def path_efficiency_plot(dataset: TrajDataset):
ped_trajectories = dataset.get_trajectories()
efficiencies = []
for traj in ped_trajectories:
if len(traj) < 2: continue
try:
p_eff = path_efficiency(traj)
efficiencies.append(p_eff * 100)
except:
print('Error in path efficiency metric')
bins = np.linspace(50, 100, 25)
hist, bins, patches = plt.hist(efficiencies,
bins,
color='pink',
density=True,
alpha=0.7)
# hist, bin_edges = np.histogram(efficiencies, bins, density=True)
plt.title(dataset.title)
plt.ylabel('path efficiency')
plt.xlabel('percent')
plt.xlim([bins[0], bins[-1]])
plt.ylim([0, 0.5])
return hist
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 speed_plot(dataset: TrajDataset):
trajectories = dataset.get_trajectories()
speeds = []
for traj in trajectories:
speeds_i = speed(traj)
speeds.extend(speeds_i)
bins = np.linspace(0, 2.5, 25)
hist, bins, patches = plt.hist(speeds,
bins,
color='blue',
density=True,
alpha=0.7)
# hist, bin_edges = np.histogram(ped_speeds, bins, density=True)
plt.suptitle(dataset.title)
plt.ylabel('histogram of speeds')
plt.xlabel('m/s')
plt.xlim([bins[0], bins[-1]])
plt.ylim([0, 6])
return hist
def acceleration_plot(dataset: TrajDataset):
trajectories = dataset.get_trajectories()
accelerations = []
for traj in trajectories:
if len(traj) < 2: continue
accelerations_i = acceleration(traj)
accelerations.extend(accelerations_i)
bins = np.linspace(-2.5, 2.5, 100)
# hist, bin_edges = np.histogram(ped_accelerations, bins, density=True)
hist, bins, patches = plt.hist(accelerations,
bins,
color='red',
density=True,
alpha=0.7)
plt.title(dataset.title)
plt.ylabel('histogram of accelerations')
plt.xlabel('m/s^2')
plt.xlim([-2.5, 2.5])
plt.ylim([0, 5])
return hist
def grouping(dataset: TrajDataset):
trajs = dataset.get_trajectories("pedestrian")
trajlets = split_trajectories(trajs)
for trajlet in trajlets:
pass
return