def test_register_scan(self):
"""Test the generation of global hypotheses."""
self.tracker.register_scan(
mht.Scan(mht.sensors.EyeOfMordor(3, 12), [
mht.Report(np.array([8.0, 8.0]), np.eye(2),
mht.models.position_measurement),
mht.Report(np.array([2.0, 2.0]), np.eye(2),
mht.models.position_measurement)
]))
def test_correct(self):
"""Correction step."""
z = np.array([2.0] * 2)
R = np.eye(2)
m = mht.Report(z, R, mht.models.velocity_measurement)
self.target.correct(m)
self.assertAlmostEqual(self.target.x[1], 0.0)
self.assertAlmostEqual(self.target.x[2], 1.0)
self.assertAlmostEqual(self.target.x[3], 1.0)
def test_icetracking(self):
"""Icetracking."""
pos = run_kmeans(image('grd'))
scan = [
mht.Report(
np.matrix([[plat], [plon]]),
np.eye(2) * 1e-4,
mht.models.position_measurement)
for plat, plon in zip(pos[0], pos[1])
]
for t in range(2):
t0 = time.time()
self.tracker.register_scan(
mht.Scan(mht.sensors.EyeOfMordor(5, 10, 12), scan))
t1 = time.time()
print(t, ': Ran tracker in', t1 - t0, 'seconds to generate',
len(self.tracker.global_hypotheses), 'hypotheses')
def test_track(self):
"""Test repeated updates from moving targets."""
targets = [
np.array([0.0, 0.0, 1.0, 1.0]),
np.array([0.0, 10.0, 1.0, -1.0])
]
for t in range(3):
if t > 0:
self.tracker.predict(1)
for t in targets:
t[0:2] += t[2:]
reports = {
mht.Report(
np.random.multivariate_normal(t[0:2],
np.diag([0.1, 0.1])), # noqa
# t[0:2],
np.eye(2) * 0.001,
mht.models.position_measurement,
i)
for i, t in enumerate(targets)
}
self.tracker.register_scan(
mht.Scan(mht.sensors.EyeOfMordor(3, 12), reports))
){kind=link}
def draw():
"""Create plot."""
tracker = mht.MHT(cparams=mht.ClusterParameters(k_max=100,
nll_limit=4,
hp_limit=5),
matching_algorithm="naive")
targets = [
np.array([0.0, 0.0, 1.0, 1.0]),
np.array([0.0, 10.0, 1.0, -1.0]),
]
hyps = None
nclusters = []
ntargets_true = []
ntargets = []
nhyps = []
for k in range(25):
if k > 0:
tracker.predict(1)
for t in targets:
t[0:2] += t[2:]
if k == 5:
targets.append(np.array([5.0, 5.0, 1.0, 0.0]))
if k % 7 == 0:
targets.append(
np.random.multivariate_normal(np.array([k, 7.0, 0.0, 0.0]),
np.diag([0.5] * 4)))
if k % 7 == 1:
del targets[-1]
if k == 10:
targets.append(np.array([10.0, -30.0, 1.0, -0.5]))
if k == 20:
targets.append(np.array([k, 0.0, 1.0, 4.0]))
reports = {
mht.Report(
np.random.multivariate_normal(t[0:2], np.diag([0.1,
0.1])), # noqa
# t[0:2],
np.eye(2) * 0.001,
mht.models.position_measurement,
i)
for i, t in enumerate(targets)
}
this_scan = mht.Scan(mht.sensors.EyeOfMordor(10, 3), reports)
tracker.register_scan(this_scan)
hyps = list(tracker.global_hypotheses())
nclusters.append(len(tracker.active_clusters))
ntargets.append(len(hyps[0].targets))
ntargets_true.append(len(targets))
nhyps.append(len(hyps))
mht.plot.plot_scan(this_scan)
plt.plot([t[0] for t in targets], [t[1] for t in targets],
marker='D',
color='y',
alpha=.5,
linestyle='None')
mht.plot.plot_hyptrace(hyps[0], covellipse=True)
mht.plot.plt.axis([-1, k + 1, -k - 1, k + 1 + 10])
mht.plot.plt.ylabel('Tracks')
mht.plot.plt.figure()
mht.plot.plt.subplot(3, 1, 1)
mht.plot.plt.plot(nclusters)
mht.plot.plt.axis([-1, k + 1, min(nclusters) - 0.1, max(nclusters) + 0.1])
mht.plot.plt.ylabel('# Clusters')
mht.plot.plt.subplot(3, 1, 2)
mht.plot.plt.plot(ntargets, label='Estimate')
mht.plot.plt.plot(ntargets_true, label='True')
mht.plot.plt.ylabel('# Targets')
mht.plot.plt.legend()
mht.plot.plt.axis([
-1, k + 1,
min(ntargets + ntargets_true) - 0.1,
max(ntargets + ntargets_true) + 0.1
])
mht.plot.plt.subplot(3, 1, 3)
mht.plot.plt.plot(nhyps)
mht.plot.plt.axis([-1, k + 1, min(nhyps) - 0.1, max(nhyps) + 0.1])
mht.plot.plt.ylabel('# Hyps')
){kind=link}
def draw():
"""Create plot."""
