Update EMT.py

EMT.py

@@ -98,51 +98,49 @@ class EMT(datasets.GeneratorBasedBuilder):
 
     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 file_path, file_obj in images:
-            img_name = os.path.basename(file_path)  # e.g., "000001.jpg"
-            video_name = os.path.basename(os.path.dirname(file_path))  # e.g., "video_1112"
-
-            ann_file = os.path.join(annotation_path, f"{video_name}.txt")
-
-            if os.path.exists(ann_file):
-                if ann_file not in annotations:
-                    annotations[ann_file] = {}
-
-                if img_name not in annotations[ann_file]:
-                    annotations[ann_file][img_name] = []
-
-                with open(ann_file, "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)
-
-                        if f"{frame_id}.jpg" == img_name:
-                            annotations[ann_file][img_name].append(
-                                {
-                                    "bbox": bbox,
-                                    "class_id": class_id,
-                                    "track_id": int(track_id),
-                                    "class_name": class_name,
-                                }
-                            )
-
+    
+        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)
+            
+            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:
+                        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))
-            ann_file = os.path.join(annotation_path, f"{video_name}.txt")
-
-            if ann_file in annotations and img_name in annotations[ann_file]:
+            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()},
-                    "objects": annotations[ann_file][img_name],
+                    "objects": annotations[key],
                 }
                 idx += 1