Saravanan666/pypi-org huggingface dataset:
Package: imggencli
ImgGenCli 💠imggencliis a community-maintained cli designed to provide users with the ability to generate, edit and filter images using the DALL-E 2 API provided by OpenAI, all from the command line.The tool provides three main commands, generate, edit, and filter.Thegeneratecommand allows the user to generate an image using a prompt, with the option to apply a filter, specify the size, and choose the number of times to generate the image. The generated image can also be saved to a specified file path.Theeditcommand provides the ability to edit an existing image by adjusting the brightness, contrast, and sharpness of the image. The edited image is then saved to a new file.Thefiltercommand allows users to apply various filters and effects to an existing image. The user can select from a range of different filters, and the filtered image can be saved to a specified file path.Thus it allows for a low end build that can work on almost all computers with high sense of flexibility with it's customization options.ConfigurationThe cli requires you to have an api token to query the OpenAI's api. You can read about and get it herehttps://platform.openai.com/account/api-keys.InstallationInstall the imggencli python package directly from pypi.pip install imggencliUsageUsage: imggencli [OPTIONS] COMMAND [ARGS]...💠 Use the Dall.E 2 api to generate, edit & filter images from the cmd line.Options:--help  Show this message and exit.Commands:edit      🎴 Change the brightness, contrast and sharpness of an image.filter    🦄 Apply filters and effects to an image.generate  🌸 Generate an image from the OpenAI Dalle api.update    🔐 Update the OpenAI API key.Commands and OptionsgenerateUsage: imggencli generate [OPTIONS]🌸 Generate an image from the OpenAI Dalle apiOptions:--prompt TEXT                   💬 The prompt to generate the image from.--size TEXT                     📐 The size of the generated image.--filter                        🎨 Apply a filter to the generated image.--iterations INTEGER            🔄 The number of times to generate the image--save-path FILE                💾 Save the generated image to the specifiedfile path--help                          Show this message and exit.editUsage: imggencli edit [OPTIONS] IMAGE_PATH🎴 Change the brightness, contrast and sharpness of an image.Options:--brightness FLOAT  💡 The brightness of the image--contrast FLOAT    🌈 The contrast of the image--sharpness FLOAT   🔪 The sharpness of the image--help              Show this message and exit.filterUsage: imggencli filter [OPTIONS] IMAGE_PATH🦄 Apply filters and effects to an image.Options:--filter [grayscale|sepia|blur|contour|detail|edge_enhance|edge_enhance_more|emboss|find_edges|sharpen|smooth|smooth_more|outline|posterize|solarize|invert|flip]🎨 The filter to apply to the image--save-path PATH                The directory to save the filtered image.Defaults to the current directory.--help                          Show this message and exit.updateUsage: imggencli update [OPTIONS]🔐 Update the OpenAI API key.Options:--help  Show this message and exit.Please feel to create issues or request for features. There will be many features added to the cli.
Package: imgInfo
imgInfo - Show image informationGet all information of image, supported imges types are png, jpej, tif etc. Tool supports API and Command line interface.InstallationImgInfo requiresPython 3.5to run.
