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0000000000000000000000000000000000000000..f80e641ca5161531564af55d21d97507b98932ad --- /dev/null +++ b/example_scripts/01_lane_changes.py @@ -0,0 +1,54 @@ +""" +Lane Change Analysis — Automatum Data T-Crossing Dataset +Analyzes lane change behavior across all vehicles in a recording. + +Usage: + python 01_lane_changes.py + +Example: + python 01_lane_changes.py ../T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2 +""" +import sys +import os +from openautomatumdronedata.dataset import droneDataset + + +def analyze_lane_changes(dataset_path): + print(f"Loading dataset from: {dataset_path}") + dataset = droneDataset(dataset_path) + dynWorld = dataset.dynWorld + + print(f"Vehicles found: {len(dynWorld)}") + + lane_change_counts = [] + + for dynObj in dynWorld.dynamicObjects.values(): + lane_ids = dynObj.lane_id_vec + if len(lane_ids) == 0: + continue + + changes = 0 + current_lane = lane_ids[0] + for lane in lane_ids[1:]: + if lane != current_lane: + changes += 1 + current_lane = lane + + lane_change_counts.append({ + "uuid": dynObj.UUID, + "type": dynObj.type, + "changes": changes, + }) + + sorted_by_changes = sorted(lane_change_counts, key=lambda x: x["changes"], reverse=True) + + print("\n--- Top 10 vehicles with most lane changes ---") + for idx, item in enumerate(sorted_by_changes[:10]): + print(f"{idx+1}. Vehicle {item['uuid']} ({item['type']}): {item['changes']} lane changes") + + +if __name__ == "__main__": + if len(sys.argv) < 2: + print("Usage: python 01_lane_changes.py ") + else: + analyze_lane_changes(sys.argv[1]) diff --git a/example_scripts/02_heatmap_density.py b/example_scripts/02_heatmap_density.py new file mode 100644 index 0000000000000000000000000000000000000000..f3080655179572d5e78a0a1afaa6e0faa0a44243 --- /dev/null +++ b/example_scripts/02_heatmap_density.py @@ -0,0 +1,58 @@ +""" +Traffic Density Heatmap — Automatum Data T-Crossing Dataset +Generates a 2D heatmap of all vehicle positions over time. + +Usage: + python 02_heatmap_density.py + +Example: + python 02_heatmap_density.py ../T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2 + +Output: + traffic_heatmap.png in the current directory +""" +import sys +import os +import numpy as np +import matplotlib.pyplot as plt +from openautomatumdronedata.dataset import droneDataset + + +def generate_density_heatmap(dataset_path, output_filename="traffic_heatmap.png"): + print(f"Loading dataset from: {dataset_path}") + dataset = droneDataset(dataset_path) + dynWorld = dataset.dynWorld + + print("Extracting position data...") + all_x, all_y = [], [] + + for dynObj in dynWorld.dynamicObjects.values(): + x_valid = [x for x in dynObj.x_vec if not np.isnan(x)] + y_valid = [y for y in dynObj.y_vec if not np.isnan(y)] + all_x.extend(x_valid) + all_y.extend(y_valid) + + if not all_x: + print("No position data found!") + return + + print(f"Extracted {len(all_x)} data points. Creating heatmap...") + + plt.figure(figsize=(12, 8)) + plt.style.use("dark_background") + plt.hist2d(all_x, all_y, bins=(200, 200), cmap="inferno", cmin=1) + plt.colorbar(label="Traffic density (data points)") + plt.title("Traffic Density Heatmap (Top-View)") + plt.xlabel("X-Position [m]") + plt.ylabel("Y-Position [m]") + plt.gca().set_aspect("equal", adjustable="box") + plt.tight_layout() + plt.savefig(output_filename, dpi=300) + print(f"Heatmap saved: {os.path.abspath(output_filename)}") + + +if __name__ == "__main__": + if len(sys.argv) < 2: + print("Usage: python 02_heatmap_density.py ") + else: + generate_density_heatmap(sys.argv[1]) diff --git a/example_scripts/03_high_acceleration.py b/example_scripts/03_high_acceleration.py new file mode 100644 index 0000000000000000000000000000000000000000..d41c3c8923eb1126e00c4bc7ca9738ad317f4aa7 --- /dev/null +++ b/example_scripts/03_high_acceleration.py @@ -0,0 +1,61 @@ +""" +High Acceleration Detection — Automatum Data T-Crossing Dataset +Detects vehicles exceeding a given acceleration threshold. + +Usage: + python 03_high_acceleration.py [threshold_m_s2] + +Example: + python 03_high_acceleration.py ../T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2 3.0 +""" +import sys +import os +import numpy as