def rig_statistics(data: DataSetBase, reconstructions):
stats = {}
permutation = np.argsort([-len(r.shots) for r in reconstructions])
for rig_camera_id, rig_camera in data.load_rig_cameras().items():
stats[rig_camera_id] = {
"initial_values": {
"rotation": list(rig_camera.pose.rotation),
"translation": list(rig_camera.pose.translation),
}
}
for idx in permutation:
rec = reconstructions[idx]
for rig_camera in rec.rig_cameras.values():
if "optimized_values" in stats[rig_camera.id]:
continue
stats[rig_camera.id]["optimized_values"] = {
"rotation": list(rig_camera.pose.rotation),
"translation": list(rig_camera.pose.translation),
}
for rig_camera_id in data.load_rig_cameras():
if "optimized_values" not in stats[rig_camera_id]:
del stats[rig_camera_id]
return stats
def _reconstruction_from_rigs_and_assignments(data: DataSetBase):
assignments = data.load_rig_assignments()
rig_cameras = data.load_rig_cameras()
data.init_reference()
reconstruction = types.Reconstruction()
reconstruction.cameras = data.load_camera_models()
for rig_instance_id, instance in assignments.items():
for image, rig_camera_id in instance:
rig_camera = rig_cameras[rig_camera_id]
reconstruction.add_rig_camera(
pymap.RigCamera(rig_camera.pose, rig_camera_id))
instance_obj = reconstruction.add_rig_instance(
pymap.RigInstance(rig_instance_id))
instance_obj.pose.set_origin(
helpers.get_image_metadata(data, image).gps_position.value)
d = data.load_exif(image)
shot = reconstruction.create_shot(
image,
camera_id=d["camera"],
rig_camera_id=rig_camera_id,
rig_instance_id=rig_instance_id,
)
shot.metadata = helpers.get_image_metadata(data, image)
return [reconstruction]
def _reconstruction_from_rigs_and_assignments(data: DataSetBase):
assignments = data.load_rig_assignments()
rig_cameras = data.load_rig_cameras()
if not data.reference_lla_exists():
data.invent_reference_lla()
base_rotation = np.zeros(3)
reconstruction = types.Reconstruction()
reconstruction.cameras = data.load_camera_models()
for instance in assignments:
for image, camera_id in instance:
rig_camera = rig_cameras[camera_id]
rig_pose = pygeometry.Pose(base_rotation)
rig_pose.set_origin(orec.get_image_metadata(data, image).gps_position.value)
d = data.load_exif(image)
shot = reconstruction.create_shot(image, d["camera"])
shot.pose = rig_camera.pose.compose(rig_pose)
shot.metadata = orec.get_image_metadata(data, image)
return [reconstruction]
def run_dataset(dataset: DataSetBase, input, output):
"""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()
gcp = dataset.load_ground_control_points()
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)
orec.bundle(reconstruction, camera_priors, rig_cameras_priors, gcp,
dataset.config)
dataset.save_reconstruction(reconstructions, output)
def rig_statistics(
data: DataSetBase,
reconstructions: List[types.Reconstruction]) -> Dict[str, Any]:
stats = {}
permutation = np.argsort([-len(r.shots) for r in reconstructions])
rig_cameras = data.load_rig_cameras()
cameras = data.load_camera_models()
for rig_camera_id, rig_camera in rig_cameras.items():
# we skip per-camera rig camera for now
if rig_camera_id in cameras:
continue
stats[rig_camera_id] = {
"initial_values": {
"rotation": list(rig_camera.pose.rotation),
"translation": list(rig_camera.pose.translation),
}
}
for idx in permutation:
rec = reconstructions[idx]
for rig_camera in rec.rig_cameras.values():
if rig_camera.id not in stats:
continue
if "optimized_values" in stats[rig_camera.id]:
continue
stats[rig_camera.id]["optimized_values"] = {
"rotation": list(rig_camera.pose.rotation),
"translation": list(rig_camera.pose.translation),
}
for rig_camera_id in rig_cameras:
if rig_camera.id not in stats:
continue
if "optimized_values" not in stats[rig_camera_id]:
del stats[rig_camera_id]
return stats
def bootstrap_reconstruction(
data: DataSetBase,
tracks_manager: pymap.TracksManager,
im1: str,
im2: str,
p1: np.ndarray,
p2: np.ndarray,
) -> Tuple[Optional[types.Reconstruction], Dict[str, Any]]:
"""Start a reconstruction using two shots."""
