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Make video (#5)

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* generate accident scene

* construction PG

* no object

* accident prob

* capture script

* update nuscenes toolds

* make video

* format

* fix test

* update readme

* update readme

* format

* format
This commit is contained in:
Quanyi Li
2023-06-17 17:19:40 +01:00
committed by GitHub
parent db50bca7fd
commit 9acdcaf321
23 changed files with 253 additions and 405 deletions
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19
README.md
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@@ -1,6 +1,23 @@
# ScenarioNet
ScenarioNet: Scalable Traffic Scenario Management System for Autonomous Driving
**Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling**
[**Webpage**](https://github.com/metadriverse/scenarionet) |
[**Code**](https://github.com/metadriverse/scenarionet) |
[**Video**](https://github.com/metadriverse/scenarionet) |
[**Paper**](https://github.com/metadriverse/scenarionet) |
ScenarioNet allows users to load scenarios from real-world dataset like Waymo, nuPlan, nuScenes, l5 and synthetic
dataset such as procedural generated ones and safety-critical ones generated by adversarial attack.
The built database provides tools for building training and test sets for ML applications.
![scenarios](docs/asset/scenarios.png)
Powered by [MetaDrive Simulator](https://github.com/metadriverse/metadrive), the scenarios can be reconstructed for
various applications like AD stack test, reinforcement learning, imitation learning, scenario generation and so on.
![sensors](docs/asset/sensor.png)
## Installation

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31
scenarionet/builder/utils.py
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@@ -116,12 +116,7 @@ def merge_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):
raise FileNotFoundError("Can not find database: {}".format(from_path))
@@ -129,19 +124,16 @@ def copy_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!"
files = os.listdir(from_path)
if ScenarioDescription.DATASET.MAPPING_FILE in files and ScenarioDescription.DATASET.SUMMARY_FILE in files and len(
files) > 2:
raise RuntimeError("The source database is not allowed to move! "
if not force_move and (ScenarioDescription.DATASET.MAPPING_FILE in files
and ScenarioDescription.DATASET.SUMMARY_FILE in files and len(files) > 2):
raise RuntimeError(
"The source database is not allowed to move! "
"This will break the relationship between this database and other database built on it."
"If it is ok for you, use 'mv' to move it manually ")
"If it is ok for you, use 'mv' to move it manually "
)
summaries, mappings = merge_database(
to_path,
from_path,
exist_ok=exist_ok,
overwrite=overwrite,
try_generate_missing_file=True,
save=False
to_path, from_path, exist_ok=exist_ok, overwrite=overwrite, try_generate_missing_file=True, save=False
)
summary_file = osp.join(to_path, ScenarioDescription.DATASET.SUMMARY_FILE)
mapping_file = osp.join(to_path, ScenarioDescription.DATASET.MAPPING_FILE)
@@ -191,9 +183,10 @@ def split_database(
assert osp.exists(abs_dir_path), "Wrong database path. Can not find database at: {}".format(abs_dir_path)
summaries, lookup, mappings = read_dataset_summary(from_path)
assert start_index >= 0 and start_index + num_scenarios <= len(
lookup), "No enough scenarios in source dataset: total {}, start_index: {}, need: {}".format(len(lookup),
start_index,
num_scenarios)
lookup
), "No enough scenarios in source dataset: total {}, start_index: {}, need: {}".format(
len(lookup), start_index, num_scenarios
)
if random:
selected = sample(lookup[start_index:], k=num_scenarios)
else:

1
scenarionet/convert_pg.py
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@@ -9,6 +9,7 @@ from scenarionet.converter.pg.utils import get_pg_scenarios, convert_pg_scenario
from scenarionet.converter.utils import write_to_directory
if __name__ == '__main__':
# For the PG environment config, see: scenarionet/converter/pg/utils.py:6
parser = argparse.ArgumentParser()
parser.add_argument(
"--database_path",

5
scenarionet/convert_waymo.py
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@@ -61,8 +61,9 @@ if __name__ == '__main__':
shutil.rmtree(output_path)
waymo_data_directory = os.path.join(SCENARIONET_DATASET_PATH, args.raw_data_path)
scenarios = get_waymo_scenarios(waymo_data_directory, args.start_file_index, args.num_files,
num_workers=8) # do not use too much worker to read data
scenarios = get_waymo_scenarios(
waymo_data_directory, args.start_file_index, args.num_files, num_workers=8
) # do not use too much worker to read data
write_to_directory(
convert_func=convert_waymo_scenario,

