def test_is_consistent_with_fixed_seed(self):
# Actually run the system using mocked images
num_frames = 20
max_time = 50
speed = 0.1
image_builder = DemoImageBuilder(mode=ImageMode.STEREO,
stereo_offset=0.15,
width=640,
height=480,
num_stars=150,
length=max_time * speed,
speed=speed,
close_ratio=0.6,
min_size=10,
max_size=100)
subject = LibVisOStereoSystem()
subject.set_camera_intrinsics(image_builder.get_camera_intrinsics(),
max_time / num_frames)
subject.set_stereo_offset(image_builder.get_stereo_offset())
subject.start_trial(ImageSequenceType.SEQUENTIAL, seed=0)
for idx in range(num_frames):
time = max_time * idx / num_frames
image = image_builder.create_frame(time)
subject.process_image(image, time)
result1 = subject.finish_trial()
subject.start_trial(ImageSequenceType.SEQUENTIAL, seed=0)
for idx in range(num_frames):
time = max_time * idx / num_frames
image = image_builder.create_frame(time)
subject.process_image(image, time)
result2 = subject.finish_trial()
has_any_estimate = False
self.assertEqual(len(result1.results), len(result2.results))
for frame_result_1, frame_result_2 in zip(result1.results,
result2.results):
self.assertEqual(frame_result_1.timestamp,
frame_result_2.timestamp)
self.assertEqual(frame_result_1.tracking_state,
frame_result_2.tracking_state)
if frame_result_1.estimated_motion is None or frame_result_2.estimated_motion is None:
self.assertEqual(frame_result_1.estimated_motion,
frame_result_2.estimated_motion)
else:
has_any_estimate = True
motion1 = frame_result_1.estimated_motion
motion2 = frame_result_2.estimated_motion
loc_diff = motion1.location - motion2.location
self.assertNPClose(loc_diff, np.zeros(3), rtol=0, atol=1e-14)
quat_diff = motion1.rotation_quat(
True) - motion2.rotation_quat(True)
self.assertNPClose(quat_diff, np.zeros(4), rtol=0, atol=1e-14)
self.assertTrue(has_any_estimate)
def test_is_different_with_changed_seed(self):
# Actually run the system using mocked images
num_frames = 20
max_time = 50
speed = 0.1
image_builder = DemoImageBuilder(mode=ImageMode.STEREO,
stereo_offset=0.15,
width=640,
height=480,
num_stars=150,
length=max_time * speed,
speed=speed,
close_ratio=0.6,
min_size=10,
max_size=100)
subject = LibVisOStereoSystem()
subject.set_camera_intrinsics(image_builder.get_camera_intrinsics(),
max_time / num_frames)
subject.set_stereo_offset(image_builder.get_stereo_offset())
subject.start_trial(ImageSequenceType.SEQUENTIAL, seed=0)
for idx in range(num_frames):
time = max_time * idx / num_frames
image = image_builder.create_frame(time)
subject.process_image(image, time)
result1 = subject.finish_trial()
subject.start_trial(ImageSequenceType.SEQUENTIAL, seed=2)
for idx in range(num_frames):
time = max_time * idx / num_frames
image = image_builder.create_frame(time)
subject.process_image(image, time)
result2 = subject.finish_trial()
self.assertEqual(len(result1.results), len(result2.results))
different_tracking = 0
loc_diff = np.zeros(3)
quat_diff = np.zeros(4)
for frame_result_1, frame_result_2 in zip(result1.results,
result2.results):
self.assertEqual(frame_result_1.timestamp,
frame_result_2.timestamp)
if frame_result_1.tracking_state != frame_result_2.tracking_state:
different_tracking += 1
elif frame_result_1.estimated_motion is not None and frame_result_2.estimated_motion is not None:
motion1 = frame_result_1.estimated_motion
motion2 = frame_result_2.estimated_motion
loc_diff += np.abs(motion1.location - motion2.location)
