Update EMT.py

EMT.py

@@ -1,37 +1,176 @@
+# 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.
+# """
+
+# # # Annotation repository
+# # _ANNOTATION_REPO = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/annotations"
+
+# # Tar file URLs for images
+# _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"
+
+# # Tar file URLs for annotations
+# _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 train/test images and annotations."""
+#         image_urls = {
+#             "train": _TRAIN_IMAGE_ARCHIVE_URL,
+#             "test": _TEST_IMAGE_ARCHIVE_URL,
+#         }
+    
+#         # Download the tar file for annotations
+#         # annotation_urls = {
+#         #     "train": _TRAIN_ANNOTATION_ARCHIVE_URL,
+#         #     "test": _TEST_ANNOTATION_ARCHIVE_URL,
+#         # }
+#         annotation_urls = {
+#         "train": "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/train_annotation.tar.gz",
+#         "test": "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/test_annotation.tar.gz",
+#         }
+#         # Download image files
+#         images = {
+#             "train": dl_manager.iter_archive(image_urls["train"]),
+#             "test": dl_manager.iter_archive(image_urls["test"]),
+#         }
+    
+#         # Download annotation files and extract them
+#         annotations = {
+#             "train": dl_manager.download_and_extract(annotation_urls["train"]),
+#             "test": dl_manager.download_and_extract(annotation_urls["test"]),
+#         }
+    
+#         return [
+#             datasets.SplitGenerator(
+#                 name=datasets.Split.TRAIN,
+#                 gen_kwargs={
+#                     "images": images["train"],
+#                     "annotation_path": annotations["train"],
+#                 },
+#             ),
+#             datasets.SplitGenerator(
+#                 name=datasets.Split.TEST,
+#                 gen_kwargs={
+#                     "images": images["test"],
+#                     "annotation_path": annotations["test"],
+#                 },
+#             ),
+#         ]
+
+#     def _generate_examples(self, images, annotation_path):
+#         """Generate dataset examples by matching images to their corresponding annotations."""
+        
+#         annotations = {}
+        
+#         # Load all annotations into memory
+#         for ann_file in os.listdir(annotation_path):
+#             video_name = os.path.splitext(ann_file)[0]  # Get video folder name from the annotation file
+#             ann_path = os.path.join(annotation_path, ann_file)
+#             print("ann_path:,",ann_path,"\nannotation_path: ",annotation_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.
-"""
-
-# # Annotation repository
-# _ANNOTATION_REPO = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/annotations"
+
+# Annotation repository
+_ANNOTATION_REPO = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/annotations"
 
 # Tar file URLs for images
 _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"
 
-# Tar file URLs for annotations
-_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."""
@@ -72,28 +211,24 @@ class EMT(datasets.GeneratorBasedBuilder):
             "train": _TRAIN_IMAGE_ARCHIVE_URL,
             "test": _TEST_IMAGE_ARCHIVE_URL,
         }
-    
-        # Download the tar file for annotations
-        # annotation_urls = {
-        #     "train": _TRAIN_ANNOTATION_ARCHIVE_URL,
-        #     "test": _TEST_ANNOTATION_ARCHIVE_URL,
-        # }
-        annotation_urls = {
-        "train": "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/train_annotation.tar.gz",
-        "test": "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/test_annotation.tar.gz",
-        }
+
         # Download image files
         images = {
             "train": dl_manager.iter_archive(image_urls["train"]),
             "test": dl_manager.iter_archive(image_urls["test"]),
         }
-    
-        # Download annotation files and extract them
+
+        # Download the annotation files from the remote repository
+        annotation_urls = {
+            "train": _ANNOTATION_REPO + "/train/",
+            "test": _ANNOTATION_REPO + "/test/",
+        }
+
         annotations = {
             "train": dl_manager.download_and_extract(annotation_urls["train"]),
             "test": dl_manager.download_and_extract(annotation_urls["test"]),
         }
-    
+
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -116,28 +251,29 @@ class EMT(datasets.GeneratorBasedBuilder):
         
         annotations = {}
         
-        # Load all annotations into memory
+        # Load all annotations into memory from the extracted remote tar file
         for ann_file in os.listdir(annotation_path):
-            video_name = os.path.splitext(ann_file)[0]  # Get video folder name from the annotation file
+            # Get video folder name (e.g., video_12211.txt)
+            video_name = os.path.splitext(ann_file)[0]
             ann_path = os.path.join(annotation_path, ann_file)
-            print("ann_path:,",ann_path,"\nannotation_path: ",annotation_path)
             
+            # Open the annotation file for reading
             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,
@@ -153,11 +289,10 @@ class EMT(datasets.GeneratorBasedBuilder):
             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
-