Saravanan666/pypi-org huggingface dataset:
Package: bob.ip.flandmark
Flandmark keypoint localization libraryThis package is part of the signal-processing and machine learning toolboxBob. It contains a simple Python wrapper to the (rather quick) open-source
facial landmark detectorFlandmark,version 1.0.7(or the github state as
of 10/february/2013). If you use this package, the author asks you to cite the
following paper:@inproceedings{Uricar-Franc-Hlavac-VISAPP-2012,
  author =      {U\v{r}i\v{c}\'a\v{r}, Michal and Franc, Vojt\v{e}ch and Hlav\'a\v{c}, V\'{a}clav},
  title =       {Detector of Facial Landmarks Learned by the Structured Output {SVM}},
  year =        {2012},
  pages =       {547-556},
  booktitle =   {VISAPP '12: Proceedings of the 7th International Conference on Computer Vision Theory and Applications},
  editor =      {Csurka, Gabriela and Braz, Jos{\'{e}}},
  publisher =   {SciTePress --- Science and Technology Publications},
  address =     {Portugal},
  volume =      {1},
  isbn =        {978-989-8565-03-7},
  book_pages =  {747},
  month =       {February},
  day =         {24-26},
  venue =       {Rome, Italy},
  keywords =    {Facial Landmark Detection, Structured Output Classification, Support Vector Machines, Deformable Part Models},
  prestige =    {important},
  authorship =  {50-40-10},
  status =      {published},
  project =     {FP7-ICT-247525 HUMAVIPS, PERG04-GA-2008-239455 SEMISOL, Czech Ministry of Education project 1M0567},
  www = {http://www.visapp.visigrapp.org},
}InstallationComplete Bob’sinstallationinstructions. Then, to install this package,
run:$ conda install bob.ip.flandmarkContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.ip.gabor
Tools for Gabor Wavelets, Transform, Jet Extraction and SimilarityThis package is part of the signal-processing and machine learning toolboxBob. It contains a set of C++ code and Python bindings for Bob’s Image
Processing tools concerning Gabor wavelets, the Gabor wavelet transform, Gabor
jet extraction in a grid graph structure and Gabor jet similarity functions
including a Gabor jet disparity estimation.If you use parts of this code in a scientific publication, please cite:@inproceedings{guenther2012disparity,
  title = {Face Recognition with Disparity Corrected {G}abor Phase Differences},
  author = {G{\"{u}}nther, Manuel and Haufe, Dennis and W{\"{u}}rtz, Rolf P.},
  editor = {Villa, Alessandro E. P. and Duch, W{\l}odzis{\l}aw and {\'{E}}rdi, P{\'{e}}ter and Masulli, Francesco and Palm, G{\"{u}}nther},
  booktitle = {Artificial Neural Networks and Machine Learning},
  series = {Lecture Notes in Computer Science},
  volume = {7552},
  year = {2012},
  month = sep,
  pages = {411-418},
  publisher = {Springer},
  pdf = {http://publications.idiap.ch/downloads/papers/2012/Gunther_ICANN2012_2012.pdf}
}InstallationComplete Bob’sinstallationinstructions. Then, to install this package,
run:$ conda install bob.ip.gaborContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.ip.mtcnn
Bob interface for MTCNN face and landmark detectionThis package wraps the MTCNN face and landmark detection from the paper@ARTICLE{7553523,author={K. Zhang and Z. Zhang and Z. Li and Y. Qiao},
journal={IEEE Signal Processing Letters},
title={Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks},
year={2016},
volume={23},
number={10},
pages={1499-1503},
keywords={Benchmark testing;Computer architecture;Convolution;Detectors;Face;Face detection;Training;Cascaded convolutional neural network (CNN);face alignment;face detection},
doi={10.1109/LSP.2016.2603342},
ISSN={1070-9908},
month={Oct},}InstallationComplete Bob’sinstallationinstructions. Then, to install this package,
run:$ conda install bob.ip.mtcnnDocumentationFor further documentation on this package, please read theLatest Versionof the documentation.
