Added annotations

emt.py

@@ -0,0 +1,267 @@
+# """EMT dataset."""
+
+# import os
+# import json
+
+# import datasets
+
+
+# _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 + tracking.",
+    
+# """
+
+
+# _LABEL_MAP = [
+#     'n01440764',
+#     'n02102040',
+#     'n02979186',
+#     'n03000684',
+#     'n03028079',
+#     'n03394916',
+#     'n03417042',
+#     'n03425413',
+#     'n03445777',
+#     'n03888257',
+# ]
+
+# # _REPO = "https://huggingface.co/datasets/frgfm/imagenette/resolve/main/metadata"
+# _REPO = "https://huggingface.co/datasets/Murdism/EMT/resolve/main/labels"
+
+
+
+# class EMTConfig(datasets.BuilderConfig):
+#     """BuilderConfig for EMT."""
+
+#     def __init__(self, data_url, metadata_urls, **kwargs):
+#         """BuilderConfig for EMT.
+#         Args:
+#           data_url: `string`, url to download the zip file from.
+#           matadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs
+#           **kwargs: keyword arguments forwarded to super.
+#         """
+#         super(EMTConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+#         self.data_url = data_url
+#         self.metadata_urls = metadata_urls
+
+
+# class EMT(datasets.GeneratorBasedBuilder):
+#     """Imagenette dataset."""
+
+#     BUILDER_CONFIGS = [
+#         EMTConfig(
+#             name="full_size",
+#             description="All images are in their original size.",
+#             data_url="https://huggingface.co/datasets/KuAvLab/EMT/blob/main/emt_images.tar.gz",
+#             metadata_urls={
+#                 "train": f"{_REPO}/train/",
+#                 "test": f"{_REPO}/test/",
+#             },
+#         )
+#     ]
+
+#     def _info(self):
+#         return datasets.DatasetInfo(
+#             description=_DESCRIPTION + self.config.description,
+#             features=datasets.Features(
+#                 {
+#                     "image": datasets.Image(),
+#                     "label": datasets.ClassLabel(
+#                         names=[
+#                             "bbox",
+#                             "class_id",
+#                             "track_id",
+#                             "class_name",
+  
+#                         ]
+#                     ),
+#                 }
+#             ),
+#             supervised_keys=None,
+#             homepage=_HOMEPAGE,
+#             license=_LICENSE,
+#             citation=_CITATION,
+#         )
+
+#     def _split_generators(self, dl_manager):
+#         archive_path = dl_manager.download(self.config.data_url)
+#         metadata_paths = dl_manager.download(self.config.metadata_urls)
+#         archive_iter = dl_manager.iter_archive(archive_path)
+#         return [
+#             datasets.SplitGenerator(
+#                 name=datasets.Split.TRAIN,
+#                 gen_kwargs={
+#                     "images": archive_iter,
+#                     "metadata_path": metadata_paths["train"],
+#                 },
+#             ),
+#             datasets.SplitGenerator(
+#                 name=datasets.Split.TEST,
+#                 gen_kwargs={
+#                     "images": os.path.join(self.config.data_url, "test"),
+#                     "metadata_path": metadata_paths["test"],
+#                 },
+#             ),
+#         ]
+
+#     def _generate_examples(self, images, metadata_path):
+#         with open(metadata_path, encoding="utf-8") as f:
+#             files_to_keep = set(f.read().split("\n"))
+#         idx = 0
+#         for file_path, file_obj in images:
+#             if file_path in files_to_keep:
+#                 label = _LABEL_MAP.index(file_path.split("/")[-2])
+#                 yield idx, {
+#                     "image": {"path": file_path, "bytes": file_obj.read()},
+#                     "label": label,
+#                 }
+#                 idx += 1
+
+"""EMT dataset."""
+
+import os
+import json
+import pandas as pd
+import datasets
+
+_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 + tracking.",
+"""
+
+_REPO = "https://huggingface.co/datasets/Murdism/EMT/resolve/main/annotations"
+
+class EMTConfig(datasets.BuilderConfig):
+    """BuilderConfig for EMT."""
+
+    def __init__(self, data_url, annotation_url, **kwargs):
+        """BuilderConfig for EMT.
+        Args:
+          data_url: `string`, URL to download the image archive (.tar file).
+          annotation_url: `string`, URL to download the annotations (Parquet file).
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(EMTConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_url = data_url
+        self.annotation_url = annotation_url
+
+
+class EMT(datasets.GeneratorBasedBuilder):
+    """EMT dataset."""
+
+    BUILDER_CONFIGS = [
+        EMTConfig(
+            name="full_size",
+            description="All images are in their original size.",
+            data_url="https://huggingface.co/datasets/KuAvLab/EMT/blob/main/emt_images.tar.gz",
+            annotation_url="https://huggingface.co/datasets/Murdism/EMT/resolve/main/annotations/",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION + self.config.description,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "objects": datasets.Sequence(
+                        {
+                            "bbox": datasets.Sequence(datasets.Float32()),
+                            "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):
+        archive_path = dl_manager.download(self.config.data_url)
+        annotation_paths = {
+            "train": dl_manager.download_and_extract(self.config.annotation_url + "train_annotations.parquet"),
+            "test": dl_manager.download_and_extract(self.config.annotation_url + "test_annotations.parquet"),
+        }
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(archive_path),
+                    "annotation_path": annotation_paths["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(archive_path),
+                    "annotation_path": annotation_paths["test"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, images, annotation_path):
+        """Generate examples from Parquet annotations and image archive."""
+        
+        # Load annotations from Parquet
+        df = pd.read_parquet(annotation_path)
+
+        # Convert DataFrame into a dictionary for faster lookups
+        annotation_dict = {}
+        for _, row in df.iterrows():
+            img_path = row["file_path"].split("/")[-2] + "/" + row["file_path"].split("/")[-1]
+            if img_path not in annotation_dict:
+                annotation_dict[img_path] = []
+            annotation_dict[img_path].append(
+                {
+                    "bbox": row["bbox"],
+                    "class_id": row["class_id"],
+                    "track_id": row["track_id"],
+                    "class_name": row["class_name"],
+                }
+            )
+
+        idx = 0
+        for file_path, file_obj in images:
+            if file_path in annotation_dict:
+                yield idx, {
+                    "image": {"path": file_path, "bytes": file_obj.read()},
+                    "objects": annotation_dict[file_path],
+                }
+                idx += 1