Saravanan666/pypi-org huggingface dataset:
Package: imgopt
ImgOpt - Compress & Optimize ImagesImgOpt is a simple and very easy to use package to Compress and Optimize images.Features:Easy to UseStandalone PackageImage Compression OptionsNo Need to use External APIsNo Need for Any API KeyInstallationPip Commandpip install imgoptHow to use?Import imgopt Packageimport imgoptInBuilt Functions:OptmizeImage Fuction:This function will compress and optimize the input image and return the output as an image. You can select your desired filename in the second parameter.imgopt.OptmizeImage(image_path,output_path_with_extension)imgopt.OptmizeImage('pro.jpg','vinay.jpg')If you don't pass any output filename, it will automatically generate a random filename with an extension.imgopt.OptmizeImage('pro.jpg')DynamicCompress Fuction:This function will compress and optimize the input image. It requires one more parameter, representing the percentage of compression. The function will return the output as an image. You can select your desired filename in the second parameter. All three parameters are mandatory when using this function.imgopt.OptmizeImage(image_path,output_path_with_extension,compress_percent)The value of Compress Percent must be between 10 and 100 (Type: int). A lower percentage will reduce the file size more.imgopt.OptmizeImage('pro.jpg','vinay.jpg',75)Note:Default Compress Percent is set to 80Code Examplesimport imgopt

imgopt.OptmizeImage('pro.jpg')

