Upload transform_longvideobench.py

transform_longvideobench.py

@@ -0,0 +1,87 @@
+import json
+import os
+from typing import List, Dict
+from collections import Counter, defaultdict
+
+def LongVideoBench2TStarFormat(dataset_path: str, video_root: str, output_path: str) -> List[Dict]:
+    """Load and transform the dataset into the required format for T*.
+
+    Args:
+        dataset_path (str): Path to the input dataset JSON file.
+        video_root (str): Root directory where video files are stored.
+        output_path (str): Path to save the transformed JSON dataset.
+
+    Returns:
+        List[Dict]: Transformed dataset formatted for T*.
+    """
+    with open(dataset_path, 'r', encoding='utf-8') as file:
+        lvb_dataset = json.load(file)
+
+    TStar_format_data = []
+    num2letter = ['A', 'B', 'C', 'D', 'E']
+    
+    question_category_counts = Counter()
+    video_question_counts = defaultdict(int)
+    
+    for idx, entry in enumerate(lvb_dataset):
+        try:
+            video_id = entry.get("video_id")
+            video_path = entry.get("video_path")
+            question = entry.get("question")
+            answer = entry.get("correct_choice", "")
+            answer = num2letter[answer]
+            question_category = entry.get("question_category", "Unknown")
+            duration_group = entry.get("duration_group", "Unknown")
+            position = entry.get("position", [])
+            options_list = entry.get("candidates", [])
+
+            # Filter out subtitle questions based on question category
+            if 'T' in question_category:
+                continue
+            # Only keep entries with duration group 3600
+            if duration_group != 3600:
+                continue
+
+            if not video_id or not question or not options_list:
+                raise ValueError(f"Missing required fields in entry {idx+1}. Skipping entry.")
+
+            options = "\n".join(f"{num2letter[i]}) {opt}" for i, opt in enumerate(options_list))
+
+            transformed_entry = {
+                "video_id": video_id,
+                "video_path": os.path.join(video_root, video_path),
+                "question": question,
+                "options": options,
+                "answer": answer,
+                "duration_group": duration_group,
+                "gt_frame_index": position,
+            }
+            
+            TStar_format_data.append(transformed_entry)
+            
+            question_category_counts[question_category] += 1
+            video_question_counts[video_id] += 1
+
+        except ValueError as e:
+            print(f"Skipping entry {idx+1}, reason: {str(e)}")
+        except Exception as e:
+            print(f"Error processing entry {idx+1}: {str(e)}")
+
+    print("Remaining question category counts:", dict(question_category_counts))
+    print("Number of questions per video:", len(video_question_counts))
+    
+    with open(output_path, "w", encoding="utf-8") as f:
+        json.dump(TStar_format_data, f, indent=4)
+    print(f"Transformed dataset saved to {output_path}")
+    
+    return TStar_format_data
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser(description="Transform LongVideoBench dataset to T* format.")
+    parser.add_argument("--dataset_path", type=str, required=True, help="Path to the dataset JSON file.")
+    parser.add_argument("--video_root", type=str, required=True, help="Root directory for video files.")
+    parser.add_argument("--output_path", type=str, required=True, help="Path to save the transformed JSON file.")
+    args = parser.parse_args()
+    
+    LongVideoBench2TStarFormat(args.dataset_path, args.video_root, args.output_path)