""" 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"])