def make_evo_traj_figures(traj_file_path):
import matplotlib.pyplot as plt
from evo import main_traj
from evo.tools import plot
# fake evo traj kitti input arguments
class evo_args:
def __init__(self, path):
self.subcommand = 'kitti'
self.pose_files = [path]
self.ref = None
testArgs = evo_args(traj_file_path)
# load trajectories using evo tools
trajectories, _ = main_traj.load_trajectories(testArgs)
# create and setup plot collection object
fig_xyz, axarr_xyz = plt.subplots(3, sharex='col')
fig_rpy, axarr_rpy = plt.subplots(3, sharex='col')
fig_traj = plt.figure()
plot_mode = plot.PlotMode['xyz']
ax_traj = plot.prepare_axis(fig_traj, plot_mode)
# make trajecory plots
for name, traj in trajectories.items():
color = next(ax_traj._get_lines.prop_cycler)['color']
plot.traj(ax_traj, plot_mode, traj, '-', color, name)
plot.traj_xyz(axarr_xyz, traj, '-', color, name, start_timestamp=None)
plot.traj_rpy(axarr_rpy, traj, '-', color, name, start_timestamp=None)
fig_rpy.text(0.,
0.005,
"euler_angle_sequence: {}".format('RPY'),
fontsize=6)
# return figures
return fig_traj, fig_xyz, fig_rpy
def plot(args, result, traj_ref, traj_est):
from evo.tools import plot
from evo.tools.settings import SETTINGS
import matplotlib.pyplot as plt
import numpy as np
logger.debug(SEP)
logger.debug("Plotting results... ")
plot_mode = plot.PlotMode(args.plot_mode)
# Plot the raw metric values.
fig1 = plt.figure(figsize=SETTINGS.plot_figsize)
if "seconds_from_start" in result.np_arrays:
seconds_from_start = result.np_arrays["seconds_from_start"]
else:
seconds_from_start = None
plot.error_array(
fig1, result.np_arrays["error_array"], x_array=seconds_from_start,
statistics={
s: result.stats[s]
for s in SETTINGS.plot_statistics if s not in ("min", "max")
}, name=result.info["label"], title=result.info["title"],
xlabel="$t$ (s)" if seconds_from_start else "index")
# Plot the values color-mapped onto the trajectory.
fig2 = plt.figure(figsize=SETTINGS.plot_figsize)
ax = plot.prepare_axis(fig2, plot_mode)
plot.traj(ax, plot_mode, traj_ref, style=SETTINGS.plot_reference_linestyle,
color=SETTINGS.plot_reference_color, label='reference',
alpha=SETTINGS.plot_reference_alpha)
if args.plot_colormap_min is None:
args.plot_colormap_min = result.stats["min"]
if args.plot_colormap_max is None:
args.plot_colormap_max = result.stats["max"]
if args.plot_colormap_max_percentile is not None:
args.plot_colormap_max = np.percentile(
result.np_arrays["error_array"], args.plot_colormap_max_percentile)
plot.traj_colormap(ax, traj_est, result.np_arrays["error_array"],
plot_mode, min_map=args.plot_colormap_min,
max_map=args.plot_colormap_max,
title="Error mapped onto trajectory")
fig2.axes.append(ax)
plot_collection = plot.PlotCollection(result.info["title"])
plot_collection.add_figure("raw", fig1)
plot_collection.add_figure("map", fig2)
if args.plot:
plot_collection.show()
if args.save_plot:
plot_collection.export(args.save_plot,
confirm_overwrite=not args.no_warnings)
if args.serialize_plot:
logger.debug(SEP)
plot_collection.serialize(args.serialize_plot,
confirm_overwrite=not args.no_warnings)
def plot_traj_colormap_rpe(rpe_metric,
traj_ref,
traj_est1,
traj_est2=None,
plot_title="",
figsize=(8, 8)):
""" Adds a trajectory colormap of RPE metrics to a plot collection.
Args:
ape_metric: an evo.core.metric object with statistics and information for RPE.
traj_ref: a PoseTrajectory3D object representing the reference trajectory.
traj_est1: a PoseTrajectory3D object representing the vio-estimated trajectory.
traj_est2: a PoseTrajectory3D object representing the pgo-estimated trajectory. Optional.
plot_title: a string representing the title of the plot.
figsize: a 2-tuple representing the figure size.
Returns:
A plt figure.
"""
fig = plt.figure(figsize=figsize)
plot_mode = plot.PlotMode.xy
ax = plot.prepare_axis(fig, plot_mode)
# We have to make deep copies to avoid altering the original data: TODO(marcus): figure out why
traj_ref = copy.deepcopy(traj_ref)
traj_est1 = copy.deepcopy(traj_est1)
traj_est2 = copy.deepcopy(traj_est2)
rpe_stats = rpe_metric.get_all_statistics()
traj_ref.reduce_to_ids(rpe_metric.delta_ids)
traj_est1.reduce_to_ids(rpe_metric.delta_ids)
plot.traj(ax, plot_mode, traj_ref, '--', 'gray', 'reference')
colormap_traj = traj_est1
if traj_est2 is not None:
traj_est2.reduce_to_ids(rpe_metric.delta_ids)
plot.traj(ax, plot_mode, traj_est1, '.', 'gray',
'reference without pgo')
colormap_traj = traj_est2
plot.traj_colormap(ax,
colormap_traj,
rpe_metric.error,
plot_mode,
min_map=0.0,
max_map=math.ceil(rpe_stats['max'] * 10) / 10,
title=plot_title)
return fig
def plot_traj_colormap_ape(ape_metric,
traj_ref,
traj_est1,
traj_est2=None,
plot_title="",
figsize=(8, 8)):
""" Adds a trajectory colormap of ATE metrics to a plot collection.
Args:
ape_metric: an evo.core.metric object with statistics and information for APE.
traj_ref: a PoseTrajectory3D object representing the reference trajectory.
traj_est1: a PoseTrajectory3D object representing the vio-estimated trajectory.
traj_est2: a PoseTrajectory3D object representing the pgo-estimated trajectory. Optional.
plot_title: a string representing the title of the plot.
figsize: a 2-tuple representing the figure size.
Returns:
A plt figure.
"""
fig = plt.figure(figsize=figsize)
plot_mode = plot.PlotMode.xy
ax = plot.prepare_axis(fig, plot_mode)
ape_stats = ape_metric.get_all_statistics()
plot.traj(ax, plot_mode, traj_ref, '--', 'gray', 'reference')
colormap_traj = traj_est1
if traj_est2 is not None:
plot.traj(ax, plot_mode, traj_est1, '.', 'gray',
'reference without pgo')
colormap_traj = traj_est2
plot.traj_colormap(ax,
colormap_traj,
ape_metric.error,
plot_mode,
min_map=0.0,
max_map=math.ceil(ape_stats['max'] * 10) / 10,
title=plot_title)
return fig
def add_traj_colormap_ape(self,
plot_collection,
ape_metric,
traj_ref,
traj_est1,
traj_est2=None,
fig_title="",
plot_title=""):
""" Adds a trajectory colormap of ATE metrics to a plot collection.
Args:
plot_collection: a PlotCollection containing plots.
ape_metric: an evo.core.metric object with statistics and information for APE.
traj_ref: a PoseTrajectory3D object representing the reference trajectory.
traj_est1: a PoseTrajectory3D object representing the vio-estimated trajectory.
traj_est2: a PoseTrajectory3D object representing the pgo-estimated trajectory. Optional.
fig_title: a string representing the title of the figure. Must be unique in the plot_collection.
plot_title: a string representing the title of the plot.
"""
fig = plt.figure(figsize=(8, 8))
plot_mode = plot.PlotMode.xy
ax = plot.prepare_axis(fig, plot_mode)
ape_stats = ape_metric.get_all_statistics()
plot.traj(ax, plot_mode, traj_ref, '--', 'gray', 'reference')
colormap_traj = traj_est1
if traj_est2 is not None:
plot.traj(ax, plot_mode, traj_est1, '.', 'gray',
'reference without pgo')
colormap_traj = traj_est2
plot.traj_colormap(ax,
colormap_traj,
ape_metric.error,
plot_mode,
min_map=0.0,
max_map=math.ceil(ape_stats['max'] * 10) / 10,
title=plot_title)
plot_collection.add_figure(fig_title, fig)
from evo.tools import plot
import matplotlib.pyplot as plt
print("plotting")
plot_collection = plot.PlotCollection("Example")
# metric values
fig_1 = plt.figure(figsize=(8, 8))
plot.error_array(fig_1, ape_metric.error, statistics=ape_statistics,
name="APE", title=str(ape_metric))
plot_collection.add_figure("raw", fig_1)
# trajectory colormapped with error
fig_2 = plt.figure(figsize=(8, 8))
plot_mode = plot.PlotMode.xy
ax = plot.prepare_axis(fig_2, plot_mode)
plot.traj(ax, plot_mode, traj_ref, '--', 'gray', 'reference')
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")
plot_collection.add_figure("traj (error)", fig_2)
# trajectory colormapped with speed
fig_3 = plt.figure(figsize=(8, 8))
plot_mode = plot.PlotMode.xy
ax = plot.prepare_axis(fig_3, plot_mode)
speeds = [trajectory.calc_speed(traj_est.positions_xyz[i], traj_est.positions_xyz[i + 1],
traj_est.timestamps[i], traj_est.timestamps[i + 1])
for i in range(len(traj_est.positions_xyz) - 1)]
speeds.append(0)
plot.traj(ax, plot_mode, traj_ref, '--', 'gray', 'reference')
plot.traj_colormap(ax, traj_est, speeds, plot_mode,
def run(args):
import os
import sys
import logging
import evo.algorithms.lie_algebra as lie
from evo.algorithms import trajectory
from evo.tools import file_interface, settings
from evo.tools.settings import SETTINGS
settings.configure_logging(verbose=True,
silent=args.silent,
debug=args.debug)
if args.debug:
import pprint
logging.debug(
"main_parser config:\n" +
pprint.pformat({arg: getattr(args, arg)
for arg in vars(args)}) + "\n")
logging.debug(SEP)
trajectories = []
ref_traj = None
if args.subcommand == "tum":
for traj_file in args.traj_files:
if traj_file != args.ref:
trajectories.append(
(traj_file,
file_interface.read_tum_trajectory_file(traj_file)))
if args.ref:
ref_traj = file_interface.read_tum_trajectory_file(args.ref)
elif args.subcommand == "kitti":
for pose_file in args.pose_files:
if pose_file != args.ref:
trajectories.append(
(pose_file,
file_interface.read_kitti_poses_file(pose_file)))
if args.ref:
ref_traj = file_interface.read_kitti_poses_file(args.ref)
elif args.subcommand == "euroc":
for csv_file in args.state_gt_csv:
if csv_file != args.ref:
trajectories.append(
(csv_file,
file_interface.read_euroc_csv_trajectory(csv_file)))
if args.ref:
ref_traj = file_interface.read_euroc_csv_trajectory(args.ref)
elif args.subcommand == "bag":
import rosbag
bag = rosbag.Bag(args.bag)
try:
if args.all_topics:
topic_info = bag.get_type_and_topic_info()
topics = sorted([
t for t in topic_info[1].keys()
if topic_info[1][t][0] == "geometry_msgs/PoseStamped"
and t != args.ref
])
if len(topics) == 0:
logging.error("no geometry_msgs/PoseStamped topics found!")
