Saravanan666/pypi-org huggingface dataset:
Package: bob.pad.face
Face presentation attack detection (anti-spoofing)This package is part of the signal-processing and machine learning toolboxBob. It implements tools for spoofing or
presentation attack detection in face biometrics.InstallationFollow ourinstallation instructions. Then,
to install this package, run:condainstallbob.pad.faceContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.pad.vein
Vein presentation attack detection (anti-spoofing)This package is part of the signal-processing and machine learning toolboxBob. It implements tools for spoofing or presentation attack detection in vein
biometrics.InstallationFollow ourinstallationinstructions. Then, to install this package, run:$ conda install bob.pad.veinContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.pad.voice
Presentation Attack Detection in Voice BiometricsThis package is part of the signal-processing and machine learning toolboxBob. This package is an extension to thebob.pad.basepackage, which provides the basic presentation attack
detection (PAD) framework.Thebob.pad.voicecontains additional functionality to run PAD experiments using speech databases.
Wrappers for speech databases are also included in this package.InstallationFollow ourinstallationinstructions. Then, using the Python interpreter
provided by the distribution, bootstrap and buildout this package:$ python bootstrap-buildout.py
$ ./bin/buildoutContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.paper.biosig2016
This package is part of theBobtoolkit and it allows to reproduce the following paper:@inproceedings{MuckenhirnBIOSIG2016,
    author = {Muckenhirn, Hannah and Magimai.-Doss, Mathew and Marcel, S{\'{e}}bastien},
    month = sep,
    title = {Presentation Attack Detection Using Long-Term Spectral Statistics for Trustworthy Speaker Verification},
    booktitle = {Proceedings of International Conference of the Biometrics Special Interest Group (BIOSIG)},
    year = {2016},
}If you use this package and/or its results, please cite the paper.InstallationTo install this package – alone or together with otherPackages of Bob– please read theInstallation Instructions.
ForBobto be able to work properly, some dependent packages are required to be installed.
Please make sure that you have read theDependenciesfor your operating system.DocumentationFor further documentation on this package, please read theDocumentation.
For a list of tutorials on this or the other packages ofBob, or information on submitting issues, asking questions and starting discussions, please visit its website.
Package: bob.paper.biosig2016_replaymobile
This package is part of theBobtoolkit. Scripts in this package can be used to reproduce face-PAD results the following paper:@inproceedings{costaBiosig2016,
author = {A. Costa-Pazo and S. Bhattacharjee and E. Vazquez-Fernandez and S. Marcel},
title = {The Replay-Mobile Face Presentation Attack Database},
year = {2016},
month = sep,
booktitle = {Proceedings of the 15th International Conference of the Biometrics Special Interest Group (BIOSIG)},
address = {Darmstadt, Germany},
}If you use this package and/or its results, please cite the paper.InstallationTo install this package – alone or together with otherPackages of Bob– please read theInstallation Instructions.
ForBobto be able to work properly, some dependent packages are required to be installed.
Please make sure that you have read theDependenciesfor your operating system.DocumentationFor a list of tutorials on this or the other packages ofBob, or information on submitting issues, asking questions and starting discussions, please visit its website.
Package: bob.paper.biosig2017-3dmaskprestudy
This package is part of theBobtoolkit. Scripts in this package can be used to reproduce face-PAD results the following paper:@inproceedings{bhattacharjeeBiosig2017,
author = {Sushil Bhattacharjee and S{\'{e}}bastien Marcel},
title = {What you can't see can help you -- extended range imaging for 3d-mask presentation attack detection},
year = {2017},
month = sep,
booktitle = {Proceedings of the 16th International Conference of the Biometrics Special Interest Group (BIOSIG)},
address = {Darmstadt, Germany},
}
                                                    §If you use this package and/or its results, please cite the paper.InstallationThe installation instructions are based oncondaand works onLinux systems only.Install condabefore continuing.Once you have installedconda, download the source code of this paper and
unpack it.  Then, you can create a conda environment with the following
command:$ cd bob.paper.biosig2017_3dmaskprestudy
$ conda env create -f environment.yml
$ source activate bob.paper.biosig2017_3dmaskprestudy  # activate the environment
$ buildoutThis will install all the required software to reproduce this paper.Downloading the datasetThis package works with theERPAdataset published by IDIAP. Please download the dataset before proceeding.Generating the plotFigure 5 in the paper shows distributions of the mean pixel-values of the face-region in the thermal images captured using the Xenics Gobi camera.
