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, # } # # 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"],"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 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, image_dir, 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[6:10])) 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 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) # Match the video folder 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 # 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