imgopt.DynamicCompress('pro.jpg', 'hello.jpg',20)Support This ProjectAuthorThe ImgOpt Package is created byVinay Chaudhary. This Project is Powered byRegem Enterprises
Package: img-optimize
A python library and GUI application to optimize image files.License:MIT.Installationpip install img-optimizeRunning the programimport imgoptimize
imgoptimize.run()The application below will appear.Operating the applicationThere are several notable features - view figure below for visual aid:Sourcing your images.If you click “Source”, you are given the option to either upload one or more image files (JPEG or PNG) or selecting a folder containing your images.If you select an image folder, only JPEG and PNG images will be uploaded.Working with images.Valid images will populate the viewport. You are able to remove images from this viewport by selecting an image (or more) and clicking “Remove”Setting image quality percentage.You can set the image quality percentage by either moving the slider under the “Image quality:” label, or manually entering the image quality value between 1 and 100.Setting destination path.Click “Destination” and select a folder to store your images.Downloading your images.Click “Download” when you are ready to download your images.Exiting the application.Simply click “Cancel” to exit the application.SupportIf you find any bug or would like to propose a new feature, please use theissues tracker. I’ll be happy to help!
Package: imgpd
Image Palette DetectorThis is a simple python application that detects dominant color of your source image and create an output palette combined with the source image.Guide:InterfaceInstallationUsageExampleInterfaceApplication has 3 types of the user-interface:the comand-line with arguments:the dialog with no-GUI:the dialog with GUI:InstallationJustdownloador clone the repogit clone https://github.com/Gourieff/img-palette-detectorand install requirements:pip install -r requirements.txtorpip install opencv-python; colorthief; argparse; pysimpleguiYou can also install it as a package:pip install imgpdUsage:python ipd.py -i "C:\my super images\image_file_name.png" -m "3" -c "5"If you installed as a package:for Windows userspy -m imgpd -i "C:\my super images\image_file_name.png" -m "3" -c "5"for Linux/MacOS users:imgpd -i /home/user/images/image_file_name.png -m 3 -c 5Where optional arguments are:-i PATH_TO_IMAGE  Path to the image file
  -m METHOD         Method to build the Palette: 1 - OpenCV based method, 2 - ColorThief based method, 3 - Both
  -c COLORS_COUNT   How much colors on the Palette must be: 2 - is minimum, don't type too much, 3-7 is optimal
  -d                To run Application in the dialog mode w/o GUIor just type for help:python ipd.py -horimgpd -hYou can also run the App in the dialog modewith GUI:python ipd.pyorimgpdwithout GUI:python ipd.py -dorimgpd -dThen you see the dialog:> How to build Palette? (Choose 1, 2 or 3)
> 1 - OpenCV based method
> 2 - ColorThief based method
> 3 - BothType 1, 2 or 3 and proceed to the next step:> Type the filename or path (ex.: image.png):Type a full or a relative path to your image or just an image name if your image is at the same folder:C:\my super images\image_file_name.pngor/home/user/images/image_file_name.pngorimage_file_name.pngProceed to the next step:> How much colors on the Palette must be? (2 - is minimum, don't type too much, 3-7 is optimal)And type how much color must be in the Palette, for example:5Then wait a little for the result.Example:$ imgpd -d
> How to build Palette? (Choose 1, 2 or 3)
> 1 - OpenCV based method
> 2 - ColorThief based method
> 3 - Both
>> 3
> Type the filename or path (ex.: image.png):
>> sample/image.png
> How much colors on the Palette must be? (2 - is minimum, don't type too much, 3-7 is optimal)
>> 5Source Image:Output Result:Method 1:Method 2:For most images Method 1 is more accurate, but for some images (as shown above) Method 2 will give you a better result, so I recommend to use 'Both' and then choose which is better.
Package: img-percent-diff
[![Build Status](https://travis-ci.org/noaa-nws-cpc/img-percent-diff.svg?branch=master)](https://travis-ci.org/noaa-nws-cpc/img-percent-diff) [![Coverage Status](https://coveralls.io/repos/github/noaa-nws-cpc/img-percent-diff/badge.svg?branch=master)](https://coveralls.io/github/noaa-nws-cpc/img-percent-diff?branch=master)Calculates the percent area that two images differ. This is useful for unit tests when you have to compare an image created by a unit test with a baseline image in the repo.Free software: Creative Commons licenseInstallationCreate your virtual environment$ virtualenv <env-name>Then activate your virtual environment and installimg-percent-diff$ source <env-name>/bin/activate
$ pip install img-percent-diffCreditsThis package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the
[audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template.# Change LogAll notable changes to this project will be documented in this file.
This project adheres to [Semantic Versioning](http://semver.org/).## [0.1.0] - 2016-07-28### AddedSomethingSomething else### ChangedSomethingSomething else### FixedSomethingSomething else
Package: img-prc
img_prcDescription.
The package img_prc is used to:
prc:
- Histogram macthing
- Structural similarity
- Resize Imame
ut:
- Read image
- Save image
- Plot image
- Plot result
- Plot histogramInstallationUse the package managerpipto install img_prcpipinstallimg_prcAuthorAdieverson SilvaLicenseMIT
Package: img-prediction
要求python >= 3.6安装pip3installimg-prediction使用$predict-h:(13023-05-17-10:05:44
usage:prediction.py[-h]-wWEIGHT_FILE_PATH-iINPUT[-oOUTPUT_DIR][-sSCORE_THRESHOLD][-v][-lLOG_FILE]ImageSegmentationAndMosaic