sys.exit(1)
else:
topics = args.topics
if not topics:
logging.warning(
"no topics used - specify topics or use the --all_topics flag"
)
sys.exit(1)
for topic in topics:
trajectories.append(
(topic, file_interface.read_bag_trajectory(bag, topic)))
if args.ref:
ref_traj = file_interface.read_bag_trajectory(bag, args.ref)
finally:
bag.close()
else:
raise RuntimeError("unsupported subcommand: " + args.subcommand)
if args.transform_left or args.transform_right:
tf_path = args.transform_left if args.transform_left else args.transform_right
t, xyz, quat = file_interface.load_transform_json(tf_path)
logging.debug(SEP)
logging.debug("applying transformation to the trajectories:\n" +
str(t))
if args.invert_transform:
t = lie.se3_inverse(t)
for name, traj in trajectories:
traj.transform(t, right_mul=args.transform_right)
if args.align or args.correct_scale:
if args.ref:
if args.subcommand == "kitti":
traj_tmp, ref_traj_tmp = trajectories, [
ref_traj for n, t in trajectories
]
else:
traj_tmp, ref_traj_tmp = [], []
from evo.algorithms import sync
for name, traj in trajectories:
logging.debug(SEP)
ref_assoc, traj_assoc = sync.associate_trajectories(
ref_traj, traj, first_name="ref", snd_name=name)
ref_traj_tmp.append(ref_assoc)
traj_tmp.append((name, traj_assoc))
trajectories = traj_tmp
correct_only_scale = args.correct_scale and not args.align
trajectories_new = []
for nt, ref_assoc in zip(trajectories, ref_traj_tmp):
logging.debug(SEP)
logging.debug("aligning " + nt[0] + " to " + args.ref + "...")
trajectories_new.append(
(nt[0],
trajectory.align_trajectory(nt[1], ref_assoc,
args.correct_scale,
correct_only_scale,
args.n_to_align)))
trajectories = trajectories_new
for name, traj in trajectories:
if args.t_offset and traj.timestamps.shape[0] != 0:
logging.debug(SEP)
logging.info("adding time offset to " + name + ": " +
str(args.t_offset) + " (s)")
traj.timestamps += args.t_offset
print_traj_info(name, traj, args.full_check)
if (args.align or args.correct_scale) and not args.ref:
logging.debug(SEP)
logging.warning("can't align without a reference! (--ref) *grunt*")
if args.ref:
print_traj_info(args.ref, ref_traj, args.full_check)
if args.plot or args.save_plot or args.serialize_plot:
from evo.tools.plot import PlotMode
plot_mode = PlotMode.xyz if not args.plot_mode else PlotMode[
args.plot_mode]
import numpy as np
from evo.tools import plot
import matplotlib.pyplot as plt
import matplotlib.cm as cm
plot_collection = plot.PlotCollection("evo_traj - trajectory plot")
fig_xyz, axarr_xyz = plt.subplots(3,
sharex="col",
figsize=tuple(SETTINGS.plot_figsize))
fig_traj = plt.figure(figsize=tuple(SETTINGS.plot_figsize))
if (args.align or args.correct_scale) and not args.ref:
plt.xkcd(scale=2, randomness=4)
fig_traj.suptitle("what if --ref?")
fig_xyz.suptitle("what if --ref?")
ax_traj = plot.prepare_axis(fig_traj, plot_mode)
if args.ref:
short_traj_name = os.path.splitext(os.path.basename(args.ref))[0]
if SETTINGS.plot_usetex:
short_traj_name = short_traj_name.replace("_", "\\_")
plot.traj(ax_traj,
plot_mode,
ref_traj,
'--',
'grey',
short_traj_name,
alpha=0 if SETTINGS.plot_hideref else 1)
plot.traj_xyz(axarr_xyz,
ref_traj,
'--',
'grey',
short_traj_name,
alpha=0 if SETTINGS.plot_hideref else 1)
cmap_colors = None
if SETTINGS.plot_multi_cmap.lower() != "none":
cmap = getattr(cm, SETTINGS.plot_multi_cmap)
cmap_colors = iter(cmap(np.linspace(0, 1, len(trajectories))))
for name, traj in trajectories:
if cmap_colors is None:
color = next(ax_traj._get_lines.prop_cycler)['color']
else:
color = next(cmap_colors)
short_traj_name = os.path.splitext(os.path.basename(name))[0]
if SETTINGS.plot_usetex:
short_traj_name = short_traj_name.replace("_", "\\_")
plot.traj(ax_traj, plot_mode, traj, '-', color, short_traj_name)
if args.ref and isinstance(ref_traj, trajectory.PoseTrajectory3D):
start_time = ref_traj.timestamps[0]
else:
start_time = None
plot.traj_xyz(axarr_xyz,
traj,
'-',
color,
short_traj_name,
start_timestamp=start_time)
plt.tight_layout()
plot_collection.add_figure("trajectories", fig_traj)
plot_collection.add_figure("xyz_view", fig_xyz)
if args.plot:
plot_collection.show()
if args.save_plot:
logging.debug(SEP)
plot_collection.export(args.save_plot,
confirm_overwrite=not args.no_warnings)
if args.serialize_plot:
logging.debug(SEP)
plot_collection.serialize(args.serialize_plot,
confirm_overwrite=not args.no_warnings)
if args.save_as_tum:
logging.debug(SEP)
for name, traj in trajectories:
dest = os.path.splitext(os.path.basename(name))[0] + ".tum"
file_interface.write_tum_trajectory_file(
dest, traj, confirm_overwrite=not args.no_warnings)
if args.ref:
dest = os.path.splitext(os.path.basename(args.ref))[0] + ".tum"
file_interface.write_tum_trajectory_file(
dest, ref_traj, confirm_overwrite=not args.no_warnings)
if args.save_as_kitti:
logging.debug(SEP)
for name, traj in trajectories:
dest = os.path.splitext(os.path.basename(name))[0] + ".kitti"
file_interface.write_kitti_poses_file(
dest, traj, confirm_overwrite=not args.no_warnings)
if args.ref:
dest = os.path.splitext(os.path.basename(args.ref))[0] + ".kitti"
file_interface.write_kitti_poses_file(
dest, ref_traj, confirm_overwrite=not args.no_warnings)
if args.save_as_bag:
logging.debug(SEP)
import datetime
import rosbag
dest_bag_path = str(
datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S.%f')) + ".bag"
logging.debug("saving trajectories to " + dest_bag_path + "...")
bag = rosbag.Bag(dest_bag_path, 'w')
try:
for name, traj in trajectories:
dest_topic = os.path.splitext(os.path.basename(name))[0]
file_interface.write_bag_trajectory(bag, traj, dest_topic)
if args.ref:
dest_topic = os.path.splitext(os.path.basename(args.ref))[0]
file_interface.write_bag_trajectory(bag, ref_traj, dest_topic)
finally:
bag.close()
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
mapper.set_array(err)
colors = [mapper.to_rgba(a) for a in err]
cbar = fig.colorbar(
mapper, ticks=[min(err), (max(err) - (max(err) - min(err)) / 2),
max(err)])
cbar.ax.set_yticklabels([
"{0:0.3f}".format(min(err)),
"{0:0.3f}".format(max(err) - (max(err) - min(err)) / 2),
"{0:0.3f}".format(max(err))
])
# Plot the ground truth and estimated trajectories against each other with APE overlaid.
ax.set_title(
"Ground-Truth Trajectory with Loop Closures (Colored-Coded by Rotation Error in Deg)"
)
plot.traj(ax, plot_mode, traj_ref, "--", "gray", "reference")
# Plot accepted loop closures
assert (len(xs) == len(ys))
for i in range(len(xs)):
x = xs[i]
y = ys[i]
color = colors[i]
plt.plot(x, y, color=color)
ax.legend()
plt.show()
# %% [markdown]
# ## LoopClosureDetector PGO-Optimized Trajectory Plotting
#
print("plotting")
plot_collection = plot.PlotCollection("Example")
# metric values
fig_1 = plt.figure(figsize=(8, 8))
plot.error_array(fig_1,
ape_metric.error,
statistics=ape_statistics,
name="APE",
title=str(ape_metric))
plot_collection.add_figure("raw", fig_1)
# trajectory colormapped with error
fig_2 = plt.figure(figsize=(8, 8))
plot_mode = plot.PlotMode.xy
ax = plot.prepare_axis(fig_2, plot_mode)
plot.traj(ax, plot_mode, traj_ref, '--', 'gray', 'reference')
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")
plot_collection.add_figure("traj (error)", fig_2)
# trajectory colormapped with speed
fig_3 = plt.figure(figsize=(8, 8))
plot_mode = plot.PlotMode.xy
ax = plot.prepare_axis(fig_3, plot_mode)
speeds = [
trajectory.calc_speed(traj_est.positions_xyz[i],
def get_and_save_results_from_folder(folder_with_predicted_poses,category):
global args
global kitti_eval_tool
global folder_with_gt_poses
global output_folder
global t
global results
values_for_excel = []
columns_for_excel = []
type_of_statistics = 'mean'
for filename in sorted(os.listdir(folder_with_predicted_poses)):
if not(os.path.exists(os.path.join(folder_with_gt_poses, filename))):
print("file with gt poses doesn't exist for "+filename)
continue
if filename.find('.txt') == -1:
continue
seq_results = {}
seq_results['name_seq'] = filename[:filename.rfind('.')]
seq_results['category'] = category
folder_name = seq_results['category']
seq_results['metrics'] = {}
seq_results['lost'] = False
os.makedirs(os.path.join(output_folder, folder_name), exist_ok=True)
output_folder_seq = os.path.join(output_folder, folder_name, filename[:filename.rfind('.')])
os.makedirs(output_folder_seq, exist_ok=True)
if os.path.isfile(os.path.join(output_folder, folder_name,"results.txt")):
file_results_txt = open(os.path.join(output_folder, folder_name,"results.txt"), "a")
else:
file_results_txt = open(os.path.join(output_folder, folder_name,"results.txt"), "w")
file_results_txt.write("translation_error(%) rotation_error(deg/m) ATE(m) APE_translation_error_median(m) APE_rotation_error_median(deg) dst_to_trgt\n")
# -------------------------------------Getting results---------------------------------------------------
if args.gt_format == 'kitti':
traj_ref = file_interface.read_kitti_poses_file(os.path.join(folder_with_gt_poses, filename))
if args.gt_format == 'tum':
traj_ref = file_interface.read_tum_trajectory_file(os.path.join(folder_with_gt_poses, filename))
seq_results["length_of_ref_traj"] = traj_ref.path_length
end_time_gt = traj_ref.get_infos()["t_end (s)"]
if args.gt_format == 'euroc':
traj_ref = file_interface.read_euroc_csv_trajectory(os.path.join(folder_with_gt_poses, filename))
if args.result_format == 'kitti':
traj_est = file_interface.read_kitti_poses_file(os.path.join(folder_with_predicted_poses, filename))
if args.result_format == 'tum':
traj_est = file_interface.read_tum_trajectory_file(os.path.join(folder_with_predicted_poses, filename))
seq_results["length_of_estimated_traj"] = traj_est.path_length
if args.result_format == 'euroc':
traj_est = file_interface.read_euroc_csv_trajectory(os.path.join(folder_with_predicted_poses, filename))
if args.result_format == 'tum' and args.gt_format == 'tum':
seq_results["num_gt_poses"] = traj_ref.num_poses
seq_results["num_predicted_poses"] = traj_est.num_poses
traj_ref, traj_est = sync.associate_trajectories(traj_ref, traj_est, args.max_diff)
end_time_est = traj_est.get_infos()["t_end (s)"]
if (abs(end_time_est - end_time_gt) > 0.2) or (traj_est.get_infos()["t_start (s)"] > 0.2):
print('LOST in track '+filename[:filename.rfind('.')])
