updated script

EMT.py

@@ -92,33 +92,45 @@ class EMT(datasets.GeneratorBasedBuilder):
                     "annotation_path": annotation_paths["test"],
                 },
             ),
-        ]
-
+            ]
     def _generate_examples(self, images, annotation_path):
         """Generate examples from annotations and image archive."""
 
-        # Load annotation files
+        # Dictionary to store annotations
         annotations = {}
-        for root, _, files in os.walk(annotation_path):
-            for file in files:
-                with open(os.path.join(root, file), "r", encoding="utf-8") as f:
+
+        # Process each image in the dataset
+        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"
+
+            # Expected annotation file
+            ann_file = os.path.join(annotation_path, f"{video_name}.txt")
+
+            # Read annotations only for the current video
+            if os.path.exists(ann_file):
+                with open(ann_file, "r", encoding="utf-8") as f:
                     for line in f:
                         parts = line.strip().split()
+                        if len(parts) < 8:  # Ensure there are enough elements
+                            continue
+                        
                         frame_id, track_id, class_name = parts[:3]
                         bbox = list(map(float, parts[4:8]))  # Extract bounding box
-                        class_id = _GT_OBJECT_CLASSES.get(class_name, -1)  # Convert class_name to numeric ID, default to -1 if not found
-
-                        img_path = f"{frame_id}.jpg"
-                        if img_path not in annotations:
-                            annotations[img_path] = []
-                        annotations[img_path].append(
-                            {
-                                "bbox": bbox,
-                                "class_id": class_id,
-                                "track_id": int(track_id),
-                                "class_name": class_name,
-                            }
-                        )
+                        class_id = _GT_OBJECT_CLASSES.get(class_name, -1)  # Convert class_name to numeric ID
+
+                        # Match annotations to the correct image
+                        if f"{frame_id}.jpg" == img_name:
+                            if img_name not in annotations:
+                                annotations[img_name] = []
+                            annotations[img_name].append(
+                                {
+                                    "bbox": bbox,
+                                    "class_id": class_id,
+                                    "track_id": int(track_id),
+                                    "class_name": class_name,
+                                }
+                            )
 
         # Yield dataset entries
         idx = 0
@@ -130,3 +142,40 @@ class EMT(datasets.GeneratorBasedBuilder):
                     "objects": annotations[img_name],
                 }
                 idx += 1
+
+    # def _generate_examples(self, images, annotation_path):
+    #     """Generate examples from annotations and image archive."""
+
+    #     # Load annotation files
+    #     annotations = {}
+    #     for root, _, files in os.walk(annotation_path):
+    #         for file in files:
+    #             with open(os.path.join(root, file), "r", encoding="utf-8") as f:
+    #                 for line in f:
+    #                     parts = line.strip().split()
+    #                     frame_id, track_id, class_name = parts[:3]
+    #                     bbox = list(map(float, parts[4:8]))  # Extract bounding box
+    #                     class_id = _GT_OBJECT_CLASSES.get(class_name, -1)  # Convert class_name to numeric ID, default to -1 if not found
+
+    #                     img_path = f"{frame_id}.jpg"
+    #                     if img_path not in annotations:
+    #                         annotations[img_path] = []
+    #                     annotations[img_path].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)
+    #         if img_name in annotations:
+    #             yield idx, {
+    #                 "image": {"path": file_path, "bytes": file_obj.read()},
+    #                 "objects": annotations[img_name],
+    #             }
+    #             idx += 1