def test_tracks_io():
d = data_generation.CubeDataset(2, 100, 0.0, 0.3)
output = StringIO()
tracks_before = d.tracks
opensfm.tracking.save_tracks_graph(output, tracks_before)
output.seek(0)
tracks_after = opensfm.tracking.load_tracks_graph(output)
assert tracks_before.nodes() == tracks_after.nodes()
assert tracks_before.edges() == tracks_after.edges()
def synthetic_reconstruction():
cube_dataset = data_generation.CubeDataset(10, 100, 0.001, 0.3)
synthetic_reconstruction = types.Reconstruction()
for shot in cube_dataset.shots.values():
synthetic_reconstruction.add_shot(shot)
for camera in cube_dataset.cameras.values():
synthetic_reconstruction.add_camera(camera)
for point_id, point in cube_dataset.points.items():
point_type = types.Point()
point_type.coordinates = point
point_type.id = point_id
synthetic_reconstruction.add_point(point_type)
return synthetic_reconstruction, cube_dataset.tracks
def test_robust_match():
d = data_generation.CubeDataset(2, 100, 0.1, 0.3)
p1 = np.array([v['feature'] for k, v in d.tracks['shot0'].iteritems()])
p2 = np.array([v['feature'] for k, v in d.tracks['shot1'].iteritems()])
camera1 = d.shots['shot0'].camera
camera2 = d.shots['shot1'].camera
num_points = len(p1)
inlier_matches = np.array([(i, i) for i in range(num_points)])
outlier_matches = np.random.randint(num_points, size=(num_points / 2, 2))
matches = np.concatenate((inlier_matches, outlier_matches))
config = opensfm.config.default_config()
rmatches = opensfm.matching.robust_match(p1, p2, camera1, camera2, matches,
config)
assert num_points <= len(rmatches) <= len(matches)