环境
- windows 10 64bit
- anaconda with python 3.7
- pyscenedetect 0.5.5
简介
了解视频剪辑的小伙伴，应该都知道转场这个词。一个视频场景转换到另一个视频场景，场景与场景之间的过渡或转换，就叫做转场。本篇介绍一个强大的开源工具 PySceneDetect，它是一款基于 opencv 的视频场景切换检测和分析工具，项目地址: https://github.com/Breakthrough/PySceneDetect
安装
首先，我们创建一个全新的虚拟环境
conda create -n pyscenedetect python=3.7
conda activate pyscenedetect接下来安装视频处理时需要用到的 ffmpeg。从官网下载编译好的二进制文件 http://ffmpeg.org/download.html，并将其路径加入到系统环境变量中
最后，进入真正的安装阶段，其实就一句话
pip install scenedetect opencv-python没错，就是这么的简单
命令行测试
pyscenedetect 提供了一个命令行工具，可以通过 -h 参数来查看它的帮助信息
Usage: scenedetect [OPTIONS] COMMAND1 [ARGS]... [COMMAND2 [ARGS]...]...
For example:
scenedetect -i video.mp4 -s video.stats.csv detect-content list-scenes
Note that the following options represent [OPTIONS] above. To list the
optional [ARGS] for a particular COMMAND, type `scenedetect help COMMAND`.
You can also combine commands (e.g. scenedetect [...] detect-content save-
images --png split-video).
Options:
-i, --input VIDEO [Required] Input video file. May be specified
multiple times to concatenate several videos
together. Also supports image sequences and
URLs.
-o, --output DIR Output directory for all files (stats file,
output videos, images, log files, etc...).
-f, --framerate FPS Force framerate, in frames/sec (e.g. -f
29.97). Disables check to ensure that all
input videos have the same framerates.
-d, --downscale N Integer factor to downscale frames by (e.g. 2,
3, 4...), where the frame is scaled to width/N
x height/N (thus -d 1 implies no downscaling).
Each increment speeds up processing by a
factor of 4 (e.g. -d 2 is 4 times quicker than
-d 1). Higher values can be used for high
definition content with minimal effect on
accuracy. [default: 2 for SD, 4 for 720p, 6
for 1080p, 12 for 4k]
-fs, --frame-skip N Skips N frames during processing (-fs 1 skips
every other frame, processing 50% of the
video, -fs 2 processes 33% of the frames, -fs
3 processes 25%, etc...). Reduces processing
speed at expense of accuracy. [default: 0]
-m, --min-scene-len TIMECODE Minimum size/length of any scene. TIMECODE can
be specified as exact number of frames, a time
in seconds followed by s, or a timecode in the
format HH:MM:SS or HH:MM:SS.nnn [default:
0.6s]
--drop-short-scenes Drop scenes shorter than `--min-scene-len`
instead of combining them with neighbors
-s, --stats CSV Path to stats file (.csv) for writing frame
metrics to. If the file exists, any metrics
will be processed, otherwise a new file will
be created. Can be used to determine optimal
values for various scene detector options, and
to cache frame calculations in order to speed
up multiple detection runs.
-v, --verbosity LEVEL Level of debug/info/error information to show.
Setting to none will suppress all output
except that generated by actions (e.g.
timecode list output). Can be overriden by
`-q`/`--quiet`.
-l, --logfile LOG Path to log file for writing application
logging information, mainly for debugging.
Make sure to set `-v debug` as well if you are
submitting a bug report.
-q, --quiet Suppresses all output of PySceneDetect except
for those from the specified commands.
Equivalent to setting `--verbosity none`.
Overrides the current verbosity level, even if
`-v`/`--verbosity` is set.
-h, --help Show this message and exit.
