PySceneDetect - Video Scene Cut Detection and Analysis Tool

PySceneDetect - Video Scene Cut Detection and Analysis Tool

PySceneDetect - Video Scene Cut Detection and Analysis Tool. Python and OpenCV-based scene cut/transition detection program & library

Python and OpenCV-based scene cut/transition detection program & library

PySceneDetect

Video Scene Cut Detection and Analysis Tool

Latest Release: v0.5.5 (January 17, 2021)

Main Webpage: py.scenedetect.com

Documentation: manual.scenedetect.com

Discord: https://discord.gg/H83HbJngk7


Quick Install: To install PySceneDetect via pip with all dependencies:

pip install scenedetect[opencv]

For servers, you can use the headless (non-GUI) version of OpenCV by installing scenedetect[opencv-headless]. To enable video splitting support, you will also need to have mkvmerge or ffmpeg installed - see the documentation on Video Splitting Support for details.

Requires Python modules click, numpy, OpenCV cv2, and (optional) tqdm for displaying progress. For details, see the dependencies on the downloads page.


Quick Start (Command Line):

Split the input video wherever a new scene is detected:

scenedetect -i video.mp4 detect-content split-video

Skip the first 10 seconds of the input video, and output a list of scenes to the terminal:

scenedetect -i video.mp4 time -s 10s detect-content list-scenes

To show a summary of all other options and commands:

scenedetect help

You can find more examples on the website or in the manual.

Quick Start (Python API):

In the code example below, we create a function find_scenes() which will load a video, detect the scenes, and return a list of tuples containing the (start, end) timecodes of each detected scene. Note that you can modify the threshold argument to modify the sensitivity of the scene detection.

# Standard PySceneDetect imports:
from scenedetect import VideoManager
from scenedetect import SceneManager

# For content-aware scene detection:
from scenedetect.detectors import ContentDetector

def find_scenes(video_path, threshold=30.0):
    # Create our video & scene managers, then add the detector.
    video_manager = VideoManager([video_path])
    scene_manager = SceneManager()
    scene_manager.add_detector(
        ContentDetector(threshold=threshold))

    # Improve processing speed by downscaling before processing.
    video_manager.set_downscale_factor()

    # Start the video manager and perform the scene detection.
    video_manager.start()
    scene_manager.detect_scenes(frame_source=video_manager)

    # Each returned scene is a tuple of the (start, end) timecode.
    return scene_manager.get_scene_list()

To get started, try printing the result from calling find_scenes on a small video clip:

    scenes = find_scenes('video.mp4')
    print(scenes)

See the manual for the full PySceneDetect API documentation.


PySceneDetect is a command-line tool and Python library, which uses OpenCV to analyze a video to find scene changes or cuts. If ffmpeg or mkvmerge is installed, the video can also be split into scenes automatically. 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 documentation 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 -s / --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 join the official PySceneDetect Discord Server, submit an issue/bug report here on Github, or contact me via my website.

Usage

Current Features & Roadmap

You can view the latest features and version roadmap on Readthedocs. See 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/.


Download Details:

Author: Breakthrough Live Demo: View The Demo Download Link: Download The Source Code Official Website: https://github.com/Breakthrough/PySceneDetect License: Licensed under BSD 3-Clause (see the LICENSE file for details).

Copyright (C) 2014-2021 Brandon Castellano. All rights reserved.

opencv python

What is Geek Coin

What is GeekCash, Geek Token

Best Visual Studio Code Themes of 2021

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

top 30 Python Tips and Tricks for Beginners

In this post, we'll learn top 30 Python Tips and Tricks for Beginners

Lambda, Map, Filter functions in python

You can learn how to use Lambda,Map,Filter function in python with Advance code examples. Please read this article

Create a Virtual Pen and Eraser with Python OpenCV - Genial Code

Learn Free how to create a virtual pen and eraser with python and OpenCV with source code and complete guide. This entire application is built fundamentally on contour detection. It can be thought of as something like closed color curves on compromises that have the same color or intensity, it's like a blob. In this project we use color masking to get the binary mask of our target color pen, then we use the counter detection to find the location of this pen and the contour to find it.

Deep Learning Project : Real Time Object Detection in Python & Opencv

Real Time Object Detection in Python And OpenCV Github Link: https://github.com/Chando0185/Object_DetectionBlog Link: https://knowledgedoctor37.blogspot.com/...

OpenCV Python | Python OpenCV Tutorial | Python for Beginners

OpenCV Python in Python will explain all the basics of OpenCV. It also explains how to create a face recognition system and a motion detector.