描述 #

视频片段切分处理器，按固定时长智能分割

核心功能 #

• 按固定秒长切分视频
• 支持剩余片段最小时长过滤
• 支持片段二进制输出或TOS存储
• 提供格式自动推断与自定义

格式支持 #

• MP4 (.mp4)
• AVI (.avi)
• MOV (.mov)
• MKV (.mkv)
• 其他常见视频格式

算子参数 #

输入 #

输出 #

处理后的结构体字段包括：

• segments: 切分后视频片段的路径列表
• segments_binary: 切分后视频片段的二进制数据列表
• video_format: 切分后视频片段的格式列表

参数 #

如参数没有默认值，则为必填参数

调用示例 #

下面的代码展示了如何使用 Daft（适用于分布式）运行算子按时长切分视频。

from __future__ import annotations

import os

import daft
from daft import col
from daft.las.functions.udf import las_udf
from daft.las.functions.video import VideoSplitByDuration

if __name__ == "__main__":

    # 更改完切分的视频会保存到指定的TOS路径下，因此，需要设置好环境变量以保证有权限写入TOS，包括：ACCESS_KEY,SECRET_KEY,TOS_ENDPOINT,TOS_REGION,TOS_TEST_DIR
    TOS_DIR = os.getenv("TOS_TEST_DIR", "tos_bucket")
    output_tos_dir = f"tos://{TOS_DIR}/video/video_split_by_duration"
    
    if os.getenv("DAFT_RUNNER", "native") == "ray":
        import logging

        import ray

        def configure_logging():
            logging.basicConfig(
                level=logging.INFO,
                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
                datefmt="%Y-%m-%d %H:%M:%S.%s".format(),
            )
            logging.getLogger("tracing.span").setLevel(logging.WARNING)
            logging.getLogger("daft_io.stats").setLevel(logging.WARNING)
            logging.getLogger("DaftStatisticsManager").setLevel(logging.WARNING)
            logging.getLogger("DaftFlotillaScheduler").setLevel(logging.WARNING)
            logging.getLogger("DaftFlotillaDispatcher").setLevel(logging.WARNING)

        ray.init(dashboard_host="0.0.0.0", runtime_env={"worker_process_setup_hook": configure_logging})
        daft.context.set_runner_ray()
    daft.set_execution_config(actor_udf_ready_timeout=600)
    daft.set_execution_config(min_cpu_per_task=0)

    # 使用环境变量构建URL
    tos_dir_url = os.getenv("TOS_DIR_URL", "las-cn-beijing-public-online.tos-cn-beijing.volces.com")
    samples = {
        "video_path": [
            f"https://{tos_dir_url}/public/shared_video_dataset/sample.mp4"
        ]
    }

    ds = daft.from_pydict(samples)

    splitter = las_udf(
        VideoSplitByDuration,
        construct_args={
            "segment_duration": 60.0,
            "min_segment_duration": 1.0,
            "output_tos_dir": output_tos_dir,
        },
    )

    ds = ds.with_column("results", splitter(col("video_path")))

    ds.show()

    # ╭─────────────────────────────────────────┬──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
    # │ video_path                              ┆ results                                                                                                                          │
    # │ ---                                     ┆ ---                                                                                                                              │
    # │ Utf8                                    ┆ Struct[segments: List[Utf8], segments_binary: List[Binary]]                                            │
    # ╞═════════════════════════════════════════╪══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╡
    # │ https://las-cn-beijing-publi-…          ┆ {segments: ["tos://tos_bucket/video/video_split_by_duration/sample/segment_1.mp4", "tos://tos_bucket/video/video_split_by_durat… │
    # ╰─────────────────────────────────────────┴──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