def run_dataset(data: DataSetBase, input, output) -> None:
recs_base = data.load_reconstruction(input)
if len(recs_base) == 0:
return
rec_base = recs_base[0]
tracks_manager = data.load_tracks_manager()
rec_base.add_correspondences_from_tracks_manager(tracks_manager)
images = data.images()
remaining_images = set(images) - set(rec_base.shots)
gcp = data.load_ground_control_points()
report = {}
rec_report = {}
report["extend_reconstruction"] = [rec_report]
rec, rec_report["grow"] = reconstruction.grow_reconstruction(
data,
tracks_manager,
rec_base,
remaining_images,
gcp,
)
rec_report["num_remaining_images"] = len(remaining_images)
report["not_reconstructed_images"] = list(remaining_images)
data.save_reconstruction([rec], output)
data.save_report(io.json_dumps(report), "reconstruction.json")
def run_dataset(data: DataSetBase, input: str, output: str) -> None:
"""Reconstruct the from a prior reconstruction."""
tracks_manager = data.load_tracks_manager()
rec_prior = data.load_reconstruction(input)
if len(rec_prior) > 0:
report, rec = reconstruction.reconstruct_from_prior(
data, tracks_manager, rec_prior[0])
data.save_reconstruction([rec], output)
data.save_report(io.json_dumps(report), "reconstruction.json")
def run_dataset(data: DataSetBase) -> None:
"""Add delaunay meshes to the reconstruction."""
tracks_manager = data.load_tracks_manager()
reconstructions = data.load_reconstruction()
all_shot_ids = set(tracks_manager.get_shot_ids())
for r in reconstructions:
for shot in r.shots.values():
if shot.id in all_shot_ids:
vertices, faces = mesh.triangle_mesh(shot.id, r,
tracks_manager)
shot.mesh.vertices = vertices
shot.mesh.faces = faces
data.save_reconstruction(reconstructions,
filename="reconstruction.meshed.json",
minify=True)
def run_dataset(data: DataSetBase,
algorithm: reconstruction.ReconstructionAlgorithm) -> None:
"""Compute the SfM reconstruction."""
tracks_manager = data.load_tracks_manager()
if algorithm == reconstruction.ReconstructionAlgorithm.INCREMENTAL:
report, reconstructions = reconstruction.incremental_reconstruction(
data, tracks_manager)
elif algorithm == reconstruction.ReconstructionAlgorithm.TRIANGULATION:
report, reconstructions = reconstruction.triangulation_reconstruction(
data, tracks_manager)
else:
raise RuntimeError(
f"Unsupported algorithm for reconstruction {algorithm}")
data.save_reconstruction(reconstructions)
data.save_report(io.json_dumps(report), "reconstruction.json")
def run_dataset(dataset: DataSetBase, input, output) -> None:
"""Bundle a reconstructions.
Args:
input: input reconstruction JSON in the dataset
output: input reconstruction JSON in the dataset
"""
reconstructions = dataset.load_reconstruction(input)
camera_priors = dataset.load_camera_models()
rig_cameras_priors = dataset.load_rig_cameras()
tracks_manager = dataset.load_tracks_manager()
# load the tracks manager and add its observations to the reconstruction
# go through all the points and add their shots
for reconstruction in reconstructions:
reconstruction.add_correspondences_from_tracks_manager(tracks_manager)
gcp = dataset.load_ground_control_points()
orec.bundle(reconstruction, camera_priors, rig_cameras_priors, gcp,
dataset.config)
dataset.save_reconstruction(reconstructions, output)