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Update nuScenes & Waymo Optimization (#47)

Browse Source 
* update can bus

* Create LICENSE

* update waymo doc

* protobuf requirement

* just warning

* Add warning for proto

* update PR template

* fix length bug

* try sharing nusc

* imu heading

* fix 161 168

* add badge

* fix doc

* update doc

* format

* update cp

* update nuscenes interface

* update doc

* prediction nuscenes

* use drivable aread for nuscenes

* allow converting prediction

* format

* fix bug

* optimize

* clean RAM

* delete more

* restore to

* add only lane

* use token

* add warning

* format

* fix bug
This commit is contained in:
Quanyi Li
2023-12-02 13:50:44 +00:00
committed by GitHub
parent 7ec01fc8c1
commit 2db22c74a8
18 changed files with 478 additions and 146 deletions
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2
.github/pull_request_template.md vendored
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@@ -6,4 +6,6 @@
* [ ] I have merged the latest main branch into current branch. * [ ] I have merged the latest main branch into current branch.
* [ ] I have run `bash scripts/format.sh` before merging. * [ ] I have run `bash scripts/format.sh` before merging.
* [ ] I updated documentation, if there are related change
* [ ] I add tests if there are new functions or bug-fixing
* Please use "squash and merge" mode. * Please use "squash and merge" mode.

201
LICENSE Normal file
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@@ -0,0 +1,201 @@
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4
README.md
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@@ -1,5 +1,9 @@
# ScenarioNet # ScenarioNet
[![Documentation Status](https://readthedocs.org/projects/scenarionet/badge/?version=latest)](https://scenarionet.readthedocs.io/en/latest/?badge=latest)
[![build](https://github.com/metadriverse/scenarionet/workflows/test/badge.svg)](http://github.com/metadriverse/scenarionet/actions)
[![GitHub license](https://img.shields.io/github/license/metadriverse/scenarionet)](https://github.com/metadriverse/scenarionet/blob/main/LICENSE.txt)
**Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling** **Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling**
[ [

11
documentation/example.rst
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@@ -28,18 +28,17 @@ After that, the scenarios will be loaded to MetaDrive simulator for closed-loop
First of all, please install `MetaDrive <https://github.com/metadriverse/metadrive>`_ and `ScenarioNet <https://github.com/metadriverse/scenarionet>`_ following these steps :ref:`installation`. First of all, please install `MetaDrive <https://github.com/metadriverse/metadrive>`_ and `ScenarioNet <https://github.com/metadriverse/scenarionet>`_ following these steps :ref:`installation`.
1. Setup Waymo toolkit 1. Setup Requirements
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
For any dataset, the first step after installing ScenarioNet is to install the corresponding official toolkit as we need to use it to parse the original data and convert to our internal scenario description. For Waymo data, please install the toolkit via:: For any dataset, the first step after installing ScenarioNet is to install the corresponding official toolkit as we need to use it to parse the original data and convert to our internal scenario description.
For Waymo data, we already have the parser in ScenarioNet so just install the TensorFlow and Protobuf via::
pip install waymo-open-dataset-tf-2-11-0
pip install tensorflow==2.11.0 pip install tensorflow==2.11.0
conda install protobuf==3.20
.. note:: .. note::
This package is only supported on Linux platform. You may fail to install ``protobuf`` if using ``pip install protobuf==3.20``.
`waymo-open-dataset` may degrade numpy, causing conflicts with `cv2` (`opencv-python`).
A workaround is to ``pip install numpy==1.24.2``
For other datasets like nuPlan and nuScenes, you need to setup `nuplan-devkit <https://github.com/motional/nuplan-devkit>`_ and `nuscenes-devkit <https://github.com/nutonomy/nuscenes-devkit>`_ respectively. For other datasets like nuPlan and nuScenes, you need to setup `nuplan-devkit <https://github.com/motional/nuplan-devkit>`_ and `nuscenes-devkit <https://github.com/nutonomy/nuscenes-devkit>`_ respectively.
Guidance on how to setup these datasets and connect them with ScenarioNet can be found at :ref:`datasets`. Guidance on how to setup these datasets and connect them with ScenarioNet can be found at :ref:`datasets`.

7
documentation/nuplan.rst
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@@ -27,6 +27,9 @@ First of all, we have to install the ``nuplan-devkit``.
pip install -r requirements.txt pip install -r requirements.txt
pip install -e . pip install -e .
# additional requirements
pip install pytorch-lightning
# 2. or install from PyPI # 2. or install from PyPI
pip install nuplan-devkit pip install nuplan-devkit
@@ -51,7 +54,7 @@ Thus please download the following files:
We recommend to download the mini split to test and make yourself familiar with the setup process. We recommend to download the mini split to test and make yourself familiar with the setup process.
All downloaded files are ``.tgz`` files and can be uncompressed by ``tar -zxf xyz.tgz``. All downloaded files are ``.zip`` files and can be uncompressed by ``unzip "*.zip"``.
All data should be placed to ``~/nuplan/dataset`` and the folder structure should comply `file hierarchy <https://nuplan-devkit.readthedocs.io/en/latest/dataset_setup.html#filesystem-hierarchy>`_. All data should be placed to ``~/nuplan/dataset`` and the folder structure should comply `file hierarchy <https://nuplan-devkit.readthedocs.io/en/latest/dataset_setup.html#filesystem-hierarchy>`_.
.. code-block:: text .. code-block:: text
@@ -85,7 +88,7 @@ All data should be placed to ``~/nuplan/dataset`` and the folder structure shoul
│ │ ├── 2021.06.09.17.23.18_veh-38_00773_01140.db │ │ ├── 2021.06.09.17.23.18_veh-38_00773_01140.db
│ │ ├── ... │ │ ├── ...
│ │ └── 2021.10.11.08.31.07_veh-50_01750_01948.db │ │ └── 2021.10.11.08.31.07_veh-50_01750_01948.db
│ └── trainval │ └── train_boston
│ ├── 2021.05.12.22.00.38_veh-35_01008_01518.db │ ├── 2021.05.12.22.00.38_veh-35_01008_01518.db
│ ├── 2021.06.09.17.23.18_veh-38_00773_01140.db │ ├── 2021.06.09.17.23.18_veh-38_00773_01140.db
│ ├── ... │ ├── ...