Install the dependencies and devDependencies and start the server.pip$pipinstallimgInfopython3$gitclonehttps://github.com/sulabh9999/imgInfo.git
$cdimgInfo
$python3setup.pyinstallDevelopmentcli interface$imginfoimage.jpg
Image:image.jpg
Width:1024Height:1024Bands:4python3 APIimportimgInfo.mainasimresult=im.info('image.jpg')print(result)pillow- The framework used to read imageContributingFor improvement pull requests are wellcome.LicenseApache
Package: imginit
imginitimginitis a CLI (Command-Line Interface) application made in Python that creates images and allows customisation for what they contain. You can customise the size, colour and the output directory (what folder the images will be placed in after creation).imginitalso comes with built-in default argument values, making the creation of test material more efficient when usingimginit.InstallationRequirementsPython 3.7.1or laterPythonYou can get the latest version of Python 3 for your OS (Operating System) by clickinghere, However, if you would like the exact version thatimginitwas made in, you can download the set-up files for that versionhere.After installation, or if you already have Python installed, you can usepipto installimginitlike so:pipinstallimginitUsageimginit n [--size/-s width height] [--colour/-c rg b] [--dir/-d path]Usingimginiton its own creates 10 white, 800x600, .PNG images within the directory the command was run in. This can be helpful for creating images made for testing purposes both quickly and efficiently.nUsing thenargument defines how many images will be made. It cannot be less than or equal to 0 and must be an integer, otherwise the program will not work accordingly.imginit 100Creates 100 of the same images as described above.sizeUsingimginit --sizeenables size manipulation for all of the images being generated. The parameter requires 2 arguments: the width and the height.imginit 100 --size 1920 1080You can also use the shortened alias-sfor faster queries.imginit 100 -s 1920 1080colourTo change the colour of the image, you can use the--colourparameter followed by 3 numbers, all of which must be a number between 0 - 255. The colour mode used is RGB, so if you would like the image to be red then you would doimginit 50 --colour 255 0 0Colour is spelled this way due to my being in the UK, but it also supports US spelling so you can do--colorinstead andimginitwill still work accordingly.The shortened alias-ccan also be used as a replacement.imginit 50 -c 255 0 0directoryimginituses the CLI's current working directory as the output directory. This can be changed by using the--dirparameter and supplying a path to a directory that exists.imginit 25 --dir C:\Users\Me\PicturesYou can always use-das the shortened alias for--dir.imginit 25 -d C:\Users\Me\PicturesNoteCurrently the only OS I have testedimginiton is Windows 7, so if there are any issues then feel free to report the issuehere.Licenseimginitgoes by the MIT License as shown inLICENSE.
Package: imgk
ImgkImgk is an image processing utility that provides various functionalities for manipulating and processing images from the command line.FeaturesImage resizing: Resize images to specific dimensions.Image conversion: Convert images to different formats.Image cropping: Crop images to a specified region.Image filtering: Apply various filters and effects to images.Image metadata extraction: Extract metadata information from images.InstallationYou can install imgk using PyPI:pipinstallimgkMake sure you have Python 3.9 or later installed.If not go to thiswebsiteand download python 3.9UsageTo use imgk, open a terminal and run theimgkcommand followed by the desired subcommand and options.Examples:Resize an image:imgkresize--width800--height600input.jpgoutput.jpgConvert an image:imgkconvert--formatpnginput.jpgoutput.pngCrop an image:imgkcrop--x100--y100--width200--height200input.jpgoutput.jpgApply a filter to an image:imgkfilter--typegrayscaleinput.jpgoutput.jpgExtract metadata from an image:imgkmetadatainput.jpgFor more information and available options, run imgk --helpLicenseThis project is licensed under the MIT License. See the LICENSE file for details.ContributingFeel free to submit issues and pull requests.AcknowledgementsPillow- Python Imaging Library (PIL)AuthorPrakhar Rathore -GitHub
Package: imgkerneliu
imgkernelAn image kernel is a small matrix used to apply effects like the ones you might find in Photoshop or Gimp, such as blurring, sharpening, outlining or embossing. They're also used in machine learning for 'feature extraction', a technique for determining the most important portions of an image.安装pipinstallimgkernel使用importimgkernelimportmatplotlib.pyplotasplt%matplotlibinlinedefshow2imgs(img1,img2,title=None):fig=plt.figure(figsize=(10,5))iftitleisnotNone:fig.suptitle(title)plt.subplot(121)plt.axis('off')plt.imshow(img1)plt.subplot(122)plt.axis('off')plt.imshow(img2)plt.show()imgpath='image.jpeg'1. 鲜明show2imgs(*imgkernel.identity(imgpath,gray=False,iden=1.6),'imgkernel.identity()')2. 模糊show2imgs(*imgkernel.blur(imgpath,gray=False),'imgkernel.blur()')3. 锐利show2imgs(*imgkernel.sharpen(imgpath,gray=False,inner=1.7,edge=-0.08),'imgkernel.sharpen()')4. 浮雕show2imgs(*imgkernel.emboss(imgpath,gray=False),'imgkernel.emboss()')5. 轮廓线show2imgs(*imgkernel.outline(imgpath,gray=False,inner=8.9,outer=-1.29),'imgkernel.outline()')6. 边沿检测6.1 上边沿show2imgs(*imgkernel.sobel(imgpath,gray=False,direction='top',base=0.03),'imgkernel.sobel(top)')6.2 下边沿show2imgs(*imgkernel.sobel(imgpath,gray=False,direction='bottom',base=0.03),'imgkernel.sobel(bottom)')6.3 左边沿show2imgs(*imgkernel.sobel(imgpath,gray=False,direction='left',base=0.03),'imgkernel.sobel(left)')6.4 右边沿show2imgs(*imgkernel.sobel(imgpath,gray=False,direction='right',base=0.03),'imgkernel.sobel(right)')
Package: imglatex
Image to LaTeXTurns a folder full of images into a LaTeX file that contains them as figures with captions.Free software: MIT licenseDocumentation:https://imglatex.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-02-16)First release on PyPI.
