Saravanan666/pypi-org huggingface dataset:
Package: image-data-split
DescripstatsThe'image-data-split'package provides a convenient tool for splitting image data into separate training, validation, and testing sets. With this package, you can easily manage the distribution of your image dataset for machine learning tasks. The package offers a simple function,'image_split', that takes input and output directories, along with desired split ratios and an optional seed for randomization. The package is designed to streamline the process of data preparation, making it easier to organize and manage image datasets for model training and evaluation.Developed by Shouke Wei, Ph.D. from Deepsim Academy, Deepsim Intelligence Technology Inc. (c) 2023Install the packagepipinstallimage-data-splitimport the packagefromimage_splitimporttrain_val_test_splitthen use thetrain_val_test_split()directly. Orimportimage_splitasspthen usesp.train_val_test_split()DocumentAn example:https://github.com/shoukewei/image_split/blob/main/docs/example.ipynbPyPI:https://pypi.org/project/image-data-split
Package: imagedb
ImageDBStore images, especially from Clipboard, in a database, and spin an image server (for usage in Jupyter Notebook).Installation$ pip install imagedbOrClone the project from GitHubpoetry add imagedb --path path/to/imagedb/folderUsageRun an image serverIn a Python script (outside Jupyter Notebook).fromimagedbimportImageDBImageDB('images.db').runserver()# Then, go to `http://localhost:8000` in your browser to register an image (from the clipboard).Get images from the image serverIn Jupyter Notebook>>> from imagedb import ImageDB
>>> idb = ImageDB('images.db')
>>> idb.last()
# The latest image in the server will be shown.  `idb.last(5)` is also supported.
>>> from IPython.display import display
>>> for image in idb.search(tags='bar'):
...     display(image)
# All images corresponding to the tag 'bar' will be shown.Screenshots
Package: imagededup
imagededup is a python package that provides functionality to find duplicates in a collection of images using a variety
of algorithms. Additionally, an evaluation and experimentation framework, is also provided. Following details the
functionality provided by the package:Finding duplicates in a directory using one of the following algorithms:Convolutional Neural NetworkPerceptual hashingDifference hashingWavelet hashingAverage hashingGeneration of features for images using one of the above stated algorithms.Framework to evaluate effectiveness of deduplication given a ground truth mapping.Plotting duplicates found for a given image file.Read the documentation at:https://idealo.github.io/imagededup/imagededup is compatible with Python 3.8+ and runs on Linux, MacOS X and Windows.
It is distributed under the Apache 2.0 license.
Package: image-deduplication
Image DeduplicationEasily find images that have a high degree of similarity using either:one line of codecli toolInstallationpip install image-deduplicationUsageFind similar images in a folder treefromimage_deduplicationimportget_image_paths,cluster_imagesimage_paths=get_image_paths("path/to/images")# Returns list of image pathsimage_clusters=cluster_images(image_paths)# Print clustersfori,clusterinenumerate(image_clusters):print(f"""Cluster{i}:{cluster}\n""")Find similar images from a list of filesfromimage_deduplicationimportcluster_imagesimage_paths=["image1.png","image2.jpg","image3.jpeg"]image_clusters=cluster_images(image_paths)# Print clustersfori,clusterinenumerate(image_clusters):print(f"""Cluster{i}:{cluster}\n""")CLI toolimage-deduplicationpath/to/imagesIf you want to analyse the current working directory, you can simply use "." as the path.MethodologyHere is an overview of how this package clusters images by similarity using computer vision techniques and a union-find algorithm to group similar images together:Feature Extraction with ORB: We utilized the ORB (Oriented FAST and Rotated BRIEF) algorithm for extracting keypoints and descriptors from images. ORB is a fast, rotation-invariant, and robust feature extractor that identifies unique points in images, facilitating the comparison of different images based on their content.Image Matching: To determine the similarity between pairs of images, we employed a brute force matcher with the Hamming distance as a metric, optimized to find the best matches for the ORB descriptors. A ratio test filters out less reliable matches, ensuring that only the most similar keypoints contribute to the similarity score.Clustering with Union-Find: An implementation of the union-find algorithm is used to dynamically cluster images based on their similarity scores. This method efficiently merges images into groups as it iterates through all pairs, identifying connected components within the dataset. The union-find structure is key for minimizing redundant comparisons and accelerating the clustering process.Homography and RANSAC: For images with a sufficient number of good matches, we calculate a homography matrix using RANSAC (Random Sample Consensus). This step further refines the matching process by considering only geometrically consistent matches, enhancing the accuracy of similarity scores.Scalable Image Clustering: The end-to-end process, from feature extraction to dynamic clustering, is designed to handle a large number of images. By efficiently comparing images and grouping them based on visual similarity, this package can organize vast datasets into manageable clusters.This technology approach combines classic computer vision techniques with modern algorithmic strategies, offering a robust solution for organizing and analyzing large collections of images based on their visual content.
