Datasets:

svjack
/

OnePromptOneStory-Examples-Vid-head2

dataset_info:
  features:
    - name: model_name
      dtype: string
    - name: id_prompt
      dtype: string
    - name: frame_prompt
      dtype: string
    - name: Image
      dtype: image
    - name: sub_images
      list:
        - name: bytes
          dtype: binary
    - name: videos
      list:
        - name: motion_bucket_id
          dtype: int64
        - name: video_bytes
          dtype: binary
  splits:
    - name: train
      num_bytes: 45316656
      num_examples: 2
  download_size: 45321507
  dataset_size: 45316656
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

from datasets import Dataset, load_dataset
from PIL import Image
import io

def bytes_to_image(image_bytes):
    """
    将字节数据（bytes）转换为 PIL.Image 对象。
    """
    image_stream = io.BytesIO(image_bytes)
    image = Image.open(image_stream)
    return image

# 加载数据集
ds = load_dataset("svjack/OnePromptOneStory-Examples-Vid-head2")["train"]

# 定义 motion_bucket_ids
motion_bucket_ids = [10, 20, 30, 40, 50]

# 创建一个新的数据集列表
new_data = []

# 遍历原始数据集中的每一行
for example in ds:
    sub_images = example["sub_images"]
    videos = example["videos"]
    
    # 遍历每个 sub_image
    for idx, sub_image_dict in enumerate(sub_images):
        sub_image_bytes = sub_image_dict["bytes"]
        sub_image = bytes_to_image(sub_image_bytes)
        
        # 计算对应的视频索引
        video_idx = idx * len(motion_bucket_ids)
        
        # 遍历每个 motion_bucket_id 和对应的视频
        for i, motion_bucket_id in enumerate(motion_bucket_ids):
            video_dict = videos[video_idx + i]
            video_bytes = video_dict["video_bytes"]  # 视频的二进制数据
            
            # 创建新的样本，保留原始数据的所有字段
            new_sample = {
                **example,  # 保留原始数据的所有字段
                "sub_image": sub_image,
                "motion_bucket_id": motion_bucket_id,
                "video": video_bytes  # 直接存储视频的二进制数据
            }
            
            # 添加到新数据集中
            new_data.append(new_sample)

# 将新数据转换为 Hugging Face Dataset 对象
new_dataset = Dataset.from_list(new_data)

# 查看新数据集中的第一个样本
#print(new_dataset[0])

new_dataset.push_to_hub("svjack/OnePromptOneStory-Examples-Vid-head2-Exp")