Package: imgload
imgloadimgloadis a python library to download images from several different hosters.
It's an ideal companion for writing web-crawlers, just feed the urls to imgload and get the raw bytes or a finished PIL 'Image' back.Supported Hosters:Currently the following image hosters are supported.http://imgur.com/http://www.imagevenue.com/https://fastpic.ru/http://www.imagebam.com/Further planned hosts:https://www.turboimagehost.com/If you know other hosts that should be supported, create an Issue with a test file or send a pull request.
Package: imglyb
imglybimglybaims at connecting two worlds that have been seperated for too long:Python withnumpyJava withImgLib2imglybusesjpypeto accessnumpyarrays and expose
them toImgLib2throughimglib2-imglyb.
This meansshared memorybetweennumpyandImgLib2, i.e. anyImgLib2algorithm can run onnumpyarrays without creating copies of the data!
For example, Python users can now make use of theBigDataViewer extensionto visualize dense volumetric
data.If you are interested in usingimglyb, have a look at theexamplesfolder
and extend the examples as needed!Note:NEP 18has
the potential to improvenumpy-imglibinteroperability, especially when
convertingimglib2data structures tonumpy.InstallationPrerequisitesimglybhas been tested on Linux, macOS, and Windows.The following tools are required:Python 3Java 8 or 11 JDK (JRE is not enough)Apache MavenIf you useconda, these will be installed for you.Installing with condacondainstall-cconda-forgeimglybInstalling with pipFirst, install the prerequisites above. Then run:pipinstallimglybIt is recommended to do this from inside a virtualenv or conda environment,
rather than system-wide.Installing from sourceFirst, install the prerequisites above. Then run:gitclonehttps://github.com/imglib/imglybcdimglyb
pipinstall-e.It is recommended to do this from inside a virtualenv or conda environment,
rather than system-wide.UsageIt is suggested to follow and extend the examples in theexamplesfolder
according to your needs.Or, for a higher-level way to useimglyb, check outpyimagej.Known IssuesAWT on macOSAWT and Cocoa do not get along perfectly. In general, the Cocoa event loop
needs to be started before the JVM is loaded. (Thanks to @tpietzsch for
figuring this out!) This requires some macOS specific code, written usingPyObjC, to properly start up and shut down the Cocoa application and start
the Java/Python code within it.TheOSXAWTwrapper.pyscript included in theimglyblibrary provides an
example of Cocoa code and can be used to run theimglybexamples. Two
packages fromPyObjCare required for this wrapper (pyobjc-coreandpyobjc-framework-cocoa), and they should be installed withimglybon macOS.When running the wrapper, one can either provide the name of the target module
(as if usingpython -m) or the full path to the target script. So using the
module name, the command to run the "butterfly" script inimglyb-exampleslooks like this:pythonimglyb/OSXAWTwrapper.pyimglyb-examples.butterflyRunningOSXAWTwrapper.pyviapython -mdoes not work at this time.
Package: imglyb-bdv
imglyb-bdvimglyb-bdvextendsimglybwith convenience
methods and structures fromBigDataViewerIf you are interested in usingimglyb-bdv, have a look at theexamplesfolder
and extend the examples as needed!InstallationPrerequisitesimglyb-bdvhas been tested on Linux, macOS, and Windows.The following tools are required:Python 3Java 8 or 11 JDK (JRE is not enough)Apache MavenIf you useconda, these will be installed for you.Installing with condacondainstall-cconda-forgeimglyb-bdvInstalling with pipFirst, install the prerequisites above. Then run:pipinstallimglyb-bdvIt is recommended to do this from inside a virtualenv or conda environment,
rather than system-wide.Installing from sourceFirst, install the prerequisites above. Then run:gitclonegit://github.com/imglib/imglyb-bdvcdimglyb-bdv
pipinstall-e.It is recommended to do this from inside a virtualenv or conda environment,
rather than system-wide.UsageIt is suggested to follow and extend the examples in theexamplesfolder
according to your needs. Note that the examples have additional dependencies
(hdf5and/ordask).Or, for a higher-level way to useimglyb, check outpyimagej.
