def min_dist_plot(dataset: TrajDataset):
frames = dataset.get_frames()
min_dists = np.ones(len(frames)) * 1000 # a big number
for ii, frame in enumerate(frames):
N_t = len(frame)
if N_t < 2: continue
X_t = frame[["pos_x", "pos_y"]].to_numpy()
# compute distance matrix between all pairs of agents
DD_t = euclidean_distances(X_t)
DD_t = DD_t[~np.eye(N_t, dtype=bool)].reshape(N_t, N_t - 1)
min_DD_t = np.amin(DD_t)
min_dists[ii] = min_DD_t
bins = np.linspace(0, 4, 40)
hist, bins, patches = plt.hist(min_dists,
bins,
color='green',
density=True,
alpha=0.7)
plt.title(dataset.title)
plt.ylabel('histogram of min distances')
plt.xlabel('meter')
plt.xlim([bins[0], bins[-1]])
plt.ylim([0, 2])
return hist
def density_vanilla_plot(
dataset: TrajDataset
): # over observed space of the dataset ([minx,maxx], [miny, maxy])
space = (dataset.bbox['x']['max'] - dataset.bbox['x']['min']) * \
(dataset.bbox['y']['max'] - dataset.bbox['y']['min'])
density_t = []
frames = dataset.get_frames()
for frame in frames:
density_t.append(len(frame) / space)
bins = np.linspace(0, 1, 40)
hist, bins, patches = plt.hist(density_t,
bins=bins,
color='red',
density=True,
alpha=0.7)
plt.title(dataset.title)
plt.ylabel('distribution of density')
plt.xlabel('Person per m^2')
def pcf_plot(dataset: TrajDataset):
frames = 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 frames:
pcf_values_t = pcf(frame[['pos_x', 'pos_y', 'pos_z']],
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(frames)
plt.title(dataset.title)
plt.ylabel('PCF')
plt.xlabel('meter')
plt.plot(avg_pcf, color='purple')