seq_results['lost'] = True
results.append(seq_results)
t.update(1)
continue
if args.alignment != None:
traj_est = trajectory.align_trajectory(traj_est, traj_ref, correct_scale=args.alignment.find("scale") != -1, correct_only_scale=args.alignment=="scale")
trajectory.align_trajectory_origin(traj_est, traj_ref)
data = (traj_ref, traj_est)
ape_metric_translation = metrics.APE(metrics.PoseRelation.translation_part)
ape_metric_rotation = metrics.APE(metrics.PoseRelation.rotation_angle_deg)
ape_metric_translation.process_data(data)
ape_metric_rotation.process_data(data)
ape_translation_statistics = ape_metric_translation.get_all_statistics()
ape_rotation_statistics = ape_metric_rotation.get_all_statistics()
ape_translation_statistics_plot = copy.deepcopy(ape_translation_statistics)
ape_rotation_statistics_plot = copy.deepcopy(ape_rotation_statistics)
ape_translation_statistics_plot.pop('sse')
ape_translation_statistics_plot.pop('std')
ape_translation_statistics_plot.pop('min')
ape_translation_statistics_plot.pop('max')
ape_rotation_statistics_plot.pop('sse')
ape_rotation_statistics_plot.pop('std')
ape_rotation_statistics_plot.pop('min')
ape_rotation_statistics_plot.pop('max')
kitti_trans_err, kitti_rot_err, ate = kitti_eval_tool.eval(traj_ref.poses_se3,
traj_est.poses_se3,
alignment=None)
#---------------------------------adding results to variable seq_results for excel -----------------------------
seq_results['metrics']['dist_to_trgt'] = traj_est.get_infos()['pos_end (m)'] - traj_ref.get_infos()['pos_end (m)']
seq_results['metrics']['dist_to_trgt'] = np.sum(np.array(seq_results['metrics']['dist_to_trgt'])**2)**0.5
seq_results['metrics']["Kitti trans err (%)"] = kitti_trans_err
seq_results['metrics']["Kitti rot err (deg/m)"] = kitti_rot_err
seq_results['metrics']["ATE (m)"] = ate
seq_results['metrics']["APE(trans err) median (m)"] = ape_translation_statistics["median"]
seq_results['metrics']["APE(rot err) median (deg)"] = ape_rotation_statistics["median"]
#--------------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------------
# --------------------------------printing results into console----------------------------------------------
print('Results for "'+filename+'":')
print('Kitti average translational error (%): {:.7f}'.format(kitti_trans_err))
print('Kitti average rotational error (deg/m): {:.7f}'.format(kitti_rot_err))
print('ATE (m): {:.7f}'.format(ate))
print('APE(translation error) median (m): {:.7f}'.format(ape_translation_statistics["median"]))
print('APE(rotation error) median (deg): {:.7f}'.format(ape_rotation_statistics["median"]))
print('distance to target on the last frame: {:.7f}'.format(seq_results['metrics']['dist_to_trgt']))
#------------------------------------------------------------------------------------------------------------
#---------------------------------Saving results into overall results text file------------------------------
file_results_txt.write('{:<24} '.format(filename[:filename.rfind('.')]))
file_results_txt.write('{:>7.4f} '.format(kitti_trans_err))
file_results_txt.write('{:>7.4f} '.format(kitti_rot_err))
file_results_txt.write('{:>7.4f} '.format(ate))
file_results_txt.write('{:>7.4f} '.format(ape_translation_statistics["median"]))
file_results_txt.write('{:>7.4f} '.format(ape_rotation_statistics["median"]))
file_results_txt.write('{:>7.4f}\n'.format(seq_results['metrics']['dist_to_trgt']))
#------------------------------------------------------------------------------------------------------------
# --------------------------------Saving metrics to text file for one track----------------------------------
txt_filename = filename[:filename.rfind('.')]+"_metrics.txt"
with open(os.path.join(output_folder_seq, txt_filename), "w") as txt_file:
txt_file.write('Kitti average translational error (%): {:.7f}\n'.format(kitti_trans_err))
txt_file.write('Kitti average rotational error (deg/m): {:.7f}\n'.format(kitti_rot_err))
txt_file.write('ATE (m): {:.7f}\n'.format(ate))
txt_file.write('APE(translation error) median (m): {:.7f}\n'.format(ape_translation_statistics["median"]))
txt_file.write('APE(rotation error) median (deg): {:.7f}\n'.format(ape_rotation_statistics["median"]))
txt_file.write('Distance to target on the last frame: {:.7f}\n'.format(seq_results['metrics']['dist_to_trgt']))
#---------------------------------------------------------------------------------------------------------
# ---------------------------------Saving values of errors for each frame to text file------------------------
# ------------------------------------------for translation errors----------------------------------------
txt_filename = filename[:filename.rfind('.')]+"_APE(translation)_errors.txt"
output_folder_seq_translation = os.path.join(output_folder_seq,"translation")
output_folder_seq_rotation = os.path.join(output_folder_seq,"rotation")
os.makedirs(output_folder_seq_translation, exist_ok=True)
os.makedirs(output_folder_seq_rotation, exist_ok=True)
with open(os.path.join(output_folder_seq_translation, txt_filename), "w") as txt_file:
for error in ape_metric_translation.error:
txt_file.write('{:.10f}\n'.format(error))
# -----------------------------------------for rotation degree errors--------------------------------------
txt_filename = filename[:filename.rfind('.')]+"_APE(rotation_deg)_errors.txt"
with open(os.path.join(output_folder_seq_rotation, txt_filename), "w") as txt_file:
for error in ape_metric_rotation.error:
txt_file.write('{:.10f}\n'.format(error))
#----------------------------------------------------------------------------------------------------------
# ---------------------------------------Saving plot of errors of each frame------------------------------
# ------------------------------------------for translation errors----------------------------------------
plot_collection = plot.PlotCollection("Example")
fig_1 = plt.figure(figsize=(8, 8))
plot.error_array(fig_1, ape_metric_translation.error,
name="APE", title=str(ape_metric_translation), xlabel="Index of frame", ylabel='Error')
plot_collection.add_figure("raw", fig_1)
plot_filename = filename[:filename.rfind('.')]+"_APE(translation)_errors.png"
plt.savefig(os.path.join(output_folder_seq_translation, plot_filename))
plt.close(fig_1)
# -----------------------------------------for rotation degree errors--------------------------------------
plot_collection = plot.PlotCollection("Example")
fig_1 = plt.figure(figsize=(8, 8))
plot.error_array(fig_1, ape_metric_rotation.error,
name="APE", title=str(ape_metric_rotation), xlabel="Index of frame", ylabel='Error')
plot_collection.add_figure("raw", fig_1)
plot_filename = filename[:filename.rfind('.')]+"_APE(rotation)_errors.png"
plt.savefig(os.path.join(output_folder_seq_rotation,plot_filename))
plt.close(fig_1)
#-----------------------------------------------------------------------------------------------------------
# -----------------------------------------Saving trajectory plot-------------------------------------------
# ------------------------------------------for translation errors----------------------------------------
fig_2 = plt.figure(figsize=(8, 8))
ax = plot.prepare_axis(fig_2, plot_mode)
plot.traj(ax, plot_mode, traj_ref, '--', 'gray', 'reference')
plot.traj_colormap( ax, traj_est, ape_metric_translation.error, plot_mode,
min_map=ape_translation_statistics["min"],
max_map=ape_translation_statistics["max"], title="APE translation mapped onto trajectory")
plot_collection.add_figure("traj (error)", fig_2)
plot_filename = filename[:filename.rfind('.')]+"_APE(translation)_map.png"
plt.savefig(os.path.join(output_folder_seq_translation,plot_filename))
plt.close(fig_2)
# -----------------------------------------for rotation degree errors--------------------------------------
fig_2 = plt.figure(figsize=(8, 8))
ax = plot.prepare_axis(fig_2, plot_mode)
plot.traj(ax, plot_mode, traj_ref, '--', 'gray', 'reference')
plot.traj_colormap( ax, traj_est, ape_metric_rotation.error, plot_mode,
min_map=ape_rotation_statistics["min"],
max_map=ape_rotation_statistics["max"], title="APE rotation mapped onto trajectory")
plot_collection.add_figure("traj (error)", fig_2)
plot_filename = filename[:filename.rfind('.')]+"_APE(rotation)_map.png"
plt.savefig(os.path.join(output_folder_seq_rotation,plot_filename))
plt.close(fig_2)
#-----------------------------------------------------------------------------------------------------------
print()
active_worksheet = wb['sheet1']
thin = Side(border_style="thin", color="000000")
thick = Side(border_style="thick", color="000000")
medium = Side(border_style="medium", color="000000")
font_header = Font(name='Arial',
size=10,
bold=True,
italic=False,
vertAlign=None,
underline='none',
strike=False,
color='FF000000')
font_values = Font(name='Arial',
size=10,
bold=False,
italic=False,
vertAlign=None,
underline='none',
strike=False,
color='FF000000')
active_worksheet.row_dimensions[2].height = 35
file_results_txt.close()
results.append(seq_results)
t.update(1)
logger.info("\nUmeyama alignment without scaling")
traj_est_aligned = trajectory.align_trajectory(traj_est, traj_ref)
logger.info("\nUmeyama alignment with scaling")
traj_est_aligned_scaled = trajectory.align_trajectory(
traj_est, traj_ref, correct_scale=True)
logger.info("\nUmeyama alignment with scaling only")
traj_est_aligned_only_scaled = trajectory.align_trajectory(
traj_est, traj_ref, correct_only_scale=True)
fig = plt.figure(figsize=(8, 8))
plot_mode = plot.PlotMode.xz
ax = plot.prepare_axis(fig, plot_mode, subplot_arg='221')
plot.traj(ax, plot_mode, traj_ref, '--', 'gray')
plot.traj(ax, plot_mode, traj_est, '-', 'blue')
fig.axes.append(ax)
plt.title('not aligned')
ax = plot.prepare_axis(fig, plot_mode, subplot_arg='222')
plot.traj(ax, plot_mode, traj_ref, '--', 'gray')
plot.traj(ax, plot_mode, traj_est_aligned, '-', 'blue')
fig.axes.append(ax)
plt.title('$\mathrm{SE}(3)$ alignment')
ax = plot.prepare_axis(fig, plot_mode, subplot_arg='223')
plot.traj(ax, plot_mode, traj_ref, '--', 'gray')
plot.traj(ax, plot_mode, traj_est_aligned_scaled, '-', 'blue')
fig.axes.append(ax)
plt.title('$\mathrm{Sim}(3)$ alignment')
def plot_multi(args, result, traj_ref_list, traj_est_list):
from evo.tools import plot
from evo.tools.settings import SETTINGS
import matplotlib.pyplot as plt
import numpy as np
logger.debug(SEP)
logger.debug("Plotting results... ")
plot_mode = plot.PlotMode(args.plot_mode)
figs = []
# Plot the raw metric values.
error_array_comb = np.array([])
for i in range(len(result)):
figs.append(plt.figure(figsize=SETTINGS.plot_figsize))
if "seconds_from_start" in result[i].np_arrays:
seconds_from_start = result[i].np_arrays["seconds_from_start"]
else:
seconds_from_start = None
plot.error_array(
figs[i], result[i].np_arrays["error_array"], x_array=seconds_from_start,
statistics={
s: result[i].stats[s]
for s in SETTINGS.plot_statistics if s not in ("min", "max")
}, name=result[i].info["label"], title=result[i].info["title"],
xlabel="$t$ (s)" if seconds_from_start else "index")