quat_diff += np.abs(
motion1.rotation_quat(True) - motion2.rotation_quat(True))
if different_tracking <= 0:
# If the tracking is the same, make sure the estimates are at least different
self.assertNotNPClose(loc_diff, np.zeros(3), rtol=0, atol=1e-10)
self.assertNotNPClose(quat_diff, np.zeros(4), rtol=0, atol=1e-10)
def run_orbslam_with_vocab(vocab_path, temp_folder, seed=1000, num_frames=25):
# Actually run the system using mocked images
max_time = 50
speed = 0.1
path_manager = PathManager([Path(__file__).parent], temp_folder)
image_builder = DemoImageBuilder(seed=seed,
mode=ImageMode.STEREO,
stereo_offset=0.15,
width=320,
height=240,
num_stars=500,
length=max_time * speed,
speed=speed,
min_size=4,
max_size=50)
subject = OrbSlam2(vocabulary_file=vocab_path,
mode=SensorMode.STEREO,
orb_ini_threshold_fast=12,
orb_min_threshold_fast=7)
subject.resolve_paths(path_manager)
subject.set_camera_intrinsics(image_builder.get_camera_intrinsics(),
max_time / num_frames)
subject.set_stereo_offset(image_builder.get_stereo_offset())
subject.start_trial(ImageSequenceType.SEQUENTIAL)
for idx in range(num_frames):
time = max_time * idx / num_frames
image = image_builder.create_frame(time)
subject.process_image(image, time)
return subject.finish_trial()
def setUpClass(cls):
dbconn.setup_image_manager()
cls.temp_folder.mkdir(parents=True, exist_ok=True)
cls.path_manager = PathManager([Path(__file__).parent],
cls.temp_folder)
image_builder = DemoImageBuilder(mode=ImageMode.STEREO,
stereo_offset=0.15,
width=320,
height=240,
num_stars=500,
length=cls.max_time * cls.speed,
speed=cls.speed,
min_size=4,
max_size=50)
# Make an image source from the image builder
images = []
for time in range(cls.num_frames):
image = image_builder.create_frame(time)
images.append(image)
cls.image_collection = ImageCollection(
images=images,
timestamps=list(range(len(images))),
sequence_type=ImageSequenceType.SEQUENTIAL)
def test_can_run_on_colour_images(self):
# Actually run the system using mocked images
num_frames = 100
max_time = 50
speed = 0.1
path_manager = PathManager([Path(__file__).parent], self.temp_folder)
image_builder = DemoImageBuilder(mode=ImageMode.MONOCULAR,
width=640,
height=480,
num_stars=150,
length=max_time * speed,
speed=speed,
close_ratio=0.6,
min_size=10,
max_size=100,
colour=True)
# image_builder.visualise_sequence(max_time, frame_interval=0.5)
# return
subject = OrbSlam2(vocabulary_file=self.vocab_path,
mode=SensorMode.MONOCULAR,
orb_num_features=1000,
orb_num_levels=8,
orb_scale_factor=1.2,
orb_ini_threshold_fast=7,
orb_min_threshold_fast=12)
subject.resolve_paths(path_manager)
subject.set_camera_intrinsics(image_builder.get_camera_intrinsics(),
max_time / num_frames)
subject.start_trial(ImageSequenceType.SEQUENTIAL)
for idx in range(num_frames):
time = max_time * idx / num_frames
image = image_builder.create_frame(time)
subject.process_image(image, time)
result = subject.finish_trial()
self.assertIsInstance(result, SLAMTrialResult)
with no_auto_dereference(SLAMTrialResult):
self.assertEqual(subject.pk, result.system)
self.assertTrue(result.success)
self.assertFalse(result.has_scale)
self.assertIsNotNone(result.run_time)
self.assertIsNotNone(result.settings)
self.assertEqual(num_frames, len(result.results))
has_been_found = False
for idx, frame_result in enumerate(result.results):
self.assertEqual(max_time * idx / num_frames,
frame_result.timestamp)
self.assertIsNotNone(frame_result.pose)
