def get_datasets(opentraj_root, dataset_names):
datasets = {}
# Make a temp dir to store and load trajdatasets (no postprocess anymore)
trajdataset_dir = os.path.join(opentraj_root, 'trajdatasets__temp')
if not os.path.exists(trajdataset_dir): os.makedirs(trajdataset_dir)
for dataset_name in dataset_names:
dataset_h5_file = os.path.join(trajdataset_dir, dataset_name + '.h5')
if os.path.exists(dataset_h5_file):
datasets[dataset_name] = TrajDataset()
datasets[dataset_name].data = pd.read_pickle(dataset_h5_file)
datasets[dataset_name].title = dataset_name
print("loading dataset from pre-processed file: ", dataset_h5_file)
continue
print("Loading dataset:", dataset_name)
# ========== ETH ==============
if 'eth-univ' == dataset_name.lower():
eth_univ_root = os.path.join(opentraj_root, 'datasets/ETH/seq_eth/obsmat.txt')
datasets[dataset_name] = load_eth(eth_univ_root, title=dataset_name, scene_id='Univ',
use_kalman=True)
elif 'eth-hotel' == dataset_name.lower():
eth_hotel_root = os.path.join(opentraj_root, 'datasets/ETH/seq_hotel/obsmat.txt')
datasets[dataset_name] = load_eth(eth_hotel_root, title=dataset_name, scene_id='Hotel')
# ******************************
# ========== UCY ==============
elif 'ucy-zara' == dataset_name.lower(): # all 3 zara sequences
zara01_dir = os.path.join(opentraj_root, 'datasets/UCY/zara01')
zara02_dir = os.path.join(opentraj_root, 'datasets/UCY/zara02')
zara03_dir = os.path.join(opentraj_root, 'datasets/UCY/zara03')
zara_01_ds = load_crowds(zara01_dir + '/annotation.vsp',
homog_file=zara01_dir + '/H.txt',
scene_id='1', use_kalman=True)
zara_02_ds = load_crowds(zara02_dir + '/annotation.vsp',
homog_file=zara02_dir + '/H.txt',
scene_id='2', use_kalman=True)
zara_03_ds = load_crowds(zara03_dir + '/annotation.vsp',
homog_file=zara03_dir + '/H.txt',
scene_id='3', use_kalman=True)
datasets[dataset_name] = merge_datasets([zara_01_ds, zara_02_ds, zara_03_ds], dataset_name)
elif 'ucy-univ' == dataset_name.lower(): # all 3 sequences
st001_dir = os.path.join(opentraj_root, 'datasets/UCY/students01')
st003_dir = os.path.join(opentraj_root, 'datasets/UCY/students03')
uni_ex_dir = os.path.join(opentraj_root, 'datasets/UCY/uni_examples')
#st001_ds = load_Crowds(st001_dir + '/students001.txt',homog_file=st001_dir + '/H.txt',scene_id='1',use_kalman=True)
st001_ds = load_crowds(st001_dir + '/annotation.vsp',
homog_file=st003_dir + '/H.txt',
scene_id='1', use_kalman=True)
st003_ds = load_crowds(st003_dir + '/annotation.vsp',
homog_file=st003_dir + '/H.txt',
scene_id='3', use_kalman=True)
uni_ex_ds = load_crowds(uni_ex_dir + '/annotation.vsp',
homog_file=st003_dir + '/H.txt',
scene_id='ex', use_kalman=True)
datasets[dataset_name] = merge_datasets([st001_ds, st003_ds, uni_ex_ds], dataset_name)
elif 'ucy-zara1' == dataset_name.lower():
zara01_root = os.path.join(opentraj_root, 'datasets/UCY/zara01/obsmat.txt')
datasets[dataset_name] = load_eth(zara01_root, title=dataset_name)
elif 'ucy-zara2' == dataset_name.lower():
zara02_root = os.path.join(opentraj_root, 'datasets/UCY/zara02/obsmat.txt')
datasets[dataset_name] = load_eth(zara02_root, title=dataset_name)
elif 'ucy-univ3' == dataset_name.lower():
students03_root = os.path.join(opentraj_root, 'datasets/UCY/students03/obsmat.txt')
datasets[dataset_name] = load_eth(students03_root, title=dataset_name)
# ******************************
# ========== HERMES ==============
elif 'bn' in dataset_name.lower().split('-'):
[_, exp_flow, cor_size] = dataset_name.split('-')
if exp_flow == '1d' and cor_size == 'w180': # 'Bottleneck-udf-180'
bottleneck_path = os.path.join(opentraj_root, 'datasets/HERMES/Corridor-1D/uo-180-180-120.txt')
elif exp_flow == '2d' and cor_size == 'w160': # 'Bottleneck-bdf-160'
bottleneck_path = os.path.join(opentraj_root, "datasets/HERMES/Corridor-2D/bo-360-160-160.txt")
else:
"Unknown Bottleneck dataset!"