37
scenarionet/converter/nuscenes/utils.py
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@@ -244,7 +244,16 @@ def get_tracks_from_frames(nuscenes: NuScenes, scene_info, frames, num_to_interp
# if id == "ego":
# ego is valid all time, so we can calculate the velocity in this way
return interpolate_tracks
# Normalize place all object to (0,0)
map_center = np.array(interpolate_tracks["ego"]["state"]["position"][0])
map_center[-1] = 0
normalized_ret = {}
for id, track, in interpolate_tracks.items():
pos = track["state"]["position"] - map_center
track["state"]["position"] = np.asarray(pos)
normalized_ret[id] = track
return normalized_ret, map_center
def get_map_features(scene_info, nuscenes: NuScenes, map_center, radius=500, points_distance=1):
@@ -299,32 +308,36 @@ def get_map_features(scene_info, nuscenes: NuScenes, map_center, radius=500, poi
for idx, boundary in enumerate(boundaries[0]):
block_points = np.array(list(i for i in zip(boundary.coords.xy[0], boundary.coords.xy[1])))
id = "boundary_{}".format(idx)
ret[id] = {SD.TYPE: MetaDriveType.LINE_SOLID_SINGLE_WHITE, SD.POLYLINE: block_points}
ret[id] = {
SD.TYPE: MetaDriveType.LINE_SOLID_SINGLE_WHITE,
SD.POLYLINE: block_points - np.asarray(map_center)[:2]
}
for id in map_objs["lane_divider"]:
line_info = map_api.get("lane_divider", id)
assert line_info["token"] == id
line = map_api.extract_line(line_info["line_token"]).coords.xy
line = [[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}
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]}
for id in map_objs["road_divider"]:
line_info = map_api.get("road_divider", id)
assert line_info["token"] == id
line = map_api.extract_line(line_info["line_token"]).coords.xy
line = [[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}
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]}
for id in map_objs["lane"]:
lane_info = map_api.get("lane", id)
assert lane_info["token"] == id
boundary = map_api.extract_polygon(lane_info["polygon_token"]).boundary.xy
boundary_polygon = [[boundary[0][i], boundary[1][i]] for i in range(len(boundary[0]))]
boundary_polygon = np.asarray([[boundary[0][i], boundary[1][i]] for i in range(len(boundary[0]))])
# boundary_polygon += [[boundary[0][i], boundary[1][i]] for i in range(len(boundary[0]))]
ret[id] = {
SD.TYPE: MetaDriveType.LANE_SURFACE_STREET,
SD.POLYLINE: discretize_lane(map_api.arcline_path_3[id], resolution_meters=points_distance),
SD.POLYGON: boundary_polygon,
SD.POLYLINE: np.asarray(discretize_lane(map_api.arcline_path_3[id], resolution_meters=points_distance)) -
np.asarray(map_center),
SD.POLYGON: boundary_polygon - np.asarray(map_center)[:2],
}
for id in map_objs["lane_connector"]:
@@ -335,7 +348,8 @@ def get_map_features(scene_info, nuscenes: NuScenes, map_center, radius=500, poi
# boundary_polygon += [[boundary[0][i], boundary[1][i], 0.] for i in range(len(boundary[0]))]
ret[id] = {
SD.TYPE: MetaDriveType.LANE_SURFACE_STREET,
SD.POLYLINE: discretize_lane(map_api.arcline_path_3[id], resolution_meters=points_distance),
SD.POLYLINE: np.asarray(discretize_lane(map_api.arcline_path_3[id], resolution_meters=points_distance)) -
np.asarray(map_center),
# SD.POLYGON: boundary_polygon,
"speed_limit_kmh": 100
}
@@ -375,14 +389,13 @@ def convert_nuscenes_scenario(scene, version, nuscenes: NuScenes):
result[SD.METADATA]["sample_rate"] = scenario_log_interval
result[SD.METADATA][SD.TIMESTEP] = np.arange(0., (len(frames) - 1) * 0.5 + 0.1, 0.1)
# interpolating to 0.1s interval
result[SD.TRACKS] = get_tracks_from_frames(nuscenes, scene_info, frames, num_to_interpolate=5)
result[SD.TRACKS], map_center = get_tracks_from_frames(nuscenes, scene_info, frames, num_to_interpolate=5)
result[SD.METADATA][SD.SDC_ID] = "ego"
# No traffic light in nuscenes at this stage
result[SD.DYNAMIC_MAP_STATES] = {}
# map
map_center = result[SD.TRACKS]["ego"]["state"]["position"][0]
result[SD.MAP_FEATURES] = get_map_features(scene_info, nuscenes, map_center, 500)
return result

