Saravanan666/pypi-org huggingface dataset:
Package: blunix-toolkit
# Blunix ToolkitThe Blunix Toolkit is a collection of Python glue-code that helps ourengineering team to uniformly collaborate on the development of thealready 100+ repositories we are maintaining inside our Githuborganization.The code is currently still undergoing rapid development but is alreadydistributed via PyPI.## Installation### System-wideTo install the `blunix-toolkit` system-wide, run:```bashsudo pip install blunix-toolkit```### User-HomeTo install the `blunix-toolkit` in your user's home-directory, run:```bashpip install --user blunix-toolkit```Remember to add ~/.local/bin to your `$PATH` variable in your shell'src-file.### Install in Python virtualenvTo install the `blunix-toolkit` into a Python virtualenv for contributingto it, tun:```bashmkvirtualenv blunix-toolkitsetvirtualenvprojectmake install```When the configuration file is accessed for the very first time, thetoolkit will ask you a set of questions regarding your name, email,Github account, as well as your local development workspace layout.Your answers will be marshaled to `~/.config/blunix/blunix.yaml`. Thisis your individual Blunix engineering configuration file. Have a look atit with:```bashblunix-config-edit```Please note, that you will need to use an[API access token](https://github.com/settings/tokens) as yourGithub password if you have enabled 2FA.The toolkit core consists of a set of Python packages and modules around abovestated configuration file, as well as executable entry-point scripts thatleverage this API. These scripts can be found in the `bin/` directory.In addition to this "core", it is possible to extend the local $SHELLwith some very useful environment variables and shell functions.```bash# Generate the shell-codeblunix-config-bash# or automatically source it like thissource <(blunix-config-bash)```Take a look at your new environment. You should see all sorts of `BLUNIX_*`variables and more.```bashset | grep -i blunix```# Getting startedOnce your configuration file has been created you can run a set of usefulGithub API requests, such as:```bash# list all repositoriesblunix-gh-repos# list all role-* repositoriesblunix-gh-roles# list all playbook-* repositoriesblunix-gh-playbooks```You can now combine these scripts with the above shell-magic:```bash# Clone all role-* repositories to your $BLUNIX_ROOT_DIR_ROLES directory while# additionally creating a virtualenv for each repo and setting the project# directory to the repositories workspace directory.blunix-gh-roles |\cut -d/ -f2 |\blunixForEach 'create=y clone=y blunixWorkon "$item"'# Similar to the above, you can do the same with all Github playbook-* repositories.blunix-gh-playbooks |\cut -d/ -f2 |\blunixForEach 'create=y clone=y blunixWorkon "$item"'```
Package: bluprint
BluprintBluprintis a command line utility for streamlined exploratory data science projects. Bluprint projects allow Jupyter and RMarkdown notebooks seamless access to configuration, data and shared code, using best coding practices, in this type of project structure:my_project
├── conf
│   └── data.yaml
├── data
│   ├── emailed
│   │   └── messy.xlsx
│   └── user_processed.csv
├── notebooks
│   └── process.ipynb
└── my_project
    └── shared_code.pyFeaturesConfiguration, data and shared code (Python/R scripts) separated from notebooks.Mixing of any or all Python/R scripts and Jupyter/RMarkdown notebooks.Consistent access to configuration and data, e.g.data.emailed.messy(Python) automatically resolves to/path/to/my_project/data/emailed/messy.xlsx.Consistent access to project modules, e.g.from my_project import shared_codein any notebook in any sub-directory.Share project easily; just copy the project directory and runpdm install.Reproducibility: Python and R dependencies are version locked.Works with tools for notebook linting, testing, CI/CD and workflows.Bluprint projects are Python packages; usepip install /path/to/my_projectto reuse shared code across projects.Usagebluprint create my_projectcreates a project skeleton similar to the example shown above. Once created, we can add data files and store all file paths relative to themy_project/datadirectory, in thedata.yaml:emailed:messy:'emailed/messy.xlsx'user:processed:'user_processed.csv'Then retrieve the automatically parsed full paths, for example inprocess.ipynbabove:# bluprint_conf is a helper package for loading configsfrombluprint_confimportload_data_yamlfrommy_project.shared_codeimportprocess_dataimportpandasaspddata=load_data_yaml()# default arg: conf/data.yamlprint(data)#> {#>   'emailed': {#>     'messy': '/path/to/my_project/data/emailed/messy.xlsx'#>   },#>   'user': {#>     'processed': '/path/to/my_project/data/user_processed.csv'#>   }#> }messy_df=pd.read_xlsx(data.emailed.messy)processed_df=process_data(messy_df)processed_df.to_csv(data.user.processed)For a working demonstration of a shareable projecthttps://github.com/igor-sb/bluprint-demo/.DocumentationFull documentation available at:https://igor-sb.github.io/bluprint/.InstallationInstallpipxandPDM. Then run:pipxinstallbluprintNoteFor R projects, installrenvbefore attempting to create a Bluprint project with R support.ReferencesBluprint integrates:PDMOmegaConfPython’s native import systemR packagerenvR packagehereR packagereticulateBluprint is heavily inspired by these resources:Author’s own frustration of dealing with malfunctioning notebooks for over a decade.Cookiecutter Data ScienceRStudio ProjectsPloomberMicrosoft Team Data Science ProcessR for Data Science (2e): 6. Workflow: scripts and projectsVincent D. Warmerdam: Untitled12.ipynb | PyData Eindhoven 2019LicenseBluprint is released underMIT license.