Package: bob.ip.optflow.hornschunck
Implementation of Horn & Schunck’s Optical Flow Framework for BobThis package is part of the signal-processing and machine learning toolboxBob. It contains a simple Python wrapper to an open-source Optical Flow
estimator based on the works by Horn & Schunck:@article{Horn_Schunck_1981,
  author = {Horn, B. K. P. and Schunck, B. G.},
  title = {Determining optical flow},
  year = {1981},
  booktitle = {Artificial Intelligence},
  volume = {17},
  pages = {185--203},
}InstallationComplete Bob’sinstallationinstructions. Then, to install this package,
run:$ conda install bob.ip.optflow.hornschunckContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.ip.optflow.liu
Liu’s Optical Flow Framework for BobThis package is part of the signal-processing and machine learning toolboxBob. It contains a simple Python wrapper to the open-source Optical Flow
estimator developed by C. Liu during his Ph.D.  The code was originally
conceived to operate over Matlab. This is a Python/Bobport.  If you use
this code, the author asks you to cite his thesis:@phdthesis{Liu_PHD_2009,
  title = {{Beyond Pixels: Exploring New Representations and Applications for Motion Analysis}},
  author = {Liu, Ce},
  institution = {{Massachusetts Institute of Technology}},
  year = {2009},
  type = {{Ph.D. Thesis}},
}InstallationComplete Bob’sinstallationinstructions. Then, to install this package,
run:$ conda install bob.ip.optflow.liuContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.ip.pytorch-extractor
Bob interface for feature extraction using PyTorchThis package is part of the signal-processing and machine learning toolboxBob. This package contains functionality to extract features from CNNs trained with PyTorch.InstallationComplete Bob’sinstallationinstructions. Then, to install this package,
run:$ conda install bob.ip.pytorch_extractorContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.ip.qualitymeasure
Bob’s library of image-quality feature-extractorsThis package is part of the signal-processing and machine learning toolboxBob. It provides functions for extracting image-quality features proposed
for PAD experiments by different research groups. Image quality measures
proposed by Galbally et al. (IEEE TIP 2014) and by Wen et al. (IEEE TIFS 2015)
are implemented in this package.InstallationComplete Bob’sinstallationinstructions. Then, to install this package,
run:$ conda install bob.ip.qualitymeasureContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.ip.skincolorfilter
Skin Color FilterThis package is part of the signal-processing and machine learning toolboxBob. It implements the skin color filter described in “Adaptive skin segmentation via feature-based face detection”, M.J. Taylor and T. Morris, Proc SPIE Photonics Europe, 2014.InstallationComplete Bob’sinstallationinstructions. Then, to install this package,
run:$ conda install bob.ip.skincolorfilterContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.ip.stereo
Bob IP StereoThis package is part of the signal-processing and machine learning toolboxBob.This package implements stereo mapping and projection functionalities and provides
them asbob.io.stream.StreamFilterto easiily integrate them in processing
pipelines.InstallationComplete bob’sinstallationinstructions. Then, to install this
package, run:$ conda install bob.ip.stereoExampleHere is an example of how to use the package to load data recorded with 3 different cameras, build a depth map using
2 of them and use it to project the third camera’s data.# Import Stream and StreamFile classes# Generic processing filters are available through the Stream classfrombob.io.streamimportStreamFile,Stream# Import bob.ip.stereo: this updates the Stream class with the stereo filters# It also upates the StreamFile class to allow to set camera configuration information.f=StreamFile(resource_path("test/data/input_example.h5","bob.io.stream"),resource_path("config/idiap_face_streams.json","bob.io.stream"),)f.set_camera_configs(resource_path("config/idiap_face_calibration.json"))# stream for stereo and projection testscolor=Stream("color",f)nir_left=Stream("nir_left_stereo",f).adjust(color)nir_right=Stream("nir_right_stereo",f).adjust(color)# reproject operationsmap_3d=nir_left.stereo(nir_right)depth=map_3d.select(channel=2).colormap(colormap="jet")rep_color=color.reproject(nir_left,nir_right,map_3d)# Use the stereo data:rep_color[0:2]# ...ContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.ip.tensorflow-extractor
Bob interface for feature extraction using TensorflowThis package is part of the signal-processing and machine learning toolboxBob. This package contains functionality to extract features from CNNs trained
with tensorflowhttp://http://tensorflow.org/InstallationComplete Bob’sinstallationinstructions. Then, to install this package,
run:$ conda install bob.ip.tensorflow_extractorContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bobj
This library provides a solution for automating SAP Business Objects (BOBJ) tasks. It has been designed to accelerate processes and empower practitioners with the following capabilities:User ManagementListing: View all usersCreating: Add new usersModifying/Updating: Edit existing user detailsDeleting: Remove usersFolder ManagementListing: View all foldersCreating: Create new foldersModifying/Updating: Edit existing folder detailsDeleting: Remove foldersServer ManagementUtilization: Monitor server usageHung-up Notification: Receive notifications for hung-up issuesEvent Server Health: Check the health status of event servers... (Additional features)Instance ManagementRunning Instances: View details includingHistorical Running TimeDatabase DetailsHistory of Failures and TotalsFailed Instances: Examine details includingHistorical Running TimeDatabase DetailsHistory of Failures and TotalsUpcoming Instances: Preview details includingHistorical Running TimeDatabase DetailsHistory of Failures and TotalsThis library serves as an essential tool for managing and maintaining SAP Business Objects efficiently and effectively.
