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"""EMT dataset.""" |
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|
|
import os |
|
import json |
|
import pandas as pd |
|
import datasets |
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|
|
_HOMEPAGE = "https://github.com/AV-Lab/emt-dataset" |
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_LICENSE = "CC-BY-SA 4.0" |
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_CITATION = """ |
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@article{EMTdataset2025, |
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title={EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving in the Arab Gulf Region}, |
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author={Nadya Abdel Madjid and Murad Mebrahtu and Abdelmoamen Nasser and Bilal Hassan and Naoufel Werghi and Jorge Dias and Majid Khonji}, |
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year={2025}, |
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eprint={2502.19260}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2502.19260} |
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} |
|
""" |
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_DESCRIPTION = """\ |
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A multi-task dataset for detection, tracking, prediction, and intention prediction. |
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This dataset includes 34,386 annotated frames collected over 57 minutes of driving, with annotations for detection + tracking.", |
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""" |
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|
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_REPO = "https://huggingface.co/datasets/Murdism/EMT/resolve/main/annotations" |
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|
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class EMTConfig(datasets.BuilderConfig): |
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"""BuilderConfig for EMT.""" |
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def __init__(self, data_url, annotation_url, **kwargs): |
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"""BuilderConfig for EMT. |
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Args: |
|
data_url: `string`, URL to download the image archive (.tar file). |
|
annotation_url: `string`, URL to download the annotations (Parquet file). |
|
**kwargs: keyword arguments forwarded to super. |
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""" |
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super(EMTConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs) |
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self.data_url = data_url |
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self.annotation_url = annotation_url |
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|
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class EMT(datasets.GeneratorBasedBuilder): |
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"""EMT dataset.""" |
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BUILDER_CONFIGS = [ |
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EMTConfig( |
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name="full_size", |
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description="All images are in their original size.", |
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data_url="https://huggingface.co/datasets/KuAvLab/EMT/blob/main/emt_images.tar.gz", |
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annotation_url="https://huggingface.co/datasets/Murdism/EMT/resolve/main/annotations/", |
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) |
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] |
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|
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION + self.config.description, |
|
features=datasets.Features( |
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{ |
|
"image": datasets.Image(), |
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"objects": datasets.Sequence( |
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{ |
|
"bbox": datasets.Sequence(datasets.Float32()), |
|
"class_id": datasets.Value("int32"), |
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"track_id": datasets.Value("int32"), |
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"class_name": datasets.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=_HOMEPAGE, |
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license=_LICENSE, |
|
citation=_CITATION, |
|
) |
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|
|
def _split_generators(self, dl_manager): |
|
archive_path = dl_manager.download(self.config.data_url) |
|
annotation_paths = { |
|
"train": dl_manager.download_and_extract(self.config.annotation_url + "train_annotations.parquet"), |
|
"test": dl_manager.download_and_extract(self.config.annotation_url + "test_annotations.parquet"), |
|
} |
|
|
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"images": dl_manager.iter_archive(archive_path), |
|
"annotation_path": annotation_paths["train"], |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
gen_kwargs={ |
|
"images": dl_manager.iter_archive(archive_path), |
|
"annotation_path": annotation_paths["test"], |
|
}, |
|
), |
|
] |
|
|
|
def _generate_examples(self, images, annotation_path): |
|
"""Generate examples from Parquet annotations and image archive.""" |
|
|
|
|
|
df = pd.read_parquet(annotation_path) |
|
|
|
|
|
annotation_dict = {} |
|
for _, row in df.iterrows(): |
|
img_path = row["file_path"].split("/")[-2] + "/" + row["file_path"].split("/")[-1] |
|
print("img_path: ",img_path) |
|
if img_path not in annotation_dict: |
|
annotation_dict[img_path] = [] |
|
annotation_dict[img_path].append( |
|
{ |
|
"bbox": row["bbox"], |
|
"class_id": row["class_id"], |
|
"track_id": row["track_id"], |
|
"class_name": row["class_name"], |
|
} |
|
) |
|
|
|
idx = 0 |
|
for file_path, file_obj in images: |
|
if file_path in annotation_dict: |
|
yield idx, { |
|
"image": {"path": file_path, "bytes": file_obj.read()}, |
|
"objects": annotation_dict[file_path], |
|
} |
|
idx += 1 |
|
|