8
documentation/nuscenes.rst
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@@ -60,7 +60,9 @@ Secondly, all files should be organized to the following structure::
├── maps/ ├── maps/
| ├──basemap/ | ├──basemap/
| ├──prediction/ | ├──prediction/
| ├──expansion/ | └──expansion/
├── can_bus/
| ├──scene-1110_meta.json
| └──... | └──...
├── samples/ ├── samples/
| ├──CAM_BACK | ├──CAM_BACK
@@ -95,9 +97,9 @@ Please try ``nuscenes-devkit/python-sdk/tutorials/nuscenes_tutorial.ipynb`` and
After setup the raw data, convertors in ScenarioNet can read the raw data, convert scenario format and build the database. After setup the raw data, convertors in ScenarioNet can read the raw data, convert scenario format and build the database.
Here we take converting raw data in ``nuscenes-mini`` as an example:: Here we take converting raw data in ``nuscenes-mini`` as an example::
python -m scenarionet.convert_nuscenes -d /path/to/your/database --version v1.0-mini --dataroot /nuscens/data/path python -m scenarionet.convert_nuscenes -d /path/to/your/database --split v1.0-mini --dataroot /nuscens/data/path
The ``version`` is to determine which split to convert. ``dataroot`` is set to ``/data/sets/nuscenes`` by default, The ``split`` is to determine which split to convert. ``dataroot`` is set to ``/data/sets/nuscenes`` by default,
but you need to specify it if your data is stored in any other directory. but you need to specify it if your data is stored in any other directory.
Now all converted scenarios will be placed at ``/path/to/your/database`` and are ready to be used in your work. Now all converted scenarios will be placed at ``/path/to/your/database`` and are ready to be used in your work.

40
documentation/operations.rst
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@@ -113,8 +113,12 @@ Convert nuScenes (Lyft)
.. code-block:: text .. code-block:: text
python -m scenarionet.convert_nuscenes [-h] [--database_path DATABASE_PATH] python -m scenarionet.convert_nuscenes [-h] [--database_path DATABASE_PATH]
[--dataset_name DATASET_NAME] [--version VERSION] [--dataset_name DATASET_NAME]
[--overwrite] [--num_workers NUM_WORKERS] [--split
{v1.0-mini,mini_val,v1.0-test,train,train_val,val,mini_train,v1.0-trainval}]
[--dataroot DATAROOT] [--map_radius MAP_RADIUS]
[--future FUTURE] [--past PAST] [--overwrite]
[--num_workers NUM_WORKERS]
Build database from nuScenes/Lyft scenarios Build database from nuScenes/Lyft scenarios
@@ -124,13 +128,31 @@ Convert nuScenes (Lyft)
directory, The path to place the data directory, The path to place the data
--dataset_name DATASET_NAME, -n DATASET_NAME --dataset_name DATASET_NAME, -n DATASET_NAME
Dataset name, will be used to generate scenario files Dataset name, will be used to generate scenario files
--version VERSION, -v VERSION --split
version of nuscenes data, scenario of this version {v1.0-mini,mini_val,v1.0-test,train,train_val,val,mini_train,v1.0-trainval}
will be converted Which splits of nuScenes data should be sued. If set
to ['v1.0-mini', 'v1.0-trainval', 'v1.0-test'], it
will convert the full log into scenarios with 20
second episode length. If set to ['mini_train',
'mini_val', 'train', 'train_val', 'val'], it will
convert segments used for nuScenes prediction
challenge to scenarios, resulting in more converted
scenarios. Generally, you should choose this parameter
from ['v1.0-mini', 'v1.0-trainval', 'v1.0-test'] to
get complete scenarios for planning unless you want to
use the converted scenario files for prediction task.
--dataroot DATAROOT The path of nuscenes data
--map_radius MAP_RADIUS The size of map
--future FUTURE 6 seconds by default. How many future seconds to
predict. Only available if split is chosen from
['mini_train', 'mini_val', 'train', 'train_val',
'val']
--past PAST 2 seconds by default. How many past seconds are used
for prediction. Only available if split is chosen from
['mini_train', 'mini_val', 'train', 'train_val',
'val']
--overwrite If the database_path exists, whether to overwrite it --overwrite If the database_path exists, whether to overwrite it
--num_workers NUM_WORKERS --num_workers NUM_WORKERS
number of workers to use
This script converted the recorded nuScenes scenario into our scenario descriptions. This script converted the recorded nuScenes scenario into our scenario descriptions.
@@ -187,7 +209,7 @@ we can aggregate them freely and enlarge the database.
.. code-block:: text .. code-block:: text
python -m scenarionet.merge [-h] --database_path DATABASE_PATH --from FROM [FROM ...] python -m scenarionet.merge [-h] --to DATABASE_PATH --from FROM [FROM ...]
[--exist_ok] [--overwrite] [--filter_moving_dist] [--exist_ok] [--overwrite] [--filter_moving_dist]
[--sdc_moving_dist_min SDC_MOVING_DIST_MIN] [--sdc_moving_dist_min SDC_MOVING_DIST_MIN]
@@ -196,7 +218,7 @@ we can aggregate them freely and enlarge the database.
optional arguments: optional arguments:
-h, --help show this help message and exit -h, --help show this help message and exit
--database_path DATABASE_PATH, -d DATABASE_PATH --database_path DATABASE_PATH, -d DATABASE_PATH, --to DATABASE_PATH
The name of the new combined database. It will create The name of the new combined database. It will create
a new directory to store dataset_summary.pkl and a new directory to store dataset_summary.pkl and
dataset_mapping.pkl. If exists_ok=True, those two .pkl dataset_mapping.pkl. If exists_ok=True, those two .pkl