Package: image-dehazer
Single-Image-Dehazing-Pythonpython implementation of the paper: "Efficient Image Dehazing with Boundary Constraint and Contextual Regularization"QuickstartThis library performs image dehazing.InstallationTo install, run:$ pip install image_dehazerUsage:importimage_dehazer# Load the libraryHazeImg=cv2.imread('image_path')# read input image -- **must be a color image**HazeCorrectedImg,HazeMap=image_dehazer.remove_haze(HazeImg)# Remove Hazecv2.imshow('input image',HazeImg);# display the original hazy imagecv2.imshow('enhanced_image',HazeCorrectedImg);# display the resultcv2.imshow('HazeMap',HazeMap);# display the HazeMapcv2.waitKey(0)# hold the display window### user controllable parameters (with their default values):airlightEstimation_windowSze=15boundaryConstraint_windowSze=3C0=20C1=300regularize_lambda=0.1sigma=0.5delta=0.85showHazeTransmissionMap=TrueAs easy as that!
Package: image-depot
介绍在上传图片的时候, 经常会用到图床功能.此工具聚合了一些常用的图床, 以便于快速使用.演示站点注意: 因免费图床的稳定性无法保证, 因此请勿用其保存重要文件说明因为使用各种免费服务, 稳定性无法保证若各位在使用过程中碰到问题, 欢迎下列任选一种方式补充:提交isuse说明问题将问题写到DepotType的对应文档上如果你有其他优秀的图床推荐, 欢迎提交isuse或PR当前支持的所有图床, 可查看文件DepotType使用安装:pipinstallimage-depot指定单个图床进行上传fromimage_depotimportimage_depot,DepotType# 选择图床对象d=image_depot(DepotType.CatBox)# 上传图片. 二进制内容image_content=''image_url=d.upload(image_content)ifnotimage_url:# 图片上传失败, 获取失败原因print(d.error())# 上传图片, 使用本地文件路径file_path=''image_url=d.upload_file(file_path)多个图床依次尝试上传:fromimage_depotimportupload,upload_file,DepotTypetype_list=[DepotType.SmMs]# 上传图片. 二进制内容. 若所有图床都上传失败, 则返回空字符串以及所有错误信息image_content=''url,err=upload(image_content,type_list=type_list)# 依次尝试所有图床, 返回第一个成功的.file_path=''upload_file(file_path,type_list=type_list)图床配置:fromimage_depotimportimage_depot,DepotType,upload,upload_file,DepotConfig,set_global_config# 部分图床需要添加配置才能使用# 全局配置config=DepotConfig()config.sm_ms.token=''set_global_config(config)# 可以为每次上传使用不同的配置信息# 此配置优先级高于 global_configd=image_depot(DepotType.CatBox)d.set_config(config)upload('',config=config)upload_file('',config=config)
Package: image-diff
image-diffCLI tool for comparing imagesInstallationInstall this tool usingpip:$ pip install image-diffImage diffTo generate an image showing the difference between two images:image-diff first.jpg second.jpg -o diff.pngCount differing pixelsTo count the number of pixels that have changed, useimage-diff count:image-diff first.jpg second.jpgThis will output a number followed by a newline.Compile an imageTheimage-diff compilecommand exists mainly to make this tool easier to test.It can be used to compile an image from a JSON definition - for example:echo '[
    [[0, 0, 0], [0, 0, 0], [0, 0, 0]],
    [[0, 0, 0], [255, 0, 0], [0, 0, 0]],
    [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
]' | image-diff compile - -o one-pixel.pngSimilar projectspixelmatchby Mapbox is a JavaScript library with an accompanying CLI tool.DevelopmentTo contribute to this tool, first checkout the code. Then create a new virtual environment:cd image-diff
python -m venv venv
source venv/bin/activateOr if you are usingpipenv:pipenv shellNow install the dependencies and test dependencies:pip install -e '.[test]'To run the tests:pytest
Package: imagediffer
Image Differ is a tool for image comparison. It loads 2 images (either local files or from the internet) and generates the diff image that shows where the images are different. It also calculates:Percentage of pixels where the images differ (with adjustable threshold for difference)Mean Square Error for the imagesStructural Similarity IndexIt can also calculate comparison stats for individual color channels or convert images to grayscale.Image Differ can be used as a library for other projects or it provides GUI application.InstallationThe easiest way to install imagediffer is using pippython -m pip install imagedifferYou can also clone this repository, install requirements using pip and then install imagediffer
using setup.py.python setup.py installDocumentationAPI DocumentationIf you want to build documentation yourself, you need to havesphinx docinstalled and
