os.path.join(RESULT_SRC.format(evalType), tracker.name)
result_src = os.path.join(trk_src, subS.name + '.json')
if os.path.exists(result_src):
r = UAV_script.load_seq_result(result_src)
seqResults.append(r)
continue
return seqResults
def main():
if OVERWRITE_RESULT:
# tracker = KCFTracker(feature_type='multi_cnn', sub_feature_type='dsst',
# sub_sub_feature_type='adapted_lr', load_model=True, vgglayer='',
#
# model_path='./trained_models/CNN_Model_OBT100_multi_cnn_best_cifar_big_valid.h5',
# name_suffix='_best_valid_CNN')
# tracker = KCFTracker(feature_type='multi_cnn', sub_feature_type='dsst',
# sub_sub_feature_type='adapted_lr', load_model=True, vgglayer='',
# model_path='./trained_models/CNN_Model_OBT100_multi_cnn_best_cifar_big_valid.h5',
# adaptation_rate_range_max=0.002,
# adaptation_rate_scale_range_max=0.005,
# saliency='grabcut', saliency_percent=0.5,
# grabcut_mask_path='../../UAV/UAV123_10fps/grab_cut_figures/',
# optical_flow=True)
tracker = KCFTracker(
feature_type='HDT',
sub_feature_type='dsst',
sub_sub_feature_type='adapted_lr',
load_model=True,
vgglayer='',
model_path=
'./trained_models/CNN_Model_OBT100_multi_cnn_best_cifar_big_valid.h5',
adaptation_rate_range_max=0.002,
adaptation_rate_scale_range_max=0.005,
saliency='grabcut',
saliency_percent=0.5,
grabcut_mask_path='../../UAV/UAV123_10fps/grab_cut_figures/',
optical_flow=True)
else:
tracker = Tracker(
name='KCFmulti_cnn_dsst_adapted_lr_grabcut_0.5_optical_flow')
# evalTypes = ['OPE', 'SRE', 'TRE']
evalTypes = ['OPE']
loadSeqs = 'UAV123'
if SETUP_SEQ:
print('Setup sequences ...')
UAV_script.setup_seqs(loadSeqs, SRC_DIR, ANNO_DIR, IMG_DIR)
print('Starting benchmark for trackers: {0}, evalTypes : {1}'.format(
tracker.name, evalTypes))
for evalType in evalTypes:
seqNames = UAV_script.get_seq_names(loadSeqs, ANNO_DIR)
seqs = UAV_script.load_seq_configs(seqNames, ANNO_DIR)
######################################################################
results = run_trackers(tracker, seqs, evalType)
######################################################################
if len(results) > 0:
######################################################################
evalResults, attrList = butil.calc_result(tracker, seqs, results,
evalType, SRC_DIR)
######################################################################
print("Result of Sequences\t -- '{0}'".format(tracker.name))
for i, seq in enumerate(seqs):
try:
print(
'\t{0}:\'{1}\'{2}\taveCoverage : {3:.3f}%\taveErrCenter : {4:.3f}'
.format(
i, seq.name, " " * (12 - len(seq.name)),
sum(seq.aveCoverage) / len(seq.aveCoverage) * 100,
sum(seq.aveErrCenter) / len(seq.aveErrCenter)))
except:
print('\t\'{0}\' ERROR!!'.format(seq.name))
print("Result of attributes\t -- '{0}'".format(tracker.name))
for attr in attrList:
print(
"\t\'{}\'\t overlap : {:04.2f}% \t\t failures : {:04.2f}".
format(attr.name, attr.overlap, attr.error))
if SAVE_RESULT:
UAV_script.save_scores(attrList, RESULT_SRC)
def main(argv):
trackers = [Tracker(name='SRDCF')]
#trackers = [Tracker(name='KCFmulti_cnn_dsst_adapted_lr_best_valid_CNN')]
evalTypes = ['OPE']
loadSeqs = 'UAV123'
try:
opts, args = getopt.getopt(argv, "ht:e:s:", ["tracker=", "evaltype=", "sequence="])
except getopt.GetoptError:
print('usage : run_trackers.py -t <trackers> -s <sequences>' + '-e <evaltypes>')
sys.exit(1)
for opt, arg in opts:
if opt == '-h':
print('usage : run_trackers.py -t <trackers> -s <sequences>' + '-e <evaltypes>')
sys.exit(0)
elif opt in ("-t", "--tracker"):
trackers = [x.strip() for x in arg.split(',')]
# trackers = [arg]
elif opt in ("-s", "--sequence"):
loadSeqs = arg
if loadSeqs != 'All' and loadSeqs != 'all' and \
loadSeqs != 'tb50' and loadSeqs != 'tb100' and \
loadSeqs != 'cvpr13':
loadSeqs = [x.strip() for x in arg.split(',')]
elif opt in ("-e", "--evaltype"):
evalTypes = [x.strip() for x in arg.split(',')]
print('Starting benchmark for {0} trackers, evalTypes : {1}'.format(
len(trackers), evalTypes))
for evalType in evalTypes:
seqNames = UAV_script.get_seq_names(loadSeqs, ANNO_DIR)
seqs = UAV_script.load_seq_configs(seqNames, ANNO_DIR)
######################################################################
trackerResults = run_trackers(trackers, seqs, evalType)
######################################################################
for tracker in trackers:
results = trackerResults[tracker]
if len(results) > 0:
######################################################################
evalResults, attrList = butil.calc_result(tracker, seqs, results, evalType, SRC_DIR)
######################################################################
print ("Result of Sequences\t -- '{0}'".format(tracker.name))
for i, seq in enumerate(seqs):
try:
print('\t{0}:\'{1}\'{2}\taveCoverage : {3:.3f}%\taveErrCenter : {4:.3f}'.format(
i,
seq.name,
" " * (12 - len(seq.name)),
sum(seq.aveCoverage) / len(seq.aveCoverage) * 100,
sum(seq.aveErrCenter) / len(seq.aveErrCenter)))
except:
print('\t\'{0}\' ERROR!!'.format(seq.name))
print("Result of attributes\t -- '{0}'".format(tracker.name))
for attr in attrList:
print("\t\'{}\'\t overlap : {:04.2f}% \t\t failures : {:04.2f}".format(attr.name, attr.overlap, attr.error))
if SAVE_RESULT:
butil.save_scores(attrList)