def _reconstruction_from_rigs_and_assignments(data: DataSetBase):
assignments = data.load_rig_assignments()
models = data.load_rig_models()
if not data.reference_lla_exists():
data.invent_reference_lla()
base_rotation = np.zeros(3)
reconstructions = []
for rig_id, instances in assignments.items():
rig_cameras = models[rig_id]["rig_cameras"]
reconstruction = types.Reconstruction()
reconstruction.cameras = data.load_camera_models()
for instance in instances:
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)
rig_camera_pose = pygeometry.Pose(rig_camera["rotation"],
rig_camera["translation"])
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)
reconstructions.append(reconstruction)
return reconstructions
def rig_statistics(data: DataSetBase, reconstructions):
stats = {}
permutation = np.argsort([-len(r.shots) for r in reconstructions])
for rig_model_id, rig_model in data.load_rig_models().items():
stats[rig_model_id] = {
"initial_values": _rig_model_statistics(rig_model)
}
for idx in permutation:
rec = reconstructions[idx]
for rig_model in rec.rig_models.values():
if "optimized_values" in stats[rig_model.id]:
continue
stats[rig_model.id]["optimized_values"] = _rig_model_statistics(
rig_model)
for rig_model_id in data.load_rig_models():
if "optimized_values" not in stats[rig_model_id]:
del stats[rig_model_id]
return stats
def bootstrap_reconstruction(data: DataSetBase, tracks_manager, im1, im2, p1,
p2):
"""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_model_priors = data.load_rig_models()
rig_assignments = data.load_rig_assignments_per_image()
reconstruction = types.Reconstruction()
reconstruction.reference = data.load_reference()
reconstruction.cameras = camera_priors
reconstruction.rig_models = rig_model_priors
new_shots = add_shot(data, reconstruction, rig_assignments, im1,
pygeometry.Pose())
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_model_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_model_priors, data.config)
report["decision"] = "Success"
report["memory_usage"] = current_memory_usage()
return reconstruction, report
def grow_reconstruction(data: DataSetBase, tracks_manager, reconstruction,
images, gcp):
"""Incrementally add shots to an initial reconstruction."""
config = data.config
report = {"steps": []}
camera_priors = data.load_camera_models()
rig_model_priors = data.load_rig_models()
paint_reconstruction(data, tracks_manager, reconstruction)
align_reconstruction(reconstruction, gcp, config)
bundle(reconstruction, camera_priors, rig_model_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
new_shots = set(new_shots)
images -= new_shots
bundle_shot_poses(reconstruction, new_shots, camera_priors,
rig_model_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_model_priors,
None, config)
rrep = retriangulate(tracks_manager, reconstruction, config)
b2rep = bundle(reconstruction, camera_priors, rig_model_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_model_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_model_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_model_priors, gcp, config)
remove_outliers(reconstruction, config)
paint_reconstruction(data, tracks_manager, reconstruction)
return reconstruction, report