# Plot the values color-mapped onto the trajectory.
figs.append(plt.figure(figsize=SETTINGS.plot_figsize))
ax = plot.prepare_axis(figs[-1], plot_mode)
if args.ros_map_yaml:
plot.ros_map(ax, args.ros_map_yaml, plot_mode)
if args.plot_colormap_min is None:
args.plot_colormap_min = min([result[i].stats["min"] for i in range(len(result))])
if args.plot_colormap_max is None:
args.plot_colormap_max = max([result[i].stats["max"] for i in range(len(result))])
if args.plot_colormap_max_percentile is not None:
args.plot_colormap_max = np.percentile(
error_array_comb, args.plot_colormap_max_percentile)
traj_est_list_comb = np.array([])
for i in range(len(result)):
plot.traj(ax, plot_mode, traj_ref_list[i], style=SETTINGS.plot_reference_linestyle,
color=multirobot_reference_color[i], label='reference'+str(i),
alpha=0.8)
plot.draw_coordinate_axes(ax, traj_ref_list[i], plot_mode,
SETTINGS.plot_axis_marker_scale)
plot.draw_coordinate_axes(ax, traj_est_list[i], plot_mode,
SETTINGS.plot_axis_marker_scale)
figs[-1].axes.append(ax)
plot.traj_colormap_multi(ax, traj_est_list, [r.np_arrays["error_array"] for r in result],
plot_mode, min_map=args.plot_colormap_min,
max_map=args.plot_colormap_max,
title="Error mapped onto trajectory")
plot_collection = plot.PlotCollection("Multi-robot APE analysis")
for i in range(len(result)):
plot_collection.add_figure("raw" + str(i), figs[i])
plot_collection.add_figure("map", figs[-1])
if args.plot:
plot_collection.show()
if args.save_plot:
plot_collection.export(args.save_plot,
confirm_overwrite=not args.no_warnings)
if args.serialize_plot:
logger.debug(SEP)
plot_collection.serialize(args.serialize_plot,
confirm_overwrite=not args.no_warnings)
logging.info("\nUmeyama alignment without scaling")
traj_est_aligned = trajectory.align_trajectory(traj_est, traj_ref)
logging.info("\nUmeyama alignment with scaling")
traj_est_aligned_scaled = trajectory.align_trajectory(traj_est,
traj_ref,
correct_scale=True)
logging.info("\nUmeyama alignment with scaling only")
traj_est_aligned_only_scaled = trajectory.align_trajectory(
traj_est, traj_ref, correct_only_scale=True)
fig = plt.figure(figsize=(8, 8))
# fig.suptitle("Umeyama $\mathrm{Sim}(3)$ alignment")
plot_mode = plot.PlotMode.xz
ax = plot.prepare_axis(fig, plot_mode,
subplot_arg='221') # 122 = 1*2 grid, second
plot.traj(ax, plot_mode, traj_ref, '--', 'gray') #, 'reference')
plot.traj(ax, plot_mode, traj_est, '-', 'blue') #, 'not aligned')
fig.axes.append(ax)
#plt.legend()
plt.title('not aligned')
ax = plot.prepare_axis(fig, plot_mode, subplot_arg='222')
plot.traj(ax, plot_mode, traj_ref, '--', 'gray') #, 'reference')
plot.traj(ax, plot_mode, traj_est_aligned, '-',
'blue') #, '$\mathrm{SE}(3)$ alignment')
fig.axes.append(ax)
#plt.legend()
plt.title('$\mathrm{SE}(3)$ alignment')
ax = plot.prepare_axis(fig, plot_mode, subplot_arg='223')
plot.traj(ax, plot_mode, traj_ref, '--', 'gray') #, 'reference')
plot.traj(ax, plot_mode, traj_est_aligned_scaled, '-',
'blue') #, '$\mathrm{Sim}(3)$ alignment')
def run_analysis(traj_ref_path,
traj_est_path,
segments,
save_results,
display_plot,
save_plots,
save_folder,
confirm_overwrite=False,
dataset_name="",
discard_n_start_poses=0,
discard_n_end_poses=0):
""" Run analysis on given trajectories, saves plots on given path:
:param traj_ref_path: path to the reference (ground truth) trajectory.
:param traj_est_path: path to the estimated trajectory.
:param save_results: saves APE, and RPE per segment results.
:param save_plots: whether to save the plots.
:param save_folder: where to save the plots.
:param confirm_overwrite: whether to confirm overwriting plots or not.
:param dataset_name: optional param, to allow setting the same scale on different plots.
"""
# Load trajectories.
from evo.tools import file_interface
traj_ref = None
try:
traj_ref = file_interface.read_euroc_csv_trajectory(
traj_ref_path) # TODO make it non-euroc specific.
except file_interface.FileInterfaceException as e:
raise Exception(
"\033[91mMissing ground truth csv! \033[93m {}.".format(e))
traj_est = None
try:
traj_est = file_interface.read_swe_csv_trajectory(traj_est_path)
except file_interface.FileInterfaceException as e:
log.info(e)
raise Exception("\033[91mMissing vio output csv.\033[99m")
evt.print_purple("Registering trajectories")
traj_ref, traj_est = sync.associate_trajectories(traj_ref, traj_est)
evt.print_purple("Aligning trajectories")
traj_est = trajectory.align_trajectory(
traj_est,
traj_ref,
correct_scale=False,
discard_n_start_poses=int(discard_n_start_poses),
discard_n_end_poses=int(discard_n_end_poses))
num_of_poses = traj_est.num_poses
traj_est.reduce_to_ids(
range(int(discard_n_start_poses),
int(num_of_poses - discard_n_end_poses), 1))
traj_ref.reduce_to_ids(
range(int(discard_n_start_poses),
int(num_of_poses - discard_n_end_poses), 1))
results = dict()
evt.print_purple("Calculating APE translation part")
data = (traj_ref, traj_est)
ape_metric = metrics.APE(metrics.PoseRelation.translation_part)
ape_metric.process_data(data)
ape_result = ape_metric.get_result()
results["absolute_errors"] = ape_result
log.info(ape_result.pretty_str(info=True))
# TODO(Toni): Save RPE computation results rather than the statistics
# you can compute statistics later...
evt.print_purple("Calculating RPE translation part for plotting")
rpe_metric_trans = metrics.RPE(metrics.PoseRelation.translation_part, 1.0,
metrics.Unit.frames, 0.0, False)
rpe_metric_trans.process_data(data)
rpe_stats_trans = rpe_metric_trans.get_all_statistics()
log.info("mean: %f" % rpe_stats_trans["mean"])
evt.print_purple("Calculating RPE rotation angle for plotting")
rpe_metric_rot = metrics.RPE(metrics.PoseRelation.rotation_angle_deg, 1.0,
metrics.Unit.frames, 1.0, False)
rpe_metric_rot.process_data(data)
rpe_stats_rot = rpe_metric_rot.get_all_statistics()
log.info("mean: %f" % rpe_stats_rot["mean"])
results["relative_errors"] = dict()
# Read segments file
for segment in segments:
results["relative_errors"][segment] = dict()
evt.print_purple("RPE analysis of segment: %d" % segment)
evt.print_lightpurple("Calculating RPE segment translation part")
rpe_segment_metric_trans = metrics.RPE(
metrics.PoseRelation.translation_part, float(segment),
metrics.Unit.meters, 0.01, True)
rpe_segment_metric_trans.process_data(data)
rpe_segment_stats_trans = rpe_segment_metric_trans.get_all_statistics()
results["relative_errors"][segment][
"rpe_trans"] = rpe_segment_stats_trans
# print(rpe_segment_stats_trans)
# print("mean:", rpe_segment_stats_trans["mean"])
evt.print_lightpurple("Calculating RPE segment rotation angle")
rpe_segment_metric_rot = metrics.RPE(
metrics.PoseRelation.rotation_angle_deg, float(segment),
metrics.Unit.meters, 0.01, True)
rpe_segment_metric_rot.process_data(data)
rpe_segment_stats_rot = rpe_segment_metric_rot.get_all_statistics()
results["relative_errors"][segment]["rpe_rot"] = rpe_segment_stats_rot
# print(rpe_segment_stats_rot)
# print("mean:", rpe_segment_stats_rot["mean"])
if save_results:
# Save results file
results_file = os.path.join(save_folder, 'results.yaml')
evt.print_green("Saving analysis results to: %s" % results_file)
with open(results_file, 'w') as outfile:
if confirm_overwrite:
if evt.user.check_and_confirm_overwrite(results_file):
outfile.write(yaml.dump(results, default_flow_style=False))
else:
log.info("Not overwritting results.")
else:
outfile.write(yaml.dump(results, default_flow_style=False))
# For each segment in segments file
# Calculate rpe with delta = segment in meters with all-pairs set to True
# Calculate max, min, rmse, mean, median etc
# Plot boxplot, or those cumulative figures you see in evo (like demographic plots)
if display_plot or save_plots:
evt.print_green("Plotting:")
log.info(dataset_name)
plot_collection = plot.PlotCollection("Example")
# metric values
fig_1 = plt.figure(figsize=(8, 8))
ymax = -1
if dataset_name is not "" and FIX_MAX_Y:
ymax = Y_MAX_APE_TRANS[dataset_name]
ape_statistics = ape_metric.get_all_statistics()
plot.error_array(
fig_1,
ape_metric.error,
statistics=ape_statistics,
name="APE translation",
title="" #str(ape_metric)
,
xlabel="Keyframe index [-]",
ylabel="APE translation [m]",
y_min=0.0,
y_max=ymax)
plot_collection.add_figure("APE_translation", fig_1)
# trajectory colormapped with error
fig_2 = plt.figure(figsize=(8, 8))
plot_mode = plot.PlotMode.xy
ax = plot.prepare_axis(fig_2, plot_mode)
plot.traj(ax, plot_mode, traj_ref, '--', 'gray', 'reference')
plot.traj_colormap(ax,
traj_est,
ape_metric.error,
plot_mode,
min_map=0.0,
max_map=math.ceil(ape_statistics['max'] * 10) / 10,
title="ATE mapped onto trajectory [m]")
plot_collection.add_figure("APE_translation_trajectory_error", fig_2)
# RPE
## Trans
### metric values
fig_3 = plt.figure(figsize=(8, 8))
if dataset_name is not "" and FIX_MAX_Y:
ymax = Y_MAX_RPE_TRANS[dataset_name]
plot.error_array(
fig_3,
rpe_metric_trans.error,
statistics=rpe_stats_trans,
name="RPE translation",
title="" #str(rpe_metric_trans)
,
xlabel="Keyframe index [-]",
ylabel="RPE translation [m]",
y_max=ymax)
plot_collection.add_figure("RPE_translation", fig_3)
### trajectory colormapped with error
fig_4 = plt.figure(figsize=(8, 8))
plot_mode = plot.PlotMode.xy
ax = plot.prepare_axis(fig_4, plot_mode)
traj_ref_trans = copy.deepcopy(traj_ref)
traj_ref_trans.reduce_to_ids(rpe_metric_trans.delta_ids)
traj_est_trans = copy.deepcopy(traj_est)
traj_est_trans.reduce_to_ids(rpe_metric_trans.delta_ids)
plot.traj(ax, plot_mode, traj_ref_trans, '--', 'gray', 'Reference')
plot.traj_colormap(
ax,
traj_est_trans,
rpe_metric_trans.error,
plot_mode,
min_map=0.0,
max_map=math.ceil(rpe_stats_trans['max'] * 10) / 10,
title="RPE translation error mapped onto trajectory [m]")
plot_collection.add_figure("RPE_translation_trajectory_error", fig_4)
## Rot
### metric values
fig_5 = plt.figure(figsize=(8, 8))
if dataset_name is not "" and FIX_MAX_Y:
ymax = Y_MAX_RPE_ROT[dataset_name]
plot.error_array(
fig_5,
rpe_metric_rot.error,
statistics=rpe_stats_rot,
name="RPE rotation error",
title="" #str(rpe_metric_rot)
,
xlabel="Keyframe index [-]",
ylabel="RPE rotation [deg]",
y_max=ymax)
plot_collection.add_figure("RPE_rotation", fig_5)
### trajectory colormapped with error
fig_6 = plt.figure(figsize=(8, 8))
plot_mode = plot.PlotMode.xy
ax = plot.prepare_axis(fig_6, plot_mode)
traj_ref_rot = copy.deepcopy(traj_ref)
traj_ref_rot.reduce_to_ids(rpe_metric_rot.delta_ids)
traj_est_rot = copy.deepcopy(traj_est)
traj_est_rot.reduce_to_ids(rpe_metric_rot.delta_ids)
plot.traj(ax, plot_mode, traj_ref_rot, '--', 'gray', 'Reference')
plot.traj_colormap(
ax,
traj_est_rot,
rpe_metric_rot.error,
plot_mode,
min_map=0.0,
max_map=math.ceil(rpe_stats_rot['max'] * 10) / 10,
title="RPE rotation error mapped onto trajectory [deg]")
plot_collection.add_figure("RPE_rotation_trajectory_error", fig_6)
if display_plot:
evt.print_green("Displaying plots.")
