Saravanan666/pypi-org huggingface dataset:
Package: image-transfer
Image TransferBasic package to transfer images and videos into a date structure.This package looks through both images and videos for creation time metadata.
If found it can copy or move the files from a source directory to a destination directory.
The sub directory of the destination directory will be based on creation time. As default
year/month/day e.g 2023/01/27.UsageCopy from one directory to anotherimgtrfcopy{source/directory}{destination/directory}Move from one directory to anotherimgtrfmove{source/directory}{destination/directory}Change format of destination directory structure.Defaults toY/m/dfor Year/Month/Day. Directories are seperated by/and the format codes can be foundhere.imgtrfcopy--dir-format{dir_format}{source/directory}{destination/directory}You can also remove empty directories.imgtrfremovedirs{target/root/directory}You can always find specific help with the--helpflag.# Either for the applicationimgtrf--help# Or for a specific commandimgtrfcopy--helpInstallationInstallation of image-transfer is simply done via a pip command.pipinstallimage-transferFFmpegTo support gathering metadata from video files, you need to haveFFmpeginstalled and added to your PATH environment variable. FFmpeg can either be installed from their siteffmpeg.orgIt can also be donwloaded via winget in windows or apt in linux.wingetinstall--idGyan.FFmpegor if on linuxsudoaptinstallffmpegMake sure to check your environment variables. Some times even a restart of your computer is necessary for them to be read in properly.
Package: image-trimmer
Image Cropping and Text ExtractionThis code is designed to crop images and extract text from them using Optical Character Recognition (OCR). It is particularly useful for processing images of documents or forms where specific information needs to be extracted. Please install tesseract ocr first!Installationpipinstallimage-trimmerUsageThe code can be run from the command line using the following syntax:image-trimmer-i./path/to/folder-o./path/to/output-tscbExample
Package: imagetyperz-async
imagetyperz-asyncAn asynchronous client for theImageTyperz CAPTCHA-solving API.httpxpowers the HTTP requests.At the moment, only image CAPTCHAS and reCAPTCHAs are supported.Installationpipinstallimagetyperz-asyncUsageimportasynciofromimagetyperzimportImageTyperzClient,reCAPTCHATypefromimagetyperz.exceptionsimportNotDecodedasyncdefdemo():#### Context manager will handle the closing of connections in the underlying# httpx AsyncClient at block end.## Alternatively, `await ita.aclose()` may be manually called to perform# cleanup.## If no cleanup is performed, a warning may be emitted at Python exit.#asyncwithImageTyperzClient('6F0848592604C9E14F0EBEA7368493C5')asita:print(awaitita.retrieve_balance())#: 8.8325# Submit reCAPTCHA jobjob_id=awaitita.submit_recaptcha(page_url='https://example.com/login',site_key='scraped-site-key',recaptcha_type=reCAPTCHAType.INVISIBLE,)print(job_id)#: 176140709# Check for results of the reCAPTCHA jobwhileTrue:try:g_response=awaitita.retrieve_recaptcha(job_id)exceptNotDecoded:awaitasyncio.sleep(5)continueelse:print(g_response)#: 03AGdBq25hDTCjOq4QywdrY...break# Alternatively, use complete_recaptcha to automatically handle the polling# for results — returning with the result when ready.g_response=awaitita.complete_recaptcha(page_url='https://example.com/login',site_key='scraped-site-key',recaptcha_type=reCAPTCHAType.INVISIBLE,)print(g_response)#: 03AGdBq25hDTCjOq4QywdrY...
