Update PR with scripts and README.md

README.md

@@ -17,6 +17,28 @@ size_categories:
 # Dataset Card for All-Angles Bench
 
 
+## Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+The dataset presents a comprehensive benchmark consistin---
+license: mit
+language:
+- en
+size_categories:
+- 1K<n<10K
+---
+
+<h1>Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs</h1>
+
+
+<a href='https://danielchyeh.github.io/All-Angles-Bench/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> 
+<a href='https://arxiv.org/pdf/2504.15280'><img src='https://img.shields.io/badge/Paper-PDF-orange'></a>
+<a href='https://arxiv.org/abs/2504.15280'><img src='https://img.shields.io/badge/Arxiv-Page-purple'></a>
+<a href="https://github.com/Chenyu-Wang567/All-Angles-Bench/tree/main"><img src='https://img.shields.io/badge/Code-Github-red'></a>
+
+# Dataset Card for All-Angles Bench
+
+
 ## Dataset Description
 
 <!-- Provide a longer summary of what this dataset is. -->
@@ -31,6 +53,98 @@ The dataset presents a comprehensive benchmark consisting of over 2,100 human-an
 - **[Ego-Exo4D](https://github.com/facebookresearch/Ego4d)** - Large-scale egocentric and exocentric video dataset for multi-person interaction understanding
 