Package: bluprint-conf
Bluprint_confBluprint_conf is a Python package for loading YAML configurations in your Python
code or Jupyter notebooks, that automatically resolves YAML file paths, so
there is no need to think about absolute or relative paths.Seedocumentationfor usage.InstallationBluprint_conf is automatically added and installed by Bluprint, but if you wish
to use it as a standalone package, it can be installed with:pipinstallbluprint_confBluprint_conf usesOmegaConfto read and
parse YAML configuration files.
Package: blur-clip-board-image-cli
Blur clip board image for macOSBlur clip board image command line tool for macOS.Installationpippipinstallblur-clip-board-image-cliDirectlycurl-sLhttps://raw.githubusercontent.com/kenchan0130/blur-clip-board-image-for-macOS/master/install.sh|bashIt installs on/usr/local/binas default.
If you want to change directory, please setBLUR_CLIP_BOARD_IMAGE_INSTALL_DIRlike follows.exportBLUR_CLIP_BOARD_IMAGE_INSTALL_DIR="/path/to/directory"curl-sLhttps://raw.githubusercontent.com/kenchan0130/blur-clip-board-image-for-macOS/master/install.sh|bashUsageCopy imageRun theblur-clip-board-imagecommandDefault blur rate is8.5You can also select blur rate with argument likeblur-clip-board-image -r 10Paste imageWhen you want to more infomation, please runblur-clip-board-imag -h.DemoLicenceMIT
Package: blur-detector
Spatially-Varying-Blur-Detection-pythonpython implementation of the paper "Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes" - cvpr 2017brief Algorithm overviewUses discrete cosine transform coefficients at multiple scales and uses max pooling on the high frequency coefficients to get the sharp areas in an image.QuickstartThis library performs Spatially Varying Blur Detection which is can be used in many applications such as Depth of field estimation, Depth from Focus estimation, Blur Magnification, Deblurring etc.InstallationTo install, run:pip install blur-detectorUsage:import blur_detector
import cv2
if __name__ == '__main__':
	img = cv2.imread('image_name', 0)
	blur_map = blur_detector.detectBlur(img, downsampling_factor=4, num_scales=4, scale_start=2, num_iterations_RF_filter=3, show_progress=True)

	cv2.imshow('ori_img', img)
	cv2.imshow('blur_map', blur_map)
	cv2.waitKey(0)As easy as that!!
Package: blurhashify
blurhashifyGenerateBlurHashimages from an existing image.InstallationViapip:pipinstallblurhashifyViapipx:pipxinstallblurhashifyUsageblurhashifyandUsage: blurhashify [OPTIONS] FILE_PATH

  Generate BlurHash images from an existing image.

Options:
  -w, --width INTEGER   The width of the output images (in px).  [default: input image
                        width]
  -h, --height INTEGER  The height of the output images (in px).  [default: input
                        image height]
  -s, --scale INTEGER   The number to multiply the width and height of the output
                        images by.  [default: 1]
  -p, --punch INTEGER   The punch of output images. This design option adjusts the
                        contrast in output images. Smaller values make the effect more
                        subtle, while larger values make it stronger.  [default: 1]
  -v, --verbose         Enable verbose logging.
  --version             Show the version and exit.