self.assertIsNotNone(frame_result.motion)
if frame_result.tracking_state is TrackingState.OK:
has_been_found = True
self.assertTrue(has_been_found)
def test_run_with_loops(self):
# Actually run the system using mocked images
num_frames = [20, 20, 27, 25]
max_time = 25
speed = 1
path_manager = PathManager([Path(__file__).parent], self.temp_folder)
image_builder = DemoImageBuilder(mode=ImageMode.STEREO,
stereo_offset=0.15,
width=320,
height=240,
num_stars=300,
length=max_time * speed,
speed=speed,
min_size=4,
max_size=50,
close_ratio=0.5)
subject = OrbSlam2(vocabulary_file=self.vocab_path,
mode=SensorMode.STEREO,
orb_ini_threshold_fast=12,
orb_min_threshold_fast=7)
subject.resolve_paths(path_manager)
subject.set_camera_intrinsics(
image_builder.get_camera_intrinsics(),
max_time * len(num_frames) / sum(num_frames))
subject.set_stereo_offset(image_builder.get_stereo_offset())
subject.start_trial(ImageSequenceType.SEQUENTIAL)
for loop_idx, loop_frames in enumerate(num_frames):
for idx in range(loop_frames):
time = max_time * idx / loop_frames + max_time * loop_idx
if loop_idx % 2 == 1:
builder_time = max_time * (1 - idx / loop_frames)
else:
builder_time = max_time * idx / loop_frames
image = image_builder.create_frame(builder_time)
subject.process_image(image, time)
result = subject.finish_trial()
self.assertIsInstance(result, SLAMTrialResult)
with no_auto_dereference(SLAMTrialResult):
self.assertEqual(subject.pk, result.system)
self.assertTrue(result.success)
self.assertTrue(result.has_scale)
self.assertIsNotNone(result.run_time)
self.assertIsNotNone(result.settings)
self.assertEqual(sum(num_frames), len(result.results))
has_been_found = False
for idx, frame_result in enumerate(result.results):
self.assertIsNotNone(frame_result.pose)
self.assertIsNotNone(frame_result.motion)
if frame_result.tracking_state is TrackingState.OK:
has_been_found = True
self.assertTrue(has_been_found)
def test_result_saves(self):
# Make an image collection with some number of images
images = []
image_builder = DemoImageBuilder(mode=ImageMode.STEREO,
stereo_offset=0.15,
width=160,
height=120)
num_images = 10
for time in range(num_images):
image = image_builder.create_frame(time / num_images)
image.save()
images.append(image)
image_collection = ImageCollection(
images=images,
timestamps=list(range(len(images))),
sequence_type=ImageSequenceType.SEQUENTIAL)
image_collection.save()
subject = LibVisOStereoSystem()
subject.save()
# Actually run the system using mocked images
subject.set_camera_intrinsics(image_builder.get_camera_intrinsics(),
1 / 10)
subject.set_stereo_offset(image_builder.get_stereo_offset())
subject.start_trial(ImageSequenceType.SEQUENTIAL)
for time, image in enumerate(images):
subject.process_image(image, time)
result = subject.finish_trial()
self.assertIsInstance(result, SLAMTrialResult)
self.assertEqual(len(image_collection), len(result.results))
result.image_source = image_collection
result.save()
# Load all the entities
all_entities = list(SLAMTrialResult.objects.all())
self.assertGreaterEqual(len(all_entities), 1)
self.assertEqual(all_entities[0], result)
all_entities[0].delete()
SLAMTrialResult._mongometa.collection.drop()
ImageCollection._mongometa.collection.drop()
StereoImage._mongometa.collection.drop()
def create_vocab(vocab_path='ORBvoc-synth.txt'):
"""
Tiny script to create a vocabulary from the demo image builder
This gives me a vocab designed to handle the synthetic images I throw at it while testing.