then runmake htmlin the docs folder.DevelopmentSimply clone the project and install dependencies fromrequirements.txtfile using pip.python -m pip install -r requirements.txtUsed librariesNumPyPillowPyQt5pyssimpytestrequestsSciPySphinx
Package: imagedit
Projeto: Pacote de EdiÃ§Ã£o de ImagensAutora do Projeto: Alexandre SilvaTeste de criaÃ§Ã£o de pacotes com o PypiDescriÃ§Ã£oO pacote "imagedit" Ã© usado para:MÃ³dulo "Processing":Adicionar ou remover ColoraÃ§Ã£o, Brilho, Contraste;Rotacionar, Inverter ou Juntar Imagens;Adicionar efeitos de borda e Blur(EmbaÃ§ar);MÃ³dulo "Utils":Abrir a imagem;Salvar imagem;Adicionar texto na imagem;Criar thumbnails (miniaturas);InstalaÃ§Ã£o do pacote (hospedado no Pypi)Use o gerenciador de pacotespip install -i https://test.pypi.org/simple/ imageditpara instalar imageditpipinstallimageditComo usar em projetosPara importar todos os mÃ³dulos, utilize:fromimageditimport*
Package: imagedominantcolor
ImageDominantColorGet the dominant color of any imageIntroductionImageDominantColor is a Python package/library fordetecting dominant color of images.It can take any input image and tell the dominant color in image. It does not uses k-means clustering for detecting dominant color but instead quantize the individual pixels and calculates the statistical mode of the quantized values. And yes, it works!ImageDominantColor does not depend on numpy unlike most of the other implementations for the same task and is also fast and minimalist.What ImageDominantColor is not?ImageDominantColor does not calculates the average color of the image. Also note that the average color of an image is not same as its dominant color.InstallationUsingpip:pipinstallimagedominantcolor-UInstall directly from GitHub:pipinstallgit+https://github.com/akamhy/imagedominantcolor.gitUsage>>>fromimagedominantcolorimportDominantColor>>>file_path="blue_butterfly.jpg"# Blue color is dominant>>>dominantcolor=DominantColor(file_path)>>>dominantcolor.dominant_color'b'>>>dominantcolor.rgb(3,6,244)>>>dir(dominantcolor)['__class__','__delattr__','__dict__','__dir__','__doc__','__eq__','__format__','__ge__','__getattribute__','__gt__','__hash__','__init__','__init_subclass__','__le__','__lt__','__module__','__ne__','__new__','__reduce__','__reduce_ex__','__repr__','__setattr__','__sizeof__','__str__','__subclasshook__','__weakref__','b','counter','dominant_color','dominant_color_of_pixel','dominant_color_of_pixels_of_image_array','g','generate_dominant_color_of_pixels_of_image_array','image','image_data','image_path','l','minimum_percent_difference_of_rgb','mpd','r','resize_value','resized_image','rgb','rgbl','set_dominat_color_of_image','set_rgbl_value_of_image','total_pixels']>>>dominantcolor.total_pixels256>>>dominantcolor.r3>>>dominantcolor.g6>>>dominantcolor.b244>>>dominantcolor.l3>>>dominantcolor.rgbl(3,6,244,3)>>>repr(dominantcolor)'DominantColor(r:3 g:6 b:244 l:3; dominant_color:b; resize_value:16; minimum_percent_difference_of_rgb:10)'>>>str(dominantcolor)'b'>>>Output dominant color and what their meanings are:r- Red is the dominant color in the image.g- Green is the dominant color for the image.b- Blue is the dominant color.l- It is lowercase L and it implies that the image is a mostly a grayscale image. L for luminance and most of the image lacks color.n- None of the color out of r, g and b are dominant but the image is not grayscale. It implies that the image has equal regions where 2 or 3 colors dominate,example here. You can check the rgb attribute to decide what to do with such cases.What arer,g,bandlattributes ofDominantColorobjects?The library shrinks the image before checking the dominant color and the default resize value is 256. Thus every image is shrunk to a 256 pixels image.
The r,g,b and l attributes indicate the number of pixels which have r,g,b and l as dominating value.LicenseCopyright (c) 2022 Akash Mahanty.Released under the MIT License. Seelicensefor details.
