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f80e641ca5161531564af55d21d97507b98932ad..0000000000000000000000000000000000000000 --- a/example_scripts/01_lane_changes.py +++ /dev/null @@ -1,54 +0,0 @@ -""" -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 deleted file mode 100644 index f3080655179572d5e78a0a1afaa6e0faa0a44243..0000000000000000000000000000000000000000 --- a/example_scripts/02_heatmap_density.py +++ /dev/null @@ -1,58 +0,0 @@ -""" -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 deleted file mode 100644 index d41c3c8923eb1126e00c4bc7ca9738ad317f4aa7..0000000000000000000000000000000000000000 --- a/example_scripts/03_high_acceleration.py +++ /dev/null @@ -1,61 +0,0 @@ -""" -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 deleted file mode 100644 index 9694593d5a875e5ad4c447af8ed8c5748d8daa4d..0000000000000000000000000000000000000000 --- a/example_scripts/README.md +++ /dev/null @@ -1,33 +0,0 @@ -# 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 deleted file mode 100644 index 8b5950f621770b64602ffab258ec67a3577cef05..0000000000000000000000000000000000000000 --- a/example_scripts/example_export_objects.py +++ /dev/null @@ -1,164 +0,0 @@ -""" - 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"]) - - - diff --git a/website_information/.DS_Store b/website_information/.DS_Store deleted file mode 100644 index 8e77dc492b90879d7a72d1d215aff384e843b8bd..0000000000000000000000000000000000000000 Binary files a/website_information/.DS_Store and /dev/null differ diff --git a/website_information/T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2/T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2_map.jpg b/website_information/T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2/T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2_map.jpg deleted file mode 100644 index 28e346734c7fbf31b52a2acff87f310372358a27..0000000000000000000000000000000000000000 --- a/website_information/T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2/T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2_map.jpg +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:7ca1f2dd2426858509d5030e5fc57a261309a5deaa78a925f3927b401be309a4 -size 83178 diff --git a/website_information/T-Crossing-St2214-DuenzlauUmgehung_1b9b-1b9bf4b8-9fa6-4d23-abd2-2b715d087e8f/T-Crossing-St2214-DuenzlauUmgehung_1b9b-1b9bf4b8-9fa6-4d23-abd2-2b715d087e8f_map.jpg b/website_information/T-Crossing-St2214-DuenzlauUmgehung_1b9b-1b9bf4b8-9fa6-4d23-abd2-2b715d087e8f/T-Crossing-St2214-DuenzlauUmgehung_1b9b-1b9bf4b8-9fa6-4d23-abd2-2b715d087e8f_map.jpg deleted file mode 100644 index 7c3a15f4778843413c9e61eb31d8d7540232de05..0000000000000000000000000000000000000000 --- a/website_information/T-Crossing-St2214-DuenzlauUmgehung_1b9b-1b9bf4b8-9fa6-4d23-abd2-2b715d087e8f/T-Crossing-St2214-DuenzlauUmgehung_1b9b-1b9bf4b8-9fa6-4d23-abd2-2b715d087e8f_map.jpg +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:9eb8fb6a882258d989dffbccfd381943c7089bdfb8beacbec2d4c53d1a23f44c -size 56736 diff --git a/website_information/bokeh_license.txt b/website_information/bokeh_license.txt deleted file mode 100644 index 951b36ac18b98a7c24ce3760e0f2fb92d51a9e56..0000000000000000000000000000000000000000 --- a/website_information/bokeh_license.txt +++ /dev/null @@ -1,28 +0,0 @@ -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 EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF -THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/website_information/img/Illustration-3.jpg b/website_information/img/Illustration-3.jpg deleted file mode 100644 index 746a1c8878e3c37b49cdde2fcc555ce1c0bde3cf..0000000000000000000000000000000000000000 --- a/website_information/img/Illustration-3.jpg +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0b9287acfe10df4dfaa33c3a1fc579acacca950477da2b18e397ad7363f91bf4 -size 2760658 diff --git "a/website_information/img/automatum_logo_waagerecht_gro\303\237.png" "b/website_information/img/automatum_logo_waagerecht_gro\303\237.png" deleted file mode 100644 index 1610c293a8144d88884a8125f47199c78f0e8466..0000000000000000000000000000000000000000 --- "a/website_information/img/automatum_logo_waagerecht_gro\303\237.png" +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:eded7ce43163ecb898166a7bc587235f59b15bd17856235867d404760ca54c3e -size 42970 diff --git a/website_information/img/icon_automatum_data_crossing.jpg b/website_information/img/icon_automatum_data_crossing.jpg deleted file mode 100644 index df289fa01a7d887a5f3f3bc8a2216c8c0b0cf2db..0000000000000000000000000000000000000000 --- a/website_information/img/icon_automatum_data_crossing.jpg +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:3a969dfd2a5f7d1dca585c5f7f388fc7809191b5d97f99dd642756c5fb3159f9 -size 2953515 diff --git a/website_information/website_licence_information.json b/website_information/website_licence_information.json deleted file mode 100644 index 933c4f3cdec10bb32584c6b2e9d09427f64dd8bc..0000000000000000000000000000000000000000 --- a/website_information/website_licence_information.json +++ /dev/null @@ -1 +0,0 @@ -{"number_of_hours": 0.4934286137989842, "number_of_kilometers": 108.80835032797071, "number_of_recordings": 2, "number_of_locations": 2, "date": "27.03.2023, 12:56:37", "version": "26bfa8c6-831b-4e31-a81e-48be9fe0123c", "max_length_trajectory": 160.27761661822345, "object_deviation": [{"title": "Car", "value": 623, "color": "#763626"}, {"title": "Truck", "value": 47, "color": "#336B87"}, {"title": "Van", "value": 13, "color": "#2A3132"}], "locations": {"DuenzlauUmgehung": {"name": "DuenzlauUmgehung", "number_of_recordings": 1, "number_of_hours": 0.31268305342379416, "number_of_kilometers": 60.88534295912189, "lat": 48.776199999999996, "long": 11.3196, "max_length_trajectory": 158.55558062271328, "number_of_objects": 384, "path_slider_img_one": "website_licence_information/T-Crossing-St2214-DuenzlauUmgehung_1b9b-1b9bf4b8-9fa6-4d23-abd2-2b715d087e8f/sliding_img_base_cropped.jpg", "path_slider_img_two": "website_licence_information/T-Crossing-St2214-DuenzlauUmgehung_1b9b-1b9bf4b8-9fa6-4d23-abd2-2b715d087e8f/sliding_img_whie_overlay_cropped.jpg", "path_map_png": "website_licence_information/T-Crossing-St2214-DuenzlauUmgehung_1b9b-1b9bf4b8-9fa6-4d23-abd2-2b715d087e8f/T-Crossing-St2214-DuenzlauUmgehung_1b9b-1b9bf4b8-9fa6-4d23-abd2-2b715d087e8f_map.jpg"}, "GaimersheimStadtweg": {"name": "GaimersheimStadtweg", "number_of_recordings": 1, "number_of_hours": 0.18074556037519002, "number_of_kilometers": 47.923007368848815, "lat": 48.788199999999996, "long": 11.3855, "max_length_trajectory": 160.27761661822345, "number_of_objects": 299, "path_slider_img_one": "website_licence_information/T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2/sliding_img_base_cropped.jpg", "path_slider_img_two": "website_licence_information/T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2/sliding_img_whie_overlay_cropped.jpg", "path_map_png": "website_licence_information/T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2/T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2_map.jpg"}}} \ No newline at end of file