To reproduce this plot, use the following command:$./bin/thermal_mean_hist.py-ip<input-ERPA-folder>/Xenics_Gobi/-op<output-plot-folder>where <input-ERPA-folder> should be the folder containing theERPAdataset, and <output-plot-folder> should specify the folder where you want the plot to be created as a pdf file.
This command will process images from the ‘Xenics_Gobi’ sub-folder in the specified input-folder (<input-ERPA-folder>), and create a file calledthermal_profile_hist.pdfin the specified output folder.
Package: bob.paper.btas_c2016
This package is part of theBobtoolkit and it allows to reproduce the following paper:@inproceedings{KorshunovBtas2016c,
    author = {P. Korshunov AND S. Marcel AND H. Muckenhirn AND A. R. Goncalves AND A. G. Souza Mello AND
            R. P. Velloso Violato AND F. O. Simoes AND M. U. Neto AND
            M. de Assis Angeloni AND J. A. Stuchi AND H. Dinkel AND N. Chen AND
            Y. Qian AND D. Paul AND G. Saha AND Md Sahidullah},
    title = {Overview of BTAS 2016 Speaker Anti-spoofing Competition},
    year = {2016},
    month = sep,
    booktitle = {IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)},
    address = {Niagara Falls, NY, USA},
}If you use this package and/or its results, please cite the paper.InstallationTo install this package – alone or together with otherPackages of Bob– please read theInstallation Instructions.
ForBobto be able to work properly, some dependent packages are required to be installed.
Please make sure that you have read theDependenciesfor your operating system.DocumentationFor further documentation on this package, please read theDocumentation.
For a list of tutorials on this or the other packages ofBob, or information on submitting issues, asking questions and starting discussions, please visit its website.
Package: bob.paper.btas_j2016
Reproducing results of paper published in BTAS 2016This package is part of theBobtoolkit and it allows to reproduce the following paper:@inproceedings{KorshunovBtas2016j,
    author = {P. Korshunov AND S. Marcel},
    title = {Joint Operation of Voice Biometrics and Presentation Attack Detection},
    year = {2016},
    month = sep,
    booktitle = {IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)},
    address = {Niagara Falls, NY, USA},
}If you use this package and/or its results, please cite the paper.InstallationThe installation instructions are based oncondaand works onLinuxandMac OSsystems
onlyInstall condabefore continuing.Once you have installedconda, download the source code of this paper and
unpack it.  Then, you can create a conda environment with the following
command:$ cd bob.paper.btas_j2016
$ conda env create -f environment.yml
$ source activate bob.paper.btas_j2016  # activate the environment
$ python -c "import bob.bio.base"  # test the installation
$ buildoutThis will install all the required software to reproduce this paper.DocumentationFor further documentation on this package, please read theDocumentation.
For a list of tutorials on this or the other packages ofBob, or information on submitting issues, asking questions and starting discussions, please visit its website.
Package: bob.paper.CVPRW_2016
Heterogeneous Face Recognition using Inter-Session Variability ModellingThis package provides the source code to run the experiments published in the paperHeterogeneous Face Recognition using Inter-Session Variability Modelling.If you use this package and/or its results, please cite the following publications:The original paper with the counter-measure explained in details:@inproceedings{Pereira_CVPRW2016,
  author = {Pereira, Tiago de Freitas and Marcel, S{\'{e}}bastien},
  keywords = {Face Recognition, Session Variability Modelling, Heterogeneous Face Recognition},
  month = jun,
  year = {2016},
  title = {Heterogeneous Face Recognition using Inter-Session Variability Modelling},
  journal = {IEEE Computer Society Workshop on Biometrics - CVPRW 2016},
}Bob as the core framework used to run the experiments:@inproceedings{Anjos_ACMMM_2012,
  author = {A. Anjos AND L. El Shafey AND R. Wallace AND M. G\"unther AND C. McCool AND S. Marcel},
  title = {Bob: a free signal processing and machine learning toolbox for researchers},
  year = {2012},
  month = oct,
  booktitle = {20th ACM Conference on Multimedia Systems (ACMMM), Nara, Japan},
  publisher = {ACM Press},
}Raw DataThis package does not provide the dataset used in the paper.
