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() -> synthetic_scene.SyntheticInputData:
np.random.seed(42)
data = synthetic_examples.synthetic_circle_scene()
maximum_depth = 40
projection_noise = 1.0
gps_noise = 5.0
return synthetic_scene.SyntheticInputData(data.get_reconstruction(),
maximum_depth, projection_noise,
gps_noise, False)
def scene_synthetic():
np.random.seed(42)
data = synthetic_examples.synthetic_circle_scene()
maximum_depth = 40
projection_noise = 1.0
gps_noise = 5.0
exifs = data.get_scene_exifs(gps_noise)
features, desc, colors, graph = data.get_tracks_data(
maximum_depth, projection_noise)
return data, exifs, features, desc, colors, graph
def scene_synthetic() -> synthetic_scene.SyntheticInputData:
np.random.seed(42)
data = synthetic_examples.synthetic_circle_scene()
maximum_depth = 40
projection_noise = 1.0
gps_noise = 5.0
reference = geo.TopocentricConverter(47.0, 6.0, 0)
return synthetic_scene.SyntheticInputData(
data.get_reconstruction(),
reference,
maximum_depth,
projection_noise,
gps_noise,
False,
)
