def test_evaluate(self):
"""Tests that the evaluation is generated correctly."""
e = OdlcEvaluator(self.submitted_odlcs, self.real_odlcs)
d = e.evaluate()
td = {t.real_odlc: t for t in d.odlcs}
self.assertEqual(self.submit1.pk, td[self.real1.pk].submitted_odlc)
self.assertEqual(True, td[self.real1.pk].image_approved)
self.assertEqual(1.0, td[self.real1.pk].classifications_ratio)
self.assertEqual(0.0, td[self.real1.pk].geolocation_accuracy_ft)
self.assertEqual(True, td[self.real1.pk].actionable_submission)
self.assertEqual(1.0, td[self.real1.pk].classifications_score_ratio)
self.assertEqual(1.0, td[self.real1.pk].geolocation_score_ratio)
self.assertEqual(1.0, td[self.real1.pk].actionable_score_ratio)
self.assertEqual(1.0, td[self.real1.pk].autonomous_score_ratio)
self.assertAlmostEqual(1.0, td[self.real1.pk].score_ratio)
self.assertEqual(self.submit2.pk, td[self.real2.pk].submitted_odlc)
self.assertEqual(True, td[self.real2.pk].image_approved)
self.assertEqual(0.6, td[self.real2.pk].classifications_ratio)
self.assertAlmostEqual(109.444,
td[self.real2.pk].geolocation_accuracy_ft,
places=3)
self.assertEqual(False, td[self.real2.pk].actionable_submission)
self.assertEqual(0.6, td[self.real2.pk].classifications_score_ratio)
self.assertAlmostEqual(0.270,
td[self.real2.pk].geolocation_score_ratio,
places=3)
self.assertEqual(0.0, td[self.real2.pk].actionable_score_ratio)
self.assertAlmostEqual(0.201, td[self.real2.pk].score_ratio, places=3)
self.assertEqual(True, td[self.real6.pk].description_approved)
self.assertAlmostEqual(0.350, d.score_ratio, places=3)
self.assertEqual(2, d.unmatched_odlc_count)
def test_evaluate_no_submitted_odlcs(self):
"""Tests that evaluation works with no submitted odlcs."""
e = OdlcEvaluator([], self.real_odlcs)
d = e.evaluate()
self.assertEqual(0, d.matched_score_ratio)
self.assertEqual(0, d.unmatched_odlc_count)
self.assertEqual(6, len(d.odlcs))
def test_evaluate_no_real_odlcs(self):
"""Tests that evaluation works with no real odlcs."""
e = OdlcEvaluator(self.submitted_odlcs, [])
d = e.evaluate()
self.assertEqual(0, d.matched_score_ratio)
self.assertEqual(7, d.unmatched_odlc_count)
self.assertAlmostEqual(-0.35, d.score_ratio, places=3)
self.assertEqual(0, len(d.odlcs))
def generate_feedback(mission_config, user, team_eval):
"""Generates mission feedback for the given team and mission.
Args:
mission_config: The mission to evaluate the team against.
user: The team user object for which to evaluate and provide feedback.
team_eval: The team evaluation to fill.
"""
feedback = team_eval.feedback
# Find the user's flights.
flight_periods = TakeoffOrLandingEvent.flights(mission_config, user)
for period in flight_periods:
if period.duration() is None:
team_eval.warnings.append('Infinite flight period.')
uas_period_logs = [
UasTelemetry.dedupe(logs)
for logs in UasTelemetry.by_time_period(user, flight_periods)
]
uas_logs = list(itertools.chain.from_iterable(uas_period_logs))
if not uas_logs:
team_eval.warnings.append('No UAS telemetry logs.')
# Determine interop telemetry rates.
telem_max, telem_avg = UasTelemetry.rates(
user, flight_periods, time_period_logs=uas_period_logs)
if telem_max:
feedback.uas_telemetry_time_max_sec = telem_max
if telem_avg:
feedback.uas_telemetry_time_avg_sec = telem_avg
# Determine if the uas hit the waypoints.
feedback.waypoints.extend(
UasTelemetry.satisfied_waypoints(
mission_config.home_pos,
mission_config.mission_waypoints.order_by('order'), uas_logs))
# Evaluate the object detections.
user_odlcs = Odlc.objects.filter(user=user).filter(
mission=mission_config.pk).all()
evaluator = OdlcEvaluator(user_odlcs,
mission_config.odlcs.all(), flight_periods)
feedback.odlc.CopyFrom(evaluator.evaluate())
# Determine collisions with stationary.
for obst in mission_config.stationary_obstacles.all():
obst_eval = feedback.stationary_obstacles.add()
obst_eval.id = obst.pk
obst_eval.hit = obst.evaluate_collision_with_uas(uas_logs)
# Add judge feedback.
try:
judge_feedback = MissionJudgeFeedback.objects.get(
mission=mission_config.pk, user=user.pk)
feedback.judge.CopyFrom(judge_feedback.proto())
except MissionJudgeFeedback.DoesNotExist:
team_eval.warnings.append('No MissionJudgeFeedback for team.')
def test_match_odlcs(self):
"""Tests that matching odlcs produce maximal matches."""
e = OdlcEvaluator(self.submitted_odlcs, self.real_odlcs)
self.assertDictEqual({
self.submit1: self.real1,
self.submit2: self.real2,
self.submit5: self.real5,
self.submit6: self.real6,
self.real1: self.submit1,
self.real2: self.submit2,
self.real5: self.submit5,
self.real6: self.submit6,
}, e.match_odlcs(self.submitted_odlcs, self.real_odlcs))
def generate_feedback(mission_config, user, team_eval):
"""Generates mission feedback for the given team and mission.
