def setup_with_config_file(self, config_file, **kwargs):
"""
:param config_file: address to config file which contains scenario parameters
:param kwargs: you can override the parameters in the config file , by passing them directly
:return:
"""
with open(config_file) as stream:
config = yaml.load(stream, Loader=yaml.FullLoader)
biped_mode = config['General']['biped_mode']
# opentraj_root = config['Dataset']['OpenTrajRoot']
robot_replacement_id = config['Dataset']['RobotId']
obstacles = config['Dataset']['Obstacles']
fps = config['Dataset']['fps']
annotation_file = config['Dataset']['Annotation']
title = config['Dataset']['Title'] if 'Title' in config[
'Dataset'] else ''
parser_type = config['Dataset']['Parser']
if parser_type == "ParserETH":
dataset = load_eth(annotation_file,
title=title,
use_kalman=False)
dataset.interpolate_frames(inplace=True)
elif parser_type == "ParserHermes":
dataset = load_bottleneck(annotation_file, title=title)
self.video_files = config['Dataset']['Video']
world_boundary = []
if 'WorldBoundary' in config['Dataset']:
world_x_min = config['Dataset']['WorldBoundary']['x_min']
world_x_max = config['Dataset']['WorldBoundary']['x_max']
world_y_min = config['Dataset']['WorldBoundary']['y_min']
world_y_max = config['Dataset']['WorldBoundary']['y_max']
world_boundary = [[world_x_min, world_x_max],
[world_y_min, world_y_max]]
# override parameters by direct arguments
self.title = kwargs.get("title", title)
dataset = kwargs.get("dataset", dataset)
fps = kwargs.get("fps", fps)
robot_replacement_id = kwargs.get("robot_id", robot_replacement_id)
biped_mode = kwargs.get("biped_mode", biped_mode)
self.setup(dataset=dataset,
fps=fps,
robot_id=robot_replacement_id,
biped_mode=biped_mode,
obstacles=obstacles,
world_boundary=world_boundary)
def run(module_directory, args):
print("\n\n-----------------------------\nRunning test metrics\n-----------------------------")
eth_dataset = load_eth(module_directory + '/tests/toy trajectories/ETH/seq_eth/obsmat.txt',
args.separator)
all_trajs = eth_dataset.get_trajectories()
for traj in all_trajs:
speed = motion.speed(traj)
p_len = path_length.path_length(traj)
p_eff = path_length.path_efficiency(traj)
all_frames = eth_dataset.get_frames()
# calc pcf on a dataset
pcf_accum = []
pcf_range = np.arange(0.2, # starting radius
8, # end radius
0.2) # step size
for frame in all_frames:
pcf_values_t = pcf.pcf(frame[['pos_x', 'pos_y']],
list(pcf_range), sigma=0.25)
if not len(pcf_accum):
pcf_accum = pcf_values_t
else:
pcf_accum += pcf_values_t
avg_pcf = pcf_accum / len(all_frames)
print('average pcf = ', avg_pcf)
# social spaces
social_spaces = distance.social_space(eth_dataset.data)
print(social_spaces.values())
plt.hist(social_spaces.values(), bins=np.arange(0, 5, 0.2), density=True)
plt.show()
# traj.plot(y='acc x')
# ax = plt.gca()
# for data in np.unique(traj.index.get_level_values('agent id')):
# random_color = np.random.rand(3,)
# traj[traj.index.get_level_values('agent id') == data].plot(y='acc x', c = random_color, ax=ax)
#
# plt.show()
# print(np.unique(traj.index.get_level_values('agent id')))
print("\n\n-----------------------------\nTest metrics done\n-----------------------------")
def __init__(self, mode, trajectory_interval, context_length,
agent_buffer_size):
"""
mode: str ['train', 'val', 'test']
trajectory_interval: int time window for a batch data, each frame is 0.4 sec
context_length: int known past trajectories
agent_buffer_size: int max number of agents allowed in one data item
"""
self.mode = mode
assert trajectory_interval > context_length
self.trajectory_interval = trajectory_interval
self.agent_buffer_size = agent_buffer_size
self.context_length = context_length
annot_file = os.path.join('datasets', 'ETH/seq_eth/obsmat.txt')
traj_dataset = load_eth(annot_file)
total = traj_dataset.data['timestamp'].last_valid_index()
total_time = traj_dataset.data['timestamp'][total] - traj_dataset.data[
'timestamp'][0]
# split datasets based on ratio of total time
# checked labels for the dataset. all are pedestrians.
if mode == 'train':
start_time = traj_dataset.data['timestamp'][0]
end_time = traj_dataset.data['timestamp'][0] + total_time * 0.8
elif mode == 'val':
start_time = traj_dataset.data['timestamp'][0] + total_time * 0.8
end_time = traj_dataset.data['timestamp'][0] + total_time * 0.9
else:
start_time = traj_dataset.data['timestamp'][0] + total_time * 0.8
end_time = traj_dataset.data['timestamp'][0] + total_time
self.traj_dataset = self.extract_dataset(traj_dataset, start_time,
end_time)
self.traj_dataset.data.reset_index(inplace=True)
if mode != 'train':
self.start_indices = np.arange(
0,
len(self.traj_dataset.data) - self.trajectory_interval,
self.trajectory_interval)
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
'--background',
'--b',
default='image',
choices=['image', 'video'],
help='select background type. video does not exist for all datasets,'
'you might need to download it first.'
