def vot_lt_2020_local4():
trackers = trackerlist('dimp', 'prdimp18', None)+ \
trackerlist('dimp', 'prdimp50', None)+ \
trackerlist('dimp', 'super_dimp', None)
dataset = get_dataset('vot')
return trackers, dataset
def eval_atom_compression_lasot_trackingnet():
# run atom_cfkd on LaSOT, TrackingNet, VOT2018
trackers = trackerlist('atom', 'atom_compression')
dataset = get_dataset('lasot', 'trackingnet')
return trackers, dataset
def main():
parser = argparse.ArgumentParser(
description='Generate success and precision plots')
parser.add_argument('tracker_name', type=str)
parser.add_argument('tracker_param', type=str)
parser.add_argument('--run_id', type=int, default=None)
parser.add_argument('--dataset', type=str)
args = parser.parse_args()
trackers = []
display_name = args.tracker_param + str(args.run_id)
trackers.extend(
trackerlist(args.tracker_name, args.tracker_param, args.run_id,
display_name))
dataset = get_dataset(args.dataset)
report_name = args.tracker_param + '_' + args.dataset
plot_results(trackers,
dataset,
report_name,
merge_results=True,
plot_types=('success', 'prec'),
skip_missing_seq=True,
force_evaluation=True,
plot_bin_gap=0.05,
exclude_invalid_frames=False)
def eval_drnet_cfkd_lasot_trackingnet():
# run atom_cfkd on LaSOT, TrackingNet, VOT2018
trackers = trackerlist('drnet', 'drnet_cfkd')
dataset = get_dataset('lasot', 'trackingnet')
return trackers, dataset
def run_tracker(tracker_name,
tracker_param,
run_id=None,
dataset_name='otb',
sequence=None,
debug=0,
threads=0,
visdom_info=None):
"""Run tracker on sequence or dataset.
args:
tracker_name: Name of tracking method.
tracker_param: Name of parameter file.
run_id: The run id.
dataset_name: Name of dataset (otb, nfs, uav, tpl, vot, tn, gott, gotv, lasot).
sequence: Sequence number or name.
debug: Debug level.
threads: Number of threads.
visdom_info: Dict optionally containing 'use_visdom', 'server' and 'port' for Visdom visualization.
"""
visdom_info = {} if visdom_info is None else visdom_info
dataset = get_dataset(dataset_name)
if sequence is not None:
dataset = [dataset[sequence]]
trackers = [Tracker(tracker_name, tracker_param, run_id)]
run_dataset(dataset, trackers, debug, threads, visdom_info=visdom_info)
def transt_readout_test_encoder_mult_from_front():
trackers = trackerlist('transt_readout_test_encoder_mult_from_front',
'default', range(1))
dataset = get_dataset(
'got10k_test') # , 'lasot') # _val, 'got10k_ltrval')
# dataset = get_dataset('got10k_test') # _val, 'got10k_ltrval')
return trackers, dataset
def transt_readout_test_encoder_mult_control():
trackers = trackerlist('transt_circuit_control', 'default', range(1))
# dataset = get_dataset('got10k_test', 'lasot') # _val, 'got10k_ltrval')
# dataset = get_dataset('lasot') # _val, 'got10k_ltrval')
# dataset = get_dataset('got10k_test') # _val, 'got10k_ltrval')
dataset = get_dataset('got10k_test')
return trackers, dataset
def uav_test():
# Run DiMP18, ATOM and ECO on the UAV dataset
trackers = trackerlist('dimp', 'dimp18', range(1)) + \
trackerlist('dimp', 'dimp18_vot', range(1))
dataset = get_dataset('uav')
return trackers, dataset
def vot_lt_2020_local3():
trackers = trackerlist('dimp', 'dimp50', None) + \
trackerlist('dimp', 'dimp50_vot', None) + \
trackerlist('dimp', 'dimp50_vot19', None)
dataset = get_dataset('vot')
return trackers, dataset
def main():
trackers = []
trackers.extend(
trackerlist('dimpRank', 'dimpRank18', range(0, 1), 'dimpRank'))
# trackers.extend(trackerlist('ECO', 'default_deep', range(1), 'ECO'))
# trackers.extend(trackerlist('dimpFMFAttnAw_49', 'dimp50', range(1), 'TRAT'))
# trackers.extend(trackerlist('MDNet', 'default', range(1), 'MDNet'))
dataset = get_dataset('otb')
sequence = [dataset['Basketball']]
playback_results(trackers, sequence[0])
def vot_lt_2020_all_test():
# Run DiMP18, ATOM and ECO on the UAV dataset
trackers = trackerlist('dimp', 'dimp18', range(1)) + \
trackerlist('dimp', 'dimp18_vot', range(1))+ \
trackerlist('dimp', 'dimp50', range(1))+ \
trackerlist('dimp', 'dimp50_vot', range(1))+ \