Package: imglyb-examples
imglyb-examplesThis is a repository of examples forimglyb.DependenciesAll dependencies will be installed automatically when installingimglyb-examplesthrough conda.imglybnumpyscikit-imageimglybis available on conda or you can install it manually following the build instructions. All other depdendencies are available through your package manager,pip, or various anaconda channels.InstallManualFirst, make sure that all dependencies are installed, then runpythonsetup.pyinstallCondacondainstall-chanslovskyimglyb-examplesIfimglybwas not previously installed, re-source the current conda environment after installing to set the environment properly.RunThese examples are available.# bash# Basic visualization and application of Python and ImgLib2 filterspython-mimglyb_examples.butterfly# Python implemented overlays for BigDataViewerpython-mimglyb_examples.bdv_hello_world# Python implementation of a painting tool for BigDataViewerpython-mimglyb_examples.bdv_painter# Embed BigDataViewer in Qt application (only Linux, and Qt and python-xlib dependencies)python-mimglyb_examples.qt_awt# Basic visualization of series of 2D NumPy arrayspython-mimglyb_examples.views_stackI recommend starting your own code from these examples and extend as you need. On Linux and Windows you can run these commands like this, as well:imglyb-examples.butterfly
imglyb-examples.bdv-hello-world
imglyb-examples.bdv-painter
mglyb-examples.qt-awt
imglyb-examples.views-stack
Package: imgmanip
Image manipulation tool in which you can select a large number of photos and
perform a lot of different operations on them.InstallationInstall from PyPIpipinstallimgmanipIf you want the program to be displayed in the system applications menu. (Linux only)sudoln-s$(whereisimgmanip)/usr/bin/imgmanip
curlhttps://raw.githubusercontent.com/hawier-dev/imgmanip/develop/imgmanip.desktop>./imgmanip.desktop
sudomv./imgmanip.desktop/usr/share/applications/imgmanip.desktopThe current list of operationsResize- resizes the images to the given resolution or by a specified percentage.Compress- compresses the image. The lower the 'quality',
the smaller the file size.Invert- inverts the colors of the image.Flip- flips the image in horizontal or vertical axis.Color detection- marks where the given color appears in the image.
Additionally, it can save themaskin .png format.Convert- converts the image to the other format.Convert color mode- This task converts the image color mode to the selected one.
Package: imgmatch
imgmatch: Image Template Matching Toolimgmatchis a command-line interface (CLI) tool designed for efficient template matching in images, making it a great asset for computer vision, digital forensics, and automated image analysis. It offers a streamlined approach to detect and analyze templates in a variety of image sizes and formats.Key FeaturesMulti-Scale Template Matching: Detects templates at multiple scales, providing thorough matching across diverse image dimensions.Rotation and Flip Detection: Capable of recognizing templates that are rotated or flipped, enhancing its effectiveness in complex imaging scenarios.Parallel Processing: Leverages multi-processing for speedy template matching, especially useful for processing large image datasets.Customizable Search Parameters: Allows adjustments of scale range, rotation angles, and confidence thresholds to meet specific requirements.User-Friendly Interface: Offers a simple and intuitive CLI, making it accessible for both beginners and experienced users. Clear documentation ensures ease of use.Python Integration: Built using Python and popular libraries like OpenCV and NumPy, ensuring robust and reliable performance.Ideal Use CasesSuitable for computer vision professionals and enthusiasts.Useful for researchers and students in digital image processing and related fields.A tool for practitioners in digital forensics and content authentication.Applicable for automated quality inspection in manufacturing and industrial environments.Getting StartedInstallationTo installimgmatch, use the following command:pipxinstallimgmatchUsageRun imgmatch on your desired image or directory of images:imgmatch/path/to/image/or/directory--scale-start0.5--scale-end2.1--confidence0.8--num-processes4Customizing ParametersYou can customize the search parameters to fit your specific needs. Here are some of the options you can adjust:--scale-start: Starting scale (default is 0.5).--scale-end: Ending scale (default is 5.1).--confidence: Confidence threshold for template matching (default is 0.8).--num-processes: Number of processes for parallel execution (default is 6).--angle-start: Starting angle for rotation (default is 0).--angle-end: Ending angle for rotation (default is 360).--angle-step: Angle step for rotation (default is 90).--template: Path to template image (default is None).--output-dir: Path to output directory (default is current directory).ContributingContributions to imgmatch are welcome! Whether it involves fixing bugs, improving documentation, or suggesting new features, we value your input.Licenseimgmatch is released under the MIT License.
