| # 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 (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, | |
| # } | |
| # # Based on the requested split, we only download the relevant data | |
| # split = self.config.name # Determine the requested split (train or test) | |
| # # Ensure paths are correctly resolved for the requested split | |
| # extracted_paths = dl_manager.download_and_extract({split: annotation_urls[split]}) | |
| # image_archives = dl_manager.download_and_extract({split: image_urls[split]}) | |
| # # Ensure annotation paths point to the correct subdirectory | |
| # annotation_path = os.path.join(extracted_paths[split], "annotations", split) | |
| # image_path = image_archives[split] | |
| # return [ | |
| # datasets.SplitGenerator( | |
| # name=datasets.Split.TRAIN if split == "train" else datasets.Split.TEST, | |
| # gen_kwargs={ | |
| # "images": dl_manager.iter_archive(image_path), | |
| # "annotation_path": annotation_path, | |
| # }, | |
| # ), | |
| # ] | |
| # # 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, | |
| # # } | |
| # # # Ensure paths are correctly resolved | |
| # # extracted_paths = dl_manager.download_and_extract(annotation_urls) | |
| # # image_archives = dl_manager.download_and_extract(image_urls) | |
| # # # ✅ Ensure annotation paths point to the correct subdirectory | |
| # # train_annotation_path = os.path.join(extracted_paths["train"], "annotations", "train") | |
| # # test_annotation_path = os.path.join(extracted_paths["test"], "annotations", "test") | |
| # # return [ | |
| # # datasets.SplitGenerator( | |
| # # name=datasets.Split.TRAIN, | |
| # # gen_kwargs={ | |
| # # "images": dl_manager.iter_archive(image_archives["train"]), | |
| # # "annotation_path": train_annotation_path, # ✅ Corrected path | |
| # # }, | |
| # # ), | |
| # # datasets.SplitGenerator( | |
| # # name=datasets.Split.TEST, | |
| # # gen_kwargs={ | |
| # # "images": dl_manager.iter_archive(image_archives["test"]), | |
| # # "annotation_path": test_annotation_path, # ✅ Corrected path | |
| # # }, | |
| # # ), | |
| # # ] | |
| # def _generate_examples(self, images, annotation_path): | |
| # """Generate dataset examples by matching images to their corresponding annotations.""" | |
| # annotations = {} | |
| # # Determine whether we're processing train or test split | |
| # 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}") | |
| # # Check if annotation directory exists | |
| # if not os.path.exists(ann_dir): | |
| # raise FileNotFoundError(f"Annotation directory does not exist: {ann_dir}") | |
| # # Extract annotation files and read their contents | |
| # for ann_file in os.listdir(ann_dir): | |
| # video_name = os.path.splitext(ann_file)[0] # Extract video folder name from file | |
| # ann_path = os.path.join(ann_dir, 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"{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" | |
| _GT_OBJECT_CLASSES = { | |
| 0: "Pedestrian", | |
| 1: "Cyclist", | |
| 2: "Motorbike", | |
| 3: "Small_motorised_vehicle", | |
| 4: "Car", | |
| 5: "Medium_vehicle", | |
| 6: "Large_vehicle", | |
| 7: "Bus", | |
| 8: "Emergency_vehicle", | |
| } | |
| 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, | |
| # } | |
| # # Ensure paths are correctly resolved for the requested split | |
| # extracted_paths = dl_manager.download_and_extract(annotation_urls) | |
| # image_archives = dl_manager.download_and_extract(image_urls) | |
| # # Ensure annotation paths point to the correct subdirectory | |
| # train_annotation_path = os.path.join(extracted_paths["train"], "EMT", "annotations", "train") | |
| # test_annotation_path = os.path.join(extracted_paths["test"], "EMT", "annotations", "test") | |
| # return [ | |
| # datasets.SplitGenerator( | |
| # name=datasets.Split.TRAIN, | |
| # gen_kwargs={ | |
| # "images": dl_manager.iter_archive(image_archives["train"]), | |
| # "annotation_path": train_annotation_path, | |
| # }, | |
| # ), | |
| # datasets.SplitGenerator( | |
| # name=datasets.Split.TEST, | |
| # gen_kwargs={ | |
| # "images": dl_manager.iter_archive(image_archives["test"]), | |
| # "annotation_path": test_annotation_path, | |
| # }, | |
| # ), | |
| # ] | |
| def _split_generators(self, dl_manager): | |
| """Download (if not cached) and prepare dataset splits.""" | |
| # Define dataset URLs | |
| image_urls = { | |
| "train": _TRAIN_IMAGE_ARCHIVE_URL, | |
| "test": _TEST_IMAGE_ARCHIVE_URL, | |
| } | |
| annotation_urls = { | |
| "train": _TRAIN_ANNOTATION_ARCHIVE_URL, | |
| "test": _TEST_ANNOTATION_ARCHIVE_URL, | |
| } | |
| # Extract all data (both splits) | |
| extracted_images = dl_manager.download_and_extract(image_urls) | |
| extracted_annotations = dl_manager.download_and_extract(annotation_urls) | |
| # Define paths | |
| train_annotation_path = os.path.join(extracted_annotations["train"], "annotations", "train") | |
| test_annotation_path = os.path.join(extracted_annotations["test"], "annotations", "test") | |
| train_image_path = extracted_images["train"] | |
| test_image_path = extracted_images["test"] | |
| # Return available splits (Hugging Face will filter based on user request) | |
| 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, images, annotation_path): | |
| """Generate dataset examples by matching images to their corresponding annotations.""" | |
| annotations = {} | |
| # Determine whether we're processing train or test split | |
| 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}") | |
| # Check if annotation directory exists | |
| if not os.path.exists(ann_dir): | |
| raise FileNotFoundError(f"Annotation directory does not exist: {ann_dir}") | |
| # Extract annotation files and read their contents | |
| for ann_file in os.listdir(ann_dir): | |
| video_name = os.path.splitext(ann_file)[0] # Extract video folder name from file | |
| ann_path = os.path.join(ann_dir, 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"{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 | |