Saravanan666/pypi-org huggingface dataset:
Package: imgrender-djentleman
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.UsageCommand 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
Package: imgrep
IMGrepWant to find that one meme with the funny punchline?
Looking for a picture of a PowerPoint presentation on a specific topic, that you took 5 years ago?
IMGrep might help.It works likegrep, but for images, and with a lot less features ... and it's also much slower ... and not suuuper
accurate, especially for handwriting, or weird fonts ;Dimgrepis built on top ofTesseract-OCRand uses
thepytesseractbindings to interface with it.InstallYou can installimgrepfromPyPIwithpippip install imgrepUsageGet the usage withimgrep -h.usage: imgrep [-h] [-i] [-r] [-f] [-0] pattern file

Grep for text in images.

positional arguments:
  pattern               A Python regex, to search for.
  file                  Path of the image(s) to search through. (Or folder(s), if `--recursive' is specified).

options:
  -h, --help            show this help message and exit
  -i, --ignore-case     Ignore case distinctions in patterns and input data.
  -r, --recursive       Grep through every file under a given directory.
  -f, --filenames-only  Only print the file names, not the contents. Makes no sense without `--recursive', and will be ignored if `--recursive' is not specified.
  -0, --null            Print the output seperated by null characters, this is useful for badly named files. Makes no sense without `--filenames-only', but will be done regardless, if specified!

Be patient. It uses multiple cores, but this just takes a while. Searching for a specific string in my ca. 2000 image strong memes folder took about 8 minutes and 30 seconds.PerformanceIs abysmal. You've been warned.Neither accuracy, nor execution time are that great, but it works for my use case.
And it is still much faster, than combing through my photos one by one, when I'm looking for something specific.TODOHaving-aand-bflags to include N lines of output after, and before the match would be nice.Also coloring the output on smart terminals would be cool.That opens a whole can of worms with determining whether the terminal supports it or not.Or whether the user wants color (NOCOLOR, or TERM=dumb etc.?).Fuzzy searchPreprocessing/Indexing (?)Pro:Done once it heavily improves performance for subsequent searchesCon:I don't want to put garbage files into the users' filesystem.This could be done, by having only one index file in XDG_CONFIG_HOME (or similar on other OS's),
that gets uninstalled with imgrep in the end.Conclusion:Probably worth itAlternativesI noticed, that there is a similar project, even with the same nameimgrep.
I am in no way affiliated with that project, but it looks cool, and it might actually suit you better, because they
already allow for preprocessing and fuzzy search, both of which are not currently implemented in this project.OTOH for a quick and dirty one off job the convenience ofpython -m pip install imgrep; imgrep -rif pattern imagesis
probably nice.
Package: imgresizer
Image ReSizerThis simple package re-sizes images from a given list of urls.Installationpip install imgresizerUsageIt expects you to pass ita configuration as command line parameter.imgresizer -c <path/to/conf_json>A sample json:{"targets":[400,200,100],"data":"data","num_threads":8,"input_dir":"incoming","output_dir":"outgoing","image_urls":"/path/to/urls.txt"}Firstly, it downloads them and stores them insidedata/input_dirdirectory. Then it starts processing images to re-size and store re-sized image todata/output_dirwith its new size added to its name.Additionally, you can pass-l trueso that it logs the process.IssuesNothing yet. But it will need to speed up.TasksImplement thread poolingMake it re-size local imagesLicenseImgReSizer is released under the [MIT license] (https://choosealicense.com/licenses/mit/#)
Package: img_resizer
# img_resizer - easy to resize image for iOS app![img_resizer demo](/screencast.gif?raw=true )# Installation``pip install img_resizer``# Usageimg_resizer --helpOptions:--path TEXT    the directory of images (default: ".")--format TEXT  image format (default: png)--postfix TEXT  mapping @3x image (default: @3x.png)
Package: imgrid
imgridTool for extracting gridified images from LabelMe JSON annotation format.InstallationRequires Python>=3.7Install using pip:pip install imgridUsageUse below command to use the tool:imgrid <json_dir> <out_dir> <width> <height> [--x_stride=<x_stride>] [--y_stride=<y_stride>] [--no_label]json_diris the directory where the LabelMe JSON annotations are saved.out_diris the directory where the extracted images will be saved.widthis the width of each extracted image.heightis the height of each extracted image.x_strideis the stride in horizontal direction while scanning through each shape. By default this is the same aswidth.y_strideis the stride in vertical direction while scanning through each shape. By default this is the same asheight.Add--no_labelif no label needs to be saved. If it is not added, a txt file containing the image class name will be saved along each extracted image.The tool will ignore any non-area annotations in the LabelMe JSONs, i.e., it will only consider shape_type that is eitherpolygon,rectangle, orcircle.
Package: imgroi
imgroiA Python library for working with ROIs from images.Free software: BSD 3-ClauseDocumentation:https://imgroi.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand thenanshe-org/nanshe-cookiecutterproject template.History0.1.0 (2016-10-28)First release on PyPI.
Package: imgscrape
Imgscrape is aseleniumandpillowbased image scraper.Supported WebsitesFlipkartAmazonFeatures!Scrapes  image linkConvert image into thumbnailEncode image into Base64Save image directly to folder  :In developmentImgscrape is aseleniumandpillowbased image scraper. You just provide the product link or a list of the same and it will provide you the links in image or list of it based on your argument.The reason behind this idea is to scrape image links are unreachable by usingBeautifulsoup4. Thus switching to selenium gets you the images you need.Installation & Usage$pipinstallimgscrapeImgscrape requireschromedriverto run. Download it.After downloading you’ll need to provide the path of chromedriver to the constructer.from imgscrape import imgscrape

