def test_get_instance_returns_an_existing_instance_simple(self):
obj = LibVisOMonoSystem()
obj.save()
result = LibVisOMonoSystem.get_instance()
self.assertEqual(obj.pk, result.pk)
self.assertEqual(obj, result)
def test_stores_and_loads(self):
kwargs = {
'matcher_nms_n': np.random.randint(0, 5),
'matcher_nms_tau': np.random.randint(0, 100),
'matcher_match_binsize': np.random.randint(0, 100),
'matcher_match_radius': np.random.randint(0, 500),
'matcher_match_disp_tolerance': np.random.randint(0, 10),
'matcher_outlier_disp_tolerance': np.random.randint(0, 10),
'matcher_outlier_flow_tolerance': np.random.randint(0, 10),
'matcher_multi_stage': np.random.choice([True, False]),
'matcher_half_resolution': np.random.choice([True, False]),
'matcher_refinement': np.random.choice(MatcherRefinement),
'bucketing_max_features': np.random.randint(0, 10),
'bucketing_bucket_width': np.random.randint(0, 100),
'bucketing_bucket_height': np.random.randint(0, 100),
'height': np.random.uniform(0.0, 3.0),
'pitch': np.random.uniform(0.0, 3.0),
'ransac_iters': np.random.randint(0, 100),
'inlier_threshold': np.random.uniform(0.0, 3.0),
'motion_threshold': np.random.uniform(0.0, 100.0)
}
obj = LibVisOMonoSystem(**kwargs)
obj.save()
# Load all the entities
all_entities = list(VisionSystem.objects.all())
self.assertGreaterEqual(len(all_entities), 1)
self.assertEqual(all_entities[0], obj)
all_entities[0].delete()
def test_get_columns_returns_column_list(self):
subject = LibVisOMonoSystem()
self.assertEqual({
'seed',
'in_width',
'in_height',
'in_fx',
'in_fy',
'in_cx',
'in_cy',
'matcher_nms_n',
'matcher_nms_tau',
'matcher_match_binsize',
'matcher_match_radius',
'matcher_match_disp_tolerance',
'matcher_outlier_disp_tolerance',
'matcher_outlier_flow_tolerance',
'matcher_multi_stage',
'matcher_half_resolution',
'matcher_refinement',
'bucketing_max_features',
'bucketing_bucket_width',
'bucketing_bucket_height',
'height',
'pitch',
'ransac_iters',
'inlier_threshold',
'motion_threshold',
}, subject.get_columns())
def test_get_properties_returns_only_requested_columns_that_exist(self):
matcher_nms_n = 10
matcher_nms_tau = 35
matcher_match_binsize = 16
matcher_match_radius = 155
matcher_match_disp_tolerance = 4
matcher_outlier_disp_tolerance = 3
matcher_outlier_flow_tolerance = 6
matcher_multi_stage = False
matcher_half_resolution = False
matcher_refinement = MatcherRefinement.SUBPIXEL
bucketing_max_features = 6
bucketing_bucket_width = 45
bucketing_bucket_height = 66
height = 0.8
pitch = 0.22
ransac_iters = 1444
inlier_threshold = 0.0006
motion_threshold = 78.4
subject = LibVisOMonoSystem(
matcher_nms_n=matcher_nms_n,
matcher_nms_tau=matcher_nms_tau,
matcher_match_binsize=matcher_match_binsize,
matcher_match_radius=matcher_match_radius,
matcher_match_disp_tolerance=matcher_match_disp_tolerance,
matcher_outlier_disp_tolerance=matcher_outlier_disp_tolerance,
matcher_outlier_flow_tolerance=matcher_outlier_flow_tolerance,
matcher_multi_stage=matcher_multi_stage,
matcher_half_resolution=matcher_half_resolution,
matcher_refinement=matcher_refinement,
bucketing_max_features=bucketing_max_features,
bucketing_bucket_width=bucketing_bucket_width,
bucketing_bucket_height=bucketing_bucket_height,
height=height,
pitch=pitch,
ransac_iters=ransac_iters,
