Saravanan666/pypi-org huggingface dataset:
Package: image-array-and-histogram-rrsc
Image array and histogramThis python package helps you to get an array and histogram of a greyscale image. You can also convert an array to greyscale image.Installation$pipinstallimage-array-and-histogram-rrscFunctionsgetImageArray(image_object): Takes an image object and returns image array. The dimensions of the array is the size of the image.getHist(image_array): Takes an image array (of pixels) and returns an array of size 256 as the histogram of the image.getImageFromArray(image_array, width, height): Takes an array, width and height and returns an image formed from the passed array. The dimensions of the image arewidth*height
Package: imageatm
Image ATM is a one-click tool that automates the workflow of a typical
image classification pipeline in an opinionated way, this includes:Preprocessing and validating input images and labelsStarting/terminating cloud instance with GPU supportTrainingModel evaluationRead the documentation at:https://idealo.github.io/imageatm/Image ATM is compatible with Python 3.6 and is distributed under the Apache 2.0 license.
Package: image-augmenter
Table of ContentsScopeSetupVersionsVirtualenvInstallationTestsAPIsLabellerNormalizerAugmenterPersisterZipperScopeThe scope of this library is to augment the dataset for an image classification ML system.SetupVersionsThe library is compatible with python3.6on.VirtualenvWe suggest to isolate your installation via python virtualenv:python3-mvenv.imgaug
...source.imgaug/bin/activateInstallationUpdatepippackage manager:pipinstallpip--upgrade
...
pipinstall-rrequirements.txtTestsThe library is covered, by fast, isolated unit and doc testing (the latter to grant reliable documentation):python-munittestdiscover-simgaug-p'*'APIsThe library is composed by different collaborators, each with its specific responsibility.
Each class tries to expose a minimal public APIs in the form of__call__or__iter__methods (when generators are used).The classes are aimed to work with one image at time, in case you need to transform and augment multiple images, avoid creating multiple instances of the classes, just change the argument of the__call__function (but forPersister, which need a new instance and/or instance attribute modification).LabellerThe target label is extracted directly by inspecting the image name and trying to extract meaningful information (customisable).lbl=Labeller(digits=10)lbl('resources/bag.png')'bag'lbl('resources/109-602-3906-001-c-suit-veletta-albino.jpg')'1096023906'NormalizerThe images are normalized by:resizing them to the specified max size (default to 256 pixels)optionally applying a squared, transparent/backgound canvas and centering the image on it, thus avoiding any deformationnorm=Normalizer(size=128,canvas=True)img=norm('resources/bag.png')img.shape(128,128,4)AugmenterThe number of images is augmented by two orders of magnitude (depending on the cutoff float attribute) by applying different transformations to the original one.Transformations are applied by using generators, thus saving memory consumption.aug=Augmenter(cutoff=.5)aug('resources/bag.png')<generatorobjectAugmenter.__call__at0x125354480>PersisterImages are persisted upon normalization and augmentation, by specifying an action function that accepts the name of the file (original basename suffixed by an index) and aBytesIOobject containing the image data stream.The persister supports both a filename path and, optionally, a stream-like object (in case the file is not yet persisted to disk).The persister supports iteration by yielding the image label and the function return value (typically the saved path), allowing to generate CSV files specific to cloud platforms (i.e.Google Vision APIs).defpersist(name,stream):filename=f'temp/{name}'withopen(filename,'wb')asf:f.write(stream.getvalue())returnfilenamepers=Persister('resources/skirt.jpg',action=perist)forlabel,filenameinpers:print(label,filename)ZipperIn case you need an archive with each normalised augmentations within the recognised label subfolder, you can rely on theZipperinterface: it creates a ZIP file on current path, by scanning the specified folder forPNGorJPGimages.zipper=Zipper('.resources/',normalizer=image.Normalizer(16),augmenter=image.Augmenter(.05))zipper()