20
scenarionet/converter/pg/utils.py
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@@ -1,8 +1,28 @@
import logging
from metadrive.envs.metadrive_env import MetaDriveEnv
from metadrive.policy.idm_policy import IDMPolicy
from metadrive.scenario.scenario_description import ScenarioDescription as SD
def make_env(start_index, num_scenarios, extra_config=None):
config = dict(
start_seed=start_index,
num_scenarios=num_scenarios,
traffic_density=0.15,
agent_policy=IDMPolicy,
accident_prob=0.5,
crash_vehicle_done=False,
crash_object_done=False,
store_map=False,
map=2
)
extra_config = extra_config or {}
config.update(extra_config)
env = MetaDriveEnv(config)
return env
def convert_pg_scenario(scenario_index, version, env):
"""
Simulate to collect PG Scenarios

17
scenarionet/converter/utils.py
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@@ -12,13 +12,11 @@ from functools import partial
import numpy as np
import psutil
import tqdm
from metadrive.envs.metadrive_env import MetaDriveEnv
from metadrive.policy.idm_policy import IDMPolicy
from metadrive.scenario import ScenarioDescription as SD
from scenarionet.builder.utils import merge_database
from scenarionet.common_utils import save_summary_anda_mapping
from scenarionet.converter.pg.utils import convert_pg_scenario
from scenarionet.converter.pg.utils import convert_pg_scenario, make_env
logger = logging.getLogger(__file__)
@@ -189,18 +187,7 @@ def write_to_directory_single_worker(
# for pg scenario only
if convert_func is convert_pg_scenario:
env = MetaDriveEnv(
dict(
start_seed=scenarios[0],
num_scenarios=len(scenarios),
traffic_density=0.15,
agent_policy=IDMPolicy,
crash_vehicle_done=False,
store_map=False,
map=2
)
)
kwargs["env"] = env
kwargs["env"] = make_env(start_index=scenarios[0], num_scenarios=len(scenarios))
count = 0
for scenario in tqdm.tqdm(scenarios, desc="Worker Index: {}".format(worker_index)):

6
scenarionet/copy_database.py
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@@ -13,11 +13,7 @@ if __name__ == '__main__':
"If exists_ok=True, those two .pkl files will be stored in an existing directory and turn "
"that directory into a database."
)
parser.add_argument(
"--remove_source",
action="store_true",
help="Remove the `from_database` if set this flag"
)
parser.add_argument("--remove_source", action="store_true", help="Remove the `from_database` if set this flag")
parser.add_argument(
"--copy_raw_data",
action="store_true",

40
scenarionet/filter_database.py
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@@ -15,10 +15,7 @@ if __name__ == '__main__':
"that directory into a database."
)
parser.add_argument(
'--from',
required=True,
type=str,
help="Which dataset to filter. It takes one directory path as input"
'--from', required=True, type=str, help="Which dataset to filter. It takes one directory path as input"
)
parser.add_argument(
"--exist_ok",
@@ -38,47 +35,26 @@ if __name__ == '__main__':
help="add this flag to select cases with SDC moving dist > sdc_moving_dist_min"
)
parser.add_argument(
"--sdc_moving_dist_min",
default=10,
type=float,
help="Selecting case with sdc_moving_dist > this value. "
"--sdc_moving_dist_min", default=10, type=float, help="Selecting case with sdc_moving_dist > this value. "
)
parser.add_argument(
"--num_object",
action="store_true",
help="add this flag to select cases with object_num < max_num_object"
"--num_object", action="store_true", help="add this flag to select cases with object_num < max_num_object"
)
parser.add_argument(
"--max_num_object",
default=30,
type=float,
help="case will be selected if num_obj < this argument"
"--max_num_object", default=30, type=float, help="case will be selected if num_obj < this argument"
)
parser.add_argument(
"--no_overpass",
action="store_true",
help="Scenarios with overpass WON'T be selected"
)
parser.add_argument("--no_overpass", action="store_true", help="Scenarios with overpass WON'T be selected")
parser.add_argument(
"--no_traffic_light",
action="store_true",
help="Scenarios with traffic light WON'T be selected"
"--no_traffic_light", action="store_true", help="Scenarios with traffic light WON'T be selected"
)
parser.add_argument(
"--id_filter",
action="store_true",
help="Scenarios with indicated name will NOT be selected"
)
parser.add_argument("--id_filter", action="store_true", help="Scenarios with indicated name will NOT be selected")
parser.add_argument(
"--exclude_ids",
nargs='+',
default=[],
help="Scenarios with indicated name will NOT be selected"
"--exclude_ids", nargs='+', default=[], help="Scenarios with indicated name will NOT be selected"
)
args = parser.parse_args()

