|
|
| import os |
| import datasets |
| import tarfile |
|
|
| _HOMEPAGE = "https://github.com/AV-Lab/emt-dataset" |
| _LICENSE = "CC-BY-SA 4.0" |
| _CITATION = """ |
| @article{EMTdataset2025, |
| title={EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving in the Arab Gulf Region}, |
| author={Nadya Abdel Madjid and Murad Mebrahtu and Abdelmoamen Nasser and Bilal Hassan and Naoufel Werghi and Jorge Dias and Majid Khonji}, |
| year={2025}, |
| eprint={2502.19260}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2502.19260} |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| A multi-task dataset for detection, tracking, prediction, and intention prediction. |
| This dataset includes 34,386 annotated frames collected over 57 minutes of driving, with annotations for detection and tracking. |
| """ |
|
|
| _TRAIN_IMAGE_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/train_images.tar.gz" |
| _TEST_IMAGE_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/test_images.tar.gz" |
|
|
| _TRAIN_ANNOTATION_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/train_annotation.tar.gz" |
| _TEST_ANNOTATION_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/test_annotation.tar.gz" |
|
|
| _GT_OBJECT_CLASSES = { |
| "Pedestrian": 0, |
| "Cyclist" : 1, |
| "Motorbike" : 2, |
| "Small_motorised_vehicle" : 3, |
| "Car" : 4, |
| "Medium_vehicle" : 5, |
| "Large_vehicle" : 6, |
| "Bus" : 7, |
| "Emergency_vehicle" : 8, |
| } |
|
|
| class EMT(datasets.GeneratorBasedBuilder): |
| """EMT dataset.""" |
| |
| BUILDER_CONFIGS = [ |
| datasets.BuilderConfig( |
| name="emt", |
| description="Training split of the EMT dataset", |
| version=datasets.Version("1.0.0"), |
| ), |
| ] |
|
|
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features( |
| { |
| "image": datasets.Image(), |
| "objects": datasets.Sequence( |
| { |
| "bbox": datasets.Sequence(datasets.Value("float32"), length=4), |
| "class_id": datasets.Value("int32"), |
| "track_id": datasets.Value("int32"), |
| "class_name": datasets.Value("string"), |
| } |
| ), |
| } |
| ), |
| supervised_keys=None, |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| citation=_CITATION, |
| ) |
|
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| def _split_generators(self, dl_manager): |
| """Download (if not cached) and prepare dataset splits.""" |
| |
| |
| image_urls = { |
| "train": _TRAIN_IMAGE_ARCHIVE_URL, |
| "test": _TEST_IMAGE_ARCHIVE_URL, |
| } |
| |
| annotation_urls = { |
| "train": _TRAIN_ANNOTATION_ARCHIVE_URL, |
| "test": _TEST_ANNOTATION_ARCHIVE_URL, |
| } |
| |
| |
| extracted_images = dl_manager.download_and_extract(image_urls) |
| extracted_annotations = dl_manager.download_and_extract(annotation_urls) |
| |
| |
| train_annotation_path = os.path.join(extracted_annotations["train"],"EMT", "annotations", "train") |
| test_annotation_path = os.path.join(extracted_annotations["test"],"EMT", "annotations", "test") |
| |
| train_image_path = extracted_images["train"] |
| test_image_path = extracted_images["test"] |
| |
| |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "image_dir": train_image_path, |
| "annotation_path": train_annotation_path, |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "image_dir": test_image_path, |
| "annotation_path": test_annotation_path, |
| }, |
| ), |
| ] |
|
|
| |
| def _generate_examples(self, image_dir, annotation_path): |
| """Generate dataset examples by matching images to their corresponding annotations.""" |
| |
| annotations = {} |
| |
| |
| if "train" in annotation_path: |
| annotation_split = "train" |
| elif "test" in annotation_path: |
| annotation_split = "test" |
| else: |
| raise ValueError(f"Unknown annotation path: {annotation_path}") |
| |
| ann_dir = annotation_path |
| |
| print(f"Extracted annotations path: {annotation_path}") |
| print(f"Looking for annotations in: {ann_dir}") |
| |
| |
| if not os.path.exists(ann_dir): |
| raise FileNotFoundError(f"Annotation directory does not exist: {ann_dir}") |
| |
| |
| for ann_file in os.listdir(ann_dir): |
| video_name = os.path.splitext(ann_file)[0] |
| ann_path = os.path.join(ann_dir, ann_file) |
| |
| if os.path.isdir(ann_path): |
| continue |
| |
| print("Processing annotation file:", ann_path) |
| |
| with open(ann_path, "r", encoding="utf-8") as f: |
| for line in f: |
| parts = line.strip().split() |
| if len(parts) < 8: |
| continue |
| |
| frame_id, track_id, class_name = parts[:3] |
| bbox = list(map(float, parts[6:10])) |
| class_id = _GT_OBJECT_CLASSES.get(class_name, -1) |
| img_name = f"{frame_id}.jpg" |
| |
| |
| key = f"{video_name}/{img_name}" |
| if key not in annotations: |
| annotations[key] = [] |
| |
| annotations[key].append( |
| { |
| "bbox": bbox, |
| "class_id": class_id, |
| "track_id": int(track_id), |
| "class_name": class_name, |
| } |
| ) |
| |
| |
| idx = 0 |
| for root, _, files in os.walk(image_dir): |
| for file_name in files: |
| if not file_name.endswith((".jpg", ".png")): |
| continue |
| |
| file_path = os.path.join(root, file_name) |
| video_name = os.path.basename(root) |
| key = f"{video_name}/{file_name}" |
| |
| if key in annotations: |
| with open(file_path, "rb") as img_file: |
| yield idx, { |
| "image": {"path": file_path, "bytes": img_file.read()}, |
| "objects": annotations[key], |
| } |
| idx += 1 |
|
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
