def run(args):
import evo.common_ape_rpe as common
from evo import EvoException
from evo.core import sync
from evo.tools import file_interface, log
from evo.tools.settings import SETTINGS
log.configure_logging(args.verbose, args.silent, args.debug,
local_logfile=args.logfile)
if args.debug:
from pprint import pformat
parser_str = pformat({arg: getattr(args, arg) for arg in vars(args)})
logger.debug("main_parser config:\n{}".format(parser_str))
logger.debug(SEP)
if (args.plot or args.save_plot or args.serialize_plot) and args.all_pairs:
raise EvoException(
"all_pairs mode cannot be used with plotting functions")
traj_ref, traj_est, ref_name, est_name = common.load_trajectories(args)
pose_relation = common.get_pose_relation(args)
delta_unit = common.get_delta_unit(args)
traj_ref_full = None
if args.plot_full_ref:
import copy
traj_ref_full = copy.deepcopy(traj_ref)
if args.subcommand != "kitti":
logger.debug("Synchronizing trajectories...")
traj_ref, traj_est = sync.associate_trajectories(
traj_ref, traj_est, args.t_max_diff, args.t_offset,
first_name=ref_name, snd_name=est_name)
result = rpe(
traj_ref=traj_ref,
traj_est=traj_est,
pose_relation=pose_relation,
delta=args.delta,
delta_unit=delta_unit,
rel_delta_tol=args.delta_tol,
all_pairs=args.all_pairs,
align=args.align,
correct_scale=args.correct_scale,
align_origin=args.align_origin,
ref_name=ref_name,
est_name=est_name,
)
if args.plot or args.save_plot or args.serialize_plot:
common.plot(args, result, traj_ref, result.trajectories[est_name],
traj_ref_full=traj_ref_full)
if args.save_results:
logger.debug(SEP)
if not SETTINGS.save_traj_in_zip:
del result.trajectories[ref_name]
del result.trajectories[est_name]
file_interface.save_res_file(args.save_results, result,
confirm_overwrite=not args.no_warnings)
def run(args: argparse.Namespace) -> None:
log.configure_logging(args.verbose,
args.silent,
args.debug,
local_logfile=args.logfile)
if args.debug:
from pprint import pformat
parser_str = pformat({arg: getattr(args, arg) for arg in vars(args)})
logger.debug("main_parser config:\n{}".format(parser_str))
logger.debug(SEP)
traj_ref, traj_est, ref_name, est_name = common.load_trajectories(args)
traj_ref_full = None
if args.plot_full_ref:
import copy
traj_ref_full = copy.deepcopy(traj_ref)
if isinstance(traj_ref, PoseTrajectory3D) and isinstance(
traj_est, PoseTrajectory3D):
logger.debug("Synchronizing trajectories...")
traj_ref, traj_est = sync.associate_trajectories(traj_ref,
traj_est,
args.t_max_diff,
args.t_offset,
first_name=ref_name,
snd_name=est_name)
pose_relation = common.get_pose_relation(args)
result = ape(
traj_ref=traj_ref,
traj_est=traj_est,
pose_relation=pose_relation,
align=args.align,
correct_scale=args.correct_scale,
n_to_align=args.n_to_align,
align_origin=args.align_origin,
ref_name=ref_name,
est_name=est_name,
)
if args.plot or args.save_plot or args.serialize_plot:
common.plot_result(args,
result,
traj_ref,
result.trajectories[est_name],
traj_ref_full=traj_ref_full)
if args.save_results:
logger.debug(SEP)
if not SETTINGS.save_traj_in_zip:
del result.trajectories[ref_name]
del result.trajectories[est_name]
file_interface.save_res_file(args.save_results,
result,
confirm_overwrite=not args.no_warnings)
def test_write_read_integrity(self):
result_out = Result()
result_out.add_np_array("test-array", np.ones(1000))
result_out.add_info({"name": "test", "number": 666})
result_out.add_trajectory("traj", helpers.fake_trajectory(1000, 0.1))
file_interface.save_res_file(self.mock_file, result_out)
result_in = file_interface.load_res_file(self.mock_file,
load_trajectories=True)
self.assertEqual(result_in, result_out)
def run(args):
import evo.common_ape_rpe as common
from evo.tools import file_interface, log
from evo.tools.settings import SETTINGS
log.configure_logging(args.verbose,
args.silent,
args.debug,