continue
datasets[dataset_name] = load_bottleneck(bottleneck_path, sampling_rate=6,
use_kalman=True,
title=dataset_name)
# ******************************
# ========== PETS ==============
elif 'pets-s2l1' == dataset_name.lower():
pets_root = os.path.join(opentraj_root, 'datasets/PETS-2009/data')
datasets[dataset_name] = load_pets(os.path.join(pets_root, 'annotations/PETS2009-S2L1.xml'), #Pat:was PETS2009-S2L2
calib_path=os.path.join(pets_root, 'calibration/View_001.xml'),
sampling_rate=2,
title=dataset_name)
# ******************************
# ========== GC ==============
elif 'gc' == dataset_name.lower():
gc_root = os.path.join(opentraj_root, 'datasets/GC/Annotation')
datasets[dataset_name] = load_gcs(gc_root, world_coord=True, title=dataset_name,
use_kalman=True
)
# ******************************
# ========== InD ==============
elif 'ind-1' == dataset_name.lower():
ind_root = os.path.join(opentraj_root, 'datasets/InD/inD-dataset-v1.0/data')
file_ids = range(7, 17 + 1) # location_id = 1
ind_1_datasets = []
for id in file_ids:
dataset_i = load_ind(os.path.join(ind_root, '%02d_tracks.csv' % id),
scene_id='1-%02d' %id,
sampling_rate=10,
use_kalman=True)
ind_1_datasets.append(dataset_i)
datasets[dataset_name] = merge_datasets(ind_1_datasets, new_title=dataset_name)
elif 'ind-2' == dataset_name.lower():
ind_root = os.path.join(opentraj_root, 'datasets/InD/inD-dataset-v1.0/data')
file_ids = range(18, 29 + 1) # location_id = 1
ind_2_datasets = []
for id in file_ids:
dataset_i = load_ind(os.path.join(ind_root, '%02d_tracks.csv' % id),
scene_id='1-%02d' % id,
sampling_rate=10,
use_kalman=True)
ind_2_datasets.append(dataset_i)
datasets[dataset_name] = merge_datasets(ind_2_datasets, new_title=dataset_name)
elif 'ind-3' == dataset_name.lower():
ind_root = os.path.join(opentraj_root, 'datasets/InD/inD-dataset-v1.0/data')
file_ids = range(30, 32 + 1) # location_id = 1
ind_3_datasets = []
for id in file_ids:
dataset_i = load_ind(os.path.join(ind_root, '%02d_tracks.csv' % id),
scene_id='1-%02d' % id,
sampling_rate=10,
use_kalman=True)
ind_3_datasets.append(dataset_i)
datasets[dataset_name] = merge_datasets(ind_3_datasets, new_title=dataset_name)
elif 'ind-4' == dataset_name.lower():
ind_root = os.path.join(opentraj_root, 'datasets/InD/inD-dataset-v1.0/data')
file_ids = range(0, 6 + 1) # location_id = 1
ind_4_datasets = []
for id in file_ids:
dataset_i = load_ind(os.path.join(ind_root, '%02d_tracks.csv' % id),
scene_id='1-%02d' % id,
sampling_rate=10,
use_kalman=True)
ind_4_datasets.append(dataset_i)
datasets[dataset_name] = merge_datasets(ind_4_datasets, new_title=dataset_name)
# ******************************
# ========== KITTI ==============
elif 'kitti' == dataset_name.lower():
kitti_root = os.path.join(opentraj_root, 'datasets/KITTI/data')
datasets[dataset_name] = load_kitti(kitti_root, title=dataset_name,
use_kalman=True,
sampling_rate=1) # FixMe: apparently original_fps = 2.5
# ******************************
# ========== L-CAS ==============
elif 'lcas-minerva' == dataset_name.lower():
lcas_root = os.path.join(opentraj_root, 'datasets/L-CAS/data')
datasets[dataset_name] = load_lcas(lcas_root, title=dataset_name,
use_kalman=True,
sampling_rate=1) # FixMe: apparently original_fps = 2.5
# ******************************
# ========== Wild-Track ==============
elif 'wildtrack' == dataset_name.lower():
wildtrack_root = os.path.join(opentraj_root, 'datasets/Wild-Track/annotations_positions')