They must be downloaded separately from CUHK_CUFS (http://mmlab.ie.cuhk.edu.hk/archive/facesketch.html) and CBSR NIR-VIS-2.0 (http://www.cbsr.ia.ac.cn/english/NIR-VIS-2.0-Database.html).InstallationNoteIf you are reading this page through our GitHub portal and not through PyPI,
notethe development tip of the package may not be stableor become
unstable in a matter of moments.Go tohttp://pypi.python.org/pypi/antispoofing.lbptopto download the latest
stable version of this package.There are 2 options you can follow to get this package installed and
operational on your computer: you can use automatic installers likepip(oreasy_install) or manually download, unpack and
usezc.buildoutto create a
virtual work environment just for this package.Using an automatic installerUsingpipis the easiest (shell commands are marked with a$signal):$ pip install bob.paper.CVPRW_2016You can also do the same witheasy_install:$ easy_install bob.paper.CVPRW_2016This will download and install this package plus any other required
dependencies. It will also verify if the version of Bob you have installed
is compatible.This scheme works well with virtual environments byvirtualenvor if you have root access to your
machine. Otherwise, we recommend you use the next option.Usingzc.buildoutDownload the latest version of this package fromPyPIand unpack it in your
working area. The installation of the toolkit itself usesbuildout. You don’t need to understand its inner workings
to use this package. Here is a recipe to get you started:$ python bootstrap.py
$ ./bin/buildoutReproducibilityPlease, check our documentation in order to reproduce the results of the paper.
Package: bob.paper.eusipco2018
Speaker Inconsistency Detection in Tampered VideoThis package is part of theBobtoolkit and it allows to reproduce the experimental results published in the following paper:@inproceedings{KorshunovEusipco2018,
    author = {P. Korshunov AND S. Marcel},
    title = {Speaker Inconsistency Detection in Tampered Video},
    year = {2018},
    month = sep,
    booktitle = {{EUSIPCO 2018}},
    address = {Rome, Italy},
}If you use this package and/or its results, please cite the paper.InstallationThe installation instructions are based oncondaand works onLinuxandMac OSsystems
only.Install condabefore continuing.Once you have installedconda, download the source code of this paper and
unpack it or checkout from Gitlab.  Then, you can create a conda environment with the following
command:$ cd bob.paper.eusipco2018
$ conda env create -f environment.yml
$ source activate bob.paper.eusipco2018  # activate the environment
$ python -c "import bob.bio.base"  # test the installation
$ buildoutThis will install all the required software to reproduce this paper.DocumentationFor further documentation on this package, please read theDocumentation.
For a list of tutorials on this or the other packages ofBob, or information on submitting issues, asking questions and starting discussions, please visit its website.
Package: bob.paper.interspeech_2016
Cross-database evaluation of audio-based spoofing detection systemsThis package is part of theBobtoolkit and it allows to reproduce the experimental results published in the following paper:@inproceedings{KorshunovInterspeech2016,
    author = {P. Korshunov AND S. Marcel},
    title = {Cross-database evaluation of audio-based spoofing detection systems},
    year = {2016},
    month = sep,
    booktitle = {Interspeech},
    pages={1705--1709},
    address = {San Francisco, CA, USA},
}If you use this package and/or its results, please cite the paper.InstallationThe installation instructions are based oncondaand works onLinuxandMac OSsystems
onlyInstall condabefore continuing.Once you have installedconda, download the source code of this paper and
unpack it.  Then, you can create a conda environment with the following
command:$ cd bob.paper.interspeech_2016
$ conda env create -f environment.yml
$ source activate bob.paper.interspeech_2016  # activate the environment
$ python -c "import bob.bio.base"  # test the installation
$ buildoutThis will install all the required software to reproduce this paper.DocumentationFor further documentation on this package, please read theDocumentation.