local_logfile=args.logfile)
if args.debug:
from pprint import pformat
parser_str = pformat({arg: getattr(args, arg) for arg in vars(args)})
logger.debug("main_parser config:\n{}".format(parser_str))
logger.debug(SEP)
if (args.plot or args.save_plot or args.serialize_plot) and args.all_pairs:
raise NotImplementedError(
"all_pairs mode cannot be used with plotting functions")
traj_ref, traj_est, ref_name, est_name = common.load_trajectories(args)
pose_relation = common.get_pose_relation(args)
delta_unit = common.get_delta_unit(args)
result = rpe(
traj_ref=traj_ref,
traj_est=traj_est,
pose_relation=pose_relation,
delta=args.delta,
delta_unit=delta_unit,
rel_delta_tol=args.delta_tol,
all_pairs=args.all_pairs,
align=args.align,
correct_scale=args.correct_scale,
ref_name=ref_name,
est_name=est_name,
)
if args.plot or args.save_plot or args.serialize_plot:
common.plot(args, result, result.trajectories[ref_name],
result.trajectories[est_name])
if args.save_results:
logger.debug(SEP)
if not SETTINGS.save_traj_in_zip:
del result.trajectories[ref_name]
del result.trajectories[est_name]
file_interface.save_res_file(args.save_results,
result,
confirm_overwrite=not args.no_warnings)
def run(args):
import evo.common_ape_rpe as common
from evo.tools import file_interface, log
from evo.tools.settings import SETTINGS
log.configure_logging(args.verbose,
args.silent,
args.debug,
local_logfile=args.logfile)
if args.debug:
from pprint import pformat
parser_str = pformat({arg: getattr(args, arg) for arg in vars(args)})
logger.debug("main_parser config:\n{}".format(parser_str))
logger.debug(SEP)
traj_ref, traj_est, ref_name, est_name = common.load_trajectories(args)
pose_relation = common.get_pose_relation(args)
result = ape(
traj_ref=traj_ref,
traj_est=traj_est,
pose_relation=pose_relation,
align=args.align,
correct_scale=args.correct_scale,
ref_name=ref_name,
est_name=est_name,
)
if args.plot or args.save_plot or args.serialize_plot:
common.plot(args, result, result.trajectories[ref_name],
result.trajectories[est_name])
if args.save_results:
logger.debug(SEP)
if not SETTINGS.save_traj_in_zip:
del result.trajectories[ref_name]
del result.trajectories[est_name]
file_interface.save_res_file(args.save_results,
result,
confirm_overwrite=not args.no_warnings)
def run(args):
import evo.common_ape_rpe as common
from evo.tools import file_interface, log
from evo.tools.settings import SETTINGS
log.configure_logging(args.verbose, args.silent, args.debug)
if args.debug:
from pprint import pformat
parser_str = pformat({arg: getattr(args, arg) for arg in vars(args)})
logger.debug("main_parser config:\n{}".format(parser_str))
logger.debug(SEP)
traj_ref, traj_est, ref_name, est_name = common.load_trajectories(args)
pose_relation = common.get_pose_relation(args)
result = ape(
traj_ref=traj_ref,
traj_est=traj_est,
pose_relation=pose_relation,
align=args.align,
correct_scale=args.correct_scale,
ref_name=ref_name,
est_name=est_name,
)
if args.plot or args.save_plot or args.serialize_plot:
common.plot(
args, result,
result.trajectories[ref_name],
result.trajectories[est_name])
if args.save_results:
logger.debug(SEP)
if not SETTINGS.save_traj_in_zip:
del result.trajectories[ref_name]
del result.trajectories[est_name]
file_interface.save_res_file(
args.save_results, result, confirm_overwrite=not args.no_warnings)
def main_ape(traj_ref,
traj_est,
pose_relation,
align=True,
correct_scale=False,
ref_name="",
est_name="",
show_plot=False,
save_plot=None,
plot_mode=None,
save_results=None,
no_warnings=False,
serialize_plot=None):
from evo.algorithms import metrics
from evo.algorithms import trajectory
from evo.tools import file_interface
from evo.tools.settings import SETTINGS
only_scale = correct_scale and not align
if align or correct_scale:
logging.debug(SEP)
if only_scale:
logging.debug("correcting scale...")
