From b54b286aeba1caf9b0e3786c7194eba5f9178cf0 Mon Sep 17 00:00:00 2001 From: PENG-AO <71684142+PENG-AO@users.noreply.github.com> Date: Fri, 12 May 2023 22:56:11 +0900 Subject: [PATCH] Scripts for twist evaluation and eagleye evaluation (#14) * feat: support multi-file evalutaion for ros2bag (a mixture of csv and ros2bag is allowed) * fix: fix the dependencies of corresponding sub_scripts * feat: update README * feat: enrich error message for undefined data type * hotfix: fix the dependencies for corresponding sub_scripts (to be refactored) * feat: implement the display of progress info * feat: update the README * feat: added evaluation scripts for single eagleye log file * feat: changed some file names to prevent conflicts * feat: implemented the graphs of xyz and xyz error * feat: rearranged file strucuter * feat: updated README * feat: added the scripts of twist evaluation * feat: added ellipse graph * fix: fixed the imappropriate radian formatter * fix: fixed the typos of config files * fix: removed unnecessary files * feat: simplified README --- README.md | 283 ++++++-- config/ajust_time_stamp.yaml | 38 -- config/evaluation_eagleye.yaml | 29 + .../{evaluation.yaml => evaluation_pose.yaml} | 0 config/evaluation_twist.yaml | 101 +++ sample_data/adjust_time_test_data/offest.csv | 200 ------ sample_data/adjust_time_test_data/target.csv | 200 ------ sample_data/config/ajust_time_stamp.yaml | 38 -- sample_data/output_sample/2d_error.png | Bin 151690 -> 152276 bytes sample_data/output_sample/2d_trajectory.png | Bin 55828 -> 54593 bytes sample_data/output_sample/3d_error.png | Bin 139939 -> 140560 bytes sample_data/output_sample/3d_trajectory.png | Bin 0 -> 180324 bytes sample_data/output_sample/height_error.png | Bin 69934 -> 0 bytes sample_data/output_sample/lateral_error.png | Bin 139216 -> 139143 bytes .../output_sample/longitudinal_error.png | Bin 141747 -> 143361 bytes sample_data/output_sample/rpy.png | Bin 176116 -> 176399 bytes sample_data/output_sample/rpy_error.png | Bin 244416 -> 244414 bytes sample_data/output_sample/sync_EKF_df.csv | 444 ++++++------- .../output_sample/sync_reference_df.csv | 628 +++++++++--------- sample_data/output_sample/xyz.png | Bin 0 -> 114020 bytes sample_data/output_sample/xyz_error.png | Bin 0 -> 217425 bytes scripts/{main.py => pose_main.py} | 6 +- scripts/twist_main.py | 25 + scripts/util/{ => pose}/configer.py | 0 scripts/util/{ => pose}/packer.py | 2 +- scripts/util/{ => pose}/plotter.py | 84 ++- scripts/util/twist/configer.py | 94 +++ scripts/util/twist/packer.py | 271 ++++++++ scripts/util/twist/plotter.py | 108 +++ sub_scripts/adjust_time_stamp.py | 137 ---- sub_scripts/develop/ani_convergence_viewer.py | 240 ------- sub_scripts/develop/conv_trj.py | 42 -- sub_scripts/develop/convergence_viewer.py | 270 -------- sub_scripts/develop/kl.py | 101 --- sub_scripts/sub_eagleye_evaluation.py | 123 ++++ sub_scripts/sub_ndt_evaluation.py | 407 +++++++++--- sub_scripts/util_eagleye/calc.py | 521 +++++++++++++++ sub_scripts/util_eagleye/plot.py | 333 ++++++++++ sub_scripts/util_eagleye/preprocess.py | 481 ++++++++++++++ 39 files changed, 3200 insertions(+), 2006 deletions(-) delete mode 100644 config/ajust_time_stamp.yaml create mode 100644 config/evaluation_eagleye.yaml rename config/{evaluation.yaml => evaluation_pose.yaml} (100%) create mode 100644 config/evaluation_twist.yaml delete mode 100644 sample_data/adjust_time_test_data/offest.csv delete mode 100644 sample_data/adjust_time_test_data/target.csv delete mode 100644 sample_data/config/ajust_time_stamp.yaml create mode 100644 sample_data/output_sample/3d_trajectory.png delete mode 100644 sample_data/output_sample/height_error.png create mode 100644 sample_data/output_sample/xyz.png create mode 100644 sample_data/output_sample/xyz_error.png rename scripts/{main.py => pose_main.py} (81%) create mode 100644 scripts/twist_main.py rename scripts/util/{ => pose}/configer.py (100%) rename scripts/util/{ => pose}/packer.py (99%) rename scripts/util/{ => pose}/plotter.py (85%) create mode 100644 scripts/util/twist/configer.py create mode 100644 scripts/util/twist/packer.py create mode 100644 scripts/util/twist/plotter.py delete mode 100644 sub_scripts/adjust_time_stamp.py delete mode 100644 sub_scripts/develop/ani_convergence_viewer.py delete mode 100644 sub_scripts/develop/conv_trj.py delete mode 100644 sub_scripts/develop/convergence_viewer.py delete mode 100644 sub_scripts/develop/kl.py create mode 100755 sub_scripts/sub_eagleye_evaluation.py create mode 100644 sub_scripts/util_eagleye/calc.py create mode 100644 sub_scripts/util_eagleye/plot.py create mode 100644 sub_scripts/util_eagleye/preprocess.py diff --git a/README.md b/README.md index 77effe2..78ab355 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,15 @@ -# localization_evaluation_toolkit -Ver.6.1 (Update 2023/03/14) -You can evaluate your localization result by comparing it to a reliable pose trajectory. -The start time, end time, and period can be different for each data. -The evaluation is automatically aligned with the one with the smaller number of data. +# localization_evaluation_toolkit -## Releases -| Version | Release Date | Python version | Note | -| ------- | ------------------ | -------------------- | ------------------------------------------------------------ | -| 6.0 | Feburary 3, 2023 | python 3.7 or higher | Support multi-file evaluation (mixtures of csv and ros2 bag) | -| 5.0 | January 20, 2023 | python 3.7 or higher | Support multi-file evaluation (CSV only) | -| 3.2 | September 29, 2022 | python 3.6 or higher | 1:1 evaluation | +An evaluator for localization results, which compares with a reliable pose trajectory. Specifically, comparisons between different start times, end times, and periods are supported, since evaluation will be automatically aligned with the one with fewer data. -## Installation +## 1. Installation & Preparation + +Clone this repo to the local environment. ```sh $ git clone https://github.com/MapIV/localization_evaluation_toolkit.git ``` -## Preparation - Evaluatable data types are limited to the format of CSV or ros2 bag, and the start time, end time, and period can be different for each data. Specifically, a valid **CSV file** requires at least the following entries. @@ -30,9 +22,11 @@ And a valid ros2 bag file should have one of the following types of topics. - `geometry_msgs/msg/PoseWithCovarianceStamped` - `nav_msgs/msg/Odometry` -## Execution +## 2. Main-evaluation + +### 2.1 Pose evaluation -A standard configuration file for evaluation consists of the following parts. +A standard configuration file for pose evaluation consists of the following parts. - Reference - Result1, Result2, ... @@ -40,7 +34,7 @@ A standard configuration file for evaluation consists of the following parts. It is required that the reference block should have the name of ***Reference*** and the names of result blocks should begin with ***Result***. When specifying the paths, it is recommended to use the absolute path. -There is a sample YAML file at `config/evaluation.yaml`. +There is a sample YAML file at `config/evaluation_pose.yaml`. To create your own configuration file, just use the following templates for CSV or ros2 bag,
@@ -171,81 +165,236 @@ and run with ```sh $ cd localization_evaluation_toolkit -$ python3 scripts/main.py config/evaluation.yaml +$ python3 scripts/pose_main.py config/evaluation_pose.yaml ``` -## Sub-evaluation and adjustment - -**A. Evaluate TP, NVTL, execution time and iteration with ros2 bag files** - -Please input a rosbag containing the following (at least one) topic. -``` -/localization/pose_estimator/pose_with_covariance -/localization/pose_estimator/nearest_voxel_transformation_likelihood -/localization/pose_estimator/transform_probability -/localization/pose_estimator/exe_time_ms -/localization/pose_estimator/iteration_num -``` +For example, you may output the following graphs with the sample data. ```sh -$ cd localization_evaluation_toolkit/sub_scripts -$ source ~/xxxxxx/install/setup.bash -$ python3 sub_ndt_evaluation.py [bag_path] [output_folder_path] +$ cd localization_evaluation_toolkit +$ python3 scripts/pose_main.py sample_data/config/sasashima_evaluation.yaml ``` -**B. Adjust time stamp with CSV files** -```sh -$ cd localization_evaluation_toolkit/sub_scripts -$ python3 adjust_time_stamp.py [target_csv_path] [offset_csv_path] [ajust_time_stamp.yaml path] [output_folder_path] -``` +
+2D Trajectory + +When you zoom in on the graph, you can see the correspondence. + +
-**C. Create CSV file with covariance column from rosbag file** -In detail, refer to [qiita article](https://qiita.com/koki2022/items/148d56e0f8eee45a0a62) -```sh -$ cd localization_evaluation_toolkit/sub_scripts -$ python covariance_to_csv.py [input_bag_path] [output_folder_path] +
+3D Trajectory + +
+ +
+X Y Z + +
+ +
+X Y Z error + +
+ +
+2D Error + +
+ +
+3D Error + +
+ +
+Longitudinal Error + +
+ +
+Lateral Error + +
+ +
+Roll Pitch Yaw +You can choose the unit of the vertical axis between radian and degree. + +
+ +
+Roll Pitch Yaw Error +You can choose the unit of the vertical axis between radian and degree. + +
+ +### 2.2 Twist evaluation + +A standard configuration file for twist evaluation consists of the following parts. + +- Reference +- Twist +- Optional settings + +It is required that the reference block should have the name of ***Reference*** and the names of result blocks should begin with ***Twist***. +When specifying the paths, it is recommended to use the absolute path. +There is a sample YAML file at `config/evaluation_twist.yaml`. +To create your own configuration file, just use the following templates, + +
+Template of Reference (click to expand) + +```yaml +Reference: + ## Auxiliary info + label: reference + path: /path/to/reference/csv + + ## Time + separate_time_stamp: false # [true]:Set secs_stamp_column and nsecs_stamp_column / [false]:Set stamp_column + #--------true--------# + secs_stamp_column: 1 + nsecs_stamp_column: 2 + #--------false-------# + stamp_column: 2 + + ## Position + x_column: 4 + y_column: 5 + z_column: 6 + + ## Rotation + use_quaternion: true # [true]:Set Quaternion / [false]:Set Euler + #--------true--------# + # Quaternion + ori_x_column: 7 + ori_y_column: 8 + ori_z_column: 9 + ori_w_column: 10 + #--------false-------# + # Euler + use_radian: true # [true]:radian / [false]:degree + roll_column: 7 + pitch_column: 8 + yaw_column: 9 + + ## Enu velocity + use_enu_vel: false # [true]: use enu velocity / [false]: ignored + vel_x_column: 0 + vel_y_column: 0 + vel_z_column: 0 + + ## Angular + use_angular: false # [true]: use angular / [false]: ignored + angular_x_column: 0 + angular_y_column: 0 + angular_z_column: 0 + + # GNSS quality + use_gnss_qual: false # [true]: use GNSS quality / [false]: ignored + gnss_qual: 10 ``` -## Output graphs -Sample output graphs are shown below. You can test this evaluation script easily with the following command. All graphs are output in full HD. -```sh -$ cd localization_evaluation_toolkit/scripts -$ python3 main.py ../sample_data/config/sasashima_evaluation.yaml +
+ +
+Template of Twist (click to expand) + +```yaml +Twist: + ## Auxiliary info + label: twist + path: /path/to/twist/csv + + ## Time + separate_time_stamp: false # [true]:Set secs_stamp_column and nsecs_stamp_column / [false]:Set stamp_column + #--------true--------# + secs_stamp_column: 1 + nsecs_stamp_column: 2 + #--------false-------# + stamp_column: 2 + + ## Linear + linear_x_column: 4 + linear_y_column: 5 + linear_z_column: 6 + + ## Angular + angular_x_column: 7 + angular_y_column: 8 + angular_z_column: 9 ``` -1. 2D Trajectory +
-![2d_traj](/sample_data/output_sample/2d_trajectory.png) +
+Template of optional settings (click to expand) -When you zoom in on the graph, you can see the correspondence. -![2D_traj_zoom](/sample_data/output_sample/2d_trajectory_zoom.png) +```yaml +# Trajectory graph numbering +progress_info: 0 # [0]:off, [1]:number, [2]:time, [3]:ros time, [4]:distance +interval: 0 # progress_info is [2]:second, [3]:second, [4]:meter -2. 2D Error +# Misc +sync_time_threshold: 0.01 # Time threshold for judgment for time synchronization [s] +leap_time: 0.0 # Offset correction for time synchronizatio [s] +based_heading_angle: false # [true]:The heading angle is based on North / [false]:The heading angle is based on East (ros data) +distance_length: 100 # Distance to calculate relative trajectory [m] +distance_step: 50 # Calculate relative trajectories step [m] +eval_step_max: 3.0 # Maximum value of error to be evaluated default [m] -![2d_error](/sample_data/output_sample/2d_error.png) +# Font +title_font_size: 14 +label_font_size: 10 +ticks_font_size: 8 -3. Height Error +# Save +save_figures: true +save_extension_type: png # without "." +save_dataframe: true +output_directory: /path/to/output/directory +``` -![height_error](/sample_data/output_sample/height_error.png) +
-4. 3D Error +and run with -![3d_error](/sample_data/output_sample/3d_error.png) +```sh +$ cd localization_evaluation_toolkit +$ python3 scripts/twist_main.py config/evaluation_twist.yaml +``` -5. Longitudinal Error +## 3. Sub-evaluation -![longitudinal_error](/sample_data/output_sample/longitudinal_error.png) +### 3.1 NDT performance -6. Lateral Error +Evaluation of TP, NVTL, execution time, iteration and error ellipse with ros2 bag files. -![lateral_error](/sample_data/output_sample/lateral_error.png) +The input rosbag file should contain the following (at least one) topic. +``` +/localization/pose_estimator/pose_with_covariance +/localization/pose_estimator/nearest_voxel_transformation_likelihood +/localization/pose_estimator/transform_probability +/localization/pose_estimator/exe_time_ms +/localization/pose_estimator/iteration_num +``` + +```sh +$ cd localization_evaluation_toolkit/sub_scripts +$ source ~/xxxxxx/install/setup.bash +$ python3 sub_ndt_evaluation.py [bag_path] [output_folder_path] +``` -7. Roll Pitch Yaw +### 3.2 Eagleye log evaluation -You can choose the unit of the vertical axis between radian and degree. -![rpy](/sample_data/output_sample/rpy.png) +Currently, refer to [the original doc page](https://github.com/MapIV/eagleye/tree/main-ros1/eagleye_util/trajectory_plot#eagleye_pp_single_evaluation) for details. -8. Roll Pitch Yaw Error +### 3.3 Covariance file generation -You can choose the unit of the vertical axis between radian and degree. -![rpy_error](/sample_data/output_sample/rpy_error.png) +Create a CSV file with covariance columns from rosbag file. In detail, refer to [qiita article](https://qiita.com/koki2022/items/148d56e0f8eee45a0a62) for details. + +```sh +$ cd localization_evaluation_toolkit/sub_scripts +$ python covariance_to_csv.py [input_bag_path] [output_folder_path] +``` diff --git a/config/ajust_time_stamp.yaml b/config/ajust_time_stamp.yaml deleted file mode 100644 index 840367c..0000000 --- a/config/ajust_time_stamp.yaml +++ /dev/null @@ -1,38 +0,0 @@ -Target: - ## Time - separate_time_stamp: true #[true]:Set secs_stamp_column and nsecs_stamp_column / [false]:Set stamp_column - #--------true--------# - secs_stamp_column: 2 - nsecs_stamp_column: 3 - #--------false-------# - stamp_column: 0 - - ## Velocity - direct_vel: true - #--------true--------# - vel_colmn: 5 - #--------false-------# - x_column: 0 - y_column: 0 - z_column: 0 - -Offset: - ## Time - separate_time_stamp: true #[true]:Set secs_stamp_column and nsecs_stamp_column / [false]:Set stamp_column - #--------true--------# - secs_stamp_column: 2 - nsecs_stamp_column: 3 - #--------false-------# - stamp_column: 0 - - ## Velocity - direct_vel: true - #--------true--------# - vel_colmn: 5 - #--------false-------# - x_column: 0 - y_column: 0 - z_column: 0 - -search_range: 2 #[s] -delta: 0.01 #[s] diff --git a/config/evaluation_eagleye.yaml b/config/evaluation_eagleye.yaml new file mode 100644 index 0000000..59c99b9 --- /dev/null +++ b/config/evaluation_eagleye.yaml @@ -0,0 +1,29 @@ +eagleye_log: + tf_num: 0 # 0:plane, 1:mgrs + time_unit: 0 # 0:[s], 1:[ns] + missing_gnss_type: 0 # 0:use all type, 1:rtklib_nav, 2:nmea + ros_reverse_imu: False # [True]:Use reverse_imu in ros + +param: + reverse_imu_flag: False + plane_num: 7 # Plane Cartesian coordinate system number (default:7 = 7) + sync_threshold_time_data_param: 0.01 # Time threshold for judgment at synchronized. (default:0.01 = 0.01[s]) + leap_time_param: 0.0 # Offset correction for time synchronization. (default:0.0 = 0.0[s]) + tf_x_param: 0.0 # Correction amount in East-West direction[m] + tf_y_param: 0.0 # Correction amount in North-South direction[m] + tf_across_param: 0.0 # Correction amount in vehicle orthogonal direction[m] + tf_along_param: 0.0 # Correction amount in the direction of vehicle travel[m] + tf_height_param: 0.0 # Correction amount in height direction[m] + tf_yaw_param: 0.0 # Correction amount in yaw[deg] + based_heaing_angle: False # [True]:The heading angle is based on North /[False]:The heading angle is based on East (ros data) + distance_length_param: 100 # Distance to calculate relative trajectory. (default:100 = 100[m]) + distance_step_param: 50 # Calculate relative trajectories step (default:50 = 50[m]) + eval_step_max_param: 3.0 # Maximum value of error to be evaluated default (default:3.0 = 3.0[m]) + ref_data_name_param: 'ref data' # Reference Legend Name. (default:'ref data') + data_name_param: 'eagleye' # Target Legend Name. (default:'eagleye') + font_size_param: 12 # label size + +evaluation_plot: + dr_error_ylim: 3.0 # ylabel data lim + plot_text_data: 'elapsed_time' # Text data to be displayed in the graph of trajectory(distance or elapsed_time) + plot_text_step: 50 # Text data step to be displayed in the graph of trajectory(default:50 = 50[m]) diff --git a/config/evaluation.yaml b/config/evaluation_pose.yaml similarity index 100% rename from config/evaluation.yaml rename to config/evaluation_pose.yaml diff --git a/config/evaluation_twist.yaml b/config/evaluation_twist.yaml new file mode 100644 index 0000000..b18219e --- /dev/null +++ b/config/evaluation_twist.yaml @@ -0,0 +1,101 @@ +###################### +## Necessary Params ## +###################### + +Reference: + ## Auxiliary info + label: reference + path: /path/to/reference/csv + + ## Time + separate_time_stamp: false # [true]:Set secs_stamp_column and nsecs_stamp_column / [false]:Set stamp_column + #--------true--------# + secs_stamp_column: 1 + nsecs_stamp_column: 2 + #--------false-------# + stamp_column: 2 + + ## Position + x_column: 4 + y_column: 5 + z_column: 6 + + ## Rotation + use_quaternion: true # [true]:Set Quaternion / [false]:Set Euler + #--------true--------# + # Quaternion + ori_x_column: 7 + ori_y_column: 8 + ori_z_column: 9 + ori_w_column: 10 + #--------false-------# + # Euler + use_radian: true # [true]:radian / [false]:degree + roll_column: 7 + pitch_column: 8 + yaw_column: 9 + + ## Enu velocity + use_enu_vel: false # [true]: use enu velocity / [false]: ignored + vel_x_column: 0 + vel_y_column: 0 + vel_z_column: 0 + + ## Angular + use_angular: false # [true]: use angular / [false]: ignored + angular_x_column: 0 + angular_y_column: 0 + angular_z_column: 0 + + # GNSS quality + use_gnss_qual: false # [true]: use GNSS quality / [false]: ignored + gnss_qual: 10 + +Twist: + ## Auxiliary info + label: twist + path: /path/to/twist/csv + + ## Time + separate_time_stamp: false # [true]:Set secs_stamp_column and nsecs_stamp_column / [false]:Set stamp_column + #--------true--------# + secs_stamp_column: 1 + nsecs_stamp_column: 2 + #--------false-------# + stamp_column: 2 + + ## Linear + linear_x_column: 4 + linear_y_column: 5 + linear_z_column: 6 + + ## Angular + angular_x_column: 7 + angular_y_column: 8 + angular_z_column: 9 + +############ +## Option ## +############ +# Trajectory graph numbering +progress_info: 0 # [0]:off, [1]:number, [2]:time, [3]:ros time, [4]:distance +interval: 0 # progress_info is [2]:second, [3]:second, [4]:meter + +# Misc +sync_time_threshold: 0.01 # Time threshold for judgment for time synchronization [s] +leap_time: 0.0 # Offset correction for time synchronizatio [s] +based_heading_angle: false # [true]:The heading angle is based on North / [false]:The heading angle is based on East (ros data) +distance_length: 100 # Distance to calculate relative trajectory [m] +distance_step: 50 # Calculate relative trajectories step [m] +eval_step_max: 3.0 # Maximum value of error to be evaluated default [m] + +# Font +title_font_size: 14 +label_font_size: 10 +ticks_font_size: 8 + +# Save +save_figures: true +save_extension_type: png # without "." +save_dataframe: true +output_directory: /path/to/output/directory \ No newline at end of file diff --git a/sample_data/adjust_time_test_data/offest.csv b/sample_data/adjust_time_test_data/offest.csv deleted file mode 100644 index 83ae71a..0000000 --- a/sample_data/adjust_time_test_data/offest.csv +++ /dev/null @@ -1,200 +0,0 @@ -time,.header.seq,.header.stamp.secs,.header.stamp.nsecs,.header.frame_id,.twist.linear.x,.twist.linear.y,.twist.linear.z,.twist.angular.x,.twist.angular.y,.twist.angular.z -2021/10/14/10:27:49.825464,494010,1634174870,824802908,1634174870,2.00138902664,0,0,0,0,0 -2021/10/14/10:27:49.838232,494011,1634174870,837338428,1634174870,2.00972223282,0,0,0,0,0 -2021/10/14/10:27:49.849831,494012,1634174870,849269484,1634174870,2.02361106873,0,0,0,0,0 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-2021/10/14/10:27:50.668690,494079,1634174870,668046899,,2.58333349228,0,0,0,0,0 -2021/10/14/10:27:50.680754,494080,1634174870,680278842,,2.59999990463,0,0,0,0,0 -2021/10/14/10:27:50.692708,494081,1634174870,692417246,,2.63194465637,0,0,0,0,0 -2021/10/14/10:27:50.704838,494082,1634174870,704552235,,2.63750004768,0,0,0,0,0 -2021/10/14/10:27:50.717309,494083,1634174870,717036975,,2.63194441795,0,0,0,0,0 -2021/10/14/10:27:50.730166,494084,1634174870,729910869,,2.65416669846,0,0,0,0,0 -2021/10/14/10:27:50.742188,494085,1634174870,741905212,,2.67638897896,0,0,0,0,0 -2021/10/14/10:27:50.753766,494086,1634174870,753446889,,2.67083334923,0,0,0,0,0 -2021/10/14/10:27:50.766870,494087,1634174870,766422028,,2.67361116409,0,0,0,0,0 -2021/10/14/10:27:50.779133,494088,1634174870,778595889,,2.69444441795,0,0,0,0,0 -2021/10/14/10:27:50.792057,494089,1634174870,791012525,,2.71250009537,0,0,0,0,0 -2021/10/14/10:27:50.803566,494090,1634174870,802906281,,2.72499990463,0,0,0,0,0 -2021/10/14/10:27:50.815994,494091,1634174870,815377737,,2.73611116409,0,0,0,0,0 -2021/10/14/10:27:50.828833,494092,1634174870,828246284,,2.74861097336,0,0,0,0,0 -2021/10/14/10:27:50.840483,494093,1634174870,840038013,,2.76249980927,0,0,0,0,0 -2021/10/14/10:27:50.852787,494094,1634174870,852167560,,2.76666665077,0,0,0,0,0 -2021/10/14/10:27:50.864880,494095,1634174870,864198071,,2.78888893127,0,0,0,0,0 -2021/10/14/10:27:50.877684,494096,1634174870,876989931,,2.81111097336,0,0,0,0,0 -2021/10/14/10:27:50.889945,494097,1634174870,889466548,,2.82777786255,0,0,0,0,0 -2021/10/14/10:27:50.901524,494098,1634174870,900791806,,2.8444442749,0,0,0,0,0 -2021/10/14/10:27:50.914204,494099,1634174870,913701985,,2.8527777195,0,0,0,0,0 -2021/10/14/10:27:50.926569,494100,1634174870,926037716,,2.86666679382,0,0,0,0,0 -2021/10/14/10:27:50.938015,494101,1634174870,937254675,,2.87916660309,0,0,0,0,0 -2021/10/14/10:27:50.950274,494102,1634174870,949517591,,2.89166665077,0,0,0,0,0 -2021/10/14/10:27:50.963088,494103,1634174870,962416557,,2.90000009537,0,0,0,0,0 -2021/10/14/10:27:50.974850,494104,1634174870,974229770,,2.91666650772,0,0,0,0,0 -2021/10/14/10:27:50.986774,494105,1634174870,986106331,,2.93611097336,0,0,0,0,0 -2021/10/14/10:27:50.999260,494106,1634174870,998749007,,2.93611097336,0,0,0,0,0 -2021/10/14/10:27:51.010913,494107,1634174871,10450035,,2.95833325386,0,0,0,0,0 -2021/10/14/10:27:51.024045,494108,1634174871,23421960,,2.97361111641,0,0,0,0,0 -2021/10/14/10:27:51.035465,494109,1634174871,34801145,,2.98055553436,0,0,0,0,0 -2021/10/14/10:27:51.047804,494110,1634174871,47211823,,2.99444437027,0,0,0,0,0 -2021/10/14/10:27:51.060194,494111,1634174871,59499856,,3.00555562973,0,0,0,0,0 -2021/10/14/10:27:51.073089,494112,1634174871,72438618,,3.02361106873,0,0,0,0,0 -2021/10/14/10:27:51.084687,494113,1634174871,84003082,,3.03333330154,0,0,0,0,0 -2021/10/14/10:27:51.097670,494114,1634174871,96942801,,3.05000019073,0,0,0,0,0 -2021/10/14/10:27:51.109304,494115,1634174871,108716383,,3.05694437027,0,0,0,0,0 -2021/10/14/10:27:51.121705,494116,1634174871,121181917,,3.06944465637,0,0,0,0,0 -2021/10/14/10:27:51.134001,494117,1634174871,133329025,,3.08194446564,0,0,0,0,0 -2021/10/14/10:27:51.146876,494118,1634174871,146194339,,3.09166669846,0,0,0,0,0 -2021/10/14/10:27:51.158404,494119,1634174871,157762296,,3.10555553436,0,0,0,0,0 -2021/10/14/10:27:51.171354,494120,1634174871,170783105,,3.11527776718,0,0,0,0,0 -2021/10/14/10:27:51.183042,494121,1634174871,182346901,,3.12361097336,0,0,0,0,0 -2021/10/14/10:27:51.195156,494122,1634174871,194524975,,3.13055562973,0,0,0,0,0 -2021/10/14/10:27:51.207232,494123,1634174871,206644568,,3.14027786255,0,0,0,0,0 -2021/10/14/10:27:51.220588,494124,1634174871,219894390,,3.1555557251,0,0,0,0,0 -2021/10/14/10:27:51.231723,494125,1634174871,231039193,,3.16111111641,0,0,0,0,0 -2021/10/14/10:27:51.243912,494126,1634174871,243321744,,3.17361116409,0,0,0,0,0 -2021/10/14/10:27:51.256408,494127,1634174871,255729051,,3.19027757645,0,0,0,0,0 -2021/10/14/10:27:51.268604,494128,1634174871,268023948,,3.20277786255,0,0,0,0,0 -2021/10/14/10:27:51.280520,494129,1634174871,279954689,,3.21388888359,0,0,0,0,0 -2021/10/14/10:27:51.292938,494130,1634174871,292233013,,3.23055553436,0,0,0,0,0 -2021/10/14/10:27:51.305244,494131,1634174871,304587547,,3.23611116409,0,0,0,0,0 -2021/10/14/10:27:51.317589,494132,1634174871,316962811,,3.24722242355,0,0,0,0,0 -2021/10/14/10:27:51.330093,494133,1634174871,329466382,,3.25694441795,0,0,0,0,0 -2021/10/14/10:27:51.341990,494134,1634174871,341293663,,3.26388883591,0,0,0,0,0 -2021/10/14/10:27:51.354344,494135,1634174871,353599467,,3.28333330154,0,0,0,0,0 -2021/10/14/10:27:51.367271,494136,1634174871,366603303,,3.29305553436,0,0,0,0,0 -2021/10/14/10:27:51.379024,494137,1634174871,378413563,,3.31666660309,0,0,0,0,0 -2021/10/14/10:27:51.391437,494138,1634174871,390782780,,3.330555439,0,0,0,0,0 -2021/10/14/10:27:51.403543,494139,1634174871,402894728,,3.33611106873,0,0,0,0,0 -2021/10/14/10:27:51.416300,494140,1634174871,415510040,,3.35138893127,0,0,0,0,0 -2021/10/14/10:27:51.428497,494141,1634174871,427931530,,3.36666679382,0,0,0,0,0 -2021/10/14/10:27:51.440248,494142,1634174871,439628692,,3.38055562973,0,0,0,0,0 -2021/10/14/10:27:51.452801,494143,1634174871,452313709,,3.39583349228,0,0,0,0,0 -2021/10/14/10:27:51.465175,494144,1634174871,464617069,,3.40972232819,0,0,0,0,0 -2021/10/14/10:27:51.476933,494145,1634174871,476413058,,3.41805553436,0,0,0,0,0 -2021/10/14/10:27:51.488925,494146,1634174871,488320876,,3.43194437027,0,0,0,0,0 -2021/10/14/10:27:51.501226,494147,1634174871,500503399,,3.45277786255,0,0,0,0,0 -2021/10/14/10:27:51.514240,494148,1634174871,513623206,,3.4597222805,0,0,0,0,0 -2021/10/14/10:27:51.525576,494149,1634174871,525142977,,3.48333334923,0,0,0,0,0 -2021/10/14/10:27:51.537886,494150,1634174871,537144707,,3.48888897896,0,0,0,0,0 -2021/10/14/10:27:51.550681,494151,1634174871,549925832,,3.50138902664,0,0,0,0,0 -2021/10/14/10:27:51.562559,494152,1634174871,561898528,,3.518055439,0,0,0,0,0 -2021/10/14/10:27:51.575227,494153,1634174871,574601838,,3.53888893127,0,0,0,0,0 -2021/10/14/10:27:51.587174,494154,1634174871,586456072,,3.53472232819,0,0,0,0,0 -2021/10/14/10:27:51.598762,494155,1634174871,598159909,,3.54166674614,0,0,0,0,0 -2021/10/14/10:27:51.611021,494156,1634174871,610346131,,3.55555558205,0,0,0,0,0 -2021/10/14/10:27:51.623340,494157,1634174871,622558972,,3.56388902664,0,0,0,0,0 -2021/10/14/10:27:51.635987,494158,1634174871,635276738,,3.57777786255,0,0,0,0,0 -2021/10/14/10:27:51.647869,494159,1634174871,647290398,,3.59861111641,0,0,0,0,0 -2021/10/14/10:27:51.660291,494160,1634174871,659653647,,3.61249995232,0,0,0,0,0 -2021/10/14/10:27:51.672489,494161,1634174871,671905669,,3.62222218513,0,0,0,0,0 -2021/10/14/10:27:51.685538,494162,1634174871,684826763,,3.63472223282,0,0,0,0,0 -2021/10/14/10:27:51.697318,494163,1634174871,696759777,,3.65833330154,0,0,0,0,0 -2021/10/14/10:27:51.709314,494164,1634174871,708724959,,3.6569442749,0,0,0,0,0 -2021/10/14/10:27:51.721619,494165,1634174871,721116985,,3.67638874054,0,0,0,0,0 -2021/10/14/10:27:51.733986,494166,1634174871,733329730,,3.68333339691,0,0,0,0,0 -2021/10/14/10:27:51.746253,494167,1634174871,745591359,,3.69722223282,0,0,0,0,0 -2021/10/14/10:27:51.759173,494168,1634174871,758516816,,3.71250009537,0,0,0,0,0 -2021/10/14/10:27:51.771320,494169,1634174871,770775713,,3.72222232819,0,0,0,0,0 -2021/10/14/10:27:51.782836,494170,1634174871,782236770,,3.74722218513,0,0,0,0,0 -2021/10/14/10:27:51.795153,494171,1634174871,794520114,,3.75833320618,0,0,0,0,0 -2021/10/14/10:27:51.807376,494172,1634174871,806743692,,3.77777767181,0,0,0,0,0 -2021/10/14/10:27:51.819692,494173,1634174871,819007101,,3.78194451332,0,0,0,0,0 -2021/10/14/10:27:51.832024,494174,1634174871,831286863,,3.79305553436,0,0,0,0,0 -2021/10/14/10:27:51.844121,494175,1634174871,843471003,,3.80138897896,0,0,0,0,0 -2021/10/14/10:27:51.856267,494176,1634174871,855627538,,3.8041665554,0,0,0,0,0 -2021/10/14/10:27:51.868887,494177,1634174871,868227309,,3.80416679382,0,0,0,0,0 -2021/10/14/10:27:51.880868,494178,1634174871,880140883,,3.81666660309,0,0,0,0,0 -2021/10/14/10:27:51.892925,494179,1634174871,892293525,,3.8527777195,0,0,0,0,0 -2021/10/14/10:27:51.905264,494180,1634174871,904573277,,3.85138893127,0,0,0,0,0 -2021/10/14/10:27:51.917468,494181,1634174871,916805139,,3.86388897896,0,0,0,0,0 -2021/10/14/10:27:51.929722,494182,1634174871,929094621,,3.88194417953,0,0,0,0,0 -2021/10/14/10:27:51.942090,494183,1634174871,941461422,,3.91111111641,0,0,0,0,0 -2021/10/14/10:27:51.954685,494184,1634174871,954122317,,3.90416669846,0,0,0,0,0 -2021/10/14/10:27:51.966674,494185,1634174871,966114743,,3.9152777195,0,0,0,0,0 -2021/10/14/10:27:51.978939,494186,1634174871,978436393,,3.92638874054,0,0,0,0,0 -2021/10/14/10:27:51.991819,494187,1634174871,991556393,,3.93055534363,0,0,0,0,0 -2021/10/14/10:27:52.003169,494188,1634174872,2988039,,3.90555548668,0,0,0,0,0 -2021/10/14/10:27:52.015863,494189,1634174872,15555621,,3.90000009537,0,0,0,0,0 -2021/10/14/10:27:52.028089,494190,1634174872,27849384,,3.93194437027,0,0,0,0,0 -2021/10/14/10:27:52.040280,494191,1634174872,40034850,,3.95694446564,0,0,0,0,0 -2021/10/14/10:27:52.052109,494192,1634174872,51770580,,3.95416688919,0,0,0,0,0 -2021/10/14/10:27:52.064943,494193,1634174872,64661268,,3.96388888359,0,0,0,0,0 -2021/10/14/10:27:52.076968,494194,1634174872,76624166,,3.99305582047,0,0,0,0,0 -2021/10/14/10:27:52.089316,494195,1634174872,88661614,,4.00416660309,0,0,0,0,0 -2021/10/14/10:27:52.101390,494196,1634174872,100669682,,3.98888874054,0,0,0,0,0 -2021/10/14/10:27:52.113405,494197,1634174872,112784268,,3.98472213745,0,0,0,0,0 -2021/10/14/10:27:52.126263,494198,1634174872,125580993,,3.99583339691,0,0,0,0,0 -2021/10/14/10:27:52.138278,494199,1634174872,137360762,,3.99305534363,0,0,0,0,0 -2021/10/14/10:27:52.150105,494200,1634174872,149425059,,4.018055439,0,0,0,0,0 -2021/10/14/10:27:52.162229,494201,1634174872,161572535,,4.04166650772,0,0,0,0,0 -2021/10/14/10:27:52.174837,494202,1634174872,174238245,,4.05138874054,0,0,0,0,0 -2021/10/14/10:27:52.187224,494203,1634174872,186585766,,4.04861116409,0,0,0,0,0 -2021/10/14/10:27:52.198968,494204,1634174872,198185581,,4.07499980927,0,0,0,0,0 -2021/10/14/10:27:52.210852,494205,1634174872,210276613,,4.0930557251,0,0,0,0,0 -2021/10/14/10:27:52.223193,494206,1634174872,222586033,,4.08888912201,0,0,0,0,0 -2021/10/14/10:27:52.236097,494207,1634174872,235414672,,4.11388874054,0,0,0,0,0 -2021/10/14/10:27:52.247970,494208,1634174872,247323055,,4.13194417953,0,0,0,0,0 diff --git a/sample_data/config/ajust_time_stamp.yaml b/sample_data/config/ajust_time_stamp.yaml deleted file mode 100644 index 840367c..0000000 --- a/sample_data/config/ajust_time_stamp.yaml +++ /dev/null @@ -1,38 +0,0 @@ -Target: - ## Time - separate_time_stamp: true #[true]:Set secs_stamp_column and nsecs_stamp_column / [false]:Set stamp_column - #--------true--------# - secs_stamp_column: 2 - nsecs_stamp_column: 3 - #--------false-------# - stamp_column: 0 - - ## Velocity - direct_vel: true - #--------true--------# - vel_colmn: 5 - #--------false-------# - x_column: 0 - y_column: 0 - z_column: 0 - -Offset: - ## Time - separate_time_stamp: true #[true]:Set secs_stamp_column and nsecs_stamp_column / [false]:Set stamp_column - #--------true--------# - secs_stamp_column: 2 - nsecs_stamp_column: 3 - #--------false-------# - stamp_column: 0 - - ## Velocity - direct_vel: true - #--------true--------# - vel_colmn: 5 - #--------false-------# - x_column: 0 - y_column: 0 - z_column: 0 - -search_range: 2 #[s] -delta: 0.01 #[s] diff --git a/sample_data/output_sample/2d_error.png b/sample_data/output_sample/2d_error.png index 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zKcuPPF>2*N=Zui!uS1RY;7Q10K>t(lp{J)usob$v3sDBx{p;6BPt1Ny%E>rVxNCw>4hCQweO8xC?wQ~_+4+R+8Ax`_*3+CPsao0j#s`+*S$+y|+E zw?1TME}A@82Eq(l-=m#HFg!SD6xip4mlldOd0NJWv*ptRE-tlD1vRy?%uN=@edFJn zQ$XP0&`{aRnRLQal7dZh4(mZh zKmsJ^Pvz4jfJqV!saq2h8k3go^iXJHrpadyq`h1EhOa$+FM9WMoW>8a8 zQZn?r;~@$IJFmoirV%S2|6<9r1%qz>duDv`)PG^R{(p1+Q`fTC VcNlGYDn(EiqJmN{@&sPG{~xt|y6*r0 literal 0 HcmV?d00001 diff --git a/scripts/main.py b/scripts/pose_main.py similarity index 81% rename from scripts/main.py rename to scripts/pose_main.py index 2444a10..42cfa6b 100644 --- a/scripts/main.py +++ b/scripts/pose_main.py @@ -3,7 +3,7 @@ sys.dont_write_bytecode = True -from util import configer, packer, plotter +from util.pose import configer, packer, plotter if __name__ == "__main__": print("Loading yaml file...", end="", flush=True) @@ -16,10 +16,10 @@ ref_pack, res_packs = packer.param2pack(ref_param, res_params, opt_param) print("Completed!!") - print("Plot graph...", end="", flush=True) + print("Plotitng graph...", end="", flush=True) figs = plotter.plot(ref_pack, res_packs, opt_param) print("Completed!!") - print("Save data...", end="", flush=True) + print("Saving data...", end="", flush=True) plotter.save(ref_pack, res_packs, figs, opt_param) print("Completed!!") diff --git a/scripts/twist_main.py b/scripts/twist_main.py new file mode 100644 index 0000000..8bb4c22 --- /dev/null +++ b/scripts/twist_main.py @@ -0,0 +1,25 @@ +import sys +import yaml + +sys.dont_write_bytecode = True + +from util.twist import configer, packer, plotter + +if __name__ == "__main__": + print("Loading yaml file...", end="", flush=True) + with open(sys.argv[1], "r") as yml: + config = yaml.safe_load(yml) + ref_param, twist_param, opt_param = configer.yaml2params(config) + print("Completed!!") + + print("Loading data files...", end="", flush=True) + data_pack = packer.param2pack(ref_param, twist_param, opt_param) + print("Completed!!") + + print("Plotting graph...", end="", flush=True) + figs = plotter.plot(data_pack, opt_param) + print("Completed!!") + + print("Saving data ...", end="", flush=True) + plotter.save(data_pack, figs, opt_param) + print("Completed!!") diff --git a/scripts/util/configer.py b/scripts/util/pose/configer.py similarity index 100% rename from scripts/util/configer.py rename to scripts/util/pose/configer.py diff --git a/scripts/util/packer.py b/scripts/util/pose/packer.py similarity index 99% rename from scripts/util/packer.py rename to scripts/util/pose/packer.py index 34bb4e4..eeb3a15 100644 --- a/scripts/util/packer.py +++ b/scripts/util/pose/packer.py @@ -8,7 +8,7 @@ from scipy.spatial.transform import Rotation from typing import List, Tuple -from util.configer import DataParam, OptParam +from util.pose.configer import DataParam, OptParam class DataPack: def __init__(self, param: DataParam, opt_param: OptParam) -> None: diff --git a/scripts/util/plotter.py b/scripts/util/pose/plotter.py similarity index 85% rename from scripts/util/plotter.py rename to scripts/util/pose/plotter.py index 56208e3..4aa8ed7 100644 --- a/scripts/util/plotter.py +++ b/scripts/util/pose/plotter.py @@ -8,8 +8,8 @@ from functools import reduce from typing import List, Dict -from util.configer import OptParam -from util.packer import RefDataPack, ResDataPack +from util.pose.configer import OptParam +from util.pose.packer import RefDataPack, ResDataPack def plot_2d_traj(ref_pack: RefDataPack, res_packs: List[ResDataPack], opt_param: OptParam) -> Dict[str, Figure]: fig_2d_trj = plt.figure("2D_Trajectory", figsize=(16, 9), dpi=120) @@ -55,7 +55,7 @@ def plot_2d_traj(ref_pack: RefDataPack, res_packs: List[ResDataPack], opt_param: ref_df = ref_pack.accumulate_df(ref_dfs) ax_2d_trj.scatter(ref_df["x"], ref_df["y"], c="k", zorder=-1, label=ref_pack.label) - ax_2d_trj.set_title("2D trajectory", fontsize=opt_param.title_font_size) + ax_2d_trj.set_title("2D Trajectory", fontsize=opt_param.title_font_size) ax_2d_trj.set_xlabel("x[m]", fontsize=opt_param.label_font_size) ax_2d_trj.set_ylabel("y[m]", fontsize=opt_param.label_font_size) ax_2d_trj.tick_params(labelsize=opt_param.ticks_font_size) @@ -73,7 +73,7 @@ def plot_3d_traj(ref_pack: RefDataPack, res_packs: List[ResDataPack], opt_param: for res_pack in res_packs: ax_3d_trj.plot3D(res_pack.data["x"], res_pack.data["y"], res_pack.data["z"], label=res_pack.label) - ax_3d_trj.set_title("3D trajectory", fontsize=opt_param.title_font_size) + ax_3d_trj.set_title("3D Trajectory", fontsize=opt_param.title_font_size) ax_3d_trj.set_xlabel('x[m]', fontsize=opt_param.label_font_size) ax_3d_trj.set_ylabel('y[m]', fontsize=opt_param.label_font_size) ax_3d_trj.set_zlabel('z[m]', fontsize=opt_param.label_font_size) @@ -82,6 +82,54 @@ def plot_3d_traj(ref_pack: RefDataPack, res_packs: List[ResDataPack], opt_param: return {"3d_trajectory": fig_3d_trj} +def plot_xyz(ref_pack: RefDataPack, res_packs: List[ResDataPack], opt_param: OptParam) -> Dict[str, Figure]: + fig_xyz = plt.figure("X_Y_Z", figsize=(16, 9), dpi=120) + + for i, name in enumerate(["x", "y", "z"]): + ax = fig_xyz.add_subplot(311 + i) + + ax.plot( + ref_pack.axis_array, ref_pack.data[name], + marker="o", c="k", markersize=3, linewidth=0.5, label=ref_pack.label + ) + for res_pack in res_packs: + ax.plot( + res_pack.axis_array, res_pack.data[name], + marker="o", markersize=1, linewidth=0.5, label=res_pack.label + ) + + ax.set_title(name.capitalize(), fontsize=opt_param.title_font_size) + ax.set_xlabel(ref_pack.axis_unit, fontsize=opt_param.label_font_size) + ax.set_ylabel("[m]", fontsize=opt_param.label_font_size) + ax.tick_params(labelsize=opt_param.ticks_font_size) + ax.grid() + ax.legend() + plt.tight_layout() + + return {"xyz": fig_xyz} + +def plot_xyz_error(ref_pack: RefDataPack, res_packs: List[ResDataPack], opt_param: OptParam) -> Dict[str, Figure]: + fig_xyz = plt.figure("X_Y_Z_Error", figsize=(16, 9), dpi=120) + + for i, name in enumerate(["x", "y", "z"]): + ax = fig_xyz.add_subplot(311 + i) + + for res_pack in res_packs: + ax.plot( + res_pack.axis_array, res_pack.data[f"error_{name}"], + marker="o", markersize=1, linewidth=0.5, label=res_pack.label + ) + + ax.set_title(f"{name.capitalize()} Error", fontsize=opt_param.title_font_size) + ax.set_xlabel(ref_pack.axis_unit, fontsize=opt_param.label_font_size) + ax.set_ylabel("error[m]", fontsize=opt_param.label_font_size) + ax.tick_params(labelsize=opt_param.ticks_font_size) + ax.grid() + ax.legend() + plt.tight_layout() + + return {"xyz_error": fig_xyz} + def plot_2d_error(ref_pack: RefDataPack, res_packs: List[ResDataPack], opt_param: OptParam) -> Dict[str, Figure]: fig_2d_error = plt.figure("2D_Error", figsize=(16, 9), dpi=120) ax_2d_error = fig_2d_error.add_subplot(111) @@ -101,27 +149,6 @@ def plot_2d_error(ref_pack: RefDataPack, res_packs: List[ResDataPack], opt_param return {"2d_error": fig_2d_error} -def plot_height_error(ref_pack: RefDataPack, res_packs: List[ResDataPack], opt_param: OptParam) -> Dict[str, Figure]: - fig_height_error = plt.figure("Height_Error", figsize=(16, 9), dpi=120) - ax_height_error = fig_height_error.add_subplot(111) - - for res_pack in res_packs: - ax_height_error.plot( - res_pack.axis_array, res_pack.data["error_z"], - marker="o", markersize=2, linewidth=0.5, label=res_pack.label - ) - - ax_height_error.set_title("Height Error", fontsize=opt_param.title_font_size) - ax_height_error.set_xlabel(ref_pack.axis_unit, fontsize=opt_param.label_font_size) - ax_height_error.set_ylabel("error[m]", fontsize=opt_param.label_font_size) - ylim = max(map(abs, ax_height_error.get_ylim())) - ax_height_error.set_ylim(-ylim, ylim) - ax_height_error.tick_params(labelsize=opt_param.ticks_font_size) - ax_height_error.legend() - ax_height_error.grid() - - return {"height_error": fig_height_error} - def plot_3d_error(ref_pack: RefDataPack, res_packs: List[ResDataPack], opt_param: OptParam) -> Dict[str, Figure]: fig_3d_error = plt.figure("3D_Error", figsize=(16, 9), dpi=120) ax_3d_error = fig_3d_error.add_subplot(111) @@ -209,7 +236,7 @@ def plot_rpy(ref_pack: RefDataPack, res_packs: List[ResDataPack], opt_param: Opt marker="o", markersize=1, linewidth=0.5, label=res_pack.label ) - ax.set_title(name, fontsize=opt_param.title_font_size) + ax.set_title(name.capitalize(), fontsize=opt_param.title_font_size) ax.set_xlabel(ref_pack.axis_unit, fontsize=opt_param.label_font_size) ax.set_ylabel(ref_pack.degree_unit, fontsize=opt_param.label_font_size) ylim = max(map(abs, ax.get_ylim())) @@ -290,9 +317,10 @@ def plot_velocity_error(ref_pack: RefDataPack, res_packs: List[ResDataPack], opt def plot(ref_pack: RefDataPack, res_packs: List[ResDataPack], opt_param: OptParam) -> Dict[str, Figure]: figs = reduce(lambda x, y: {**x, **y}, [ plot_2d_traj(ref_pack, res_packs, opt_param), # 2d trajectory - # plot_3d_traj(ref_pack, res_packs, opt_param), # 3d trajectory + plot_3d_traj(ref_pack, res_packs, opt_param), # 3d trajectory + plot_xyz(ref_pack, res_packs, opt_param), # xyz + plot_xyz_error(ref_pack, res_packs, opt_param), # xyz error