Package: image-utils
Utils to manipulate ceph ceph image.currently there are two seperate modules. they have no relations.### Install```pip install image-utils```print version:```from image_utils import versionversion.version_string()```### Usagecurrently there are two modules.#### ceph.pythis module is used to interact with ceph image.##### Example: copy a image from source ceph cluster to dest ceph.```from image_utils import ceph# get source store.src_store = ceph.get_store(conffile_path, ceph_user, keyring_path)# get dest store.dst_store = ceph.get_store(conffile_path2, ceph_user, keyring_path2)# get an image location from source ceph pool.src_loc = src_store.get_location(pool, image_name)# copy image from src_store to dst_store, in dst_poolceph.copy(src_store, src_loc, dst_store, dst_pool)```#### fish.pythis module is used to modify disk image. require `guestfs`. while guestfs python bindings are not foundin pypi, you should install rpm package```yum install python-libguestfs```Then you are ready##### Example: modify a image.```from image_utils import fishf = fish.Fish(mon_host, mon_port, client, key, pool, image_name)f.launch()f.add_mtu('192.168.1.100', 1450)f.remove_file('/root/abc.txt')f.shutdown()```while there are few apis in `Fish` class, tweak the class and add more apis as your wish.
Package: image-view
image-viewdisplays images (PNM, PNG, JPEG, etc.) usingpygame.The primary motivation is to support viewing PNM images on OS X. For common
image types, Preview works great. For PNM images,Xeeworks fine for the
most part, but it doesn’t work properly when displaying a subset of images from
command line. This is important if there are a series of images captured from a
camera (captured-00*.pgm), and you want to display a subset of them.InstallationFirst install thepygamedependency somehow. One option is to use this
fork, which avoids X11 on OS X:$ pip install --upgrade git+https://github.com/myint/pygameThen install the actual program:$ pip install --upgrade image-viewUsageTypical usage:$ image-view captured-070*.pgmUse the left and right arrow keys to navigate through images.Use+/-keys to scale the image. Press0to reset.Options:usage: image-view [-h] [--colorize] [--little-endian] [--version]
                  files [files ...]

positional arguments:
  files            paths to images

optional arguments:
  -h, --help       show this help message and exit
  --colorize       color 16-bit PGM images with a rainbow gradient
  --little-endian  interpret 16-bit PGM images as little endian; this is the
                   opposite of Netpbm (and ImageMagick)
  --version        show program's version number and exitExample screenshot
Package: image-viewer
Image Vieweriviewis a command line app for viewing images using open_cv.InstallationUsing Pip$pipinstallimage_viewerManual$gitclonehttps://github.com/John-Lee-Cooper/image-viewer$cdimage-viewer$pythonsetup.pyinstallUsage$iview--version
$iview--help
$iview-r~/PicturesUser InterfaceKeyResultSPACEGo to the next imageBACKSPACEGo to the previous imageDELETEDelete the current imageENTERToggle full screenESCAPEExitotherHelp screenHolding the left mouse button down and moving the mouse will pan the image.Rolling the mouse wheel up and down will zoom out and in where the mouse is.TodoUI errorPan/zoom via keypadConfig dynamic singletonRequirements linux vs macDisplay image name, size, location, date takenCropTransitionWritten byJohn Lee Cooperjohn.lee.cooper@gatech.edu
Package: image-viewer-mk2
Image viewer Mk2Viewer for spectral images, in particular suitable from multispectral optoacoustic tomography.Installationpip install image-viewer-mk2==1.0.0Seedependenciesfor versions of dependency libraries that were tested with this software but are not strictly required during installation.
