def main():
"""
The quaternions of the groundtruth will be normalized,
and saved to another filename.
"""
print("Normalizing quaternions of groundtruth camera trajectory...")
dataset_tools.save_cam_trajectory_TUM(
join_path("sin2_tex2_h1_v8_d", "traj_groundtruth.txt"),
dataset_tools.load_cam_trajectory_TUM(join_path("sin2_tex2_h1_v8_d", "trajectory_nominal.txt")),
)
print("Done.")
def repair_ICL_NUIM_cam_trajectory(filename_in,
filename_out,
initial_location=None,
rebuild_timestamps=True,
delta_timestamp=0.,
fps=30):
"""
After this repair, you will be able to use the output in conjunction with
the original (i.e. non-mirrored) 3D scene of the dataset,
provided that you don't apply the negative Y-factor of the camera calibration matrix.
"filename_in" : filename of the input camera trajectory
"filename_out" : filename of the repaired output camera trajectory
"initial_location" : location of the first camera pose
Note that all ICL NUIM camera trajectories are undetermined up to an unknown translation.
To find the correct "initial_location", you can do the following:
Ps = load_cam_poses_POV("traj1.posesRenderingCommands.sh")
_, locs_exact, _ = dataset_tools.convert_cam_poses_to_cam_trajectory_TUM(Ps)
initial_location = [-locs_exact[0, 0], locs_exact[0, 1], locs_exact[0, 2]]
"rebuild_timestamps" :
if True, regenerate timestamps starting from start-time "delta_timestamp",
at a rate of "fps"
The new "timestps", "locations", "quaternions" will be returned.
Note: this is not the recommended way to go,
for accuracy, consider using "load_cam_poses_POV()" instead.
"""
timestps, locations, quaternions = dataset_tools.load_cam_trajectory_TUM(
filename_in)
if initial_location != None:
delta_location = initial_location - locations[0]
else:
delta_location = np.zeros(3)
if rebuild_timestamps:
timestps = delta_timestamp + (1 +
np.arange(len(timestps))) / float(fps)
for i, (location, quaternion) in enumerate(zip(locations, quaternions)):
lx, ly, lz = location
locations[i] = np.array([lx, ly, -lz]) + delta_location
qx, qy, qz, qw = quaternion
quaternions[i] = np.array([qw, qz, qy, -qx])
dataset_tools.save_cam_trajectory_TUM(filename_out,
(timestps, locations, quaternions))
return timestps, locations, quaternions
def main():
"""
The quaternions of the groundtruth will be normalized,
and saved to another filename.
"""
print ("Normalizing quaternions of groundtruth camera trajectory...")
dataset_tools.save_cam_trajectory_TUM(
join_path("sin2_tex2_h1_v8_d", "traj_groundtruth.txt"),
dataset_tools.load_cam_trajectory_TUM(
join_path("sin2_tex2_h1_v8_d", "trajectory_nominal.txt") ) )
print ("Done.")
def main():
"""
The exact trajectories will be extracted, along with the repaired 3D model.
For now only the "Living Room Dataset" is supported.
"""
print("Mirroring 3D model...")
mirror_wavefront_obj_file(
join_path("living_room_obj_mtl", "living-room.obj"),
join_path("living_room_obj_mtl", "living-room_Xmirrored.obj"))
print("Extracting exact ground-truth camera trajectories...")
pov_bash_script_filenames = [
"livingroomlcmlog-2013-08-07.00.posesRenderingCommands.sh",
"livingroomlcmlog-2013-08-07.01.posesRenderingCommands_copy.sh",
"livingroomlcmlog-2013-08-07.02.posesRenderingCommands.sh",
"livingroomlcmlog-2013-08-07.03.posesRenderingCommands.sh"
]
for i, pov_bash_script_filename in enumerate(pov_bash_script_filenames):
dataset_tools.save_cam_trajectory_TUM(
join_path("living_room_traj%sn_frei_png" % i,
"traj_groundtruth%s.txt" % i),
dataset_tools.convert_cam_poses_to_cam_trajectory_TUM(
load_cam_poses_POV(
join_path("living_room_code", pov_bash_script_filename))))
if "--repair-noisy-trajectories" in sys.argv:
print("Repairing (noisy) camera trajectories...")
