def scene_synthetic_triangulation() -> synthetic_scene.SyntheticInputData:
np.random.seed(42)
reference = geo.TopocentricConverter(47.0, 6.0, 0)
data = synthetic_examples.synthetic_circle_scene(reference)
maximum_depth = 40
projection_noise = 1.0
gps_noise = 0.1
imu_noise = 1.0
gcp_noise = (0.0, 0.0)
gcps_count = 10
gcps_shift = [10.0, 0.0, 100.0]
return synthetic_scene.SyntheticInputData(
data.get_reconstruction(),
reference,
maximum_depth,
projection_noise,
gps_noise,
imu_noise,
gcp_noise,
False,
gcps_count,
gcps_shift,
)
def scene_synthetic_cube():
np.random.seed(42)
data = synthetic_examples.synthetic_cube_scene()
reconstruction = data.get_reconstruction()
input_data = synthetic_scene.SyntheticInputData(reconstruction, 40, 0.0,
0.0, False)
return reconstruction, input_data.tracks_manager
def scene_synthetic_cube():
np.random.seed(42)
data = synthetic_examples.synthetic_cube_scene()
reference = geo.TopocentricConverter(47.0, 6.0, 0)
reconstruction = data.get_reconstruction()
input_data = synthetic_scene.SyntheticInputData(reconstruction, reference,
40, 0.0, 0.0, False)
return reconstruction, input_data.tracks_manager
def scene_synthetic_rig() -> synthetic_scene.SyntheticInputData:
np.random.seed(42)
data = synthetic_examples.synthetic_rig_scene()
maximum_depth = 40
projection_noise = 1.0
gps_noise = 0.1
return synthetic_scene.SyntheticInputData(data.get_reconstruction(),
maximum_depth, projection_noise,
gps_noise, False)
def scene_synthetic_rig() -> synthetic_scene.SyntheticInputData:
np.random.seed(42)
data = synthetic_examples.synthetic_rig_scene()
maximum_depth = 40
projection_noise = 1.0
gps_noise = 0.1
reference = geo.TopocentricConverter(47.0, 6.0, 0)
return synthetic_scene.SyntheticInputData(
data.get_reconstruction(),
reference,
maximum_depth,
projection_noise,
gps_noise,
False,
)
def pairs_and_poses():
np.random.seed(42)
data = synthetic_examples.synthetic_cube_scene()
reconstruction = data.get_reconstruction()
input_data = synthetic_scene.SyntheticInputData(reconstruction, 40, 0.0,
0.0, False)
features, tracks_manager = input_data.features, input_data.tracks_manager
points_keys = list(reconstruction.points.keys())
pairs, poses = defaultdict(list), defaultdict(list)
for im1, im2 in tracks_manager.get_all_pairs_connectivity():
tuples = tracks_manager.get_all_common_observations(im1, im2)
f1 = [p.point for k, p, _ in tuples if k in points_keys]
f2 = [p.point for k, _, p in tuples if k in points_keys]
pairs[im1, im2].append((f1, f2))
pose1 = reconstruction.shots[im1].pose
pose2 = reconstruction.shots[im2].pose
poses[im1, im2] = pose2.relative_to(pose1)
camera = list(reconstruction.cameras.values())[0]
return pairs, poses, camera, features, tracks_manager, reconstruction