Commands:
about Print license/copyright info.
detect-content Perform content detection algorithm on input video(s).
detect-threshold Perform threshold detection algorithm on input video(s).
export-html Exports scene list to a HTML file.
help Print help for command (help [command]).
list-scenes Prints scene list and outputs to a CSV file.
save-images Create images for each detected scene.
split-video Split input video(s) using ffmpeg or mkvmerge.
time Set start/end/duration of input video(s).
version Print version of PySceneDetect.找个包含多场景切换的视频测试一下，执行命令
scenedetect.exe -i lldq.mp4 detect-content split-video脚本运行结束后，会在当前目录下生成一个个的短视频
每个视频只包含一个场景
如果想从视频的某个时间点开始，可以使用参数 time
scenedetect.exe -i lldq.mp4 time -s 5s detect-content split-video还可以将检测后的场景图片保存下来，同时生成统计文件 csv
scenedetect.exe -i lldq.mp4 -o video_scenes detect-content save-images
场景切割算法
pyscenedetect 使用了2种场景切割的方法，它们是 detect-content 和 detect-threshold，除此之外，更牛皮的是，它还支持自定义检测算法。
-
detect-content顾名思义，这种方法就是根据前后图像的内容来进行判断，与我们常识中所说的视频转场是一样的。算法会根据前后2帧的视频数据，计算出它们不同的区域大小，如果这个区域大于某个预先设定的值(默认是30，可以通过
--threshold参数来指定)，那么就认为场景已经切换了。 -
detect-threshold这是比较传统的检测方法，有点像
ffmpeg中的blackframe滤镜。它会用特定的值去跟数据帧的亮度比较进行，如果大于某个预先设定的值，就认为场景已经切换了。在pyscenedetect中，这个值是由视频帧的每个像素的RGB的平均值计算而来。 -
自定义检测算法
所有的检测算法必须继承自
SceneDetector这个类from scenedetect.scene_detector import SceneDetector class CustomDetector(SceneDetector): """CustomDetector class to implement a scene detection algorithm.""" def __init__(self): pass def process_frame(self, frame_num, frame_img, frame_metrics, scene_list): """Computes/stores metrics and detects any scene changes. Prototype method, no actual detection. """ return def post_process(self, scene_list): pass类中主要有2个方法，
process_frame负责处理所有的视频帧；post_process是可选的，它在process_frame结束后执行，主要用来做一些后期处理，比如场景切换数据的文件保存。下面主要来看看
process_frame方法，它有如下几个重要参数- frame_num: 当前处理到的帧数
- frame_img: 返回的帧数据，格式是
numpy数组 - frame_metrics: 保存检测算法计算结果的字典
- scene_list: 视频中所有场景切换包含的帧数列表
更加实现细节方面，可以参考源码目录下的
scenedetect/detectors/content_detector.py或scenedetect/detectors/threshold_detector.py
Python API的使用
如果需要在自己的代码中去使用 pyscenedetect，除了使用命令行调用的方式外，pyscenedetect 还提供了基于 python的API。
下面是一个简单的 demo，程序读取视频文件，使用 content-detector 算法进行检测，最后将所有场景的开始时间、结束时间和总的帧数分别打印输出。
from scenedetect.video_manager import VideoManager
from scenedetect.scene_manager import SceneManager
from scenedetect.stats_manager import StatsManager
from scenedetect.detectors.content_detector import ContentDetector
def find_scenes(video_path):
video_manager = VideoManager([video_path])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
# 使用contect-detector
scene_manager.add_detector(ContentDetector())
try:
video_manager.set_downscale_factor()
video_manager.start()
scene_manager.detect_scenes(frame_source=video_manager)
scene_list = scene_manager.get_scene_list()
print('List of scenes obtained:')
for i, scene in enumerate(scene_list):
print(
'Scene %2d: Start %s / Frame %d, End %s / Frame %d' % (
i + 1,
scene[0].get_timecode(), scene[0].get_frames(),
scene[1].get_timecode(), scene[1].get_frames(),))
finally:
video_manager.release()
if __name__ == '__main__':
find_scenes('lldq.mp4')代码执行的结果是这样的
