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. mkvmerge, 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.
Note that PySceneDetect is currently in alpha; see Current Features & Roadmap below for details. For help or other issues, you can visit #pyscenedetect on Freenode, or contact me on my website. Feel free to submit any bugs or feature requests to the Issue Tracker here on Github. Development right now is a tad slow as I'm nearing the completion of my thesis, but should resume to a normal pace by fall of this year (September/October 2015).
The latest version of PySceneDetect (v0.2.2-alpha) can be downloaded here; to run it, you will need:
- Python 2 / 3 (tested on 2.7.X, untested but should work on 3.X)
- OpenCV Python Module (usually found in Linux package repos as
python-opencv, Windows users can find prebuilt binaries for Python 2.7 here) - Numpy
To ensure you have all the requirements, open a python interpreter, 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).
To run PySceneDetect, you can invoke python scenedetect.py or ./scenedetect.py directly. To display the help file, detailing usage parameters:
./scenedetect.py --help
To perform threshold-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):
./scenedetect.py --input myvideo.mp4 --output myvideo_scenes.csv
To perform threshold-based analysis, with a threshold intensity of 16, and a match percent of 90:
./scenedetect.py --input myvideo.mp4 --threshold 16 --minpercent 90
Detailed descriptions of the above parameters, as well as their default values, can be obtained by using the --help flag.
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 -s argument).
See 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.
- analyzes passed video file for changes in intensity/content (currently based on mean pixel value/brightness)
- detects fade-in and fade-out based on user-defined threshold
- exports list of scenes to .CSV file (both timecodes and frame numbers) (
-o) - exports timecodes in
mkvmergeformat:HH:MM:SS.nnnnn, comma-separated - statistics/analysis mode to export frame-by-frame video metrics (
-s)
- add output-suppression mode for better automation with external scripts/programs
- content-aware scene detection based on changes in the HSV color space
- adaptive or user-defined bias for fade in/out interpolation
- export scenes in chapter/XML format
- improve robustness of content-aware detection by combining with edge detection (similar to MATLAB-based scene change detector)
- additional timecode formats
- interactive/guided mode, eventually moving to a graphical interface
You can find additional information regarding PySceneDetect at the following URL: http://www.bcastell.com/projects/pyscenedetect/
Licensed under BSD 2-Clause (see the LICENSE file for details).
Copyright (C) 2013-2014 Brandon Castellano. All rights reserved.