Package: image-augmentor
An Image augmentation and image pre-processing library for machine learning. Implemented in python this library is designed to be platform and framework independent. Implements a variety of useful image augmentation methods which stochastically transforms an image set into an augmented image set.InstallationThis package is known to work with python 3.5.4 and Python 2.7.12.You can install the library withpip:pip install image-augmentorYou can clone the repo and get this library running like this:git clonehttps://github.com/michaelmendoza/image_augmentor.git
Package: imagebackup
vntfsclone, vpartclone & vpartimage - Mount Image Backups as Virtual PartitionsAt a low level, this package provides Python code to read the building blocks -
headers, bitmaps, and blocks - ofpartclone,partimageandntfsclonebackup
images. These components may be used in other Python projects. Refer to theAPI documentationfor a comprehensive description of these components.Virtual PartitionsAt a higher level, three command-line utilities based on this low-level code are
also included. Their features are as follows:They read partclone, partimage and ntfsclone images, verify checksums and
list contents.They mount partclone, partimage and ntfsclone images - the backup of a
partition - as virtual partitions.These virtual partitions have the contents of the partition. They are created
without allocating additional disk space. Just like a restored partition, for
example, a virtual partition can be subjected to a file system consistency
check (fsck).A virtual partition can be mounted as a file system. This is done with the
help of a loop device and allows you to inspect the contents. Individual
files and directories can be copied from image backups.Full DocumentationOur documentation is atimagebackup.readthedocs.org.UtilitiesInstallationAPI TutorialAPI Reference
Package: image-bbox-slicer
This easy-to-use library is a data transformer sometimes useful in Object Detection     and Segmentation tasks. With only a few lines of code, one can slice images and their bounding box annotations       into smaller tiles, both into specific sizes and into any arbitrary number of equal parts.         The tool also supports resizing of images and their bounding box annotations, both by specific sizes           and by a resizing/scaling factor.Read the docs athttps://image-bbox-slicer.readthedocs.io/en/latest/.
Package: image-blobs
BLOBsHomePYPIAnalysis of gray scale images containing gaussian shaped blobs.
Blobs should not overlap in 2d, but may overlap in projection.
Entry point isblobs.find_blobs().fromimage_blobsimportfind_blobsfromimage_blobs.utilimportmake_image,show_featuresshape=(100,200)# 200x100Fs=[#  X   Y  W  H  A    IDX (ignored as input, unique in output)(160,25,4,4,4,0),(150,50,5,3,5,0),(40,25,4,4,3,0),(100,50,4,4,2.5,0),]print('Actual')print(Fs)img=make_image(shape,Fs,dtype='u1')features=find_blobs(img)print('Computed (order may differ)')print(features)show_features(img,features,sigma=3)
Package: imageBuild
imageBuild该功能是使用超分辨率来对图像进行重建，可以简单理解成让图片变大变清晰。效果重建前 与 重建后食用方法 - Hugging Face简单介绍下 Hugging Face中文名：拥抱脸 ，英文名就和这个表情包一样🤗同样的名字。Hugging Face 在国内应用非常广泛，入门者可以用它快速用得上科研大牛们训练出的超牛模型，你甚至不需要知道什么是GPT，BERT就可以用他的模型了Hugging Face 官网：https://huggingface.co/安装拥抱脸使用 pip 安装（不建议从 pip 安装，推荐将源码部署在huggingface中使用，简单快捷）pip install imageBuild从 Github 安装git clone https://github.com/mute23-code/imageBuild.git从 HuggingFace 安装git clone https://huggingface.co/mute23/imageBuild别忘了切换文件夹cd imageBuild下载 huggingface hub Clipip install huggingface_hub登录huggingface-cli login创建存储库和 Github 差不多，不多赘述点此创建添加存储库git lfs install推送git add .
