Saravanan666/pypi-org huggingface dataset:
Package: image-regist
Image RegistrationThe image-regist library is used to find the best parameters (rotation and translation of the x and y axes) to transform the target image to align with the reference image.ConceptUsing the Particle Swarm Optimization (PSO) algorithm for optimization and Mutual Information as a measurement metric for two images. The image-regist library tries to find the best parameters (rotation and translation of the x and y axes) of the target image so that they can be aligned with the reference image.InstallationFor installation, you can use pippipinstallimage-registor clone from the repositorygitclonehttps://github.com/mufis-coder/image_registRequirementsInstall the packages below according to the version listed to use the image-regist librarynumpy==1.21.6Pillow==9.3.0scipy==1.7.3UsagefromPILimportImagefromimage_regist.toolsimportTransform,findTransformation,transform_image_2d# Load reference and target imagereference_image=Image.open("your-reference-image-file")target_image=Image.open("your-target-image-file")"""Find best parameters to transform target image----In this example three transformations are used [Transform.ROTATION, Transform.TRANSLATION X,Transform.TRANSLATION_Y]. You can use less than three and you don't have to use them sequentially.The result of ---best_params--- is a list in the order according to the parameter ---params---.---If you want the algorithm to run faster, you can set parameter ---faster=True---"""best_params=findTransformation(data1=reference_image,data2=target_image,params=[Transform.ROTATION,Transform.TRANSLATION_X,Transform.TRANSLATION_Y],faster=False)# Transform the target image according to the parameters that have been searched fortranformed_image=transform_image_2d(target_image,[Transform.ROTATION,Transform.TRANSLATION_X,Transform.TRANSLATION_Y],best_params)# Display transformed imagetranformed_image.show()
Package: image-registration
Image Registration Methods for Astronomy(intended forextended emission, not stellar images!)Documentation:http://image-registration.rtfd.orgUsagepip install image_registrationorgit clone https://github.com/keflavich/image_registrationfromimage_registrationimportchi2_shiftfromimage_registration.fft_toolsimportshiftimportimage_registration#Generate Sample Imageimage=image_registration.tests.make_extended(100)offset_image=image_registration.tests.make_offset_extended(image,4.76666,-12.33333333333333333333333,noise=0.1)#Get Fused Imagexoff,yoff,exoff,eyoff=chi2_shift(image,offset_image)corrected_image2=shift.shiftnd(offset_image,(-yoff,-xoff))Requirements:Install the following version of the packages to replicate this repository:FITS_tools==0.2matplotlib==3.4.2astropy==4.2.1For the following packages latest version should work:scipypytestTo replicate in conda you can use environment.yml given in repositoryAcknowledgments:Borrows heavily fromhttp://www.mathworks.com/matlabcentral/fileexchange/18401-efficient-subpixel-image-registration-by-cross-correlation/content/html/efficient_subpixel_registration.htmlReplicates much of the functionality ofhttp://www.astro.ucla.edu/~mperrin/IDL/sources/subreg.proAlso implementshttp://solarmuri.ssl.berkeley.edu/~welsch/public/software/cross_cor_taylor.pro
Package: image-rename
Rename the picture by looking through the human eye and typing. (you don’t need to open the file by yourself)INTRODUCTIONIn response to the vigorous development of machine learning, label data often processed in supervised learning, so how to quickly name it manually has become a topic.
The purpose of this tool: I hope that users can quickly name tag data.Installpip installimage-renameUSAGEopen your CMD, and then:img_rename "PATH OF YOUR SETTING.PY"NoteAbout the setting file you can reference inhere.get help:img_rename-hDEMOyou can add otherpluginsto see more information {[IFD],[EXIF][GPS], …}, like below:REFERENCEFOOTNOTES[IFD]https://www.awaresystems.be/imaging/tiff/tifftags/privateifd.html[EXIF]Exchangeable Image File Format[GPS]Global Positioning System tag
Package: image-renamer
image-renamerCommand developed to rename your image files using timestamp information stored in metadata(EXIF) of image.InstallationUsing pip managerpip install image-renamerManuallygit clone <github-link>
cd <repository>
python3 setup.py installUsageSimple image renamer
Command developed to rename your image files using timestamp information stored in metadata(EXIF) of image.