any_var = #path to driver
var = imgscrape(producturl, path= any_var)you can feed a list of product urls and get the image links in a list.from imgscrape import imgscrape

any_var = #path to driver
lst_var = ['link1', 'link2', 'link3']
var = imgscrape(*lst_var, path= any_var)DevelopmentWant to contribute? Great!
Create a pull request and we’ll see.Supportlinuxwindows### TodosAdd support for MacOSAdd more supported websitesLicenseMITFree Software, Hell Yeah!
Package: imgscraper
imgscraper: yet another web scraperimgscraper is a simple library that allows you to retrieve image information from meme sites.InstallationTo installimgscraper, you can use pip:pipinstallimgscraperImgScrapersupports Python 3.10+Usagefromimgscraper.scraper_constructorimportcreate_scraperimg_scraper=create_scraper(website_url="https://imagocms.webludus.pl/",container_class="image-holder",pagination_class="pagination")img_scraper.start_sync()print(img_scraper.synchronization_data)Output:[Image(source="https://imagocms.webludus.pl/images/01/",url_address="https://imagocms.webludus.pl/img/01.jpg",title="String"),Image(source="https://imagocms.webludus.pl/images/02/",url_address="https://imagocms.webludus.pl/img/02.jpg",title="String")]Pages to scan and scraperThe user can specify how many subpages should be scraped and what tool the application should use.fromimgscraper.scraper_constructorimportcreate_scraperimg_scraper=create_scraper(website_url="https://imagocms.webludus.pl/",container_class="image-holder",pagination_class="pagination",pages_to_scan=1,scraper="bs4")Last sync dataWhen starting the synchronization process, the user can provide data from the last synchronization (img.src).
If the application encounters a provided image, the process is terminated. All previously synced images are available.scraper.start_sync(("https://imagocms.webludus.pl/img/01.jpg","https://imagocms.webludus.pl/img/02.jpg",))Image ObjectThe Image object provides the.as_dict()method to turn it into a dictionary.img=Image(source="https://imagocms.webludus.pl/images/01/",url_address="https://imagocms.webludus.pl/img/01.jpg",title="String").as_dict()Output:img={"source":"https://imagocms.webludus.pl/images/01/","url_address":"https://imagocms.webludus.pl/img/01.jpg","title":"String"}
Package: img-scrapper
img_scrapperA Python package to scrape and download images from Google search using
BeautifulSoup and Requests libraries.InstallationUse the package managerpipto install
img_scrapper.#PyPipipinstallimg-scrapperUsagefromimg_scrapperimportget_original_imagesimages=get_original_images('Spotify',download=False)The script will display the first image it finds in the search
results. If you want to download the image, set download=True in the
get_original_images function call.The downloaded images will be saved in the Bs4_Img directory.ContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details.
Package: imgscrapy
imgscrapyA simple CLI image scraper written in python inspired byImageScraperwith support for headless scraping of dynamic websites.InstallationBuild from sourcegit clone https://github.com/arutselvan/ImgScrapycd ImgScrapypython setup.py installAs a Python packagepip install --user imgscrapyRequirementspython>=3.6Usageusage: imgscrapy [-h] [-d DIRECTORY] [-i] [-n NFIRST] [-t NTHREADS] [-hd] [-to TIMEOUT] target_url