inlier_threshold=inlier_threshold,
motion_threshold=motion_threshold
)
self.assertEqual({
'matcher_match_binsize': matcher_match_binsize,
'matcher_match_disp_tolerance': matcher_match_disp_tolerance,
'matcher_outlier_disp_tolerance': matcher_outlier_disp_tolerance,
'matcher_refinement': matcher_refinement,
'pitch': pitch,
}, subject.get_properties({
'matcher_match_binsize', 'matcher_match_disp_tolerance', 'matcher_outlier_disp_tolerance',
'not_a_column',
'matcher_refinement', 'pitch',
'also_not_a_column', 'sir_not_appearing_in_these_columns'
}))
def test_simple_trial_run_generated(self):
sequence_folder, left_path, right_path = ndds_loader.find_files(
NDDS_SEQUENCE)
camera_intrinsics = ndds_loader.read_camera_intrinsics(
left_path / '_camera_settings.json')
max_img_id = ndds_loader.find_max_img_id(
lambda idx: left_path / ndds_loader.IMG_TEMPLATE.format(idx))
with (NDDS_SEQUENCE / 'timestamps.json').open('r') as fp:
timestamps = json.load(fp)
subject = LibVisOMonoSystem(
matcher_nms_n=10,
matcher_nms_tau=66,
matcher_match_binsize=50,
matcher_match_radius=245,
matcher_match_disp_tolerance=2,
matcher_outlier_disp_tolerance=5,
matcher_outlier_flow_tolerance=2,
matcher_multi_stage=False,
matcher_half_resolution=False,
matcher_refinement=MatcherRefinement.SUBPIXEL,
bucketing_max_features=6,
bucketing_bucket_width=136,
bucketing_bucket_height=102,
height=1.0,
pitch=0.0,
ransac_iters=439,
inlier_threshold=4.921875,
motion_threshold=609.375)
subject.set_camera_intrinsics(camera_intrinsics, 0.1)
subject.start_trial(ImageSequenceType.SEQUENTIAL, seed=0)
image_group = 'test'
with image_manager.get().get_group(image_group, allow_write=True):
for img_idx in range(max_img_id + 1):
pixels = image_utils.read_colour(
left_path / ndds_loader.IMG_TEMPLATE.format(img_idx))
image = Image(
_id=bson.ObjectId(),
pixels=pixels,
image_group=image_group,
metadata=imeta.ImageMetadata(camera_pose=Transform()))
subject.process_image(image, timestamps[img_idx])
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(max_img_id + 1, len(result.results))
def test_get_instance_can_create_an_instance(self):
matcher_nms_n = np.random.randint(1, 5)
matcher_nms_tau = np.random.randint(20, 100)
matcher_match_binsize = np.random.randint(20, 100)
matcher_match_radius = np.random.randint(20, 100)
matcher_match_disp_tolerance = np.random.randint(1, 5)
matcher_outlier_disp_tolerance = np.random.randint(2, 10)
matcher_outlier_flow_tolerance = np.random.randint(2, 10)
matcher_multi_stage = np.random.choice([True, False])
matcher_half_resolution = np.random.choice([True, False])
matcher_refinement = np.random.choice(MatcherRefinement)
bucketing_max_features = np.random.randint(2, 10)
bucketing_bucket_width = np.random.randint(20, 100)
bucketing_bucket_height = np.random.randint(20, 100)
height = np.random.uniform(0.8, 1.2)
pitch = np.random.uniform(-0.1, 0.1)
ransac_iters = np.random.randint(500, 3000)
inlier_threshold = np.random.uniform(0, 0.0001)
motion_threshold = np.random.uniform(20.0, 300.0)
obj = LibVisOMonoSystem.get_instance(
matcher_nms_n=matcher_nms_n,
matcher_nms_tau=matcher_nms_tau,
matcher_match_binsize=matcher_match_binsize,
matcher_match_radius=matcher_match_radius,
matcher_match_disp_tolerance=matcher_match_disp_tolerance,