options:-h,--helpshowthishelpmessageandexit-wWEIGHT_FILE_PATH,--weight-file-pathWEIGHT_FILE_PATHTheweightfilepath-iINPUT,--inputINPUTInputimagefile-oOUTPUT_DIR,--output-dirOUTPUT_DIRThedirofprocessedimages-sSCORE_THRESHOLD,--score-thresholdSCORE_THRESHOLDThefractionalthresholdoftheidentificationarea-v,--verboseOutputdetailedlog-lLOG_FILE,--log-fileLOG_FILEThepathoflogfile,defaultis./prediction.log-w指定权重文件-i输入的图片文件-o输出路径-s预测分数阈值(0-1之间, 默认0.5，值越大越精确)-v输出日志到文件-l日志文件路径
Package: imgpro
# imgproDescriptionThe imgpro package has the following tools:Processing:
- Histogram matching
- Structural similarity
- Resizing imagesUtils:
- Reading images
- Saving images
- Plotting images
- Plotting similarity tests
- Plotting rgb histograms## InstallationUse the package manager [pip](https://pip.pypa.io/en/stable/) to install imgpropip install imgpro## Usagefrom image_processing.processing import combination.py
combination.find_difference()## Author
Marianne Costa Avalone## License
[MIT](https://chosealicense.com/licenses/mit/)
Package: img-processing
img_processingDescription.
The package img_processing is used to:
- Processing:
- Histogram matching
- Structural similarity
- Resize image
- Utils:
- Read Image
- Save Image
- Plot Image
- Plot Result
- Plot Histogram
-InstallationUse the package managerpipto install img_processingpipinstallimg_processingAuthorFelipe F. PortoLicenseMIT
Package: img-processing-dio
package_nameDescription.
The package package_name is used to:
Processing:
- Histogram matching
- Structural similarity
- Resize image
Utils:
- Read image
- Save image
- Plot image
- Plot result
- Plot histogramInstallationUse the package managerpipto install package_namepipinstallimg_processing_dioAuthorThiagoLicenseMITProjeto desenvolvido no lab "Descomplicando a criaÃ§Ã£o de pacotes de processamento de imagens em Python" da Digital Innovation One.
Package: img-processing-hg
img_processing_hgDescription.Este projeto Ã© um fork do projeto desenvolvido no Bootcamp Cognizant Cloud Data Engineer 2, modulo apresentado pela desenvolvedora karina Kato. 

Este package consiste na reproduÃ§Ã£o de um pacote de processamento de imagens em python e sua publicaÃ§Ã£o utilizando o Pypi.The package img_processing_hg is used to:- Histogram matching
- Structural similarity
- Resize image
- Read image
- Save image
- Plot image
- Plot result
- Plot histogramInstallationUse the package managerpipto install package_namepipinstallimg_processing_hgUsagefromimg_processing_hgimportcombinationcombination()AuthorHuguemberg M.
Projeto by Karina KatoLicenseMIT
Package: img-processing-pcg
Projeto de aprendizado de criaÃ§Ã£o de pacotes de processamento de imagens em PythonAutora do Projeto: Karina KatoTutorial de instalaÃ§Ã£o inspirado no README do projetohttps://github.com/HenriqueMAP/image-processing-packageAula: Descomplicando a criaÃ§Ã£o de pacotes de processamento de imagens em Python - Digital Innovation OneBootcamp: GeraÃ§Ã£o Tech Unimed-BH - CiÃªncia de Dados(clique aqui para ver o meu perfil na plataforma)Tecnologia: PythonData: 02/11/2022DescriÃ§Ã£oO pacote "image_processing-test" Ã© usado para:MÃ³dulo "Processing":CorrespondÃªncia de histograma;Similaridade estrutural;Redimensionar imagem;MÃ³dulo "Utils":Ler imagem;Salvar imagem;Plotar imagem;Resultado do grÃ¡fico;Plotar histograma;Passo a passo da configuraÃ§Ã£o para hospedar um pacote em Python no ambiente de testes Test PypiInstalaÃ§Ã£o das Ãºltimas versÃµes de "setuptools" e "wheel"py -m pip install --user --upgrade setuptools wheelTenha certeza que o diretÃ³rio no terminal seja o mesmo do arquivo "setup.py"C:\User\Isabella\image-processing-package> py setup.py sdist bdist_wheelApÃ³s completar a instalaÃ§Ã£o, verifique se as pastas abaixo foram adicionadas ao projeto:build;dist;image_processing_test.egg-info.Basta subir os arquivos, usando o Twine, para o Test Pypi:py -m twine upload --repository testpypi dist/*ApÃ³s rodar o comando acima no terminal, serÃ¡ pedido para inserir o usuÃ¡rio e senha. Feito isso, o projeto estarÃ¡ hospedado no Test Pypi.hospedÃ¡-lo no Pypi diretamente.Nesse projeto, meu objetivo nÃ£o Ã© usar o projeto de Karina Kato para postar em meu perfil do Pypi pessoal, pois a autoria do projeto Ã© dela.Ainda nÃ£o tenho projetos que possam ser utilizados como pacote, portanto apenas estou utilizando seu projeto como exemplo para o aprendizado da criaÃ§Ã£o de pacotes.No entanto, tenha em mente que o Test Pypi, como o prÃ³prio nome diz, Ã© apenas um ambiente de testes. Para que o projeto esteja disponÃ­vel como um pacote para ser usado publicamente, Ã© necessÃ¡rio hospedÃ¡-lo no site oficial do Pypi.InstalaÃ§Ã£o local, apÃ³s hospedagem no Test PypiInstalaÃ§Ã£o de dependÃªnciaspip install -r requirements.txtInstalÃ§Ã£o do PacoteUse o gerenciador de pacotespip install -i https://test.pypi.org/simple/ image-processing-testpara instalar image_processing-testpipinstallimage-processing-testComo usar em qualquer projetofromimage-processing-test.processingimportcombinationcombination.find_difference(image1,image2)Autor (quem hospedou o projeto no Test Pypi)Isabella FirminoLicenÃ§aMIT
Package: img-processing-project-dio
img_processing_project_dioDescription:This project is part of Cloud Data Engineer bootcamp from DIO - Digital Innovation One.The packageimg_processing_project_diois used to:Processing:
- Histrogam matching
- Structural similarity
- Resize image