Seereleasesfor older versions.UsageFrom command line as a standalone application.> imvmk2 [-i filename] [-c config_filename] [-d (debug)]From within python scripts and interactive sessions.The viewer can be either used as an interactive image viewer, giving the user the ability to manually adjust the settings. The rendered image is returned back so that it can be further used inside the script.importimage_viewer_mk2.appasimvimg=np.zeros((height,width,channels))render=imv.start(image=img)Alternatively, it can be used to apply existing configuration and directly return a rendered image without opening the interactive GUI:importimage_viewer_mk2.appasimvimg=np.zeros((height,width,channels))render=imv.render(image=img,config_filename='config.json')Configuration files can be saved from the interactive GUI or they can be returned as a second return value in the script by passing parameterreturn_config=Trueto the callimv.start().TroubleshootingTclError: image "pyimageXX" doesn't existWhen using in interactive session, all other matplotlib figures have to be closed.CreditsThis software reuses code and icons produced by: Alistair Muldal, cilame, Benjamin Johnson, Remin Emonet,Icon home,Gregor Cresnar,Freepik,Google, Uptal Barman,Arkinasi,Royyan Wijaya, and Pancracysdh.LicenseThe software is provided under theMIT open license.CitationIf you use this software for your research, please cite it as:Kukačka, Jan (2021). Image Viewer MK2 (v1.0.0) [Computer software]. https://github.com/jankukacka/image_viewer_mk2
Package: image-vision
image_visionPython image_vision provides easy to use functionalities for performing image processingFree software: MIT licenseDocumentation:https://image-vision.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-07-13)First release on PyPI.
Package: imageworker
Usagefile to ndarray>>>fromimageworkerimportfile_to_array>>>data=file_to_array("test.jpg")url to ndarray>>>fromimageworkerimporturl_to_array>>>data=url_to_array("https://n.sinaimg.cn/spider2021326/106/w1024h682/20210326/5927-kmvwsvy1040641.jpg")upload ndarray to qiniu cdn>>>key=QINIU_KEY>>>secret=QINIU_SECRET>>>domain=HOST>>>bucket=QINIU_BUCKET>>>url=put_qiniu(data,key,secret,domain,bucket)
Package: imageZIP
imageZIPThis is a small python package to archive (encrypt) files and directories into a single image file.This project is made just for fun, it has no practical use, output "ZIP" image will always have the same or greater file size than it's content. Also, this is not easy-to-scan like QR codes.Algorithm is pretty easy. If folder to zip is chosen, it will iterate through all folders and files inside it. If folder is found, it will write relative path of folder in bytes and add special folder delimiter at the end. If file is found, it will write it's relative path and name in bytes, add special file delimiter and then write all it's content in bytes with additional second file delimiter. When bytes array is ready to go, it will build an image, where individual pixels representing particular byte number.ExampleThis is an "imageZIP archive", containing this whole "imageZIP" repository.It was created withimageZIP.zip("some path/imageZIP/", scale=4).You can actually download this image and unzip it withimageZIP.unzip("some path/demo_zip.png", scale=4), it will create "imageZIP" folder with all repository files.How to useTo download this package, you can usepip install imageZIP.Then go to python terminal and writeimport imageZIP.Then you can use 2 available functions:imageZIP.zip(path: str, scale: int = 1, color_mode: str = 'heat');imageZIP.unzip(path: str, scale: int = 1, color_mode: str = 'heat').Where:path- path (str) to a single file or whole directory to "zip", or image file to "unzip";scale* - scale (int) of input or output image file. Default:1;color_mode* - color mode (str) of input or output image file. Default:"heat".*not necessary attributes, which can be omitted.Notes:Forzipandunzipyou have to provide samescaleandcolor_modesettings;Availablecolor_modes are:bw,heat,heat_toxic,rainbow,red,blue;scaleis have to beint >= 1;pathcan be absolute or relative;If a good rectangle image can't be created, output image will be a square with some empty pixels at the end.
Package: imaginairy-normal-map
Image-to-Normal-MapCreate normal maps from images (using AI)Quick Startpipinstallimaginairy-normal-mapfromPILimportImagefromimaginairy_normal_map.modelimportcreate_normal_map_pil_imgimg=Image.open("oval-office-large.jpg")normal_img=create_normal_map_pil_img(img)normal_img.save("oval-office-large-normal.jpg")This project was made to support the AI image generation project,imaginAIry.Credit to Gwangbin Bae, Ignas Budvytis, and Roberto Cipolla for creation of
theoriginal algorithm and code.
