136 lines
4.3 KiB
Python
136 lines
4.3 KiB
Python
import ast
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import copy
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import inspect
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import logging
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import math
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import os
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import pickle
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import shutil
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from scenarionet.common_utils import save_summary_anda_mapping
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import numpy as np
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import tqdm
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from metadrive.scenario import ScenarioDescription as SD
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logger = logging.getLogger(__file__)
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def nuplan_to_metadrive_vector(vector, nuplan_center=(0, 0)):
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"All vec in nuplan should be centered in (0,0) to avoid numerical explosion"
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vector = np.array(vector)
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vector -= np.asarray(nuplan_center)
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return vector
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def compute_angular_velocity(initial_heading, final_heading, dt):
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"""
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Calculate the angular velocity between two headings given in radians.
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Parameters:
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initial_heading (float): The initial heading in radians.
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final_heading (float): The final heading in radians.
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dt (float): The time interval between the two headings in seconds.
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Returns:
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float: The angular velocity in radians per second.
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"""
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# Calculate the difference in headings
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delta_heading = final_heading - initial_heading
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# Adjust the delta_heading to be in the range (-π, π]
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delta_heading = (delta_heading + math.pi) % (2 * math.pi) - math.pi
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# Compute the angular velocity
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angular_vel = delta_heading / dt
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return angular_vel
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def mph_to_kmh(speed_in_mph: float):
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speed_in_kmh = speed_in_mph * 1.609344
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return speed_in_kmh
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def contains_explicit_return(f):
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return any(isinstance(node, ast.Return) for node in ast.walk(ast.parse(inspect.getsource(f))))
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def write_to_directory(
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convert_func, scenarios, output_path, dataset_version, dataset_name, force_overwrite=False, **kwargs
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):
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"""
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Convert a batch of scenarios.
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"""
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if not contains_explicit_return(convert_func):
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raise RuntimeError("The convert function should return a metadata dict")
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if "version" in kwargs:
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kwargs.pop("version")
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logger.info("the specified version in kwargs is replaced by argument: 'dataset_version'")
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save_path = copy.deepcopy(output_path)
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output_path = output_path + "_tmp"
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# meta recorder and data summary
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if os.path.exists(output_path):
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shutil.rmtree(output_path)
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os.makedirs(output_path, exist_ok=False)
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# make real save dir
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delay_remove = None
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if os.path.exists(save_path):
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if force_overwrite:
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delay_remove = save_path
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else:
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raise ValueError("Directory already exists! Abort")
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summary_file = SD.DATASET.SUMMARY_FILE
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mapping_file = SD.DATASET.MAPPING_FILE
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summary_file_path = os.path.join(output_path, summary_file)
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mapping_file_path = os.path.join(output_path, mapping_file)
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summary = {}
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mapping = {}
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for scenario in tqdm.tqdm(scenarios):
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# convert scenario
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sd_scenario = convert_func(scenario, dataset_version, **kwargs)
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scenario_id = sd_scenario[SD.ID]
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export_file_name = SD.get_export_file_name(dataset_name, dataset_version, scenario_id)
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# add agents summary
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summary_dict = {}
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ego_car_id = sd_scenario[SD.METADATA][SD.SDC_ID]
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summary_dict[ego_car_id] = SD.get_object_summary(
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state_dict=sd_scenario.get_sdc_track()["state"], id=ego_car_id, type=sd_scenario.get_sdc_track()["type"]
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)
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for track_id, track in sd_scenario[SD.TRACKS].items():
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summary_dict[track_id] = SD.get_object_summary(state_dict=track["state"], id=track_id, type=track["type"])
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sd_scenario[SD.METADATA][SD.SUMMARY.OBJECT_SUMMARY] = summary_dict
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# count some objects occurrence
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sd_scenario[SD.METADATA][SD.SUMMARY.NUMBER_SUMMARY] = SD.get_number_summary(sd_scenario)
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# update summary/mapping dicy
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summary[export_file_name] = copy.deepcopy(sd_scenario[SD.METADATA])
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mapping[export_file_name] = "" # in the same dir
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# sanity check
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sd_scenario = sd_scenario.to_dict()
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SD.sanity_check(sd_scenario, check_self_type=True)
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# dump
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p = os.path.join(output_path, export_file_name)
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with open(p, "wb") as f:
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pickle.dump(sd_scenario, f)
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# store summary file
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save_summary_anda_mapping(summary_file_path, mapping_file_path, summary, mapping)
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# rename and save
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if delay_remove is not None:
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assert delay_remove == save_path
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shutil.rmtree(delay_remove)
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os.rename(output_path, save_path)
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return summary, mapping
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