7
scenarionet/num_scenarios.py
View File

@@ -7,12 +7,7 @@ logger = logging.getLogger(__file__)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
"--database_path",
"-d",
required=True,
help="Database to check number of scenarios"
)
parser.add_argument("--database_path", "-d", required=True, help="Database to check number of scenarios")
args = parser.parse_args()
summary, _, _, = read_dataset_summary(args.database_path)
logger.info("Number of scenarios: {}".format(len(summary)))

8
scenarionet/split_database.py
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@@ -19,9 +19,13 @@ if __name__ == '__main__':
)
parser.add_argument("--num_scenarios", type=int, default=64, help="how many scenarios to extract (default: 30)")
parser.add_argument("--start_index", type=int, default=0, help="which index to start")
parser.add_argument("--random", action="store_true", help="If set to true, it will choose scenarios randomly "
parser.add_argument(
"--random",
action="store_true",
help="If set to true, it will choose scenarios randomly "
"from all_scenarios[start_index:]. "
"Otherwise, the scenarios will be selected sequentially")
"Otherwise, the scenarios will be selected sequentially"
)
parser.add_argument(
"--exist_ok",
action="store_true",

59
scenarionet/tests/script/capture_database.py Normal file
View File

@@ -0,0 +1,59 @@
import pygame
from metadrive.envs.scenario_env import ScenarioEnv
from metadrive.policy.replay_policy import ReplayEgoCarPolicy
if __name__ == "__main__":
env = ScenarioEnv(
{
"use_render": True,
"agent_policy": ReplayEgoCarPolicy,
"show_interface": False,
"image_observation": False,
"show_logo": False,
"no_traffic": False,
"drivable_region_extension": 15,
"sequential_seed": True,
"reactive_traffic": False,
"show_fps": False,
"render_pipeline": True,
"daytime": "07:10",
"window_size": (1600, 900),
"camera_dist": 9,
"start_scenario_index": 1000,
"num_scenarios": 4000,
"horizon": 1000,
"store_map": False,
"vehicle_config": dict(
show_navi_mark=False,
no_wheel_friction=True,
use_special_color=False,
image_source="depth_camera",
lidar=dict(num_lasers=120, distance=50),
lane_line_detector=dict(num_lasers=0, distance=50),
side_detector=dict(num_lasers=12, distance=50)
),
"data_directory": "D:\\scenarionet_testset\\nuplan_test\\nuplan_test_w_raw"
}
)
# env.reset()
#
#
def capture():
env.capture("rgb_deluxe_{}_{}.jpg".format(env.current_seed, t))
ret = env.render(
mode="topdown", screen_size=(1600, 900), film_size=(10000, 10000), target_vehicle_heading_up=True
)
pygame.image.save(ret, "top_down_{}_{}.png".format(env.current_seed, env.episode_step))
#
#
# env.engine.accept("c", capture)
# for seed in [1001, 1002, 1005, 1011]:
env.reset(force_seed=1020)
for t in range(10000):
capture()
o, r, d, info = env.step([1, 0.88])
if env.episode_step >= env.engine.data_manager.current_scenario_length:
break

