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# 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.
# """
# # # Annotation repository
# # _ANNOTATION_REPO = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/annotations"
# # Tar file URLs for images
# _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"
# # Tar file URLs for annotations
# _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"
# class EMT(datasets.GeneratorBasedBuilder):
# """EMT dataset."""
# BUILDER_CONFIGS = [
# datasets.BuilderConfig(
# name="full_size",
# description="All images are in their original size.",
# 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 train/test images and annotations."""
# image_urls = {
# "train": _TRAIN_IMAGE_ARCHIVE_URL,
# "test": _TEST_IMAGE_ARCHIVE_URL,
# }
# # Download the tar file for annotations
# # annotation_urls = {
# # "train": _TRAIN_ANNOTATION_ARCHIVE_URL,
# # "test": _TEST_ANNOTATION_ARCHIVE_URL,
# # }
# annotation_urls = {
# "train": "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/train_annotation.tar.gz",
# "test": "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/test_annotation.tar.gz",
# }
# # Download image files
# images = {
# "train": dl_manager.iter_archive(image_urls["train"]),
# "test": dl_manager.iter_archive(image_urls["test"]),
# }
# # Download annotation files and extract them
# annotations = {
# "train": dl_manager.download_and_extract(annotation_urls["train"]),
# "test": dl_manager.download_and_extract(annotation_urls["test"]),
# }
# return [
# datasets.SplitGenerator(
# name=datasets.Split.TRAIN,
# gen_kwargs={
# "images": images["train"],
# "annotation_path": annotations["train"],
# },
# ),
# datasets.SplitGenerator(
# name=datasets.Split.TEST,
# gen_kwargs={
# "images": images["test"],
# "annotation_path": annotations["test"],
# },
# ),
# ]
# def _generate_examples(self, images, annotation_path):
# """Generate dataset examples by matching images to their corresponding annotations."""
# annotations = {}
# # Load all annotations into memory
# for ann_file in os.listdir(annotation_path):
# video_name = os.path.splitext(ann_file)[0] # Get video folder name from the annotation file
# ann_path = os.path.join(annotation_path, ann_file)
# print("ann_path:,",ann_path,"\nannotation_path: ",annotation_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[4:8]))
# class_id = _GT_OBJECT_CLASSES.get(class_name, -1)
# img_name = f"{frame_id}.jpg"
# # Store annotation in a dictionary
# 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,
# }
# )
# # Yield dataset entries
# idx = 0
# for file_path, file_obj in images:
# img_name = os.path.basename(file_path)
# video_name = os.path.basename(os.path.dirname(file_path)) # Match the video folder
# key = f"{video_name}/{img_name}"
# if key in annotations:
# yield idx, {
# "image": {"path": file_path, "bytes": file_obj.read()},
# "objects": annotations[key],
# }
# idx += 1
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"
class EMT(datasets.GeneratorBasedBuilder):
"""EMT dataset."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="full_size",
description="All images are in their original size.",
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 train/test images and annotations."""
image_urls = {
"train": _TRAIN_IMAGE_ARCHIVE_URL,
"test": _TEST_IMAGE_ARCHIVE_URL,
}
annotation_urls = {
"train": _TRAIN_ANNOTATION_ARCHIVE_URL,
"test": _TEST_ANNOTATION_ARCHIVE_URL,
}
# Download image files
images = {
"train": dl_manager.iter_archive(image_urls["train"]),
"test": dl_manager.iter_archive(image_urls["test"]),
}
# Download annotation files and extract them
annotations = {
"train": dl_manager.download_and_extract(annotation_urls["train"]),
"test": dl_manager.download_and_extract(annotation_urls["test"]),
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images": images["train"],
"annotation_path": annotations["train"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"images": images["test"],
"annotation_path": annotations["test"],
},
),
]
def _generate_examples(self, images, annotation_path):
"""Generate dataset examples by matching images to their corresponding annotations."""
annotations = {}
# Extract annotation tar file and read its contents
for ann_file in os.listdir(annotation_path):
# Ensure that we're dealing with the annotation file
ann_path = os.path.join(annotation_path, ann_file)
if os.path.isdir(ann_path):
continue # Skip directories
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[4:8]))
class_id = _GT_OBJECT_CLASSES.get(class_name, -1)
img_name = f"{frame_id}.jpg"
# Store annotation in a dictionary
key = f"{ann_file}/{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,
}
)
# Yield dataset entries
idx = 0
for file_path, file_obj in images:
img_name = os.path.basename(file_path)
video_name = os.path.basename(os.path.dirname(file_path)) # Match the video folder
key = f"{video_name}/{img_name}"
if key in annotations:
yield idx, {
"image": {"path": file_path, "bytes": file_obj.read()},
"objects": annotations[key],
}
idx += 1
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