Downloads images from the given URL

positional arguments:
  target_url            URL to scrape images from
optional arguments:
  -h, --help            show this help message and exit
  -d DIRECTORY, --directory DIRECTORY
                        Directory in which images should be downloaded
  -i, --injected        Scrape images from a dynamic website and JS injected images
  -n NFIRST, --nfirst NFIRST
                        Scrape the first n images
  -t NTHREADS, --nthreads NTHREADS
                        Maximum number of threads to use
  -hd, --head           Open chromium for scraping JS injected source/images
  -to TIMEOUT, --timeout TIMEOUT
                        Timeout value for obtaining page sourceExamplesDownload all images from a static websiteimgscrapy <Target URL>Download the first 5 images from a dynamic websiteimgscrapy <Target URL> -i --nfirst 5NoteImgScrapy usespyppeteerwhich uses Chromium for headless scraping. When scraping a dynamic website for the first time, Chromium will be downloaded automatically which might take some time.To DoWrite testsAdd support for Base64 imagesAdd support for embedded/inline svg filesFix issues with headless browsing of dynamic site with modal/popupFix issue with missing trailing slash in URL resolutionAdd option to dump URL of downloaded/failed imagesLicenseMIT
Package: imgsegmentationmq
DescriptionRepository for the image segmentation package.Installing the packageTo install the package in Unix/macOS, execute the following command:python3 -m pip install imgsegmentationmqTo install the package in Windows, execute the following command:py -m pip install imgsegmentationmqImporting and uding the packageTo import the package to your python script run the following line:import imgsegmentationmqTo import all the algorithms to your script run the following line:from imgsegmentationmq import *Once you have run the previous line, you can start using the algorithms by using the respective calls:For the k-means algorithm:

    imgsegmentationmq.means.kmeans(k,image, max_it, init_centroids)

For the c-means algorithm:

    imgsegmentationmq.means.kmeans(k,image,m,max_it,init_centroids)

For the k-quantile algorithm:

    imgsegmentationmq.means.kmeans(k,image,k1,k2,max_it,init_centroids)

For the c-quantile algorithm:

