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_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_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_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)