matcher_outlier_disp_tolerance=matcher_outlier_disp_tolerance,
matcher_outlier_flow_tolerance=matcher_outlier_flow_tolerance,
matcher_multi_stage=matcher_multi_stage,
matcher_half_resolution=matcher_half_resolution,
matcher_refinement=matcher_refinement,
bucketing_max_features=bucketing_max_features,
bucketing_bucket_width=bucketing_bucket_width,
bucketing_bucket_height=bucketing_bucket_height,
height=height,
pitch=pitch,
ransac_iters=ransac_iters,
inlier_threshold=inlier_threshold,
motion_threshold=motion_threshold
)
self.assertEqual(matcher_nms_n, obj.matcher_nms_n)
self.assertEqual(matcher_nms_tau, obj.matcher_nms_tau)
self.assertEqual(matcher_match_binsize, obj.matcher_match_binsize)
self.assertEqual(matcher_match_radius, obj.matcher_match_radius)
self.assertEqual(matcher_match_disp_tolerance, obj.matcher_match_disp_tolerance)
self.assertEqual(matcher_outlier_disp_tolerance, obj.matcher_outlier_disp_tolerance)
self.assertEqual(matcher_outlier_flow_tolerance, obj.matcher_outlier_flow_tolerance)
self.assertEqual(matcher_multi_stage, obj.matcher_multi_stage)
self.assertEqual(matcher_half_resolution, obj.matcher_half_resolution)
self.assertEqual(matcher_refinement, obj.matcher_refinement)
self.assertEqual(bucketing_max_features, obj.bucketing_max_features)
self.assertEqual(bucketing_bucket_width, obj.bucketing_bucket_width)
self.assertEqual(bucketing_bucket_height, obj.bucketing_bucket_height)
self.assertEqual(height, obj.height)
self.assertEqual(pitch, obj.pitch)
self.assertEqual(ransac_iters, obj.ransac_iters)
self.assertEqual(inlier_threshold, obj.inlier_threshold)
self.assertEqual(motion_threshold, obj.motion_threshold)
def test_creates_an_instance_with_defaults_by_default(self):
obj = LibVisOMonoSystem()
result = LibVisOMonoSystem.get_instance()
self.assertEqual(obj.matcher_nms_n, result.matcher_nms_n)
self.assertEqual(obj.matcher_nms_tau, result.matcher_nms_tau)
self.assertEqual(obj.matcher_match_binsize, result.matcher_match_binsize)
self.assertEqual(obj.matcher_match_radius, result.matcher_match_radius)
self.assertEqual(obj.matcher_match_disp_tolerance, result.matcher_match_disp_tolerance)
self.assertEqual(obj.matcher_outlier_disp_tolerance, result.matcher_outlier_disp_tolerance)
self.assertEqual(obj.matcher_outlier_flow_tolerance, result.matcher_outlier_flow_tolerance)
self.assertEqual(obj.matcher_multi_stage, result.matcher_multi_stage)
self.assertEqual(obj.matcher_half_resolution, result.matcher_half_resolution)
self.assertEqual(obj.matcher_refinement, result.matcher_refinement)
self.assertEqual(obj.bucketing_max_features, result.bucketing_max_features)
self.assertEqual(obj.bucketing_bucket_width, result.bucketing_bucket_width)
self.assertEqual(obj.bucketing_bucket_height, result.bucketing_bucket_height)
self.assertEqual(obj.height, result.height)
self.assertEqual(obj.pitch, result.pitch)
self.assertEqual(obj.ransac_iters, result.ransac_iters)
self.assertEqual(obj.inlier_threshold, result.inlier_threshold)
self.assertEqual(obj.motion_threshold, result.motion_threshold)
def test_profile_mono(self, ):
import cProfile as profile
stats_file = "libviso_mono.prof"
system = LibVisOMonoSystem()
image_builder = DemoImageBuilder(mode=ImageMode.MONOCULAR,
stereo_offset=0.15,
width=640,
height=480,