for i, pov_bash_script_filename in enumerate(
pov_bash_script_filenames):
_, locs_exact, _ = dataset_tools.convert_cam_poses_to_cam_trajectory_TUM(
load_cam_poses_POV(
join_path("living_room_code", pov_bash_script_filename)))
repair_ICL_NUIM_cam_trajectory(
join_path("living_room_traj%sn_frei_png" % i,
"livingRoom%sn.gt.freiburg" % i),
join_path("living_room_traj%sn_frei_png" % i,
"livingRoom%sn.gt.freiburg_repaired" % i),
np.array(
[-locs_exact[0, 0], locs_exact[0, 1], locs_exact[0, 2]]))
print("Done.")
def repair_ICL_NUIM_cam_trajectory(filename_in, filename_out,
initial_location=None,
rebuild_timestamps=True, delta_timestamp=0., fps=30):
"""
After this repair, you will be able to use the output in conjunction with
the original (i.e. non-mirrored) 3D scene of the dataset,
provided that you don't apply the negative Y-factor of the camera calibration matrix.
"filename_in" : filename of the input camera trajectory
"filename_out" : filename of the repaired output camera trajectory
"initial_location" : location of the first camera pose
Note that all ICL NUIM camera trajectories are undetermined up to an unknown translation.
To find the correct "initial_location", you can do the following:
Ps = load_cam_poses_POV("traj1.posesRenderingCommands.sh")
_, locs_exact, _ = dataset_tools.convert_cam_poses_to_cam_trajectory_TUM(Ps)
initial_location = [-locs_exact[0, 0], locs_exact[0, 1], locs_exact[0, 2]]
"rebuild_timestamps" :
if True, regenerate timestamps starting from start-time "delta_timestamp",
at a rate of "fps"
The new "timestps", "locations", "quaternions" will be returned.
Note: this is not the recommended way to go,
for accuracy, consider using "load_cam_poses_POV()" instead.
"""
timestps, locations, quaternions = dataset_tools.load_cam_trajectory_TUM(filename_in)
if initial_location != None:
delta_location = initial_location - locations[0]
else:
delta_location = np.zeros(3)
if rebuild_timestamps:
timestps = delta_timestamp + (1 + np.arange(len(timestps))) / float(fps)
for i, (location, quaternion) in enumerate(zip(locations, quaternions)):
lx, ly, lz = location
locations[i] = np.array([lx, ly, -lz]) + delta_location
qx, qy, qz, qw = quaternion
quaternions[i] = np.array([qw, qz, qy, -qx])
dataset_tools.save_cam_trajectory_TUM(filename_out, (timestps, locations, quaternions))
return timestps, locations, quaternions
def main():
traj_to_file, traj_from_file, traj_input_files, map_input_files, at_frame, offset_time = parse_cmd_args(
)
print("Calculating transformation...")
cam_trajectory_from = dataset_tools.load_cam_trajectory_TUM(traj_from_file)
cam_trajectory_to = dataset_tools.load_cam_trajectory_TUM(traj_to_file)
transformation = dataset_tools.transform_between_cam_trajectories(
cam_trajectory_from,
cam_trajectory_to,
at_frame=at_frame,
offset_time=offset_time)
print("Results:")
delta_quaternion, delta_scale, delta_location = transformation
print("delta_quaternion:")
print("\t %s" % delta_quaternion)
print("delta_scale:")
print("\t %s" % delta_scale)
print("delta_location:")
print("\t %s" % delta_location)
print()
for traj_input_file in traj_input_files:
print('Transforming traj "%s"...' % traj_input_file)
dataset_tools.save_cam_trajectory_TUM(
"%s-trfm%s" % tuple(os.path.splitext(traj_input_file)),
dataset_tools.transformed_cam_trajectory(
dataset_tools.load_cam_trajectory_TUM(traj_input_file),
transformation))
for map_input_file in map_input_files:
print('Transforming map "%s"...' % map_input_file)
points, colors, _ = dataset_tools.load_3D_points_from_pcd_file(
map_input_file, use_alpha=True)
dataset_tools.save_3D_points_to_pcd_file(
"%s-trfm%s" % tuple(os.path.splitext(map_input_file)),
dataset_tools.transformed_points(points, transformation), colors)
print("Done.")
def main():
"""
The exact trajectories will be extracted, along with the repaired 3D model.