For a list of tutorials on this or the other packages ofBob, or information on submitting issues, asking questions and starting discussions, please visit its website.
Package: bob.paper.isba2018-pad-dnn
Reproducing results of paper published in ISBA 2018This package is part of theBobtoolkit and it allows to reproduce the following paper:@inproceedings{KorshunovISBA2018,
    author = {Pavel Korshunov, Andr\'e Gon\c{c}alves, Ricardo Violato, Fl\'avio Sim\~oes, and S\'ebastien Marcel},
    title = {On the Use of Convolutional Neural Networks for Speech Presentation Attack Detection},
    booktitle = {International Conference on Identity, Security and Behavior Analysis (ISBA 2018)},
    year = {2018},
    month = jan,
    address = {Singapore},
}If you use this package and/or its results, please cite the paper.InstallationFollow ourinstallationinstructions and installBobusing Conda. Then, run the following commands:$ conda install zc.buildout
$ buildoutContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.paper.jstsp_2017
Reproducing results of paper published in J-STSP 2017This package is part of theBobtoolkit and it allows to reproduce the following paper:@article{KorshunovJSTSP2017,
    author = {P. Korshunov and S. Marcel},
    title = {Impact of score fusion on voice biometrics and presentation attack detection in cross-database evaluations},
    year = {2017},
    journal = {IEEE Journal of Selected Topics in Signal Processing},
}If you use this package and/or its results, please cite the paper.InstallationFollow ourinstallationinstructions and installBobusing Conda. Then, run the following commands:$ conda install zc.buildout
$ buildoutContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.paper.lipsync2019
Speaker Inconsistency Detection in Tampered VideoThis package is part of theBobtoolkit and it allows to reproduce the experimental results published in the following paper:@inproceedings{KorshunovICML2019,
     author = {Korshunov, Pavel and Halstead, Michael and Castan, Diego and Graciarena, Martin and McLaren, Mitchell and Burns, Brian and Lawson, Aaron and Marcel, S{\'{e}}bastien},
   keywords = {inconsistencies detection, lip-syncing, Video tampering},
      month = jul,
      title = {Tampered Speaker Inconsistency Detection with Phonetically Aware Audio-visual Features},
  booktitle = {International Conference on Machine Learning},
     series = {Synthetic Realities: Deep Learning for Detecting AudioVisual Fakes},
       year = {2019},
}If you use this package and/or its results, please cite the paper.InstallationThe installation instructions are based oncondaand works onLinuxandMac OSsystems
only.Install condabefore continuing.Once you have installedconda, download the source code of this paper and
unpack it or checkout from Gitlab.  Then, you can create a conda environment with the following
command:$ cd bob.paper.lipsync2019
$ conda env create -f environment.yml
$ source activate bob.paper.lipsync2019  # activate the environment
$ python -c "import bob.io.base"  # test the installation
$ buildoutThis will install all the required software to reproduce this paper.DocumentationDownload databases and generate tampered videosReproduce the experimentsContactFor questions or reporting issues to this software package, contact Pavel Korshunov (pavel.korshunov@idiap.ch).
Package: bob.paper.taslp-2017
Reproducing results of paper submitted to IEEE/ACM Transactions on Audio, Speech, and Language ProcessingThis package is part of the signal-processing and machine learning toolboxBobandit allows to reproduce the following paper:@inproceedings{Muckenhirntaslp_2017,
    author = {Muckenhirn, Hannah and Korshunov, Pavel and Magimai.-Doss, Mathew and Marcel, S{\'{e}}bastien},
    title = {Long Term Spectral Statistics for Voice Presentation Attack Detection,
    booktitle = {To appear in IEEE/ACM Transactions on Audio, Speech and Language Processing},
    year = {2017},
}If you use this package and/or its results, please cite the paper.InstallationFollow ourinstallationinstructions. Then, using the Python interpreter
provided by the distribution, bootstrap and buildout this package:$ python bootstrap-buildout.py
$ ./bin/buildoutContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.paper.tifs2018-dsu
Python package to reproduce the experiments from the paper “Heterogeneous Face Recognition Using Domain Specific Units”This package is part of the signal-processing and machine learning toolboxBob.