    imgsegmentationmq.means.kmeans(k,image,m,k1,k2, max_it, init_centroids)Please make sure you have the arguments in the appropiate form. Specifically note that to use these algorithms
the image to which you will apply it needs to be a numpy array with shape (image length, image width, image colorscale),
this means that for a 60x40 image in gray colorscale, the shape should be (60,40,1) while for the a 60x40 image with RGB
colorscale, the shape of the numpy array should be (60,40,3).
Package: imgsimilarity
imgsimilarityimgsimilarity tool automates the process of comparing image pairs to check if they are similar or not and produces a score that quantifies the comparison by providing a score between 0 to 1, 0 being exactly identical.How to use the toolThe tool is published in the pypi repository and the latest version can be installed with just a single line of command.PrerequisitesPython should be installed version 3 and above.Python package manager "pip" should be installed.InstallationOpen a terminal and paste:pip install imgsimilarityThis will download the tool and set it up by installing all its dependenciesExecutionThe module imgsimilarity takes a command line argument, i.e the path to the location of the csv file which needs to be provided when running it like in the example belowpython -m imgsimilarity.diff C://pathtocsvfileThe tool will execute and a comparison_results.csv file will be generated in the directory the command was run.How does the tool work?The tool uses dhash algorithm to find a hashcode for each image and then find how many bits differ between the hashcodes to find the score for similarity. Identical images have
difference as 0.The dhash pypi module is being used here, its an out of the box python module to compute hashes and determine the difference for images.
The hash for an image is stored in a dictionary to avoid repeated execution of dhash function and this reduces the cost of similarity detection.
The execution time for each pair is calculated by taking the difference of process time at the start of the iteration and the process time at the end of the iteration.Below is a flowchart that briefly illustrates how the tool functions.How does the tool achieve hassle free installation and usage?It is by using the Python Package Index repository!Once the latest code is pushed and tagged, the gitlab ci is configured to run pytests to make sure the code works and the once the tests succeed it creates the build and a distributable with the help of setup.py file and twindle.
and its uploaded to the PyPi Repository automatically.The user can install the latest version using pip install or upgrade command. Thanks to the setup.py file all the dependencies are installed automaticallyReference:PyPi repo:https://pypi.org/project/imgsimilarity/dhash :https://pypi.org/project/dhash/noteThe source code is well documented and pydoc can be genrated.
Package: imgspy
imgspy finds the metadata (type, size) of an image given its url by fetching
as little as needed. This is a python implementation offastimage. Supports
image types BMP, CUR, GIF, ICO, JPEG, PNG, PSD, TIFF, WEBP.usage>>> imgspy.info('http://via.placeholder.com/1920x1080')
{'type': 'png', 'width': 1920, 'height': 1080}
>>> with requests.get('http://via.placeholder.com/1920x1080', stream=True) as res:
...     imgspy.info(res.raw)
{'type': 'png', 'width': 1920, 'height': 1080}
>>> imgspy.info('/path/to/image.jpg')
{'type': 'jpg', 'width': 420, 'height': 240}
>>> with open('/path/to/image.jpg') as f:
...     imgspy.info(f)
{'type': 'jpg', 'width': 420, 'height': 240}
Package: imgsteg
This is a python library for image steganography. Using this library user can encrypt and decrypt text message inside an image.How to use this packageneed to install packagepip install imgstegIn .py filefrom imgsteg import imgstegcreate objectobj = imgstegHow encryption workimage = "name and path of the image including extension"  #image = "car.png"
new_image = "new.png"
message = "your message that want to encrypt"
obj.Encrypt(image, message, new_image)How decryption workimage = "name and path of the image including extension"  #image = "car.png"
data = obj.Decrypt(image)
print(data.message)
Package: imgsync
OpenStack Glance Image Synchronization toolThis application will download images from the official distribution
repository, and upload them to OpenStack Glance. It is possible to define