144
scenarionet/tests/script/capture_pg.py
View File

@@ -1,119 +1,39 @@
import pygame
from metadrive.envs.metadrive_env import MetaDriveEnv
from metadrive.utils import setup_logger
if __name__ == "__main__":
setup_logger(True)
env = MetaDriveEnv(
{
"num_scenarios": 1,
"traffic_density": 0.15,
"traffic_mode": "hybrid",
"start_seed": 74,
# "_disable_detector_mask":True,
# "debug_physics_world": True,
# "debug": True,
# "global_light": False,
# "debug_static_world": True,
"show_interface": False,
"cull_scene": False,
"random_spawn_lane_index": False,
"random_lane_width": False,
# "image_observation": True,
# "controller": "joystick",
# "show_coordinates": True,
"random_agent_model": False,
"manual_control": True,
"use_render": True,
"accident_prob": 1,
"decision_repeat": 5,
"interface_panel": [],
"need_inverse_traffic": False,
"rgb_clip": True,
"map": 2,
# "agent_policy": ExpertPolicy,
"random_traffic": False,
# "random_lane_width": True,
"driving_reward": 1.0,
# "pstats": True,
"force_destroy": False,
# "show_skybox": False,
"show_fps": False,
"render_pipeline": True,
# "camera_dist": 8,
"window_size": (1600, 900),
"camera_dist": 9,
# "camera_pitch": 30,
# "camera_height": 1,
# "camera_smooth": False,
# "camera_height": -1,
"vehicle_config": {
"enable_reverse": False,
# "vehicle_model": "xl",
# "rgb_camera": (1024, 1024),
# "spawn_velocity": [8.728615581032535, -0.24411703918728195],
"spawn_velocity_car_frame": True,
# "image_source": "depth_camera",
# "random_color": True
# "show_lidar": True,
"spawn_lane_index": None,
# "destination":"2R1_3_",
# "show_side_detector": True,
# "show_lane_line_detector": True,
# "side_detector": dict(num_lasers=2, distance=50),
# "lane_line_detector": dict(num_lasers=2, distance=50),
# "show_line_to_navi_mark": True,
"show_navi_mark": False,
# "show_dest_mark": True
},
}
)
o = env.reset()
from scenarionet.converter.pg.utils import make_env
def capture():
env.capture()
ret = env.render(mode="topdown", screen_size=(1600, 900), film_size=(2000, 2000), track_target_vehicle=True)
pygame.image.save(ret, "top_down_{}.png".format(env.current_seed))
env.capture("rgb_deluxe_{}_{}.jpg".format(env.current_seed, t))
ret = env.render(mode="topdown", screen_size=(1600, 900), film_size=(6000, 6000), target_vehicle_heading_up=True)
pygame.image.save(ret, "top_down_{}_{}.png".format(env.current_seed, env.episode_step))
env.engine.accept("c", capture)
# env.main_camera.set_follow_lane(True)
# env.vehicle.get_camera("rgb_camera").save_image(env.vehicle)
# for line in env.engine.coordinate_line:
# line.reparentTo(env.vehicle.origin)
# env.vehicle.set_velocity([5, 0], in_local_frame=True)
for s in range(1, 100000):
# env.vehicle.set_velocity([1, 0], in_local_frame=True)
if __name__ == "__main__":
env = make_env(
0,
50000,
extra_config=dict(
use_render=True,
show_logo=False,
show_fps=False,
show_interface=False,
drivable_region_extension=15,
window_size=(1600, 900),
render_pipeline=True,
camera_dist=9,
random_spawn_lane_index=False,
vehicle_config=dict(show_navi_mark=False),
daytime="07:10"
)
)
# o = env.reset(force_seed=0)
# env.engine.accept("c", capture)
for s in range(6, 1000):
env.reset(force_seed=16)
for t in range(10000):
capture()
o, r, d, info = env.step([0, 0])
# env.vehicle.set_pitch(-np.pi/4)
# [0.09231533, 0.491018, 0.47076905, 0.7691619, 0.5, 0.5, 1.0, 0.0, 0.48037243, 0.8904728, 0.81229943, 0.7317231, 1.0, 0.85320455, 0.9747932, 0.65675277, 0.0, 0.5, 0.5]
# else:
# if s % 100 == 0:
# env.close()
# env.reset()
# info["fuel"] = env.vehicle.energy_consumption
# env.render(
# text={
# # "heading_diff": env.vehicle.heading_diff(env.vehicle.lane),
# # "lane_width": env.vehicle.lane.width,
# # "lane_index": env.vehicle.lane_index,
# # "lateral": env.vehicle.lane.local_coordinates(env.vehicle.position),
# "current_seed": env.current_seed
# }
# )
# if d:
# env.reset()
# # assert env.observation_space.contains(o)
# if (s + 1) % 100 == 0:
# # print(
# "Finish {}/10000 simulation steps. Time elapse: {:.4f}. Average FPS: {:.4f}".format(
# s + 1,f
# time.time() - start, (s + 1) / (time.time() - start)
# )
# )
# if d:
# # # env.close()
# # # print(len(env.engine._spawned_objects))
# env.reset()
if info["arrive_dest"]:
break