11
documentation/waymo.rst
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@@ -26,18 +26,17 @@ The dataset includes:
- Adjusted some road edge boundary height estimates - Adjusted some road edge boundary height estimates
1. Install Waymo Toolkit 1. Install Requirements
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
First of all, we have to install the waymo toolkit and tensorflow:: First of all, we have to install tensorflow and Protobuf::
pip install waymo-open-dataset-tf-2-11-0
pip install tensorflow==2.11.0 pip install tensorflow==2.11.0
conda install protobuf==3.20
.. note:: .. note::
This package is only supported on Linux platform. You may fail to install ``protobuf`` if using ``pip install protobuf==3.20``.
`waymo-open-dataset` may degrade numpy, causing conflicts with cv2.
A workaround is ``pip install numpy==1.24.2``
2. Download TFRecord 2. Download TFRecord
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

23
scenarionet/builder/utils.py
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@@ -115,17 +115,17 @@ def merge_database(
return summaries, mappings return summaries, mappings
def copy_database( def copy_database(from_path, to_path, exist_ok=False, overwrite=False, copy_raw_data=False, remove_source=False):
from_path, to_path, exist_ok=False, overwrite=False, copy_raw_data=False, remove_source=False, force_move=False
):
if not os.path.exists(from_path): if not os.path.exists(from_path):
raise FileNotFoundError("Can not find database: {}".format(from_path)) raise FileNotFoundError("Can not find database: {}".format(from_path))
if os.path.exists(to_path): if os.path.exists(to_path):
assert exist_ok, "to_directory already exists. Set exists_ok to allow turning it into a database" assert exist_ok, "to_directory already exists. Set exists_ok to allow turning it into a database"
assert not os.path.samefile(from_path, to_path), "to_directory is the same as from_directory. Abort!" assert not os.path.samefile(from_path, to_path), "to_directory is the same as from_directory. Abort!"
files = os.listdir(from_path) files = os.listdir(from_path)
if not force_move and (ScenarioDescription.DATASET.MAPPING_FILE in files official_file_num = sum(
and ScenarioDescription.DATASET.SUMMARY_FILE in files and len(files) > 2): [ScenarioDescription.DATASET.MAPPING_FILE in files, ScenarioDescription.DATASET.SUMMARY_FILE in files]
)
if remove_source and len(files) > official_file_num:
raise RuntimeError( raise RuntimeError(
"The source database is not allowed to move! " "The source database is not allowed to move! "
"This will break the relationship between this database and other database built on it." "This will break the relationship between this database and other database built on it."
@@ -146,9 +146,16 @@ def copy_database(
mappings = {key: "./" for key in summaries.keys()} mappings = {key: "./" for key in summaries.keys()}
save_summary_anda_mapping(summary_file, mapping_file, summaries, mappings) save_summary_anda_mapping(summary_file, mapping_file, summaries, mappings)
if remove_source and ScenarioDescription.DATASET.MAPPING_FILE in files and \ if remove_source:
ScenarioDescription.DATASET.SUMMARY_FILE in files and len(files) == 2: if ScenarioDescription.DATASET.MAPPING_FILE in files and ScenarioDescription.DATASET.SUMMARY_FILE in files \
shutil.rmtree(from_path) and len(files) == 2:
shutil.rmtree(from_path)
logger.info("Successfully remove: {}".format(from_path))
else:
logger.info(
"Failed to remove: {}, as it might contain scenario files "
"or has no summary file or mapping file".format(from_path)
)
def split_database( def split_database(