num_stars=150,
length=self.max_time * self.speed,
speed=self.speed,
close_ratio=0.6,
min_size=10,
max_size=100)
profile.runctx(
"run_libviso(system, image_builder, self.num_frames, self.max_time, 0)",
locals=locals(),
globals=globals(),
filename=stats_file)
def test_result_saves(self):
# Make an image collection with some number of images
images = []
image_builder = DemoImageBuilder(mode=ImageMode.MONOCULAR, 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 = LibVisOMonoSystem()
subject.save()
# Actually run the system using mocked images
subject.set_camera_intrinsics(image_builder.get_camera_intrinsics(), 1 / 10)
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()
Image._mongometa.collection.drop()
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)
def test_is_different_with_changed_seed(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=10, max_size=100
)
subject = LibVisOMonoSystem(motion_threshold=1000)
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 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.MONOCULAR,
width=640, height=480, num_stars=150,
length=max_time * speed, speed=speed,
close_ratio=0.6, min_size=10, max_size=100
)
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)
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_get_properties_returns_the_value_of_all_columns(self):
matcher_nms_n = 10
matcher_nms_tau = 35
matcher_match_binsize = 16
matcher_match_radius = 155
matcher_match_disp_tolerance = 4
matcher_outlier_disp_tolerance = 3
matcher_outlier_flow_tolerance = 6
matcher_multi_stage = False
matcher_half_resolution = False
matcher_refinement = MatcherRefinement.SUBPIXEL
bucketing_max_features = 6
bucketing_bucket_width = 45
bucketing_bucket_height = 66
height = 0.8
pitch = 0.22
ransac_iters = 1444
inlier_threshold = 0.0006
motion_threshold = 78.4
subject = LibVisOMonoSystem(
matcher_nms_n=matcher_nms_n,
matcher_nms_tau=matcher_nms_tau,
matcher_match_binsize=matcher_match_binsize,
matcher_match_radius=matcher_match_radius,
matcher_match_disp_tolerance=matcher_match_disp_tolerance,
matcher_outlier_disp_tolerance=matcher_outlier_disp_tolerance,
matcher_outlier_flow_tolerance=matcher_outlier_flow_tolerance,
matcher_multi_stage=matcher_multi_stage,
matcher_half_resolution=matcher_half_resolution,
matcher_refinement=matcher_refinement,
bucketing_max_features=bucketing_max_features,
bucketing_bucket_width=bucketing_bucket_width,
bucketing_bucket_height=bucketing_bucket_height,
height=height,
pitch=pitch,
ransac_iters=ransac_iters,
inlier_threshold=inlier_threshold,
motion_threshold=motion_threshold
)
properties = subject.get_properties()
for key, value in {
'seed': np.nan,
'in_width': np.nan,
'in_height': np.nan,
'in_fx': np.nan,
'in_fy': np.nan,
'in_cx': np.nan,
'in_cy': np.nan,
'matcher_nms_n': matcher_nms_n,
'matcher_nms_tau': matcher_nms_tau,
'matcher_match_binsize': matcher_match_binsize,
'matcher_match_radius': matcher_match_radius,
'matcher_match_disp_tolerance': matcher_match_disp_tolerance,
'matcher_outlier_disp_tolerance': matcher_outlier_disp_tolerance,
'matcher_outlier_flow_tolerance': matcher_outlier_flow_tolerance,
'matcher_multi_stage': matcher_multi_stage,
'matcher_half_resolution': matcher_half_resolution,
'matcher_refinement': matcher_refinement,