57
scenarionet/tests/script/generate_sensor.py
View File

@@ -1,4 +1,3 @@
import time
import pygame
from metadrive.engine.asset_loader import AssetLoader
from metadrive.envs.scenario_env import ScenarioEnv
@@ -15,12 +14,13 @@ if __name__ == "__main__":
# "need_lane_localization": False,
"show_logo": False,
"no_traffic": False,
"drivable_region_extension": 15,
"sequential_seed": True,
"reactive_traffic": False,
"show_fps": False,
# "debug": True,
# "render_pipeline": True,
"daytime": "11:01",
"render_pipeline": True,
"daytime": "08:10",
"window_size": (1600, 900),
"camera_dist": 0.8,
"camera_height": 1.5,
@@ -31,7 +31,7 @@ if __name__ == "__main__":
# "force_reuse_object_name": True,
# "data_directory": "/home/shady/Downloads/test_processed",
"horizon": 1000,
# "no_static_vehicles": True,
"no_static_vehicles": False,
# "show_policy_mark": True,
# "show_coordinates": True,
# "force_destroy": True,
@@ -50,48 +50,23 @@ if __name__ == "__main__":
side_detector=dict(num_lasers=12, distance=50)
),
"data_directory": AssetLoader.file_path("nuscenes", return_raw_style=False),
"image_observation": True,
# "image_observation": True,
}
)
# 0,1,3,4,5,6
success = []
reset_num = 0
start = time.time()
reset_used_time = 0
s = 0
while True:
# for i in range(10):
start_reset = time.time()
env.reset(force_seed=0)
reset_used_time += time.time() - start_reset
reset_num += 1
for seed in range(10):
env.reset(force_seed=seed)
for t in range(10000):
if t==30:
# env.capture("camera_deluxe.jpg")
# ret = env.render(mode="topdown", screen_size=(1600, 900), film_size=(5000, 5000), track_target_vehicle=True)
# pygame.image.save(ret, "top_down.png")
env.vehicle.get_camera("depth_camera").save_image(env.vehicle, "camera.jpg")
env.capture("rgb_deluxe_{}_{}.jpg".format(env.current_seed, t))
ret = env.render(
mode="topdown", screen_size=(1600, 900), film_size=(9000, 9000), target_vehicle_heading_up=True
)
pygame.image.save(ret, "top_down_{}_{}.png".format(env.current_seed, t))
# env.vehicle.get_camera("depth_camera").save_image(env.vehicle, "depth_{}.jpg".format(t))
# env.vehicle.get_camera("rgb_camera").save_image(env.vehicle, "rgb_{}.jpg".format(t))
o, r, d, info = env.step([1, 0.88])
assert env.observation_space.contains(o)
s += 1
# if env.config["use_render"]:
# env.render(text={"seed": env.current_seed,
# # "num_map": info["num_stored_maps"],
# "data_coverage": info["data_coverage"],
# "reward": r,
# "heading_r": info["step_reward_heading"],
# "lateral_r": info["step_reward_lateral"],
# "smooth_action_r": info["step_reward_action_smooth"]})
if d:
print(
"Time elapse: {:.4f}. Average FPS: {:.4f}, AVG_Reset_time: {:.4f}".format(
time.time() - start, s / (time.time() - start - reset_used_time),
reset_used_time / reset_num
)
)
print("seed:{}, success".format(env.engine.global_random_seed))
print(list(env.engine.curriculum_manager.recent_success.dict.values()))
# if d:
if env.episode_step >= env.engine.data_manager.current_scenario_length:
break