48
scenarionet/convert_nuscenes.py
View File

@@ -1,11 +1,16 @@
desc = "Build database from nuScenes/Lyft scenarios" desc = "Build database from nuScenes/Lyft scenarios"
if __name__ == '__main__': prediction_split = ["mini_train", "mini_val", "train", "train_val", "val"]
scene_split = ["v1.0-mini", "v1.0-trainval", "v1.0-test"]
if __name__ == "__main__":
import pkg_resources # for suppress warning import pkg_resources # for suppress warning
import argparse import argparse
import os.path import os.path
from functools import partial
from scenarionet import SCENARIONET_DATASET_PATH from scenarionet import SCENARIONET_DATASET_PATH
from scenarionet.converter.nuscenes.utils import convert_nuscenes_scenario, get_nuscenes_scenarios from scenarionet.converter.nuscenes.utils import convert_nuscenes_scenario, get_nuscenes_scenarios, \
get_nuscenes_prediction_split
from scenarionet.converter.utils import write_to_directory from scenarionet.converter.utils import write_to_directory
parser = argparse.ArgumentParser(description=desc) parser = argparse.ArgumentParser(description=desc)
@@ -19,12 +24,29 @@ if __name__ == '__main__':
"--dataset_name", "-n", default="nuscenes", help="Dataset name, will be used to generate scenario files" "--dataset_name", "-n", default="nuscenes", help="Dataset name, will be used to generate scenario files"
) )
parser.add_argument( parser.add_argument(
"--version", "--split",
"-v", default="v1.0-mini",
default='v1.0-mini', choices=scene_split + prediction_split,
help="version of nuscenes data, scenario of this version will be converted " help="Which splits of nuScenes data should be sued. If set to {}, it will convert the full log into scenarios"
" with 20 second episode length. If set to {}, it will convert segments used for nuScenes prediction"
" challenge to scenarios, resulting in more converted scenarios. Generally, you should choose this "
" parameter from {} to get complete scenarios for planning unless you want to use the converted scenario "
" files for prediction task.".format(scene_split, prediction_split, scene_split)
) )
parser.add_argument("--dataroot", default="/data/sets/nuscenes", help="The path of nuscenes data") parser.add_argument("--dataroot", default="/data/sets/nuscenes", help="The path of nuscenes data")
parser.add_argument("--map_radius", default=500, type=float, help="The size of map")
parser.add_argument(
"--future",
default=6,
help="6 seconds by default. How many future seconds to predict. Only "
"available if split is chosen from {}".format(prediction_split)
)
parser.add_argument(
"--past",
default=2,
help="2 seconds by default. How many past seconds are used for prediction."
" Only available if split is chosen from {}".format(prediction_split)
)
parser.add_argument("--overwrite", action="store_true", help="If the database_path exists, whether to overwrite it") parser.add_argument("--overwrite", action="store_true", help="If the database_path exists, whether to overwrite it")
parser.add_argument("--num_workers", type=int, default=8, help="number of workers to use") parser.add_argument("--num_workers", type=int, default=8, help="number of workers to use")
args = parser.parse_args() args = parser.parse_args()
@@ -32,10 +54,14 @@ if __name__ == '__main__':
overwrite = args.overwrite overwrite = args.overwrite
dataset_name = args.dataset_name dataset_name = args.dataset_name
output_path = args.database_path output_path = args.database_path
version = args.version version = args.split
scenarios, nuscs = get_nuscenes_scenarios(args.dataroot, version, args.num_workers)
if version in scene_split:
scenarios, nuscs = get_nuscenes_scenarios(args.dataroot, version, args.num_workers)
else:
scenarios, nuscs = get_nuscenes_prediction_split(
args.dataroot, version, args.past, args.future, args.num_workers
)
write_to_directory( write_to_directory(
convert_func=convert_nuscenes_scenario, convert_func=convert_nuscenes_scenario,
scenarios=scenarios, scenarios=scenarios,
@@ -45,4 +71,8 @@ if __name__ == '__main__':
overwrite=overwrite, overwrite=overwrite,
num_workers=args.num_workers, num_workers=args.num_workers,
nuscenes=nuscs, nuscenes=nuscs,
past=[args.past for _ in range(args.num_workers)],
future=[args.future for _ in range(args.num_workers)],
prediction=[version in prediction_split for _ in range(args.num_workers)],
map_radius=[args.map_radius for _ in range(args.num_workers)],
) )

2
scenarionet/converter/nuplan/utils.py
View File

@@ -35,7 +35,7 @@ try:
NUPLAN_PACKAGE_PATH = os.path.dirname(nuplan.__file__) NUPLAN_PACKAGE_PATH = os.path.dirname(nuplan.__file__)
except ImportError as e: except ImportError as e:
logger.warning("Can not import nuplan-devkit: {}".format(e)) raise RuntimeError(e)
EGO = "ego" EGO = "ego"