positional arguments:
  path        Path to folder

optional arguments:
  -h, --help  show this help message and exit
  -f FORMAT   format of the new file name
  -r          search images recursively
  --dry-run   run renamer without changing anything

File name is created based on format specified by user(required). 
Following tags are supported. Tags must be located within '{}':
YYYY - Year
MM   - Month
DD   - Day
hh   - Hours
mm   - Minutes
ss   - Seconds

Example: "{YYYY}-{MM}-{DD}"
Output: 2021-09-03.jpg
Package: image-reorder
Image ReorderReorder images from multiple cameras into a single folderTo use, create a source directory (say,pictures) with a subdirectory for
each camera (pictures/mom,pictures/dad, etc.).  Run theanalyzecommand
to get some basic info about what images exist in the source directory:$ reorder analyze --help
Usage: reorder analyze [OPTIONS] <source-dir>

  Analyze images in a source directory.

  Finds all images in a source directory and generates some information about
  those images, including camera models.

Options:
  -h, --help  Show this message and exit.Then, run thecopycommand to copy files from the source directory to some
target directory:$ reorder copy --help
Usage: reorder copy [OPTIONS] <source-dir> <target-dir>

  Reorder images from a source directory into a target directory.

  Finds all images in a source directory and reorders them into a target
  directory by EXIF creation date, taking into account any offsets.  The
  target folder will be created if it does not already exist.

  The copied filenames will get a prefix like "image001__".  This way, you can
  sort the images by filename, and they'll have the correct order.

  If the clocks on the cameras are not in sync, you may optionally provide a
  time offset by camera model.  The configured hours and minutes will be added
  to or removed from the the actual EXIF time.  Use a format like "PowerShot
  A70=+06:55" or "Pixel 2=-00:03".  The `reorder analyze` command will show
  you all of the different camera models among your images.  You can provide
  the --offset switch multiple times.

