Update EMT.py

EMT.py

@@ -1,4 +1,212 @@
 
+# 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
@@ -34,8 +242,8 @@ class EMT(datasets.GeneratorBasedBuilder):
 
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(
-            name="full_size",
-            description="All images are in their original size.",
+            name="default",
+            description="Dataset with train and test splits",
             version=datasets.Version("1.0.0"),
         )
     ]
@@ -75,66 +283,31 @@ class EMT(datasets.GeneratorBasedBuilder):
             "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]})
-    
+        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
-        annotation_path = os.path.join(extracted_paths[split], "annotations", split)
-        image_path = image_archives[split]
+        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 if split == "train" else datasets.Split.TEST,
+                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_path),
-                    "annotation_path": annotation_path,
+                    "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."""
-        
-    #     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."""
     
@@ -205,5 +378,3 @@ class EMT(datasets.GeneratorBasedBuilder):
                     "objects": annotations[key],
                 }
                 idx += 1
-
-