Update EMT.py

EMT.py

@@ -163,14 +163,33 @@
 
 import os
 import datasets
+import tarfile
 
-# Annotation repository
-_ANNOTATION_REPO = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/annotations"
+_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.
+"""
 
-# 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"
 
+_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."""
@@ -211,24 +230,24 @@ class EMT(datasets.GeneratorBasedBuilder):
             "train": _TRAIN_IMAGE_ARCHIVE_URL,
             "test": _TEST_IMAGE_ARCHIVE_URL,
         }
-
+    
+        annotation_urls = {
+            "train": _TRAIN_ANNOTATION_ARCHIVE_URL,
+            "test": _TEST_ANNOTATION_ARCHIVE_URL,
+        }
+        
         # Download image files
         images = {
             "train": dl_manager.iter_archive(image_urls["train"]),
             "test": dl_manager.iter_archive(image_urls["test"]),
         }
-
-        # Download the annotation files from the remote repository
-        annotation_urls = {
-            "train": _ANNOTATION_REPO + "/train/",
-            "test": _ANNOTATION_REPO + "/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,
@@ -251,29 +270,32 @@ class EMT(datasets.GeneratorBasedBuilder):
         
         annotations = {}
         
-        # Load all annotations into memory from the extracted remote tar file
+        # Extract annotation tar file and read its contents
         for ann_file in os.listdir(annotation_path):
-            # Get video folder name (e.g., video_12211.txt)
-            video_name = os.path.splitext(ann_file)[0]
+            # Ensure that we're dealing with the annotation file
             ann_path = os.path.join(annotation_path, ann_file)
             
-            # Open the annotation file for reading
+            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}"
+                    key = f"{ann_file}/{img_name}"
                     if key not in annotations:
                         annotations[key] = []
-
+    
                     annotations[key].append(
                         {
                             "bbox": bbox,
@@ -289,10 +311,11 @@ 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
+