'bucketing_max_features': bucketing_max_features,
'bucketing_bucket_width': bucketing_bucket_width,
'bucketing_bucket_height': bucketing_bucket_height,
'height': height,
'pitch': pitch,
'ransac_iters': ransac_iters,
'inlier_threshold': inlier_threshold,
'motion_threshold': motion_threshold
}.items():
self.assertIn(key, properties)
if isinstance(value, float) and np.isnan(value):
self.assertTrue(np.isnan(properties[key]))
else:
self.assertEqual(value, properties[key])
def test_can_start_and_stop_trial(self):
subject = LibVisOMonoSystem()
subject.start_trial(ImageSequenceType.SEQUENTIAL)
result = subject.finish_trial()
self.assertIsInstance(result, SLAMTrialResult)
def test_get_instance_returns_an_existing_instance_complex(self):
matcher_nms_n = np.random.randint(1, 5)
matcher_nms_tau = np.random.randint(20, 100)
matcher_match_binsize = np.random.randint(20, 100)
matcher_match_radius = np.random.randint(20, 100)
matcher_match_disp_tolerance = np.random.randint(1, 5)
matcher_outlier_disp_tolerance = np.random.randint(2, 10)
matcher_outlier_flow_tolerance = np.random.randint(2, 10)
matcher_multi_stage = np.random.choice([True, False])
matcher_half_resolution = np.random.choice([True, False])
matcher_refinement = np.random.choice(MatcherRefinement)
bucketing_max_features = np.random.randint(2, 10)
bucketing_bucket_width = np.random.randint(20, 100)
bucketing_bucket_height = np.random.randint(20, 100)
height = np.random.uniform(0.8, 1.2)
pitch = np.random.uniform(-0.1, 0.1)
ransac_iters = np.random.randint(500, 3000)
inlier_threshold = np.random.uniform(0, 0.0001)
motion_threshold = np.random.uniform(20.0, 300.0)
obj = LibVisOMonoSystem(
matcher_nms_n=matcher_nms_n,
matcher_nms_tau=matcher_nms_tau,
matcher_match_binsize=matcher_match_binsize,
matcher_match_radius=matcher_match_radius,
matcher_match_disp_tolerance=matcher_match_disp_tolerance,
matcher_outlier_disp_tolerance=matcher_outlier_disp_tolerance,
matcher_outlier_flow_tolerance=matcher_outlier_flow_tolerance,
matcher_multi_stage=matcher_multi_stage,
matcher_half_resolution=matcher_half_resolution,
matcher_refinement=matcher_refinement,
bucketing_max_features=bucketing_max_features,
bucketing_bucket_width=bucketing_bucket_width,
bucketing_bucket_height=bucketing_bucket_height,
height=height,
pitch=pitch,
ransac_iters=ransac_iters,
inlier_threshold=inlier_threshold,
motion_threshold=motion_threshold
)
obj.save()
result = LibVisOMonoSystem.get_instance(
matcher_nms_n=matcher_nms_n,
matcher_nms_tau=matcher_nms_tau,
matcher_match_binsize=matcher_match_binsize,
matcher_match_radius=matcher_match_radius,
matcher_match_disp_tolerance=matcher_match_disp_tolerance,
matcher_outlier_disp_tolerance=matcher_outlier_disp_tolerance,
matcher_outlier_flow_tolerance=matcher_outlier_flow_tolerance,
matcher_multi_stage=matcher_multi_stage,
matcher_half_resolution=matcher_half_resolution,
matcher_refinement=matcher_refinement,
bucketing_max_features=bucketing_max_features,
bucketing_bucket_width=bucketing_bucket_width,
bucketing_bucket_height=bucketing_bucket_height,
height=height,
pitch=pitch,
ransac_iters=ransac_iters,
inlier_threshold=inlier_threshold,
motion_threshold=motion_threshold
)
self.assertEqual(obj.pk, result.pk)
self.assertEqual(obj, result)