Utils:
- Read image
- Save image
- Plot result
- Plot histogramInstallationUse the package managerpipto installimg_processing_project_diopipinstallimg-processing-project-dioUsagefromimage_processing_desafio_dio.processingimportcombination,transformationfromimage_processing_desafio_dio.utilsimportio,plotAuthorThielson AlmendraLicenseMIT
Package: img-processing-tm
img-processing-tmDescription.
The package image_processing is used to:
Processing
- Histogram matching
- Structural similarity
- Resize image
Utils:
- Read Image
- Save Image
- Plot image
- Plot Result
- Plot histogramInstallationUse the package managerpipto install package_namepipinstallimg-processing-tmUsageAuthorThais_MenesesLicenseMIT
Package: imgProcessor
Based onscikit-image,`numba <http://numba.pydata.org>`_ andOpenCV2.4Browse theAPI DocumentationFork the code ongithubInstallationimgProcessoris listed in the Python Package Index. You can install it typing:pip install imgProcessorTestsfancyWidgetsuses mostly the ‘one class/function per file’ rule. Running each module as program, like:python -m imgProcessor.camera.PerspectiveCorrectionwill execute the test procedure of this module.To run all tests type:python -m imgProcessor.testsv0.1, <24/04/2016> – Initial release.Karl Bedrich 2014 -
Package: img-processor
img_processorPython package for taking an image and doing a thingFree software: MIT licenseDocumentation:https://img-processor.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-06-13)First release on PyPI.0.5.0 (2020-09-22)Adds autorotation code0.6.0 (2020-09-28)Adds additional validation and error handling0.7.0 (2020-09-29)Fixes missing requirements in setup.py0.8.0 (2021-01-29)Adds tiff conversion function0.9.0 (2021-02-08)Adds movie creation function to make time lapse movies from image files0.10.0 (2021-02-09)Adds compression to make_movie function0.11.0 (2021-02-10)Cleans up requirements, removing libtiff and pinning versions
Package: img-proof
overviewimg-proof(IPA) provides a command line utility to test
images in the Public Cloud (AWS, Azure, GCE, etc.).Withimg-proofyou can now test custom images in a cloud framework agnostic way
with one tool and one API. In the first release,img-proofsupports the
openSUSE and SLES distributions. It also supports the three largest
cloud frameworks (AWS, Azure and GCE). However, it is intended to be
distribution agnostic and framework transparent so both are easily
extensible.For each distribution there are specific synchronization points that
must be provided. These currently include soft reboot and system update.
The synch points not only test functionality but also act as dividers to
separate distinct sections of a test suite. For example you can run a
test to ensure the proper repos exist before and after a system update.
The system update synch point will guarantee the order of tests.
Speaking of tests, if you're already familiar with Pytest conventions
there's no need to learn a whole new unit testing framework.img-proofis
written in Python and leverages the Pytest framework through Testinfra.InstallationTo install the package use the following commands as root:$zypperarhttp://download.opensuse.org/repositories/Cloud:/Tools/<distribution>
$zypperrefresh
$zypperinpython3-img-proofRequirementsboto3apache-libcloudazure-commonazure-mgmt-computeazure-mgmt-networkazure-mgmt-resourceClickparamikopytestPyYamltestinfraociDocsTestsimg-proofuses the Testinfra package for writing unit tests. Testinfra
leverages Pytest and provides modules such as Package, Process and
Service to test the state of images. See theTestinfra
Docsfor more information
