pybgs 3.3.1

Official Python wrapper for BGSLibrary

BGSLibrary: A Background Subtraction Library

Introduction

The BGSLibrary (Background Subtraction Library) is a comprehensive C++ framework designed for background subtraction in computer vision applications, particularly for detecting moving objects in video streams. It provides an easy-to-use and extensible platform for researchers and developers to experiment with and implement various background subtraction techniques.

Library Version

3.3.0 (see Build Status and Release Notes for more info)

Background and Development

The BGSLibrary was developed in early 2012 by Andrews Cordolino Sobral as a C++ framework with wrappers available for Python, Java, and MATLAB. It aims to facilitate foreground-background separation in videos using the OpenCV library.

Compatibility

The library is compatible with OpenCV versions 2.4.x, 3.x, and 4.x. It can be compiled and used on Windows, Linux, and Mac OS X systems.

Licensing

The library's source code is available under the MIT license, making it free for both academic and commercial use.

Getting started

• List of available algorithms
• Algorithms benchmark
• Which algorithms really matter?
• Library architecture

#include <iostream>
#include <algorithm>
#include <iterator>
#include <vector>

// Include the OpenCV and BGSLibrary libraries
#include <opencv2/opencv.hpp>
#include <bgslibrary/algorithms/algorithms.h>

int main( int argc, char** argv )
{
    // Gets the names of the background subtraction algorithms registered in the BGSLibrary factory
    auto algorithmsName = BGS_Factory::Instance()->GetRegisteredAlgorithmsName();

    // Displays the number of available background subtraction algorithms in the BGSLibrary
    std::cout << "Number of available algorithms: " << algorithmsName.size() << std::endl;

    // Displays the list of available background subtraction algorithms in the BGSLibrary
    std::cout << "List of available algorithms:" << std::endl;
    std::copy(algorithmsName.begin(), algorithmsName.end(), std::ostream_iterator<std::string>(std::cout, "\n"));

    // Returns 0 to indicate that the execution was successful
    return 0;
}

Installation instructions

You can either install BGSLibrary via pre-built binary package or build it from source.

Supported Compilers:

• GCC 4.8 and above
• Clang 3.4 and above
• MSVC 2015, 2017, 2019 or newer

Other compilers might work, but are not officially supported. The bgslibrary requires some features from the ISO C++ 2014 standard.

🚀 Build Using Pixi (Recommended)

Pixi provides a clean and reproducible way to build BGSLibrary with no manual OpenCV installation. It works on macOS, Linux, and Windows (WSL recommended).

0. Install Pixi

curl -fsSL https://pixi.sh/install.sh | sh

Restart your terminal after installation.

1. Dependencies and build tasks are preconfigured

The repository already ships a pixi.toml that declares all build dependencies — OpenCV, CMake, Ninja, compilers and pkg-config, plus Python and NumPy for the Python wrapper — and all the build tasks used below. Just clone the repository; there is no need to run pixi init or add anything by hand.

List the available tasks at any time with:

pixi task list

2. Activate the Pixi environment

pixi shell

3. Configure the build

pixi run configure

4. Build bgslibrary

pixi run build

This generates:

• build/bgslibrary
• build/libbgslibrary_core.*

5. Build the Python wrapper (pybgs)

To build and use the Python bindings (import pybgs):

pixi run build_python     # configure + build pybgs (BGS_PYTHON_SUPPORT=ON, into build_py/)
pixi run install_python   # install pybgs into the Pixi environment's site-packages
python -c "import pybgs"  # verify the import works

pybgs is compiled against the OpenCV, Python and NumPy provided by the Pixi environment, so no system Python or OpenCV installation is required.

6. Build C++ examples (optional)

pixi run build_examples

This produces:

• examples/build/bgs_demo
• examples/build/bgs_demo2

7. Run bgslibrary

Camera demo:

pixi run run

Demo using a video file:

pixi run run_bgs_demo

Demo using an image sequence:

pixi run run_bgs_demo2

8. Install bgslibrary into the Pixi environment (optional)

pixi run install

The library and headers go into .pixi/envs/default/

You can then use bgslibrary from other CMake projects in the same environment, for example:

find_package(BGSLibrary REQUIRED)

9. Clean and rebuild

To clean build artifacts:

pixi run clean          # Clean main build
pixi run clean_examples # Clean examples

To rebuild everything from scratch:

pixi run rebuild     # Rebuild main project
pixi run rebuild_all # Rebuild main project + examples

These tasks automatically handle dependencies, ensuring a consistent build state.