Package: imagine-client-py
Imagine Client for PythonThis library allows you to easily connect to doc.ai's Imagine API.Supported Image ModelsModel IdTypeDescriptionphenomenal-faceimageGiven a facial image, this model infers the age, sex, height, and weight of a personhappy-faceimageGiven a facial image, this model infers the mood of a personmannaimageGiven a food image, this model infers whether the food is healthy/unhealthyface-detectorimageGiven an image, this model detects whether a face is presentfood-detectorimageGiven an image, this model detects whether food is presentAPIImageBasedModel.create(imagine_client, model_id)Given an ImagineClient (imagine_client) and string (model_id), creates an image model. See the"Supported Image Models"section for a list of supported model ids.ImageBasedModel.infer(image_file_stream, store=false, metadata=None)Performs the model's inference on an image. A read stream should be passed as the parameter (image_file_stream).ImageBasedModel.insert_correction(corrections_dict, metadata=None)Uploads a correction label to the imagine API, for a given inference.ImageBasedModel.insert_prediction(predictions_dict, metadata=None)(FOR EDGE PREDICTIONS) Uploads a prediction label to the imagine API, for a given inference.Examplefromimagine_client_pyimportImagineClient,ImageBasedModel# Required parametersAPI_KEY=<VALID_IMAGINE_API_KEY>IMAGE_PATH=<IMAGE_FILE_PATH>CLIENT_ID=<ANY_CLIENT_ID># Initialize imagine clientimagine_client=ImagineClient({'api_key':API_KEY,'client_id':CLIENT_ID})# Create the 'happy-face' model interfacemodel=ImageBasedModel.create(imagine_client,'happy-face')# Create a read stream to a image filewithopen(IMAGE_PATH,'rb')asimage_face_file:# Perform the model's inference, on the image.result=model.infer(image_face_file)##TestsTo run tests:export IMAGINE_API_KEY=<API key> IMAGINE_CLIENT_ID=test
pytest
Package: imaginenc
ImaginencConvert any file into an image, and images back to files.InstallationImaginenc is available on PyPI:$python-mpipinstallimaginencImaginenc requires Python 3.8+.Usageusage: imaginenc [-h] (-e | -d) -i INPUT [-o OUTPUT] [-s SIGN]Convert any file into an image, and images back to files. Run without args for interactive input mode.optional arguments:-h, --help            show this help message and exit-e, --encode          encode file to image-d, --decode          decode image to file-i INPUT, --input INPUTinput file-o OUTPUT, --output OUTPUToutput folder-s SIGN, --sign SIGN  sign the encoded image (max 50 characters)Examples of imaginenc commandEncodeimaginenc-e-iimaginenc\imaginenc.py-oimages-s"Thank you for using this tool!"Decodeimaginenc-d-iimages\imaginenc.py.png-ooutputExamples of imaginenc moduleImportimportimaginencEncode file name and save as imageimaginenc.encode_file_name(input_file_path='imaginenc/imaginenc.py',output_file_path='images',sign='signature')Encode file bytes and return imagewithopen('imaginenc/imaginenc.py')asf:image=imaginenc.encode_bytes_to_image(input_file_bytes=f.read(),file_name=f.name(),sign='signature')Decode image name and save as original filemetadata=imaginenc.decode_image_name(input_file_path='images/imaginenc.py.png',output_file_path='output')Decode PIL image to original file bytesfromPILimportImageimage=Image.open('images/imaginenc.py.png')file_bytes,metadata=imaginenc.decode_image_to_bytes(image=image)Planned FeaturesEncryption.
Package: imaginepy
ImaginePyFeatures🎨 Turn words into art👓 Choose from an array of art styles🔧 Adjust your masterpiece with creative controls!📦 Stay ahead of the game with the ever-growing art library!🌇 Generate wallpapers🔎 Discover and explore similar artistic designsThis is refactored and improved version of the original fromhyugogirubatoInstallationNote: RequiresPython3.7.0 or newer with PIP installed.$pythonsetup.pyinstallYou now have theimaginepypackage installed.PyPi Installationpip install imaginepyFrom Source CodeThe following steps are instructions on download, preparing, and running the code under a Venv environment.