Options:
  -o, --offset <offset>  Time offset like 'PowerShot A70=+06:55'
  -h, --help             Show this message and exit.
Package: image-resize-python-script
image-resize-python-scriptThis repository contains a Python script for batch processing of images (PNG). The script allows you to resize multiple images at once, making it convenient for tasks such as creating thumbnails or resizing images for web applications.UsageClone the repository to your local machine.Install the required dependencies by runningpip install -r requirements.txt.Place the images you want to resize in theinputfolder.Open a terminal or command prompt and navigate to the project directory.Run the script using the commandpython resize_images.py.The resized images will be saved in theoutputfolder.Feel free to customize the script according to your specific requirements. Happy image resizing!LicenseThis project is licensed under the MIT License - see theMIT LICENSEfile for details.
Package: image-resizer
Python tool to resize a bunch of images quickly.InstallationInstall via pip using:pip installimage-resizeGet the cutting edge version using:pip install-egit+https://github.com/ssundarraj/image_resizer.git#egg=image_resizerorClone/download the git repo, and install usingpython setup.py.Usageimage-resizeinput_file.jpg
Package: image-restoration-tools
# A package of image restoration by leoThere are three methods in our package, which are# import the packageimport image_restoration_tools.irt as irt## 1. input the images dir and the dir path where enhanced images saveirt.restore_dir(imgdir, savedir, path_length=3, isJPEG=False, isBLUR=False)## 2. input the image path and the path where the enhanced image saveirt.restore_path(imgpath, savepath, path_length=3, isJPEG=False, isBLUR=False)## 3. input the image and the path where the enhanced image saveimport image_restoration_tools.utilsimg = utils.imread_uint(imgpath, 3)irt.restore_image(img, savepath, path_length=3, isJPEG=False, isBLUR=False)You can set enhancement path by set $path_length$, the smaller the value, the shorter the recovery time will be.if the degrading process is known as JPEG/Gaussian BLUR, set $isJPEG/isBLUR$ true separately
Package: imageretriever
ImageRetrieverImage retriver is a simple Django app to help you get the missing
image from the server when developping (for example when you get a
fresh database and don’t want to download again the 3Gb of images
for the few images you miss since last sync).InstallIf you’re using buildout, just add ‘imageretriever’ to your list of
eggs.If not, you can install the egg to your python setup using:easy_install imageretrieverYou can also download the sources and add them to your local environment.Using image retrieverStart the django shell, then type:from imageretriever.utils import sync_images
sync_images('http://localhost:8000/', 'http://example.com/')It will list all image fields in the models and download them if they
are not on your local filesystem.You can also specify the number of milliseconds between each image
download (to avoid being too agressive with the server) and if you
want more information:sync_images('http://localhost:8000/', 'http://example.com/', 100, True)imageretriever logs0.1 (2011-08-26)added base functionalities. [vincent]
Package: imagerie-lite
Imagerie liteLightweight python package grouping together useful computer vision functions and operationsThis package was initially created for myself to extract some of the most useful important functions,from packages likescipy,skimage, etc...This was done in order to expose all these very useful computer vision functions,and be able to use them in aServerlessenvironment.DocumentationDocumentation related files are stored indocs/directory.Getting startedPrerequisitesInstallationDocumentationFunctionsCreditsIbragim Abubakarov<ibragim.ai95@gmail.com>
Package: imageryclient
ImageryClientConnectomics data often involves a combination of microscopy imagery and segmentation, labels of distinct objects applied to this imagery. While exploring the data in tools like Neuroglancer is great, a common task is often to make figures overlaying 2d images and segmentation sliced from the larger data. ImageryClient is designed to make it easy to generate aligned cutouts from imagery and segmentation, and make it efficient to produce attractive, publication-ready overlay images.Because of the size of these volumes, cloud-based serverless n-d array file storage systems are often used to host this data. CloudVolume has become an excellent general purpose tool for accessing such data. However, imagery and segmentation for the same data are hosted at distinct cloud locations and can differ in basic properties like base resolution. Moreover, imagery and segmentation have data that means intrensically different things. Values in imagery indicate pixel intensity in order to produce a picture, while values in segmentation indicate the object id at a given location. ImageryClient acts as a front end for making aligned cutouts from multiple cloudvolume sources, splitting segmentations into masks for each object, and more.
Package: images2video