🛠 Build Using CMake (Classic Method)

• Windows installation
• Ubuntu / OS X installation

Graphical User Interface

• C++ QT (Official)
• C++ MFC (Deprecated)
• Java (Obsolete)

Wrappers

• Python
• MATLAB
• Java

Usage examples

• BGSlibrary examples folder
• • https://github.com/andrewssobral/bgslibrary/tree/master/examples
• BGSlibrary examples in C++
• • https://github.com/andrewssobral/bgslibrary-examples-cpp
• BGSlibrary examples in Python
• • https://github.com/andrewssobral/bgslibrary-examples-python

More

• Docker images
• How to integrate BGSLibrary in your own CPP code
• How to contribute
• List of collaborators
• Release notes

Algorithm compatibility across OpenCV versions

Algorithm	OpenCV < 3.0 (42)	3.0 <= OpenCV <= 3.4.7 (41)	3.4.7 < OpenCV < 4.0 (39)	OpenCV >= 4.0 (26)
AdaptiveBackgroundLearning	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
AdaptiveSelectiveBackgroundLearning	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
CodeBook	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
DPAdaptiveMedian	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:
DPEigenbackground	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:
DPGrimsonGMM	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:
DPMean	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:
DPPratiMediod	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:
DPTexture	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:
DPWrenGA	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:
DPZivkovicAGMM	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:
FrameDifference	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
FuzzyChoquetIntegral	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
FuzzySugenoIntegral	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
GMG	:heavy_check_mark:	:x:	:x:	:x:
IndependentMultimodal	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
KDE	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
KNN	:x:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
LBAdaptiveSOM	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
LBFuzzyAdaptiveSOM	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
LBFuzzyGaussian	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
LBMixtureOfGaussians	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
LBP_MRF	:heavy_check_mark:	:heavy_check_mark:	:x:	:x:
LBSimpleGaussian	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
LOBSTER	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
MixtureOfGaussianV1	:heavy_check_mark:	:x:	:x:	:x:
MixtureOfGaussianV2	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
MultiCue	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:
MultiLayer	:heavy_check_mark:	:heavy_check_mark:	:x:	:x:
PAWCS	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
PixelBasedAdaptiveSegmenter	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
SigmaDelta	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
StaticFrameDifference	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
SuBSENSE	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
T2FGMM_UM	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:
T2FGMM_UV	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:
T2FMRF_UM	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:
T2FMRF_UV	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:
TwoPoints	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
ViBe	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
VuMeter	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
WeightedMovingMean	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
WeightedMovingVariance	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:

Stargazers over time

Citation

If you use this library for your publications, please cite it as:

@inproceedings{bgslibrary,
author    = {Sobral, Andrews},
title     = {{BGSLibrary}: An OpenCV C++ Background Subtraction Library},
booktitle = {IX Workshop de Visão Computacional (WVC'2013)},
address   = {Rio de Janeiro, Brazil},
year      = {2013},
month     = {Jun},
url       = {https://github.com/andrewssobral/bgslibrary}
}

A chapter about the BGSLibrary has been published in the handbook on Background Modeling and Foreground Detection for Video Surveillance.

@incollection{bgslibrarychapter,
author    = {Sobral, Andrews and Bouwmans, Thierry},
title     = {BGS Library: A Library Framework for Algorithm’s Evaluation in Foreground/Background Segmentation},
booktitle = {Background Modeling and Foreground Detection for Video Surveillance},
publisher = {CRC Press, Taylor and Francis Group.}
year      = {2014},
}

References

• Sobral, Andrews. BGSLibrary: An OpenCV C++ Background Subtraction Library. IX Workshop de Visão Computacional (WVC'2013), Rio de Janeiro, Brazil, Jun. 2013. (PDF in brazilian-portuguese containing an english abstract).

• Sobral, Andrews; Bouwmans, Thierry. "BGS Library: A Library Framework for Algorithm’s Evaluation in Foreground/Background Segmentation". Chapter on the handbook "Background Modeling and Foreground Detection for Video Surveillance", CRC Press, Taylor and Francis Group, 2014. (PDF in english).

Some algorithms of the BGSLibrary were used successfully in the following papers:

• (2014) Sobral, Andrews; Vacavant, Antoine. A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Computer Vision and Image Understanding (CVIU), 2014. (Online) (PDF)

• (2013) Sobral, Andrews; Oliveira, Luciano; Schnitman, Leizer; Souza, Felippe. (Best Paper Award) Highway Traffic Congestion Classification Using Holistic Properties. In International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA'2013), Innsbruck, Austria, Feb 2013. (Online) (PDF)

Videos