:return:
"""
total_time = 10 # seconds
num_frames = 20
speed = 3.0
vocab_builder = VocabularyBuilder()
for seed in tqdm(range(100), total=100):
image_builder = DemoImageBuilder(mode=ImageMode.MONOCULAR,
seed=seed,
length=total_time * speed)
for idx in range(num_frames):
time = total_time * idx / num_frames
image = image_builder.create_frame(time)
vocab_builder.add_image(image.pixels)
vocab_builder.build_vocabulary(str(vocab_path))
def create_vocab(vocab_builder: VocabularyBuilder,
vocab_path: Path,
branching_factor: int,
depth: int,
num_variants: int = 10,
seed=100):
"""
Tiny script to create a vocabulary from the demo image builder
This gives me a vocab designed to handle the synthetic images I throw at it while testing.
:return:
"""
total_time = 10 # seconds
num_frames = 20 # Total frames to pull
speed = 3.0 # Units / second
for img_seed in range(num_variants):
image_builder = DemoImageBuilder(mode=ImageMode.MONOCULAR,
seed=img_seed,
length=total_time * speed)
for idx in range(num_frames):
time = total_time * idx / num_frames
image = image_builder.create_frame(time)
vocab_builder.add_image(image.pixels)
vocab_builder.build_vocabulary(str(vocab_path), branching_factor, depth,
seed)
def test_can_run_on_colour_images(self):
# Actually run the system using mocked images
num_frames = 50
max_time = 50
speed = 0.1
image_builder = DemoImageBuilder(
mode=ImageMode.MONOCULAR,
width=640, height=480, num_stars=150,
length=max_time * speed, speed=speed,
close_ratio=0.6, min_size=1, max_size=50, colour=True
)
subject = LibVisOMonoSystem(motion_threshold=1000)
subject.set_camera_intrinsics(image_builder.get_camera_intrinsics(), max_time / num_frames)
subject.start_trial(ImageSequenceType.SEQUENTIAL, seed=0)
for idx in range(num_frames):
time = max_time * idx / num_frames
image = image_builder.create_frame(time)
subject.process_image(image, time)
result = subject.finish_trial()
self.assertIsInstance(result, SLAMTrialResult)
self.assertEqual(subject, result.system)
self.assertTrue(result.success)
self.assertFalse(result.has_scale)
self.assertIsNotNone(result.run_time)
self.assertEqual({
'seed': 0,
'in_fx': image_builder.focal_length,
'in_fy': image_builder.focal_length,
'in_cu': image_builder.width / 2,
'in_cv': image_builder.height / 2,
'in_width': image_builder.width,
'in_height': image_builder.height
}, result.settings)
self.assertEqual(num_frames, len(result.results))
has_been_found = False
has_been_lost = False
for idx, frame_result in enumerate(result.results):
self.assertEqual(max_time * idx / num_frames, frame_result.timestamp)
self.assertIsNotNone(frame_result.pose)
self.assertIsNotNone(frame_result.motion)
# If we're lost, our tracking state should depend of if we've been lost before
is_first_frame = False
if frame_result.tracking_state != TrackingState.OK:
if has_been_found:
has_been_lost = True
self.assertEqual(frame_result.tracking_state, TrackingState.LOST)
else:
self.assertEqual(frame_result.tracking_state, TrackingState.NOT_INITIALIZED)
elif has_been_found is False:
is_first_frame = True
has_been_found = True
# Motion should be none when we are lost, and on the first found frame
if is_first_frame or frame_result.tracking_state != TrackingState.OK:
self.assertIsNone(frame_result.estimated_motion)
else:
self.assertIsNotNone(frame_result.estimated_motion)
# Estimates will be none until we get a successful estimate, or after it has lost
if not has_been_found or has_been_lost:
self.assertIsNone(frame_result.estimated_pose)
else:
self.assertIsNotNone(frame_result.estimated_pose)
self.assertTrue(has_been_found)