You can skip steps 3-5 with a simplepython setup.py installcall instead, but you miss out on a wide array of benefits.git clone https://github.com/ItsCEED/Imaginepycd Imaginepypython -m venv envsource env/bin/activatepython setup.py installAs seen in Step 5, running theimaginepyexecutable is somewhat different to a normal PIP installation.
SeeVenv's Docson various ways of making calls under the virtual-environment.UsageThe following is a minimal example of using ImaginePy in a script. It gets the generated image
from the text and increases the quality.fromimaginepyimportImaginefromimaginepy.constantsimport*defmain():imagine=Imagine()img_data=imagine.sdprem(prompt="Woman sitting on a table, looking at the sky, seen from behind",style=Style.NO_STYLE,ratio=Ratio.RATIO_16X9,negative="",seed=1000,cfg=16,model=Model.REALISTIC,asbase64=False# default is false, putting it here as presentation.)ifimg_dataisNone:print("An error occurred while generating the image.")returnimg_data=imagine.upscale(img_data)ifimg_dataisNone:print("An error occurred while upscaling the image.")returntry:withopen("example.jpeg",mode="wb")asimg_file:img_file.write(img_data)exceptExceptionase:print(f"An error occurred while writing the image to file:{e}")if__name__=="__main__":main()Async versionimportasynciofromimaginepyimportAsyncImaginefromimaginepy.constantsimport*asyncdefmain():imagine=AsyncImagine()img_data=imagine.sdprem(prompt="Woman sitting on a table, looking at the sky, seen from behind",style=Style.NO_STYLE,ratio=Ratio.RATIO_16X9,negative="",seed=1000,cfg=16,model=Model.REALISTIC,asbase64=False# default is false, putting it here as presentation.)ifimg_dataisNone:print("An error occurred while generating the image.")returnimg_data=awaitimagine.upscale(image=img_data)ifimg_dataisNone:print("An error occurred while upscaling the image.")returntry:withopen("example.png",mode="wb")asimg_file:img_file.write(img_data)exceptExceptionase:print(f"An error occurred while writing the image to file:{e}")awaitimagine.close()if__name__=="__main__":asyncio.run(main())CreditImagine Icon © Vyro AI & APIOriginal reverse and version by:hyugogirubatoLicenseGNU General Public License, Version 3.0
Package: imagines
ImagineSImagineS (Image Engine Search) tool augments an image dataset by searching a series of queries in Google Images.InstallWe recommend first creating an environment by using eitherVirtual EnvsorConda.Then install the library usingpip:pip install imaginesUsageExample:Downloadappleandwaterrelated images by adding the query searches that would belong to each class.fromimaginesimportDatasetAugmentationdataset_augmentation=DatasetAugmentation(driver_type='chrome')label_queries={'apple':['apple'],'water':['water','water bottle'],}max_links_to_fetch=3sleep_between_interactions=1image_shape=(224,224)resize_images=Trueoutput_dir='images/'dataset_augmentation.augment_dataset(label_queries,output_dir,max_links_to_fetch,image_shape,resize_images,sleep_between_interactions)LicenseMIT License
Package: imagingplus
Imaging+imaging+ is a Python package and tool-suite for essential processing and analysis of 2photon imaging data.
Especially, there is a fully implemented pipeline for data structuring, processing, analysis and plotting of 2photon Ca2+ imaging experiments, and experiments based around 2photon imaging such as all optical experiments (i.e. 2photon optogenetic stim or 1photon optogenetic stim, with combined 2photon Ca2+ imaging).OverviewThis package is designed for experiments that follow the general structure of an "imaging+" experiment shown below. We have additionally provided specific sub-modules to suit imaging experiments performed using PackIO, a Bruker 2pPlus microscope and using Suite2p for Ca2+ imaging
data processing for ROI segmentation.