else:
logging.debug("aligning using Umeyama's method..." + (
" (with scale correction)" if correct_scale else ""))
traj_est = trajectory.align_trajectory(traj_est, traj_ref,
correct_scale, only_scale)
logging.debug(SEP)
# calculate APE
data = (traj_ref, traj_est)
ape_metric = metrics.APE(pose_relation)
ape_metric.process_data(data)
ape_statistics = ape_metric.get_all_statistics()
title = str(ape_metric)
if align and not correct_scale:
title += "\n(with SE(3) Umeyama alignment)"
elif align and correct_scale:
title += "\n(with Sim(3) Umeyama alignment)"
elif only_scale:
title += "\n(scale corrected)"
else:
title += "\n(not aligned)"
logging.debug(SEP)
res_str = ""
for name, val in sorted(ape_statistics.items()):
res_str += "{:>10}".format(name) + "\t" + "{0:.6f}".format(val) + "\n"
logging.info("\nstatistics of " + title + ":\n\n" + res_str)
if show_plot or save_plot or save_results or serialize_plot:
if isinstance(traj_est, trajectory.PoseTrajectory3D):
seconds_from_start = [
t - traj_est.timestamps[0] for t in traj_est.timestamps
]
else:
seconds_from_start = None
if show_plot or save_plot or serialize_plot:
from evo.tools import plot
import matplotlib.pyplot as plt
logging.debug(SEP)
logging.debug("plotting results... ")
fig1 = plt.figure(figsize=(SETTINGS.plot_figsize[0],
SETTINGS.plot_figsize[1]))
# metric values
plot.error_array(
fig1,
ape_metric.error,
x_array=seconds_from_start,
statistics=ape_statistics,
name="APE" + (" (" + ape_metric.unit.value + ")")
if ape_metric.unit else "",
title=title,
xlabel="$t$ (s)" if seconds_from_start else "index")
# info text
if SETTINGS.plot_info_text and est_name and ref_name:
ax = fig1.gca()
ax.text(0,
-0.12,
"estimate: " + est_name + "\nreference: " + ref_name,
transform=ax.transAxes,
fontsize=8,
color="gray")
# trajectory colormapped
fig2 = plt.figure(figsize=(SETTINGS.plot_figsize[0],
SETTINGS.plot_figsize[1]))
plot_mode = plot_mode if plot_mode is not None else plot.PlotMode.xyz
ax = plot.prepare_axis(fig2, plot_mode)
plot.traj(ax,
plot_mode,
traj_ref,
'--',
'gray',
'reference',
alpha=0.0 if SETTINGS.plot_hideref else 1.0)
plot.traj_colormap(ax,
traj_est,
ape_metric.error,
plot_mode,
min_map=ape_statistics["min"],
max_map=ape_statistics["max"],
title="APE mapped onto trajectory")
fig2.axes.append(ax)
plot_collection = plot.PlotCollection(title)
plot_collection.add_figure("raw", fig1)
plot_collection.add_figure("map", fig2)
if show_plot:
plot_collection.show()
if save_plot:
plot_collection.export(save_plot,
confirm_overwrite=not no_warnings)
if serialize_plot:
logging.debug(SEP)
plot_collection.serialize(serialize_plot,
confirm_overwrite=not no_warnings)
if save_results:
logging.debug(SEP)
file_interface.save_res_file(save_results,
ape_metric,
ape_statistics,
title,
ref_name,
est_name,
seconds_from_start,
traj_ref,
traj_est,
confirm_overwrite=not no_warnings)
return ape_statistics, ape_metric.error
def run(args: argparse.Namespace) -> None:
print('using this main_ape')
log.configure_logging(args.verbose,
args.silent,
args.debug,
local_logfile=args.logfile)
if args.debug:
from pprint import pformat
parser_str = pformat({arg: getattr(args, arg) for arg in vars(args)})
logger.debug("main_parser config:\n{}".format(parser_str))
logger.debug(SEP)
traj_ref, traj_est, ref_name, est_name = common.load_trajectories(args)
traj_ref_full = None
if args.plot_full_ref:
import copy
traj_ref_full = copy.deepcopy(traj_ref)
if args.flip_xy:
print("flip xy", args.flip_xy)
flip_rotation = transformations.rotation_matrix(math.pi / 2, [0, 0, 1])
print('transformation matrix,', flip_rotation)
traj_est.transform_rotation_only(flip_rotation)
if isinstance(traj_ref, PoseTrajectory3D) and isinstance(
traj_est, PoseTrajectory3D):
logger.debug(SEP)
if args.t_start or args.t_end:
if args.t_start:
logger.info("Using time range start: {}s".format(args.t_start))
if args.t_end:
logger.info("Using time range end: {}s".format(args.t_end))
traj_ref.reduce_to_time_range(args.t_start, args.t_end)
logger.debug("Synchronizing trajectories...")
