def num_trajlets(dataset: TrajDataset, length=4.8, overlap=2):
trajs = dataset.get_trajectories(label="pedestrian")
trajlets = split_trajectories(trajs,
length,
overlap,
static_filter_thresh=0.)
non_static_trajlets = split_trajectories(trajs,
length,
overlap,
static_filter_thresh=1.)
return len(trajlets), len(non_static_trajlets)
def ttc(all_frames, name, trajlets):
all_ttc = []
Rp = 0.33 #assume pedestrians radius is 0.33
new_frames = []
for frame in all_frames:
frame.reset_index(inplace=True)
#if there is only one pedestrian at that time, or encounter invalid vel value
if len(frame.index) < 2 or frame['vel_x'].isnull().values.any(
) or frame['vel_y'].isnull().values.any():
continue
#calculate ttc for each pair
x_4d = np.stack((frame.pos_x.values, frame.pos_y.values,
frame.vel_x.values, frame.vel_y.values),
axis=1)
DCA, TTCA = DCA_MTX(x_4d)
for i in range(len(TTCA)):
#find out ttc of one agent
ttc = [
TTCA[i][j] for j in range(len(TTCA[i]))
if DCA[i][j] < 2 * Rp and TTCA[i][j] > 0
]
#find out min ttc for one agent
if len(ttc) > 0:
min_ttc = np.min(ttc)
frame.loc[i, 'ttc'] = min_ttc
min_dca = np.min([j for j in DCA[i] if j > 0])
frame.loc[i, 'dca'] = min_dca
new_frames.append(frame)
new_frames = pd.concat(new_frames)
new_traj = TrajDataset()
new_traj.data = new_frames
trajs = new_traj.get_trajectories(label="pedestrian")
trajlets[name] = split_trajectories(trajs, to_numpy=False)
#average local density o each trajlet
avg_traj_ttc = []
avg_traj_dca = []
for trajlet in trajlets[name]:
avg_traj_ttc.append(np.min(trajlet['ttc'].dropna())) #min of min
avg_traj_dca.append(np.min(trajlet['dca'].dropna())) #min of min
return avg_traj_ttc, avg_traj_dca
def local_density(all_frames,trajlets,name):
#define local density function
#for all pedestrians at that time, find its distance to NN
distNN = []
dens_t = []
a=1
new_frames = []
for frame in all_frames:
if len(frame)>1:
#find pairwise min distance
distNN.append([])
dens_t.append([])
dist = squareform(pdist(frame[['pos_x','pos_y']].values))
pair_dist = []
for pi in dist:
pair_dist.append(np.array(pi))
min_pi = [j for j in pi if j>0.01]
if len(min_pi) == 0:
min_dist = 0.01
else:
min_dist = np.min(min_pi)
distNN[-1].append(min_dist)
#calculate local density for agent pj
for pj in range(len(dist)):
dens_t_i = 1/(2*np.pi)*np.sum(1/((a*np.array(distNN[-1]))**2)*np.exp(-np.divide((pair_dist[pj]**2),(2*(a*np.array(distNN[-1]))**2))))
dens_t[-1].append(dens_t_i)
frame.loc[frame.index[pj],'p_local'] = dens_t_i
new_frames.append(frame)
new_frames = pd.concat(new_frames)
new_traj = TrajDataset()
new_traj.data = new_frames
trajs = new_traj.get_trajectories(label="pedestrian")
trajlets[name] = split_trajectories(trajs, to_numpy=False)
#average local density for each trajlet
avg_traj_plocal=[]
for trajlet in trajlets[name]:
avg_traj_plocal.append(np.max(trajlet['p_local']))
return avg_traj_plocal
def get_trajlets(opentraj_root, dataset_names):
trajlets = {}
# Make a temp dir to store and load trajlets (no splitting anymore)
trajlet_dir = os.path.join(opentraj_root, 'trajlets')
if not os.path.exists(trajlet_dir): os.makedirs(trajlet_dir)
for dataset_name in dataset_names:
trajlet_npy_file = os.path.join(trajlet_dir, dataset_name + '-trl.npy')
if os.path.exists(trajlet_npy_file):
trajlets[dataset_name] = np.load(trajlet_npy_file)
print("loading trajlets from: ", trajlet_npy_file)
else:
ds = get_datasets(opentraj_root, [dataset_name])[dataset_name]
trajs = ds.get_trajectories(label="pedestrian")
trajlets[dataset_name] = split_trajectories(trajs, to_numpy=True)
np.save(trajlet_npy_file, trajlets[dataset_name])
print("writing trajlets ndarray into: ", trajlet_npy_file)
return trajlets
# 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)
def grouping(dataset: TrajDataset):
trajs = dataset.get_trajectories("pedestrian")
trajlets = split_trajectories(trajs)
for trajlet in trajlets:
pass
return