It should be completely usable and understandable for anyone with the correct data in hand and basic knowledge of Python.
There are tutorials to help along the way. Ultimately, the goal of this package is to jump-start your own analysis of your awesome experiment.We hope that it provides a useful structure to organize your experimental data, and some functionality to interact and process your data in an efficient manner.Getting startedRecommended tools:python >=3.9conda - environments for python package managementRunconda create -n <insert-name> python=3.9to create a new conda environment with python 3.9Note:imaging+should install and function normally within existing conda environments as well.Jupyter - for creating python based notebooksVS Code or PyCharm - IDE for python code developmentInstallation instructionsNote: The package is installable as a stand-alone python package. You can install the package into an existing conda environment, or you may choose to skip using a conda environment all together (steps 2 and 3).Clone this github repository usinggit clone https://github.com/Packer-Lab/imaging+.gitin the terminal.Create the preset conda environment provided in this repository (plitest.yml) usingconda env create -f plitest.ymlfrom the terminal.Activate the conda environmentconda activate plitest.cdto the parent directory of where this repo was downloaded to.From this parent directly, runpip install -e imaging+from terminal to install this packageimaging+.Testimaging+is successfully installed (import package in python):Ensure that the conda environment from whichimaging+was installed is activated.start python from command line using:python, or start python from the same conda environment in your preferred method (e.g. jupyter notebook or IDE).Import the package:import imaging+orimport imaging+ as ip.DocumentationThe documentation forimaging+is available athttps://imagingplus.readthedocs.io/en/latest/
Package: imaging-research
miresearchOrganisation and automation tools for medical imaging research dataInstallation:pipinstallimaging-researchNote: pypi reported naming conflicts for miresearchAboutThis is a collection of classes for following OOP principles for organisation of research data for medical imaging research studies.It takes advantage of the dicom standard and the packagespydcmtkfor automating and generalising many typical steps with the intention of making the researcher's life easier.This package may be easily adapted and expanded upon for a high level control over your research data. Or, it may be used as is for basic structure and organisation of data and automation of common tasks.Class structureAbstractSubjectclass is top level class taking inputs:subjectNumber: an integerdataRoot: the root directory where subjects to be storedsubjectPrefix: a prefix to be combined withsubjectNumberfor naming each subjectOptional: will be guessed from subjects already present indataRootif not given.DIRECTORY_STRUCTURE_TREE: DirectoryStructureTree class to define directory structure for each subject directory (see wiki for construction shortcuts)Optional: Defaults toRAWandMETAdirectories.This is the basic parent class containing fundamental methods for organisation and management. Seewikifor advanced usage, epsecially via inheritance and polymorphism.Exposed commandline tool: miresearchmiresearch-h
usage:...Actions:-LoadLOADPATHPathtoloaddicomsfrom(file/directory/tar/tar.gz/zip)-LOAD_MULTICombinewith"Load":LoadnewsubjectforeachsubdirectoryunderloadPath-LOAD_MULTI_FORCECombinewith"Load":ForcetoignorestudyUIDsandloadnewIDpersubdirectory-WatchDirectoryWATCHDIRECTORYWillwatchgivendirectoryfornewdataandloadasnewstudy-SummaryCSVSUMMARYCSVWritesummaryCSVfile(giveoutputfilename)Configurationmiresearch uses a miresearch.conf file for configuration.By default miresearch.conf files are search for in the following locations:source_code_directory/miresearch.conf (file with default settings)$HOME/miresearch.conf$HOME/.miresearch.conf$HOME/.config/miresearch.confFull file path defined at environment variable: "MIRESEARCH_CONF"Full path passed as commandline argument tomiresearchFiles are read in the above order with each subsequent variable present overwritting any previously defined.
For information on files found and variables used run:miresearch -INFODocumentationFor full documentation seewiki