def add_metric_plot(self, plot_collection, dataset_name, metric, fig_title="", plot_title="", metric_units=""): """ Adds a metric plot to a plot collection. Args: plot_collection: a PlotCollection containing plots. dataset_name: a string representing the name of the dataset being evaluated. metric: an evo.core.metric object with statistics and information. 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. metric_units: a string representing the units of the metric being plotted. """ fig = plt.figure(figsize=(8, 8)) stats = metric.get_all_statistics() plot.error_array(fig, metric.error, statistics=stats, name=plot_title, title=plot_title, xlabel="Keyframe index [-]", ylabel=plot_title + " " + metric_units) plot_collection.add_figure(fig_title, fig)
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_metric(metric, plot_title="", figsize=(8, 8)): """ Adds a metric plot to a plot collection. Args: plot_collection: a PlotCollection containing plots. metric: an evo.core.metric object with statistics and information. 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) stats = metric.get_all_statistics() plot.error_array(fig, metric.error, statistics=stats, title=plot_title, xlabel="Keyframe index [-]", ylabel=plot_title + " " + metric.unit.value) return fig
print("calculating APE") data = (traj_ref, traj_est) ape_metric = metrics.APE(metrics.PoseRelation.translation_part) ape_metric.process_data(data) ape_statistics = ape_metric.get_all_statistics() print("mean:", ape_statistics["mean"]) print("loading plot modules") 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
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 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)
ape_metric = metrics.APE(metrics.PoseRelation.translation_part) ape_metric.process_data(data) ape_statistics = ape_metric.get_all_statistics() print("mean:", ape_statistics["mean"]) print("loading plot modules") 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"],
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_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
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
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)
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)
for pose_relation in metrics.PoseRelation: print("\n------------------------------------------------------------------\n") data = (traj_ref, traj_est) start = time.clock() ape_metric = metrics.APE(pose_relation) ape_metric.process_data(data) ape_stats = ape_metric.get_all_statistics() stop = time.clock() ape_time = stop-start print("APE statistics w.r.t. " + ape_metric.pose_relation.value + ": ") pretty_printer.pprint(ape_stats) print("\nelapsed time for running the APE algorithm (seconds):\t", "{0:.6f}".format(ape_time)) print("elapsed time for trajectory loading and APE (seconds):\t", "{0:.6f}".format(load_time+ape_time)) if show_plots: plot.error_array(ape_metric.error, statistics=ape_stats, name="APE w.r.t. " + ape_metric.pose_relation.value) if show_plots: plt.show() print("\n------------------------------------------------------------------") print("------------------------------------------------------------------\n") print("calling offical TUM ATE script for comparison...") cmd = ["tum_benchmark_tools/evaluate_ate.py", ref_file, est_file, "--max_difference", str(max_diff), "--verbose"] time_cmd = timeit(stmt="sp.call("+str(cmd)+")", setup="import subprocess as sp", number=1) print("\nelapsed time for full TUM ATE script (seconds):\t", "{0:.6f}".format(time_cmd))
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)