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 pairs_and_poses():
np.random.seed(42)
data = synthetic_examples.synthetic_cube_scene()
reconstruction = data.get_reconstruction()
scale = 0.0
features, _, _, tracks_manager = data.get_tracks_data(40, scale)
points_keys = list(reconstruction.points.keys())
pairs, poses = defaultdict(list), defaultdict(list)
for (im1, im2), tuples in tracks_manager.get_all_common_observations_all_pairs().items():
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))
poses[im1, im2] = reconstruction.shots[im2].pose.\
compose(reconstruction.shots[im1].pose.inverse())
camera = list(reconstruction.cameras.values())[0]
return pairs, poses, camera, features, tracks_manager, reconstruction
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
def pairs_and_poses():
np.random.seed(42)
data = synthetic_examples.synthetic_cube_scene()
reconstruction = data.get_reconstruction()
scale = 0.0
features, _, _, graph = data.get_tracks_data(40, scale)
pairs, poses = defaultdict(list), defaultdict(list)
for track in reconstruction.points:
for im1, im2 in combinations(graph[track].keys(), 2):
f1 = features[im1][graph[track][im1]['feature_id']][:2]
f2 = features[im2][graph[track][im2]['feature_id']][:2]
if im1 > im2:
im1, im2 = im2, im1
f1, f2, = f2, f1
pairs[im1, im2].append((f1, f2))
if not poses[im1, im2]:
poses[im1, im2] = reconstruction.shots[im2].pose.\
compose(reconstruction.shots[im1].pose.inverse())
camera = list(reconstruction.cameras.values())[0]
return pairs, poses, camera, features, graph, reconstruction
def scene_synthetic_cube():
np.random.seed(42)
data = synthetic_examples.synthetic_cube_scene()
_, _, _, tracks_manager = data.get_tracks_data(40, 0.0)
return data.get_reconstruction(), tracks_manager