Video Scene Cut Detection and Analysis Tool
Latest Release: v0.5 (August 31, 2018)
Main Webpage: py.scenedetect.com
Quick Install: Requires Python modules
cv2, and (optional)
tqdm for displaying progress. To install PySceneDetect via
pip install scenedetect
To test if you have the required prerequisites, open a
python prompt, and run the following:
import numpy import cv2
If both of those commands execute without any problems, you should be able to install PySceneDetect without any issues. To enable video splitting support, you will also need to have
ffmpeg installed on your system. See getting started guide after installation for details.
Also see the
USAGE.md file for details on detection modes, default values/thresholds to try, and how to effectively choose the optimal detection parameters. Full documentation for PySceneDetect can be found on Readthedocs, or by visiting py.scenedetect.com.
To install from source instead, download the latest release and call
python setup.py install (see the download page for details.
PySceneDetect is a command-line tool, written in Python and using OpenCV, which analyzes a video, looking for scene changes or cuts. The output timecodes can then be used with another tool (e.g.
ffmpeg) to split the video into individual clips. A frame-by-frame analysis can also be generated for a video, to help with determining optimal threshold values or detecting patterns/other analysis methods for a particular video. See the
USAGE.md file for details.
There are two main detection methods PySceneDetect uses:
detect-threshold (comparing each frame to a set black level, useful for detecting cuts and fades to/from black), and
detect-content (compares each frame sequentially looking for changes in content, useful for detecting fast cuts between video scenes, although slower to process). Each mode has slightly different parameters, and is described in detail below.
In general, use
detect-threshold mode if you want to detect scene boundaries using fades/cuts in/out to black. If the video uses a lot of fast cuts between content, and has no well-defined scene boundaries, you should use the
detect-content mode. Once you know what detection mode to use, you can try the parameters recommended below, or generate a statistics file (using the
--statsfile flag) in order to determine the correct paramters - specifically, the proper threshold value.
Note that PySceneDetect is currently in beta; see Current Features & Roadmap below for details. For help or other issues, you can contact me on my website, or we can chat in #pyscenedetect on Freenode. Feel free to submit any bugs or feature requests to the Issue Tracker here on Github.
Download & Installation
Downloading: The latest version of PySceneDetect (
v0.4) can be downloaded here; to run it, you will need:
- Python 2 / 3
- OpenCV Python Module (usually found in Linux package repos as
python-opencv, Windows users can find prebuilt binaries for Python 2.7 here)
- tqdm (optional, can install via
pip install tqdm)
To enable video splitting support, you also need to have one of the following tools installed (Linux users can usually grab them from your package manager):
More complete documentation and installation instructions can be found on Readthedocs, including a detailed guide on how to install the above dependencies. Note that in some cases the Windows version may require an additional
opencv_ffmpeg.dll file for the specific version of OpenCV installed.
To ensure you have all the system requirements installed, open a
python interpreter/REPL, and ensure you can
import numpy and
import cv2 without any errors. You can download a test video and view the expected output from the resources branch (see the end of the Usage section below for details).
Installing: Once you have all the system requirements, go to where you downloaded PySceneDetect and extract the archive. To install PySceneDetect, run the following command in the folder containing the extracted files (the one containing
python setup.py install
After installation, you can use PySceneDetect as the
scenedetect command from any terminal/command prompt. To verify the installation, run the following command to display what version of PySceneDetect you have installed:
There is now a dedicated
USAGE.md file (here) containing more detailed usage instructions. Documentation is also being added to Readthedocs, which will eventually replace the content of this file (see the PySceneDetect Quickstart Section for details)..
To run PySceneDetect, use the
scenedetect command if you have it installed to your system. Otherwise, if you are running from source, you can invoke
python scenedetect.py or
./scenedetect.py (instead of
scenedetect in the examples shown below and elsewhere). To display the help file, detailing the command line parameters:
To perform content-based analysis with the default parameters, on a video named
myvideo.mp4, saving a list of scenes to
myvideo_scenes.csv (they are also printed to the terminal when
list-scenes is specified):
scenedetect --input myvideo.mp4 detect-content list-scenes -o myvideo_scenes.csv
To automatically split the input video into scenes using stream copying (default) with a statsfile specified (requires
mkvmerge to be installed):
scenedetect --input myvideo.mp4 --statsfile myvideo.stats.csv detect-content split-video
To automatically split the input video in precise mode (re-encodes input, slower but frame-perfect accuracy for output files, requires
ffmpeg to be installed):
scenedetect --input myvideo.mp4 --statsfile myvideo.stats.csv detect-content split-video -p
To perform content-based analysis, with a threshold intensity of 30:
scenedetect --input myvideo.mp4 detect-content --threshold 30
To perform threshold-based analysis, with a threshold intensity of 16 and a match percent of 90:
scenedetect --input myvideo.mp4 detect-threshold --threshold 16 --min-percent 90
Detailed descriptions of the above parameters, as well as their default values, can be obtained by using the
Below is a visual example of the parameters used in threshold mode (click for full-view):
You can download the file
testvideo.mp4, as well as the expected output
testvideo-results.txt, from the resources branch, for testing the operation of the program. Data for the above graph was obtained by running PySceneDetect on
testvideo.mp4 in statistics mode (by specifying the
Current Features & Roadmap
You can view the latest features and version roadmap on Readthedocs.
docs/changelog.md for a list of changes in each version, or visit the Releases page to download a specific version. Feel free to submit any bugs/issues or feature requests to the Issue Tracker.
Additional features being planned or in development can be found here (tagged as
feature) in the issue tracker. You can also find additional information about PySceneDetect at http://www.bcastell.com/projects/pyscenedetect/.
Licensed under BSD 3-Clause (see the
LICENSE file for details).
Copyright (C) 2012-2018 Brandon Castellano. All rights reserved.