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# """EMT dataset."""
# import os
# import json
# import datasets
# _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 + tracking.",
# """
# _LABEL_MAP = [
# 'n01440764',
# 'n02102040',
# 'n02979186',
# 'n03000684',
# 'n03028079',
# 'n03394916',
# 'n03417042',
# 'n03425413',
# 'n03445777',
# 'n03888257',
# ]
# # _REPO = "https://huggingface.co/datasets/frgfm/imagenette/resolve/main/metadata"
# _REPO = "https://huggingface.co/datasets/Murdism/EMT/resolve/main/labels"
# class EMTConfig(datasets.BuilderConfig):
# """BuilderConfig for EMT."""
# def __init__(self, data_url, metadata_urls, **kwargs):
# """BuilderConfig for EMT.
# Args:
# data_url: `string`, url to download the zip file from.
# matadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs
# **kwargs: keyword arguments forwarded to super.
# """
# super(EMTConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
# self.data_url = data_url
# self.metadata_urls = metadata_urls
# class EMT(datasets.GeneratorBasedBuilder):
# """Imagenette dataset."""
# BUILDER_CONFIGS = [
# EMTConfig(
# name="full_size",
# description="All images are in their original size.",
# data_url="https://huggingface.co/datasets/KuAvLab/EMT/blob/main/emt_images.tar.gz",
# metadata_urls={
# "train": f"{_REPO}/train/",
# "test": f"{_REPO}/test/",
# },
# )
# ]
# def _info(self):
# return datasets.DatasetInfo(
# description=_DESCRIPTION + self.config.description,
# features=datasets.Features(
# {
# "image": datasets.Image(),
# "label": datasets.ClassLabel(
# names=[
# "bbox",
# "class_id",
# "track_id",
# "class_name",
# ]
# ),
# }
# ),
# supervised_keys=None,
# homepage=_HOMEPAGE,
# license=_LICENSE,
# citation=_CITATION,
# )
# def _split_generators(self, dl_manager):
# archive_path = dl_manager.download(self.config.data_url)
# metadata_paths = dl_manager.download(self.config.metadata_urls)
# archive_iter = dl_manager.iter_archive(archive_path)
# return [
# datasets.SplitGenerator(
# name=datasets.Split.TRAIN,
# gen_kwargs={
# "images": archive_iter,
# "metadata_path": metadata_paths["train"],
# },
# ),
# datasets.SplitGenerator(
# name=datasets.Split.TEST,
# gen_kwargs={
# "images": os.path.join(self.config.data_url, "test"),
# "metadata_path": metadata_paths["test"],
# },
# ),
# ]
# def _generate_examples(self, images, metadata_path):
# with open(metadata_path, encoding="utf-8") as f:
# files_to_keep = set(f.read().split("\n"))
# idx = 0
# for file_path, file_obj in images:
# if file_path in files_to_keep:
# label = _LABEL_MAP.index(file_path.split("/")[-2])
# yield idx, {
# "image": {"path": file_path, "bytes": file_obj.read()},
# "label": label,
# }
# idx += 1
"""EMT dataset."""
import os
import json
import pandas as pd
import datasets
_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 + tracking.",
"""
_REPO = "https://huggingface.co/datasets/Murdism/EMT/resolve/main/annotations"
class EMTConfig(datasets.BuilderConfig):
"""BuilderConfig for EMT."""
def __init__(self, data_url, annotation_url, **kwargs):
"""BuilderConfig for EMT.
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.
"""
super(EMTConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
self.data_url = data_url
self.annotation_url = annotation_url
class EMT(datasets.GeneratorBasedBuilder):
"""EMT dataset."""
BUILDER_CONFIGS = [
EMTConfig(
name="full_size",
description="All images are in their original size.",
data_url="https://huggingface.co/datasets/KuAvLab/EMT/blob/main/emt_images.tar.gz",
annotation_url="https://huggingface.co/datasets/Murdism/EMT/resolve/main/annotations/",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION + self.config.description,
features=datasets.Features(
{
"image": datasets.Image(),
"objects": datasets.Sequence(
{
"bbox": datasets.Sequence(datasets.Float32()),
"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):
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."""
# Load annotations from Parquet
df = pd.read_parquet(annotation_path)
# Convert DataFrame into a dictionary for faster lookups
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
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