updated

emt.py

@@ -1,144 +1,6 @@
-# """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"
@@ -158,41 +20,22 @@ _CITATION = """
 
 _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.",
+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."""
+# Image archive URL
+_IMAGE_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/blob/main/emt_images.tar.gz"
 
-    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
+# Annotations URL (organized in train/test subfolders)
+_ANNOTATION_REPO = "https://huggingface.co/datasets/Murdism/EMT/resolve/main/annotations"
 
 
 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,
+            description=_DESCRIPTION,
             features=datasets.Features(
                 {
                     "image": datasets.Image(),
@@ -213,12 +56,12 @@ class EMT(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download(self.config.data_url)
+        archive_path = dl_manager.download(_IMAGE_ARCHIVE_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"),
+            "train": dl_manager.download_and_extract(f"{_ANNOTATION_REPO}/train/"),
+            "test": dl_manager.download_and_extract(f"{_ANNOTATION_REPO}/test/"),
         }
-        
+
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -237,32 +80,38 @@ class EMT(datasets.GeneratorBasedBuilder):
         ]
 
     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]
-            print("img_path: ",img_path)
-            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"],
-                }
-            )
-
+        """Generate examples from annotations and image archive."""
+
+        # Load annotation files
+        annotations = {}
+        for root, _, files in os.walk(annotation_path):
+            for file in files:
+                with open(os.path.join(root, file), "r", encoding="utf-8") as f:
+                    for line in f:
+                        parts = line.strip().split()
+                        frame_id, track_id, class_name = parts[:3]
+                        bbox = list(map(float, parts[4:8]))  # Extract bounding box
+                        class_id = hash(class_name) % 1000  # Convert class_name to numeric ID
+
+                        img_path = f"{frame_id}.jpg"
+                        if img_path not in annotations:
+                            annotations[img_path] = []
+                        annotations[img_path].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:
-            if file_path in annotation_dict:
+            img_name = os.path.basename(file_path)
+            if img_name in annotations:
                 yield idx, {
                     "image": {"path": file_path, "bytes": file_obj.read()},
-                    "objects": annotation_dict[file_path],
+                    "objects": annotations[img_name],
                 }
                 idx += 1