104
scenarionet/tests/script/replay_origin.py
View File

@@ -1,104 +0,0 @@
import time
import pygame
from metadrive.envs.scenario_env import ScenarioEnv
from metadrive.policy.replay_policy import ReplayEgoCarPolicy
NuScenesEnv = ScenarioEnv
if __name__ == "__main__":
env = NuScenesEnv(
{
"use_render": True,
"agent_policy": ReplayEgoCarPolicy,
"show_interface": False,
"image_observation": False,
"show_logo": False,
"no_traffic": False,
"drivable_region_extension": 15,
"sequential_seed": True,
"reactive_traffic": False,
"show_fps": False,
# "debug": True,
"render_pipeline": True,
"daytime": "19:30",
"window_size": (1600, 900),
"camera_dist": 9,
# "camera_height": 1.5,
# "camera_pitch": None,
# "camera_fov": 60,
"start_scenario_index": 0,
"num_scenarios": 4,
# "force_reuse_object_name": True,
# "data_directory": "/home/shady/Downloads/test_processed",
"horizon": 1000,
# "no_static_vehicles": True,
# "show_policy_mark": True,
# "show_coordinates": True,
# "force_destroy": True,
# "default_vehicle_in_traffic": True,
"vehicle_config": dict(
# light=True,
# random_color=True,
show_navi_mark=False,
use_special_color=False,
image_source="depth_camera",
# rgb_camera=(1600, 900),
# depth_camera=(1600, 900, True),
# no_wheel_friction=True,
lidar=dict(num_lasers=120, distance=50),
lane_line_detector=dict(num_lasers=0, distance=50),
side_detector=dict(num_lasers=12, distance=50)
),
"data_directory": "D:\\code\\scenarionet\\scenarionet\\tests\\script\\waymo_scenes_adv"
}
)
# 0,1,3,4,5,6
success = []
reset_num = 0
start = time.time()
reset_used_time = 0
s = 0
env.reset()
def capture():
env.capture()
ret = env.render(mode="topdown", screen_size=(1600, 900), film_size=(7000, 7000), track_target_vehicle=True)
pygame.image.save(ret, "top_down_{}.png".format(env.current_seed))
env.engine.accept("c", capture)
while True:
# for i in range(10):
start_reset = time.time()
env.reset()
reset_used_time += time.time() - start_reset
reset_num += 1
for t in range(10000):
o, r, d, info = env.step([1, 0.88])
assert env.observation_space.contains(o)
s += 1
# if env.config["use_render"]:
# env.render(text={"seed": env.current_seed,
# # "num_map": info["num_stored_maps"],
# "data_coverage": info["data_coverage"],
# "reward": r,
# "heading_r": info["step_reward_heading"],
# "lateral_r": info["step_reward_lateral"],
# "smooth_action_r": info["step_reward_action_smooth"]})
if d:
print(
"Time elapse: {:.4f}. Average FPS: {:.4f}, AVG_Reset_time: {:.4f}".format(
time.time() - start, s / (time.time() - start - reset_used_time),
reset_used_time / reset_num
)
)
print("seed:{}, success".format(env.engine.global_random_seed))
print(list(env.engine.curriculum_manager.recent_success.dict.values()))
break

7
scenarionet/tests/test_filter_overpass.py
View File

@@ -21,12 +21,7 @@ def test_filter_overpass():
filters.append(ScenarioFilter.make(ScenarioFilter.no_overpass))
summaries, _ = merge_database(
output_path,
*dataset_paths,
exist_ok=True,
overwrite=True,
try_generate_missing_file=True,
filters=filters
output_path, *dataset_paths, exist_ok=True, overwrite=True, try_generate_missing_file=True, filters=filters
)
assert len(summaries) == 3
for scenario in summaries:

4
scenarionet/tests/test_move.py
View File

@@ -19,7 +19,7 @@ def test_copy_database():
# move
for k, from_path in enumerate(dataset_paths):
to = os.path.join(TMP_PATH, str(k))
copy_database(from_path, to)
copy_database(from_path, to, force_move=True, exist_ok=True, overwrite=True)
moved_path.append(to)
assert os.path.exists(from_path)
merge_database(output_path, *moved_path, exist_ok=True, overwrite=True, try_generate_missing_file=True)
@@ -37,7 +37,7 @@ def test_copy_database():
for k, from_path in enumerate(moved_path):
new_p = os.path.join(TMP_PATH, str(k) + str(k))
new_move_pathes.append(new_p)
copy_database(from_path, new_p, exist_ok=True, overwrite=True)
copy_database(from_path, new_p, exist_ok=True, overwrite=True, remove_source=True)
assert not os.path.exists(from_path)
merge_database(output_path, *new_move_pathes, exist_ok=True, overwrite=True, try_generate_missing_file=True)
# verify