trackerlist('dimp', 'dimp50_vot19', range(1))+ \
trackerlist('dimp', 'prdimp18', range(1))+ \
trackerlist('dimp', 'prdimp50', range(1))+ \
trackerlist('dimp', 'super_dimp', range(1))
dataset = get_dataset('vot')
return trackers, dataset
def transt_readout_mixed():
# Evaluate GOT on a circuit KYS I trained
trackers = trackerlist('transt_readout_mixed', 'default', range(1))
dataset = get_dataset('got10k_test') # _val, 'got10k_ltrval')
return trackers, dataset
def trsiam_trackingnet():
trackers = trackerlist('trdimp', 'trsiam', range(1))
dataset = get_dataset('trackingnet')
return trackers, dataset
def trsiam_otb():
trackers = trackerlist('trdimp', 'trsiam', range(1))
dataset = get_dataset('otb')
return trackers, dataset
trackers.extend(trackerlist('MDNet', 'default', range(0, 1), 'MDNet'))
trackers.extend(trackerlist('CCOT', 'default', range(0, 1), 'CCOT'))
# trackers.extend(trackerlist('DaSiamRPN', 'default', range(0,1), 'DaSiamRPN'))
# trackers.extend(trackerlist('SiamRPN++', 'default', range(0,1), 'SiamRPN++'))
trackers.extend(trackerlist('dimp', 'dimp50', range(0, 5), 'DiMP50'))
# trackers.extend(trackerlist('dimpFMFAttnAw', 'dimp50_49', range(1,2), 'TRAT'))
trackers.extend(trackerlist('TRAT', 'trat', range(0, 3), 'TRAT'))
# trackers.extend(trackerlist('atom_R50_ours', 'default', range(0,5), 'ATOM50*'))
trackers.extend(trackerlist('atom', 'default', range(0, 5), 'ATOM'))
# trackers.extend(trackerlist('siamban_results', 'SiamBAN_LaSOT', range(0,1), 'SiamBAN'))
# trackers.extend(trackerlist('Roam', 'LaSOT-ROAM++', range(0,1), 'Roam++'))
# trackers.extend(trackerlist('Roam', 'LaSOT-ROAM', range(0,1), 'Roam'))
filter_criteria = None
dataset = get_dataset('nfs')
# sequence = [dataset['book-10']]
# print_per_sequence_results(trackers, dataset, 'lasot', merge_results=True, filter_criteria=filter_criteria, force_evaluation=False)
# playback_results(trackers, sequence[0])
plot_results(trackers,
dataset,
'nfs',
merge_results=True,
plot_types=('success', 'prec', 'norm_prec'),
skip_missing_seq=False,
force_evaluation=True,
plot_bin_gap=0.05,
exclude_invalid_frames=False)
# print_results(trackers, dataset, 'lasot', merge_results=True, plot_types=('success', 'prec', 'norm_prec'))
from pytracking.evaluation import Tracker, get_dataset, trackerlist, Sequence
from pytracking.analysis.playback_results import playback_results
trackers = []
#
# trackers.extend(trackerlist('TratCatAttn31-32', 'default2', range(0,1), 'TratCatAttn32_1'))
# trackers.extend(trackerlist('TratCatAttn31-32', 'default2', range(1,2), 'TratCatAttn32_2'))
# trackers.extend(trackerlist('TratCatAttn31-32', 'default2', range(2,3), 'TratCatAttn32_3'))
# trackers.extend(trackerlist('TratCatAttn31-32', 'default2', range(3,4), 'TratCatAttn32_4'))
# trackers.extend(trackerlist('TratCatAttn31-32', 'default2', range(4,5), 'TratCatAttn32_5'))
# trackers.extend(trackerlist('dimpFMFAttnAw', 'dimp50_49', range(1,2), 'dimp50_4_2'))
# trackers.extend(trackerlist('dimpFMFAttnAw', 'dimp50_49', range(2,3), 'dimp50_46_1'))
trackers.extend(trackerlist('RankingFast', 'default', range(1), 'RafnkinFast'))
filter_criteria = None
dataset = get_dataset('otb')
# sequence = [dataset['Basketball']]
# print_per_sequence_results(trackers, dataset, 'nfs', merge_results=True, filter_criteria=filter_criteria, force_evaluation=False)
# playback_results(trackers, sequence[0])
# plot_results(trackers, dataset, 'uav', merge_results=True, plot_types=('success', 'prec', 'norm_prec'),
# skip_missing_seq=False, force_evaluation=True, plot_bin_gap=0.05, exclude_invalid_frames=False)
print_results(trackers,
dataset,
'otb',
merge_results=True,
plot_types=('success', 'prec', 'norm_prec'))
def dimp_lasot():
# Run three runs of ATOM on NFS and UAV datasets
trackers = trackerlist('dimp', 'dimp18', range(2))
dataset = get_dataset('lasot')
return trackers, dataset
def Ranking():
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# Run three runs of ATOM on NFS and UAV datasets
trackers = trackerlist('Ranking', 'default', range(2))
dataset = get_dataset('otb')