 
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ch-chenyu/All-Angles-Bench")
+```
+
+
+## Prepare Full Benchmark Data on Local Machine
+
+1. **Set up Git lfs and clone the benchmark:**
+```bash
+$ conda install git-lfs
+$ git lfs install
+
+$ git lfs clone https://huggingface.co/datasets/ch-chenyu/All-Angles-Bench
+```
+
+
+2. **Download Ego4D-Exo dataset and extract the frames for the benchmark scenes:**
+
+We provide the image files for the EgoHumans dataset. For the Ego-Exo4D dataset, due to licensing restrictions, you will need to first sign the license agreement from the official Ego-Exo4D repository at https://ego4ddataset.com/egoexo-license/. After signing the license, you can download the dataset (`downscaled_takes/448`) and then use the preprocessing scripts to extract the corresponding images.
+
+```bash
+$ pip install ego4d --upgrade
+$ egoexo -o All-Angles-Bench/ --parts downscaled_takes/448
+
+$ python All-Angles-Bench/scripts/process_ego4d_exo.py --input All-Angles-Bench
+```
+
+3. **Transform JSON metadata into benchmark TSV format:**
+
+To convert the metadata from JSON format into a structured TSV format compatible with benchmark evaluation scripts in [VLMEvalKit](https://github.com/open-compass/VLMEvalKit), run:
+```bash
+$ python All-Angles-Bench/scripts/json2tsv_pair.py --input All-Angles-Bench/data.json
+
+```
+
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+
+The JSON data contains the following key-value pairs:
+
+| Key              | Type       | Description                                                                 |
+|------------------|------------|-----------------------------------------------------------------------------|
+| `index`          | Integer    | Unique identifier for the data entry (e.g. `1221`)                          |
+| `folder`         | String     | Directory name where the scene is stored (e.g. `"05_volleyball"`)           |
+| `category`       | String     | Task category (e.g. `"counting"`)                                           |
+| `pair_idx`       | String     | Index of a corresponding paired question (if applicable)                    |
+| `image_path`     | List       | Array of input image paths                                                  |
+| `question`       | String     | Natural language query about the scene                                      |
+| `A`/`B`/`C`      | String     | Multiple choice options                                                     |
+| `answer`         | String     | Correct option label (e.g. `"B"`)                                           |
+| `sourced_dataset`| String     | Source dataset name (e.g. `"EgoHumans"`)                                    |
+
+
+
+
+
+## Citation 
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+```bibtex
+@article{yeh2025seeing,
+  title={Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs},
+  author={Chun-Hsiao Yeh, Chenyu Wang, Shengbang Tong, Ta-Ying Cheng, Ruoyu Wang, Tianzhe Chu, Yuexiang Zhai, Yubei Chen, Shenghua Gao and Yi Ma},
+  journal={arXiv preprint arXiv:2504.15280},
+  year={2025}
+}
+```
+
+## Acknowledgements
+You may refer to related work that serves as foundations for our framework and code repository, 
+[EgoHumans](https://github.com/rawalkhirodkar/egohumans),
+[Ego-Exo4D](https://github.com/facebookresearch/Ego4d),
+[VLMEvalKit](https://github.com/open-compass/VLMEvalKit).
+Thanks for their wonderful work and data.g of over 2,100 human-annotated multi-view question-answer (QA) pairs, spanning 90 real-world scenes. Each scene is captured from multiple viewpoints, providing diverse perspectives and context for the associated questions.
+
+
+## Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **[EgoHumans](https://github.com/rawalkhirodkar/egohumans)** - Egocentric multi-view human activity understanding dataset
+- **[Ego-Exo4D](https://github.com/facebookresearch/Ego4d)** - Large-scale egocentric and exocentric video dataset for multi-person interaction understanding
+
+
 ## Usage
 
 ```python

folder_list.txt

@@ -0,0 +1,83 @@
+uniandes_bouldering_012_29
+georgiatech_bike_15_4
+georgiatech_cooking_11_02_6
+utokyo_omelet_5_1001_4
+utokyo_pcr_2001_23_2
+indiana_cooking_23_2
+nus_cooking_17_5
+indiana_bike_11_9
+nus_cpr_25_1
+georgiatech_covid_09_2
+cmu_soccer03_1
+minnesota_cooking_060_2
+sfu_cooking027_9
+iiith_piano_003_2
+cmu_soccer12_2
+utokyo_salad_12_1018_4
+cmu_bike20_3
+cmu_bike21_3
+cmu_bike18_6
+utokyo_sushi_4_1008_4
+sfu_cooking032_3
+utokyo_soccer_8000_17_6
+cmu_bike19_5
+cmu_bike15_3
+cmu_bike14_3
+unc_music_04-26-23_02_11
+cmu_bike16_3
+cmu_bike17_3
+sfu_cooking031_10
+iiith_guitar_002_10
+cmu_bike11_5
+cmu_bike12_5
+fair_cooking_05_6
+sfu_cooking024_2
+sfu_cooking022_2
+cmu_bike01_7
+sfu_cooking029_2
+sfu_cooking015_2
+cmu_soccer08_3
+sfu_cooking030_6
+cmu_bike06_2
+cmu_soccer11_2
+nus_covidtest_49_1
+sfu_basketball016_5
+sfu_basketball_04_2
+sfu_basketball014_9
+uniandes_dance_020_31
+sfu_basketball017_9
+unc_soccer_09-22-23_01_13
+indiana_music_04_2
+minnesota_rockclimbing_021_32
+sfu_basketball012_12
+iiith_cooking_90_2
+unc_basketball_03-31-23_02_38
+fair_bike_06_7
+uniandes_basketball_005_10
+utokyo_cpr_2005_28_2
+nus_soccer_17_3
+sfu_cooking_005_4
+cmu_bike08_2
+cmu_bike09_4
+iiith_soccer_033_6
+sfu_covid_013_12
+cmu_bike05_2
+cmu_bike02_5
+sfu_basketball015_7
+cmu_soccer16_6
+cmu_soccer15_5
+cmu_bike13_4
+uniandes_cooking_001_5
+sfu_basketball013_28
+cmu_bike10_7
+sfu_cooking020_8
+sfu_cooking026_4
+sfu_cooking023_8
+sfu_cooking025_5
+sfu_cooking028_8
+cmu_bike03_2
+cmu_soccer06_6