traj_ref, traj_est = sync.associate_trajectories(traj_ref,
traj_est,
args.t_max_diff,
args.t_offset,
first_name=ref_name,
snd_name=est_name)
pose_relation = common.get_pose_relation(args)
print('align_odom: ', args.align_odom)
result = ape(
traj_ref=traj_ref,
traj_est=traj_est,
pose_relation=pose_relation,
align=args.align,
correct_scale=args.correct_scale,
n_to_align=args.n_to_align,
align_origin=args.align_origin,
align_odom=args.align_odom,
ref_name=ref_name,
est_name=est_name,
)
if args.plot or args.save_plot or args.serialize_plot:
common.plot_result(args,
result,
traj_ref,
result.trajectories[est_name],
traj_ref_full=traj_ref_full)
if args.save_results:
logger.debug(SEP)
if not SETTINGS.save_traj_in_zip:
del result.trajectories[ref_name]
del result.trajectories[est_name]
file_interface.save_res_file(args.save_results,
result,
confirm_overwrite=not args.no_warnings)
def main_rpe_for_each(traj_ref,
traj_est,
pose_relation,
mode,
bins,
rel_tols,
align=False,
correct_scale=False,
ref_name="",
est_name="",
show_plot=False,
save_plot=None,
save_results=None,
no_warnings=False,
serialize_plot=None):
from evo.algorithms import metrics
from evo.algorithms import filters
from evo.algorithms import trajectory
from evo.tools import file_interface
from evo.tools.settings import SETTINGS
if not bins or not rel_tols:
raise RuntimeError(
"bins and tolerances must have more than one element")
if len(bins) != len(rel_tols):
raise RuntimeError(
"bins and tolerances must have the same number of elements")
if mode in {"speed", "angular_speed"
} and traj_est is trajectory.PosePath3D:
raise RuntimeError("timestamps are required for mode: " + mode)
bin_unit = None
if mode == "speed":
bin_unit = metrics.VelUnit.meters_per_sec
elif mode == "path":
bin_unit = metrics.Unit.meters
elif mode == "angle":
bin_unit = metrics.Unit.degrees
elif mode == "angular_speed":
bin_unit = metrics.VelUnit.degrees_per_sec
rpe_unit = None
if pose_relation is metrics.PoseRelation.translation_part:
rpe_unit = metrics.Unit.meters
elif pose_relation is metrics.PoseRelation.rotation_angle_deg:
rpe_unit = metrics.Unit.degrees
elif pose_relation is metrics.PoseRelation.rotation_angle_rad:
rpe_unit = metrics.Unit.radians
correct_only_scale = correct_scale and not align
if align or correct_scale:
logging.debug(SEP)
if correct_only_scale:
logging.debug("correcting scale...")
else:
logging.debug("aligning using Umeyama's method..." + (
" (with scale correction)" if correct_scale else ""))
traj_est = trajectory.align_trajectory(traj_est, traj_ref,
correct_scale,
correct_only_scale)
results = []
for bin, rel_tol, in zip(bins, rel_tols):
logging.debug(SEP)
logging.info("calculating RPE for each sub-sequence of " + str(bin) +
" (" + bin_unit.value + ")")
tol = bin * rel_tol
id_pairs = []
if mode == "path":
id_pairs = filters.filter_pairs_by_path(traj_ref.poses_se3,
bin,
tol,
all_pairs=True)
elif mode == "angle":
id_pairs = filters.filter_pairs_by_angle(traj_ref.poses_se3,
bin,
tol,
degrees=True)
elif mode == "speed":
id_pairs = filters.filter_pairs_by_speed(traj_ref.poses_se3,
traj_ref.timestamps, bin,
tol)
elif mode == "angular_speed":
id_pairs = filters.filter_pairs_by_angular_speed(
traj_ref.poses_se3, traj_ref.timestamps, bin, tol, True)
if len(id_pairs) == 0:
raise RuntimeError("bin " + str(bin) + " (" + str(bin_unit.value) +
") " +
"produced empty index list - try other values")
# calculate RPE with all IDs (delta 1 frames)
data = (traj_ref, traj_est)
# the delta here has nothing to do with the bin - 1f delta just to use all poses of the bin
rpe_metric = metrics.RPE(pose_relation,
delta=1,
delta_unit=metrics.Unit.frames,