plot_2d_error(ref_pack, res_packs, opt_param), # 2d error - plot_height_error(ref_pack, res_packs, opt_param), # height error plot_3d_error(ref_pack, res_packs, opt_param), # 3d error plot_longitudinal_error(ref_pack, res_packs, opt_param), # longitudinal error plot_lateral_error(ref_pack, res_packs, opt_param), # lateral error diff --git a/scripts/util/twist/configer.py b/scripts/util/twist/configer.py new file mode 100644 index 0000000..1669b77 --- /dev/null +++ b/scripts/util/twist/configer.py @@ -0,0 +1,94 @@ +# configuration reader + +from typing import Tuple + +class DataParam: + def __init__(self, config: dict, key: str) -> None: + # auxiliary info + self.label = config[key]["label"] + self.path = config[key]["path"] + # time + self.separate_time_stamp = config[key]["separate_time_stamp"] + self.stamp_column = config[key]["stamp_column"] + self.secs_stamp_column = config[key]["secs_stamp_column"] + self.nsecs_stamp_column = config[key]["nsecs_stamp_column"] + # other entries + if key == "Reference": + self.init_ref_param(config, key) + elif key == "Twist": + self.init_twist_param(config, key) + else: + raise RuntimeError(f"Unexpected config key: {key}") + + def init_ref_param(self, config: dict, key: str) -> None: + try: + # position + self.x_column = config[key]["x_column"] + self.y_column = config[key]["y_column"] + self.z_column = config[key]["z_column"] + # rotation + self.use_quaternion = config[key]["use_quaternion"] + self.ori_x_column = config[key]["ori_x_column"] + self.ori_y_column = config[key]["ori_y_column"] + self.ori_z_column = config[key]["ori_z_column"] + self.ori_w_column = config[key]["ori_w_column"] + self.use_radian = config[key]["use_radian"] + self.roll_column = config[key]["roll_column"] + self.pitch_column = config[key]["pitch_column"] + self.yaw_column = config[key]["yaw_column"] + # enu velocity + self.use_enu_velocity = config[key]["use_enu_vel"] + self.vel_x_column = config[key]["vel_x_column"] + self.vel_y_column = config[key]["vel_y_column"] + self.vel_z_column = config[key]["vel_z_column"] + # angular + self.use_angular = config[key]["use_angular"] + self.angular_x_column = config[key]["angular_x_column"] + self.angular_y_column = config[key]["angular_y_column"] + self.angular_z_column = config[key]["angular_z_column"] + # GNSS quality + self.use_gnss_quality = config[key]["use_gnss_qual"] + self.gnss_quality = config[key]["gnss_qual"] + except KeyError: + raise RuntimeError("Undefined config key for the reference data") + + def init_twist_param(self, config: dict, key: str) -> None: + try: + # linear + self.linear_x_column = config[key]["linear_x_column"] + self.linear_y_column = config[key]["linear_y_column"] + self.linear_z_column = config[key]["linear_z_column"] + # angular + self.angular_x_column = config[key]["angular_x_column"] + self.angular_y_column = config[key]["angular_y_column"] + self.angular_z_column = config[key]["angular_z_column"] + except KeyError: + raise RuntimeError("Undefined config key for the twist data") + +class OptParam: + def __init__(self, config: dict) -> None: + # trajectory + self.progress_info = config["progress_info"] + self.interval = config["interval"] + # misc + self.sync_time_threshold = config["sync_time_threshold"] + self.leap_time = config["leap_time"] + self.based_heading_angle = config["based_heading_angle"] + self.distance_length = config["distance_length"] + self.distance_step = config["distance_step"] + self.eval_step_max = config["eval_step_max"] + # font size + self.title_font_size = config["title_font_size"] + self.label_font_size = config["label_font_size"] + self.ticks_font_size = config["ticks_font_size"] + # saving option + self.save_figures = config["save_figures"] + self.save_extension_type = config["save_extension_type"] + self.save_dataframe = config["save_dataframe"] + self.output_directory = config["output_directory"] + +def yaml2params(config: dict) -> Tuple[DataParam, DataParam, OptParam]: + ref_param = DataParam(config, "Reference") + twist_param = DataParam(config, "Twist") + opt_param = OptParam(config) + return ref_param, twist_param, opt_param diff --git a/scripts/util/twist/packer.py b/scripts/util/twist/packer.py new file mode 100644 index 0000000..ccca8b7 --- /dev/null +++ b/scripts/util/twist/packer.py @@ -0,0 +1,271 @@ +# csv data packer + +import math +import numpy as np +import pandas as pd +from scipy.spatial.transform import Rotation +from typing import Tuple + +from util.twist.configer import DataParam, OptParam + +class DataPack: + def __init__(self, ref_param: DataParam, twist_param, opt_param: OptParam) -> None: + # basic info + self.ref_label = ref_param.label + self.twist_label = twist_param.label + # read data + self.ref_data = self.read_ref_data(ref_param) + self.twist_data = self.read_twist_data(twist_param) + # sync time + self.sync_time(opt_param) + # pre-calculation + self.ref_data = self.ref_data.assign(**self.calc_distance(self.ref_data)) + self.ref_data = self.ref_data.assign(**self.calc_elapsed(self.ref_data)) + self.dr_error, self.dr_traj = self.get_dr_twist(opt_param) + self.dr_rate = self.get_dr_rate(opt_param) + + def sync_time(self, opt_param: OptParam) -> None: + sync_index = np.zeros(len(self.twist_data["time"])) + + set_target_index = [] + set_ref_index = [] + + if self.ref_data["time"][0] > self.twist_data["time"][0]: + self.twist_data = self.twist_data[self.twist_data["time"] > self.ref_data["time"][0] - opt_param.sync_time_threshold] + self.twist_data = self.twist_data.reset_index(drop=True) + else: + self.ref_data = self.ref_data[self.ref_data["time"] > self.twist_data["time"][0] - opt_param.sync_time_threshold] + self.ref_data = self.ref_data.reset_index(drop=True) + + if len(self.twist_data) == 0 or len(self.ref_data) == 0: + raise RuntimeError("Target time and ref time ranges do not match.") + + if self.ref_data.iloc[-1]["time"] < self.twist_data.iloc[-1]["time"]: + self.twist_data = self.twist_data[self.twist_data["time"] < self.ref_data.iloc[-1]["time"] + opt_param.sync_time_threshold] + self.twist_data = self.twist_data.reset_index(drop=True) + else: + self.ref_data = self.ref_data[self.ref_data["time"] < self.twist_data.iloc[-1]["time"] + opt_param.sync_time_threshold] + self.ref_data = self.ref_data.reset_index(drop=True) + + if len(self.twist_data) == 0 or len(self.ref_data) == 0: + raise RuntimeError("Target time and ref time ranges do not match.") + + sync_ref_time_tmp = self.ref_data["time"] + + num = 0 + len_drop_num = 0 + for i in range(len(self.twist_data)): + if i == 0: continue + time_tmp = self.twist_data.iloc[i]["time"] - sync_ref_time_tmp + opt_param.leap_time + sync_index[i] = np.argmin(abs(time_tmp)) + sync_time_tmp = time_tmp[sync_index[i]] + if sync_time_tmp < opt_param.sync_time_threshold: + tmp_num = int(sync_index[i]) + set_target_index.append(i) + num = num + len_drop_num + drop_num = list(range(0,tmp_num - len_drop_num - 1, 1)) + if not drop_num == None: + sync_ref_time_tmp = sync_ref_time_tmp.drop(drop_num, axis=0) + len_drop_num = len(drop_num) + sync_ref_time_tmp = sync_ref_time_tmp.reset_index(drop=True) + set_ref_index.append(num) + + self.ref_data = self.ref_data.iloc[set_ref_index].reset_index(drop=True) + self.twist_data = self.twist_data.iloc[set_target_index].reset_index(drop=True) + + def get_dr_twist(self, opt_param: OptParam) -> Tuple[pd.DataFrame, pd.DataFrame]: + last_distance = 0 + last_heading_integtation = 0 + set_dr_trajcetory = [] + set_calc_error = [] + for i in range(len(self.ref_data["time"])): + if i == 0: continue + if last_distance + opt_param.distance_step < self.ref_data["distance"][i]: + data_set_flag = True + start_distance = self.ref_data["distance"][i] + start_pos_x = self.ref_data["x"][i] + start_pos_y = self.ref_data["y"][i] + previous_pos_x = 0 + previous_pos_y = 0 + last_distance = start_distance + last_time = self.ref_data["time"][i] + for j in range(i, len(self.ref_data["time"])): + if data_set_flag == True: + last_time = self.ref_data["time"][j] + last_heading_integtation = np.deg2rad(self.ref_data["yaw"])[j] + distance_data = self.ref_data["distance"][j] - start_distance + absolute_pos_x = self.ref_data["x"][j] - start_pos_x + absolute_pos_y = self.ref_data["y"][j] - start_pos_y + data_set_flag = False + continue + else: + heading_integtation = last_heading_integtation + self.twist_data["angular_z"][j] * (self.ref_data["time"][j] - last_time) + if opt_param.based_heading_angle == True: + usr_vel_x = math.sin(heading_integtation) * self.twist_data["linear_x"][j] + usr_vel_y = math.cos(heading_integtation) * self.twist_data["linear_x"][j] + else: + usr_vel_x = math.cos(heading_integtation) * self.twist_data["linear_x"][j] + usr_vel_y = math.sin(heading_integtation) * self.twist_data["linear_x"][j] + if self.ref_data["distance"][j] - start_distance < opt_param.distance_length: + dr_pos_x = previous_pos_x + usr_vel_x * (self.ref_data["time"][j] - last_time) + dr_pos_y = previous_pos_y + usr_vel_y * (self.ref_data["time"][j] - last_time) + last_heading_integtation = heading_integtation + previous_pos_x = dr_pos_x + previous_pos_y = dr_pos_y + last_time = self.ref_data["time"][j] + distance_data = self.ref_data["distance"][j] - start_distance + absolute_pos_x = self.ref_data["x"][j] - start_pos_x + absolute_pos_y = self.ref_data["y"][j] - start_pos_y + absolute_dr_pos_x = start_pos_x + dr_pos_x + absolute_dr_pos_y = start_pos_y + dr_pos_y + set_dr_trajcetory.append([absolute_dr_pos_x, absolute_dr_pos_y]) + else: + error_x = absolute_pos_x - dr_pos_x + error_y = absolute_pos_y - dr_pos_y + error_2d_fabs = math.fabs(error_x ** 2 + error_y ** 2) + error_2d = math.sqrt(error_2d_fabs) + set_calc_error.append([start_distance, distance_data, absolute_pos_x, dr_pos_x, absolute_pos_y, dr_pos_y, error_x, error_y, error_2d]) + break + dr_error = pd.DataFrame(set_calc_error, columns=[ + "start_distance", "distance", + "absolute_pos_x", "absolute_pos_y", + "dr_pos_x", "dr_pos_y", + "error_x", "error_y", "error_2d" + ]) + dr_trajcetory = pd.DataFrame(set_dr_trajcetory, columns=["x", "y"]) + return dr_error, dr_trajcetory + + def get_dr_rate(self, opt_param: OptParam) -> pd.DataFrame: + ErrTra_cnt = [] + set_ErrTra_middle = [] + set_ErrTra = [] + ErrTra_cnt = len(self.dr_error["error_2d"]) + + step = 0.01 + eval_step_middle = 0.1 + for i in np.arange(0, eval_step_middle, step): + cnt = 0 + eval_step = i + for data in self.dr_error["error_2d"]: + if data < eval_step: + cnt = cnt + 1 + rate = cnt / ErrTra_cnt + set_ErrTra_middle.append([eval_step,rate,rate *100]) + ErrTra_Rate_middle = pd.DataFrame(set_ErrTra_middle,columns=["x_label", "ErrTra_Rate", "ErrTra"]) + + step = 0.1 + for i in np.arange(eval_step_middle, opt_param.eval_step_max, step): + cnt = 0 + eval_step = i + for data in self.dr_error["error_2d"]: + if data < eval_step: + cnt = cnt + 1 + rate = cnt / ErrTra_cnt + set_ErrTra.append([eval_step,rate,rate *100]) + ErrTra_Rate_tmp = pd.DataFrame(set_ErrTra,columns=["x_label", "ErrTra_Rate", "ErrTra"]) + ErrTra_Rate = pd.concat([ErrTra_Rate_middle,ErrTra_Rate_tmp]) + return ErrTra_Rate + + @staticmethod + def calc_distance(df: pd.DataFrame) -> dict: + if all(key in df.columns for key in ["vel_x", "vel_y", "vel_z"]): + elapsed = df["time"] - df["time"][0] + velocity = (df["vel_x"] ** 2 + df["vel_y"] ** 2 + df["vel_z"] ** 2) ** 0.5 + displacement = elapsed * velocity + else: + displacement = [0] + [np.sqrt(pow(dx, 2) + pow(dy, 2)) for dx, dy in zip( + df["x"].iloc[1:].values - df["x"].iloc[:-1].values, + df["y"].iloc[1:].values - df["y"].iloc[:-1].values, + )] + return {"distance": [sum(displacement[:idx + 1]) for idx in range(len(displacement))]} + + @staticmethod + def calc_elapsed(df: pd.DataFrame) -> dict: + return {"elapsed": df["time"] - df["time"][0]} + + @staticmethod + def read_ref_data(param: DataParam) -> pd.DataFrame: + original_data = pd.read_csv(param.path) + adjusted_data = pd.DataFrame() + + # time + if param.separate_time_stamp: + adjusted_data["time"] = original_data.iloc[:, param.secs_stamp_column] + original_data.iloc[:, param.nsecs_stamp_column] / 10 ** 9 + elif len(str(int(original_data.iloc[0, param.stamp_column]))) > 10: + adjusted_data["time"] = original_data.iloc[:, param.stamp_column] / 10 ** 9 + else: + adjusted_data["time"] = original_data.iloc[:, param.stamp_column] + + # position + adjusted_data["x"] = original_data.iloc[:, param.x_column] + adjusted_data["y"] = original_data.iloc[:, param.y_column] + adjusted_data["z"] = original_data.iloc[:, param.z_column] + + # rotation + round2pipi = lambda v: (v % (math.pi * 2)) - (0 if (v % (math.pi * 2)) < math.pi else (math.pi * 2)) + if param.use_quaternion: + for i in range(len(original_data)): + ref_q_temp = [ + original_data.iloc[i, param.ori_x_column], + original_data.iloc[i, param.ori_y_column], + original_data.iloc[i, param.ori_z_column], + original_data.iloc[i, param.ori_w_column], + ] + ref_e_temp = Rotation.from_quat([ref_q_temp[0], ref_q_temp[1], ref_q_temp[2], ref_q_temp[3]]) + adjusted_data.at[i, "roll"] = ref_e_temp.as_euler("ZYX", degrees=True)[2] + adjusted_data.at[i, "pitch"] = ref_e_temp.as_euler("ZYX", degrees=True)[1] + adjusted_data.at[i, "yaw"] = ref_e_temp.as_euler("ZYX", degrees=True)[0] + elif (not param.use_quaternion) and param.use_radian: + adjusted_data["roll"] = original_data.iloc[:, param.roll_column].map(round2pipi).map(np.rad2deg) + adjusted_data["pitch"] = original_data.iloc[:, param.pitch_column].map(round2pipi).map(np.rad2deg) + adjusted_data["yaw"] = original_data.iloc[:, param.yaw_column].map(round2pipi).map(np.rad2deg) + elif (not param.use_quaternion) and (not param.use_radian): + adjusted_data["roll"] = original_data.iloc[:, param.roll_column].map(np.deg2rad).map(round2pipi).map(np.rad2deg) + adjusted_data["pitch"] = original_data.iloc[:, param.pitch_column].map(np.deg2rad).map(round2pipi).map(np.rad2deg) + adjusted_data["yaw"] = original_data.iloc[:, param.yaw_column].map(np.deg2rad).map(round2pipi).map(np.rad2deg) + + # enu velocity + if param.use_enu_velocity: + adjusted_data["vel_x"] = original_data.iloc[:, param.vel_x_column] + adjusted_data["vel_y"] = original_data.iloc[:, param.vel_y_column] + adjusted_data["vel_z"] = original_data.iloc[:, param.vel_z_column] + + # angular + if param.use_angular: + adjusted_data["angular_x"] = original_data.iloc[:, param.angular_x_column] + adjusted_data["angular_y"] = original_data.iloc[:, param.angular_y_column] + adjusted_data["angular_z"] = original_data.iloc[:, param.angular_z_column] + + # GNSS quality + if param.use_gnss_quality: + adjusted_data["gnss_quality"] = original_data.iloc[:, param.gnss_quality] + + return adjusted_data + + @staticmethod + def read_twist_data(param: DataParam) -> pd.DataFrame: + original_data = pd.read_csv(param.path) + adjusted_data = pd.DataFrame() + + # time + if param.separate_time_stamp: + adjusted_data["time"] = original_data.iloc[:, param.secs_stamp_column] + original_data.iloc[:, param.nsecs_stamp_column] / 10 ** 9 + elif len(str(int(original_data.iloc[0, param.stamp_column]))) > 10: + adjusted_data["time"] = original_data.iloc[:, param.stamp_column] / 10 ** 9 + else: + adjusted_data["time"] = original_data.iloc[:, param.stamp_column] + + # linear + adjusted_data["linear_x"] = original_data.iloc[:, param.linear_x_column] # velocity + adjusted_data["linear_y"] = original_data.iloc[:, param.linear_y_column] + adjusted_data["linear_z"] = original_data.iloc[:, param.linear_z_column] + + # angluar + adjusted_data["angular_x"] = original_data.iloc[:, param.angular_x_column] + adjusted_data["angular_y"] = original_data.iloc[:, param.angular_y_column] + adjusted_data["angular_z"] = original_data.iloc[:, param.angular_z_column] + + return adjusted_data + +def param2pack(ref_param: DataParam, twist_param: DataParam, opt_param: OptParam) -> DataPack: + return DataPack(ref_param, twist_param, opt_param) diff --git a/scripts/util/twist/plotter.py b/scripts/util/twist/plotter.py new file mode 100644 index 0000000..8798fe9 --- /dev/null +++ b/scripts/util/twist/plotter.py @@ -0,0 +1,108 @@ +# graph plotter + +import matplotlib.pyplot as plt +import matplotlib.ticker +from matplotlib.figure import Figure +from functools import reduce +from typing import Dict + +from util.twist.configer import OptParam +from util.twist.packer import DataPack + +def plot_relative_position_error(data_pack: DataPack, opt_param: OptParam) -> Dict[str, Figure]: + fig_rel_pos_err = plt.figure("Relative_Position_Error", figsize=(16, 9), dpi=120) + ax_rel_pos_err = fig_rel_pos_err.add_subplot(111) + + ax_rel_pos_err.plot(data_pack.dr_error["start_distance"], data_pack.dr_error["error_2d"], markersize=1) + ax_rel_pos_err.set_title("Relative Position Error", fontsize=opt_param.title_font_size) + ax_rel_pos_err.set_xlabel("start distance [m]", fontsize=opt_param.label_font_size) + ax_rel_pos_err.set_ylabel("2D error [m]", fontsize=opt_param.label_font_size) + ax_rel_pos_err.tick_params(labelsize=opt_param.ticks_font_size) + ax_rel_pos_err.grid() + + return {"relative_position_error": fig_rel_pos_err} + +def plot_cumulative_error_distribution(data_pack: DataPack, opt_param: OptParam) -> Dict[str, Figure]: + fig_cum_err_dis = plt.figure("Cumulative_Error_Distribution", figsize=(16, 9), dpi=120) + ax_cum_err_dis = fig_cum_err_dis.add_subplot(111) + + ax_cum_err_dis.plot(data_pack.dr_rate["x_label"], data_pack.dr_rate["ErrTra"], marker="s", markersize=10) + ax_cum_err_dis.set_title("Cumulative Error Distribution (relative position)", fontsize=opt_param.title_font_size) + ax_cum_err_dis.set_xlabel("2D error [m]", fontsize=opt_param.label_font_size) + ax_cum_err_dis.set_xscale("log") + ax_cum_err_dis.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + ax_cum_err_dis.set_xticks([0.01, 0.05, 0.1, 0.5, 1.0, 3.0]) + ax_cum_err_dis.set_ylabel("Rate [%]", fontsize=opt_param.label_font_size) + ax_cum_err_dis.tick_params(labelsize=opt_param.ticks_font_size) + ax_cum_err_dis.grid() + + return {"cumulative_error_distribution": fig_cum_err_dis} + +def plot_dr_trajectory(data_pack: DataPack, opt_param: OptParam) -> Dict[str, Figure]: + fig_dr_traj = plt.figure("DR_Trajectory", figsize=(16, 9), dpi=120) + ax_dr_traj = fig_dr_traj.add_subplot(111) + + ax_dr_traj.plot( + data_pack.ref_data["x"] - data_pack.ref_data["x"][0], data_pack.ref_data["y"] - data_pack.ref_data["y"][0], + marker=".", markersize=3, linestyle="None", label=data_pack.ref_label + ) + ax_dr_traj.plot( + data_pack.dr_traj["x"] - data_pack.ref_data["x"][0], data_pack.dr_traj["y"] - data_pack.ref_data["y"][0], + marker="s", markersize=3, linestyle="None", alpha=0.3, label=data_pack.twist_label + ) + # show progress + if opt_param.progress_info: + if opt_param.progress_info == 1: + data = data_pack.ref_data.index + elif opt_param.progress_info == 2: + data = data_pack.ref_data["elapsed"] + elif opt_param.progress_info == 3: + data = data_pack.ref_data["time"] + elif opt_param.progress_info == 4: + data = data_pack.ref_data["distance"] + else: + raise RuntimeError("Unexpected progress info type") + counter = 0 + for i in data_pack.ref_data.index: + if (data[i] - data[0]) < counter: continue + ax_dr_traj.text( + data_pack.ref_data["x"][i] - data_pack.ref_data["x"][0], + data_pack.ref_data["y"][i] - data_pack.ref_data["y"][0], + round(data[i], 2), va="bottom" + ) + counter += opt_param.interval + ax_dr_traj.set_title("DR Trajectory", fontsize=opt_param.title_font_size) + ax_dr_traj.set_xlabel("East [m]", fontsize=opt_param.label_font_size) + ax_dr_traj.set_ylabel("West [m]", fontsize=opt_param.label_font_size) + ax_dr_traj.legend() + ax_dr_traj.tick_params(labelsize=opt_param.ticks_font_size) + ax_dr_traj.grid() + ax_dr_traj.set_aspect("equal") + ax_dr_traj.axis("square") + + return {"dr_trajectory": fig_dr_traj} + +def plot(data_pack: DataPack, opt_param: OptParam) -> Dict[str, Figure]: + figs = reduce(lambda x, y: {**x, **y}, [ + plot_relative_position_error(data_pack, opt_param), + plot_cumulative_error_distribution(data_pack, opt_param), + plot_dr_trajectory(data_pack, opt_param), + ]) + plt.show() + return figs + +def save_df(data_pack: DataPack, opt_param: OptParam) -> None: + cols = ["start_distance", "error_2d"] + data_pack.dr_error[cols].to_csv(f"{opt_param.output_directory}/target_dr_error.csv", header=True, index=False, float_format="%.9f") + +def save_figures(figs: Dict[str, Figure], opt_param: OptParam) -> None: + for name, fig in figs.items(): + fig.savefig(f"{opt_param.output_directory}/{name}.