Package: bob.kaldi
Python Bindings for KaldiThis package provides pythonic bindings forKaldifunctionality so it can be
seamlessly integrated with Python-based workflows. It is a part fo the signal-
processing and machine learning toolboxBob. If you make use of bob.kaldi,
please cite our publication:Cernak, A. Komaty, A. Mohammadi, A. Anjos, and S. Marcel, “Bob speaks kaldi,” Aug. 2017.InstallationFollow ourinstallationinstructions. Then, to install this package, run:$ conda install bob.kaldiKaldiis bundled in our conda channnels as well which means installing
bob.kaldi will automatically installKaldias well.ContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.learn.activation
Activation functors of BobThis package is part of the signal-processing and machine learning toolboxBob. It contains Bob’s Activation functors, which used in several learning
packages of Bob in the namespacebob.learn.InstallationComplete Bob’sinstallationinstructions. Then, to install this package,
run:$ conda install bob.learn.activationContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.learn.boosting
Boosted Learning AlgorithmsThis package is part of the signal-processing and machine learning toolboxBob.  It implements a generalized boosting framework, which incorporates
different boosting approaches.InstallationComplete Bob’sinstallationinstructions. Then, to install this package,
run:$ conda install bob.learn.boostingContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.learn.em
Expectation Maximization Machine Learning ToolsThis package is part of the signal-processing and machine learning toolboxBob. It contains routines for learning
probabilistic models via Expectation Maximization (EM).The EM algorithm is an iterative method that estimates parameters for
statistical models, where the model depends on unobserved latent variables. The
EM iteration alternates between performing an expectation (E) step, which
creates a function for the expectation of the log-likelihood evaluated using
the current estimate for the parameters, and a maximization (M) step, which
computes parameters maximizing the expected log-likelihood found on the E step.
These parameter-estimates are then used to determine the distribution of the
latent variables in the next E step.The package includes the machine definition per se and a selection of different
trainers for specialized purposes:Maximum Likelihood (ML)Maximum a Posteriori (MAP)K-MeansInter Session Variability Modelling (ISV)Joint Factor Analysis (JFA)Total Variability Modeling (iVectors)Probabilistic Linear Discriminant Analysis (PLDA)EM Principal Component Analysis (EM-PCA)InstallationComplete Bob'sinstallationinstructions. Then, to install this package, run:condainstallbob.learn.emContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.learn.libsvm
Bob’s Python bindings for LIBSVMThis package is part of the signal-processing and machine learning toolboxBob. It contains a set of Pythonic bindings for LIBSVM.InstallationComplete Bob’sinstallationinstructions. Then, to install this package,
run:$ conda install bob.learn.libsvmContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.learn.misc
Miscelaneous Machines and Trainers in BobThis package contains various Bob’s machines and trainers which have not yet been fully ported into the new framework.Functionality in this package is subject to modifications, including deletions of classes and functions (which will be moved to other packages).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 theStable Versionor theLatest Versionof the documentation.
For a list of tutorials on this or the other packages obBob, or information on submitting issues, asking questions and starting discussions, please visit its website.