on writing infrastructure tests.img-proofcurrently passes the Pytest option-x(stop on first
failure) through as--early-exit. If there's an interest or need for
any other options/args please submit an issue toGithub.CLI OverviewThe CLI provides multiple subcommands to initiate image testing:img-proof testTest image in the given framework using the supplied test files.img-proof resultsInvokes the default show subcommandimg-proof results show 1.img-proof results clearClear the results from the history file.img-proof results deleteDelete the specified history item from the history log.img-proof results list`Display list of results history.img-proof results showDisplay the results or log file for a history item.img-proof listPrint a list of test files or test cases.Issues/EnhancementsPlease submit issues and requests toGithub.ContributingContributions toipaare welcome and encouraged. SeeCONTRIBUTINGfor info on getting started.LicenseCopyright (c) 2018 SUSE LLC.Distributed under the terms of GPL-3.0+ license, seeLICENSEfor details.
Package: imgproxy-python
imgproxy_pythonA tiny library to simplify generation of imgproxy urlsinstallationpip install imgproxy_pythonusageFor example you want to simply resize your image to other height and width using imgproxy. To get proper imgproxy url you need the followingfrom imgproxy_python.urls import UrlGenerator
config = {
        'imgproxy_key': '943b421c9eb07c830af81030552c86009268de4e532ba2ee2eab8247c6da0881',
        'imgproxy_salt': '520f986b998545b4785e0defbc4f3c1203f22de2374a3d53cb7a7fe9fea309c5',
        'imgproxy_host': 'http://localhost:8000',
        'options': {
            'resize': 'fill',
            'width': 300,
            'height': 300,
        }
    }
    url = 'http://img.example.com/pretty/image.jpg'
    urlgen = UrlGenerator(url, config)
    print(urlgen.get_full_signed_url())You will get a ready-to-go imgproxy linkhttp://localhost:8000/6cy4IRuQ1nQW8qGVmNvPdynjmkuwUEx-XhGw_C_kQmA/fill/300/300/ce/0/aHR0cDovL2ltZy5l/eGFtcGxlLmNvbS9w/cmV0dHkvaW1hZ2Uu/anBnwarningIf you want to have generated link working you need to have imgproxy instance up and running onconfig['imgproxy_host'], in our example it ishttp://localhost:8000diclaimerfor now only basic urls are supported in version 0.0.1. Advanced url generation is coming
Package: imgpy
Image processing library for Python with animated GIFs support - proxy wrapper aroundPillowlibrary with simple usable interface and access to each frame.FeaturesAttributes:infoexif - dict with EXIF tags and GPS dict with GPS tags if presentedformatsizewidthheightmodemode_desc - image mode descriptionframe_count - frame countanimated - flag, which shows if image is animatedframes - frame listMethods:convertcropfilterpasteresizerotatethumbnailtransformtransposeload - gets called when you accessframesattribute for the first time or call any processing or save method, call this method explicitly to load all or limited number of frames (first n frames or random n frames without reordering)save - save imageclose - close imageRequirementsPython 3.5+Pillow 4.1.1+Setuppython-3.6-mvenv.envsource.env/bin/activatepipinstallimgpyUsagefromimgpyimportImg# Crop imagewithImg(fp='test.gif')asim:im.crop(box=(10,10,110,110))im.save(fp='crop.gif')# Create thumbnail imagewithImg(fp='test.gif')asim:im.thumbnail(size=(100,100))im.save(fp='thumb.gif')# Save 10 random GIF frameswithImg(fp='test.gif')asim:im.load(limit=10,first=False)im.save(fp='random.gif')TestsRun tests: py.test
Package: imgraph
IMGRAPHUsed for converting image to graph, uses superpixel method for node creation, extract features from CNN models.Example Usage:from imgraph.pipeline import create_graph_pipleline

