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import os |
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from datasets import GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split, Features, Value, Sequence, Image |
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class TruckV2X(GeneratorBasedBuilder): |
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VERSION = "1.0.0" |
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def _info(self): |
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return DatasetInfo( |
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description="TruckV2X: A Truck-Centered Perception Dataset", |
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features=Features({ |
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"town": Value("string"), |
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"frame_id": Value("string"), |
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"agent_data": { |
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"cav": { |
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"camera_images": Sequence(Image()), |
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"lidar_files": Sequence(Value("string")), |
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"yaml_file": Value("string"), |
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}, |
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"roadside": { |
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"camera_images": Sequence(Image()), |
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"lidar_files": Sequence(Value("string")), |
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"yaml_file": Value("string"), |
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}, |
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"tractor": { |
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"camera_images": Sequence(Image()), |
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"lidar_files": Sequence(Value("string")), |
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"yaml_file": Value("string"), |
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}, |
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"trailer": { |
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"camera_images": Sequence(Image()), |
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"lidar_files": Sequence(Value("string")), |
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"yaml_file": Value("string"), |
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}, |
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} |
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}), |
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supervised_keys=None, |
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homepage="TBD", |
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license="MIT" |
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) |
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def _split_generators(self, dl_manager): |
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train_zips = [ |
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"train/Town1_1.zip", "train/Town1_4.zip", "train/Town1_5.zip", |
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"train/Town2_0.zip", "train/Town2_1.zip", "train/Town2_2.zip", |
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"train/Town2_3.zip", "train/Town3_1.zip", "train/Town3_4.zip", |
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"train/Town3_6.zip", "train/Town3_7.zip", "train/Town3_8.zip", |
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"train/Town3_9.zip", "train/Town4_0.zip", "train/Town4_1.zip", |
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"train/Town4_3.zip", "train/Town4_4.zip", "train/Town4_6.zip", |
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"train/Town5_0.zip", "train/Town5_3.zip", "train/Town5_4.zip", |
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"train/Town5_5.zip", "train/Town5_6.zip", "train/Town5_7.zip", |
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"train/Town5_8.zip", "train/Town6_0.zip", "train/Town6_2.zip", |
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"train/Town6_3.zip", "train/Town6_4.zip", "train/Town6_5.zip", |
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"train/Town7_1.zip", "train/Town7_3.zip", "train/Town10_0.zip", |
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"train/Town10_1.zip", "train/Town10_6.zip", "train/Town10_7.zip", |
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"train/Town10_8.zip", "train/Town15_1.zip" |
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] |
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val_zips = [ |
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"val/Town3_0.zip", "val/Town4_5.zip", "val/Town5_2.zip", |
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"val/Town5_9.zip", "val/Town6_6.zip", "val/Town7_4.zip", |
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"val/Town10_4.zip", "val/Town10_9.zip", "val/Town10_10.zip" |
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] |
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test_zips = [ |
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"test/Town1_0.zip", "test/Town1_2.zip", "test/Town1_3.zip", |
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"test/Town2_4.zip", "test/Town2_5.zip", "test/Town3_2.zip", |
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"test/Town3_3.zip", "test/Town3_5.zip", "test/Town4_2.zip", |
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"test/Town5_1.zip", "test/Town6_1.zip", "test/Town7_0.zip", |
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"test/Town7_2.zip", "test/Town10_2.zip", "test/Town10_3.zip", |
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"test/Town10_5.zip", "test/Town15_0.zip" |
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] |
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train_dirs = [dl_manager.extract(dl_manager.download(p)) for p in train_zips] |
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val_dirs = [dl_manager.extract(dl_manager.download(p)) for p in val_zips] |
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test_dirs = [dl_manager.extract(dl_manager.download(p)) for p in test_zips] |
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return [ |
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SplitGenerator(name=Split.TRAIN, gen_kwargs={"town_dirs": train_dirs}), |
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SplitGenerator(name=Split.VALIDATION, gen_kwargs={"town_dirs": val_dirs}), |
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SplitGenerator(name=Split.TEST, gen_kwargs={"town_dirs": test_dirs}), |
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] |
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def _generate_examples(self, town_dirs): |
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id_ = 0 |
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agents = ["cav", "roadside", "tractor", "trailer"] |
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camera_counts = {"cav": 4, "roadside": 1, "tractor": 5, "trailer": 5} |
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lidar_counts = {"cav": 1, "roadside": 1, "tractor": 2, "trailer": 2} |
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for town_dir in town_dirs: |
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subfolders = [f for f in os.listdir(town_dir) if os.path.isdir(os.path.join(town_dir, f))] |
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if len(subfolders) == 1: |
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town_dir = os.path.join(town_dir, subfolders[0]) |
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town_name = os.path.basename(town_dir.rstrip("/\\")) |
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cav_dir = os.path.join(town_dir, "cav") |
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if not os.path.exists(cav_dir): |
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continue |
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frame_ids = sorted([ |
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fname.replace(".yaml", "") |
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for fname in os.listdir(cav_dir) |
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if fname.endswith(".yaml") |
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]) |
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for frame_id in frame_ids: |
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example = { |
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"town": town_name, |
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"frame_id": frame_id, |
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"agent_data": {} |
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} |
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for agent in agents: |
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agent_dir = os.path.join(town_dir, agent) |
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data = { |
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"camera_images": [], |
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"lidar_files": [], |
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"yaml_file": "" |
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} |
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for i in range(camera_counts[agent]): |
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cam_path = os.path.join(agent_dir, f"{frame_id}_camera{i}.jpg") |
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if os.path.exists(cam_path): |
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data["camera_images"].append(cam_path) |
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for i in range(lidar_counts[agent]): |
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lidar_path = os.path.join(agent_dir, f"{frame_id}_lidar{i}.pcd") |
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if os.path.exists(lidar_path): |
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data["lidar_files"].append(lidar_path) |
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yaml_path = os.path.join(agent_dir, f"{frame_id}.yaml") |
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if os.path.exists(yaml_path): |
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data["yaml_file"] = yaml_path |
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example["agent_data"][agent] = data |
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yield id_, example |
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id_ += 1 |