git commit -f "first commit"
git push到此，你的图像重建的工具就搭建完成了，拥抱脸贴心的为你准备了api，你也可以通过它实现图像重建。不过，由于使用api，线上算力存在限制，所以调用的是较小的模型，导致线上的模型似乎没有本地推断那么好，事实上，效果差距还是很大的。想要获得更好的重建效果，还是得靠你刚才搭建的程序API = "https://hf.space/embed/{你的id名}/{你的仓库名}/api/predict/"鸣谢xinntao/Real-ESRGAN超分辨率原理便是基于该仓库ppxxxg22/nonebot_plugin_RealESRGAN提供了封装后的执行文件许可证由于Real-ESRGAN使用了BSD 3-Clause许可证，本项目也同样使用该许可注意！BSD许可证禁止在未经书面同意的情况下使用版权所有者或其贡献者的名义来推广衍生产品。BSD 3-Clause License

Copyright (c) 2023, mute.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this
   list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice,
   this list of conditions and the following disclaimer in the documentation
   and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its
   contributors may be used to endorse or promote products derived from
   this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Package: image-builder-deployer
Installation QUICK STARTTo install this script from source clone this repo usinggit clone. after cloning the repo make sure that all python requirements are met. Install all python packages that are needed for buildibng from source; On linux run:apt updateapt install python3-pipapt install python3-venvpip3 install buildAfter that the script should be ready for build. To build it usepython -m build -sfrom the repo root directory. After build just runpip3 install dist/imageBuilderDeployer-<version number>.tar.gzand it should be ready for use.Supported versionsTo build and rus this package you will need to use python >=3.6How to buildTo build this python package you will need to have python build installed as a package. To do this please runpython -m pip install build. To build ImageBuilderAPI navigate to root directory of this repo and runpython -m build. When build finfishes new directory will appear namedistHow to installTo install package globaly simply runpython -m pip install dist/image_builder_deployer<version>.tag.gz>ConfigurationBefore using this script it needs a configuration file, the template for this file can be found inconfig/config_template. Simply fill in the template file and store it somewhere in your system.How to run this packageAfter installing this package runpython -m image_builder_deployer <path_to_your_config_file>and it should just work.TroubleshootingTODO
Package: imagecaptioner
Image CaptionerCommand line python script for adding captions to the images based on the metadata, filename or user input.ExampleSource Image:imagecaptioner kitten.jpg --align=trresult:imagecaptioner kitten.jpg -c "LOVELY KITTEN, ##Software                      ##DateTime" --color=#e4eeed --size=30 --bold=2 -o "captioned2_kitten.jpg"result:InstallationIt is required to installimagecaptionerdependency from pypi repository.pip install imagecaptionerUsageCLI➜  ~/pictures imagecaptioner --helpusage: imagecaptioner path caption_expression [options]positional arguments:path                  File or directory locationoptional arguments:-h, --help            show this help message and exit-c CAPTION, --caption CAPTIONCaption with variables support taken from metatags. To access variable in caption use e.g. ##DateTime-m, --metatags        Show file metatags-df DATEFORMAT, --dateformat DATEFORMATDate time format-o OUTPUT, --output OUTPUTOutput file or directory, by default adds 'captioned_' prefix-r, --overwrite       Overwrite already captioned photo with new one-p, --preview         Preview mode. Show only mode instead of save. Useful for testing. Only for specific file.--font FONT           Font type--size SIZE           Font size, by default automatically chosen.--color COLOR         Font color--bold BOLD           Font bold--align ALIGN         Text alignment. Possible values: BOTTOM_LEFT, BOTTOM_RIGHT, TOP_LEFT, TOP_RIGHT, CENTER.It can be used also with shorter versions like: bl, br, tl, tr, c--singlethread        Use single thread (only works for directory)-v, --verbose         Increase logging severityExamples:> imagecaptioner 20190116_111115.jpg -c "Party, ##DateTime - Captured with phone ##Make ##Model"> imagecaptioner images/ --output=captioned_images> imagecaptioner 20190116_111115.jpg -p --font=fonts/Lato-Regular.ttf --size=120 --color=#030360 --bold=3> imagecaptioner 20190116_111115.jpg -r -dateformat "%H:%M" --output=captioned.jpgShow metatags:> imagecaptioner 20190116_111115.jpg -m> imagecaptioner images/ -mPython libraryIt is possible to importimagecaptionerlib to your python projectImport library:fromimagecaptioner.captionerimportImageCaptionerIt could be simply used:ImageCaptioner.add_captions(path="images/",output="captined_images",overwrite=1)Overwrite image with caption:ImageCaptioner.add_captions(path="20190116_111115.jpg",caption="SUPER PARTY, ##DateTime captured with ##Made",dateformat="%H:%M",output="20190116_111115.jpg",overwrite=1,preview=None,font=None,size=86,color="#F5992A",bold=2,)LicenceMIT