plot_collection.show()
if save_plots:
evt.print_green("Saving plots to: ")
log.info(save_folder)
# Config output format (pdf, eps, ...) using evo_config...
plot_collection.export(os.path.join(save_folder, "plots.eps"),
False)
plot_collection.export(os.path.join(save_folder, "plots.pdf"),
False)
def plot_result(args: argparse.Namespace,
result: Result,
traj_ref: PosePath3D,
traj_est: PosePath3D,
traj_ref_full: typing.Optional[PosePath3D] = None) -> None:
from evo.tools import plot
from evo.tools.settings import SETTINGS
import matplotlib.pyplot as plt
import numpy as np
logger.debug(SEP)
logger.debug("Plotting results... ")
plot_mode = plot.PlotMode(args.plot_mode)
# Plot the raw metric values.
fig1 = plt.figure(figsize=SETTINGS.plot_figsize)
if "seconds_from_start" in result.np_arrays:
seconds_from_start = result.np_arrays["seconds_from_start"]
else:
seconds_from_start = None
plot.error_array(
fig1.gca(),
result.np_arrays["error_array"],
x_array=seconds_from_start,
statistics={
s: result.stats[s]
for s in SETTINGS.plot_statistics if s not in ("min", "max")
},
name=result.info["label"],
title=result.info["title"],
xlabel="$t$ (s)" if seconds_from_start is not None else "index")
# Plot the values color-mapped onto the trajectory.
fig2 = plt.figure(figsize=SETTINGS.plot_figsize)
ax = plot.prepare_axis(fig2, plot_mode)
if args.ros_map_yaml:
plot.ros_map(ax, args.ros_map_yaml, plot_mode)
plot.traj(ax,
plot_mode,
traj_ref_full if traj_ref_full else traj_ref,
style=SETTINGS.plot_reference_linestyle,
color=SETTINGS.plot_reference_color,
label='reference',
alpha=SETTINGS.plot_reference_alpha)
plot.draw_coordinate_axes(ax, traj_ref, plot_mode,
SETTINGS.plot_axis_marker_scale)
if args.plot_colormap_min is None:
args.plot_colormap_min = result.stats["min"]
if args.plot_colormap_max is None:
args.plot_colormap_max = result.stats["max"]
if args.plot_colormap_max_percentile is not None:
args.plot_colormap_max = np.percentile(
result.np_arrays["error_array"], args.plot_colormap_max_percentile)
plot.traj_colormap(ax,
traj_est,
result.np_arrays["error_array"],
plot_mode,
min_map=args.plot_colormap_min,
max_map=args.plot_colormap_max,
title="Error mapped onto trajectory")
plot.draw_coordinate_axes(ax, traj_est, plot_mode,
SETTINGS.plot_axis_marker_scale)
if SETTINGS.plot_pose_correspondences:
plot.draw_correspondence_edges(
ax,
traj_est,
traj_ref,
plot_mode,
style=SETTINGS.plot_pose_correspondences_linestyle,
color=SETTINGS.plot_reference_color,
alpha=SETTINGS.plot_reference_alpha)
fig2.axes.append(ax)
plot_collection = plot.PlotCollection(result.info["title"])
plot_collection.add_figure("raw", fig1)
plot_collection.add_figure("map", fig2)
if args.plot:
plot_collection.show()
if args.save_plot:
plot_collection.export(args.save_plot,
confirm_overwrite=not args.no_warnings)
if args.serialize_plot:
logger.debug(SEP)
plot_collection.serialize(args.serialize_plot,
confirm_overwrite=not args.no_warnings)
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
traj_est_aligned = trajectory.align_trajectory(traj_est, traj_ref)
logger.info("\nUmeyama alignment with scaling")
traj_est_aligned_scaled = trajectory.align_trajectory(traj_est,
traj_ref,
correct_scale=True)
logger.info("\nUmeyama alignment with scaling only")
traj_est_aligned_only_scaled = trajectory.align_trajectory(
traj_est, traj_ref, correct_only_scale=True)
fig = plt.figure(figsize=(8, 8))
plot_mode = plot.PlotMode.xz
ax = plot.prepare_axis(fig, plot_mode, subplot_arg='221')
plot.traj(ax, plot_mode, traj_ref, '--', 'gray')
plot.traj(ax, plot_mode, traj_est, '-', 'blue')
fig.axes.append(ax)
plt.title('not aligned')
ax = plot.prepare_axis(fig, plot_mode, subplot_arg='222')
plot.traj(ax, plot_mode, traj_ref, '--', 'gray')
plot.traj(ax, plot_mode, traj_est_aligned, '-', 'blue')
fig.axes.append(ax)
plt.title('$\mathrm{SE}(3)$ alignment')
ax = plot.prepare_axis(fig, plot_mode, subplot_arg='223')
plot.traj(ax, plot_mode, traj_ref, '--', 'gray')
plot.traj(ax, plot_mode, traj_est_aligned_scaled, '-', 'blue')
fig.axes.append(ax)
plt.title('$\mathrm{Sim}(3)$ alignment')
def run(args):
import os
import sys
import numpy as np
import evo.core.lie_algebra as lie
from evo.core import trajectory
from evo.core.trajectory import PoseTrajectory3D
from evo.tools import file_interface, log
from evo.tools.settings import SETTINGS
log.configure_logging(verbose=args.verbose,
silent=args.silent,
debug=args.debug)
if args.debug:
import pprint
logger.debug(
"main_parser config:\n" +
pprint.pformat({arg: getattr(args, arg)
for arg in vars(args)}) + "\n")
logger.debug(SEP)
trajectories = []
ref_traj = None
if args.subcommand == "tum":
for traj_file in args.traj_files:
if traj_file != args.ref:
trajectories.append(
(traj_file,
file_interface.read_tum_trajectory_file(traj_file)))
if args.ref:
ref_traj = file_interface.read_tum_trajectory_file(args.ref)
elif args.subcommand == "kitti":
for pose_file in args.pose_files:
if pose_file != args.ref:
trajectories.append(
(pose_file,
file_interface.read_kitti_poses_file(pose_file)))
if args.ref:
ref_traj = file_interface.read_kitti_poses_file(args.ref)
elif args.subcommand == "euroc":
for csv_file in args.state_gt_csv:
if csv_file != args.ref:
trajectories.append(
(csv_file,
file_interface.read_euroc_csv_trajectory(csv_file)))
if args.ref:
ref_traj = file_interface.read_euroc_csv_trajectory(args.ref)
elif args.subcommand == "bag":
import rosbag
bag = rosbag.Bag(args.bag)
try:
if args.all_topics:
topic_info = bag.get_type_and_topic_info()
topics = sorted([
t for t in topic_info[1].keys()
if topic_info[1][t][0] == "geometry_msgs/PoseStamped"
and t != args.ref
])
if len(topics) == 0:
logger.error("No geometry_msgs/PoseStamped topics found!")
sys.exit(1)
else:
topics = args.topics
if not topics:
logger.warning(
"No topics used - specify topics or set --all_topics.")
sys.exit(1)
for topic in topics:
trajectories.append(
(topic, file_interface.read_bag_trajectory(bag, topic)))
if args.ref:
ref_traj = file_interface.read_bag_trajectory(bag, args.ref)
finally:
bag.close()
else:
raise RuntimeError("unsupported subcommand: " + args.subcommand)
if args.merge:
if args.subcommand == "kitti":
raise TypeError(
"can't merge KITTI files - but you can append them with 'cat'")
if len(trajectories) == 0:
raise RuntimeError("no trajectories to merge (excluding --ref)")
merged_stamps = trajectories[0][1].timestamps
merged_xyz = trajectories[0][1].positions_xyz
merged_quat = trajectories[0][1].orientations_quat_wxyz
for _, traj in trajectories[1:]:
merged_stamps = np.concatenate((merged_stamps, traj.timestamps))
merged_xyz = np.concatenate((merged_xyz, traj.positions_xyz))
merged_quat = np.concatenate(
(merged_quat, traj.orientations_quat_wxyz))
order = merged_stamps.argsort()
merged_stamps = merged_stamps[order]
merged_xyz = merged_xyz[order]
merged_quat = merged_quat[order]
trajectories = [("merged_trajectory",
PoseTrajectory3D(merged_xyz, merged_quat,
merged_stamps))]
if args.transform_left or args.transform_right:
tf_type = "left" if args.transform_left else "right"
tf_path = args.transform_left if args.transform_left else args.transform_right
t, xyz, quat = file_interface.load_transform_json(tf_path)
logger.debug(SEP)
if not lie.is_se3(t):
logger.warning("Not a valid SE(3) transformation!")
if args.invert_transform:
t = lie.se3_inverse(t)
logger.debug("Applying a {}-multiplicative transformation:\n{}".format(
tf_type, t))
for name, traj in trajectories:
traj.transform(t, right_mul=args.transform_right)
if args.t_offset:
logger.debug(SEP)
for name, traj in trajectories:
if type(traj) is trajectory.PosePath3D:
logger.warning(
"{} doesn't have timestamps - can't add time offset.".
format(name))
else:
logger.info("Adding time offset to {}: {} (s)".format(
name, args.t_offset))
traj.timestamps += args.t_offset
if args.align or args.correct_scale:
if not args.ref:
logger.debug(SEP)
logger.warning("Can't align without a reference! (--ref) *grunt*")
else:
if args.subcommand == "kitti":
traj_tmp, ref_traj_tmp = trajectories, [
ref_traj for n, t in trajectories
]
else:
traj_tmp, ref_traj_tmp = [], []
from evo.core import sync
for name, traj in trajectories:
logger.debug(SEP)
ref_assoc, traj_assoc = sync.associate_trajectories(
ref_traj,
traj,
max_diff=args.t_max_diff,
first_name="ref",
snd_name=name)
ref_traj_tmp.append(ref_assoc)
traj_tmp.append((name, traj_assoc))
trajectories = traj_tmp
correct_only_scale = args.correct_scale and not args.align
trajectories_new = []
for nt, ref_assoc in zip(trajectories, ref_traj_tmp):
logger.debug(SEP)
logger.debug("Aligning " + nt[0] + " to " + args.ref + "...")
trajectories_new.append(
(nt[0],
trajectory.align_trajectory(nt[1], ref_assoc,
args.correct_scale,
correct_only_scale,
args.n_to_align)))
trajectories = trajectories_new
for name, traj in trajectories:
print_traj_info(name, traj, args.verbose, args.full_check)
if args.ref:
print_traj_info(args.ref, ref_traj, args.verbose, args.full_check)
if args.plot or args.save_plot or args.serialize_plot:
from evo.tools.plot import PlotMode
plot_mode = PlotMode.xyz if not args.plot_mode else PlotMode[
args.plot_mode]
import numpy as np
from evo.tools import plot
import matplotlib.pyplot as plt
import matplotlib.cm as cm
plot_collection = plot.PlotCollection("evo_traj - trajectory plot")
fig_xyz, axarr_xyz = plt.subplots(3,
sharex="col",
figsize=tuple(SETTINGS.plot_figsize))
fig_rpy, axarr_rpy = plt.subplots(3,
sharex="col",
figsize=tuple(SETTINGS.plot_figsize))
fig_traj = plt.figure(figsize=tuple(SETTINGS.plot_figsize))
if (args.align or args.correct_scale) and not args.ref:
plt.xkcd(scale=2, randomness=4)
fig_traj.suptitle("what if --ref?")
fig_xyz.suptitle("what if --ref?")