246
scenarionet/converter/nuscenes/utils.py
View File

@@ -5,12 +5,16 @@ import geopandas as gpd
import numpy as np import numpy as np
from metadrive.scenario import ScenarioDescription as SD from metadrive.scenario import ScenarioDescription as SD
from metadrive.type import MetaDriveType from metadrive.type import MetaDriveType
from nuscenes.eval.prediction.splits import get_prediction_challenge_split
from shapely.ops import unary_union from shapely.ops import unary_union
from scenarionet.converter.nuscenes.type import ALL_TYPE, HUMAN_TYPE, BICYCLE_TYPE, VEHICLE_TYPE from scenarionet.converter.nuscenes.type import ALL_TYPE, HUMAN_TYPE, BICYCLE_TYPE, VEHICLE_TYPE
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
try: try:
import logging
logging.getLogger('shapely.geos').setLevel(logging.CRITICAL)
from nuscenes import NuScenes from nuscenes import NuScenes
from nuscenes.can_bus.can_bus_api import NuScenesCanBus from nuscenes.can_bus.can_bus_api import NuScenesCanBus
from nuscenes.eval.common.utils import quaternion_yaw from nuscenes.eval.common.utils import quaternion_yaw
@@ -199,11 +203,16 @@ def get_tracks_from_frames(nuscenes: NuScenes, scene_info, frames, num_to_interp
interpolate_tracks[id]["state"]["position"] = interpolate( interpolate_tracks[id]["state"]["position"] = interpolate(
track["state"]["position"], track["state"]["valid"], new_valid track["state"]["position"], track["state"]["valid"], new_valid
) )
if id == "ego": if id == "ego" and not scene_info.get("prediction", False):
assert "prediction" not in scene_info
# We can get it from canbus # We can get it from canbus
canbus = NuScenesCanBus(dataroot=nuscenes.dataroot) try:
imu_pos = np.asarray([state["pos"] for state in canbus.get_messages(scene_info["name"], "pose")[::5]]) canbus = NuScenesCanBus(dataroot=nuscenes.dataroot)
interpolate_tracks[id]["state"]["position"][:len(imu_pos)] = imu_pos imu_pos = np.asarray([state["pos"] for state in canbus.get_messages(scene_info["name"], "pose")[::5]])
min_len = min(len(imu_pos), new_episode_len)
interpolate_tracks[id]["state"]["position"][:min_len] = imu_pos[:min_len]
except:
logger.info("Fail to get canbus data for {}".format(scene_info["name"]))
# velocity # velocity
interpolate_tracks[id]["state"]["velocity"] = interpolate( interpolate_tracks[id]["state"]["velocity"] = interpolate(
@@ -217,7 +226,6 @@ def get_tracks_from_frames(nuscenes: NuScenes, scene_info, frames, num_to_interp
interpolate_tracks[id]["state"]["velocity"][k - 1] = np.array([0., 0.]) interpolate_tracks[id]["state"]["velocity"][k - 1] = np.array([0., 0.])
# speed outlier check # speed outlier check
max_vel = np.max(np.linalg.norm(interpolate_tracks[id]["state"]["velocity"], axis=-1)) max_vel = np.max(np.linalg.norm(interpolate_tracks[id]["state"]["velocity"], axis=-1))
assert max_vel < 50, "Abnormal velocity!"
if max_vel > 30: if max_vel > 30:
print("\nWARNING: Too large speed for {}: {}".format(id, max_vel)) print("\nWARNING: Too large speed for {}: {}".format(id, max_vel))
@@ -225,16 +233,21 @@ def get_tracks_from_frames(nuscenes: NuScenes, scene_info, frames, num_to_interp
# then update position # then update position
new_heading = interpolate_heading(track["state"]["heading"], track["state"]["valid"], new_valid) new_heading = interpolate_heading(track["state"]["heading"], track["state"]["valid"], new_valid)
interpolate_tracks[id]["state"]["heading"] = new_heading interpolate_tracks[id]["state"]["heading"] = new_heading
if id == "ego": if id == "ego" and not scene_info.get("prediction", False):
assert "prediction" not in scene_info
# We can get it from canbus # We can get it from canbus
canbus = NuScenesCanBus(dataroot=nuscenes.dataroot) try:
imu_heading = np.asarray( canbus = NuScenesCanBus(dataroot=nuscenes.dataroot)
[ imu_heading = np.asarray(
quaternion_yaw(Quaternion(state["orientation"])) [
for state in canbus.get_messages(scene_info["name"], "pose")[::5] quaternion_yaw(Quaternion(state["orientation"]))
] for state in canbus.get_messages(scene_info["name"], "pose")[::5]
) ]
interpolate_tracks[id]["state"]["heading"][:len(imu_heading)] = imu_heading )
min_len = min(len(imu_heading), new_episode_len)
interpolate_tracks[id]["state"]["heading"][:min_len] = imu_heading[:min_len]
except:
logger.info("Fail to get canbus data for {}".format(scene_info["name"]))
for k, v in track["state"].items(): for k, v in track["state"].items():
if k in ["valid", "heading", "position", "velocity"]: if k in ["valid", "heading", "position", "velocity"]:
@@ -256,7 +269,7 @@ def get_tracks_from_frames(nuscenes: NuScenes, scene_info, frames, num_to_interp
return normalized_ret, map_center return normalized_ret, map_center
def get_map_features(scene_info, nuscenes: NuScenes, map_center, radius=500, points_distance=1): def get_map_features(scene_info, nuscenes: NuScenes, map_center, radius=500, points_distance=1, only_lane=False):
""" """
Extract map features from nuscenes data. The objects in specified region will be returned. Sampling rate determines Extract map features from nuscenes data. The objects in specified region will be returned. Sampling rate determines
the distance between 2 points when extracting lane center line. the distance between 2 points when extracting lane center line.
@@ -286,49 +299,72 @@ def get_map_features(scene_info, nuscenes: NuScenes, map_center, radius=500, poi
map_objs = map_api.get_records_in_radius(map_center[0], map_center[1], radius, layer_names) map_objs = map_api.get_records_in_radius(map_center[0], map_center[1], radius, layer_names)