datasets[dataset_name] = load_wildtrack(wildtrack_root, title=dataset_name,
use_kalman=True,
sampling_rate=1) # original_annot_framerate=2
# ******************************
# ========== Edinburgh ==============
elif 'edinburgh' in dataset_name.lower():
edinburgh_dir = os.path.join(opentraj_root, 'datasets/Edinburgh/annotations')
if 'edinburgh' == dataset_name.lower(): # all files
# edinburgh_path = edinburgh_dir
# select 1-10 Sep
Ed_selected_days = ['01Sep', '02Sep', '04Sep', '05Sep', '06Sep', '10Sep']
partial_ds = []
for selected_day in Ed_selected_days:
edinburgh_path = os.path.join(edinburgh_dir, 'tracks.%s.txt' % selected_day)
partial_ds.append(load_edinburgh(edinburgh_path, title=dataset_name,
use_kalman=True, scene_id=selected_day,
sampling_rate=4) # original_framerate=9
)
merge_datasets(partial_ds)
else:
seq_date = dataset_name.split('-')[1]
edinburgh_path = os.path.join(edinburgh_dir, 'tracks.%s.txt' %seq_date)
datasets[dataset_name] = load_edinburgh(edinburgh_path, title=dataset_name,
use_kalman=True,
sampling_rate=4) # original_framerate=9
# ******************************
# ========== Town-Center ==============
elif 'towncenter' == dataset_name.lower():
towncenter_root = os.path.join(opentraj_root, 'datasets/Town-Center')
# FixMe: might need Kalman Smoother
datasets[dataset_name] = load_town_center(towncenter_root + '/TownCentre-groundtruth-top.txt',
calib_path=towncenter_root + '/TownCentre-calibration-ci.txt',
title=dataset_name,
use_kalman=True,
sampling_rate=10) # original_framerate=25
# ******************************
# ========== SDD ==============
elif 'sdd-' in dataset_name.lower():
scene_name = dataset_name.split('-')[1]
sdd_root = os.path.join(opentraj_root, 'datasets', 'SDD')
annot_files_sdd = sorted(glob.glob(sdd_root + '/' + scene_name + "/**/annotations.txt", recursive=True))
sdd_scales_yaml_file = os.path.join(sdd_root, 'estimated_scales.yaml')
with open(sdd_scales_yaml_file, 'r') as f:
scales_yaml_content = yaml.load(f, Loader=yaml.FullLoader)
scene_datasets = []
for file_name in annot_files_sdd:
filename_parts = file_name.split('/')
scene_name = filename_parts[-3]
scene_video_id = filename_parts[-2]
scale = scales_yaml_content[scene_name][scene_video_id]['scale']
sdd_dataset_i = load_sdd(file_name, scale=scale,
scene_id=scene_name + scene_video_id.replace('video', ''),
drop_lost_frames=False,
use_kalman=True,
sampling_rate=12) # original_framerate=30
scene_datasets.append(sdd_dataset_i)
scene_dataset = merge_datasets(scene_datasets, dataset_name)
datasets[dataset_name] = scene_dataset
# ******************************
else:
print("Error! invalid dataset name:", dataset_name)
# save to h5 file
datasets[dataset_name].data.to_pickle(dataset_h5_file)
print("saving dataset into pre-processed file: ", dataset_h5_file)
return datasets
test_file = True
if 'Train' in trajnet_file:
test_file = False
# find the corresponding file in SDD
scene_name = trajnet_file[trajnet_file.rfind('/') + 1:-6]
scene_id = int(trajnet_file[-5])
sdd_file = os.path.join(opentraj_root, 'datasets/SDD/', scene_name, 'video%d' % scene_id, 'annotations.txt')
if not os.path.exists(sdd_file):
# print('Error: sdd file does not exist:', sdd_file)
continue
# read from trajnet
trajnet_dataset = load_trajnet(trajnet_file)
# read from SDD
sdd_dataset = load_sdd(sdd_file)
# plot them for manula debug
# fig, axes = plt.subplots(nrows=2, ncols=1)
# trajnet_dataset.data.plot.scatter("pos_x", "pos_y", ax=axes[0])
# sdd_dataset.data.plot.scatter("pos_x", "pos_y", ax=axes[1])
# plt.show()
# take one traj from trajnet
trajnet_ids = trajnet_dataset.get_agent_ids()