logger.info("Starting reconstruction with {} and {}".format(im1, im2))
report: Dict[str, Any] = {
"image_pair": (im1, im2),
"common_tracks": len(p1),
}
camera_priors = data.load_camera_models()
camera1 = camera_priors[data.load_exif(im1)["camera"]]
camera2 = camera_priors[data.load_exif(im2)["camera"]]
threshold = data.config["five_point_algo_threshold"]
min_inliers = data.config["five_point_algo_min_inliers"]
iterations = data.config["five_point_refine_rec_iterations"]
R, t, inliers, report[
"two_view_reconstruction"] = two_view_reconstruction_general(
p1, p2, camera1, camera2, threshold, iterations)
logger.info("Two-view reconstruction inliers: {} / {}".format(
len(inliers), len(p1)))
if len(inliers) <= 5:
report["decision"] = "Could not find initial motion"
logger.info(report["decision"])
return None, report
rig_camera_priors = data.load_rig_cameras()
rig_assignments = data.load_rig_assignments_per_image()
reconstruction = types.Reconstruction()
reconstruction.reference = data.load_reference()
reconstruction.cameras = camera_priors
reconstruction.rig_cameras = rig_camera_priors
new_shots = add_shot(data, reconstruction, rig_assignments, im1,
pygeometry.Pose())
if im2 not in new_shots:
new_shots |= add_shot(data, reconstruction, rig_assignments, im2,
pygeometry.Pose(R, t))
align_reconstruction(reconstruction, None, data.config)
triangulate_shot_features(tracks_manager, reconstruction, new_shots,
data.config)
logger.info("Triangulated: {}".format(len(reconstruction.points)))
report["triangulated_points"] = len(reconstruction.points)
if len(reconstruction.points) < min_inliers:
report["decision"] = "Initial motion did not generate enough points"
logger.info(report["decision"])
return None, report
to_adjust = {s for s in new_shots if s != im1}
bundle_shot_poses(reconstruction, to_adjust, camera_priors,
rig_camera_priors, data.config)
retriangulate(tracks_manager, reconstruction, data.config)
if len(reconstruction.points) < min_inliers:
report[
"decision"] = "Re-triangulation after initial motion did not generate enough points"
logger.info(report["decision"])
return None, report
bundle_shot_poses(reconstruction, to_adjust, camera_priors,
rig_camera_priors, data.config)
report["decision"] = "Success"
report["memory_usage"] = current_memory_usage()
return reconstruction, report
def grow_reconstruction(
data: DataSetBase,
tracks_manager: pymap.TracksManager,
reconstruction: types.Reconstruction,
images: Set[str],
gcp: List[pymap.GroundControlPoint],
) -> Tuple[types.Reconstruction, Dict[str, Any]]:
"""Incrementally add shots to an initial reconstruction."""
config = data.config
report = {"steps": []}
camera_priors = data.load_camera_models()
rig_camera_priors = data.load_rig_cameras()
paint_reconstruction(data, tracks_manager, reconstruction)
align_reconstruction(reconstruction, gcp, config)
bundle(reconstruction, camera_priors, rig_camera_priors, None, config)
remove_outliers(reconstruction, config)
paint_reconstruction(data, tracks_manager, reconstruction)
should_bundle = ShouldBundle(data, reconstruction)
should_retriangulate = ShouldRetriangulate(data, reconstruction)
while True:
if config["save_partial_reconstructions"]:
paint_reconstruction(data, tracks_manager, reconstruction)
data.save_reconstruction(
[reconstruction],
"reconstruction.{}.json".format(
datetime.datetime.now().isoformat().replace(":", "_")),
)
candidates = reconstructed_points_for_images(tracks_manager,
reconstruction, images)
if not candidates:
break
logger.info("-------------------------------------------------------")
threshold = data.config["resection_threshold"]
min_inliers = data.config["resection_min_inliers"]
for image, _ in candidates:
ok, new_shots, resrep = resect(
data,
tracks_manager,
reconstruction,
image,
threshold,
min_inliers,
)
if not ok:
continue
images -= new_shots
bundle_shot_poses(
reconstruction,
new_shots,
camera_priors,
rig_camera_priors,
data.config,
)
logger.info(
f"Adding {' and '.join(new_shots)} to the reconstruction")
step = {
"images": list(new_shots),
"resection": resrep,
"memory_usage": current_memory_usage(),
}
report["steps"].append(step)
np_before = len(reconstruction.points)
triangulate_shot_features(tracks_manager, reconstruction,
new_shots, config)
np_after = len(reconstruction.points)
step["triangulated_points"] = np_after - np_before
if should_retriangulate.should():
logger.info("Re-triangulating")
align_reconstruction(reconstruction, gcp, config)
b1rep = bundle(reconstruction, camera_priors,
rig_camera_priors, None, config)
rrep = retriangulate(tracks_manager, reconstruction, config)
b2rep = bundle(reconstruction, camera_priors,
rig_camera_priors, None, config)
remove_outliers(reconstruction, config)
step["bundle"] = b1rep
step["retriangulation"] = rrep
step["bundle_after_retriangulation"] = b2rep
should_retriangulate.done()
should_bundle.done()
elif should_bundle.should():
align_reconstruction(reconstruction, gcp, config)
brep = bundle(reconstruction, camera_priors, rig_camera_priors,
None, config)
remove_outliers(reconstruction, config)
step["bundle"] = brep
should_bundle.done()
elif config["local_bundle_radius"] > 0:
bundled_points, brep = bundle_local(
reconstruction,
camera_priors,
rig_camera_priors,
None,
image,
config,
)
remove_outliers(reconstruction, config, bundled_points)
step["local_bundle"] = brep
break
else:
logger.info("Some images can not be added")
break
logger.info("-------------------------------------------------------")
align_reconstruction(reconstruction, gcp, config)
bundle(reconstruction, camera_priors, rig_camera_priors, gcp, config)
remove_outliers(reconstruction, config)
paint_reconstruction(data, tracks_manager, reconstruction)
return reconstruction, report