all_pairs=True)
rpe_metric.process_data(data, id_pairs)
mean = rpe_metric.get_statistic(metrics.StatisticsType.mean)
results.append(mean)
if SETTINGS.plot_usetex:
mode.replace("_", "\_")
title = "mean RPE w.r.t. " + pose_relation.value + "\nfor different " + mode + " sub-sequences"
if align and not correct_scale:
title += "\n(with SE(3) Umeyama alignment)"
elif align and correct_scale:
title += "\n(with Sim(3) Umeyama alignment)"
elif correct_only_scale:
title += "\n(scale corrected)"
else:
title += "\n(not aligned)"
logging.debug(SEP)
logging.info("\n" + title + "\n")
res_str = ""
for bin, result in zip(bins, results):
res_str += "{:>10}".format(str(bin) + "(" + bin_unit.value + ")")
res_str += "\t" + "{0:.6f}".format(result) + "\n"
logging.info(res_str)
if show_plot or save_plot or serialize_plot:
from evo.tools import plot
import matplotlib.pyplot as plt
plot_collection = plot.PlotCollection(title)
fig = plt.figure(figsize=(SETTINGS.plot_figsize[0],
SETTINGS.plot_figsize[1]))
plot.error_array(fig,
results,
x_array=bins,
name="mean RPE" +
(" (" + rpe_unit.value + ")") if rpe_unit else "",
marker="o",
title=title,
xlabel=mode + " sub-sequences " + " (" +
bin_unit.value + ")")
# info text
if SETTINGS.plot_info_text and est_name and ref_name:
ax = fig.gca()
ax.text(0,
-0.12,
"estimate: " + est_name + "\nreference: " + ref_name,
transform=ax.transAxes,
fontsize=8,
color="gray")
plt.title(title)
plot_collection.add_figure("raw", fig)
if show_plot:
plot_collection.show()
if save_plot:
plot_collection.export(save_plot,
confirm_overwrite=not no_warnings)
if serialize_plot:
logging.debug(SEP)
plot_collection.serialize(serialize_plot,
confirm_overwrite=not no_warnings)
rpe_statistics = {bin: result for bin, result in zip(bins, results)}
if save_results:
logging.debug(SEP)
# utility class to trick save_res_file
class Metric:
unit = rpe_unit
error = results
file_interface.save_res_file(save_results,
Metric,
rpe_statistics,
title,
ref_name,
est_name,
bins,
traj_ref,
traj_est,
xlabel=mode + " sub-sequences " + " (" +
bin_unit.value + ")",
confirm_overwrite=not no_warnings)
return rpe_statistics, results
res.info["title"] = title
res.info["est_name"] = est_name
err_arr = []
ts_arr = []
for j in range(len(csv_data[i])):
err_arr.append(csv_data[i][j][0])
ts_arr.append(j)
res.add_np_array("error_array", err_arr)
res.add_np_array("timestamps", ts_arr)
rmse_arr = []
for j in range(len(csv_data[i])):
rmse_arr.append(csv_data[i][j][2])
statsdict = {
"mean": mean(err_arr),
"median": median(err_arr),
"rmse": mean(rmse_arr)
}
res.add_stats(statsdict)
save_path = args.save + est_name
save_path = save_path[:-4] + ".zip"
print("\nSaving consolidated results to:\n {}".format(save_path))
evof.save_res_file(save_path, res)
def main(res_file, new_name):
result = file_interface.load_res_file(res_file)
result.info["est_name"] = new_name
file_interface.save_res_file(res_file, result)
def main_rpe(traj_ref,
traj_est,
pose_relation,
delta,
delta_unit,
rel_delta_tol=0.1,
all_pairs=False,
align=False,
correct_scale=False,
ref_name="",
est_name="",
show_plot=False,
save_plot=None,
plot_mode=None,
save_results=None,
no_warnings=False,
support_loop=False,
serialize_plot=None):
from evo.core import metrics, result
from evo.core import trajectory
from evo.tools import file_interface
from evo.tools.settings import SETTINGS
if (show_plot or save_plot or serialize_plot) and all_pairs:
raise metrics.MetricsException(
"all_pairs mode cannot be used with plotting functions")
only_scale = correct_scale and not align
if align or correct_scale:
logging.debug(SEP)
if only_scale:
logging.debug("correcting scale...")