Package: bob.learn.mlp
Multi-Layer Perceptron and Trainers in BobThis package is part of the signal-processing and machine learning toolboxBob. It contains a set of Pythonic bindings for Bob’s MLP implementation and
Trainers.InstallationComplete Bob’sinstallationinstructions. Then, to install this package,
run:$ conda install bob.learn.mlpContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.learn.pytorch
Bob support for PyTorchThis package is part of the signal-processing and machine learning toolboxBob. It provides tools to define and train Neural Networks based on PyTorch.InstallationComplete Bob’sinstallationinstructions. Then, to install this package,
run:$ conda install bob.learn.pytorchContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bob.learn.tensorflow
Bob support for tensorflowThis package is part of the signal-processing and machine learning toolboxBob. It provides tools 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.learn.tensorflowContactFor questions or reporting issues to this software package, contact our
developmentmailing list.Before doing that, check our documentation by clicking in the links on the top.
Package: bob.med.tb
Active Tuberculosis Detection On CXR Package for BobThis package is part of the signal-processing and machine learning toolboxBob. It implements training and inference on frontal CXR for Tuberculosis
detection.InstallationComplete bob’sinstallationinstructions. Then, to install this
package, run:$ conda install bob.med.tbCitationIf you use this software package in a publication, we would appreciate if you
could cite our work:@TECHREPORT{Raposo_Idiap-Com-01-2021,
   author = {Raposo, Geoffrey},
   keywords = {deep learning, generalization, Interpretability, transfer learning, Tuberculosis Detection},
   projects = {Idiap},
   month = {7},
   title = {Active tuberculosis detection from frontal chest X-ray images},
   type = {Idiap-Com},
   number = {Idiap-Com-01-2021},
   year = {2021},
   institution = {Idiap},
   url = {https://gitlab.idiap.ch/bob/bob.med.tb},
   pdf = {https://publidiap.idiap.ch/downloads//reports/2021/Raposo_Idiap-Com-01-2021.pdf}
}ContactFor questions or reporting issues to this software package, contact our
developmentmailing list.
Package: bobo
BoboBobo is a light-weight framework for creating WSGI web applications.Its goal is to be easy to use and remember.It addresses 2 problems:Mapping URLs to objectsCalling objects to generate HTTP responsesBobo doesn’t have a templating language, a database integration layer,
or a number of other features that are better provided by WSGI
middle-ware or application-specific libraries.Bobo builds on other frameworks, most notably WSGI and WebOb.To learn more. visit:http://bobo.digicool.comChange History2.4.0 (2017-05-17)Dropped support for Python 2.6 and 3.3.Added support for Python 3.4, 3.5, and 3.6 (mostly verifying that tests pass).Don’t encode text locations in redirects, as that breaks with
modern versions of WebOb.2.3.0 2014-11-21Bobo will encode Unicode URLs for redirects automatically.2.2.0 2014-07-10Bobo will pass resource function arguments from data in JSON request bodies.2.2.0 2014-07-10Bobo will now pass resource function arguments from data in JSON
request bodies.2.1.1 2014-07-06Fixed: Reloading modules sometimes failed2.1.0 2014-04-06Thebobo.Applicationconstructor now accepts objects as well as
strings for the bobo options. This makes application definition from
Python a bit cleaner.A newbobo_handle_exceptionsoptions makes it easy to tell bobo
not to catch application exceptions.  This is helpful is you’re
using WSGI middleware to handle exceptions.The object provided tobobo_errorsoption can now provide a
subset of error handlers.2.0.0 2014-02-09Added Python 3 support!Add decorators to define resource handlers for additional HTTP methods
(put,get,head,delete,options).With the addition ofbobo.put,bobo.postwas changed to no longer
handle PUT requests.1.0.0 2012-04-29Minimum supported Python version is 2.6.Updated to work with WebOb 1.2Added backtracking when searching for resources to deal with a case
when a route doesn’t handle a request method, but a later-matching
route does.Bobo now catches application exceptions and generates 500 responses
by default.0.2.3 2012-03-12Bugs fixed:Sanitize the request path included in the message on the default
404 page.0.2.2 2010-01-19Bugs fixed:An intended optimization to cache resource decorator computations
didn’t work, making request handling slower than it should have
been.URLs were sometimes treated as if they had extra slashes when
traversing subroutes.boboserver.File must explicitly open files in binary mode, which is not
the default on Windows.0.2.1 2009-06-16Packaging update to update documentation.0.2.0 2009-05-26Initial Public Release
Package: bobocep
BoboCEPBoboCEPis aComplex Event Processing(CEP) engine
designed foredge computinginInternet of Things(IoT) systems
to facilitate inferential reasoning and decision-making on streaming data.