  --help                Show this message and exit.Referenceshttps://github.com/woltapp/blurhashhttps://github.com/halcy/blurhash-pythonhttps://github.com/woltapp/blurhash-pythonhttps://github.com/joaopalmeiro/flake8-import-as-modulehttps://python-poetry.org/docs/dependency-specification/#exact-requirements+https://github.com/python-poetry/poetry/issues/1440#issuecomment-1105165877https://dataewan.com/blog/poetry-python-command-line/https://github.com/alvarobartt/python-package-templatehttps://github.com/ofek/hatch-vcshttps://github.com/aka-raccoon/hatch-awshttps://hatch.pypa.io/latest/blog/2022/10/08/hatch-v160/#virtual-environment-location+https://github.com/pypa/hatch/issues/715DevelopmentVS Code profile:Pythonhatchconfigsetdirs.env.virtual.hatchhatchconfigshow--allhatchenvcreatedevhatchenvfindhatchenvshowhatchversionhatch-edevrunpipshowblurhashifyhatch-edevrunblurhashify--helphatch-edevrunblurhashify06_IMG_1974.jpgorhatch-edevrunblurhashify06_IMG_1974.jpg--verbosehatchrundev:checkCopy the output of the help command (hatch -e dev run blurhashify --help) and paste it in theUsagesection.Deploymenthatchprojectmetadatahatchversiongittaggitls-remote--tagsoriginhatchversionmicroorhatchversionminorCommit theblurhashify/__init__.pyfile with the updated version.gittag"v$(hatchversion)"gittaggitpushorigin--tags
Package: blurhash-numba
blurhash-numba : The fastest Python 3 BlurHash implementationpowered bynumbaWhat is BlurHash?BlurHash is a compact representation of a placeholder for an image.BlurHash encoder consumes an image, and provides a short string (only 20-30 characters!) that represents the placeholder for the image. You perform this on the backend of your service, and store the string along with the image. When you send data to any client, you send both the URL to the image, and the BlurHash string. Your client then takes the string, and decodes it into an image that it shows while the real image is loading over the network. The string is short enough that it comfortably fits into whatever data format you use. For instance, it can easily be added as a field in a JSON object.In summary:Read more about the algorithmhere.InstallationYou can installblurhash-numbausing pip3$ pip3 install blurhash-numbaYou can also optionally installPillow(PIL) along withblurhash-numbain case it is not already installed$ pip3 install blurhash-numba[pillow]UsageEncodingAsblurhash_numba.encodeaccepts the image in the form of anumpyarray. You can convert an image file using thePillowpython library.fromblurhash_numbaimportencodefromPILimportImageimportnumpyasnpimage=Image.open("256.jpg")imageimage_array=np.array(image.convert("RGB"),dtype=np.float)blurhash_code=encode(image_array,x_components=4,y_components=3)blurhash_code'LtL#LZR*x]jG.TRkoeayIUofM{R*'y_componentsandx_componentsparameters adjust the amount of
vertical and horizontal AC components in hashed image. Both parameters must
be>= 1and<= 9.Decodingfromblurhash_numbaimportdecodefromPILimportImageimportnumpyasnpblur_img=Image.fromarray(np.array(decode(blur_hash,256,256)).astype('uint8'))blur_imgTestsRun test suite withpytestin virtual environment$ pytestFAQsWhy should I use blurhash-numba?This is the fastest implementation of the BlurHash algorithm (both encoding & decoding) in Python currently as it usesnumbato directly convert the Python+NumPy code into fast machine code. It is 30-70x faster thanhalcy/blurhash-pythonand 2-4x faster thanwoltapp/blurhash.How do I pick the number of X and Y components?It depends a bit on taste. The more components you pick, the more information is retained in the placeholder, but the longer the BlurHash string will be. Also, it doesn't always look good with too many components. We usually go with 4 by 3, which seems to strike a nice balance.However, you should adjust the number of components depending on the aspect ratio of your images. For instance, very wide images should have more X components and fewer Y components.What is thepunchparameter in this implementations?It is a parameter that adjusts the contrast on the decoded image. 1 means normal, smaller values will make the effect more subtle, and larger values will make it stronger. This is basically a design parameter, which lets you adjust the look.Technically, what it does is scale the AC components up or down.CreditsThis project is based on the pure python BlurHash implementation byhalcy/blurhash-python.Also credit goes to the original implementation bywoltapp/blurhash.