# build map boundary if not only_lane:
polygons = [] # build map boundary
# for id in map_objs["drivable_area"]: polygons = []
# seg_info = map_api.get("drivable_area", id) for id in map_objs["drivable_area"]:
# assert seg_info["token"] == id seg_info = map_api.get("drivable_area", id)
# for polygon_token in seg_info["polygon_tokens"]: assert seg_info["token"] == id
# polygon = map_api.extract_polygon(polygon_token) for polygon_token in seg_info["polygon_tokens"]:
# polygons.append(polygon) polygon = map_api.extract_polygon(polygon_token)
for id in map_objs["road_segment"]: polygons.append(polygon)
seg_info = map_api.get("road_segment", id) # for id in map_objs["road_segment"]:
assert seg_info["token"] == id # seg_info = map_api.get("road_segment", id)
polygon = map_api.extract_polygon(seg_info["polygon_token"]) # assert seg_info["token"] == id
polygons.append(polygon) # polygon = map_api.extract_polygon(seg_info["polygon_token"])
for id in map_objs["road_block"]: # polygons.append(polygon)
seg_info = map_api.get("road_block", id) # for id in map_objs["road_block"]:
assert seg_info["token"] == id # seg_info = map_api.get("road_block", id)
polygon = map_api.extract_polygon(seg_info["polygon_token"]) # assert seg_info["token"] == id
polygons.append(polygon) # polygon = map_api.extract_polygon(seg_info["polygon_token"])
polygons = [geom if geom.is_valid else geom.buffer(0) for geom in polygons] # polygons.append(polygon)
boundaries = gpd.GeoSeries(unary_union(polygons)).boundary.explode(index_parts=True) polygons = [geom if geom.is_valid else geom.buffer(0) for geom in polygons]
for idx, boundary in enumerate(boundaries[0]): boundaries = gpd.GeoSeries(unary_union(polygons)).boundary.explode(index_parts=True)
block_points = np.array(list(i for i in zip(boundary.coords.xy[0], boundary.coords.xy[1]))) for idx, boundary in enumerate(boundaries[0]):
id = "boundary_{}".format(idx) block_points = np.array(list(i for i in zip(boundary.coords.xy[0], boundary.coords.xy[1])))
ret[id] = { id = "boundary_{}".format(idx)
SD.TYPE: MetaDriveType.LINE_SOLID_SINGLE_WHITE, ret[id] = {
SD.POLYLINE: block_points - np.asarray(map_center)[:2] SD.TYPE: MetaDriveType.LINE_SOLID_SINGLE_WHITE,
} SD.POLYLINE: block_points - np.asarray(map_center)[:2]
}
# broken line # broken line
for id in map_objs["lane_divider"]: for id in map_objs["lane_divider"]:
line_info = map_api.get("lane_divider", id) line_info = map_api.get("lane_divider", id)
assert line_info["token"] == id assert line_info["token"] == id
line = map_api.extract_line(line_info["line_token"]).coords.xy line = map_api.extract_line(line_info["line_token"]).coords.xy
line = np.asarray([[line[0][i], line[1][i]] for i in range(len(line[0]))]) line = np.asarray([[line[0][i], line[1][i]] for i in range(len(line[0]))])
ret[id] = {SD.TYPE: MetaDriveType.LINE_BROKEN_SINGLE_WHITE, SD.POLYLINE: line - np.asarray(map_center)[:2]} ret[id] = {SD.TYPE: MetaDriveType.LINE_BROKEN_SINGLE_WHITE, SD.POLYLINE: line - np.asarray(map_center)[:2]}
# solid line # solid line
for id in map_objs["road_divider"]: for id in map_objs["road_divider"]:
line_info = map_api.get("road_divider", id) line_info = map_api.get("road_divider", id)
assert line_info["token"] == id assert line_info["token"] == id
line = map_api.extract_line(line_info["line_token"]).coords.xy line = map_api.extract_line(line_info["line_token"]).coords.xy
line = np.asarray([[line[0][i], line[1][i]] for i in range(len(line[0]))]) line = np.asarray([[line[0][i], line[1][i]] for i in range(len(line[0]))])
ret[id] = {SD.TYPE: MetaDriveType.LINE_SOLID_SINGLE_YELLOW, SD.POLYLINE: line - np.asarray(map_center)[:2]} ret[id] = {SD.TYPE: MetaDriveType.LINE_SOLID_SINGLE_YELLOW, SD.POLYLINE: line - np.asarray(map_center)[:2]}
# crosswalk
for id in map_objs["ped_crossing"]:
info = map_api.get("ped_crossing", id)
assert info["token"] == id
boundary = map_api.extract_polygon(info["polygon_token"]).exterior.xy
boundary_polygon = np.asarray([[boundary[0][i], boundary[1][i]] for i in range(len(boundary[0]))])
ret[id] = {
SD.TYPE: MetaDriveType.CROSSWALK,
SD.POLYGON: boundary_polygon - np.asarray(map_center)[:2],
}
# walkway
for id in map_objs["walkway"]:
info = map_api.get("walkway", id)
assert info["token"] == id
boundary = map_api.extract_polygon(info["polygon_token"]).exterior.xy
boundary_polygon = np.asarray([[boundary[0][i], boundary[1][i]] for i in range(len(boundary[0]))])
ret[id] = {
SD.TYPE: MetaDriveType.BOUNDARY_SIDEWALK,
SD.POLYGON: boundary_polygon - np.asarray(map_center)[:2],
}
# normal lane # normal lane
for id in map_objs["lane"]: for id in map_objs["lane"]:
@@ -365,28 +401,6 @@ def get_map_features(scene_info, nuscenes: NuScenes, map_center, radius=500, poi
SD.EXIT: map_api.get_outgoing_lane_ids(id) SD.EXIT: map_api.get_outgoing_lane_ids(id)
} }
# crosswalk
for id in map_objs["ped_crossing"]:
info = map_api.get("ped_crossing", id)
assert info["token"] == id
boundary = map_api.extract_polygon(info["polygon_token"]).exterior.xy
boundary_polygon = np.asarray([[boundary[0][i], boundary[1][i]] for i in range(len(boundary[0]))])
ret[id] = {
SD.TYPE: MetaDriveType.CROSSWALK,
SD.POLYGON: boundary_polygon - np.asarray(map_center)[:2],
}
# walkway
for id in map_objs["walkway"]:
info = map_api.get("walkway", id)
assert info["token"] == id
boundary = map_api.extract_polygon(info["polygon_token"]).exterior.xy