ax_traj = plot.prepare_axis(fig_traj, plot_mode)
if args.ref:
short_traj_name = os.path.splitext(os.path.basename(args.ref))[0]
if SETTINGS.plot_usetex:
short_traj_name = short_traj_name.replace("_", "\\_")
plot.traj(ax_traj,
plot_mode,
ref_traj,
'--',
'grey',
short_traj_name,
alpha=0 if SETTINGS.plot_hideref else 1)
plot.traj_xyz(axarr_xyz,
ref_traj,
'--',
'grey',
short_traj_name,
alpha=0 if SETTINGS.plot_hideref else 1)
plot.traj_rpy(axarr_rpy,
ref_traj,
'--',
'grey',
short_traj_name,
alpha=0 if SETTINGS.plot_hideref else 1)
cmap_colors = None
if SETTINGS.plot_multi_cmap.lower() != "none":
cmap = getattr(cm, SETTINGS.plot_multi_cmap)
cmap_colors = iter(cmap(np.linspace(0, 1, len(trajectories))))
for name, traj in trajectories:
if cmap_colors is None:
color = next(ax_traj._get_lines.prop_cycler)['color']
else:
color = next(cmap_colors)
short_traj_name = os.path.splitext(os.path.basename(name))[0]
if SETTINGS.plot_usetex:
short_traj_name = short_traj_name.replace("_", "\\_")
plot.traj(ax_traj, plot_mode, traj, '-', color, short_traj_name)
if args.ref and isinstance(ref_traj, trajectory.PoseTrajectory3D):
start_time = ref_traj.timestamps[0]
else:
start_time = None
plot.traj_xyz(axarr_xyz,
traj,
'-',
color,
short_traj_name,
start_timestamp=start_time)
plot.traj_rpy(axarr_rpy,
traj,
'-',
color,
short_traj_name,
start_timestamp=start_time)
plt.tight_layout()
plot_collection.add_figure("trajectories", fig_traj)
plot_collection.add_figure("xyz_view", fig_xyz)
plot_collection.add_figure("rpy_view", fig_rpy)
if args.plot:
plot_collection.show()
if args.save_plot:
logger.info(SEP)
plot_collection.export(args.save_plot,
confirm_overwrite=not args.no_warnings)
if args.serialize_plot:
logger.info(SEP)
plot_collection.serialize(args.serialize_plot,
confirm_overwrite=not args.no_warnings)
if args.save_as_tum:
logger.info(SEP)
for name, traj in trajectories:
dest = os.path.splitext(os.path.basename(name))[0] + ".tum"
file_interface.write_tum_trajectory_file(
dest, traj, confirm_overwrite=not args.no_warnings)
if args.ref:
dest = os.path.splitext(os.path.basename(args.ref))[0] + ".tum"
file_interface.write_tum_trajectory_file(
dest, ref_traj, confirm_overwrite=not args.no_warnings)
if args.save_as_kitti:
logger.info(SEP)
for name, traj in trajectories:
dest = os.path.splitext(os.path.basename(name))[0] + ".kitti"
file_interface.write_kitti_poses_file(
dest, traj, confirm_overwrite=not args.no_warnings)
if args.ref:
dest = os.path.splitext(os.path.basename(args.ref))[0] + ".kitti"
file_interface.write_kitti_poses_file(
dest, ref_traj, confirm_overwrite=not args.no_warnings)
if args.save_as_bag:
logger.info(SEP)
import datetime
import rosbag
dest_bag_path = str(
datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S.%f')) + ".bag"
logger.info("Saving trajectories to " + dest_bag_path + "...")
bag = rosbag.Bag(dest_bag_path, 'w')
try:
for name, traj in trajectories:
dest_topic = os.path.splitext(os.path.basename(name))[0]
frame_id = traj.meta[
"frame_id"] if "frame_id" in traj.meta else ""
file_interface.write_bag_trajectory(bag, traj, dest_topic,
frame_id)
if args.ref:
dest_topic = os.path.splitext(os.path.basename(args.ref))[0]
frame_id = ref_traj.meta[
"frame_id"] if "frame_id" in ref_traj.meta else ""
file_interface.write_bag_trajectory(bag, ref_traj, dest_topic,
frame_id)
finally:
bag.close()
print("traj_ref: ", traj_ref)
print("traj_est: ", traj_est)
# %%
# plot ground truth trajectory with pose
plot_mode = plot.PlotMode.xy
fig = plt.figure()
ax = plot.prepare_axis(fig, plot_mode)
draw_coordinate_axes(ax,
traj_ref_complete,
marker_scale=2,
downsample_ratio=20,
plot_mode=plot_mode)
draw_start_and_end(ax, traj_ref_complete, plot_mode)
plot.traj(ax, plot_mode, traj_ref_complete, "--", "gray", "reference")
plt.title("Reference trajectory with pose")
plt.show()
# plot unaligned trajectory with pose
plot_mode = plot.PlotMode.xy
fig = plt.figure()
ax = plot.prepare_axis(fig, plot_mode)
draw_coordinate_axes(ax,
traj_est_unaligned,
marker_scale=0.3,
plot_mode=plot_mode)
draw_start_and_end(ax, traj_est_unaligned, plot_mode)
plot.traj(ax, plot_mode, traj_est_unaligned, "--", "gray", "reference")
plt.title("Estimated trajectory with pose")
plt.show()
def run(args):
import os
import sys
import numpy as np
import evo.core.lie_algebra as lie
from evo.core import trajectory
from evo.core.trajectory import PoseTrajectory3D
from evo.tools import file_interface, log
from evo.tools.settings import SETTINGS
log.configure_logging(verbose=args.verbose,
silent=args.silent,
debug=args.debug)
if args.debug:
import pprint
logger.debug(
"main_parser config:\n" +
pprint.pformat({arg: getattr(args, arg)
for arg in vars(args)}) + "\n")
logger.debug(SEP)
trajectories, ref_traj = load_trajectories(args)
if args.merge:
if args.subcommand == "kitti":
die("Can't merge KITTI files.")
if len(trajectories) == 0:
die("No trajectories to merge (excluding --ref).")
trajectories = {
"merged_trajectory": trajectory.merge(trajectories.values())
}
if args.transform_left or args.transform_right:
tf_type = "left" if args.transform_left else "right"
tf_path = args.transform_left \
if args.transform_left else args.transform_right
transform = file_interface.load_transform_json(tf_path)
logger.debug(SEP)
if not lie.is_se3(transform):
logger.warning("Not a valid SE(3) transformation!")
if args.invert_transform:
transform = lie.se3_inverse(transform)
logger.debug("Applying a {}-multiplicative transformation:\n{}".format(
tf_type, transform))
for traj in trajectories.values():
traj.transform(transform, right_mul=args.transform_right)
if args.t_offset:
logger.debug(SEP)
for name, traj in trajectories.items():
if type(traj) is trajectory.PosePath3D:
die("{} doesn't have timestamps - can't add time offset.".
format(name))
logger.info("Adding time offset to {}: {} (s)".format(
name, args.t_offset))
traj.timestamps += args.t_offset
if args.sync or args.align or args.correct_scale:
from evo.core import sync
if not args.ref:
logger.debug(SEP)
die("Can't align or sync without a reference! (--ref) *grunt*")
for name, traj in trajectories.items():
if args.subcommand == "kitti":
ref_traj_tmp = ref_traj
else:
logger.debug(SEP)
ref_traj_tmp, trajectories[name] = sync.associate_trajectories(
ref_traj,
traj,
max_diff=args.t_max_diff,
first_name="reference",
snd_name=name)
if args.align or args.correct_scale:
logger.debug(SEP)
logger.debug("Aligning {} to reference.".format(name))
trajectories[name] = trajectory.align_trajectory(
trajectories[name],
ref_traj_tmp,
correct_scale=args.correct_scale,
correct_only_scale=args.correct_scale and not args.align,
n=args.n_to_align)
for name, traj in trajectories.items():
print_traj_info(name, traj, args.verbose, args.full_check)
if args.ref:
print_traj_info(args.ref, ref_traj, args.verbose, args.full_check)
if args.plot or args.save_plot or args.serialize_plot:
from evo.tools.plot import PlotMode
plot_mode = PlotMode.xyz if not args.plot_mode else PlotMode[
args.plot_mode]
import numpy as np
from evo.tools import plot
import matplotlib.pyplot as plt
import matplotlib.cm as cm
plot_collection = plot.PlotCollection("evo_traj - trajectory plot")
fig_xyz, axarr_xyz = plt.subplots(3,
sharex="col",
figsize=tuple(SETTINGS.plot_figsize))
fig_rpy, axarr_rpy = plt.subplots(3,
sharex="col",
figsize=tuple(SETTINGS.plot_figsize))
fig_traj = plt.figure(figsize=tuple(SETTINGS.plot_figsize))
ax_traj = plot.prepare_axis(fig_traj, plot_mode)
pltstart = 0
pltend = traj.num_poses
if args.plotstart:
pltstart = args.plotstart
if args.plotend != -1:
pltend = args.plotend
if args.ref:
short_traj_name = os.path.splitext(os.path.basename(args.ref))[0]
if SETTINGS.plot_usetex:
short_traj_name = short_traj_name.replace("_", "\\_")
plot.traj(ax_traj,
plot_mode,
ref_traj,
style=SETTINGS.plot_reference_linestyle,
color=SETTINGS.plot_reference_color,
label=short_traj_name,
alpha=SETTINGS.plot_reference_alpha,
start=pltstart,
end=pltend)
plot.traj_xyz(axarr_xyz,
ref_traj,
style=SETTINGS.plot_reference_linestyle,
color=SETTINGS.plot_reference_color,
label=short_traj_name,
alpha=SETTINGS.plot_reference_alpha,
start=pltstart,
end=pltend)
plot.traj_rpy(axarr_rpy,
ref_traj,
style=SETTINGS.plot_reference_linestyle,
color=SETTINGS.plot_reference_color,
label=short_traj_name,
alpha=SETTINGS.plot_reference_alpha,
start=pltstart,
end=pltend)
cmap_colors = None
if SETTINGS.plot_multi_cmap.lower() != "none":
cmap = getattr(cm, SETTINGS.plot_multi_cmap)
cmap_colors = iter(cmap(np.linspace(0, 1, len(trajectories))))
fig_3 = plt.figure(figsize=tuple(SETTINGS.plot_figsize))
#plot_mode = plot.PlotMode.xz
ax = plot.prepare_axis(fig_3, plot_mode)
fig_3.axes.append(ax)
for name, traj in trajectories.items():
num = traj.positions_xyz.shape[0]
if pltstart >= num:
print(name, "plotstart > len!", num)
pltstart = 0
if pltend != -1 and (pltend > num or pltend < pltstart):
print(name, "plotend > len!", num)
pltend = traj.num_poses
pltstart = int(pltstart)
pltend = int(pltend)
if cmap_colors is None:
color = next(ax_traj._get_lines.prop_cycler)['color']
else:
color = next(cmap_colors)
short_traj_name = os.path.splitext(os.path.basename(name))[0]
if SETTINGS.plot_usetex:
short_traj_name = short_traj_name.replace("_", "\\_")
plot.traj(ax_traj,
plot_mode,
traj,
'-',
color,
short_traj_name,
start=pltstart,
end=pltend)
if args.ref and isinstance(ref_traj, trajectory.PoseTrajectory3D):
start_time = ref_traj.timestamps[0]
else:
start_time = None
plot.traj_xyz(axarr_xyz,
traj,
'-',
color,
short_traj_name,
start_timestamp=start_time,
start=pltstart,
end=pltend)
plot.traj_rpy(axarr_rpy,
traj,
'-',
color,
short_traj_name,
start_timestamp=start_time,
start=pltstart,
end=pltend)
speeds = [
trajectory.calc_speed(traj.positions_xyz[i],
traj.positions_xyz[i + 1],
traj.timestamps[i],
traj.timestamps[i + 1])
for i in range(pltstart, pltend - 1)
]
speeds.append(0)
#plot.traj(ax, plot_mode, traj, '--', 'gray', 'reference')
plot.traj_colormap(ax,
traj,
speeds,
plot_mode,
min_map=min(speeds),
max_map=max(max(speeds), 10),
title="speed mapped onto trajectory",
start=pltstart,
end=pltend)
plot_collection.add_figure("trajectories", fig_traj)
plot_collection.add_figure("xyz_view", fig_xyz)
plot_collection.add_figure("rpy_view", fig_rpy)
plot_collection.add_figure("traj (speed)", fig_3)
if args.plot:
plot_collection.show()
if args.save_plot:
logger.info(SEP)
plot_collection.export(args.save_plot,
confirm_overwrite=not args.no_warnings)
if args.serialize_plot:
logger.info(SEP)
plot_collection.serialize(args.serialize_plot,
confirm_overwrite=not args.no_warnings)
if args.save_as_tum:
logger.info(SEP)
for name, traj in trajectories.items():
dest = os.path.splitext(os.path.basename(name))[0] + ".tum"
file_interface.write_tum_trajectory_file(
dest, traj, confirm_overwrite=not args.no_warnings)
if args.ref:
dest = os.path.splitext(os.path.basename(args.ref))[0] + ".tum"
file_interface.write_tum_trajectory_file(
dest, ref_traj, confirm_overwrite=not args.no_warnings)
if args.save_as_kitti:
logger.info(SEP)
for name, traj in trajectories.items():
dest = os.path.splitext(os.path.basename(name))[0] + ".kitti"
file_interface.write_kitti_poses_file(
dest, traj, confirm_overwrite=not args.no_warnings)
if args.ref:
dest = os.path.splitext(os.path.basename(args.ref))[0] + ".kitti"
file_interface.write_kitti_poses_file(
dest, ref_traj, confirm_overwrite=not args.no_warnings)
if args.save_as_bag:
import datetime
import rosbag
dest_bag_path = str(
datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")) + ".bag"
logger.info(SEP)
logger.info("Saving trajectories to " + dest_bag_path + "...")