Package: blurhash-python
blurhash-pythonThis is an encoder for the BlurHash algorithm. To find out more about BlurHash, seehttps://github.com/woltapp/blurhash.InstallationInstall blurhash with pip$ pip install blurhash-pythonor pipenv$ pipenv install blurhash-pythonUsageCreate blurhash from image fileimportblurhashwithopen('image.jpg','rb')asimage_file:hash=blurhash.encode(image_file,x_components=4,y_components=3)Alternatively, scale the image to produce a faster hash, and create a blurhash from the in-memory image directlyimportblurhashfromPILimportImagewithImage.open('image.jpg')asimage:image.thumbnail((100,100))hash=blurhash.encode(image,x_components=4,y_components=3)You can also pass file name as parameter to the functionimportblurhashhash=blurhash.encode('image.jpg',x_components=4,y_components=3)y_componentsandx_componentsparameters adjust the amount of
vertical and horizontal AC components in hashed image. Both parameters must
be>= 1and<= 9.DevelopmentInstall development requirements and package in editable mode$ pipenv install --devTestsRun test suite withpytestin virtual environment$ pytestUsetoxto run test suite against all supported python versions$ tox
Package: blurit
Blur ItThis package is aimed to build to blur different portions of a image. Currently it blurs eyes in human photograph.InstallpipinstallbluritUsagefrombluritimportblur_eyes_in_images#blur_eyes_in_images('[YOUR-IMAGES-FOLDER-PATH]','[GENERATED-IMAGES-FOLDER PATH]','VALUE-OF-HEIGHT-AND-WIDTH-OF-BLUR')blur_eyes_in_images('./images','./images_generated',15)DemoI have used a selfie of elen fromhereand compiled the code. looks as below.Pretained model used in extracting featuresFacial landmarks are found using apre-trained 68 point predictor:.I have used the help from below posts to build this packageHow to draw a rectangle around a region of interest in pythonHow to use opencv (python) to blur faces?Gaussian blurring with OpenCV: only blurring a subregion of an image?
Package: blurlab
Pyblur (Renamed asblurlab)IMPORTANT: The package now is renamed asblurlab.Fix Python 3 compatibility issues ofpyblur.Fix Skimageconvolve2derror frompyblur3.##Python image blurring routines.
Pyblur is a collection of simple image blurring routines.It supports Gaussian, Disk, Box, and Linear Motion Blur Kernels as well as the Point Spread Functions
used inConvolutional Neural Networks for Direct Text Deblurring.Functions receive a PIL image as input, and return another as output.Kernel sizes can either be specified as input, or randomized.Finally, there's a RandomizedBlur function that applies a random type of blurring kernel with a random width/strength.pypi:https://pypi.python.org/pypi?:action=display&name=pyblur&version=0.2.3InstallationFrom Pip:pip install blurlabOr alternativelygit clonethis repo and run locallyUsagefrom pyblur import *Gaussian BlurBlurs image using a Gaussian Kernelblurred = GaussianBlur(img, bandwidth)Randomized kernel bandwidth (between 0.5 and 3.5)blurred = GaussianBlur_random(img)Defocus (Disk) BlurBlurs image using a Disk Kernelblurred = DefocusBlur(img, kernelsize)Randomized kernel size (between 3 and 9)blurred = DefocusBlur_random(img)Box BlurBlurs image using a Box Kernelblurred = BoxBlur(img, kernelsize)Randomized kernel size (between 3 and 9)blurred = BoxBlur_random(img)Linear Motion BlurBlurs image using a Line Kernelblurred = LinearMotionBlur(img, dim, angle, linetype)ParametersdimKernel Size: {3,5,7,9}angleAngle of the line of motion. Will be floored to the closest one available for the given kernel size.linetype = {left, right, full}Controls whether the blur kernel will be applied in full or only the left/right halves of it.Randomized kernel size, angle, and line typeblurred = LinearMotionBlur_random(img)PSF BlurBlurs image using one of the Point Spread Functions (Kernels) used in:Convolutional Neural Networks for Direct Text Deblurringblurred = PsfBlur(img, psfid)ParameterspsfidId of the Point Spread Function to apply [0, 99]Randomized kernel size, angle, and line typeblurred = PsfBlur_random(img)Random BlurRandomly applies one of the supported blur types, with a randomized bandwidth/strenght.blurred = RandomizedBlur(img)
Package: blurple