boundary_polygon = np.asarray([[boundary[0][i], boundary[1][i]] for i in range(len(boundary[0]))])
ret[id] = {
SD.TYPE: MetaDriveType.BOUNDARY_SIDEWALK,
SD.POLYGON: boundary_polygon - np.asarray(map_center)[:2],
}
# # stop_line # # stop_line
# for id in map_objs["stop_line"]: # for id in map_objs["stop_line"]:
# info = map_api.get("stop_line", id) # info = map_api.get("stop_line", id)
@@ -404,22 +418,41 @@ def get_map_features(scene_info, nuscenes: NuScenes, map_center, radius=500, poi
return ret return ret
def convert_nuscenes_scenario(scene, version, nuscenes: NuScenes): def convert_nuscenes_scenario(
token, version, nuscenes: NuScenes, map_radius=500, prediction=False, past=2, future=6, only_lane=False
):
""" """
Data will be interpolated to 0.1s time interval, while the time interval of original key frames are 0.5s. Data will be interpolated to 0.1s time interval, while the time interval of original key frames are 0.5s.
""" """
scene_token = scene["token"] if prediction:
scenario_log_interval = 0.1 past_num = int(past / 0.5)
scene_info = nuscenes.get("scene", scene_token) future_num = int(future / 0.5)
frames = [] nusc = nuscenes
current_frame = nuscenes.get("sample", scene_info["first_sample_token"]) instance_token, sample_token = token.split("_")
while current_frame["token"] != scene_info["last_sample_token"]: current_sample = last_sample = next_sample = nusc.get("sample", sample_token)
frames.append(parse_frame(current_frame, nuscenes)) past_samples = []
current_frame = nuscenes.get("sample", current_frame["next"]) future_samples = []
frames.append(parse_frame(current_frame, nuscenes)) for _ in range(past_num):
assert current_frame["next"] == "" if last_sample["prev"] == "":
assert len(frames) == scene_info["nbr_samples"], "Number of sample mismatches! " break
last_sample = nusc.get("sample", last_sample["prev"])
past_samples.append(parse_frame(last_sample, nusc))
for _ in range(future_num):
if next_sample["next"] == "":
break
next_sample = nusc.get("sample", next_sample["next"])
future_samples.append(parse_frame(next_sample, nusc))
frames = past_samples[::-1] + [parse_frame(current_sample, nusc)] + future_samples
scene_info = copy.copy(nusc.get("scene", current_sample["scene_token"]))
scene_info["name"] = scene_info["name"] + "_" + token
scene_info["prediction"] = True
frames_scene_info = [frames, scene_info]
else:
frames_scene_info = extract_frames_scene_info(token, nuscenes)
scenario_log_interval = 0.1
frames, scene_info = frames_scene_info
result = SD() result = SD()
result[SD.ID] = scene_info["name"] result[SD.ID] = scene_info["name"]
result[SD.VERSION] = "nuscenes" + version result[SD.VERSION] = "nuscenes" + version
@@ -430,7 +463,7 @@ def convert_nuscenes_scenario(scene, version, nuscenes: NuScenes):
result[SD.METADATA]["map"] = nuscenes.get("log", scene_info["log_token"])["location"] result[SD.METADATA]["map"] = nuscenes.get("log", scene_info["log_token"])["location"]
result[SD.METADATA]["date"] = nuscenes.get("log", scene_info["log_token"])["date_captured"] result[SD.METADATA]["date"] = nuscenes.get("log", scene_info["log_token"])["date_captured"]
result[SD.METADATA]["coordinate"] = "right-handed" result[SD.METADATA]["coordinate"] = "right-handed"
result[SD.METADATA]["scenario_token"] = scene_token # result[SD.METADATA]["dscenario_token"] = scene_token
result[SD.METADATA][SD.ID] = scene_info["name"] result[SD.METADATA][SD.ID] = scene_info["name"]
result[SD.METADATA]["scenario_id"] = scene_info["name"] result[SD.METADATA]["scenario_id"] = scene_info["name"]
result[SD.METADATA]["sample_rate"] = scenario_log_interval result[SD.METADATA]["sample_rate"] = scenario_log_interval
@@ -443,16 +476,39 @@ def convert_nuscenes_scenario(scene, version, nuscenes: NuScenes):
result[SD.DYNAMIC_MAP_STATES] = {} result[SD.DYNAMIC_MAP_STATES] = {}
# map # map
result[SD.MAP_FEATURES] = get_map_features(scene_info, nuscenes, map_center, 500) result[SD.MAP_FEATURES] = get_map_features(scene_info, nuscenes, map_center, map_radius, only_lane=only_lane)
del frames_scene_info
del frames
del scene_info
return result return result
def extract_frames_scene_info(scene, nuscenes):
scene_token = scene["token"]
scene_info = nuscenes.get("scene", scene_token)
frames = []
current_frame = nuscenes.get("sample", scene_info["first_sample_token"])
while current_frame["token"] != scene_info["last_sample_token"]:
frames.append(parse_frame(current_frame, nuscenes))
current_frame = nuscenes.get("sample", current_frame["next"])
frames.append(parse_frame(current_frame, nuscenes))
assert current_frame["next"] == ""
assert len(frames) == scene_info["nbr_samples"], "Number of sample mismatches! "
return frames, scene_info
def get_nuscenes_scenarios(dataroot, version, num_workers=2): def get_nuscenes_scenarios(dataroot, version, num_workers=2):
nusc = NuScenes(version=version, dataroot=dataroot) nusc = NuScenes(version=version, dataroot=dataroot)
scenarios = nusc.scene
def _get_nusc(): def _get_nusc():
return NuScenes(version=version, dataroot=dataroot) return NuScenes(version=version, dataroot=dataroot)
return scenarios, [_get_nusc() for _ in range(num_workers)] return nusc.scene, [nusc for _ in range(num_workers)]
def get_nuscenes_prediction_split(dataroot, version, past, future, num_workers=2):
def _get_nusc():
return NuScenes(version="v1.0-mini" if "mini" in version else "v1.0-trainval", dataroot=dataroot)
nusc = _get_nusc()
return get_prediction_challenge_split(version, dataroot=dataroot), [nusc for _ in range(num_workers)]