Package: image-download
image-search📱이 모듈은 쉽게 이미지를 다운로드할수있게 하기위해서 개발된 라이브러리입니다.📥 Download윈도우나 리눅스의 터미널에서 다음과 같이 입력합니다.pipinstallimage-search오류가 나는 경우,python -m pip install --upgrade pip로 pip를 업데이트 해주세요.How to use?importsearchresponse=search.searchresponse().download(str("< 검색하고 싶은 것 >"),int(<maxdownloadlimit>))# 이미지 다운로드print(response().urls("< 검색하고 싶은 것 >",int(<maxdownloadlimit>)))# 이미지 url 검색
Package: imagedownloader
Image DownloaderThis is a Python Package calledimagedownloaderthat defines three methods:fromList(),fromCsv(), andzipFolder(). The class is designed to download images from either a list of URLs or a CSV file containing multiple URLs, and then save them to a specified folder location. Additionally, it provides functionality to zip the folder where the downloaded images are saved.ThefromList()method takes a list of URLs and a folder path as arguments. If the specified folder does not exist, the method creates it. Then, it loops through the list of URLs, sends a request to download the image, and saves the image to the folder using the last part of the URL as the filename. If any errors occur during the process, the method prints an error message.ThefromCsv()method takes a CSV file path and a folder path as arguments. If the specified folder does not exist, the method creates it. Then, it reads the CSV file using thecsv.reader()function and loops through each row. For each row, it extracts the URL, sends a request to download the image, and saves the image to the folder using the last part of the URL as the filename. If any errors occur during the process, the method prints an error message.ThezipFolder()method takes a folder path and an output path as arguments. It creates a new zip file at the output path and loops through each file in the specified folder using theos.walk()function. For each file, it adds the file to the zip file with the same filename. If any errors occur during the process, the method prints an error message.The class also provides two dunder methods,str() andrepr(), which return a string representation of the class instance.InstallationPytubev3 requires an installation of Python 3.7 or greater, as well as pip. (Pip is typically bundled with Pythoninstallations)To install from PyPI with pip:python -m pip install imgdownloaderExamplesCSV File Should have all the urls in the first column as followsfrom imagedownloader import ImageDownloader

if __name__ == "__main__":
    
    image_urls = [
    'https://cdn.pixabay.com/photo/2017/08/30/01/05/milky-way-2695569_960_720.jpg',
    ]
    
    save_path = "./images"
    csv_path = "./test.csv"
    output_path = "./images.zip"
    folder_path = "./images"
    
    imgDownloader = ImageDownloader()
    imgDownloader.fromList(image_urls, save_path)
    imgDownloader.fromCsv(csv_path, save_path)
    imgDownloader.zipFolder(folder_path, output_path)Development/ContributingFork it!Create your feature branch:git checkout -b my-new-featureCommit your changes:git commit -am 'Add Some Feature'Push to the branch:git push origin my-new-featureSubmit a pull request.Email me atmazqoty.01@gmail.combecause I do not check those messages often.History1.0.3
Package: image-downloader-san
Image-downloader-sanInstalationpip install image-downloader-sanorpip install git+https://github.com/TaprisSugarbell/Image-downloader-san.gitDependenciesPython >= 3.8requestsbeautifulsoup4Example of useSite RecognizefromIdimportBruteforceb=Bruteforce()b.site_recognize("https://e-shuushuu.net/image/1056471/")SafeboorufromIdimportBruteforceb=Bruteforce()b.site_recognize("https://safebooru.donmai.us/posts/4510322?q=order%3Arank")Login examplefromIdimportBruteforceb=Bruteforce(username="YOUR-USERNAME",api_key="YOUR-API-KEY")b.safebooru("https://safebooru.donmai.us/posts/4510322?q=order%3Arank")Sankaku ComplexfromIdimportBruteforceb=Bruteforce()b.sankaku("https://chan.sankakucomplex.com/post/show/25048523")Download ImagefromIdimportBruteforceb=Bruteforce()b.yandere("https://yande.re/post/show/782256").download()Force DownloadfromIdimportBruteforceb=Bruteforce()b.download_image("https://pbs.twimg.com/media/E0uKGhTWUAgXb3a?format=jpg&name=large",force=True)InformationfromIdimportBruteforceb=Bruteforce()b.konachan("https://konachan.com/post/show/326520/bikini-breast_hold-close-gray_eyes-gray_hair-infin",info=True)
Package: imagedupes
usage: imagedupes [-h] [-a ALGORITHM] [-d DIRECTORY] [-D DATABASE] [-l][-o OPTIONS] [-p PROGRAM] [-r] [-R] [-s HASH_SIZE]Finds visually similar images and opens them in an image viewer, one group of
matches at a time. If no options are specified, it defaults to searching the
current working directory non-recursively using a perceptual image hash
algorithm with a hash size of 8, opens images in the system default image
handler (all members of a group of matches at once), and does not follow
symbolic links or use a persistent database.optional arguments:-h,--helpshow this help message and exit-aALGORITHM,--algorithmALGORITHMSpecify a hash algorithm to use. Acceptable inputs:
‘dhash’ (horizontal difference hash),
‘dhash_vertical’, ‘ahash’ (average hash), ‘phash’
(perceptual hash), ‘phash_simple’, ‘whash_haar’ (Haar
wavelet hash), ‘whash_db4’ (Daubechles wavelet hash).
