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


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="default",
            description="Dataset with train and test splits",
            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 _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