hf-internal-testing/spaghetti-video-8-frames

---
---

This is the code that was used to generate this video:

```
from decord import VideoReader, cpu
from huggingface_hub import hf_hub_download
import numpy as np

np.random.seed(0)

def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
converted_len = int(clip_len * frame_sample_rate)
end_idx = np.random.randint(converted_len, seg_len)
start_idx = end_idx - converted_len
indices = np.linspace(start_idx, end_idx, num=clip_len)
indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
return indices

file_path = hf_hub_download(
repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
)
vr = VideoReader(file_path, num_threads=1, ctx=cpu(0))

# sample 8 frames
vr.seek(0)
indices = sample_frame_indices(clip_len=8, frame_sample_rate=1, seg_len=len(vr))
buffer = vr.get_batch(indices).asnumpy()

# create a list of NumPy arrays
video = [buffer[i] for i in range(buffer.shape[0])]

video_numpy = np.array(video)
with open('spaghetti_video_8_frames.npy', 'wb') as f:
np.save(f, video_numpy)
```

原始信息汇总

数据集概述

数据集内容

• 数据集包含一个名为 "eating_spaghetti.mp4" 的视频文件。
• 该视频文件被用于生成一个包含8帧的NumPy数组，存储在 "spaghetti_video_8_frames.npy" 文件中。

数据处理方法

• 使用 decord 库读取视频文件。
• 通过 huggingface_hub 库从 "nielsr/video-demo" 仓库下载视频文件。
• 使用随机种子 0 生成帧索引。
• 从视频中采样8帧，并将这些帧存储为NumPy数组。

代码示例

python from decord import VideoReader, cpu from huggingface_hub import hf_hub_download import numpy as np

np.random.seed(0)

def sample_frame_indices(clip_len, frame_sample_rate, seg_len): converted_len = int(clip_len * frame_sample_rate) end_idx = np.random.randint(converted_len, seg_len) start_idx = end_idx - converted_len indices = np.linspace(start_idx, end_idx, num=clip_len) indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) return indices

file_path = hf_hub_download( repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" ) vr = VideoReader(file_path, num_threads=1, ctx=cpu(0))

sample 8 frames

vr.seek(0) indices = sample_frame_indices(clip_len=8, frame_sample_rate=1, seg_len=len(vr)) buffer = vr.get_batch(indices).asnumpy()

create a list of NumPy arrays

video = [buffer[i] for i in range(buffer.shape[0])]

video_numpy = np.array(video) with open(spaghetti_video_8_frames.npy, wb) as f: np.save(f, video_numpy)