Defaults to ‘phash’ if not specified.-dDIRECTORY,--directoryDIRECTORYDirectory to search for images. Defaults to the
current working directory if not specified.-DDATABASE,--databaseDATABASEUse a database to cache hash results and speed up hash
comparisons. Argument should be the path to which you
want to save or read from the database. Warning:
runnning the program multiple times with the same
database but a different hash algorithm (or different
hash size) will lead to missed matches. Defaults to no
database if not specified.-l,--linksFollow symbolic links. Defaults to off if not
specified.-oOPTIONS,--optionsOPTIONSOption parameters to pass to the program opened by the
–program flag. Defaults to no options if not
specified.-pPROGRAM,--programPROGRAMProgram to open the matched images with. Defaults to
your system’s default image handler if not specified.-r,--recursiveSearch through directories recursively. Defaults to
off if not specified.-R,--rawsProcess and hash raw image files. Note: Very slow. You
might want to leave it running overnight for large
image sets. Using the –database option in tandem is
highly recommended. Defaults to off if not specified.-sHASH_SIZE,--hash_sizeHASH_SIZEResolution of the hash; higher is more sensitive to
differences. Some hash algorithms require that it be a
power of 2 (2, 4, 8, 16…) so using a power of two is
recommended. Defaults to 8 if not specified. Values
lower than 8 may not work with some hash algorithms.
Package: imagee
ImageeSimple image optimization toolInstall with pippip install imageeOptimaze image with simple wayfrom imagee import Imagee
image_path = "images/peach.jpg"
output_path = "images/min_peach.png"
im = Imagee()
im.read(image_path)
print(im.format)
print(im.size)
im.optimaze()
print(im.optimized_size)
print(im.optimization_rate)
im.save(output_path)
Package: image-ema-project-test1
Image-EMA IDI ProjectA simple example project for the 2019 High-speed Image Based Experimental Modal Analysis & Open Source Tools Summer School.QuickstartAt the end of the summer school, the image-ema-project will be available on PyPI and can be installed withpip.$pipinstallimage_ema_projectAfter installing image-ema-project you can use it like any other Python module.Here is a simple example:importimage_ema_projectasiepimportnumpyasnpimportmatplotlib.pyplotaspltvideo=np.load('examples/speckle.npy',mmap_mode='r')results=iep.get_displacements(video,point=[5,5],roi_size=[7,7])plt.figure()plt.plot(results[0],label='x [px]')plt.plot(results[1],label='y [px]')plt.legend()plt.show()You can also run this basic example by running the following commain in the project base direcotry:$python-mexamples.basic_exampleTheRead the Docs pageprovides the project documentation.
Package: image-enc-dec
Image_Encryption_DecryptionThis Software or package is used for Encrypting and Decrypting ImagesHow to use this applicationFollow thisstepsto use this applicationMain requiremet is you must havePythonandpipinstallIf you Don't have python Don't worry follow this stepsFor Unix and Linux based Opreting systemsBy deafult unix and linux users have python preinstallJust open Terminal and typepython3Then install pip using apt for Debian Familysudo apt install python3-pipFor RHEL Familysudo yum install python3-pipFor WindowsFor installetion of Python on windowsclick hearNow You Sucessecfully installed Python Don't worry Pip is install automaticaly.Then, we will move to installetion of Image-Encryption-Decryption Application.Use this commond for Unix/Linux distro's (Terminal)pip3installimage-enc-decUse this commond for Windows Commond Prompt (CMD)importImageEncDecNow You able to use this Project.<<<<<<< HEAD=======8f54c370102c29198ba6160f0ecea91f5a3f6b80Snapshots<<<<<<< HEAD8f54c370102c29198ba6160f0ecea91f5a3f6b80