This package is part of thebob.biopackages, which allow to run comparable and reproducible biometric recognition experiments on publicly available databases.InstallationFollow ourinstallationinstructions. Then, using the Python interpreter
provided by the distribution, bootstrap and buildout this package:$ conda install bob.thesis.tiago
$ bob bio htface --helpContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.pipelines
Tools to build robust and extensible pipelinesThis package is part of the signal-processing and machine learning toolboxBob.The goal is to provide more flexible pipeline mechanisms forbob.bio.baseandbob.pad.base.It is based on thescikit-learn pipelineand adds a layer to route data and metadata through the pipeline (which is not
supported by scikit-learn yet).InstallationComplete bob'sinstallation instructions. Then,
to install this package, run:condainstallbob.pipelinesContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bobros
BobrosMaking your life a bit more beautifulpipinstallbobros
bobros--helpbobrosis a tool for setting the background colour for files in PyCharm project
navigator.It was mainly created for working with Django projects where you can have lots
of app folders, all of which may contain files with identical names likeviewsormodels, etc. It's much easier to work with them when you can easily
tell them apart and can easily see where one app ends and another one begins in
your project navigation panel. Colour-coding the app folders makes that task a
lot easier.However, it turned out that doing that using the standard PyCharm mechanisms is
not that easy. You have to navigate through several settings tabs and do a lot
of clicking and typing. Well, there must be a better way!Bobros takes a simple config and generates the correct xml files to make your
project files colour-coded. It also supports different colour-themes.Sample config file (i.e.my_config.yml):themes:dark:# defines colours for a theme named "dark", names can be arbitraryone:aabb00# defines colour "one" to have the hex value of aabb00two:bbaa00# defines colour "two"light:# defines colours for a theme named "light". Should contain the same colours as other themesone:99ff88two:9988eeitems:# defines colours for the items on diskmy_file.py:onemy_file_2.py:twomy_folder:oneSome special values:~The "home" folder: in Django projects the settings folder by default has
the same name as the project folder. It's nice to have the settings folder
the same colour in all your projectsProblems,Non-Project Filesspecial names used by Idea for files/folders
containing errors or not belonging to the current projectTo apply a theme in a config, run the following from the root folder of your
project:bobroscharmmy_config.yml-tdarkYou could also have a less fancy config, without themes:colors:one:aabb00two:bbaa00items:my_file.py:onemy_file_2.py:twomy_folder:oneTo apply this config, runbobroscharmmy_config.ymlIt is an error to try to apply a config with themes and not providing a theme
name on the command line (i.e. the-toption), to specify a non-existing
theme or to use a config without themes and providing the-toption on the
command line.Generating coloursI like my files to have colours of the same saturation and luminance, but
of different hues, so bobros has a command to generate such colours from a
given initial colour:bobrosmake-colorsb1e3be--points10This will generate 10 such colours, you can then copy the values to your
config file and assign them to your items.Checking configsYou can runbobrosscheckmy_config.ymlfrom the root of a PyCharm (or Idea) project. For now, it will check if the
config specifies files that do not exist in the actual project.
Package: bobross
[![bobrossquotes](https://snapcraft.io/bobrossquotes/badge.svg)](https://snapcraft.io/bobrossquotes)# BobRossQuotesA collection of quotes from Bob Ross.## To install:sudo snap install bobrossquotes## To run:From your terminal:bobrossquotes## Star History[![Star History Chart](https://api.star-history.com/svg?repos=kz6fittycent/BobRossQuotes&type=Date)](https://star-history.com/#kz6fittycent/BobRossQuotes&Date)
Package: bob.rppg.base
Remote Heart Rate MeasurementThis package is part of the signal-processing and machine learning toolboxBob. It provides a set of algorithm to infer the heart rate from face video sequences.InstallationFollow ourinstallationinstructions. Then, if you would like to use
this package, just do:$ conda install bob.rppg.baseOr if you would like to develop the package:$ git clone git@gitlab.idiap.ch:bob/bob.rppg.base.git
$ cd bob.rppg.base
$ buildoutContactFor questions or reporting issues to this software package, contact our
developmentmailing list.