np +from openautomatumdronedata.dataset import droneDataset + + +def detect_high_accelerations(dataset_path, acc_threshold=3.0): + print(f"Loading dataset from: {dataset_path}") + dataset = droneDataset(dataset_path) + dynWorld = dataset.dynWorld + + print(f"Searching for accelerations > {acc_threshold} m/s^2...") + + high_accel_vehicles = [] + + for dynObj in dynWorld.dynamicObjects.values(): + length = min(len(dynObj.ax_vec), len(dynObj.ay_vec)) + if length == 0: + continue + + ax = np.array(dynObj.ax_vec[:length]) + ay = np.array(dynObj.ay_vec[:length]) + total_accel = np.sqrt(ax**2 + ay**2) + valid_accel = total_accel[~np.isnan(total_accel)] + + if len(valid_accel) > 0: + max_accel = np.max(valid_accel) + if max_accel > acc_threshold: + high_accel_vehicles.append({ + "uuid": dynObj.UUID, + "type": dynObj.type, + "max_accel": max_accel, + }) + + if not high_accel_vehicles: + print(f"No vehicles with acceleration > {acc_threshold} m/s^2 found.") + return + + sorted_vehicles = sorted(high_accel_vehicles, key=lambda x: x["max_accel"], reverse=True) + + print(f"\n--- {len(sorted_vehicles)} vehicles with notable acceleration ---") + for item in sorted_vehicles: + print(f"Vehicle {item['uuid']} ({item['type']}): max {item['max_accel']:.2f} m/s^2") + + +if __name__ == "__main__": + if len(sys.argv) < 2: + print("Usage: python 03_high_acceleration.py [threshold]") + else: + threshold = float(sys.argv[2]) if len(sys.argv) >= 3 else 3.0 + detect_high_accelerations(sys.argv[1], threshold) diff --git a/example_scripts/README.md b/example_scripts/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9694593d5a875e5ad4c447af8ed8c5748d8daa4d --- /dev/null +++ b/example_scripts/README.md @@ -0,0 +1,33 @@ +# Example Scripts — Automatum Data T-Crossing Dataset + +These scripts demonstrate how to work with the Automatum drone traffic data using the `openautomatumdronedata` Python library. + +## Prerequisites + +```bash +pip install openautomatumdronedata numpy matplotlib +``` + +## Scripts + +| Script | Description | +|--------|-------------| +| `01_lane_changes.py` | Analyzes lane change frequency per vehicle and shows the top 10 | +| `02_heatmap_density.py` | Creates a traffic density heatmap from all position data | +| `03_high_acceleration.py` | Detects vehicles exceeding an acceleration threshold | +| `example_export_objects.py` | Exports per-vehicle JSON files with surrounding object data | + +## Usage + +All scripts take the path to a recording folder as their first argument: + +```bash +python 01_lane_changes.py ../T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2 +python 02_heatmap_density.py ../T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2 +python 03_high_acceleration.py ../T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2 3.0 +``` + +## Learn More + +- [Full Documentation](https://openautomatumdronedata.readthedocs.io) +- [Automatum Data Website](https://automatum-data.com) diff --git a/example_scripts/example_export_objects.py b/example_scripts/example_export_objects.py new file mode 100644 index 0000000000000000000000000000000000000000..8b5950f621770b64602ffab258ec67a3577cef05 --- /dev/null +++ b/example_scripts/example_export_objects.py @@ -0,0 +1,164 @@ +""" + 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": 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0000000000000000000000000000000000000000..951b36ac18b98a7c24ce3760e0f2fb92d51a9e56 --- /dev/null +++ b/website_information/bokeh_license.txt @@ -0,0 +1,28 @@ +Copyright (c) 2012 - 2023, Anaconda, Inc., and Bokeh Contributors +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +Redistributions of source code must retain the above copyright notice, +this list of conditions and the following disclaimer. + +Redistributions in binary form must reproduce the above copyright notice, +this list of conditions and the following disclaimer in the documentation +and/or other materials provided with the distribution. + +Neither the name of Anaconda nor the names of any contributors +may be used to endorse or promote products derived from this software +without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY 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