# plt.figure()
suggested_scale = -1
for trajnet_id in trajnet_ids:
# trajnet_id = trajnet_ids[3]
trajnet_traj = trajnet_dataset.get_trajectories([trajnet_id])[0]
# trajnet_traj_0.plot.scatter("pos_x", "pos_y", ax=axes[0], color='red')
## SDD datasets
scenes = [
# ['bookstore', 'video0'],
# ['bookstore', 'video1'],
['coupa', 'video3']
]
sdd_scales_yaml_file = os.path.join(OPENTRAJ_ROOT, 'datasets/SDD',
'estimated_scales.yaml')
with open(sdd_scales_yaml_file, 'r') as f:
scales_yaml_content = yaml.load(f, Loader=yaml.FullLoader)
for scene_i in scenes:
scale = scales_yaml_content[scene_i[0]][scene_i[1]]['scale']
sdd_dataset_i = load_sdd(
os.path.join(OPENTRAJ_ROOT, "datasets/SDD", scene_i[0], scene_i[1],
"annotations.txt"),
scene_id="SDD-" + scene_i[0] + scene_i[1],
title="SDD-" + scene_i[0] + "-" + scene_i[1][-1], # use_kalman=True,
scale=scale,
drop_lost_frames=False,
sampling_rate=6) # original fps=30
datasets.append(sdd_dataset_i)
# annot_file = os.path.join(OPENTRAJ_ROOT, 'datasets/ETH/seq_eth/obsmat.txt')
# datasets.append(load_eth(annot_file, title="ETH-Univ"))
eth_hotel_annot_file = os.path.join(OPENTRAJ_ROOT,
'datasets/ETH/seq_hotel/obsmat.txt')
datasets.append(load_eth(eth_hotel_annot_file, title="ETH-Hotel"))
zara01_annot_file = os.path.join(OPENTRAJ_ROOT,
'datasets/UCY/zara01/annotation.vsp')
zara01 = load_crowds(zara01_annot_file,
def run(path, args):
print("\n-----------------------------\nRunning test load\n-----------------------------")
if 'eth/' in path.lower():
print("[Javad]: Directly reading ETH Dataset (seq_eth):")
traj_dataset = load_eth(path)
all_trajs = traj_dataset.get_trajectories()
all_frames = traj_dataset.get_frames()
if '/sdd' in path.lower():
if os.path.isdir(path):
traj_dataset = load_sdd_dir(path)
else:
traj_dataset = load_sdd(path)
trajs = traj_dataset.get_trajectories()
print("total number of trajectories = ", len(trajs))
if 'gc/' in path.lower():
kwargs = {}
for arg in args:
if 'homog_file=' in arg:
kwargs['homog_file'] = arg.replace("homog_file=", "")
gc_dataset = load_gcs(path, **kwargs)
trajs = gc_dataset.get_trajectories()
print("GC: number of trajs = ", len(trajs))
if 'pets-2009/' in path.lower():
kwargs = {}
for arg in args:
if 'calib_path=' in arg:
kwargs['calib_path'] = arg.replace("calib_path=", "")
load_pets(path, **kwargs)
if 'ind/' in path.lower():
# Test the InD Dataset
traj_dataset = load_ind(path)
all_trajs = traj_dataset.get_trajectories()
print('------------------------')
print('First trajectory (InD)')
print('------------------------')
print(all_trajs[0])
all_frames = traj_dataset.get_frames()
if 'wild-track/' in path.lower():
traj_dataset = load_wildtrack(path)
if 'town' in path.lower():
# Construct arguments dictionary
kwargs = {}
for arg in args:
if 'calib_path=' in arg:
kwargs['calib_path'] = arg.replace("calib_path=", "")
# Test the Town Center Dataset
traj_dataset = load_town_center(path, **kwargs)
all_trajs = traj_dataset.get_trajectories()
print('------------------------')
print('First trajectory (Town Center)')
print('------------------------')
print(all_trajs[0])
all_frames = traj_dataset.get_frames()
if 'chaos' in path.lower():
print("\n")
print("ChAOS Style :")
print(loaders.loadChAOS(path, args.separator))
print("\n\n-----------------------------\nTest load done\n-----------------------------")