bag = rosbag.Bag(dest_bag_path, 'w')
try:
for name, traj in trajectories.items():
dest_topic = os.path.splitext(os.path.basename(name))[0]
frame_id = traj.meta[
"frame_id"] if "frame_id" in traj.meta else ""
file_interface.write_bag_trajectory(bag, traj, dest_topic,
frame_id)
if args.ref:
dest_topic = os.path.splitext(os.path.basename(args.ref))[0]
frame_id = ref_traj.meta[
"frame_id"] if "frame_id" in ref_traj.meta else ""
file_interface.write_bag_trajectory(bag, ref_traj, dest_topic,
frame_id)
finally:
bag.close()
def run(args):
import sys
import numpy as np
import evo.core.lie_algebra as lie
from evo.core import trajectory
from evo.core.trajectory import PoseTrajectory3D
from evo.tools import file_interface, log
log.configure_logging(verbose=args.verbose,
silent=args.silent,
debug=args.debug,
local_logfile=args.logfile)
if args.debug:
import pprint
logger.debug(
"main_parser config:\n" +
pprint.pformat({arg: getattr(args, arg)
for arg in vars(args)}) + "\n")
logger.debug(SEP)
trajectories, ref_traj = load_trajectories(args)
if args.merge:
if args.subcommand == "kitti":
die("Can't merge KITTI files.")
if len(trajectories) == 0:
die("No trajectories to merge (excluding --ref).")
trajectories = {
"merged_trajectory": trajectory.merge(trajectories.values())
}
if args.t_offset:
logger.debug(SEP)
for name, traj in trajectories.items():
if type(traj) is trajectory.PosePath3D:
die("{} doesn't have timestamps - can't add time offset.".
format(name))
logger.info("Adding time offset to {}: {} (s)".format(
name, args.t_offset))
traj.timestamps += args.t_offset
if args.n_to_align != -1 and not (args.align or args.correct_scale):
die("--n_to_align is useless without --align or/and --correct_scale")
# TODO: this is fugly, but is a quick solution for remembering each synced
# reference when plotting pose correspondences later...
synced = (args.subcommand == "kitti" and ref_traj) or any(
(args.sync, args.align, args.correct_scale, args.align_origin))
synced_refs = {}
if synced:
from evo.core import sync
if not args.ref:
logger.debug(SEP)
die("Can't align or sync without a reference! (--ref) *grunt*")
for name, traj in trajectories.items():
if args.subcommand == "kitti":
ref_traj_tmp = ref_traj
else:
logger.debug(SEP)
ref_traj_tmp, trajectories[name] = sync.associate_trajectories(
ref_traj,
traj,
max_diff=args.t_max_diff,
first_name="reference",
snd_name=name)
if args.align or args.correct_scale:
logger.debug(SEP)
logger.debug("Aligning {} to reference.".format(name))
trajectories[name].align(ref_traj_tmp,
correct_scale=args.correct_scale,
correct_only_scale=args.correct_scale
and not args.align,
n=args.n_to_align)
if args.align_origin:
logger.debug(SEP)
logger.debug("Aligning {}'s origin to reference.".format(name))
trajectories[name].align_origin(ref_traj_tmp)
if SETTINGS.plot_pose_correspondences:
synced_refs[name] = ref_traj_tmp
if args.transform_left or args.transform_right:
tf_type = "left" if args.transform_left else "right"
tf_path = args.transform_left \
if args.transform_left else args.transform_right
transform = file_interface.load_transform_json(tf_path)
logger.debug(SEP)
if not lie.is_se3(transform):
logger.warning("Not a valid SE(3) transformation!")
if args.invert_transform:
transform = lie.se3_inverse(transform)
logger.debug("Applying a {}-multiplicative transformation:\n{}".format(
tf_type, transform))
for traj in trajectories.values():
traj.transform(transform,
right_mul=args.transform_right,
propagate=args.propagate_transform)
for name, traj in trajectories.items():
print_traj_info(to_compact_name(name, args), traj, args.verbose,
args.full_check)
if args.ref:
print_traj_info(to_compact_name(args.ref, args), ref_traj,
args.verbose, args.full_check)
if args.plot or args.save_plot or args.serialize_plot:
import numpy as np
from evo.tools import plot
import matplotlib.pyplot as plt
import matplotlib.cm as cm
plot_collection = plot.PlotCollection("evo_traj - trajectory plot")
fig_xyz, axarr_xyz = plt.subplots(3,
sharex="col",
figsize=tuple(SETTINGS.plot_figsize))
fig_rpy, axarr_rpy = plt.subplots(3,
sharex="col",
figsize=tuple(SETTINGS.plot_figsize))
fig_traj = plt.figure(figsize=tuple(SETTINGS.plot_figsize))
plot_mode = plot.PlotMode[args.plot_mode]
ax_traj = plot.prepare_axis(fig_traj, plot_mode)
# for x-axis alignment starting from 0 with --plot_relative_time
start_time = None
if args.ref:
if isinstance(ref_traj, trajectory.PoseTrajectory3D) \
and args.plot_relative_time:
start_time = ref_traj.timestamps[0]
short_traj_name = to_compact_name(args.ref, args,
SETTINGS.plot_usetex)
plot.traj(ax_traj,
plot_mode,
ref_traj,
style=SETTINGS.plot_reference_linestyle,
color=SETTINGS.plot_reference_color,
label=short_traj_name,
alpha=SETTINGS.plot_reference_alpha)
plot.draw_coordinate_axes(
ax_traj, ref_traj, plot_mode,
SETTINGS.plot_reference_axis_marker_scale)
plot.traj_xyz(axarr_xyz,
ref_traj,
style=SETTINGS.plot_reference_linestyle,
color=SETTINGS.plot_reference_color,
label=short_traj_name,
alpha=SETTINGS.plot_reference_alpha,
start_timestamp=start_time)
plot.traj_rpy(axarr_rpy,
ref_traj,
style=SETTINGS.plot_reference_linestyle,
color=SETTINGS.plot_reference_color,
label=short_traj_name,
alpha=SETTINGS.plot_reference_alpha,
start_timestamp=start_time)
if args.ros_map_yaml:
plot.ros_map(ax_traj, args.ros_map_yaml, plot_mode)
cmap_colors = None
if SETTINGS.plot_multi_cmap.lower() != "none":
cmap = getattr(cm, SETTINGS.plot_multi_cmap)
cmap_colors = iter(cmap(np.linspace(0, 1, len(trajectories))))
for name, traj in trajectories.items():
if cmap_colors is None:
color = next(ax_traj._get_lines.prop_cycler)['color']
else:
color = next(cmap_colors)
short_traj_name = to_compact_name(name, args, SETTINGS.plot_usetex)
plot.traj(ax_traj,
plot_mode,
traj,
SETTINGS.plot_trajectory_linestyle,
color,
short_traj_name,
alpha=SETTINGS.plot_trajectory_alpha)
plot.draw_coordinate_axes(ax_traj, traj, plot_mode,
SETTINGS.plot_axis_marker_scale)
if ref_traj and synced and SETTINGS.plot_pose_correspondences:
plot.draw_correspondence_edges(
ax_traj,
traj,
synced_refs[name],
plot_mode,
color=color,
style=SETTINGS.plot_pose_correspondences_linestyle,
alpha=SETTINGS.plot_trajectory_alpha)
plot.traj_xyz(axarr_xyz,
traj,
SETTINGS.plot_trajectory_linestyle,
color,
short_traj_name,
alpha=SETTINGS.plot_trajectory_alpha,
start_timestamp=start_time)
plot.traj_rpy(axarr_rpy,
traj,
SETTINGS.plot_trajectory_linestyle,
color,
short_traj_name,
alpha=SETTINGS.plot_trajectory_alpha,
start_timestamp=start_time)
if not SETTINGS.plot_usetex:
fig_rpy.text(0.,
0.005,
"euler_angle_sequence: {}".format(
SETTINGS.euler_angle_sequence),
fontsize=6)
plot_collection.add_figure("trajectories", fig_traj)
plot_collection.add_figure("xyz_view", fig_xyz)
plot_collection.add_figure("rpy_view", fig_rpy)
if args.plot:
plot_collection.show()
if args.save_plot:
logger.info(SEP)
plot_collection.export(args.save_plot,
confirm_overwrite=not args.no_warnings)
if args.serialize_plot:
logger.info(SEP)
plot_collection.serialize(args.serialize_plot,
confirm_overwrite=not args.no_warnings)
if args.save_as_tum:
logger.info(SEP)
for name, traj in trajectories.items():
dest = to_filestem(name, args) + ".tum"
file_interface.write_tum_trajectory_file(
dest, traj, confirm_overwrite=not args.no_warnings)
if args.ref:
dest = to_filestem(args.ref, args) + ".tum"
file_interface.write_tum_trajectory_file(
dest, ref_traj, confirm_overwrite=not args.no_warnings)
if args.save_as_kitti:
logger.info(SEP)
for name, traj in trajectories.items():
dest = to_filestem(name, args) + ".kitti"
file_interface.write_kitti_poses_file(
dest, traj, confirm_overwrite=not args.no_warnings)
if args.ref:
dest = to_filestem(args.ref, args) + ".kitti"
file_interface.write_kitti_poses_file(
dest, ref_traj, confirm_overwrite=not args.no_warnings)
if args.save_as_bag or args.save_as_bag2:
from rosbags.rosbag1 import Writer as Rosbag1Writer
from rosbags.rosbag2 import Writer as Rosbag2Writer
writers = []
if args.save_as_bag:
dest_bag_path = str(
datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")) + ".bag"
writers.append(Rosbag1Writer(dest_bag_path))
if args.save_as_bag2:
dest_bag_path = str(
datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S"))
writers.append(Rosbag2Writer(dest_bag_path))
for writer in writers:
logger.info(SEP)
logger.info("Saving trajectories to " + str(writer.path) + "...")