+cmu_soccer07_2
+sfu_cooking017_4
+cmu_soccer09_2
+cmu_soccer14_4

scripts/json2tsv_pair.py

@@ -0,0 +1,60 @@
+import json
+import csv
+import os
+import argparse
+from pathlib import Path
+
+def process_question(question, A, B, C):
+    options = [A, B, C]
+    question_no_options = question.replace(A, "").replace(B, "").replace(C, "")
+    question_no_options = question_no_options.strip().replace(",", "").strip()
+
+    while len(options) < 3:
+        options.append("")
+    return question_no_options, options
+
+def main(json_path):
+    json_path = Path(json_path).resolve()
+    base_path = json_path.parent  # All-Angles-Bench/
+    output_dir = base_path / "bench_tsv"
+    output_dir.mkdir(parents=True, exist_ok=True)
+    output_path = output_dir / "all_angles_bench_huggingface.tsv"
+
+    with open(json_path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+
+    tsv_data = []
+
+    for entry in data:
+        folder = entry["folder"]
+        category = entry["category"]
+        image_paths = entry["image_path"]
+        modified_input_images = [str(base_path / p) for p in image_paths]
+
+        question = entry["question"]
+        answer = entry["answer"]
+        index = entry["index"]
+        pair_idx = entry["pair_idx"]
+
+        A = entry["A"]
+        B = entry["B"]
+        C = entry["C"]
+        question_no_options, options = process_question(question, A, B, C)
+
+        tsv_data.append([
+            index, folder, category, pair_idx, modified_input_images,
+            question_no_options
+        ] + options + [answer])
+
+    with open(output_path, 'w', newline='', encoding='utf-8') as tsv_file:
+        tsv_writer = csv.writer(tsv_file, delimiter='\t')
+        tsv_writer.writerow(['index', 'folder', 'category', 'pair_idx', 'image_path', 'question', 'A', 'B', 'C', 'answer'])
+        tsv_writer.writerows(tsv_data)
+
+    print(f"Saved TSV to {output_path}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input', required=True, help='Path to the data.json file (e.g., All-Angles-Bench/data.json)')
+    args = parser.parse_args()
+    main(args.input)

scripts/process_ego4d_exo.py

@@ -0,0 +1,89 @@
+import os
+import shutil
+import argparse
+from pathlib import Path
+import cv2
+
+def extract_frames_from_video(video_path, output_folder, num_frames=16):
+    cap = cv2.VideoCapture(video_path)
+    
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    
+    interval = total_frames // num_frames
+    
+    output_folder.mkdir(parents=True, exist_ok=True)
+
+    for i in range(num_frames):
+        cap.set(cv2.CAP_PROP_POS_FRAMES, i * interval)
+        
+        ret, frame = cap.read()
+        if not ret:
+            break
+        
+        frame_filename = output_folder / f"frame{i + 1}_{Path(video_path).stem}.jpg"
+        cv2.imwrite(str(frame_filename), frame)
+    
+    cap.release()
+
+def process_subfolders(root_path, folder_list, output_root):
+    for subfolder in Path(root_path).iterdir():
+        if subfolder.is_dir():
+            folder_name = subfolder.name
+            if folder_name not in folder_list:
+                continue
+            
+            target_folder = subfolder / "frame_aligned_videos/downscaled/448"
+
+            if target_folder.exists() and target_folder.is_dir():
+                for video_file in target_folder.glob("cam*.mp4"):
+                    print(f"Processing video: {video_file}")
+                    new_subfolder = Path(output_root) / subfolder.name
+                    new_subfolder.mkdir(parents=True, exist_ok=True)
+                    
+                    extract_frames_from_video(video_file, new_subfolder)
+
+def move_and_rename_frames_from_file(file_path, destination_root, source_root):
+    with open(file_path, 'r') as f:
+        folder_list = [line.strip() for line in f.readlines()]
+
+    for folder in folder_list:
+        folder_path = Path(os.path.join(source_root, folder))
+        if folder_path.exists() and folder_path.is_dir():
+            target_folder = Path(destination_root) / folder_path.name
+            target_folder.mkdir(parents=True, exist_ok=True)
+            
+            for frame_file in folder_path.glob("frame2_*.jpg"):
+                new_filename = frame_file.name.replace("frame2_", "")
+                
+                destination_file = target_folder / new_filename
+                
+                shutil.move(str(frame_file), str(destination_file))
+                print(f"Moved and renamed {frame_file} to {destination_file}")
+        else:
+            print(f"Folder {folder} does not exist or is not a directory.")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input', required=True, help='Path to All-Angles-Bench (root folder)')
+    args = parser.parse_args()
+
+    root_input = Path(args.input)
+    folder_list_file = root_input / "folder_list.txt"
+    input_path = root_input / "takes"
+    output_root = root_input / "extracted_frames_huggingface"
+    destination_root = root_input / "ego_exo4d_scenes"
+
+    with open(folder_list_file, 'r') as f:
+        folder_list = [line.strip() for line in f.readlines()]
+
+    process_subfolders(input_path, folder_list, output_root)
+    print("Processing frames extraction completed!")
+    
+    move_and_rename_frames_from_file(folder_list_file, destination_root, output_root)
+    print("Files moved and renamed successfully!")
+
+        # Clean up
+    if output_root.exists() and output_root.is_dir():
+        shutil.rmtree(output_root)
+        print(f"Removed intermediate folder: {output_root}")