{opt_param.save_extension_type}") + +def save(data_pack: DataPack, figs: Dict[str, Figure], opt_param: OptParam) -> None: + # save dataframes + if opt_param.save_dataframe: + save_df(data_pack, opt_param) + # save figures + if opt_param.save_figures: + save_figures(figs, opt_param) diff --git a/sub_scripts/adjust_time_stamp.py b/sub_scripts/adjust_time_stamp.py deleted file mode 100644 index 1715c05..0000000 --- a/sub_scripts/adjust_time_stamp.py +++ /dev/null @@ -1,137 +0,0 @@ -import sys -import os - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import yaml -from scipy.spatial import KDTree - -sys.path.append(os.path.join(os.path.dirname(__file__), "..")) -# sys.path.append("../scripts") -from scripts.deprecated import extract, yaml_param - - -def input_yaml_param(config_path, target_param, offset_param): - with open(config_path, "r") as yml: - config = yaml.safe_load(yml) - - target_param.separate_time_stamp = config["Target"]["separate_time_stamp"] - target_param.stamp_column = config["Target"]["stamp_column"] - target_param.secs_stamp_column = config["Target"]["secs_stamp_column"] - target_param.nsecs_stamp_column = config["Target"]["nsecs_stamp_column"] - target_param.direct_vel = config["Target"]["direct_vel"] - target_param.vel_colmn = config["Target"]["vel_colmn"] - target_param.x_column = config["Target"]["x_column"] - target_param.y_column = config["Target"]["y_column"] - target_param.z_column = config["Target"]["z_column"] - - offset_param.separate_time_stamp = config["Offset"]["separate_time_stamp"] - offset_param.stamp_column = config["Offset"]["stamp_column"] - offset_param.secs_stamp_column = config["Offset"]["secs_stamp_column"] - offset_param.nsecs_stamp_column = config["Offset"]["nsecs_stamp_column"] - offset_param.direct_vel = config["Offset"]["direct_vel"] - offset_param.vel_colmn = config["Offset"]["vel_colmn"] - offset_param.x_column = config["Offset"]["x_column"] - offset_param.y_column = config["Offset"]["y_column"] - offset_param.z_column = config["Offset"]["z_column"] - - search_range = config["search_range"] - delta = config["delta"] - - return (search_range, delta) - - -def initial_graph(target_param, offset_param): - initial_fig = plt.figure("Initial Graph", figsize=(16, 9), dpi=120) - ax_initial = initial_fig.add_subplot(111) - ax_initial.set_title("Initial Graph") - ax_initial.scatter(target_param.df["time"], target_param.df["vel"], c="k") - ax_initial.scatter(offset_param.df["time"], offset_param.df["vel"], c="r", s=2) - ax_initial.set_xlabel("time[s]") - ax_initial.set_ylabel("data") - ax_initial.grid() - ax_initial.set_aspect("equal") - - -def all_search(target_df, offset_df, search_range, delta): - num_off = search_range / delta - target = target_df.to_numpy() - offset_df["time"] -= search_range - calc_df = offset_df.copy(deep=True) - dist_sum = [] - - # kd_tree - kd_tree = KDTree(target) - for i in range(int(num_off) * 2): - offset = calc_df.to_numpy() - dist_temp, indexes_temp = kd_tree.query(offset) - dist_sum.append(np.sum(dist_temp)) - calc_df["time"] += delta - if i % 10 == 0: - print(i, "/", int(num_off) * 2) - min_idx = dist_sum.index(min(dist_sum)) - offset_df["time"] += delta * min_idx - fit_width = delta * min_idx - search_range - return fit_width - - -def output(fit_width, target_param, offset_param, output_dir): - - if offset_param.separate_time_stamp == True: - offset_param.df_temp.iloc[:, offset_param.secs_stamp_column] = offset_param.df["time"] - offset_param.df_temp.drop(offset_param.df_temp.columns[offset_param.nsecs_stamp_column], axis=1, inplace=True) - else: - offset_param.df_temp.iloc[:, offset_param.stamp_column] = offset_param.df["time"] - - # Output as csv - offset_param.df_temp.to_csv(output_dir + "/after_time_offset.csv", index=False) - - # Output matched graph - fit_fig = plt.figure("Matched Graph", figsize=(16, 9), dpi=120) - ax_fit = fit_fig.add_subplot(111) - ax_fit.set_title("Matched Graph") - ax_fit.scatter(target_param.df["time"], target_param.df["vel"], c="k") - ax_fit.scatter(offset_param.df["time"], offset_param.df["vel"], c="r", s=2) - ax_fit.text(0.1, 1.01, "Offset: {} [s]".format(fit_width), fontsize=15, transform=ax_fit.transAxes) - ax_fit.set_xlabel("time[s]") - ax_fit.set_ylabel("data") - ax_fit.grid() - ax_fit.set_aspect("equal") - print("Completed!!") - plt.show() - - -if __name__ == "__main__": - argv = sys.argv - target_dir = argv[1] - offset_dir = argv[2] - config_path = argv[3] - output_dir = argv[4] - - print("Loading yaml file ...", end="") - target_param = yaml_param.YamlParam() - offset_param = yaml_param.YamlParam() - search_range, delta = input_yaml_param(config_path, target_param, offset_param) - print("Completed!!") - - print("Loading csv files ...", end="") - target_param.df_temp = pd.read_csv(target_dir) - offset_param.df_temp = pd.read_csv(offset_dir) - print("Completed!!") - - print("Extraction ...", end="") - extract.extract_time(target_param) - extract.extract_vel(target_param) - extract.extract_time(offset_param) - extract.extract_vel(offset_param) - del target_param.df_temp - initial_graph(target_param, offset_param) - print("Completed!!") - - print("Find minimum ...", end="") - fit_width = all_search(target_param.df, offset_param.df, search_range, delta) - print("Completed!!") - - print("Output data ...") - output(fit_width, target_param, offset_param, output_dir) diff --git a/sub_scripts/develop/ani_convergence_viewer.py b/sub_scripts/develop/ani_convergence_viewer.py deleted file mode 100644 index 0703a33..0000000 --- a/sub_scripts/develop/ani_convergence_viewer.py +++ /dev/null @@ -1,240 +0,0 @@ -import math -import os -import sys - -import matplotlib.animation as animation -import matplotlib.patches as patches -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import rosbag2_py -import yaml -from rcl_interfaces.msg import Log -from rclpy.serialization import deserialize_message -from rosidl_runtime_py.utilities import get_message -from scipy.spatial.transform import Rotation as R - -sys.path.append(os.path.join(os.path.dirname(__file__), "..")) -# sys.path.append("../scripts") -from scripts.deprecated import adjust, read_ros2bag, yaml_param - -import kl - - -def remove_cm(param, init_aray, converged_array): - delnum = 0 - for i in range(len(init_aray)): - if param.df["time"][i - delnum] != init_aray[i - delnum].time: - del init_aray[i - delnum] - del converged_array[i - delnum] - delnum += 1 - - -def ellipse_compare( - i, - ref_param, - result_param, - ref_init_aray, - ref_converged_array, - result_init_array, - result_converged_array, - kl_list, - mismatch, -): - ax_route.cla() - ax_kl.cla() - ax_ref_conv.cla() - ax_result_conv.cla() - - # route - ax_route.set_title("Route") - ax_route.scatter(result_param.df["x"], result_param.df["y"], c="k", marker="o", s=5, zorder=1) - ax_route.set_xlabel("x [m]") - ax_route.set_ylabel("y [m]") - ax_route.grid() - - # KL - ax_kl.set_title("KL divergence") - ax_kl.plot(ref_param.df["time"] - ref_param.df["time"][0], kl_list, marker="o", c="k", markersize=1, zorder=1) - ax_kl.text( - 0.05, 1.03, "mismatch:{}/{}".format(mismatch, int(len(result_param.df))), fontsize=10, transform=ax_kl.transAxes - ) - ax_kl.set_ylim(-1, max(kl_list) + 10) - ax_kl.set_xlabel("time[s]") - ax_kl.set_ylabel("KL divergence") - ax_kl.grid() - - # convergence evaluation - ax_ref_conv.set_title(ref_param.df["time"][i]) - ax_ref_conv.set_xlim(-3, 3) - ax_ref_conv.set_ylim(-3, 3) - ax_ref_conv.set_xlabel("x [m]") - ax_ref_conv.set_ylabel("y [m]") - ax_ref_conv.grid() - ax_ref_conv.set_aspect("equal") - - # propsoed method - ax_result_conv.set_title(result_param.df["time"][i]) - ax_result_conv.set_xlim(-3, 3) - ax_result_conv.set_ylim(-3, 3) - ax_result_conv.set_xlabel("x [m]") - ax_result_conv.set_ylabel("y [m]") - ax_result_conv.grid() - ax_result_conv.set_aspect("equal") - - # route - ax_route.scatter(result_param.df["x"][i], result_param.df["y"][i], c="r", marker="o", s=15, zorder=2) - # KL - ax_kl.scatter(ref_param.df["time"][i] - ref_param.df["time"][0], kl_list[i], c="r", marker="o", s=15, zorder=2) - - # convergence evaluation - trans_ref_init_x = ref_init_aray[i].x - ref_param.df["x"][i] - trans_ref_init_y = ref_init_aray[i].y - ref_param.df["y"][i] - trans_ref_conv_x = ref_converged_array[i].x - ref_param.df["x"][i] - trans_ref_conv_y = ref_converged_array[i].y - ref_param.df["y"][i] - ax_ref_conv.scatter(trans_ref_init_x, trans_ref_init_y, c="r", marker="o", s=5, zorder=2) - ax_ref_conv.scatter(trans_ref_conv_x, trans_ref_conv_y, c="k", marker="o", s=10, zorder=3) - line_x = [trans_ref_init_x, trans_ref_conv_x] - line_y = [trans_ref_init_y, trans_ref_conv_y] - ax_ref_conv.plot(line_x, line_y, "g-", linewidth=0.2, zorder=1) - e = patches.Ellipse( - xy=(0, 0), - width=ref_param.df["ellipse_long"][i] * 2, - height=ref_param.df["ellipse_short"][i] * 2, - angle=math.degrees(ref_param.df["ellipse_yaw"][i]), - fill=False, - ec="r", - zorder=4, - ) - ax_ref_conv.add_patch(e) - - # proposed method - trans_result_init_x = result_init_array[i].x - result_param.df["x"][i] - trans_result_init_y = result_init_array[i].y - result_param.df["y"][i] - trans_result_conv_x = result_converged_array[i].x - result_param.df["x"][i] - trans_result_conv_y = result_converged_array[i].y - result_param.df["y"][i] - ax_result_conv.scatter(trans_result_init_x, trans_result_init_y, c="r", marker="o", s=5, zorder=2) - ax_result_conv.scatter(trans_result_conv_x, trans_result_conv_y, c="k", marker="o", s=10, zorder=3) - line_x = [trans_result_init_x, trans_result_conv_x] - line_y = [trans_result_init_y, trans_result_conv_y] - ax_result_conv.plot(line_x, line_y, "g-", linewidth=0.2, zorder=1) - e = patches.Ellipse( - xy=(0, 0), - width=result_param.df["ellipse_long"][i] * 2, - height=result_param.df["ellipse_short"][i] * 2, - angle=math.degrees(result_param.df["ellipse_yaw"][i]), - fill=False, - ec="r", - zorder=4, - ) - ax_result_conv.add_patch(e) - - print("\r", str(i + 1) + " / " + str(len(ref_param.df)), end="") - - -if __name__ == "__main__": - - argv = sys.argv - ref_path = argv[1] - result_path = argv[2] - config_path = argv[3] - output_dir = argv[4] - - print("Loading yaml file ...", end="") - with open(config_path, "r") as yml: - config = yaml.safe_load(yml) - ref_param = yaml_param.YamlParam() - result_param = yaml_param.YamlParam() - save_param = yaml_param.SaveParam() - op_param = yaml_param.OpParam() - adjust.input_yaml_ros2(config, ref_param, "Reference") - adjust.set_tf(config, ref_param, "Reference") - adjust.input_yaml_ros2(config, result_param, "Result") - adjust.set_tf(config, result_param, "Result") - adjust.input_save_param(config, save_param) - adjust.input_op_param_ros2(config, op_param) - op_param.display_ellipse = True - print("Completed!!") - - print("Loading ros2 bag files ...", end="") - read_ros2bag.read_ros2bag(ref_path, ref_param, op_param) - read_ros2bag.read_ros2bag(result_path, result_param, op_param) - print("Completed!!") - - ref_init_aray = [] - ref_converged_array = [] - result_init_array = [] - result_converged_array = [] - print("Loading convergence evaluation topic ...", end="") - read_ros2bag.read_pose_array(ref_path, ref_init_aray, "/ndt_convergence_evaluator_initial_array") - read_ros2bag.read_pose_array(ref_path, ref_converged_array, "/ndt_convergence_evaluator_result_array") - read_ros2bag.read_pose_array( - result_path, result_init_array, "/localization/pose_estimator/limited_convergence_evaluation_initial" - ) - read_ros2bag.read_pose_array( - result_path, result_converged_array, "/localization/pose_estimator/limited_convergence_evaluation" - ) - print("Completed!!") - - print("Adjusting the start time ...", end="") - if adjust.adjust_start_time(ref_param, result_param) == -1: - sys.exit(1) - print("Completed!!") - - print("Adjusting the end time ...", end="") - adjust.adjust_end_time(ref_param, result_param) - print("Completed!!") - - print("Synchronizing time...", end="") - ref_param.df, result_param.df = adjust.sync_time(ref_param, result_param, op_param) - # del ref_param.df_temp, result_param.df_temp - print("Completed!!") - - print("Calculating error ellipse ...", end="") - adjust.calc_ellipse(ref_param) - print("Completed!!") - - print("Calculating error ellipse ...", end="") - adjust.calc_ellipse(result_param) - print("Completed!!") - - print("Calculating KL divergence for 2D distributions...", end="") - kl_list, mismatch = kl.calc_kl(ref_param, result_param) - print("Completed!!") - - print("Remove the components...", end="") - remove_cm(ref_param, ref_init_aray, ref_converged_array) - remove_cm(result_param, result_init_array, result_converged_array) - print("Completed!!") - - print(len(ref_param.df)) - print(len(result_param.df)) - - print("Output convergence evaluation graph...", end="") - - conv_fig = plt.figure("Result", figsize=(16, 9), dpi=120) - ax_route = conv_fig.add_subplot(223) - ax_kl = conv_fig.add_subplot(221) - ax_ref_conv = conv_fig.add_subplot(222) - ax_result_conv = conv_fig.add_subplot(224) - - ani = animation.FuncAnimation( - conv_fig, - ellipse_compare, - fargs=( - ref_param, - result_param, - ref_init_aray, - ref_converged_array, - result_init_array, - result_converged_array, - kl_list, - mismatch, - ), - interval=100, - frames=len(ref_param.df), - ) - # plt.show() - ani.save(output_dir + "/animation.mp4", writer="ffmpeg") - - print("Completed!!") diff --git a/sub_scripts/develop/conv_trj.py b/sub_scripts/develop/conv_trj.py deleted file mode 100644 index 27eddb7..0000000 --- a/sub_scripts/develop/conv_trj.py +++ /dev/null @@ -1,42 +0,0 @@ -import os -import sys - -import matplotlib.pyplot as plt -from matplotlib import cm -from rclpy.serialization import deserialize_message -from rosidl_runtime_py.utilities import get_message - -sys.path.append(os.path.join(os.path.dirname(__file__), "..")) -# sys.path.append("../scripts") -from scripts.deprecated import adjust, read_ros2bag, yaml_param - -if __name__ == "__main__": - - argv = sys.argv - bag_path = argv[1] - - print("Read convergence_trajectory topic") - frame = [] - read_ros2bag.read_pose_array(bag_path, frame, "/localization/pose_estimator/convergence_trajectory") - - print("Output graph") - conv_trj_fig = plt.figure("Convergence Trajectory", figsize=(16, 9), dpi=120) - ax_ref_conv = conv_trj_fig.add_subplot(111) - itr = [] - result_x = [] - result_y = [] - type(len(frame[1].x)) - for i in range(len(frame)): - ax_ref_conv.plot(frame[i].x, frame[i].y, marker="o", c="k", markersize=2, linewidth=0.5, zorder=1) - result_x.append(frame[i].x[-1]) - result_y.append(frame[i].y[-1]) - itr.append(len(frame[i].x)) - - scatter = ax_ref_conv.scatter(result_x, result_y, c=itr, cmap=cm.jet, s=12, linewidth=0, zorder=2) - cbar = plt.colorbar(scatter, label="Iteration") - - ax_ref_conv.set_xlabel("x [m]") - ax_ref_conv.set_ylabel("y [m]") - ax_ref_conv.grid() - ax_ref_conv.set_aspect("equal") - plt.show() diff --git a/sub_scripts/develop/convergence_viewer.py b/sub_scripts/develop/convergence_viewer.py deleted file mode 100644 index 8f84a00..0000000 --- a/sub_scripts/develop/convergence_viewer.py +++ /dev/null @@ -1,270 +0,0 @@ -import math -import os -import sys - -import matplotlib.patches as patches -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import rosbag2_py -import yaml -from rcl_interfaces.msg import Log -from rclpy.serialization import deserialize_message -from rosidl_runtime_py.utilities import get_message -from scipy.spatial.transform import Rotation as R - -sys.path.append(os.path.join(os.path.dirname(__file__), "..")) -# sys.path.append("../scripts") -from scripts.deprecated import adjust, read_ros2bag, yaml_param - - -class FrameCM: - def __init__(self): - self.time = "" - self.x = [] - self.y = [] - self.z = [] - self.roll = [] - self.pitch = [] - self.yaw = [] - - -def read_ce_topic(bag_file, frame_array, topic_name): - bag_path = str(bag_file) - storage_options = rosbag2_py.StorageOptions(uri=bag_path, storage_id="sqlite3") - converter_options = rosbag2_py.ConverterOptions(input_serialization_format="cdr", output_serialization_format="cdr") - - reader = rosbag2_py.SequentialReader() - reader.open(storage_options, converter_options) - topic_types = reader.get_all_topics_and_types() - - type_map = {topic_types[i].name: topic_types[i].type for i in range(len(topic_types))} - - storage_filter = rosbag2_py.StorageFilter(topics=[topic_name]) - reader.set_filter(storage_filter) - - while reader.has_next(): - (topic, data, t) = reader.read_next() - msg_type = get_message(type_map[topic]) - msg = deserialize_message(data, msg_type) - - fram_cm = FrameCM() - - fram_cm.time = (msg.header.stamp.sec) + (msg.header.stamp.nanosec) / 10**9 - for i in range(len(msg.poses)): - q_temp = [ - msg.poses[i].orientation.x, - msg.poses[i].orientation.y, - msg.poses[i].orientation.z, - msg.poses[i].orientation.w, - ] - e_temp = R.from_quat([q_temp[0], q_temp[1], q_temp[2], q_temp[3]]) - fram_cm.x.append(msg.poses[i].position.x) - fram_cm.y.append(msg.poses[i].position.y) - fram_cm.z.append(msg.poses[i].position.z) - fram_cm.roll.append(e_temp.as_euler("ZYX", degrees=False)[2]) - fram_cm.pitch.append(e_temp.as_euler("ZYX", degrees=False)[1]) - fram_cm.yaw.append(e_temp.as_euler("ZYX", degrees=False)[0]) - frame_array.append(fram_cm) - - -def calc_kl(ref_param, result_param, output_dir): - kl_list = [] - mismatch = 0 - for i in range(len(result_param.df)): - - ref_cov = np.array( - [ - [ref_param.df.at[i, "cov_xx"], ref_param.df.at[i, "cov_xy"]], - [ref_param.df.at[i, "cov_yx"], ref_param.df.at[i, "cov_yy"]], - ] - ) - result_cov = np.array( - [ - [result_param.df.at[i, "cov_xx"], result_param.df.at[i, "cov_xy"]], - [result_param.df.at[i, "cov_yx"], result_param.df.at[i, "cov_yy"]], - ] - ) - det_ref_cov = np.linalg.det(ref_cov) - det_result_cov = np.linalg.det(result_cov) - inv_ref_cov = np.linalg.inv(ref_cov) - KL = math.log(det_ref_cov / det_result_cov) + np.trace(np.dot(inv_ref_cov, result_cov)) - 2 - kl_list.append(KL) - if KL > 10: - mismatch += 1 - - fig_kl = plt.figure("KL divergence", figsize=(16, 9), dpi=120) - ax_kl = fig_kl.add_subplot(111) - ax_kl.set_title("KL divergence") - ax_kl.plot(ref_param.df["time"] - ref_param.df.at[0, "time"], kl_list, marker="o", c="b", markersize=2) - ax_kl.text( - 0.1, 1.05, "mismatch:{}/{}".format(mismatch, int(len(result_param.df))), fontsize=15, transform=ax_kl.transAxes - ) - ax_kl.set_xlabel("time[s]") - ax_kl.set_ylabel("KL divergence") - ax_kl.grid() - fig_kl.savefig(output_dir + "/kl.png") - - -def remove_cm(param, init_aray, converged_array): - delnum = 0 - for i in range(len(init_aray)): - if param.df["time"][i - delnum] != init_aray[i - delnum].time: - del init_aray[i - delnum] - del converged_array[i - delnum] - delnum += 1 - - -def ellipse_compare( - ref_param, result_param, ref_init_aray, ref_converged_array, result_init_array, result_converged_array, output_dir -): - conv_fig = plt.figure("Result", figsize=(16, 9), dpi=120) - ax_ref_conv = conv_fig.add_subplot(121) - ax_result_conv = conv_fig.add_subplot(122) - for i in range(len(ref_param.df)): - ax_ref_conv.cla() - ax_result_conv.cla() - trans_ref_init_x = ref_init_aray[i].x - ref_param.df["x"][i] - trans_ref_init_y = ref_init_aray[i].y - ref_param.df["y"][i] - trans_ref_conv_x = ref_converged_array[i].x - ref_param.df["x"][i] - trans_ref_conv_y = ref_converged_array[i].y - ref_param.df["y"][i] - ax_ref_conv.scatter(trans_ref_init_x, trans_ref_init_y, c="r", marker="o", s=5, zorder=2) - ax_ref_conv.scatter(trans_ref_conv_x, trans_ref_conv_y, c="k", marker="o", s=10, zorder=3) - line_x = [trans_ref_init_x, trans_ref_conv_x] - line_y = [trans_ref_init_y, trans_ref_conv_y] - ax_ref_conv.plot(line_x, line_y, "g-", linewidth=0.2, zorder=1) - e = patches.Ellipse( - xy=(0, 0), - width=ref_param.df["ellipse_long"][i] * 2, - height=ref_param.df["ellipse_short"][i] * 2, - angle=math.degrees(ref_param.df["ellipse_yaw"][i]), - fill=False, - ec="r", - zorder=4, - ) - ax_ref_conv.add_patch(e) - ax_ref_conv.set_xlim(-3, 3) - ax_ref_conv.set_ylim(-3, 3) - ax_ref_conv.set_xlabel("x [m]") - ax_ref_conv.set_ylabel("y [m]") - ax_ref_conv.grid() - ax_ref_conv.set_aspect("equal") - - trans_result_init_x = result_init_array[i].x - result_param.df["x"][i] - trans_result_init_y = result_init_array[i].y - result_param.df["y"][i] - trans_result_conv_x = result_converged_array[i].x - result_param.df["x"][i] - trans_result_conv_y = result_converged_array[i].y - result_param.df["y"][i] - ax_result_conv.scatter(trans_result_init_x, trans_result_init_y, c="r", marker="o", s=5, zorder=2) - ax_result_conv.scatter(trans_result_conv_x, trans_result_conv_y, c="k", marker="o", s=10, zorder=3) - line_x = [trans_result_init_x, trans_result_conv_x] - line_y = [trans_result_init_y, trans_result_conv_y] - ax_result_conv.plot(line_x, line_y, "g-", linewidth=0.2, zorder=1) - e = patches.Ellipse( - xy=(0, 0), - width=result_param.df["ellipse_long"][i] * 2, - height=result_param.df["ellipse_short"][i] * 2, - angle=math.degrees(result_param.df["ellipse_yaw"][i]), - fill=False, - ec="r", - zorder=4, - ) - ax_result_conv.add_patch(e) - ax_result_conv.set_xlim(-3, 3) - ax_result_conv.set_ylim(-3, 3) - ax_result_conv.set_xlabel("x [m]") - ax_result_conv.set_ylabel("y [m]") - ax_result_conv.grid() - ax_result_conv.set_aspect("equal") - - conv_fig.savefig(output_dir + "/" + str(i) + ".png") - - -if __name__ == "__main__": - - argv = sys.argv - ref_path = argv[1] - result_path = argv[2] - config_path = argv[3] - output_dir = argv[4] - - print("Loading yaml file ...", end="") - with open(config_path, "r") as