custom properties and prefixes for all the synced images.Available distribution repositoriesCurrently the following images repositories are supported:CentOSCentOS 6:http://cloud.centos.org/centos/6/images/CentOS 7:http://cloud.centos.org/centos/7/images/DebianDebian 9:https://cdimage.debian.org/cdimage/openstack/current-9/Debian testing:https://cdimage.debian.org/cdimage/openstack/testing/UbuntuUbuntu 14.04:https://cloud-images.ubuntu.com/trusty/Ubuntu 16.04:https://cloud-images.ubuntu.com/xenial/Ubuntu 18.04:https://cloud-images.ubuntu.com/bionic/InstallationInstall it via PyPI:pip install imgsyncOr install it from the repo:git clone https://github.com/alvarolopez/imgsync
pip install imgsyncConfigurationCopy/etc/imgsync/imgsync.conf.sampleinto/etc/imgsync/imgsync.confand
adjust it your your needs. Take into account the following:You need to configure your OpenStack Keystone authentication under the[keystone_auth]section. The user should be able to publicize images in
your glance deployment (check your policy file).You can define a prefix to be used for all the distribution names with theprefixoption.Additionally you can add some custom image properties, using thepropertiesoption, that can be repeated multiple times for multiple properties.The list of images to be downloaded is configured via thedistributionsoption.CentOS distributions define additional options to allow the download of
all the published images, or just the latest one. This is configured in
the[centos6]and[centos6]sections. This is not possible for Debian and
Ubuntu.Image propertiesimgsyncsets a propertysource=imgsyncto all the images that donwloaded
and synced. This way we can identify if an image is uploaded into glance by us
or by anyone else. This property is hardcoded and cannot be reconfigured or
replaced by something else. Other properties set byimgsyncare are stored with theimgsync.prefix(likeimgsync.sha256orimgsync.sha512)Nevertheless, it is also possible to define additional properties in the form
"key=value" via thepropertiesoption in the configuration file (you can
specify this option several times).Therefore, it is important that you configure glance to enable the properpolicy protection rulesso that only the configured user is able to write those properties (i.e. at
leastsource,imgsync.sha512andimgsync.sha256). Moreover, you need to
configure nova to exclude those properties when nova creates and uploads an
snapshot to glance, via thenon_inheritable_image_propertiesoption in your/etc/nova/nova.confconfiguration file (again, at least addsource,imgsync.sha512andimgsync.sha256).
Package: imgtag
ImgTagSimple XMP Image Tag & Metadata Editing ModuleIt is recomended to install using Conda to create a virtual python environment, as it makes it easy to install and manage different packages. Use the following commands to set up an environment:conda env create -f environment.yml
conda activate imgtagIf you choose to install manually, you must installexempifor your OS.For Debian:sudo apt-get install -y exempiFor a Conda environment:conda install -c conda-forge exempiOnceexempiis installed, installimgtagwith:python3 -m pip install -y imgtagTo useimgtag:fromimgtagimportImgTag# Open image for tag editingtest=ImgTag(filename="test.jpg",# The image fileforce_case="lower",# Converts the case of all tags# Can be `None`, `"lower"`, `"upper"`# Default: Nonestrip=True,# Strips whitespace from the ends of all tags# Default: Trueno_duplicates=True,# Removes all duplicate tags (case sensitive)# Default: Trueuse_warnings=True# Toggles warnings# Default: Truememory_limit_ratio=0.8# The maximum percent of free memory to use# Default: 0.8)# Print existing tagsprint("Current tags:")fortagintest.get_tags():print("  Tag:",tag)# Add tagstest.add_tags(["sleepy","happy"])# Remove tagstest.remove_tags(["cute"])# Set tags, removing all existing tagstest.set_tags(["dog","good boy"])# Save changes and close filetest.close()# Re-open for tag editingtest.open()# Remove all tagstest.clear_tags()# Delete the ImgTag object, automatically saving and closing the filedel(test)
Package: img-term
Image 2 ANSIConverts an image to a representation in ANSIInstallationpipinstallimg_termUsageusage: img_term [-h] [-img IMG] [-width WIDTH] [-cam CAM] [-col {4,8,24}]

Display image to terminal

optional arguments:
  -h, --help     show this help message and exit
  -img IMG       Image file to display
  -width WIDTH   Character width of output
  -cam CAM       Show camera, this is the default
  -col {4,8,24}  Colour scheme to useDisplay an image in terminalimg_term-imgdog.jpgRender your video capture device to terminal:img_termSelect a colour palette:./img_term.py-imglena.jpg-col24ExampleGo from this:To this:Or thisTo this:Or even render a video:./img_term.py-col24-vidunrealset.mkv-width78https://gfycat.com/IdolizedSomeGemsbuckOr simply stream your USB camera to terminal!:https://gfycat.com/RemarkablePalatableKob