images2video=============Python images to video library using OpenCV## Installation```bashpip install images2video# orpip install https://github.com/messense/images2video/archive/master.zip```Note that you also need to install OpenCV and numpy to make it work.## Usage```pythonfrom images2video import ImagesToVideofrom images2video.effects import *  # NOQAvideo = ImagesToVideo('test.avi', seconds=10)video.add_image('1.jpg', ResizeEffect)video.add_image('2.jpg', CropEffect, bounce=True)video.add_image('3.jpg', RotationEffect, bounce=True)video.generate()```## LicenseThe MIT License (MIT)Copyright (c) 2014 messensePermission is hereby granted, free of charge, to any person obtaining a copyof this software and associated documentation files (the "Software"), to dealin the Software without restriction, including without limitation the rightsto use, copy, modify, merge, publish, distribute, sublicense, and/or sellcopies of the Software, and to permit persons to whom the Software isfurnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in allcopies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS ORIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THEAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHERLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THESOFTWARE.
Package: images-automate-python-souravdlboy
Automate-Images + Videos-downloadIntroduction :Data collection is one of the important steps in Data analysisÂ , Machine learning and Deep learning.
It is the process of gathering information of our interest, in our case it's the images of any type we want to gather.
Without data, we can't model our problem and do analysis of it.Objective :ðŸ‘‰ Automate the collection and building of images using Google API's for search and videos using Youtube API's.ðŸ‘‰ Option to send mails to anyone containing collected datasets as attachment.Motivation :This is a part of project - IV (Task-I) made for UCS633 - Data analytics and visualization at TIET.@Author : Sourav Kumar@Roll no. : 101883068Getting credentials for Google API and Youtube API:ForGoogle image APIForYoutube APIGetting Started Locally :After getting the credentials , we can now run the script for automation.Firstly download all the files on your system using cmd (terminal) or git bash :git clone https://github.com/souravs17031999/AUTOMATE-DATA-PIPELINEWith Tkinter GUI :For IMAGES :Move to the directory where script is downloaded.Now run the script in following way :python data_image_automation.pyand arguments can be passed from interactive GUI.For VIDEOS :Move to the directory where script is downloaded.Now run the script in following way :python data_video_automation.pyand arguments can be passed from interactive GUI.Note : Two important args :Client secrets file path: Give absolute path.Chrome driver path : Give absolute path with .exe extension.Output :Some samples of images :Limitations and Scope for future work:There are few limitations :Currently Google and Youtube allows 100 requests in one API call and so the script is able to download in total 100API's in one go but this behaviour can be changed from the script by calling it multiple times after tweaking some parameters which controls the after and before id's of returned images and videos and can be checked in their API documentation.[For Images] Considering big file sizes for sending as mails, i have resized , gray-scaled and then zipped the files and upload as attachment.This behaviour can be changed from the script by disabling some of the functions passing a optional parameter.[For videos] Considering big file sizes for sending as mails, i have compressed the file sizes and reduced the duration of videos.This behaviour can be easily manipulated in the script itself.Also, mail can be only sent for GMAIL users as of now, this can also be changed by manipulating the Ports of all email servers and controlling their behaviours in the script.
Package: images-compare
Images Compare 🖼️This Python package allows for image comparison between two provided images using a specified threshold.InstallationInstall images-compare with pippipinstallimages-compareDemoCopy the below code snippet for a quick startfromimages_compareimportverify_imagesres=verify_images("img_1.png","img_2.png",0.0001)print(res)UsageThe functionverify_imagesis designed to take three parameters: the first two parameters are the images to be compared, and the third parameter represents the threshold difference. The default value for the threshold is set to 0.0001.AuthorsDenzil RodriguesLicenseMIT
Package: imagescore
Bjorn scoreCalculate image similarity scoreTools used:Language: Python
Dev Modules Used: 
- opencv-python
- scikit-image
- click
Test Modules Used:
- tox
- pytest
- pylint
- pytest-cov
Build and Deployment: Travis-ci
App Repository: PyPiInstallation:pip3 install imagescoreHow to run:imagescore --infile 'input.csv' --outfile 'output.csv'The options are:--infile  | -i [str]    -- [Input file path]
    --outfile | -o [str]    -- [Output file path]
    --height  | -h [int]    -- [Optional: height to be resized, default = 4096]
    --width   | -w [int]    -- [Optional: width to be resized, default = 4096]Expected Input: csv file with images and its absolute pathExpected Output: csv file with images and its absolute path, image score and elapsed time in secs.Developer mode:The application is written in python3. Hence create a virtualenv with python3 and install the dependencies fromrequirements.txt.virtualenv --python=python3 venv
pip install -e .The unit test cases are located in tests directory. Install the dependenciesrequrements-dev.txt.pip install -r requirements-dev.txt
toxDeployment:The python package is build with the travis.yml script when there are changes on the master branch.
To push a new version:
Make the changes in the application, push it to any feature branch and merge with master.To deploy a new version tag it. e.ggit tag -a v1.1.0
git push origin v1.1.0When a tag is pushed, travis starts building and uploads the application to pypiPypi Release:https://pypi.org/project/imagescore/Github Release:https://github.com/sandjaie/image_score/releasesTravis Builds:https://travis-ci.com/sandjaie/image_score/Manual Deployment:We are using twine to upload the artifact to Pypi registry.