For now only the "Living Room Dataset" is supported.
"""
print "Mirroring 3D model..."
mirror_wavefront_obj_file(
join_path("living_room_obj_mtl", "living-room.obj"),
join_path("living_room_obj_mtl", "living-room_Xmirrored.obj") )
print "Extracting exact ground-truth camera trajectories..."
pov_bash_script_filenames = [
"livingroomlcmlog-2013-08-07.00.posesRenderingCommands.sh",
"livingroomlcmlog-2013-08-07.01.posesRenderingCommands_copy.sh",
"livingroomlcmlog-2013-08-07.02.posesRenderingCommands.sh",
"livingroomlcmlog-2013-08-07.03.posesRenderingCommands.sh" ]
for i, pov_bash_script_filename in enumerate(pov_bash_script_filenames):
dataset_tools.save_cam_trajectory_TUM(
join_path("living_room_traj%sn_frei_png" % i, "traj_groundtruth%s.txt" % i),
dataset_tools.convert_cam_poses_to_cam_trajectory_TUM(load_cam_poses_POV(
join_path("living_room_code", pov_bash_script_filename) )) )
if "--repair-noisy-trajectories" in sys.argv:
print "Repairing (noisy) camera trajectories..."
for i, pov_bash_script_filename in enumerate(pov_bash_script_filenames):
_, locs_exact, _ = dataset_tools.convert_cam_poses_to_cam_trajectory_TUM(
load_cam_poses_POV(join_path("living_room_code", pov_bash_script_filename)) )
repair_ICL_NUIM_cam_trajectory(
join_path("living_room_traj%sn_frei_png" % i, "livingRoom%sn.gt.freiburg" % i),
join_path("living_room_traj%sn_frei_png" % i, "livingRoom%sn.gt.freiburg_repaired" % i),
np.array([-locs_exact[0, 0], locs_exact[0, 1], locs_exact[0, 2]]) )
print "Done."
def main():
traj_to_file, traj_from_file, traj_input_files, map_input_files, at_frame, offset_time = parse_cmd_args()
print "Calculating transformation..."
cam_trajectory_from = dataset_tools.load_cam_trajectory_TUM(traj_from_file)
cam_trajectory_to = dataset_tools.load_cam_trajectory_TUM(traj_to_file)
transformation = dataset_tools.transform_between_cam_trajectories(
cam_trajectory_from, cam_trajectory_to, at_frame=at_frame, offset_time=offset_time )
print "Results:"
delta_quaternion, delta_scale, delta_location = transformation
print "delta_quaternion:"
print "\t %s" % delta_quaternion
print "delta_scale:"
print "\t %s" % delta_scale
print "delta_location:"
print "\t %s" % delta_location
print
for traj_input_file in traj_input_files:
print 'Transforming traj "%s"...' % traj_input_file
dataset_tools.save_cam_trajectory_TUM(
"%s-trfm%s" % tuple(os.path.splitext(traj_input_file)),
dataset_tools.transformed_cam_trajectory(
dataset_tools.load_cam_trajectory_TUM(traj_input_file),
transformation ) )
for map_input_file in map_input_files:
print 'Transforming map "%s"...' % map_input_file
points, colors, _ = dataset_tools.load_3D_points_from_pcd_file(map_input_file, use_alpha=True)
dataset_tools.save_3D_points_to_pcd_file(
"%s-trfm%s" % tuple(os.path.splitext(map_input_file)),
dataset_tools.transformed_points(points, transformation),
colors )
print "Done."