try:
writer.open()
for name, traj in trajectories.items():
dest_topic = to_topic_name(name, args)
frame_id = traj.meta[
"frame_id"] if "frame_id" in traj.meta else ""
file_interface.write_bag_trajectory(
writer, traj, dest_topic, frame_id)
if args.ref:
dest_topic = to_topic_name(args.ref, args)
frame_id = ref_traj.meta[
"frame_id"] if "frame_id" in ref_traj.meta else ""
file_interface.write_bag_trajectory(
writer, ref_traj, dest_topic, frame_id)
finally:
writer.close()
if args.save_table:
from evo.tools import pandas_bridge
logger.debug(SEP)
df = pandas_bridge.trajectories_stats_to_df(trajectories)
pandas_bridge.save_df_as_table(df,
args.save_table,
confirm_overwrite=not args.no_warnings)
def run(args):
import os
import sys
import numpy as np
import evo.core.lie_algebra as lie
from evo.core import trajectory
from evo.core.trajectory import PoseTrajectory3D
from evo.tools import file_interface, log
from evo.tools.settings import SETTINGS
log.configure_logging(verbose=args.verbose,
silent=args.silent,
debug=args.debug,
local_logfile=args.logfile)
if args.debug:
import pprint
logger.debug(
"main_parser config:\n" +
pprint.pformat({arg: getattr(args, arg)
for arg in vars(args)}) + "\n")
logger.debug(SEP)
trajectories, ref_traj = load_trajectories(args)
if args.merge:
if args.subcommand == "kitti":
die("Can't merge KITTI files.")
if len(trajectories) == 0:
die("No trajectories to merge (excluding --ref).")
trajectories = {
"merged_trajectory": trajectory.merge(trajectories.values())
}
if args.transform_left or args.transform_right:
tf_type = "left" if args.transform_left else "right"
tf_path = args.transform_left \
if args.transform_left else args.transform_right
transform = file_interface.load_transform_json(tf_path)
logger.debug(SEP)
if not lie.is_se3(transform):
logger.warning("Not a valid SE(3) transformation!")
if args.invert_transform:
transform = lie.se3_inverse(transform)
logger.debug("Applying a {}-multiplicative transformation:\n{}".format(
tf_type, transform))
for traj in trajectories.values():
traj.transform(transform,
right_mul=args.transform_right,
propagate=args.propagate_transform)
if args.t_offset:
logger.debug(SEP)
for name, traj in trajectories.items():
if type(traj) is trajectory.PosePath3D:
die("{} doesn't have timestamps - can't add time offset.".
format(name))
logger.info("Adding time offset to {}: {} (s)".format(
name, args.t_offset))
traj.timestamps += args.t_offset
if args.n_to_align != -1 and not (args.align or args.correct_scale):
die("--n_to_align is useless without --align or/and --correct_scale")
if args.sync or args.align or args.correct_scale or args.align_origin:
from evo.core import sync
if not args.ref:
logger.debug(SEP)
die("Can't align or sync without a reference! (--ref) *grunt*")
for name, traj in trajectories.items():
if args.subcommand == "kitti":
ref_traj_tmp = ref_traj
else:
logger.debug(SEP)
ref_traj_tmp, trajectories[name] = sync.associate_trajectories(
ref_traj,
traj,
max_diff=args.t_max_diff,
first_name="reference",
snd_name=name)
if args.align or args.correct_scale:
logger.debug(SEP)
logger.debug("Aligning {} to reference.".format(name))
trajectories[name] = trajectory.align_trajectory(
trajectories[name],
ref_traj_tmp,
correct_scale=args.correct_scale,
correct_only_scale=args.correct_scale and not args.align,
n=args.n_to_align)
if args.align_origin:
logger.debug(SEP)
logger.debug("Aligning {}'s origin to reference.".format(name))
trajectories[name] = trajectory.align_trajectory_origin(
trajectories[name], ref_traj_tmp)
print_compact_name = not args.subcommand == "bag"
for name, traj in trajectories.items():
print_traj_info(name, traj, args.verbose, args.full_check,
print_compact_name)
if args.ref:
print_traj_info(args.ref, ref_traj, args.verbose, args.full_check,
print_compact_name)
if args.plot or args.save_plot or args.serialize_plot:
import numpy as np
from evo.tools import plot
import matplotlib.pyplot as plt
import matplotlib.cm as cm
plot_collection = plot.PlotCollection("evo_traj - trajectory plot")
fig_xyz, axarr_xyz = plt.subplots(3,
sharex="col",
figsize=tuple(SETTINGS.plot_figsize))
fig_rpy, axarr_rpy = plt.subplots(3,
sharex="col",
figsize=tuple(SETTINGS.plot_figsize))
fig_traj = plt.figure(figsize=tuple(SETTINGS.plot_figsize))
plot_mode = plot.PlotMode[args.plot_mode]
ax_traj = plot.prepare_axis(fig_traj, plot_mode)
if args.ref:
short_traj_name = os.path.splitext(os.path.basename(args.ref))[0]
if SETTINGS.plot_usetex:
short_traj_name = short_traj_name.replace("_", "\\_")
plot.traj(ax_traj,
plot_mode,
ref_traj,
style=SETTINGS.plot_reference_linestyle,
color=SETTINGS.plot_reference_color,
label=short_traj_name,
alpha=SETTINGS.plot_reference_alpha)
plot.draw_coordinate_axes(ax_traj, ref_traj, plot_mode,
SETTINGS.plot_axis_marker_scale)
plot.traj_xyz(axarr_xyz,
ref_traj,
style=SETTINGS.plot_reference_linestyle,
color=SETTINGS.plot_reference_color,
label=short_traj_name,
alpha=SETTINGS.plot_reference_alpha)
plot.traj_rpy(axarr_rpy,
ref_traj,
style=SETTINGS.plot_reference_linestyle,
color=SETTINGS.plot_reference_color,
label=short_traj_name,
alpha=SETTINGS.plot_reference_alpha)
if args.ros_map_yaml:
plot.ros_map(ax_traj, args.ros_map_yaml, plot_mode)
cmap_colors = None
if SETTINGS.plot_multi_cmap.lower() != "none":
cmap = getattr(cm, SETTINGS.plot_multi_cmap)
cmap_colors = iter(cmap(np.linspace(0, 1, len(trajectories))))
for name, traj in trajectories.items():
if cmap_colors is None:
color = next(ax_traj._get_lines.prop_cycler)['color']
else:
color = next(cmap_colors)
if print_compact_name:
short_traj_name = os.path.splitext(os.path.basename(name))[0]
else:
short_traj_name = name
if SETTINGS.plot_usetex:
short_traj_name = short_traj_name.replace("_", "\\_")
plot.traj(ax_traj,
plot_mode,
traj,
SETTINGS.plot_trajectory_linestyle,
color,
short_traj_name,
alpha=SETTINGS.plot_trajectory_alpha)
plot.draw_coordinate_axes(ax_traj, traj, plot_mode,
SETTINGS.plot_axis_marker_scale)
if args.ref and isinstance(ref_traj, trajectory.PoseTrajectory3D):
start_time = ref_traj.timestamps[0]
else:
start_time = None
plot.traj_xyz(axarr_xyz,
traj,
SETTINGS.plot_trajectory_linestyle,
color,
short_traj_name,
alpha=SETTINGS.plot_trajectory_alpha,
start_timestamp=start_time)
plot.traj_rpy(axarr_rpy,
traj,
SETTINGS.plot_trajectory_linestyle,
color,
short_traj_name,
alpha=SETTINGS.plot_trajectory_alpha,
start_timestamp=start_time)
if not SETTINGS.plot_usetex:
fig_rpy.text(0.,
0.005,
"euler_angle_sequence: {}".format(
SETTINGS.euler_angle_sequence),
fontsize=6)
plot_collection.add_figure("trajectories", fig_traj)
plot_collection.add_figure("xyz_view", fig_xyz)
plot_collection.add_figure("rpy_view", fig_rpy)
if args.plot:
plot_collection.show()
if args.save_plot:
logger.info(SEP)
plot_collection.export(args.save_plot,
confirm_overwrite=not args.no_warnings)
if args.serialize_plot:
logger.info(SEP)
plot_collection.serialize(args.serialize_plot,
confirm_overwrite=not args.no_warnings)
if args.save_as_tum:
logger.info(SEP)
for name, traj in trajectories.items():
dest = os.path.splitext(os.path.basename(name))[0] + ".tum"
file_interface.write_tum_trajectory_file(
dest, traj, confirm_overwrite=not args.no_warnings)
if args.ref:
dest = os.path.splitext(os.path.basename(args.ref))[0] + ".tum"
file_interface.write_tum_trajectory_file(
dest, ref_traj, confirm_overwrite=not args.no_warnings)
if args.save_as_kitti:
logger.info(SEP)
for name, traj in trajectories.items():
dest = os.path.splitext(os.path.basename(name))[0] + ".kitti"
file_interface.write_kitti_poses_file(
dest, traj, confirm_overwrite=not args.no_warnings)
if args.ref:
dest = os.path.splitext(os.path.basename(args.ref))[0] + ".kitti"
file_interface.write_kitti_poses_file(
dest, ref_traj, confirm_overwrite=not args.no_warnings)
if args.save_as_bag:
import datetime
import rosbag
dest_bag_path = str(
datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")) + ".bag"
logger.info(SEP)
logger.info("Saving trajectories to " + dest_bag_path + "...")
bag = rosbag.Bag(dest_bag_path, 'w')
try:
for name, traj in trajectories.items():
dest_topic = os.path.splitext(os.path.basename(name))[0]
frame_id = traj.meta[
"frame_id"] if "frame_id" in traj.meta else ""
file_interface.write_bag_trajectory(bag, traj, dest_topic,
frame_id)
if args.ref:
dest_topic = os.path.splitext(os.path.basename(args.ref))[0]
frame_id = ref_traj.meta[
"frame_id"] if "frame_id" in ref_traj.meta else ""
file_interface.write_bag_trajectory(bag, ref_traj, dest_topic,
frame_id)
finally:
bag.close()
def plot(args, result, traj_ref, traj_est):
from evo.tools import plot
from evo.tools.settings import SETTINGS
import matplotlib.pyplot as plt
import numpy as np
logger.debug(SEP)
logger.debug("Plotting results... ")
plot_mode = plot.PlotMode(args.plot_mode)
# Plot the raw metric values.
fig1 = plt.figure(figsize=SETTINGS.plot_figsize)
if "seconds_from_start" in result.np_arrays:
seconds_from_start = result.np_arrays["seconds_from_start"]
else:
seconds_from_start = None
plot.error_array(
fig1, result.np_arrays["error_array"],
x_array=seconds_from_start,
statistics=result.stats,
name=result.info["label"],
title=result.info["title"],
xlabel="$t$ (s)" if seconds_from_start else "index")
# Plot the values color-mapped onto the trajectory.
fig2 = plt.figure(figsize=SETTINGS.plot_figsize)
ax = plot.prepare_axis(fig2, plot_mode)
plot.traj(ax, plot_mode, traj_ref, '--', 'black', 'reference',
alpha=0.0 if SETTINGS.plot_hideref else 0.5)
if args.plot_colormap_min is None:
args.plot_colormap_min = result.stats["min"]
if args.plot_colormap_max is None:
args.plot_colormap_max = result.stats["max"]
if args.plot_colormap_max_percentile is not None:
args.plot_colormap_max = np.percentile(
result.np_arrays["error_array"],
args.plot_colormap_max_percentile)
plot.traj_colormap(
ax, traj_est, result.np_arrays["error_array"],
plot_mode,
min_map=args.plot_colormap_min,
max_map=args.plot_colormap_max,
title="Error mapped onto trajectory")
fig2.axes.append(ax)
plot_collection = plot.PlotCollection(result.info["title"])
plot_collection.add_figure("raw", fig1)
plot_collection.add_figure("map", fig2)
if args.plot:
plot_collection.show()
if args.save_plot:
plot_collection.export(
args.save_plot, confirm_overwrite=not args.no_warnings)
if args.serialize_plot:
logger.debug(SEP)
plot_collection.serialize(
args.serialize_plot, confirm_overwrite=not args.no_warnings)