Pypi registry needs an account to be created. Enter the credentials in the cli when prompted.pip3 install twine
python3 setup.py sdist bdist_wheel
twine upload dist/*
Package: image-scramble
PycassoSplit image into tiles and scramble/unscramble them based on a seed. The demo application can be found athttps://image-scramble.herokuapp.com.ScrambleUsing scramble mode onthis sample imagewill produce the following output:UnscrambleTo revert the image to its original state, use the sameseedandslice_sizeon unscramble mode.CreditsPycasso is a Python version ofwebcaetano/image-scrambleandwebcaetano/shuffle-seed. This also uses a stripped-down port ofdavidbau/seedrandomto initialize the PRNG.Sample image is taken fromPepper&Carrotby David Revoy licensed underCC BY 4.0.SetupInstall from PyPI$pipinstallimage-scrambleInstall from sourceFirst, you should get a copy of this project in your local machine by either downloading the zip file or cloning the repository.git clone https://github.com/catsital/pycasso.gitcdintopycassodirectory.Runpython setup.py installto install package.UsageWeb applicationRunpython app/app.pyDockerBuild from local using:$dockerbuild-t"image-scramble".
$docker--namepycasso-p5000:5000-dimage-scrambleGet image fromDocker Hub:$dockerpullcatsital/image-scrambleCommand-line utility$pycassoimage_input.pngimage_outputscrambleThis will produce a scrambled image with seed based on your current system time. Hence, the output will be completely unpredictable and irreversible. Use the options-nforslice_size,-fforformat, and-sforseed, like so:$pycassoimage_input.pngimage_outputscramble-n5050-sseed-fjpegScriptInitialize aCanvasand use scramble onexportby:frompycassoimportCanvasCanvas('image_input.png',(30,30),'seed').export('scramble','image_output','jpeg')
Package: image-search
Image Search Python PackageA simple python package for saving images from Bing and Google without using API keys. This package utilizes web browsers to help scrape images found on web searches.This should only be used for educational and personal purposes only. I am not responsible for any issues that may arise by scraping such sources. All images are copyrighted and owned by their respective owners, I do not claim any ownership.Usageusage: image_search [-h] [--limit LIMIT] [--json] [--url URL]
                [--adult-filter-off]
                engine queryExample:Google Imagesimage_search google cat --limit 10 --jsonThis will download 10 cat images and metadata from Google Images.Example:Bing Imagesimage_search bing dog --limit 10 --jsonThis will download 10 dog images and metadata from Bing Images.FAQsHow can I download a specific amount of images?Use the--limitflag to define the amount of images you want to download.How do I search with custom filters on Google Images?Use the--urlflag to define your own url with the search filter.DisclaimerThis program lets you scrape/download many images from Bing and Google. Please do not download any image violating its copyright terms. Google Images and Bing Images are merely search engines that index images and allow you to find them. Neither Google nor Bing produce these images, and as such, they don't warrant a copyright on any of the images. The original creators of the images own the copyrights.Images published in the United States are automatically copyrighted by their owners, even if they do not explicitly carry a copyright warning. You may not reproduce copyright images without their owner's permission, except in "fair use" cases. You could risk running into lawyer's warnings, cease-and-desist letters, and copyright suits. Please be careful, and make sure your are not violating any laws!
Package: image_segmenter
ImageSegmenterThis Python package provides a classImageSegmenterfor annotating images with bounding boxes using various image processing techniques.Table of ContentsBackgroundInstallationUsageSupportContributingBackgroundImage segmentation and annotation is a crucial part of any machine learning project that deals with images. TheImageSegmenterclass provided in this package simplifies this process. It uses various image processing techniques like morphological operations, thresholding, and channel selection to help generate accurate bounding boxes. It also includes the ability to visualize the results, save the annotations, and apply various filters.InstallationTheImageSegmenterpackage requires:OpenCVNumPyMatplotlibVOCWriter (For saving annotations)To install these dependencies, you can use pip:pipinstallopencv-pythonnumpymatplotlibvoc-writerTo use theImageSegmenterclass, simply include it in your Python script:fromimage_segmenterimportImageSegmenterUsageHere's an example usage of theImageSegmenterclass:fromimage_segmenterimportImageSegmenter# Instantiate the ImageSegmenterannotator=ImageSegmenter()# Read an imageimage=annotator.read_image('path_to_image')# Convert to grayscaleimage_gray=annotator.select_colorsp(image,'gray')# Apply thresholdingthresholded=annotator.threshold(image_gray)# Apply morphological operationsmorphed=annotator.morph_op(thresholded,'open')# Find contoursbboxes=annotator.get_bboxes(morphed)# Visualize the results ########################### draw bounding boxesannotated=annotator.draw_bboxes(image,bboxes)# show the imageannotator.display_image(image,annotated)########################## Save the annotationsannotator.save_annotations('path_to_save_annotations',image,bboxes)TODOWrite testsadd multi-label supportadd support for other image formatsadd support for other image processing techniquesadd support for other annotation formatsSpecial ThanksA special thanks toKukil, the author ofthis guidefrom which these methods were generated