It providesfault tolerance(FT) via theactive replicationof
partially-completed complex events across multiple instances of the software.LicenseBoboCEPis open source, as per theOpen Source Definition.
The code in this repository can be redistributed and/or modified under the terms of theMIT License.
Package: bobolink
bobolink-clibobolink helps user's store bookmarks and easily search for them later. In a nutshell, bobolink provides full text search on the HTML documents associated with user's bookmarks.For more information on bobolink in general, users should refer to the documentation hosted on thewebsite.bobolink-cliis the command line interface to the public instance
of the bobolinkbackend.. User's wishing to use bobolink just need to install the cli and signup.Installation$ python -m pip install bobolinkGetting StartedFor user's without a bobolink account, the fastest way to get going is to runbobolink signupafter installation. This, followed bybobolink configureis all that is needed in order to start saving and searching your bookmarks.The cli is heavily documented and can be accessed directly viabobolink [COMMAND] --help. Also as mentioned previously, additional documentation can be found directly on the bobolinkwebsite.ExamplesPlease refer to the terminal session below for a quick exploration of what's possible with bobolink.
Package: bobo-test
# bobo_test## Change Log### 0.2.14add authfix header issue### 0.2.8Add product nameMove version to headerhide Dev version### 0.2.5change note css### 0.2.4adjust table css### 0.2.3remove PDF download button### 0.2.2add config display_header default trueadd config copyright_en and copyright_zhfix some bug### 0.2.1header update### 0.1.4show image as Full screen modechange toc deep to 8add about chanelfix some language bugs### 0.1.3Auto scroll to current selected tocAdd + to the node, if there are any children under it### 0.1.2Change toc deep to 4Hide current page toc from global toc treeFix a small bug### 0.1.1Change selected toc styleAdd PDF download buttonAdd version change dropdown list## Usage:` pip install bobo_test `Changeconf.py` html_theme = "bobo_test" `## ContributeUsegulpwebpackto compile CSS/JS and HTML template### DependenciesPython >= 2.7node >= 9.0gulp >= 4.0webpack >= 4.0sphinx### Before development> go to src/templates/layout.html, uncomment line 151 & comment line 153prepare dev` gulp pre `start dev server` gulp `### publish new version> go to src/templates/layout.html, comment line 151 and uncomment line 153update the version number in _init_.pybuild dist` gulp build `dist python package` python setup.py sdist `publish` twine upload dist/* `
Package: bobotools
bobotools收录到PytorchNetHub安装pipinstallbobotools
or
pipinstallgit+https://github.com/bobo0810/bobotools.git工具类Pytorch(torch_tools)方法功能get_model_info获取模型信息(模型大小、计算量、参数量、前向推理耗时等)vis_tensor可视化tensorvis_cam可视化注意力图图像(img_tools)方法功能download_url多进程下载URL图像filter_md5对输入的图像列表，互相去重，返回重复图像列表verify_integrity对输入的图像列表，验证完整性，返回错误图像列表plot_yolo可视化yolo结果文本(txt_tools)方法功能read_lines批量读取txt，支持指定分隔符write_lines批量写入,保存为txt列表(list_tools)方法功能chunk_N列表均分为N块chunk_per列表分块，每块为指定长度M参考Python打包
Package: bob.pad.base
Scripts to run anti-spoofing experimentsThis package is part of the signal-processing and machine learning toolboxBob.
This package is the base of thebob.padfamily of packages, which allow to
run comparable and reproducible presentation attack detection (PAD) experiments
on publicly available databases.This package contains basic functionality to run PAD experiments.
It provides a generic API for PAD including:A database and its evaluation protocolA data preprocessing algorithmA feature extraction algorithmA PAD algorithmAll these steps of the PAD system are given as configuration files.
All the algorithms are standardized on top of scikit-learn estimators.In this base package, only a core functionality is implemented. The specialized
algorithms should be provided by other packages, which are usually in thebob.padnamespace, likebob.pad.face.InstallationComplete Bob'sinstallation instructions. Then,
to install this package, run:condainstallbob.pad.baseContactFor questions or reporting issues to this software package, contact our
developmentmailing list.