2
scenarionet/converter/utils.py
View File

@@ -234,6 +234,8 @@ def write_to_directory_single_worker(
sd_scenario[SD.METADATA][SD.SUMMARY.NUMBER_SUMMARY] = SD.get_number_summary(sd_scenario) sd_scenario[SD.METADATA][SD.SUMMARY.NUMBER_SUMMARY] = SD.get_number_summary(sd_scenario)
# update summary/mapping dicy # update summary/mapping dicy
if export_file_name in summary:
logger.warning("Scenario {} already exists and will be overwritten!".format(export_file_name))
summary[export_file_name] = copy.deepcopy(sd_scenario[SD.METADATA]) summary[export_file_name] = copy.deepcopy(sd_scenario[SD.METADATA])
mapping[export_file_name] = "" # in the same dir mapping[export_file_name] = "" # in the same dir

5
scenarionet/converter/waymo/utils.py
View File

@@ -446,10 +446,7 @@ def preprocess_waymo_scenarios(files, worker_index):
:param worker_index, the index for the worker :param worker_index, the index for the worker
:return: a list of scenario_pb2 :return: a list of scenario_pb2
""" """
try: from scenarionet.converter.waymo.waymo_protos import scenario_pb2
scenario_pb2
except NameError:
raise ImportError("Please install waymo_open_dataset package: pip install waymo-open-dataset-tf-2-11-0")
for file in tqdm.tqdm(files, desc="Process Waymo scenarios for worker {}".format(worker_index)): for file in tqdm.tqdm(files, desc="Process Waymo scenarios for worker {}".format(worker_index)):
file_path = os.path.join(file) file_path = os.path.join(file)

1
scenarionet/merge.py
View File

@@ -10,6 +10,7 @@ if __name__ == '__main__':
parser.add_argument( parser.add_argument(
"--database_path", "--database_path",
"-d", "-d",
"--to",
required=True, required=True,
help="The name of the new combined database. " help="The name of the new combined database. "
"It will create a new directory to store dataset_summary.pkl and dataset_mapping.pkl. " "It will create a new directory to store dataset_summary.pkl and dataset_mapping.pkl. "

9
scenarionet/tests/test_move.py
View File

@@ -19,9 +19,16 @@ def test_copy_database():
# move # move
for k, from_path in enumerate(dataset_paths): for k, from_path in enumerate(dataset_paths):
to = os.path.join(TMP_PATH, str(k)) to = os.path.join(TMP_PATH, str(k))
copy_database(from_path, to, force_move=True, exist_ok=True, overwrite=True) copy_database(from_path, to, exist_ok=True, overwrite=True)
moved_path.append(to) moved_path.append(to)
assert os.path.exists(from_path) assert os.path.exists(from_path)
success = False
try:
copy_database(from_path, to, exist_ok=True, overwrite=True, remove_source=True)
except RuntimeError:
success = True
assert success
assert os.path.exists(to)
merge_database(output_path, *moved_path, exist_ok=True, overwrite=True, try_generate_missing_file=True) merge_database(output_path, *moved_path, exist_ok=True, overwrite=True, try_generate_missing_file=True)
# verify # verify
summary, sorted_scenarios, mapping = read_dataset_summary(output_path) summary, sorted_scenarios, mapping = read_dataset_summary(output_path)

2
tutorial/read_established_scenarionet_dataset.ipynb
View File

@@ -88,7 +88,7 @@
"\n", "\n",
"def make_GIF(frames, name=\"demo.gif\"):\n", "def make_GIF(frames, name=\"demo.gif\"):\n",
" print(\"Generate gif...\")\n", " print(\"Generate gif...\")\n",
" imgs = [pygame.surfarray.array3d(frame) for frame in frames]\n", " imgs = [frame for frame in frames]\n",
" imgs = [Image.fromarray(img) for img in imgs]\n", " imgs = [Image.fromarray(img) for img in imgs]\n",
" imgs[0].save(name, save_all=True, append_images=imgs[1:], duration=50, loop=0)" " imgs[0].save(name, save_all=True, append_images=imgs[1:], duration=50, loop=0)"
] ]

2
tutorial/simulation.ipynb
View File

@@ -82,7 +82,7 @@
"\n", "\n",
"def make_GIF(frames, name=\"demo.gif\"):\n", "def make_GIF(frames, name=\"demo.gif\"):\n",
" print(\"Generate gif...\")\n", " print(\"Generate gif...\")\n",
" imgs = [pygame.surfarray.array3d(frame) for frame in frames]\n", " imgs = [frame for frame in frames]\n",
" imgs = [Image.fromarray(img) for img in imgs]\n", " imgs = [Image.fromarray(img) for img in imgs]\n",
" imgs[0].save(name, save_all=True, append_images=imgs[1:], duration=50, loop=0)" " imgs[0].save(name, save_all=True, append_images=imgs[1:], duration=50, loop=0)"
] ]