Package: imagecaptioning-aeye
imagecaptioning-aeyeThe Image Captioning module for the A-eye project.Project Organization├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.ioProject based on thecookiecutter data science project template. #cookiecutterdatascience
Package: image-caption-scraper
image-caption-scraperAboutThis package allows downloading images and their corresponding captions from web search engines like Google Images, Yahoo Image, Flickr, and more to come.The model is particularly targetting researchers for building their own datasets of their own concepts, and can thus train machine learning models for computer vision, natural language processing, and the intersection of both (image captioning, visual relationship detection).The pipeline is completely based on Selenium web driver.Table of ContentsInstallationRequirementsUsageOptionsLicenseContactTODOInstallationpip install image-caption-scraperRequirementsPython 3.6 or later with allrequirements.txtdependencies installed. To install run:pip install -r requirements.txtAlso, make sure to download chrome-driver fromhttps://chromedriver.chromium.org/and either add it to the system variables path, or type down the full path to the .exe file in the options below.Usagefrom image_caption_scraper import Image_Caption_Scraper

scraper = Image_Caption_Scraper(
                engine="all", # or "google", "yahoo", "flickr"
                num_images=100,
                query="dog chases cat",
                out_dir="images",
                headless=True,
                driver="chromedriver",
                expand=False,
                k=3
            )

scraper.scrape(save_images=True)OptionsArgumentDescriptionOptionsengineSearch engine to scrape images from. By default it searches through all the available engines (currently supports Google, Flickr, Yahoo)."all","google","flickr","yahoo"num_imagesNumber of images targetted by the userAny number (int) > 0queryThe text query to search forAny text queryout_dirOutput directly to save the images and captionsAny text stringheadlessArgument to hide the browser while crawling the web pages or show it. True will hide, False will open it'True' or 'False'expandArgument to expand the input query. Expansions supports synonyms from wordnet at the moment. Translations are coming soon.'True' or 'False'driverThe web driver to navigate the web pages. Download the driver fromhttps://chromedriver.chromium.org/Default='chromedriver' (configured in System Path). Otherwise just type the path to the .exe filekIf expand==True, k determines how many synonyms to fetch from wordnet for each word in the query. It is assumed that words are separated by spaces. The model fetches the closest k synonyms for each word by path_similarity in the wordnet graph.'True' or 'False'save_imagesIf True the model will save the images+captions in the out_dir folder. Otherwise it will only save the meta-data (with urls) without the images.'True' or 'False'LicenseDistributed under the MIT License. SeeLICENSEfor more information.ContactEmail:alishibli97@hotmail.comTODOVerify large dataset collections (quality and time wise)Implement parallel execution for faster data collectionExpand queries using more methods like transations and other.
Package: imagecast
ImagecastAboutImagecast is like ImageMagick but for Pythonistas. Optionally provides its
features via HTTP API.Currently, this is based onPillow. However, it might be based onWandin
the future.There might still be dragons.InstallPrerequisitespip install imagecastWith service API:pip install imagecast[service]FeaturesColorspace conversion: monochrome, grayscaleCropping with negative right/bottom offsetsResizing while keeping aspect ratioOutput format: Any image formats from Pillow or raw bytesHTTP APISynopsis# Display on screen
