EMT.py

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