tracker = mht.MHT(cparams=mht.ClusterParameters(k_max=100, hp_limit=5),
matching_algorithm="naive")
target_centroids = [
([0.0, 0.0, 1.0, 1.0], np.diag([1, 1, 0.3, 0.3]), 15),
([100.0, 100.0, -1.0, -1.0], np.diag([1, 1, 0.3, 0.3]), 5), # noqa
([0.0, 70.0, 1.0, 0.0], np.diag([1, 1, 0.3, 0.3]), 0),
([50.0, 50.0, 0.0, 0.0], np.diag([4, 4, 0.3, 0.3]), 0), # noqa
]
clutter_centroids = [
([0.0, 0.0, 50.0, 50.0], np.diag([70, 70, 1, 1]), 1),
]
sensors = [
mht.sensors.Satellite((-10, 40, -10, 40), 3, 3),
mht.sensors.Satellite((45, 120, 0, 40), 3, 3),
mht.sensors.Satellite((35, 120, 45, 120), 3, 3),
mht.sensors.Satellite((-10, 30, 55, 120), 3, 3),
]
targets = [
np.random.multivariate_normal(c[0], c[1]) for c in target_centroids
for _ in range(c[2])
]
mlhyp = None
nclusters = []
ntargets_true = []
ntargets = []
nhyps = []
for k in range(35):
# print()
# print()
print("k:", k)
if k > 0:
tracker.predict(1)
for t in targets:
t[0:2] += t[2:]
clutter = [
np.random.multivariate_normal(c[0], c[1])
for c in clutter_centroids for _ in range(c[2])
]
reports = {
mht.Report(
np.random.multivariate_normal(t[0:2], np.diag([0.1,
0.1])), # noqa
# t[0:2],
np.eye(2) * 0.001,
mht.models.position_measurement,
i)
for i, t in enumerate(targets)
}
false_reports = {
mht.Report(
np.random.multivariate_normal(t[0:2], np.diag([0.1,
0.1])), # noqa
np.eye(2) * 0.3,
mht.models.position_measurement)
for t in clutter
}
ntt = 0
for s in sensors:
sr = {r for r in reports if s.in_fov(r.z[0:2])}
fsr = {r for r in false_reports if s.in_fov(r.z[0:2])}
ntt += len(sr)
reports -= sr
this_scan = mht.Scan(s, list(sr | fsr))
# mht.plot.plot_scan(this_scan)
tracker.register_scan(this_scan)
tracker._load_clusters()
mlhyp = next(tracker.global_hypotheses())
nclusters.append(len(tracker.active_clusters))
ntargets.append(len(mlhyp.targets))
ntargets_true.append(len(targets))
nhyps.append(
reduce(operator.mul,
(len(c.hypotheses) for c in tracker.active_clusters)))
plt.plot([t[0] for t in targets], [t[1] for t in targets],
marker='D',
color='y',
alpha=.5,
linestyle='None')
plt.plot([t[0] for t in targets], [t[1] for t in targets],
marker='D',
color='y',
alpha=.5,
linestyle='None')
mht.plot.plot_hyptrace(mlhyp, covellipse=False)
mht.plot.plot_hypothesis(mlhyp, cseed=2)
mht.plot.plt.axis([-30, 150, -30, 150])
mht.plot.plt.ylabel('Tracks')
for s in sensors:
mht.plot.plot_bbox(s)
tracker._load_clusters()
print("Clusters:", len(tracker.active_clusters))
for c in tracker.active_clusters:
mht.plot.plot_bbox(c)