example_scripts/example_export_objects.py

"""
    This is a demo script that was used to take the default dynamicWorld.json and extract every vehicle with its related objects and sorts them 
    to a new json file for each ego vehicle.
    This allows an analysis of each maneuver without accessing other vehicles in the dynamicWorld. 
    Please note that this will generate a lot of redundant information. 

"""
from openautomatumdronedata.dataset import droneDataset
import json 
import os
import shutil
import sys

# Get all present recording folders in the current dataset 
dataset_folders = list()
current_path = os.path.abspath(os.path.join(__file__ ,"../.."))
for item in os.listdir(current_path):
    if os.path.isdir(os.path.join(current_path, item)) and item != "img":
        dataset_folders.append(item)



for recording_folder in dataset_folders:
    path = os.path.join(current_path, recording_folder)

    # Create an output folder
    export_path = os.path.join(current_path, recording_folder, "export_single_objects")
    if not os.path.exists(export_path):
        os.mkdir(export_path)

    # Now we open each dataset and create a droneDataset object for it
    dataset = droneDataset(path)

    # Here we access the dynamic world, the global JSON file containing all recording infromation
    dynWorld = dataset.dynWorld  

    # Here we open the plain JSON file without the automatum pip utility in parallel
    f = open(os.path.join(path, "dynamicWorld.json"))
    json_dict = json.load(f)

    # Create a new dict to store all agregated values in
    relation_dict = dict()

    # Lets take every object (car, truck, etc.) from the plain JSON file and crate a new JSON containing only this object with all its surrounding objects
    for object in json_dict["objects"]:
        """ 
            Now we access the object_relation_dict_list, ttc_dict and tth_dict of the object to see which objects are the surrounding ones:
                "object_relation_dict_list": [
                {
                    "front_ego": null,
                    "behind_ego": "32499e60-30e9-4f41-8dc4-8699364db5dc",
                    "front_left": null,
                    "behind_left": null,
                    "front_right": "3e67c856-116a-4af5-96cc-39f5002f71a0",
                    "behind_right": "3002eaf3-a545-4e56-aa31-557f25e79643"
                },
                ...

                "ttc_dict_vec": [
                {
                    "front_ego": -1,
                    "behind_ego": null,
                    "front_left": null,
                    "behind_left": null,
                    "front_right": 477.62466112341815,
                    "behind_right": null
                },
                ...

                "tth_dict_vec": [
                {
                    "front_ego": null,
                    "behind_ego": 0.380621726114513,
                    "front_left": null,
                    "behind_left": null,
                    "front_right": -1,
                    "behind_right": 3.687973804225473
                },
                ...
        """
        for i, (object_relation_dict, ttc_dict, tth_dict, lat_dict, long_dict) in enumerate(zip(object["object_relation_dict_list"], object["ttc_dict_vec"], object["tth_dict_vec"], object["lat_dist_dict_vec"], object["long_dist_dict_vec"])):
            time_stamp = object["time"][i]
            for key in object_relation_dict.keys():  # key = "front_ego", relation = "UUID of the object in this position" for example
                relation = object_relation_dict[key]
                if relation is not None:  # Check if there is an object at this position at all
                    relation_object = dynWorld.get_dynObj_by_UUID(relation)  # Access the object with the automatum utility by its UUID
                    time_idx = relation_object.next_index_of_specific_time(time_stamp)  # Get the time index of the object for the time stamp of our current ego vehicle we generate the new JSON for


                    # Copy all values from this object at the specific time
                    relation_dict["UUID"] = relation_object.UUID
                    relation_dict["length"] = relation_object.length
                    relation_dict["width"] = relation_object.width
                    relation_dict["x"] = relation_object.x_vec[time_idx]
                    relation_dict["y"] = relation_object.y_vec[time_idx]
                    relation_dict["vx"] = relation_object.vx_vec[time_idx]
                    relation_dict["vy"] = relation_object.vy_vec[time_idx]
                    relation_dict["ax"] = relation_object.ax_vec[time_idx]
                    relation_dict["ay"] = relation_object.ay_vec[time_idx]
                    relation_dict["jerk_x"] = relation_object.vx_vec[time_idx]
                    relation_dict["jerk_y"] = relation_object.vx_vec[time_idx]
                    relation_dict["curvature"] = relation_object.vx_vec[time_idx]
                    relation_dict["psi"] = relation_object.psi_vec[time_idx]
                    relation_dict["lane_id"] = relation_object.lane_id_vec[time_idx]
                    relation_dict["road_id"] = relation_object.road_id_vec[time_idx]
                    relation_dict["road_type"] = relation_object.vx_vec[time_idx]
                    relation_dict["distance_left_lane_marking"] = relation_object.distance_left_lane_marking[time_idx]
                    relation_dict["distance_right_lane_marking"] = relation_object.distance_right_lane_marking[time_idx]
                    relation_dict["ttc"] = ttc_dict[key]
                    relation_dict["tth"] = tth_dict[key]
                    relation_dict["lat_dist"] = lat_dict[key]
                    relation_dict["long_dist"] = long_dict[key]

                else:
                    relation_dict = None

                 
                """
                    Now we replace the initial single UUID of the object with all information we accumulated about the object behind the UUID

                        "object_relation_dict_list": [
                        {
                            "front_left": 0decabdc-fa4f-4f25-93ed-88eed734bba0,
                        ...

                        "object_relation_dict_list": [
                        {
                            "front_left": {
                                "UUID": "0decabdc-fa4f-4f25-93ed-88eed734bba0",
                                "length": 4.172288426073395,
                                "width": 1.8141249203213998,
                                "vx": 46.54388406290268,
                                "vy": 0.005328263922638854,
                                "ax": 0.5608367460027531,
                                "ay": -0.5516711364613421,
                                "psi": -0.5643746012832805,
                                "x": 47.834595023288536,
                                "y": -32.82371510377445,
                                "lane_id": 3,
                                "road_id": 0,
                                "distance_left_lane_marking": 2.3102357971463827,
                                "distance_right_lane_marking": 1.6230526113559351
                            },
                        ---

                """
                object["object_relation_dict_list"][i][key] = relation_dict

                relation_dict = dict()  # Delete the dict for the next object


        # Delete redundant information
        del object["ttc_dict_vec"]
        del object["tth_dict_vec"]
        del object["lat_dist_dict_vec"]
        del object["long_dist_dict_vec"]

        # Finally we save each object as its own JSON    
        with open(os.path.join(export_path, object["UUID"] + ".json"), "w") as outfile:
            json.dump(object, outfile)
        print("Successfully exported object %s" % object["UUID"])