return trackers, dataset
def eco_lasot():
# Run three runs of ATOM on NFS and UAV datasets
trackers = trackerlist('eco', 'default', range(1))
dataset = get_dataset('lasot')
return trackers, dataset
def evaluate_dataset(results_path,
dset_name,
measure='J',
to_file=True,
scores=False,
sequences=None,
quiet=False):
dset = get_dataset(dset_name)
results = OrderedDict()
dset_scores = []
dset_decay = []
dset_recall = []
if to_file:
f = open(results_path / ("evaluation-%s.txt" % measure), "w")
def _print(msg):
if not quiet:
print(msg)
if to_file:
print(msg, file=f)
if sequences is not None:
sequences = [sequences] if not isinstance(sequences,
(list, tuple)) else sequences
target_names = []
for j, sequence in enumerate(dset):
if (sequences is not None) and (sequence.name not in sequences):
continue
# Load all frames
frames = sequence.ground_truth_seg
annotations = OrderedDict()
segmentations = OrderedDict()
for f in frames:
if f is None:
continue
file = Path(f)
annotations[file.name] = imread_indexed(file)
if not scores:
segmentations[file.name] = imread_indexed(
os.path.join(results_path, sequence.name, file.name))
else:
raise NotImplementedError
# Find object ids and starting frames
object_info = dict()
for f_id, d in sequence.init_data.items():
for obj_id in d['object_ids']:
object_info[int(obj_id)] = Path(d['mask']).name
if 0 in object_info: # Remove background
object_info.pop(0)
# Evaluate
n_seqs = len(dset)
n_objs = len(object_info)
seq_name = sequence.name
_print("%d/%d: %s: %d object%s" %
(j + 1, n_seqs, seq_name, n_objs, "s" if n_objs > 1 else ""))
r = evaluate_sequence(seq_name,
segmentations,
annotations,
object_info,
measure=measure)
results[seq_name] = r
# Print scores, per frame and object, ignoring NaNs
per_obj_score = [] # Per-object accuracies, averaged over the sequence
per_frame_score = [] # Per-frame accuracies, averaged over the objects
for obj_id, score in r['raw'].items():
target_names.append('{}_{}'.format(seq_name, obj_id))
per_frame_score.append(score)
s = utils.mean(score) # Sequence average for one object
per_obj_score.append(s)
if n_objs > 1:
_print("joint {obj}: acc {score:.3f} ┊{apf}┊".format(
obj=obj_id, score=s, apf=utils.text_bargraph(score)))
# Print mean object score per frame and final score
dset_decay.extend(r['decay'])
dset_recall.extend(r['recall'])
dset_scores.extend(per_obj_score)
seq_score = utils.mean(per_obj_score) # Final score
seq_mean_score = utils.nanmean(np.array(per_frame_score),
axis=0) # Mean object score per frame
# Print sequence results
_print("final : acc {seq:.3f} ({dset:.3f}) ┊{apf}┊".format(
seq=seq_score,
dset=np.mean(dset_scores),
apf=utils.text_bargraph(seq_mean_score)))
_print("%s: %.3f, recall: %.3f, decay: %.3f" %
(measure, utils.mean(dset_scores), utils.mean(dset_recall),
utils.mean(dset_decay)))
if to_file:
f.close()
return target_names, dset_scores, dset_recall, dset_decay
def trsiam_lasot():
trackers = trackerlist('trdimp', 'trsiam_lasot', range(1))
dataset = get_dataset('lasot')
return trackers, dataset
def transt_control_normal():
trackers = trackerlist('transt_control_normal', 'default', range(1))
dataset = get_dataset('got10k_test') # _val, 'got10k_ltrval')
return trackers, dataset
def transt_readout_test_v1():
trackers = trackerlist('transt_readout_test_v1', 'default', range(1))
dataset = get_dataset('got10k_test') # _val, 'got10k_ltrval')
return trackers, dataset
def rtranst():
trackers = trackerlist('rtranst', 'default', range(1))
dataset = get_dataset('got10k_test') # _val, 'got10k_ltrval')
return trackers, dataset
def vot_lt_2020_local2():
trackers = trackerlist('dimp', 'dimp18', None) + \
trackerlist('dimp', 'dimp18_vot', None)
dataset = get_dataset('vot')
return trackers, dataset
def atom_nfs_uav():
# Run three runs of ATOM on NFS and UAV datasets
trackers = trackerlist('atom', 'default', range(3))
dataset = get_dataset('nfs', 'uav')
return trackers, dataset
def dimp50_lasot():
trackers = trackerlist('dimp', 'super_dimp', range(1))
dataset = get_dataset('lasot')
return trackers, dataset
