Use Pyscenedetect Dynamically In Program

The pydantic schema can be used to describe a class in Python. In this case, the `Description` class is defined as a base model. The summary provided describes the purpose and functionality of the `pyscenedetect` library, which assists with dynamically detecting scenes in videos while accounting for possible issues like splicing and video gradients. A code snippet demonstrating how to use this library and FFmpeg to identify and segment a video into scenes is also provided.

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对于单纯拼接起来的视频 这个算法就如同手术刀一样精准

首尾有可能有一两帧看起来不太对 但是可以通过调节start和end来修正

警惕频繁转场的视频 它们可能是属于同一个小片段的 但是如果不打乱顺序有可能会触发版权识别问题

即使选出来了可以使用的片段 对于同一个视频的制作过程 依旧要隔一段时间采样 比如两个片段间隔至少5秒 不要单纯的把所有片段一次性提取出来 避免内容重复和版权问题

当然对于有渐变 转场的视频 可能需要用其他的检测方法

from scenedetect import open_video, SceneManager, split_video_ffmpeg
from scenedetect.detectors import ContentDetector
from scenedetect.video_splitter import split_video_ffmpeg
def split_video_into_scenes(video_path, threshold=27.0):
# Open our video, create a scene manager, and add a detector.
video = open_video(video_path)
scene_manager = SceneManager()
scene_manager.add_detector(
ContentDetector(threshold=threshold))
scene_manager.detect_scenes(video, show_progress=True)
scene_list = scene_manager.get_scene_list()
split_video_ffmpeg(video_path, scene_list, show_progress=True)