else:
logging.debug("aligning using Umeyama's method..." + (
" (with scale correction)" if correct_scale else ""))
traj_est = trajectory.align_trajectory(traj_est, traj_ref,
correct_scale, only_scale)
logging.debug(SEP)
# calculate RPE
data = (traj_ref, traj_est)
rpe_metric = metrics.RPE(pose_relation, delta, delta_unit, rel_delta_tol,
all_pairs)
rpe_metric.process_data(data)
rpe_statistics = rpe_metric.get_all_statistics()
title = str(rpe_metric)
if align and not correct_scale:
title += "\n(with SE(3) Umeyama alignment)"
elif align and correct_scale:
title += "\n(with Sim(3) Umeyama alignment)"
elif only_scale:
title += "\n(scale corrected)"
else:
title += "\n(not aligned)"
rpe_result = result.from_metric(rpe_metric, title, ref_name, est_name)
logging.debug(SEP)
logging.info(rpe_result.pretty_str())
# restrict trajectories to delta ids
if support_loop:
# avoid overwriting if called repeatedly e.g. in Jupyter notebook
import copy
traj_ref = copy.deepcopy(traj_ref)
traj_est = copy.deepcopy(traj_est)
traj_ref.reduce_to_ids(rpe_metric.delta_ids)
traj_est.reduce_to_ids(rpe_metric.delta_ids)
if isinstance(traj_est, trajectory.PoseTrajectory3D) and not all_pairs:
seconds_from_start = [
t - traj_est.timestamps[0] for t in traj_est.timestamps
]
rpe_result.add_np_array("seconds_from_start", seconds_from_start)
else:
seconds_from_start = None
if show_plot or save_plot or serialize_plot and not all_pairs:
from evo.tools import plot
import matplotlib.pyplot as plt
logging.debug(SEP)
logging.debug("plotting results... ")
fig1 = plt.figure(figsize=(SETTINGS.plot_figsize[0],
SETTINGS.plot_figsize[1]))
# metric values
plot.error_array(
fig1,
rpe_metric.error,
x_array=seconds_from_start,
statistics=rpe_statistics,
name="RPE" +
(" (" + rpe_metric.unit.value + ")") if rpe_metric.unit else "",
title=title,
xlabel="$t$ (s)" if seconds_from_start else "index")
# info text
if SETTINGS.plot_info_text and est_name and ref_name:
ax = fig1.gca()
ax.text(0,
-0.12,
"estimate: " + est_name + "\nreference: " + ref_name,
transform=ax.transAxes,
fontsize=8,
color="gray")
# trajectory colormapped
fig2 = plt.figure(figsize=(SETTINGS.plot_figsize[0],
SETTINGS.plot_figsize[1]))
plot_mode = plot_mode if plot_mode is not None else plot.PlotMode.xyz
ax = plot.prepare_axis(fig2, plot_mode)
plot.traj(ax,
plot_mode,
traj_ref,
'--',
'gray',
'reference',
alpha=0 if SETTINGS.plot_hideref else 1)
plot.traj_colormap(ax,
traj_est,
rpe_metric.error,
plot_mode,
min_map=rpe_statistics["min"],
max_map=rpe_statistics["max"],
title="RPE mapped onto trajectory")
fig2.axes.append(ax)
plot_collection = plot.PlotCollection(title)
plot_collection.add_figure("raw", fig1)
plot_collection.add_figure("map", fig2)
if show_plot:
plot_collection.show()
if save_plot:
plot_collection.export(save_plot,
confirm_overwrite=not no_warnings)
if serialize_plot:
logging.debug(SEP)
plot_collection.serialize(serialize_plot,
confirm_overwrite=not no_warnings)
if save_results:
logging.debug(SEP)
if SETTINGS.save_traj_in_zip:
rpe_result.add_trajectory("traj_ref", traj_ref)
rpe_result.add_trajectory("traj_est", traj_est)
file_interface.save_res_file(save_results,
rpe_result,
confirm_overwrite=not no_warnings)
return rpe_result
# print(loc_tra)
results = []
odom_result = ape(
traj_ref=mocap,
traj_est=odom,
pose_relation=metrics.PoseRelation.translation_part,
align=False,
correct_scale=False,
align_origin=True,
ref_name="mocap",
est_name="odom",
)
results.append(odom_result)
file_interface.save_res_file("/home/kostas/results/res_files/odom",
odom_result,
confirm_overwrite=False)
slam_result = ape(
traj_ref=mocap,
traj_est=slam,
pose_relation=metrics.PoseRelation.translation_part,
align=False,
correct_scale=False,
align_origin=True,
ref_name="mocap",
est_name="slam",
)
results.append(slam_result)
file_interface.save_res_file("/home/kostas/results/res_files/slam",
slam_result,