imagecast --uri="$IMGURL" --display

# Colorspace reduction to bi-level with threshold, output as bytes
imagecast --uri="$IMGURL" --monochrome=200 --format=bytes

# Colorspace reduction, cropping, resizing and format conversion
imagecast --uri="$IMGURL" --grayscale --crop=40,50,-50,-40 --width=200 --save=test.pngExample:imagecast --uri="https://unsplash.com/photos/WvdKljW55rM/download?force=true" --monochrome=80 --crop=850,1925,-950,-900 --width=640 --displayHTML CapturingImagecast can also capture screenshots of webpages, or elements thereof. It usesPlaywrightandFirefoxto convert full pages or specific DOM elements
to bitmaps.After installing Imagecast, run:playwright install firefoxThen, invoke Imagecast like:imagecast --uri="${URL}" --displayIn order to select specific elements for capturing, use the--elementoption
to express a DOM selector to apply:imagecast --uri="${URL}" --element="#panel-1" --displayHTTP APIStart the Imagecast service as daemon:imagecast serviceExample:http "localhost:9999/?uri=https%3A%2F%2Funsplash.com%2Fphotos%2FWvdKljW55rM%2Fdownload%3Fforce%3Dtrue&monochrome=80&crop=850,1925,-950,-900&width=640"NoteYou should not run the service without restricting the
list of allowed remote hosts on the public internet.To do that, invoke the service like:imagecast service --allowed-hosts=unsplash.com,media.example.orgOther projectshttps://github.com/DictGet/ecce-homohttps://github.com/agschwender/pilboxhttps://github.com/francescortiz/image
Package: imagecatalog
imagecatalogCreate a PDF contact sheet from a list of files.InstallationInstall from PyPIpipinstallimagecatalogor get latest dev version from GitHub.pipinstallgit+https://github.com/tdmorello/imagecatalog.gitUsageCommand line# for full list of optionsimagecatalog-himagecatalog-iimages/*.png-oexample.pdf--title'Image Catalog'PDF outputimagecatalog\-iimages/*.png\-oexample_landscape.pdf\--rows2\--cols4\--orientationlandscape\--title'Catalog with Landscape Layout'\PDF outputFile paths and metadata can also be supplied from a csv file with headers "image", "label", "note"$head-n5sample.csv
image,label,note
images/image_00.png,Image0,image0note
images/image_01.png,Image1,image1note
images/image_02.png,Image2,image2note
images/image_03.png,Image3,image3noteimagecatalog--csvsample.csv--title'Catalog from CSV'-oexample_csv.pdfPDF outputOther features: apply image filtersimagecatalog-iimages/*.png-ocatalog.pdf\--invert\--grayscale\--autocontrastintegrate with shell utilities# make a catalog with all images created within the last dayfind.-ctime1d|xargsimagecatalog-ocatalog.pdf-iuse regular expressions to filter files# make a catalog with images that have '1' and/or '2' in the filenameimagecatalog-iimages/*.png-ocatalog.pdf--regex'.*(1|2).*'ScriptingfromimagecatalogimportCatalog# Catalog inherits from FPDF# see https://github.com/PyFPDF/fpdf2 for more methodscatalog=Catalog()# optionally add a titlecatalog.set_title("Image Catalog")# grab a set of existing images from a local directoryimages=[f"images/image_{i:02}.png"foriinrange(12)]# optionally add labels (defaults to filenames)labels=[f"Image{i}"foriinrange(len(images))]# optionally add notesnotes=[f"note for image{i}"foriinrange(len(images))]# generate the pdfcatalog.add_page()catalog.build_table(images,labels,notes,rows=4,cols=3)# savecatalog.output("example.pdf")Contributionsimagecatalogusespoetryfor building and package management. Pull requests are welcome.
Package: image-center
image-center图像识别定位某个元素在当前屏幕中的坐标；在自动化测试中获取到元素坐标之后，可以传入到键鼠工具，从而实现对目标元素的操作。Documentation:https://linuxdeepin.github.io/image-centerSource Code:https://github.com/linuxdeepin/image-center安装pip install image-center如果想在本机直接使用图像识别，还需要在本机安装OpenCVsudoaptinstallpython3-opencv使用说明截取目标元素图片，将图片保存到某个路径；fromimage_centerimportImageCenterImageCenter.find_image("~/Desktop/test.png")返回test.png在当前屏幕中的位置。RPC服务如果不想在本机安装OpenCV，或者你的机器无法安装OpenCV，可以在服务端安装OpenCV（安装方法和上面一样）；服务端启动方法：fromimage_center.serverimportserverfromimage_center.confimportsettingsetting.PORT=8889# 默认端口是8889，可以修改为其他端口；server()执行这个代码就可以启动服务了；客户端使用方法和前面一样，唯一需要配置的是服务端的IP和端口。fromimage_centerimportImageCenterfromimage_center.confimportsettingsetting.SERCER_IP="192.168.2.1"# 服务端IPsetting.PORT=8889# 和服务端端口保持一致ImageCenter.find_image("~/Desktop/test.png")# test.png是你自己截的图，路径也修改成你自己的路径