'(default: "image")')
args = argparser.parse_args()
opentraj_root = args.data_root
traj_dataset = None
# #============================ ETH =================================
if args.dataset == 'eth':
annot_file = os.path.join(opentraj_root, 'ETH/seq_eth/obsmat.txt')
traj_dataset = load_eth(annot_file)
homog_file = os.path.join(opentraj_root, 'ETH/seq_eth/H.txt')
if args.background == 'image':
media_file = os.path.join(opentraj_root,
'ETH/seq_eth/reference.png')
elif args.background == 'video':
media_file = os.path.join(opentraj_root, 'ETH/seq_eth/video.avi')
else:
error_msg('background type is invalid')
elif args.dataset == 'hotel':
annot_file = os.path.join(opentraj_root, 'ETH/seq_hotel/obsmat.txt')
traj_dataset = load_eth(annot_file)
homog_file = os.path.join(opentraj_root, 'ETH/seq_hotel/H.txt')
# media_file = os.path.join(opentraj_root, 'ETH/seq_hotel/reference.png')
media_file = os.path.join(opentraj_root, 'ETH/seq_hotel/video.avi')
# p(Xp|Xn) = p(Xp, Xn) / p(Xn) = p(X) / p(Xn)
euclidean_distances(trajlets)
cE = 0
for traj_i in trajlets:
xi = traj_i.whole
xoi = traj_i.obsv
xpi = traj_i.pred
for traj_j in trajlets:
xj = traj_j.whole
xoj = traj_j.obsv
xpj = traj_j.pred
# K_xi_xj = np.exp(-norm(x1 - x2) ** 2 / 2)
return cE
if __name__ == "__main__":
from toolkit.loaders.loader_eth import load_eth
from toolkit.core.trajlet import split_trajectories, to_numpy
eth_dataset = load_eth(
"/home/cyrus/workspace2/OpenTraj/datasets/ETH/seq_eth/obsmat.txt")
eth_trajs = eth_dataset.get_trajectories("pedestrian")
eth_trajlets = split_trajectories(eth_trajs)
to_numpy(eth_trajlets)
eth_trajlets = np.stack(eth_trajlets)
conditional_entropy(eth_trajlets)
"pos_x", "pos_y", "vel_x", "vel_y", "timestamp"
]].to_numpy()
trl_pred_np = trl_pred[[
"pos_x", "pos_y", "vel_x", "vel_y", "timestamp"
]].to_numpy()
trl_np_list.append([trl_obsv_np, trl_pred_np])
trajlets = np.stack(trl_np_list)
return trajlets
# test
if __name__ == "__main__":
from toolkit.loaders.loader_eth import load_eth
import sys, os
opentraj_root = sys.argv[1]
# test_dataset = loadETH(os.path.join(opentraj_root, "datasets/ETH/seq_eth/obsmat.txt"))
test_dataset = load_eth(
os.path.join(opentraj_root, "datasets/ETH/seq_hotel/obsmat.txt"))
trajs = test_dataset.get_trajectories()
trajlets_4_8s = split_trajectories(trajs, length=4.8, to_numpy=True)
trajlets_8s = split_trajectories(trajs, length=8, to_numpy=True)
paired_trajlets_4_8s = split_trajectories_paired(trajs,
length=4.8,
to_numpy=True)
print("Test hotel dataset\n******************")
print("trajlets_4_8s.shape =", trajlets_4_8s.shape)
print("trajlets_8s.shape =", trajlets_8s.shape)
print("paired_trajlets_4_8s.shape =", paired_trajlets_4_8s.shape)
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-----------------------------")
pred_poss = pred_dps
for i, id in enumerate(self.cur_agent_ids):
pred_dps[:, i] += self.obsv_history[id][-1]
return pred_poss
# test
if __name__ == "__main__":
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use("TkAgg")
np.set_printoptions(precision=3)
opentraj_root = "/home/cyrus/workspace2/OpenTraj"
annot_file = os.path.join(opentraj_root, 'datasets/ETH/seq_eth/obsmat.txt')
dataset = load_eth(annot_file, title="ETH-Univ")
obstacles = [[[0, 0], [8, 0]]]
frames = dataset.get_frames()
all_agent_ids = dataset.data["agent_id"].unique()
min_x, max_x = dataset.data["pos_x"].min() * 1.2, dataset.data["pos_x"].max() * 1.2
min_y, max_y = dataset.data["pos_y"].min() * 1.2, dataset.data["pos_y"].max() * 1.2
def last_loc(df: pd.DataFrame):
return df[["pos_x", "pos_y"]].iloc[-1].to_numpy()
agent_goals = dataset.data.groupby("agent_id").apply(last_loc)
max_num_concurrent_agents = max([len(fr) for fr in frames])
predictor_A = TrajPredictor(16, obstacles, "rvo2", "crowdbag")
predictor_B = TrajPredictor(16, obstacles, "PowerLaw", "umans")