yml: - config = yaml.safe_load(yml) - ref_param = yaml_param.YamlParam() - result_param = yaml_param.YamlParam() - save_param = yaml_param.SaveParam() - op_param = yaml_param.OpParam() - adjust.input_yaml_ros2(config, ref_param, "Reference") - adjust.set_tf(config, ref_param, "Reference") - adjust.input_yaml_ros2(config, result_param, "Result") - adjust.set_tf(config, result_param, "Result") - adjust.input_save_param(config, save_param) - adjust.input_op_param_ros2(config, op_param) - op_param.display_ellipse = True - print("Completed!!") - - print("Loading ros2 bag files ...", end="") - read_ros2bag.read_ros2bag(ref_path, ref_param, op_param) - read_ros2bag.read_ros2bag(result_path, result_param, op_param) - print("Completed!!") - - ref_init_aray = [] - ref_converged_array = [] - result_init_array = [] - result_converged_array = [] - print("Loading convergence evaluation topic ...", end="") - read_ros2bag.read_pose_array(ref_path, ref_init_aray, "/ndt_convergence_evaluator_initial_array") - read_ros2bag.read_pose_array(ref_path, ref_converged_array, "/ndt_convergence_evaluator_result_array") - read_ros2bag.read_pose_array( - result_path, result_init_array, "/localization/pose_estimator/limited_convergence_evaluation_initial" - ) - read_ros2bag.read_pose_array( - result_path, result_converged_array, "/localization/pose_estimator/limited_convergence_evaluation" - ) - print("Completed!!") - - print("Adjusting the start time ...", end="") - if adjust.adjust_start_time(ref_param, result_param) == -1: - sys.exit(1) - print("Completed!!") - - print("Adjusting the end time ...", end="") - adjust.adjust_end_time(ref_param, result_param) - print("Completed!!") - - print("Synchronizing time...", end="") - ref_param.df, result_param.df = adjust.sync_time(ref_param, result_param, op_param) - # del ref_param.df_temp, result_param.df_temp - print("Completed!!") - - print("Calculating error ellipse ...", end="") - adjust.calc_ellipse(ref_param) - print("Completed!!") - - print("Calculating error ellipse ...", end="") - adjust.calc_ellipse(result_param) - print("Completed!!") - - print("Calculating KL divergence for 2D distributions...", end="") - calc_kl(ref_param, result_param, output_dir) - print("Completed!!") - - print("Remove the components...", end="") - remove_cm(ref_param, ref_init_aray, ref_converged_array) - remove_cm(result_param, result_init_array, result_converged_array) - print("Completed!!") - - print(len(ref_param.df)) - print(len(result_param.df)) - - print("Output convergence evaluation graph...", end="") - ellipse_compare( - ref_param, - result_param, - ref_init_aray, - ref_converged_array, - result_init_array, - result_converged_array, - output_dir, - ) - print("Completed!!") diff --git a/sub_scripts/develop/kl.py b/sub_scripts/develop/kl.py deleted file mode 100644 index 1e87ce6..0000000 --- a/sub_scripts/develop/kl.py +++ /dev/null @@ -1,101 +0,0 @@ -import math -import os -import sys - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import yaml - -sys.path.append(os.path.join(os.path.dirname(__file__), "..")) -# sys.path.append("../scripts") -from scripts.deprecated import adjust, read_ros2bag, yaml_param - - -def calc_kl(ref_param, result_param): - kl_list = [] - mismatch = 0 - for i in range(len(result_param.df)): - - ref_cov = np.array( - [ - [ref_param.df.at[i, "cov_xx"], ref_param.df.at[i, "cov_xy"]], - [ref_param.df.at[i, "cov_yx"], ref_param.df.at[i, "cov_yy"]], - ] - ) - result_cov = np.array( - [ - [result_param.df.at[i, "cov_xx"], result_param.df.at[i, "cov_xy"]], - [result_param.df.at[i, "cov_yx"], result_param.df.at[i, "cov_yy"]], - ] - ) - det_ref_cov = np.linalg.det(ref_cov) - det_result_cov = np.linalg.det(result_cov) - inv_ref_cov = np.linalg.inv(ref_cov) - inv_result_cov = np.linalg.inv(result_cov) - # KL = math.log(det_ref_cov / det_result_cov) + np.trace(np.dot(inv_ref_cov, result_cov)) - 2 - KL = math.log(det_result_cov / det_ref_cov) + np.trace(np.dot(inv_result_cov, ref_cov)) - 2 - kl_list.append(KL) - if KL > 10: - mismatch += 1 - return kl_list, mismatch - - -if __name__ == "__main__": - - argv = sys.argv - ref_path = argv[1] - result_path = argv[2] - config_path = argv[3] - output_dir = argv[4] - - print("Loading yaml file ...", end="") - with open(config_path, "r") as yml: - config = yaml.safe_load(yml) - ref_param = yaml_param.YamlParam() - result_param = yaml_param.YamlParam() - save_param = yaml_param.SaveParam() - op_param = yaml_param.OpParam() - adjust.input_yaml_ros2(config, ref_param, "Reference") - adjust.set_tf(config, ref_param, "Reference") - adjust.input_yaml_ros2(config, result_param, "Result") - adjust.set_tf(config, result_param, "Result") - adjust.input_save_param(config, save_param) - adjust.input_op_param_ros2(config, op_param) - op_param.display_ellipse = True - print("Completed!!") - - print("Loading ros2 bag files ...", end="") - read_ros2bag.read_ros2bag(ref_path, ref_param, op_param) - read_ros2bag.read_ros2bag(result_path, result_param, op_param) - print("Completed!!") - - print("Adjusting the start time ...", end="") - if adjust.adjust_start_time(ref_param, result_param) == -1: - sys.exit(1) - print("Completed!!") - - print("Adjusting the end time ...", end="") - adjust.adjust_end_time(ref_param, result_param) - print("Completed!!") - - print("Synchronizing time...", end="") - ref_param.df, result_param.df = adjust.sync_time(ref_param, result_param, op_param) - del ref_param.df_temp, result_param.df_temp - print("Completed!!") - - print("Calculating KL divergence for 2D distributions...", end="") - kl_list, mismatch = calc_kl(ref_param, result_param) - - fig_kl = plt.figure("KL divergence", figsize=(16, 9), dpi=120) - ax_kl = fig_kl.add_subplot(111) - ax_kl.set_title("KL divergence") - ax_kl.plot(ref_param.df["time"] - ref_param.df.at[0, "time"], kl_list, marker="o", c="b", markersize=2) - ax_kl.text( - 0.1, 1.05, "mismatch:{}/{}".format(mismatch, int(len(result_param.df))), fontsize=15, transform=ax_kl.transAxes - ) - ax_kl.set_xlabel("time[s]") - ax_kl.set_ylabel("KL divergence") - ax_kl.grid() - fig_kl.savefig(output_dir + "/kl.png") - plt.show() diff --git a/sub_scripts/sub_eagleye_evaluation.py b/sub_scripts/sub_eagleye_evaluation.py new file mode 100755 index 0000000..69ff2e7 --- /dev/null +++ b/sub_scripts/sub_eagleye_evaluation.py @@ -0,0 +1,123 @@ +# Copyright (c) 2022, Map IV, Inc. +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# * Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# * Neither the name of the Map IV, Inc. nor the names of its contributors +# may be used to endorse or promote products derived from this software +# without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL COPYRIGHT HOLDER BE LIABLE FOR ANY +# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +# sub_eagleye_evaluation.py +# Author MapIV Hoda + +import argparse +import yaml +import pandas as pd +import sys +import matplotlib.pyplot as plt + +import util_eagleye.preprocess as util_prepro +import util_eagleye.calc as util_calc +import util_eagleye.plot as util_plot + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("input_file", action="store") + parser.add_argument("input_yaml", action="store") + args= parser.parse_args() + print(args) + input_ref_path=sys.argv[1] + yaml_path=sys.argv[2] + + yaml_path_str: str = yaml_path + with open(yaml_path_str,"r") as yml: + config = yaml.safe_load(yml) + + # set param + missing_gnss_type = config["eagleye_log"]["missing_gnss_type"] + reverse_imu = config["param"]["reverse_imu_flag"] + plane = config["param"]["plane_num"] + plot_text_data = config["evaluation_plot"]["plot_text_data"] + plot_text_step = config["evaluation_plot"]["plot_text_step"] + data_name = config["param"]["data_name_param"] + font_size = config["param"]["font_size_param"] + + print('plane',plane) + + eagleye_df,raw_df = util_prepro.set_log_df(input_ref_path,plane,config) + print("set eagleye_data") + + eagleye_ecef_base = pd.concat([eagleye_df['ecef_base_x'],eagleye_df['ecef_base_y'],eagleye_df['ecef_base_z']],axis=1) + for i in range(len(eagleye_ecef_base)): + if not eagleye_ecef_base['ecef_base_x'][i] == 0 and not eagleye_ecef_base['ecef_base_y'][i] == 0: + org_x = eagleye_ecef_base['ecef_base_x'][i] + org_y = eagleye_ecef_base['ecef_base_y'][i] + org_z = eagleye_ecef_base['ecef_base_z'][i] + org_xyz = [org_x,org_y,org_z] + break + + eagleye_xyz = pd.concat([eagleye_df['x'],eagleye_df['y'],eagleye_df['z']],axis=1) + + raw_llh = pd.concat([raw_df['latitude'],raw_df['longitude'],raw_df['altitude']],axis=1) + raw_xyz = util_prepro.latlon_to_19(raw_llh,plane) + + rtk_llh_tmp = pd.concat([raw_df['rtk_latitude'],raw_df['rtk_longitude'],raw_df['rtk_altitude']],axis=1) + rtk_llh = rtk_llh_tmp.rename(columns={'rtk_latitude': 'latitude', 'rtk_longitude': 'longitude','rtk_altitude': 'altitude'}) + rtk_xyz = util_prepro.latlon_to_19(rtk_llh,plane) + + raw_xyz_vel = pd.concat([raw_df['vel_x'],raw_df['vel_y'],raw_df['vel_z']],axis=1) + vel = util_calc.xyz2enu_vel(raw_xyz_vel,org_xyz) + dopplor_heading = util_calc.calc_dopplor_heading(vel) + dopplor = pd.concat([raw_df['elapsed_time'],vel, dopplor_heading],axis=1) + + eagleye_plot_rpy = pd.concat([eagleye_df['roll'],eagleye_df['pitch'],eagleye_df['yaw']],axis=1) + eagleye_6dof = pd.concat([eagleye_xyz,eagleye_plot_rpy],axis=1) + raw_6dof = pd.concat([raw_xyz,dopplor_heading],axis=1) + + if missing_gnss_type == 0: + util_plot.plot_6DoF_single(eagleye_df['elapsed_time'],raw_df['elapsed_time'],raw_df['elapsed_time'], eagleye_xyz, rtk_xyz, raw_xyz, eagleye_plot_rpy,dopplor, font_size) + util_plot.plot_traj_text_tree('2D Trajectory', raw_xyz, rtk_xyz, eagleye_xyz, eagleye_df[plot_text_data], plot_text_step, font_size, data_name, "gnss rtk data(nmea)") + util_plot.plot_traj_qual(eagleye_xyz,eagleye_df['qual'], eagleye_df[plot_text_data], plot_text_step, font_size) + util_plot.plot_traj_3d_three(raw_xyz, rtk_xyz, eagleye_xyz, font_size, data_name, "gnss rtk data(nmea)") + elif missing_gnss_type == 1: + util_plot.plot_6DoF(eagleye_df['elapsed_time'], eagleye_6dof, rtk_xyz , data_name, "gnss rtk data(nmea)",font_size) + util_plot.plot_traj_text('2D Trajectory', rtk_xyz, eagleye_xyz, eagleye_df[plot_text_data], plot_text_step, font_size, data_name, "gnss rtk data(nmea)") + util_plot.plot_traj_qual(eagleye_xyz,eagleye_df['qual'], eagleye_df[plot_text_data], plot_text_step, font_size) + util_plot.plot_traj_3d(rtk_xyz, eagleye_xyz, font_size, data_name, "gnss rtk data(nmea)") + elif missing_gnss_type == 2: + util_plot.plot_6DoF(eagleye_df['elapsed_time'], eagleye_6dof, raw_6dof , data_name, "gnss raw data(rtklib)",font_size) + util_plot.plot_traj_text('2D Trajectory', rtk_xyz, eagleye_xyz, eagleye_df[plot_text_data], plot_text_step, font_size, data_name, "gnss raw data(rtklib)") + util_plot.plot_traj_3d(raw_xyz, eagleye_xyz, font_size, data_name, "gnss raw data(rtklib)") + + fig2 = plt.figure() + ax_sf = fig2.add_subplot(2, 1, 1) + util_plot.plot_one(ax_sf, eagleye_df, 'elapsed_time', 'sf', 'Velocity scal factor', 'Time [s]', 'Velocity scal factor []', "None", 1, font_size) + + ax_vel = fig2.add_subplot(2, 1, 2) + ax_vel.set_title('Velocity') + ax_vel.plot(dopplor['elapsed_time'] , dopplor['velocity'] , marker="s",linestyle="None",markersize=1 , color = "green", label="dopplor") + ax_vel.plot(raw_df['elapsed_time'] , raw_df['velocity'] , marker=".",linestyle="None",markersize=1, color = "red", label="can velocity") + ax_vel.plot(eagleye_df['elapsed_time'] , eagleye_df['velocity'] , marker="s",linestyle="None",markersize=1,alpha=0.3 , color = "blue", label="eagleye") + ax_vel.set_xlabel('Time [s]') + ax_vel.set_ylabel('Velocity [m/s]') + ax_vel.legend(loc='upper right') + ax_vel.grid() + + plt.show() + \ No newline at end of file diff --git a/sub_scripts/sub_ndt_evaluation.py b/sub_scripts/sub_ndt_evaluation.py index 0747548..2e41fcd 100644 --- a/sub_scripts/sub_ndt_evaluation.py +++ b/sub_scripts/sub_ndt_evaluation.py @@ -2,6 +2,7 @@ import os import sys +import matplotlib.patches as patches import matplotlib.pyplot as plt import numpy as np import pandas as pd @@ -12,7 +13,7 @@ sys.path.append(os.path.join(os.path.dirname(__file__), "..")) # sys.path.append("../scripts") -from scripts.deprecated import read_ros2bag +from scripts.deprecated import adjust, read_ros2bag, yaml_param class BagParam: @@ -28,131 +29,327 @@ def __init__(self): self.df_temp = pd.DataFrame() +def set_param(pose_param, op_param): + pose_param.topic = "" + pose_param.bag_id = "sqlite3" + pose_param.bag_format = "cdr" + pose_param.tf_time = 0.0 + pose_param.tf_x = 0.0 + pose_param.tf_y = 0.0 + pose_param.tf_z = 0.0 + pose_param.tf_roll = 0.0 + pose_param.tf_pitch = 0.0 + pose_param.tf_yaw = 0.0 + pose_param.inv_roll = 1.0 + pose_param.inv_pitch = 1.0 + pose_param.inv_yaw = 1.0 + + op_param.display_ellipse = True + + +def plot_trajectory_nvtl(pose_param, nvtl, output_dir): + for i in range(0, len(pose_param.df)): + search_sync = abs(nvtl.df_temp.iloc[:, 0] - pose_param.df.iloc[i, 0]) + sync_id = search_sync.idxmin() + nvtl.df = nvtl.df.append(nvtl.df_temp.iloc[sync_id, :], ignore_index=True) + nvtl.df.reset_index(inplace=True, drop=True) + + fig_trj_nvtl = plt.figure("Trajectory and NVTL", figsize=(16, 9), dpi=120) + ax_trj_nvtl = fig_trj_nvtl.add_subplot(111) + ax_trj_nvtl.set_title("Trajectory and NVTL") + scatter_nvtl = ax_trj_nvtl.scatter( + pose_param.df["x"], pose_param.df["y"], c=nvtl.df["data"], vmin=0.0, vmax=5, cmap="jet" + ) + plt.colorbar(scatter_nvtl, label="NVTL") + ax_trj_nvtl.set_xlabel("x[m]") + ax_trj_nvtl.set_ylabel("y[m]") + ax_trj_nvtl.set_aspect("equal") + ax_trj_nvtl.grid() + fig_trj_nvtl.savefig(output_dir + "/trj_nvtl.png") + + +def plot_trajectory_tp(pose_param, tp, output_dir): + for i in range(0, len(pose_param.df)): + search_sync = abs(tp.df_temp.iloc[:, 0] - pose_param.df.iloc[i, 0]) + sync_id = search_sync.idxmin() + tp.df = tp.df.append(tp.df_temp.iloc[sync_id, :], ignore_index=True) + tp.df.reset_index(inplace=True, drop=True) + + fig_trj_tp = plt.figure("Trajectory and TP", figsize=(16, 9), dpi=120) + ax_trj_tp = fig_trj_tp.add_subplot(111) + ax_trj_tp.set_title("Trajectory and TP") + scatter_tp = ax_trj_tp.scatter( + pose_param.df["x"], pose_param.df["y"], c=tp.df["data"], vmin=0.0, vmax=7.0, cmap="jet" + ) + plt.colorbar(scatter_tp, label="TP") + ax_trj_tp.set_xlabel("x[m]") + ax_trj_tp.set_ylabel("y[m]") + ax_trj_tp.set_aspect("equal") + ax_trj_tp.grid() + fig_trj_tp.savefig(output_dir + "/trj_tp.png") + + +def plot_nvtl(nvtl, output_dir): + fig_nvtl = plt.figure("NVTL", figsize=(16, 9), dpi=120) + ax_nvtl = fig_nvtl.add_subplot(111) + ax_nvtl.set_title("NVTL") + ax_nvtl.plot(nvtl.df_temp["time"] - nvtl.df_temp.at[0, "time"], nvtl.df_temp["data"], marker="o", markersize=2) + ax_nvtl.set_xlabel("time[s]") + ax_nvtl.set_ylabel("NVTL") + ax_nvtl.set_ylim(0, 5) + ax_nvtl.grid() + fig_nvtl.savefig(output_dir + "/nvtl.png") + + +def plot_tp(tp, output_dir): + fig_tp = plt.figure("TP", figsize=(16, 9), dpi=120) + ax_tp = fig_tp.add_subplot(111) + ax_tp.set_title("TP") + ax_tp.plot(tp.df_temp["time"] - tp.df_temp.at[0, "time"], tp.df_temp["data"], marker="o", markersize=2) + ax_tp.set_xlabel("time[s]") + ax_tp.set_ylabel("TP") + ax_tp.set_ylim(0, 7) + ax_tp.grid() + fig_tp.savefig(output_dir + "/tp.png") + + +def plot_exe_time(exe_time, output_dir): + fig_exe_time = plt.figure("Execution Time", figsize=(16, 9), dpi=120) + ax_exe_time = fig_exe_time.add_subplot(111) + ax_exe_time.set_title("Execution Time") + ax_exe_time.plot( + exe_time.df_temp["time"] - exe_time.df_temp.at[0, "time"], + exe_time.df_temp["data"], + marker="o", + markersize=2, + ) + ax_exe_time.set_xlabel("time[s]") + ax_exe_time.set_ylabel("Execution Time") + ax_exe_time.set_ylim(0, 150) + ax_exe_time.grid() + fig_exe_time.savefig(output_dir + "/exe_time.png") + + +def plot_itr(itr, output_dir): + fig_itr = plt.figure("Iteration", figsize=(16, 9), dpi=120) + ax_itr = fig_itr.add_subplot(111) + ax_itr.set_title("Iteration") + ax_itr.plot(itr.df_temp["time"] - itr.df_temp.at[0, "time"], itr.df_temp["data"], marker="o", markersize=2) + ax_itr.set_xlabel("time[s]") + ax_itr.set_ylabel("Iteration") + ax_itr.grid() + fig_itr.savefig(output_dir + "/itr.png") + + +def plot_ellipse_ls(pose_param, output_dir): + # ellipse long + start_time = pose_param.df.at[0, "time"] + fig_el = plt.figure("Error Ellipse (long/short)", figsize=(16, 9), dpi=120) + ax_el_long = fig_el.add_subplot(211) + ax_el_long.set_title("Ellipse Long Radius") + ax_el_long.plot( + pose_param.df["time"] - start_time, + pose_param.df["ellipse_long"], + marker="o", + markersize=2, + ) + ax_el_long.set_xlabel("time[s]") + ax_el_long.set_ylabel("size[m]") + ax_el_long.set_ylim(bottom=-0.01) + ax_el_long.grid() + + # ellipse short + ax_el_short = fig_el.add_subplot(212) + ax_el_short.set_title("Ellipse Short Radius") + ax_el_short.plot( + pose_param.df["time"] - start_time, + pose_param.df["ellipse_short"], + marker="o", + markersize=2, + ) + ax_el_short.set_xlabel("time[s]") + ax_el_short.set_ylabel("size[m]") + ax_el_short.set_ylim(bottom=-0.01) + ax_el_short.grid() + + plt.tight_layout() + fig_el.savefig(output_dir + "/error_ellipse_ls.png") + + +def plot_ellipse_ll(pose_param, output_dir): + # ellipse longitudinal direction + start_time = pose_param.df.at[0, "time"] + fig_el2 = plt.figure("Error Ellipse (longitudinal/lateral)", figsize=(16, 9), dpi=120) + ax_el2_longitudinal = fig_el2.add_subplot(211) + ax_el2_longitudinal.set_title("Ellipse Longitudinal Direction") + ax_el2_longitudinal.plot( + pose_param.df["time"] - start_time, + pose_param.df["ellipse_longitudinal"], + marker="o", + markersize=2, + ) + ax_el2_longitudinal.set_xlabel("time[s]") + ax_el2_longitudinal.set_ylabel("size[m]") + ax_el2_longitudinal.set_ylim(bottom=-0.01) + ax_el2_longitudinal.grid() + + # ellipse lateral direction + ax_el2_lateral = fig_el2.add_subplot(212) + ax_el2_lateral.set_title("Ellipse Lateral Direction") + ax_el2_lateral.plot( + pose_param.df["time"] - start_time, + pose_param.df["ellipse_lateral"], + marker="o", + markersize=2, + ) + ax_el2_lateral.set_xlabel("time[s]") + ax_el2_lateral.set_ylabel("size[m]") + ax_el2_lateral.set_ylim(bottom=-0.01) + ax_el2_lateral.grid() + plt.tight_layout() + fig_el2.savefig(output_dir + "/error_ellipse2_ll.png") + + +def create_ellipse(pose_param, ax): + for i in range(len(pose_param.df)): + e = patches.Ellipse( + xy=(pose_param.df["x"][i], pose_param.df["y"][i]), + width=pose_param.df["ellipse_long"][i] * 2, + height=pose_param.df["ellipse_short"][i] * 2, + angle=math.degrees(pose_param.df["ellipse_yaw"][i]), + alpha=0.3, + color="m", + ) + ax.add_patch(e) + + +def plot_ellipse_trj(pose_param, output_dir): + fig_trj_el_long = plt.figure("Trajectory and Ellipse Long", figsize=(16, 9), dpi=120) + ax_trj_el_long = fig_trj_el_long.add_subplot(111) + + fig_trj_el_short = plt.figure("Trajectory and Ellipse Short", figsize=(16, 9), dpi=120) + ax_trj_el_short = fig_trj_el_short.add_subplot(111) + + fig_trj_el2_longitudinal = plt.figure("Trajectory and Ellipse Longitudinal", figsize=(16, 9), dpi=120) + ax_trj_el2_longitudinal = fig_trj_el2_longitudinal.add_subplot(111) + + fig_trj_el2_lateral = plt.figure("Trajectory and Ellipse Lateral", figsize=(16, 9), dpi=120) + ax_trj_el2_lateral = fig_trj_el2_lateral.add_subplot(111) + + create_ellipse(pose_param, ax_trj_el_long) + create_ellipse(pose_param, ax_trj_el_short) + create_ellipse(pose_param, ax_trj_el2_longitudinal) + create_ellipse(pose_param, ax_trj_el2_lateral) + + ax_trj_el_long.set_title("Trajectory and Ellipse Long") + scatter_el_long = ax_trj_el_long.scatter( + pose_param.df["x"], pose_param.df["y"], c=pose_param.df["ellipse_long"], vmin=0.0, cmap="jet" + ) + fig_trj_el_long.colorbar(scatter_el_long, label="ellipse long[m]") + ax_trj_el_long.set_xlabel("x[m]") + ax_trj_el_long.set_ylabel("y[m]") + ax_trj_el_long.set_aspect("equal") + ax_trj_el_long.grid() + fig_trj_el_long.savefig(output_dir + "/trj_el_long.png") + + ax_trj_el_short.set_title("Trajectory and Ellipse Short") + scatter_el_short = ax_trj_el_short.scatter( + pose_param.df["x"], pose_param.df["y"], c=pose_param.df["ellipse_short"], vmin=0.0, cmap="jet" + ) + fig_trj_el_short.colorbar(scatter_el_short, label="ellipse short[m]") + ax_trj_el_short.set_xlabel("x[m]") + ax_trj_el_short.set_ylabel("y[m]") + ax_trj_el_short.set_aspect("equal") + ax_trj_el_short.grid() + fig_trj_el_short.savefig(output_dir + "/trj_el_short.png") + + ax_trj_el2_longitudinal.set_title("Trajectory and Ellipse Longitudinal Direction") + scatter_el_longitudinal = ax_trj_el2_longitudinal.scatter( + pose_param.df["x"], pose_param.df["y"], c=pose_param.df["ellipse_longitudinal"], vmin=0.0, cmap="jet" + ) + fig_trj_el2_longitudinal.colorbar(scatter_el_longitudinal, label="ellipse longitudinal[m]") + ax_trj_el2_longitudinal.set_xlabel("x[m]") + ax_trj_el2_longitudinal.set_ylabel("y[m]") + ax_trj_el2_longitudinal.set_aspect("equal") + ax_trj_el2_longitudinal.grid() + fig_trj_el2_longitudinal.savefig(output_dir + "/trj_el_longitudinal.png") + + ax_trj_el2_lateral.set_title("Trajectory and Ellipse Lateral Direction") + scatter_el_lateral = ax_trj_el2_lateral.scatter( + pose_param.df["x"], pose_param.df["y"], c=pose_param.df["ellipse_lateral"], vmin=0.0, cmap="jet" + ) + fig_trj_el2_lateral.colorbar(scatter_el_lateral, label="ellipse lateral[m]") + ax_trj_el2_lateral.set_xlabel("x[m]") + ax_trj_el2_lateral.set_ylabel("y[m]") + ax_trj_el2_lateral.set_aspect("equal") + ax_trj_el2_lateral.grid() + fig_trj_el2_lateral.savefig(output_dir + "/trj_el_lateral.png") + + if __name__ == "__main__": argv = sys.argv bag_file = argv[1] output_dir = argv[2] - pose_ = BagParam() - pose_.topic = "/localization/pose_estimator/pose_with_covariance" - read_ros2bag.read_pose(bag_file, pose_) - pose_empty = pose_.df_temp.empty + pose_param = yaml_param.YamlParam() + op_param = yaml_param.OpParam() + set_param(pose_param, op_param) + pose_param.topic = "/localization/pose_estimator/pose_with_covariance" + read_ros2bag.read_ros2bag(bag_file, pose_param, op_param) + pose_param.df = pose_param.df_temp.copy() + pose_empty = pose_param.df.empty - nvtl_ = BagParam() - nvtl_.topic = "/localization/pose_estimator/nearest_voxel_transformation_likelihood" - read_ros2bag.read_unique(bag_file, nvtl_) - nvtl_empty = nvtl_.df_temp.empty + nvtl = BagParam() + nvtl.topic = "/localization/pose_estimator/nearest_voxel_transformation_likelihood" + read_ros2bag.read_unique(bag_file, nvtl) + nvtl_empty = nvtl.df_temp.empty - tp_ = BagParam() - tp_.topic = "/localization/pose_estimator/transform_probability" - read_ros2bag.read_unique(bag_file, tp_) - tp_empty = tp_.df_temp.empty + tp = BagParam() + tp.topic = "/localization/pose_estimator/transform_probability" + read_ros2bag.read_unique(bag_file, tp) + tp_empty = tp.df_temp.empty - exe_time_ = BagParam() - exe_time_.topic = "/localization/pose_estimator/exe_time_ms" - read_ros2bag.read_unique(bag_file, exe_time_) - exe_time_empty = exe_time_.df_temp.empty + exe_time = BagParam() + exe_time.topic = "/localization/pose_estimator/exe_time_ms" + read_ros2bag.read_unique(bag_file, exe_time) + exe_time_empty = exe_time.df_temp.empty - itr_ = BagParam() - itr_.topic = "/localization/pose_estimator/iteration_num" - read_ros2bag.read_unique(bag_file, itr_) - itr_empty = itr_.df_temp.empty + itr = BagParam() + itr.topic = "/localization/pose_estimator/iteration_num" + read_ros2bag.read_unique(bag_file, itr) + itr_empty = itr.df_temp.empty # Plot + # trajectory nvtl if pose_empty == False and nvtl_empty == False: - for i in range(0, len(pose_.df_temp)): - search_sync = abs(nvtl_.df_temp.iloc[:, 0] - pose_.df_temp.iloc[i, 0]) - sync_id = search_sync.idxmin() - nvtl_.df = nvtl_.df.append(nvtl_.df_temp.iloc[sync_id, :], ignore_index=True) - nvtl_.df.reset_index(inplace=True, drop=True) - - fig_trj_nvtl = plt.figure("Trajectory and NVTL", figsize=(16, 9), dpi=120) - ax_trj_nvtl = fig_trj_nvtl.add_subplot(111) - ax_trj_nvtl.set_title("Trajectory and NVTL") - scatter_nvtl = ax_trj_nvtl.scatter( - pose_.df_temp["x"], pose_.df_temp["y"], c=nvtl_.df["data"], vmin=0.0, vmax=5, cmap="jet" - ) - plt.colorbar(scatter_nvtl, label="NVTL") - ax_trj_nvtl.set_xlabel("x[m]") - ax_trj_nvtl.set_ylabel("y[m]") - ax_trj_nvtl.set_aspect("equal") - ax_trj_nvtl.grid() - fig_trj_nvtl.savefig(output_dir + "/trj_nvtl.png") + plot_trajectory_nvtl(pose_param, nvtl, output_dir) + # trajectory tp if pose_empty == False and tp_empty == False: - for i in range(0, len(pose_.df_temp)): - search_sync = abs(tp_.df_temp.iloc[:, 0] - pose_.df_temp.iloc[i, 0]) - sync_id = search_sync.idxmin() - tp_.df = tp_.df.append(tp_.df_temp.iloc[sync_id, :], ignore_index=True) - tp_.df.reset_index(inplace=True, drop=True) - - fig_trj_tp = plt.figure("Trajectory and TP", figsize=(16, 9), dpi=120) - ax_trj_tp = fig_trj_tp.add_subplot(111) - ax_trj_tp.set_title("Trajectory and TP") - scatter_tp = ax_trj_tp.scatter( - pose_.df_temp["x"], pose_.df_temp["y"], c=tp_.df["data"], vmin=0.0, vmax=7.0, cmap="jet" - ) - plt.colorbar(scatter_tp, label="TP") - ax_trj_tp.set_xlabel("x[m]") - ax_trj_tp.set_ylabel("y[m]") - ax_trj_tp.set_aspect("equal") - ax_trj_tp.grid() - fig_trj_tp.savefig(output_dir + "/trj_tp.png") + plot_trajectory_tp(pose_param, tp, output_dir) + # nvtl if nvtl_empty == False: - fig_nvtl = plt.figure("NVTL", figsize=(16, 9), dpi=120) - ax_nvtl = fig_nvtl.add_subplot(111) - ax_nvtl.set_title("NVTL") - ax_nvtl.plot( - nvtl_.df_temp["time"] - nvtl_.df_temp.at[0, "time"], nvtl_.df_temp["data"], marker="o", c="b", markersize=2 - ) - ax_nvtl.set_xlabel("time[s]") - ax_nvtl.set_ylabel("NVTL") - ax_nvtl.set_ylim(0, 5) - ax_nvtl.grid() - fig_nvtl.savefig(output_dir + "/nvtl.png") + plot_nvtl(nvtl, output_dir) + # tp if tp_empty == False: - fig_tp = plt.figure("TP", figsize=(16, 9), dpi=120) - ax_tp = fig_tp.add_subplot(111) - ax_tp.set_title("TP") - ax_tp.plot( - tp_.df_temp["time"] - tp_.df_temp.at[0, "time"], tp_.df_temp["data"], marker="o", c="b", markersize=2 - ) - ax_tp.set_xlabel("time[s]") - ax_tp.set_ylabel("TP") - ax_tp.set_ylim(0, 7) - ax_tp.grid() - fig_tp.savefig(output_dir + "/tp.png") + plot_tp(tp, output_dir) + # execution time if exe_time_empty == False: - fig_exe_time = plt.figure("Execution Time", figsize=(16, 9), dpi=120) - ax_exe_time = fig_exe_time.add_subplot(111) - ax_exe_time.set_title("Execution Time") - ax_exe_time.plot( - exe_time_.df_temp["time"] - exe_time_.df_temp.at[0, "time"], - exe_time_.df_temp["data"], - marker="o", - c="b", - markersize=2, - ) - ax_exe_time.set_xlabel("time[s]") - ax_exe_time.set_ylabel("Execution Time") - ax_exe_time.set_ylim(0, 150) - ax_exe_time.grid() - fig_exe_time.savefig(output_dir + "/exe_time.png") + plot_exe_time(exe_time, output_dir) + # iteration if itr_empty == False: - fig_itr = plt.figure("Iteration", figsize=(16, 9), dpi=120) - ax_itr = fig_itr.add_subplot(111) - ax_itr.set_title("Iteration") - ax_itr.plot( - itr_.df_temp["time"] - itr_.df_temp.at[0, "time"], itr_.df_temp["data"], marker="o", c="b", markersize=2 - ) - ax_itr.set_xlabel("time[s]") - ax_itr.set_ylabel("Iteration") - ax_itr.grid() - fig_itr.savefig(output_dir + "/itr.png") + plot_itr(itr, output_dir) + + # plot ellipse + if pose_empty == False: + adjust.calc_ellipse(pose_param) + plot_ellipse_ls(pose_param, output_dir) + plot_ellipse_ll(pose_param, output_dir) + plot_ellipse_trj(pose_param, output_dir) - plt.show() + plt.show() \ No newline at end of file diff --git a/sub_scripts/util_eagleye/calc.py b/sub_scripts/util_eagleye/calc.py new file mode 100644 index 0000000..3556014 --- /dev/null +++ b/sub_scripts/util_eagleye/calc.py @@ -0,0 +1,521 @@ + + + +from typing import List +import pandas as pd +import numpy as np +import math +import mgrs +import sys + +from tqdm import tqdm +from scipy.spatial.transform import Rotation as R +from decimal import Decimal, ROUND_HALF_UP + +def xyz2enu(ecef_xyz,org_xyz): + org_llh = xyz2llh(org_xyz) + phi = org_llh[0] + lam = org_llh[1] + sinphi = np.sin(phi) + cosphi = np.cos(phi) + sinlam = np.sin(lam) + coslam = np.cos(lam) + R = np.matrix([[-sinlam , coslam , 0],[-sinphi*coslam , -sinphi*sinlam , cosphi],[cosphi*coslam , cosphi*sinlam , sinphi]]) + + set_enu_data: List[float] = [] + for i in range(len(ecef_xyz)): + if ecef_xyz['ecef_x'][i] == 0 and ecef_xyz['ecef_y'][i] == 0: + set_enu_data.append([0,0,0]) + continue + diff_xyz = np.array([[ecef_xyz['ecef_x'][i] - org_xyz[0]] , [ecef_xyz['ecef_y'][i] - org_xyz[1]] , [ecef_xyz['ecef_z'][i] - org_xyz[2]]]) + enu = R * diff_xyz + x: float = float(enu[0]) + y: float = float(enu[1]) + z: float = float(enu[2]) + set_enu_data.append([x,y,z]) + set_enu = pd.DataFrame(set_enu_data,columns=['x','y','z']) + return set_enu + +def xyz2llh(xyz): + x = xyz[0] + y = xyz[1] + z = xyz[2] + x2 = x**2 + y2 = y**2 + z2 = z**2 + + a = 6378137.0000 # earth radius in meters + b = 6356752.3142 # earth semiminor in meters + e = (1-(b/a)**2)**0.5 + b2 = b*b + e2 = e**2 + ep = e*(a/b) + r = (x2+y2)**0.5 + r2 = r*r + E2 = a**2 - b**2 + F = 54*b2*z2 + G = r2 + (1-e2)*z2 - e2*E2 + c = (e2*e2*F*r2)/(G*G*G) + s = ( 1 + c + (c*c + 2*c)**0.5 )**(1/3) + P = F / (3 * (s+1/s+1)**2 * G*G) + Q = (1+2*e2*e2*P)**0.5 + ro = -(P*e2*r)/(1+Q) + ((a*a/2)*(1+1/Q) - (P*(1-e2)*z2)/(Q*(1+Q)) - P*r2/2) ** 0.5 + tmp = (r - e2*ro)**2 + U = ( tmp + z2 )**0.5 + V = ( tmp + (1-e2)*z2 )**0.5 + zo = (b2*z)/(a*V) + + height = U*( 1 - b2/(a*V) ) + + lat = math.atan( (z + ep*ep*zo)/r ) + temp = math.atan(y/x) + if x >=0: + long = temp + elif (x < 0) & (y >= 0): + long = math.pi + temp + else: + long = temp - math.pi + + llh = [lat,long,height] + return llh + +def llh2xyz(llh): + set_xyz_data: List[float] = [] + for i in range(len(llh)): + lat = llh['latitude'][i] + lon = llh['longitude'][i] + ht = llh['altitude'][i] + + PI_180 = math.pi / 180.0 + A = 6378137.0 + ONE_F = 298.257223563 + E2 = (1.0 / ONE_F) * (2 - (1.0 / ONE_F)) + + n = lambda x: A / \ + math.sqrt(1.0 - E2 * math.sin(x * PI_180)**2) + x = (n(lat) + ht) \ + * math.cos(lat * PI_180) \ + * math.cos(lon * PI_180) + y = (n(lat) + ht) \ + * math.cos(lat * PI_180) \ + * math.sin(lon * PI_180) + z = (n(lat) * (1.0 - E2) + ht) \ + * math.sin(lat * PI_180) + set_xyz_data.append([x,y,z]) + set_xyz = pd.DataFrame(set_xyz_data,columns=['ecef_x','ecef_y','ecef_z']) + return set_xyz + +def change_anglel_limit(heading): + set_output: List[float] = [] + for i in range(len(heading)): + heading_tmp = heading[i] + while heading_tmp < -math.pi or math.pi < heading_tmp: + if heading_tmp < -math.pi: + heading_tmp += math.pi * 2 + else: + heading_tmp -= math.pi * 2 + set_output.append(heading_tmp) + output = pd.DataFrame(set_output,columns=['yaw']) + return output + +def xyz2enu_vel(vel,org_xyz): + set_vel_data: List[float] = [] + for i in range(len(vel)): + x_vel = vel['vel_x'][i] + y_vel = vel['vel_y'][i] + z_vel = vel['vel_z'][i] + + org_llh = xyz2llh(org_xyz) + phi = org_llh[0] + lam = org_llh[1] + + sin_phi = math.sin(phi) + cos_phi = math.cos(phi) + sin_lam = math.sin(lam) + cos_lam = math.cos(lam) + + e_vel = (-x_vel * sin_lam) + (y_vel * cos_lam) + n_vel = (-x_vel * cos_lam * sin_phi) - (y_vel * sin_lam * sin_phi) + (z_vel * cos_phi) + u_vel = (x_vel * cos_lam * cos_phi) +( y_vel * sin_lam * cos_phi) + (z_vel * sin_phi) + vel_2d = (e_vel ** 2 + n_vel ** 2) ** 0.5 + + set_vel_data.append([e_vel,n_vel,u_vel,vel_2d]) + df = pd.DataFrame(set_vel_data,columns=['east_vel','north_vel','up_vel','velocity']) + return df + +def calc_dopplor_heading(vel): + heading = np.degrees(np.arctan2(vel['east_vel'], vel['north_vel'])) + df = pd.DataFrame(heading,columns=['yaw']) + return df + +def ll2mgrs(llh): + set_mgrs: List[float] = [] + m = mgrs.MGRS() + for i in range(len(llh)): + ll = (llh['latitude'][i], llh['longitude'][i] , 4) + coords = m.toMGRS(*ll) + zone, x, y = coords[:5], float(coords[5:10]), float(coords[10:]) + height = llh['altitude'][i] + set_mgrs.append([x,y,height]) + xyz = pd.DataFrame(set_mgrs,columns=['x','y','z']) + return xyz + +def calc_distance_vel(velocity, time): + last_distance = 0 + set_distance: List[float] = [] + for i in range(len(time)): + if i == 0: + last_time = time.iloc[i] + continue + distance = last_distance + velocity.iloc[i] * (time.iloc[i] - last_time) + last_time = time.iloc[i] + last_distance = distance + set_distance.append([distance]) + distance = pd.DataFrame(set_distance,columns=['distance']) + return distance + +def calc_distance_xy(xy): + last_distance = 0 + set_distance: List[float] = [] + for i in range(len(xy)): + if i == 0: + distance = 0 + else: + distance = last_distance + (((xy.iloc[i]['x']-xy.iloc[i-1]['x']) ** 2) + (xy.iloc[i]['y']-xy.iloc[i-1]['y']) ** 2)** 0.5 + last_distance = distance + set_distance.append([distance]) + distance = pd.DataFrame(set_distance,columns=['distance']) + return distance + +def sync_time(ref_data,csv_data,sync_threshold_time,leap_time): # Time synchronization + sync_index = np.zeros(len(csv_data['TimeStamp'])) + + set_target_index: List[float] = [] + set_ref_index: List[float] = [] + + if ref_data['TimeStamp'][0] > csv_data['TimeStamp'][0]: + csv_data = csv_data[csv_data['TimeStamp'] > ref_data['TimeStamp'][0] - sync_threshold_time] + csv_data = csv_data.reset_index(drop=True) + else: + ref_data = ref_data[ref_data['TimeStamp'] > csv_data['TimeStamp'][0] - sync_threshold_time] + ref_data = ref_data.reset_index(drop=True) + + if len(csv_data) == 0 or len(ref_data) == 0: + print("[ERROR] Target time and ref time ranges do not match.") + sys.exit(1) + + if ref_data.iloc[-1]['TimeStamp'] < csv_data.iloc[-1]['TimeStamp']: + csv_data = csv_data[csv_data['TimeStamp'] < ref_data.iloc[-1]['TimeStamp'] + sync_threshold_time] + csv_data = csv_data.reset_index(drop=True) + else: + ref_data = ref_data[ref_data['TimeStamp'] < csv_data.iloc[-1]['TimeStamp'] + sync_threshold_time] + ref_data = ref_data.reset_index(drop=True) + + if len(csv_data) == 0 or len(ref_data) == 0: + print("[ERROR] Target time and ref time ranges do not match.") + sys.exit(1) + + sync_ref_time_tmp = ref_data['TimeStamp'] + + num = 0 + len_drop_num = 0 + for i in range(len(csv_data)): + if i == 0: continue + time_tmp: List[float] = [] + time_tmp = csv_data.iloc[i]['TimeStamp']-sync_ref_time_tmp + leap_time + sync_index[i] = np.argmin(abs(time_tmp)) + sync_time_tmp = time_tmp[sync_index[i]] + if sync_time_tmp < sync_threshold_time: + tmp_num = int(sync_index[i]) + set_target_index.append(i) + num = num + len_drop_num + drop_num = list(range(0,tmp_num - len_drop_num - 1,1)) + if not drop_num == None: + sync_ref_time_tmp = sync_ref_time_tmp.drop(drop_num, axis=0) + len_drop_num = len(drop_num) + sync_ref_time_tmp = sync_ref_time_tmp.reset_index(drop=True) + set_ref_index.append(num) + + data_df_output = csv_data.iloc[set_target_index] + data_df_output = data_df_output.reset_index(drop=True) + data_df_output['elapsed_time'] = data_df_output['TimeStamp'] - data_df_output['TimeStamp'][0] + ref_df_output = ref_data.iloc[set_ref_index] + ref_df_output = ref_df_output.reset_index(drop=True) + ref_df_output['elapsed_time'] = ref_df_output['TimeStamp'] - ref_df_output['TimeStamp'][0] + + return ref_df_output , data_df_output + +def calc_error_xyz(elapsed_time,ref_time,eagleye_time,ref_xyz,data_xyz,ref_yaw): + error_xyz = pd.DataFrame() + error_xyz['elapsed_time'] = elapsed_time + error_xyz['TimeStamp'] = ref_time - eagleye_time + error_xyz['x'] = data_xyz['x'] - ref_xyz['x'] + error_xyz['y'] = data_xyz['y'] - ref_xyz['y'] + error_xyz['z'] = data_xyz['z'] - ref_xyz['z'] + error_xyz['2d'] = (error_xyz['x']**2 + error_xyz['y']**2)**0.5 + + set_aa: List[float] = [] + for i in range(len(ref_yaw)): + across = error_xyz.iloc[i]['x'] * math.cos(math.radians(ref_yaw[i])) - error_xyz.iloc[i]['y'] * math.sin(math.radians(ref_yaw[i])) + along = error_xyz.iloc[i]['x'] * math.sin(math.radians(ref_yaw[i])) + error_xyz.iloc[i]['y'] * math.cos(math.radians(ref_yaw[i])) + set_aa.append([across,along]) + error_aa = pd.DataFrame(set_aa,columns=['across','along']) + + error = pd.concat([error_xyz,error_aa],axis=1) + return error + + +def calc_TraRate(diff_2d , eval_step_max): # Calculation of error , x_label , ErrTra_Rate rate + ErrTra_cnt: List[int] = [] + set_ErrTra_middle: List[float] = [] + set_ErrTra: List[float] = [] + ErrTra_cnt = len(diff_2d) + + step = 0.01 + eval_step_middle = 0.1 + for i in np.arange(0, eval_step_middle, step): + cnt = 0 + eval_step = i + for data in diff_2d: + if data < eval_step: + cnt = cnt + 1 + rate = cnt / ErrTra_cnt + set_ErrTra_middle.append([eval_step,rate,rate *100]) + ErrTra_Rate_middle = pd.DataFrame(set_ErrTra_middle,columns=['x_label','ErrTra_Rate','ErrTra']) + + step = 0.1 + for i in np.arange(eval_step_middle, eval_step_max, step): + cnt = 0 + eval_step = i + for data in diff_2d: + if data < eval_step: + cnt = cnt + 1 + rate = cnt / ErrTra_cnt + set_ErrTra.append([eval_step,rate,rate *100]) + ErrTra_Rate_tmp = pd.DataFrame(set_ErrTra,columns=['x_label','ErrTra_Rate','ErrTra']) + ErrTra_Rate = pd.concat([ErrTra_Rate_middle,ErrTra_Rate_tmp]) + return ErrTra_Rate + +def quaternion_to_euler_zyx(ori): + set_eular_angle: List[float] = [] + for i in range(len(ori)): + q = [ori.iloc[i]['ori_x'],ori.iloc[i]['ori_y'],ori.iloc[i]['ori_z'],ori.iloc[i]['ori_w']] + r = R.from_quat([q[0], q[1], q[2], q[3]]) + roll = r.as_euler('ZYX', degrees=True)[2] + pitch = r.as_euler('ZYX', degrees=True)[1] + yaw = r.as_euler('ZYX', degrees=True)[0] + set_eular_angle.append([roll,pitch,yaw]) + euler_angle = pd.DataFrame(set_eular_angle,columns=['roll','pitch','yaw']) + return euler_angle + +def calc_error_rpy(ref_angle,eagleye_angle): + error_rpy = pd.DataFrame() + error_rpy['roll'] = eagleye_angle['roll'] - ref_angle['roll'] + error_rpy['pitch'] = eagleye_angle['pitch'] - ref_angle['pitch'] + error_rpy['yaw'] = eagleye_angle['yaw'] - ref_angle['yaw'] + for i in range(len(error_rpy)): + if error_rpy['yaw'][i] > math.degrees(math.pi): + error_rpy['yaw'][i] = error_rpy['yaw'][i] - 2 * math.degrees(math.pi) + elif error_rpy['yaw'][i] < -math.degrees(math.pi): + error_rpy['yaw'][i] = error_rpy['yaw'][i] + 2 * math.degrees(math.pi) + return error_rpy + +def calc_velocity(vel): + velocity = pd.DataFrame() + velocity['velocity'] = (vel['vel_x'] ** 2 + vel['vel_y'] ** 2 + vel['vel_z'] ** 2) ** 0.5 + return velocity + +def calc_velocity_error(eagleye_velocity,ref_velocity): + error_velocity = pd.DataFrame() + error_velocity['velocity'] = eagleye_velocity - ref_velocity + return error_velocity + +def calc_dr(TimeStamp,distance,eagleye_vel_xyz,ref_xyz,distance_length,distance_step): + last_distance = 0 + set_dr_trajcetory: List[float] = [] + set_calc_error: List[float] = [] + for i in range(len(TimeStamp)): + if i == 0: continue + if last_distance + distance_step < distance[i]: + start_distance = distance[i] + start_pos_x = ref_xyz['x'][i] + start_pos_y = ref_xyz['y'][i] + previous_pos_x = 0 + previous_pos_y = 0 + last_distance = start_distance + last_time = TimeStamp[i] + for j in range(i,len(TimeStamp)): + if eagleye_vel_xyz['vel_x'][j] == 0 and eagleye_vel_xyz['vel_y'][j] == 0: + last_time = TimeStamp[j] + distance_data = distance[j] - start_distance + absolute_pos_x = ref_xyz['x'][j] - start_pos_x + absolute_pos_y = ref_xyz['y'][j] - start_pos_y + continue + if distance[j] - start_distance < distance_length: + dr_pos_x = previous_pos_x + eagleye_vel_xyz['vel_x'][j] * (TimeStamp[j] - last_time) + dr_pos_y = previous_pos_y + eagleye_vel_xyz['vel_y'][j] * (TimeStamp[j] - last_time) + previous_pos_x = dr_pos_x + previous_pos_y = dr_pos_y + last_time = TimeStamp[j] + distance_data = distance[j] - start_distance + absolute_pos_x = ref_xyz['x'][j] - start_pos_x + absolute_pos_y = ref_xyz['y'][j] - start_pos_y + absolute_dr_pos_x = start_pos_x + dr_pos_x + absolute_dr_pos_y = start_pos_y + dr_pos_y + set_dr_trajcetory.append([absolute_dr_pos_x,absolute_dr_pos_y]) + else: + error_x = absolute_pos_x - dr_pos_x + error_y = absolute_pos_y - dr_pos_y + error_2d_fabs = math.fabs(error_x ** 2 + error_y ** 2) + error_2d= math.sqrt(error_2d_fabs) + set_calc_error.append([start_distance,distance_data,absolute_pos_x,dr_pos_x,absolute_pos_y,dr_pos_y,error_x,error_y,error_2d]) + break + calc_error = pd.DataFrame(set_calc_error,columns=['start_distance','distance','absolute_pos_x','absolute_pos_y','dr_pos_x','dr_pos_y','error_x','error_y','error_2d']) + dr_trajcetory = pd.DataFrame(set_dr_trajcetory,columns=['x','y']) + return calc_error,dr_trajcetory + +def calc_dr_twist(TimeStamp,distance,twist_data,ref_heading,ref_xyz,distance_length,distance_step,based_heaing_angle): + last_distance = 0 + last_heading_integtation = 0 + set_dr_trajcetory: List[float] = [] + set_calc_error: List[float] = [] + for i in range(len(TimeStamp)): + if i == 0: continue + if last_distance + distance_step < distance[i]: + data_set_flag = True + start_distance = distance[i] + start_pos_x = ref_xyz['x'][i] + start_pos_y = ref_xyz['y'][i] + previous_pos_x = 0 + previous_pos_y = 0 + last_distance = start_distance + last_time = TimeStamp[i] + for j in range(i,len(TimeStamp)): + if data_set_flag == True: + last_time = TimeStamp[j] + last_heading_integtation = ref_heading[j] + distance_data = distance[j] - start_distance + absolute_pos_x = ref_xyz['x'][j] - start_pos_x + absolute_pos_y = ref_xyz['y'][j] - start_pos_y + data_set_flag = False + continue + else: + heading_integtation = last_heading_integtation + twist_data['angular_z'][j] * (TimeStamp[j] - last_time) + if based_heaing_angle == True: + usr_vel_x = math.sin(heading_integtation) * twist_data['velocity'][j] + usr_vel_y = math.cos(heading_integtation) * twist_data['velocity'][j] + else: + usr_vel_x = math.cos(heading_integtation) * twist_data['velocity'][j] + usr_vel_y = math.sin(heading_integtation) * twist_data['velocity'][j] + if distance[j] - start_distance < distance_length: + dr_pos_x = previous_pos_x + usr_vel_x * (TimeStamp[j] - last_time) + dr_pos_y = previous_pos_y + usr_vel_y * (TimeStamp[j] - last_time) + last_heading_integtation = heading_integtation + previous_pos_x = dr_pos_x + previous_pos_y = dr_pos_y + last_time = TimeStamp[j] + distance_data = distance[j] - start_distance + absolute_pos_x = ref_xyz['x'][j] - start_pos_x + absolute_pos_y = ref_xyz['y'][j] - start_pos_y + absolute_dr_pos_x = start_pos_x + dr_pos_x + absolute_dr_pos_y = start_pos_y + dr_pos_y + set_dr_trajcetory.append([absolute_dr_pos_x,absolute_dr_pos_y]) + else: + error_x = absolute_pos_x - dr_pos_x + error_y = absolute_pos_y - dr_pos_y + error_2d_fabs = math.fabs(error_x ** 2 + error_y ** 2) + error_2d= math.sqrt(error_2d_fabs) + set_calc_error.append([start_distance,distance_data,absolute_pos_x,dr_pos_x,absolute_pos_y,dr_pos_y,error_x,error_y,error_2d]) + break + calc_error = pd.DataFrame(set_calc_error,columns=['start_distance','distance','absolute_pos_x','absolute_pos_y','dr_pos_x','dr_pos_y','error_x','error_y','error_2d']) + dr_trajcetory = pd.DataFrame(set_dr_trajcetory,columns=['x','y']) + return calc_error,dr_trajcetory + +def calc_dr_eagleye(TimeStamp,distance,eagleye_twist_data,ref_heading,ref_xyz,distance_length,distance_step,based_heaing_angle): + last_distance = 0 + last_heading_integtation = 0 + velocity_stop_threshold = 0.01 + velocity_slip_threshold = 1 + set_dr_trajcetory: List[float] = [] + set_calc_error: List[float] = [] + for i in range(len(TimeStamp)): + if i == 0: continue + if last_distance + distance_step < distance[i]: + data_set_flag = True + start_distance = distance[i] + start_pos_x = ref_xyz['x'][i] + start_pos_y = ref_xyz['y'][i] + previous_pos_x = 0 + previous_pos_y = 0 + last_distance = start_distance + last_time = TimeStamp[i] + for j in range(i,len(TimeStamp)): + if data_set_flag == True: + last_time = TimeStamp[j] + last_heading_integtation = ref_heading[j] + distance_data = distance[j] - start_distance + absolute_pos_x = ref_xyz['x'][j] - start_pos_x + absolute_pos_y = ref_xyz['y'][j] - start_pos_y + data_set_flag = False + continue + if eagleye_twist_data['velocity'][j] > velocity_stop_threshold: + heading_integtation = last_heading_integtation + (eagleye_twist_data['angular_z'][j] + eagleye_twist_data['yawrate_offset'][j]) * (TimeStamp[j] - last_time) + else: + heading_integtation = last_heading_integtation + (eagleye_twist_data['angular_z'][j] + eagleye_twist_data['yawrate_offset_stop'][j]) * (TimeStamp[j] - last_time) + if eagleye_twist_data['velocity'][j] > velocity_slip_threshold: + heading_correction_slip = heading_integtation + eagleye_twist_data['slip'][j] + else: + heading_correction_slip = heading_integtation + if distance[j] - start_distance < distance_length: + if based_heaing_angle == True: + usr_vel_x = math.sin(heading_correction_slip) * eagleye_twist_data['velocity'][j] + usr_vel_y = math.cos(heading_correction_slip) * eagleye_twist_data['velocity'][j] + else: + usr_vel_x = math.cos(heading_correction_slip) * eagleye_twist_data['velocity'][j] + usr_vel_y = math.sin(heading_correction_slip) * eagleye_twist_data['velocity'][j] + dr_pos_x = previous_pos_x + usr_vel_x * (TimeStamp[j] - last_time) + dr_pos_y = previous_pos_y + usr_vel_y * (TimeStamp[j] - last_time) + last_heading_integtation = heading_correction_slip + previous_pos_x = dr_pos_x + previous_pos_y = dr_pos_y + last_time = TimeStamp[j] + distance_data = distance[j] - start_distance + absolute_pos_x = ref_xyz['x'][j] - start_pos_x + absolute_pos_y = ref_xyz['y'][j] - start_pos_y + absolute_dr_pos_x = start_pos_x + dr_pos_x + absolute_dr_pos_y = start_pos_y + dr_pos_y + set_dr_trajcetory.append([absolute_dr_pos_x,absolute_dr_pos_y]) + else: + error_x = absolute_pos_x - dr_pos_x + error_y = absolute_pos_y - dr_pos_y + error_2d_fabs = math.fabs(error_x ** 2 + error_y ** 2) + error_2d= math.sqrt(error_2d_fabs) + set_calc_error.append([start_distance,distance_data,absolute_pos_x,dr_pos_x,absolute_pos_y,dr_pos_y,error_x,error_y,error_2d]) + break + calc_error = pd.DataFrame(set_calc_error,columns=['start_distance','distance','absolute_pos_x','absolute_pos_y','dr_pos_x','dr_pos_y','error_x','error_y','error_2d']) + dr_trajcetory = pd.DataFrame(set_dr_trajcetory,columns=['x','y']) + return calc_error,dr_trajcetory + +def error_evaluation_each(error, elem): + data_max = max(error[elem]) + data_average = np.average(error[elem]) + data_std = np.std(error[elem]) + data_rms = np.sqrt(np.square(error[elem]).mean(axis = 0)) + digits_num = '0.01' + return [elem, \ + (Decimal(str(data_max)).quantize(Decimal(digits_num), rounding=ROUND_HALF_UP) ), \ + (Decimal(str(data_average)).quantize(Decimal(digits_num), rounding=ROUND_HALF_UP) ), \ + (Decimal(str(data_std)).quantize(Decimal(digits_num), rounding=ROUND_HALF_UP) ), \ + (Decimal(str(data_rms)).quantize(Decimal(digits_num), rounding=ROUND_HALF_UP) )] + +def error_evaluation(error): + x = error_evaluation_each(error, 'x') + y = error_evaluation_each(error, 'y') + z = error_evaluation_each(error, 'z') + xy = error_evaluation_each(error, '2d') + roll = error_evaluation_each(error, 'roll') + pitch = error_evaluation_each(error, 'pitch') + yaw = error_evaluation_each(error, 'yaw') + error_table = pd.DataFrame([x, y, z, xy, roll, pitch, yaw], columns = ['data', 'max', 'average', 'std', 'rms']) + return error_table + diff --git a/sub_scripts/util_eagleye/plot.py b/sub_scripts/util_eagleye/plot.py new file mode 100644 index 0000000..624dfa5 --- /dev/null +++ b/sub_scripts/util_eagleye/plot.py @@ -0,0 +1,333 @@ +# Copyright (c) 2022, Map IV, Inc. +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# * Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# * Neither the name of the Map IV, Inc. nor the names of its contributors +# may be used to endorse or promote products derived from this software +# without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL COPYRIGHT HOLDER BE LIABLE FOR ANY +# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +# plot.py +# Author MapIV Hoda + +from typing import List +import pandas as pd +import numpy as np +from math import log10 , floor + +import matplotlib.pyplot as plt +# plt.rcParams["font.size"] = 18 + +def judge_qual(xyz,qual,qual_num): + set_output_df: List[float] = [] + for i in range(len(xyz)): + if qual_num == qual[i]: + x = xyz['x'][i] + y = xyz['y'][i] + z = xyz['z'][i] + qual_data = qual[i] + set_output_df.append([x,y,z,qual_data]) + output_df = pd.DataFrame(set_output_df,columns=['x','y','z','qual']) + return output_df + +def plot_xyz(ax, eagleye_x_data, rtk_x_data, raw_x_data, eagleye_xyz, rtk_xyz, raw_xyz, elem, title, y_label, font_size): + if elem in raw_xyz.columns: + ax.plot(raw_x_data , raw_xyz[elem] , marker=".", linestyle="None",markersize=1, color = "red", label="gnss raw data(rtklib)") + if elem in rtk_xyz.columns: + ax.plot(rtk_x_data , rtk_xyz[elem] , marker="o", linestyle="None",markersize=1, color = "green", label="gnss rtk data(nmea)") + if elem in eagleye_xyz.columns: + ax.plot(eagleye_x_data , eagleye_xyz[elem] , marker="s", linestyle="None",markersize=1, alpha=0.3, color = "blue", label="eagleye") + ax.set_xlabel('time [s]', fontsize=font_size) + ax.set_ylabel(y_label, fontsize=font_size) + ax.set_title(title) + ax.legend(loc='upper right') + ax.tick_params(labelsize=font_size) + ax.grid() + +def plot_rpy(ax, x_data, eagleye_plot_rpy, dopplor, elem, title, y_label, font_size): + if elem in dopplor.columns: + ax.plot(x_data , dopplor[elem] , marker="o", linestyle="None",markersize=1, color = "green", label="dopplor") + if elem in eagleye_plot_rpy.columns: + ax.plot(x_data , eagleye_plot_rpy[elem] , marker="s", linestyle="None",markersize=1, color = "blue", label="eagleye") + ax.set_xlabel('time [s]', fontsize=font_size) + ax.set_ylabel(y_label, fontsize=font_size) + ax.set_title(title) + ax.legend(loc='upper right') + ax.tick_params(labelsize=font_size) + ax.grid() + +def plot_each(ax, x_data , eagleye, ref, y_data, title, y_label,data_name, ref_data_name, font_size): + if y_data in ref.columns: + ax.plot(x_data, ref[y_data] , marker="o", linestyle="None",markersize=1, color = "green", label=ref_data_name) + if y_data in eagleye.columns: + ax.plot(x_data , eagleye[y_data] , marker="s", linestyle="None",markersize=1, alpha=0.3, color = "blue", label=data_name) + ax.set_xlabel('time [s]', fontsize=font_size) + ax.set_ylabel(y_label, fontsize=font_size) + ax.set_title(title) + ax.legend(loc='upper right') + ax.tick_params(labelsize=font_size) + ax.grid() + +def plot_6DoF_single(eagleye_x_data, rtk_x_data, raw_x_data, eagleye_xyz, rtk_xyz, raw_xyz, eagleye_plot_rpy,dopplor, font_size): + fig1 = plt.figure() + ax_x = fig1.add_subplot(2, 3, 1) + ax_y = fig1.add_subplot(2, 3, 2) + ax_z = fig1.add_subplot(2, 3, 3) + ax_roll = fig1.add_subplot(2, 3, 4) + ax_pitch = fig1.add_subplot(2, 3, 5) + ax_yaw = fig1.add_subplot(2, 3, 6) + plot_xyz(ax_x, eagleye_x_data, rtk_x_data, raw_x_data, eagleye_xyz, rtk_xyz, raw_xyz, 'x', 'X (East-West)','East [m]', font_size) + plot_xyz(ax_y, eagleye_x_data, rtk_x_data, raw_x_data, eagleye_xyz, rtk_xyz, raw_xyz, 'y', 'Y (North-South)','North [m]', font_size) + plot_xyz(ax_z,eagleye_x_data, rtk_x_data, raw_x_data, eagleye_xyz, rtk_xyz, raw_xyz, 'z', 'Z (Height)','Height [m]', font_size) + plot_rpy(ax_roll, eagleye_x_data, eagleye_plot_rpy, dopplor, 'roll', 'Roll' , 'Roll [deg]', font_size) + plot_rpy(ax_pitch, eagleye_x_data, eagleye_plot_rpy, dopplor, 'pitch', 'Pitch', 'Pitch [deg]', font_size) + plot_rpy(ax_yaw, eagleye_x_data, eagleye_plot_rpy, dopplor, 'yaw', 'Yaw', 'Yaw [deg]', font_size) + +def plot_6DoF(x_data,eagleye, ref,data_name, ref_data_name, font_size): + fig1 = plt.figure() + ax_x = fig1.add_subplot(2, 3, 1) + ax_y = fig1.add_subplot(2, 3, 2) + ax_z = fig1.add_subplot(2, 3, 3) + ax_roll = fig1.add_subplot(2, 3, 4) + ax_pitch = fig1.add_subplot(2, 3, 5) + ax_yaw = fig1.add_subplot(2, 3, 6) + plot_each(ax_x, x_data, eagleye, ref, 'x', 'X (East-West)','East [m]',data_name, ref_data_name, font_size) + plot_each(ax_y, x_data ,eagleye, ref, 'y', 'Y (North-South)','North [m]',data_name, ref_data_name, font_size) + plot_each(ax_z, x_data , eagleye, ref, 'z', 'Z (Height)','Height [m]',data_name, ref_data_name, font_size) + plot_each(ax_roll, x_data , eagleye, ref, 'roll', 'Roll' , 'Roll [deg]',data_name, ref_data_name, font_size) + plot_each(ax_pitch, x_data , eagleye, ref, 'pitch', 'Pitch', 'Pitch [deg]',data_name, ref_data_name, font_size) + plot_each(ax_yaw, x_data , eagleye, ref, 'yaw', 'Yaw', 'Yaw [deg]',data_name, ref_data_name, font_size) + +def plot_each_error(ax, error_data, y_data, title, y_label, font_size): + ax.plot(error_data['elapsed_time'] , error_data[y_data] , marker="s", linestyle="None",markersize=1, color = "blue") + ax.set_xlabel('time [s]', fontsize=font_size) + ax.set_ylabel(y_label, fontsize=font_size) + ax.set_title(title) + ax.tick_params(labelsize=font_size) + ax.grid() + +def plot_one(ax, error_data, x_data, y_data, title, x_label, y_label, line_style, marker_size, font_size): + ax.plot(error_data[x_data] , error_data[y_data] , marker="s", linestyle=line_style, markersize=marker_size, color = "blue") + ax.set_xlabel(x_label, fontsize=font_size) + ax.set_ylabel(y_label, fontsize=font_size) + ax.set_title(title) + ax.tick_params(labelsize=font_size) + ax.grid() + +def plot_two(ax, ref, eagleye, x_data, y_data, title, x_label, y_label, line_style, font_size, data_name, ref_data_name): + ax.plot(ref[x_data] , ref[y_data] , marker=".", linestyle=line_style, markersize=1, color = "red", label=ref_data_name) + ax.plot(eagleye[x_data] , eagleye[y_data] , marker="s", linestyle=line_style, markersize=1, alpha=0.3, color = "blue", label=data_name) + ax.set_xlabel(x_label, fontsize=font_size) + ax.set_ylabel(y_label, fontsize=font_size) + ax.set_title(title) + ax.legend(loc='upper right') + ax.tick_params(labelsize=font_size) + ax.grid() + +def plot_three(ax, ref, eagleye, raw, x_data, y_data, title, x_label, y_label, line_style, font_size, data_name, ref_data_name): + ax.plot(ref[x_data] , ref[y_data] , marker=".", linestyle=line_style, markersize=1, color = "red", label=ref_data_name) + ax.plot(raw[x_data] , raw[y_data] , marker="o", linestyle=line_style, markersize=1, color = "green", label="initial ref heading") + ax.plot(eagleye[x_data] , eagleye[y_data] , marker="s", linestyle=line_style, markersize=1, alpha=0.3, color = "blue", label=data_name) + ax.set_xlabel(x_label, fontsize=font_size) + ax.set_ylabel(y_label, fontsize=font_size) + ax.set_title(title) + ax.legend(loc='upper right') + ax.tick_params(labelsize=font_size) + ax.grid() + +def plot_error_6DoF(error_data,ref_data_name, error_table, font_size): + fig2 = plt.figure() + fig2.suptitle(ref_data_name + ' - eagleye Error') + ax_x = fig2.add_subplot(4, 3, 1) + ax_y = fig2.add_subplot(4, 3, 2) + ax_z = fig2.add_subplot(4, 3, 3) + ax_x_table = fig2.add_subplot(4, 3, 4) + ax_y_table = fig2.add_subplot(4, 3, 5) + ax_z_table = fig2.add_subplot(4, 3, 6) + ax_roll = fig2.add_subplot(4, 3, 7) + ax_pitch = fig2.add_subplot(4, 3, 8) + ax_yaw = fig2.add_subplot(4, 3, 9) + ax_roll_table = fig2.add_subplot(4, 3, 10) + ax_pitch_table = fig2.add_subplot(4, 3, 11) + ax_yaw_table = fig2.add_subplot(4, 3, 12) + plot_each_error(ax_x, error_data, 'x', 'X (East-West) Error','East error [m]', font_size) + plot_each_error(ax_y, error_data, 'y', 'Y (North-South) Error','North error [m]', font_size) + plot_each_error(ax_z, error_data, 'z', 'Z (Height) Error','Height error [m]', font_size) + plot_each_error(ax_roll, error_data, 'roll', 'Roll Error' , 'Roll error [deg]', font_size) + plot_each_error(ax_pitch, error_data, 'pitch', 'Pitch Error', 'Pitch error [deg]', font_size) + plot_each_error(ax_yaw, error_data, 'yaw', 'Yaw Error', 'Yaw error [deg]', font_size) + plot_table(ax_x_table, error_table[error_table["data"] == "x"], "[m]", font_size) + plot_table(ax_y_table, error_table[error_table["data"] == "y"], "[m]", font_size) + plot_table(ax_z_table, error_table[error_table["data"] == "z"], "[m]", font_size) + plot_table(ax_roll_table, error_table[error_table["data"] == "roll"], "[deg]", font_size) + plot_table(ax_pitch_table, error_table[error_table["data"] == "pitch"], "[deg]", font_size) + plot_table(ax_yaw_table, error_table[error_table["data"] == "yaw"], "[deg]", font_size) + +def plot_table(ax, error_table, unit, font_size): + table_data = error_table.set_index('data') + table = ax.table(cellText = table_data.values, colLabels = table_data.columns + unit, loc = 'center') + table.auto_set_font_size(False) + table.set_fontsize(font_size) + ax.axis('tight') + ax.axis('off') + +def plot_error(error_data,ref_data_name, font_size): + fig4 = plt.figure() + fig4.suptitle(ref_data_name + ' - eagleye Error') + ax_x = fig4.add_subplot(2, 2, 1) + ax_y = fig4.add_subplot(2, 2, 2) + ax_across = fig4.add_subplot(2, 2, 3) + ax_along = fig4.add_subplot(2, 2, 4) + plot_each_error(ax_x, error_data, 'x', 'X (East-West) Error','East error [m]', font_size) + plot_each_error(ax_y, error_data, 'y', 'Y (North-South) Error','North error [m]', font_size) + plot_each_error(ax_across, error_data, 'across', 'Across Error','across error [m]', font_size) + plot_each_error(ax_along, error_data, 'along', 'Along Error','along error [m]', font_size) + +def plot_error_distributiln(error_data, font_size,ref_data_name): + fig5 = plt.figure() + fig5.suptitle(ref_data_name + ' - eagleye Error') + ax_xy = fig5.add_subplot(1, 2, 1) + ax_aa = fig5.add_subplot(1, 2, 2) + plot_one(ax_xy, error_data, 'x', 'y', 'X-Y error', 'x error [m]', 'y error [m]', 'None', 1, font_size) + plot_one(ax_aa, error_data, 'across', 'along', 'Across-Along error', 'across error [m]', 'along error [m]', 'None', 1, font_size) + ax_xy.set_aspect('equal') + ax_xy.axis('square') + ax_aa.set_aspect('equal') + ax_aa.axis('square') + +def plot_traj(ref_data, data, font_size,data_name, ref_data_name): + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.plot(ref_data['x']-ref_data['x'][0] , ref_data['y']-ref_data['y'][0] , marker=".",linestyle="None",markersize=3, color = "red", label=ref_data_name) + ax.plot(data['x']-ref_data['x'][0] , data['y']-ref_data['y'][0] , marker="s",linestyle="None",markersize=3,alpha=0.3 , color = "blue", label=data_name) + ax.set_xlabel('East [m]', fontsize=font_size) + ax.set_ylabel('North [m]', fontsize=font_size) + ax.set_title('2D Trajectory') + ax.legend(loc='upper right') + ax.tick_params(labelsize=font_size) + ax.grid() + ax.set_aspect('equal') + ax.axis('square') + +def plot_traj_text(title, ref_data, data, text, step, font_size, data_name, ref_data_name): + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.plot(ref_data['x']-ref_data['x'][0] , ref_data['y']-ref_data['y'][0] , marker=".",linestyle="None",markersize=3, color = "red", label=ref_data_name) + ax.plot(data['x']-ref_data['x'][0] , data['y']-ref_data['y'][0] , marker="s",linestyle="None",markersize=3,alpha=0.3 , color = "blue", label=data_name) + cnt = 0 + for i in np.arange(0, len(text.index), step): + if text[i] >= cnt * step: + ax.text(ref_data['x'][i] - ref_data['x'][0] , ref_data['y'][i] - ref_data['y'][0] , int(text[i]), size=font_size) + cnt = cnt + 1 + ax.set_xlabel('East [m]', fontsize=font_size) + ax.set_ylabel('North [m]', fontsize=font_size) + ax.set_title(title) + ax.legend(loc='upper right') + ax.tick_params(labelsize=font_size) + ax.grid() + ax.set_aspect('equal') + ax.axis('square') + +def plot_traj_text_tree(title, raw_data, ref_data, data, text, step, font_size, data_name, ref_data_name): + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.plot(raw_data['x']-ref_data['x'][0] , raw_data['y']-ref_data['y'][0] , marker=".",linestyle="None",markersize=1, color = "red", label="gnss raw data(rtklib)") + ax.plot(ref_data['x']-ref_data['x'][0] , ref_data['y']-ref_data['y'][0] , marker=".",linestyle="None",markersize=3, color = "green", label=ref_data_name) + ax.plot(data['x']-ref_data['x'][0] , data['y']-ref_data['y'][0] , marker="s",linestyle="None",markersize=3,alpha=0.3 , color = "blue", label=data_name) + cnt = 0 + for i in np.arange(0, len(text.index), step): + if text[i] >= cnt * step: + ax.text(ref_data['x'][i] - ref_data['x'][0] , ref_data['y'][i] - ref_data['y'][0] , int(text[i]), size=16) + cnt = cnt + 1 + ax.set_xlabel('East [m]', fontsize=font_size) + ax.set_ylabel('North [m]', fontsize=font_size) + ax.set_title(title) + ax.legend(loc='upper right') + ax.tick_params(labelsize=font_size) + ax.grid() + ax.set_aspect('equal') + ax.axis('square') + +def plot_traj_three(raw_data, ref_data, data, font_size, data_name, ref_data_name): + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1) + ax.plot(raw_data['x']-ref_data['x'][0] , raw_data['y']-ref_data['y'][0] , marker=".",linestyle="None",markersize=1, color = "red", label="gnss raw data(rtklib)") + ax.plot(ref_data['x']-ref_data['x'][0] , ref_data['y']-ref_data['y'][0] , marker=".",linestyle="None",markersize=1, color = "green", label=ref_data_name) + ax.plot(data['x']-ref_data['x'][0] , data['y']-ref_data['y'][0] , marker="s",linestyle="None",markersize=1,alpha=0.3 , color = "blue", label=data_name) + ax.set_xlabel('East [m]', fontsize=font_size) + ax.set_ylabel('North [m]', fontsize=font_size) + ax.set_title('2D Trajectory') + ax.legend(loc='upper right') + ax.tick_params(labelsize=font_size) + ax.grid() + ax.set_aspect('equal') + ax.axis('square') + +def plot_traj_3d(ref_data, data, font_size, data_name, ref_data_name): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.set_title('3D Trajectory') + ax.plot3D(ref_data['x'] - ref_data['x'][0] , ref_data['y'] - ref_data['y'][0] ,ref_data['z'] - ref_data['z'][0] , marker=".",linestyle="None",markersize=1, color = "red",label=ref_data_name) + ax.plot3D(data['x'] - ref_data['x'][0] , data['y'] - ref_data['y'][0] ,data['z'] - ref_data['z'][0] , marker="s",linestyle="None",markersize=1, alpha=0.3, color = "blue",label=data_name) + ax.set_xlabel('East [m]', fontsize=font_size) + ax.set_ylabel('North [m]', fontsize=font_size) + ax.set_zlabel('Height [m]', fontsize=font_size) + ax.legend(loc='upper right') + ax.tick_params(labelsize=font_size) + ax.grid() + +def plot_traj_3d_three(raw_data, ref_data, data, font_size, data_name, ref_data_name): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.set_title('3D Trajectory') + ax.plot3D(raw_data['x'] - ref_data['x'][0] , raw_data['y'] - ref_data['y'][0] ,raw_data['z'] - ref_data['z'][0] , marker=".",linestyle="None",markersize=1, color = "red",label="gnss raw data(rtklib)") + ax.plot3D(ref_data['x'] - ref_data['x'][0] , ref_data['y'] - ref_data['y'][0] ,ref_data['z'] - ref_data['z'][0] , marker=".",linestyle="None",markersize=1, color = "green",label=ref_data_name) + ax.plot3D(data['x'] - ref_data['x'][0] , data['y'] - ref_data['y'][0] ,data['z'] - ref_data['z'][0] , marker="s",linestyle="None",markersize=1, alpha=0.3, color = "blue",label=data_name) + ax.set_xlabel('East [m]', fontsize=font_size) + ax.set_ylabel('North [m]', fontsize=font_size) + ax.set_zlabel('Height [m]', fontsize=font_size) + ax.legend(loc='upper right') + ax.tick_params(labelsize=font_size) + ax.grid() + +def plot_traj_qual(xyz, qual, text, step, font_size): + gnss_single = judge_qual(xyz,qual,1) + gnss_differential = judge_qual(xyz,qual,2) + gnss_fix = judge_qual(xyz,qual,4) + gnss_float = judge_qual(xyz,qual,5) + + fig = plt.figure() + ax_fix = fig.add_subplot(1, 1, 1) + ax_fix.set_title('GNSS positioning solution') + ax_fix.plot(gnss_single['x']-xyz['x'][0] , gnss_single['y']-xyz['y'][0] , marker=".",linestyle="None",markersize=1, color = "red", label="gnss single") + ax_fix.plot(gnss_differential['x']-xyz['x'][0] , gnss_differential['y']-xyz['y'][0] , marker=".",linestyle="None",markersize=1, color = "blue", label="gnss differential") + ax_fix.plot(gnss_fix['x']-xyz['x'][0] , gnss_fix['y']-xyz['y'][0] , marker=".",linestyle="None",markersize=1, color = "green", label="gnss fix") + ax_fix.plot(gnss_float['x']-xyz['x'][0] , gnss_float['y']-xyz['y'][0] , marker=".",linestyle="None",markersize=1, color = "yellow", label="gnss float") + cnt = 0 + for i in np.arange(0, len(text.index), step): + if text[i] >= cnt * step: + ax_fix.text(xyz['x'][i] - xyz['x'][0] , xyz['y'][i] - xyz['y'][0] , int(text[i]), size=16) + cnt = cnt + 1 + ax_fix.set_xlabel('East [m]', fontsize=font_size) + ax_fix.set_ylabel('North [m]', fontsize=font_size) + ax_fix.legend(loc='upper right') + ax_fix.tick_params(labelsize=font_size) + ax_fix.grid() + ax_fix.set_aspect('equal','box') + ax_fix.axis('square') diff --git a/sub_scripts/util_eagleye/preprocess.py b/sub_scripts/util_eagleye/preprocess.py new file mode 100644 index 0000000..8eed66a --- /dev/null +++ b/sub_scripts/util_eagleye/preprocess.py @@ -0,0 +1,481 @@ +# Copyright (c) 2022, Map IV, Inc. +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# * Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# * Neither the name of the Map IV, Inc. nor the names of its contributors +# may be used to endorse or promote products derived from this software +# without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL COPYRIGHT HOLDER BE LIABLE FOR ANY +# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +# preprocess.py +# Author MapIV Hoda + +from typing import List +import pandas as pd +import numpy as np +import math +import yaml + +import util_eagleye.calc as util_calc + +def set_ref_data(input_path,config): # Creation of dataset with reference to column number + use_separate_time_stamp = config["ref_index"]["separate_time_stamp"] + index_time_unit = config["ref_index"]["time_unit"] + use_quaternion_flag = config["ref_index"]["use_quaternion"] + use_radian_flag = config["ref_index"]["use_radian"] + use_vel_flag = config["ref_index"]["use_vel"] + use_angular_flag = config["ref_index"]["use_angular"] + use_qual_flag = config["ref_index"]["use_qual"] + index_time_sec = config["ref_index"]["time_sec"] + index_time_nsec = config["ref_index"]["time_nsec"] + index_time = config["ref_index"]["time"] + index_x = config["ref_index"]["x"] + index_y = config["ref_index"]["y"] + index_z = config["ref_index"]["z"] + index_ori_x = config["ref_index"]["ori_x"] + index_ori_y = config["ref_index"]["ori_y"] + index_ori_z = config["ref_index"]["ori_z"] + index_ori_w = config["ref_index"]["ori_w"] + index_roll = config["ref_index"]["roll"] + index_pitch = config["ref_index"]["pitch"] + index_yaw = config["ref_index"]["yaw"] + index_vel_x = config["ref_index"]["vel_x"] + index_vel_y = config["ref_index"]["vel_y"] + index_vel_z = config["ref_index"]["vel_z"] + index_angular_x = config["ref_index"]["angular_x"] + index_angular_y = config["ref_index"]["angular_y"] + index_angular_z = config["ref_index"]["angular_z"] + tmp_df = pd.read_csv(input_path,header=0, index_col=None) + if use_separate_time_stamp == True: + ref_data_time = pd.Series(tmp_df.iloc[:,index_time_sec] + tmp_df.iloc[:,index_time_nsec] * 10 ** (-9), name='TimeStamp') + elif index_time_unit == 1: + ref_data_time = pd.Series(tmp_df.iloc[:,index_time] * 10 ** (-9), name='TimeStamp') + else: + ref_data_time = pd.Series(tmp_df.iloc[:,index_time], name='TimeStamp') + ref_data_x = pd.Series(tmp_df.iloc[:,index_x], name='x') + ref_data_y = pd.Series(tmp_df.iloc[:,index_y], name='y') + ref_data_z = pd.Series(tmp_df.iloc[:,index_z], name='z') + ref_data_xyz = pd.concat([ref_data_x,ref_data_y,ref_data_z],axis=1) + if use_quaternion_flag == True: + ref_data_ori_x = pd.Series(tmp_df.iloc[:,index_ori_x], name='ori_x') + ref_data_ori_y = pd.Series(tmp_df.iloc[:,index_ori_y], name='ori_y') + ref_data_ori_z = pd.Series(tmp_df.iloc[:,index_ori_z], name='ori_z') + ref_data_ori_w = pd.Series(tmp_df.iloc[:,index_ori_w], name='ori_w') + ref_data_ori = pd.concat([ref_data_ori_x,ref_data_ori_y,ref_data_ori_z,ref_data_ori_w],axis=1) + ref_rpy = util_calc.quaternion_to_euler_zyx(ref_data_ori) + elif use_quaternion_flag == False and use_radian_flag == True: + ref_data_roll = pd.Series(tmp_df.iloc[:,index_roll], name='roll') + ref_data_pitch = pd.Series(tmp_df.iloc[:,index_pitch], name='pitch') + ref_data_yaw_tmp = pd.Series(tmp_df.iloc[:,index_yaw], name='yaw') + ref_data_yaw = util_calc.change_anglel_limit(ref_data_yaw_tmp) + ref_rpy = pd.concat([np.rad2deg(ref_data_roll),np.rad2deg(ref_data_pitch),np.rad2deg(ref_data_yaw)],axis=1) + elif use_quaternion_flag == False and use_radian_flag == False: + ref_data_roll = pd.Series(tmp_df.iloc[:,index_roll], name='roll') + ref_data_pitch = pd.Series(tmp_df.iloc[:,index_pitch], name='pitch') + ref_data_yaw_tmp = pd.Series(tmp_df.iloc[:,index_yaw], name='yaw') + ref_data_yaw = util_calc.change_anglel_limit(np.deg2rad(ref_data_yaw_tmp)) + ref_rpy = pd.concat([ref_data_roll,ref_data_pitch,np.rad2deg(ref_data_yaw)],axis=1) + + set_ref_vel = pd.DataFrame() + if use_vel_flag == True: + ref_data_vel_x = pd.Series(tmp_df.iloc[:,index_vel_x], name='vel_x') + ref_data_vel_y = pd.Series(tmp_df.iloc[:,index_vel_y], name='vel_y') + ref_data_vel_z = pd.Series(tmp_df.iloc[:,index_vel_z], name='vel_z') + set_ref_vel_tmp = pd.concat([ref_data_vel_x, ref_data_vel_y, ref_data_vel_z],axis=1) + ref_velocity = util_calc.calc_velocity(set_ref_vel_tmp) + distance = util_calc.calc_distance_vel(ref_velocity["velocity"],ref_data_time) + set_ref_vel = pd.concat([set_ref_vel_tmp, ref_velocity],axis=1) + else: + distance = util_calc.calc_distance_xy(ref_data_xyz) + set_ref_angular = pd.DataFrame() + if use_angular_flag == True: + ref_data_angular_x = pd.Series(tmp_df.iloc[:,index_angular_x], name='angular_x') + ref_data_angular_y = pd.Series(tmp_df.iloc[:,index_angular_y], name='angular_y') + ref_data_angular_z = pd.Series(tmp_df.iloc[:,index_angular_z], name='angular_z') + set_ref_angular = pd.concat([ref_data_angular_x, ref_data_angular_y, ref_data_angular_z],axis=1) + if use_qual_flag == True: + index_qual = config["ref_index"]["qual"] + ref_data_qual = pd.Series(tmp_df.iloc[:,index_qual], name='qual') + else: + index_qual = config["ref_index"]["qual"] + ref_data_qual = pd.Series(tmp_df.iloc[:,index_qual], name='qual_not_set') + set_ref_df = pd.concat([ref_data_time, ref_data_xyz, ref_rpy, set_ref_vel, set_ref_angular, distance, ref_data_qual],axis=1) + return set_ref_df + +def set_target_data(input_path,config): # Creation of dataset with reference to column number + use_separate_time_stamp = config["target_data_index"]["separate_time_stamp"] + index_time_unit = config["target_data_index"]["time_unit"] + use_qual_flag = config["target_data_index"]["use_qual"] + index_time_sec = config["target_data_index"]["time_sec"] + index_time_nsec = config["target_data_index"]["time_nsec"] + index_time = config["target_data_index"]["time"] + index_x = config["target_data_index"]["x"] + index_y = config["target_data_index"]["y"] + index_z = config["target_data_index"]["z"] + index_ori_x = config["target_data_index"]["ori_x"] + index_ori_y = config["target_data_index"]["ori_y"] + index_ori_z = config["target_data_index"]["ori_z"] + index_ori_w = config["target_data_index"]["ori_w"] + tmp_df = pd.read_csv(input_path,header=0, index_col=None) + if use_separate_time_stamp == True: + data_time = pd.Series(tmp_df.iloc[:,index_time_sec] + tmp_df.iloc[:,index_time_nsec] * 10 ** (-9), name='TimeStamp') + elif index_time_unit == 1: + data_time = pd.Series(tmp_df.iloc[:,index_time] * 10 ** (-9), name='TimeStamp') + else: + data_time = pd.Series(tmp_df.iloc[:,index_time], name='TimeStamp') + data_x = pd.Series(tmp_df.iloc[:,index_x], name='x') + data_y = pd.Series(tmp_df.iloc[:,index_y], name='y') + data_z = pd.Series(tmp_df.iloc[:,index_z], name='z') + data_ori_x = pd.Series(tmp_df.iloc[:,index_ori_x], name='ori_x') + data_ori_y = pd.Series(tmp_df.iloc[:,index_ori_y], name='ori_y') + data_ori_z = pd.Series(tmp_df.iloc[:,index_ori_z], name='ori_z') + data_ori_w = pd.Series(tmp_df.iloc[:,index_ori_w], name='ori_w') + if use_qual_flag == True: + index_qual = config["target_data_index"]["qual"] + data_qual = pd.Series(tmp_df.iloc[:,index_qual], name='qual') + else: + index_qual = config["target_data_index"]["qual"] + data_qual = pd.Series(tmp_df.iloc[:,index_qual], name='qual_not_set') + set_df = pd.concat([data_time, data_x, data_y, data_z, data_ori_x, data_ori_y, data_ori_z, data_ori_w, data_qual],axis=1) + return set_df + +def set_twist_data(input_path,config): # Creation of dataset with reference to column number + use_separate_time_stamp = config["twist_index"]["separate_time_stamp"] + index_time_unit = config["twist_index"]["time_unit"] + index_time_sec = config["twist_index"]["time_sec"] + index_time_nsec = config["twist_index"]["time_nsec"] + index_time = config["twist_index"]["time"] + index_linear_x = config["twist_index"]["linear_x"] + index_linear_y = config["twist_index"]["linear_y"] + index_linear_z = config["twist_index"]["linear_z"] + index_angular_x = config["twist_index"]["angular_x"] + index_angular_y = config["twist_index"]["angular_y"] + index_angular_z = config["twist_index"]["angular_z"] + tmp_df = pd.read_csv(input_path,header=0, index_col=None) + if use_separate_time_stamp == True: + data_time = pd.Series(tmp_df.iloc[:,index_time_sec] + tmp_df.iloc[:,index_time_nsec] * 10 ** (-9), name='TimeStamp') + elif index_time_unit == 1: + data_time = pd.Series(tmp_df.iloc[:,index_time] * 10 ** (-9), name='TimeStamp') + else: + data_time = pd.Series(tmp_df.iloc[:,index_time], name='TimeStamp') + data_linear_x = pd.Series(tmp_df.iloc[:,index_linear_x], name='velocity') + data_linear_y = pd.Series(tmp_df.iloc[:,index_linear_y], name='linear_y') + data_linear_z = pd.Series(tmp_df.iloc[:,index_linear_z], name='linear_z') + data_angular_x = pd.Series(tmp_df.iloc[:,index_angular_x], name='angular_x') + data_angular_y = pd.Series(tmp_df.iloc[:,index_angular_y], name='angular_y') + data_angular_z = pd.Series(tmp_df.iloc[:,index_angular_z], name='angular_z') + set_df = pd.concat([data_time, data_linear_x, data_linear_y, data_linear_z, data_angular_x, data_angular_y, data_angular_z],axis=1) + return set_df + +def set_log_df(input,plane,config): # Creation of dataset with reference to labels in df + df = pd.read_csv(input,delimiter=None, header='infer', index_col=None, usecols=None) + index_time_unit = config["eagleye_log"]["time_unit"] + tf_num = config["eagleye_log"]["tf_num"] + ros_reverse_imu = config["eagleye_log"]["ros_reverse_imu"] + missing_gnss_type = config["eagleye_log"]["missing_gnss_type"] + eagleye_df = df[["timestamp", + "rtklib_nav.tow", + "velocity_scale_factor.scale_factor", + "correction_velocity.twist.linear.x", + "distance.distance", + "enu_absolute_pos_interpolate.ecef_base_pos.x", + "enu_absolute_pos_interpolate.ecef_base_pos.y", + "enu_absolute_pos_interpolate.ecef_base_pos.z", + "rolling.rolling_angle", + "pitching.pitching_angle", + "heading_interpolate_1st.heading_angle", + "heading_interpolate_2nd.heading_angle", + "heading_interpolate_3rd.heading_angle", + "yawrate_offset_stop.yawrate_offset", + "yawrate_offset_2nd.yawrate_offset", + "slip_angle.slip_angle", + "enu_vel.vector.x", + "enu_vel.vector.y", + "enu_vel.vector.z", + "eagleye_pp_llh.latitude", + "eagleye_pp_llh.longitude", + "eagleye_pp_llh.altitude", + "imu.angular_velocity.x", + "imu.angular_velocity.y", + "imu.angular_velocity.z", + 'gga_llh.gps_qual', + ]] + + eagleye_df = eagleye_df.rename(columns={'timestamp': 'TimeStamp_tmp', + 'rtklib_nav.tow': 'TOW', + 'velocity_scale_factor.scale_factor': 'sf', + 'correction_velocity.twist.linear.x': 'velocity', + 'distance.distance': 'distance', + 'enu_absolute_pos_interpolate.ecef_base_pos.x': 'ecef_base_x', + 'enu_absolute_pos_interpolate.ecef_base_pos.y': 'ecef_base_y', + 'enu_absolute_pos_interpolate.ecef_base_pos.z': 'ecef_base_z', + 'rolling.rolling_angle': 'roll_rad', + 'pitching.pitching_angle': 'pitch_rad', + 'heading_interpolate_1st.heading_angle': 'heading_1st', + 'heading_interpolate_2nd.heading_angle': 'heading_2nd', + 'heading_interpolate_3rd.heading_angle': 'yaw_rad', + 'yawrate_offset_stop.yawrate_offset': 'yawrate_offset_stop', + 'yawrate_offset_2nd.yawrate_offset': 'yawrate_offset', + 'slip_angle.slip_angle': 'slip', + 'enu_vel.vector.x': 'vel_x', + 'enu_vel.vector.y': 'vel_y', + 'enu_vel.vector.z': 'vel_z', + 'eagleye_pp_llh.latitude': 'latitude', + 'eagleye_pp_llh.longitude': 'longitude', + 'eagleye_pp_llh.altitude': 'altitude', + "imu.angular_velocity.x":'angular_x', + "imu.angular_velocity.y":'angular_y', + "imu.angular_velocity.z":'angular_z', + 'gga_llh.gps_qual': 'qual', + }) + + raw_df = df[["timestamp", + "rtklib_nav.tow", + "velocity.twist.linear.x", + "distance.distance", + "imu.angular_velocity.x", + "imu.angular_velocity.y", + "imu.angular_velocity.z", + "rtklib_nav.ecef_vel.x", + "rtklib_nav.ecef_vel.y", + "rtklib_nav.ecef_vel.z", + "rtklib_nav.status.latitude", + "rtklib_nav.status.longitude", + "rtklib_nav.status.altitude", + "gga_llh.latitude", + "gga_llh.longitude", + "gga_llh.altitude", + "gga_llh.gps_qual", + ]] + + raw_df = raw_df.rename(columns={'timestamp': 'TimeStamp_tmp', + 'rtklib_nav.tow': 'TOW', + 'velocity.twist.linear.x': 'velocity', + 'distance.distance': 'distance', + 'imu.angular_velocity.x': 'rollrate', + 'imu.angular_velocity.y': 'pitchrate', + 'imu.angular_velocity.z': 'yawrate', + 'rtklib_nav.ecef_vel.x': 'vel_x', + 'rtklib_nav.ecef_vel.y': 'vel_y', + 'rtklib_nav.ecef_vel.z': 'vel_z', + 'rtklib_nav.status.latitude': 'latitude', + 'rtklib_nav.status.longitude': 'longitude', + 'rtklib_nav.status.altitude': 'altitude', + 'gga_llh.latitude': 'rtk_latitude', + 'gga_llh.longitude': 'rtk_longitude', + 'gga_llh.altitude': 'rtk_altitude', + 'gga_llh.gps_qual': 'qual', + }) + + raw_index = raw_df[raw_df['latitude'] == 0].index + rtk_index = raw_df[raw_df['rtk_latitude'] == 0].index + if missing_gnss_type == 1: + raw_df = raw_df.drop(rtk_index) + eagleye_df = eagleye_df.drop(rtk_index) + elif missing_gnss_type == 2: + raw_df = raw_df.drop(raw_index) + eagleye_df = eagleye_df.drop(raw_index) + elif len(rtk_index) < len(raw_index): + raw_df = raw_df.drop(raw_index) + eagleye_df = eagleye_df.drop(raw_index) + else: + raw_df = raw_df.drop(rtk_index) + eagleye_df = eagleye_df.drop(rtk_index) + raw_df = raw_df.reset_index() + eagleye_df = eagleye_df.reset_index() + + if index_time_unit == 1: + eagleye_df['TimeStamp'] = eagleye_df['TimeStamp_tmp'] * 10 ** (-9) + raw_df['TimeStamp'] = raw_df['TimeStamp_tmp'] * 10 ** (-9) + elif index_time_unit == 0: + eagleye_df['TimeStamp'] = eagleye_df['TimeStamp_tmp'] + raw_df['TimeStamp'] = raw_df['TimeStamp_tmp'] + eagleye_df['elapsed_time'] = eagleye_df['TimeStamp'] - raw_df['TimeStamp'][0] + raw_df['elapsed_time'] = raw_df['TimeStamp'] - raw_df['TimeStamp'][0] + eagleye_df['yaw'] = np.rad2deg(util_calc.change_anglel_limit(eagleye_df['yaw_rad'])) + eagleye_df['roll'] = np.rad2deg(eagleye_df['roll_rad']) + eagleye_df['pitch'] = np.rad2deg(eagleye_df['pitch_rad']) + llh = pd.concat([eagleye_df['latitude'],eagleye_df['longitude'],eagleye_df['altitude']],axis=1) + if tf_num == 0: + xyz = latlon_to_19(llh,plane) + elif tf_num == 1: + xyz = util_calc.ll2mgrs(llh) + if ros_reverse_imu == True: + eagleye_df['angular_z'] = -1 * eagleye_df['angular_z'] + eagleye_df['yawrate_offset_stop'] = -1 * eagleye_df['yawrate_offset_stop'] + eagleye_df['yawrate_offset'] = -1 * eagleye_df['yawrate_offset'] + eagleye_df['slip'] = -1 * eagleye_df['slip'] + set_eagleye_df = pd.concat([eagleye_df, xyz],axis=1) + return set_eagleye_df, raw_df + +def set_tf_xy(xy,tf_x,tf_y): + xy['x'] = xy['x'] + tf_x + xy['y'] = xy['y'] + tf_y + return xy + +def correct_anntenapos(eagleye_df,ref_yaw,tf_across,tf_along,tf_height): + d = [[tf_across],[tf_along],[0]] + for i in range(len(ref_yaw)): + sinphi = math.sin(math.radians(ref_yaw[i])) + cosphi = math.cos(math.radians(ref_yaw[i])) + R = [[cosphi , sinphi , 0],[-sinphi , cosphi , 0],[0,0,0]] + diff = np.dot(R, d) + eagleye_df['x'][i] = eagleye_df['x'][i] + diff[0] + eagleye_df['y'][i] = eagleye_df['y'][i] + diff[1] + eagleye_df['z'] = eagleye_df['z'] + tf_height + return eagleye_df + +def latlon_to_19(llh,plane): + phi0_deg , lambda0_deg = plane_table(plane) + set_xy: List[float] = [] + for i in range(len(llh)): + phi_deg=llh['latitude'][i] + lambda_deg=llh['longitude'][i] + z=llh['altitude'][i] + """ Converts latitude and longitude to xy in plane rectangular coordinates + - input: + (phi_deg, lambda_deg): Latitude and longitude [degrees] to be converted + (note that these are decimal numbers, not minutes and seconds) + (phi0_deg, lambda0_deg): Latitude and longitude [degrees] of the origin of the rectangular coordinate system + (note that these are decimal degrees, not minutes and seconds) + - output: + x: Transformed plane rectangular coordinates[m] + y: Transformed plane rectangular coordinates[m] + """ + # Correcting the latitude-longitude and plane-rectangular coordinate system origin from degree to radian + phi_rad = np.deg2rad(phi_deg) + lambda_rad = np.deg2rad(lambda_deg) + phi0_rad = np.deg2rad(phi0_deg) + lambda0_rad = np.deg2rad(lambda0_deg) + + # Auxiliary function + def A_array(n): + A0 = 1 + (n**2)/4. + (n**4)/64. + A1 = - (3./2)*( n - (n**3)/8. - (n**5)/64. ) + A2 = (15./16)*( n**2 - (n**4)/4. ) + A3 = - (35./48)*( n**3 - (5./16)*(n**5) ) + A4 = (315./512)*( n**4 ) + A5 = -(693./1280)*( n**5 ) + return np.array([A0, A1, A2, A3, A4, A5]) + + def alpha_array(n): + a0 = np.nan # dummy + a1 = (1./2)*n - (2./3)*(n**2) + (5./16)*(n**3) + (41./180)*(n**4) - (127./288)*(n**5) + a2 = (13./48)*(n**2) - (3./5)*(n**3) + (557./1440)*(n**4) + (281./630)*(n**5) + a3 = (61./240)*(n**3) - (103./140)*(n**4) + (15061./26880)*(n**5) + a4 = (49561./161280)*(n**4) - (179./168)*(n**5) + a5 = (34729./80640)*(n**5) + return np.array([a0, a1, a2, a3, a4, a5]) + + # Constant (a, F: World Geodetic System - Geodetic Reference System 1980 (GRS80) Ellipsoid) + m0 = 0.9999 + a = 6378137. + F = 298.257222101 + + # (1), Calculation of n, A_i, alpha_i + n = 1. / (2*F - 1) + A_array = A_array(n) + alpha_array = alpha_array(n) + + # (2), Calculation of S, A + A_ = ( (m0*a)/(1.+n) )*A_array[0] # [m] + S_ = ( (m0*a)/(1.+n) )*( A_array[0]*phi0_rad + np.dot(A_array[1:], np.sin(2*phi0_rad*np.arange(1,6))) ) # [m] + + # (3) Calculation of lambda_c, lambda_s + lambda_c = np.cos(lambda_rad - lambda0_rad) + lambda_s = np.sin(lambda_rad - lambda0_rad) + + # (4)Calculation of t, t_ + t = np.sinh( np.arctanh(np.sin(phi_rad)) - ((2*np.sqrt(n)) / (1+n))*np.arctanh(((2*np.sqrt(n)) / (1+n)) * np.sin(phi_rad)) ) + t_ = np.sqrt(1 + t*t) + + # (5) Calculation of xi', eta' + xi2 = np.arctan(t / lambda_c) # [rad] + eta2 = np.arctanh(lambda_s / t_) + + # (6) Calculation of x, y + x = A_ * (xi2 + np.sum(np.multiply(alpha_array[1:], + np.multiply(np.sin(2*xi2*np.arange(1,6)), + np.cosh(2*eta2*np.arange(1,6)))))) - S_ # [m] + y = A_ * (eta2 + np.sum(np.multiply(alpha_array[1:], + np.multiply(np.cos(2*xi2*np.arange(1,6)), + np.sinh(2*eta2*np.arange(1,6)))))) # [m] + set_xy.append([y , x , z]) + xyz = pd.DataFrame(set_xy,columns=['x','y','z']) + return xyz + +def plane_table(plane): + if plane == 1: + phi0_deg = 33 + lambda0_deg = 129+30./60 + if plane == 2: + phi0_deg = 33 + lambda0_deg = 131 + if plane == 3: + phi0_deg = 36 + lambda0_deg = 132+10./60 + if plane == 4: + phi0_deg = 33 + lambda0_deg = 133+30./60 + if plane == 5: + phi0_deg = 36 + lambda0_deg = 134+20./60 + if plane == 6: + phi0_deg = 36 + lambda0_deg = 136 + if plane == 7: + phi0_deg = 36 + lambda0_deg = 137+10./60 + if plane == 8: + phi0_deg = 36 + lambda0_deg = 138+30./60 + if plane == 9: + phi0_deg = 36 + lambda0_deg = 139+50./60 + if plane == 10: + phi0_deg = 40 + lambda0_deg = 140+50./60 + if plane == 11: + phi0_deg = 44 + lambda0_deg = 140+15./60 + if plane == 12: + phi0_deg = 44 + lambda0_deg = 142+15./60 + if plane == 13: + phi0_deg = 44 + lambda0_deg = 144+15./60 + if plane == 14: + phi0_deg = 26 + lambda0_deg = 142 + if plane == 15: + phi0_deg = 26 + lambda0_deg = 127+30./60 + if plane == 16: + phi0_deg = 26 + lambda0_deg = 124+30./60 + if plane == 17: + phi0_deg = 26 + lambda0_deg = 131 + if plane == 18: + phi0_deg = 26 + lambda0_deg = 136 + if plane == 19: + phi0_deg = 26 + lambda0_deg = 154 + return phi0_deg , lambda0_deg \ No newline at end of file

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