    path = "path/to/image"

    create_graph_pipleline(path, 'classification', 'rag', 'resnet18', 10)Above code will create a graph from the image and save it in the directory .~/cache/imgraph or directory specified by the user in enviornment variable IMGRAPH_HOME.Expected input folder structure:image_folder
    ├── test
    │   ├── class1
    │   └── class2
    ├── train
    │   ├── class1
    │   └── class2
    └── val
        ├── class1
        └── class2The graph will be saved in the PyG Data format or pickle format.To install pytorch geometric dependencies, please follow the instructions here:PyG installationor use the following code snippet:To install full dependeciens install using setup.py with full-dependencies flag (its slow, but will install all dependencies)import torch

    def format_pytorch_version(version):
    return version.split('+')[0]

    TORCH_version = torch.__version__
    TORCH = format_pytorch_version(TORCH_version)

    def format_cuda_version(version):
    return 'cu' + version.replace('.', '')

    CUDA_version = torch.version.cuda
    CUDA = format_cuda_version(CUDA_version)

    !pip install torch-scatter     -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html
    !pip install torch-sparse      -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html
    !pip install torch-cluster     -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html
    !pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html
    !pip install torch-geometric
Package: img-renamer
Image renamerProductionMasterDescriptionimg_renameris a Python script to rename images in numberic order.UsageSeewikifor full usageBasic usageThis will rename all images in~/Picturesfolder like this:0000001.png,0000002.png,0000003.png...img_renamer ~/PicturesOutput:image.png -> 0000001.png
picture.jpg -> 0000002.jpg
img.jpeg -> 0000003.jpg
____________________
Renaming 3 of 3
Are you sure you want to rename (y/n): y
Do you want to save a log file(y/n): nPrint help textSimply run thisimg_renamer --helpAuthorMiika LauniainenDonatingIf you want to support me, feel free to use paypalLicenseThis library is licensed under GPLv3
Package: imgrender
Imgrender: Python Terminal Image ViewerTerminal image viewer in python, inspired byhttps://github.com/stefanhaustein/TerminalImageViewer. A piece of software designed to render
images on the linux/bash command line. It can either be used as a CLI or as a python library. It should work on most modern terminals, including mobaxterm for windows and jupyter notebooks.Installationto Install imgrender, runpip install imgrenderUsageCommand Lineusage: imgrender [-h] [--width WIDTH] [--height HEIGHT] path

Render images on the command line

positional arguments:
  path             the image path

optional arguments:
  -h, --help       show this help message and exit
  --width WIDTH    width of the rendered image (default 60 pixels)
  --height HEIGHT  height of the rendered image (default 60 pixels)Python LibrarySimply import the render function from the imgrender library. The function has two arguments, the image path (positional), and the scale parameteter (keyword), which takes a tuple with two integer (height, width)for instance:In [1]: from imgrender import render

In [2]: render('frog.jpg', scale=(40, 60))
(this will render the image stored in 'frog.jpg' with 40x60 pixels)The render function can be called without passing in a scale argument, this will use the default scale parameters of (60, 60)Resultsbelow is an image of the city of Portsmouth generated as a 60x60 visualization.And here is the original imageFinally, here is the same imaged visualised as 250x250 pixels