Args:
mission_config: The mission to evaluate the team against.
user: The team user object for which to evaluate and provide feedback.
team_eval: The team evaluation to fill.
"""
feedback = team_eval.feedback
# Calculate the total mission clock time.
missions = MissionClockEvent.missions(user)
mission_clock_time = datetime.timedelta(seconds=0)
for mission in missions:
duration = mission.duration()
if duration is None:
team_eval.warnings.append('Infinite mission clock.')
else:
mission_clock_time += duration
feedback.mission_clock_time_sec = mission_clock_time.total_seconds()
# Calculate total time in air.
flight_periods = TakeoffOrLandingEvent.flights(user)
if flight_periods:
flight_time = reduce(lambda x, y: x + y,
[p.duration() for p in flight_periods])
feedback.flight_time_sec = flight_time.total_seconds()
else:
feedback.flight_time_sec = 0
# Find the user's flights.
for period in flight_periods:
if period.duration() is None:
team_eval.warnings.append('Infinite flight period.')
uas_period_logs = [
UasTelemetry.dedupe(logs)
for logs in UasTelemetry.by_time_period(user, flight_periods)
]
uas_logs = list(itertools.chain.from_iterable(uas_period_logs))
if not uas_logs:
team_eval.warnings.append('No UAS telemetry logs.')
# Determine interop telemetry rates.
telem_max, telem_avg = UasTelemetry.rates(user,
flight_periods,
time_period_logs=uas_period_logs)
if telem_max:
feedback.uas_telemetry_time_max_sec = telem_max
if telem_avg:
feedback.uas_telemetry_time_avg_sec = telem_avg
# Determine if the uas went out of bounds. This must be done for
# each period individually so time between periods isn't counted as
# out of bounds time. Note that this calculates reported time out
# of bounds, not actual or possible time spent out of bounds.
out_of_bounds = datetime.timedelta(seconds=0)
feedback.boundary_violations = 0
for logs in uas_period_logs:
bv, bt = FlyZone.out_of_bounds(mission_config.fly_zones.all(), logs)
feedback.boundary_violations += bv
out_of_bounds += bt
feedback.out_of_bounds_time_sec = out_of_bounds.total_seconds()
# Determine if the uas hit the waypoints.
feedback.waypoints.extend(
UasTelemetry.satisfied_waypoints(
mission_config.home_pos,
mission_config.mission_waypoints.order_by('order'), uas_logs))
# Evaluate the object detections.
user_odlcs = Odlc.objects.filter(user=user).all()
evaluator = OdlcEvaluator(user_odlcs, mission_config.odlcs.all())
feedback.odlc.CopyFrom(evaluator.evaluate())
# Determine collisions with stationary and moving obstacles.
for obst in mission_config.stationary_obstacles.all():
obst_eval = feedback.stationary_obstacles.add()
obst_eval.id = obst.pk
obst_eval.hit = obst.evaluate_collision_with_uas(uas_logs)
for obst in mission_config.moving_obstacles.all():
obst_eval = feedback.moving_obstacles.add()
obst_eval.id = obst.pk
obst_eval.hit = obst.evaluate_collision_with_uas(uas_logs)
# Add judge feedback.
try:
judge_feedback = MissionJudgeFeedback.objects.get(
mission=mission_config.pk, user=user.pk)
feedback.judge.CopyFrom(judge_feedback.proto())
except MissionJudgeFeedback.DoesNotExist:
team_eval.warnings.append('No MissionJudgeFeedback for team.')
# Sanity check mission time.
judge_mission_clock = (feedback.judge.flight_time_sec +
feedback.judge.post_process_time_sec)
if abs(feedback.mission_clock_time_sec - judge_mission_clock) > 30:
team_eval.warnings.append(
'Mission clock differs between interop and judge.')
def test_match_value(self):
"""Tests the match value for two odlcs."""
e = OdlcEvaluator(self.submitted_odlcs, self.real_odlcs)
self.assertAlmostEqual(
1.0,
e.evaluate_match(self.submit1, self.real1).score_ratio,
places=3)
self.assertAlmostEqual(
0.174,
e.evaluate_match(self.submit2, self.real2).score_ratio,
places=3)
self.assertAlmostEqual(
0.0,
e.evaluate_match(self.submit3, self.real3).score_ratio,
places=3)
self.assertAlmostEqual(
0.0,
e.evaluate_match(self.submit4, self.real4).score_ratio,
places=3)
self.assertAlmostEqual(
0.3,
e.evaluate_match(self.submit5, self.real5).score_ratio,
places=3)
self.assertAlmostEqual(
0.7,
e.evaluate_match(self.submit6, self.real6).score_ratio,
places=3)
self.assertAlmostEqual(
0.240,
e.evaluate_match(self.submit7, self.real1).score_ratio,
places=3)
self.assertAlmostEqual(
0.814,
e.evaluate_match(self.submit1, self.real2).score_ratio,
places=3)
self.assertAlmostEqual(
0.32,
e.evaluate_match(self.submit2, self.real1).score_ratio,
places=3)