Update EMT.py

EMT.py

@@ -3,7 +3,6 @@
 import os
 import datasets
 
-
 _HOMEPAGE = "https://github.com/AV-Lab/emt-dataset"
 
 _LICENSE = "CC-BY-SA 4.0"
@@ -22,11 +21,11 @@ _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 and tracking.
 """
 
 # Annotation repository
-_ANNOTATION_REPO = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/annotations/"
+_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"
@@ -68,55 +67,57 @@ class EMT(datasets.GeneratorBasedBuilder):
 
     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 and extract images
+        image_paths = {
+            "train": dl_manager.download_and_extract(_TRAIN_IMAGE_ARCHIVE_URL),
+            "test": dl_manager.download_and_extract(_TEST_IMAGE_ARCHIVE_URL),
         }
-    
+        
+        # Download annotations (extracted automatically)
         annotation_paths = {
-            "train": dl_manager.download_and_extract(f"{_ANNOTATION_REPO}/train/"),
-            "test": dl_manager.download_and_extract(f"{_ANNOTATION_REPO}/test/"),
+            "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,
                 gen_kwargs={
-                    "images": dl_manager.iter_archive(image_urls["train"]),  
+                    "images": dl_manager.iter_archive(image_paths["train"]),  
                     "annotation_path": annotation_paths["train"],
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "images": dl_manager.iter_archive(image_urls["test"]), 
+                    "images": dl_manager.iter_archive(image_paths["test"]), 
                     "annotation_path": annotation_paths["test"],
                 },
             ),
         ]
 
-
     def _generate_examples(self, images, annotation_path):
         """Generate dataset examples by matching images to their corresponding annotations."""
         
         # Load ALL annotations into memory before iterating over images
         annotations = {}
-    
+
         for ann_file in os.listdir(annotation_path):  # Iterate over all annotation files
-            video_name = os.path.splitext(ann_file)[0]  # Extract video folder name
             ann_path = os.path.join(annotation_path, ann_file)
-            
+            video_name = os.path.splitext(ann_file)[0]  # Extract video folder name
+
             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 simple dictionary
                     key = f"{video_name}/{img_name}"
                     if key not in annotations:
@@ -130,14 +131,14 @@ class EMT(datasets.GeneratorBasedBuilder):
                             "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))
             key = f"{video_name}/{img_name}"  # Match image to preloaded annotations
-    
+
             if key in annotations:
                 yield idx, {
                     "image": {"path": file_path, "bytes": file_obj.read()},