blurple.pyA front-end framework for discord.pyBlurple.py is a framework built on top of discord.py, giving you the tools you need to build discord bots with convenience.:warning: hey! thanks for checking this library out. although this library is not actively being maintained, it is stable and you probably won't encounter any issues if you use it! if you do happen to run into any issues, feel free to make an issue and preferrably PR and i'll get to it as soon as i can :)Here's what's included:blurple.uiAll the styled components you'll ever need for building user interfaces in discord.Styled alerts, toasts that automatically dismiss, and more.blurple.ioRobust functions that enable you to build stable, multi-step commands with ease.Support for multi-event replies, and timeout handling by default.blurple.extUtilities and sane defaults for discord.ext commands.Drop-in replacements, addons, and other small gadgets.usageDocumentation can be foundhere.installationMake sure your python version is up-to-date.Install the package withpip install blurple.py.example withblurple.uiimport discordfrom discord.ext import commands+ from blurple import uibot = commands.Bot(command_prefix='!')@bot.command()async def ping(ctx):-     await ctx.send('Pong!')+     await ctx.send(embed=ui.Alert(ui.Style.SUCCESS,+         title="Pong!",+         description=f"Latency: `{round(bot.latency*1000)}ms`")+     )bot.run('token')running locallyClone the repositorygit clone https://github.com/cerusabi/blurple.py.gitCreate a.envin the repository root with your token:TOKEN=kpXVCJ9.pLY.Q6m9FThe repo contains atest.pyfile used for testing, simply run the filepython3 test.py@cerusabi•cerusabi.github.io
Package: blurple.py
blurple.pyA front-end framework for discord.pyBlurple.py is a framework built on top of discord.py, giving you the tools you need to build discord bots with convenience.:warning: hey! thanks for checking this library out. although this library is not actively being maintained, it is stable and you probably won't encounter any issues if you use it! if you do happen to run into any issues, feel free to make an issue and preferrably PR and i'll get to it as soon as i can :)Here's what's included:blurple.uiAll the styled components you'll ever need for building user interfaces in discord.Styled alerts, toasts that automatically dismiss, and more.blurple.ioRobust functions that enable you to build stable, multi-step commands with ease.Support for multi-event replies, and timeout handling by default.blurple.extUtilities and sane defaults for discord.ext commands.Drop-in replacements, addons, and other small gadgets.usageDocumentation can be foundhere.installationMake sure your python version is up-to-date.Install the package withpip install blurple.py.example withblurple.uiimport discordfrom discord.ext import commands+ from blurple import uibot = commands.Bot(command_prefix='!')@bot.command()async def ping(ctx):-     await ctx.send('Pong!')+     await ctx.send(embed=ui.Alert(ui.Style.SUCCESS,+         title="Pong!",+         description=f"Latency: `{round(bot.latency*1000)}ms`")+     )bot.run('token')running locallyClone the repositorygit clone https://github.com/cerusabi/blurple.py.gitCreate a.envin the repository root with your token:TOKEN=kpXVCJ9.pLY.Q6m9FThe repo contains atest.pyfile used for testing, simply run the filepython3 test.py@cerusabi•cerusabi.github.io
Package: blurr
Table of contentsWhat is BlurrIs Blurr for you?PlaygroundTutorial & DocsContributeData Science 'Joel Test'RoadmapWhat is Blurr?Blurr transforms structured, streamingraw dataintofeaturesfor model training and prediction using ahigh-level expressive YAML-based languagecalled the Blurr Transform Spec (BTS). The BTS merges the schema and computation model for data processing.The BTS is adata transform definitionfor structured data. The BTS encapsulates thebusiness logicof data transforms and Blurr orchestrates theexecutionof data transforms. Blurr is runner-agnostic, so BTSs can be run by event processors such as Spark, Spark Streaming or Flink.Is Blurr for you?Yes, if: you are well on your way on the ML 'curve of enlightenment', and are thinking about how to do online scoringPlaygroundLaunch playgroundTutorial and DocsComing up with features is difficult, time-consuming, requires expert knowledge. 'Applied machine learning' is basically feature engineering --- Andrew NgRead the docsStreaming BTS Tutorial|Window BTS TutorialPreparing data for specific use cases using Blurr:Dynamic in-game offers (Offer AI)Frequently Bought TogetherContribute to BlurrWelcome to the Blurr community! We are so glad that you share our passion for building MLOps!Please create anew issueto begin a discussion. Alternatively, feel free to pick up an existing issue!Please sign theContributor License Agreementbefore raising a pull request.Data Science 'Joel Test'Inspired by the (old school)Joel Testto rate software teams, here's our version for data science teams. What's your score?Data pipelines are versioned and reproduciblePipelines (re)build in one stepDeploying to production needs minimal engineering helpSuccessful ML is a long game. You play it like it isKaizen. Experimentation and iterations are a way of lifeRoadmapBlurr is currently in Developer Preview.Stay in touch!: Star this project or emailhello@blurr.aiLocal transformations onlySupport for custom functions and other python libraries in the BTSSpark runnerS3 support for data sinkDynamoDB as an Intermediate StoreFeatures server