Package: imgm-compress
一个主流图片压缩方案的自动化脚本（利用 tinify 实现）实现三种图片输入的方式用户可以指定需要压缩的图片文件名称（支持多个图片文件，使用空格隔开）如果用户未指定图片，默认压缩当前文件夹下所有的 jpg/jpeg/png/webp 图片支持图片的尺寸调整输入宽度和高度（比如 600 800），表示按比例缩小图片，使其适合给定的尺寸输入宽度（比如 600 0），表示按确定的宽度调整图片，高度适配输入高度（比如 0 800），表示按确定的高度调整图片，宽度适配如未输入宽度和高度，则表示按照原始分辨率来压缩图片调用方法import img_compress

img_compress.tinify.key = "" # 请在https://tinify.cn/developers申请密匙
img_compress.compress() # 可以指定多个图片文件，未指定则压缩当前路径下的图片注意：需先到tifiny官网申请API密匙 ->传送门
Package: imgMS
imgMSIntroductionLA-ICP-MS data require high level of manual processing to achieve results. Often the time needed for data evaluation by far exceeds the time necessary for data aquisition. During analysis, mass spectra are either written as a separate files, or as a continuous data stream into one single file. Both of these aproaches require a different workflow of processing steps.imgMSis a Python package which aims to provide an easy and intuitive way of working with LA-ICP-MS data acquired into single file.imgMSautomatically processes these steps of analysis:bacground/signal identificationsignal despikingbackground substractiontotal sum normalisationinternal standard normalisationquantification to reference materialgeneration of elemental mapsMotivationAs a Phd student in the field of analytical chemistry using LA-ICP-MS for rutine analysis as well as research tasks, I found it pretty difficult and time consuming to evaluate data without an appropriate software. Despite the fact, that there exists multiple free or open source software, I couldn't find any that would fit the workflow of our department. What started as a simple script for data reduction of LA-ICP-MS data, is now a complete python package.This package is intended to provide a quick, as well as (hopefully) easy to undestand, way of getting a results from bulk analysis or elemental imaging using LA-ICP-MS.InstallationimgMSis a python package registered at PyPI, and therefore can be installed with pip:pip install imgMSLimitationsSo far there is only option to directly import data from 2 instruments (ThermoFisher Element2 and Agilent) in 3 formats (.csv, .xlsx, .asc). In the future there will be more options.imgMS only works with multiple analysis with reference analysis acquired in one file, unlike many other software aimed on LA-ICP-MS data reduction.
Package: imgo
imgoProcess, Augment, and Balance Image Data💡 What is it?This library is designed to facilitate the preprocessing phase of image classification projects in order to get into the fun part: training the models!Features:Imgo is composed of two modules:uptoolsandaugtools.Uptoolshelps to streamline various image data preprocessing tasks, such as:Reading images from a local diskRescaling imagesNormalizing and standardizing pixel valuesConverting image datasets into numpy-arraysOne-hot-encoding label dataSplitting image datasets into training, validation, and testing subsetsMerging data subsets into a single datasetSaving numpy-arrays as images in class subdirectoriesAugtoolsallows the user to quickly and efficiently apply augmentation to image data. With Augtools, users can perform the following augmentation tasks using very few lines of code:Apply a powerful collection of transformation and corruption functionsAugment images saved on a local diskSave augmented images in class subdirectoriesAugment entire image datasetsAugment training data in place in preparation for machine learning projectsRebalance class sizes by generating new training images🛠️ SetupInstall it fromPyPIby runningpip install imgo.DependenciesThe code was written withPython 3.6, and it is recommended to run it in a virtual environment.
All the required libraries are listed in therequirements.txtfile in this repo.🚀 ExecutionOnce the package has been installed, it is simply a case of experimenting with the various classes and functions. For a quickstart, please see thedemo.📝 DocumentationDocumentation is currently available in the form of docstrings.⚖️ LicenseThe project is licensed under the MIT license.AcknowledgementsSome of theaugtoolslibrary is built as a wrapper aroundImgaug, a powerful image augmentation library. For more information, please seehttps://imgaug.readthedocs.io/en/latest/.