Saravanan666/pypi-org huggingface dataset:
Package: imagemv
imagemvMove photos and videos into a directory tree sorted by date taken.Use as a Sync and / or Backup ScriptOptionally keep the original files in the source directory (copy instead of move).
Either keep all of them or only up to a certain age (as defined by a configurable number of days). This is a useful feature if the source directory is synced with a mobile phone or similar device with limited storage.../DEST/
│
├── 2022/
│   ├── 08/
│   │   ├── 23/
│   │   │   └── IMG_20220823_154532.jpg
│   ├── 09/
│   │   ├── 03/
│   │   │   ├── Screenshot-1.png
│   │   │   └── VID_2022-08-24_08:34:56.mp4
│   │   ├── 04/
│   │   │   ├── Matrix.mov
├── 2023/
│   ├── 01/The program will recursively traverse the source directory and sort all image files found based on their metadata. If no date is found, the program will try to parse the date from the filename. Failing that, the file'sst-mtimewill be used.Original metadata and filenames are preserved. When an image with the same filename already exists at the destination path, the program will skip the file and continue processing with the next file.Usageimagemv

Usage:
  imagemv.py SOURCE DEST [--keep=<days>] [--dry-run]
  imagemv.py SOURCE DEST [--keep-all] [--dry-run]
  imagemv.py (-h | --help)
  imagemv.py (-v | --version)

Options:
  -h --help          Show this screen.
  -v --version       Show version.
  --verbose          Print more information about progress.
  --keep=<days>      Keep original files which are newer than the specified age in <days> [default: 0].
  --keep-all         Keep all original files.
  --dry-run          Simulation mode (no changes will be written to the filesystem).Exampleimagemv --dry-run --keep=365 ~/phone-sync/DCIM/Camera ~/Images
Package: imagemvs
package_nameDescription.
The package package_name is used to:
Processing:
- Histogram Matching
- Structural similarity
- Resize image
Utils:
- Read image
- Save image
- Plot image
- Plot result
- Plot histogramInstallationUse the package managerpipto install package_namepipinstallpackage_nameUsagefrompackage_name.module1_nameimportfile1_namefile1_name.my_function()AuthorMarcos Vinicius
Package: imagen
ImaGenImaGen is a generic Python library for 0D, 1D, and 2D pattern
distributions.Please seeImaGen’s websitefor
documentation and examples.InstallationImaGen requiresHoloviews,Param, andNumPy.If you want to use the image-loading features in imagen.image, you
will also need eitherPillow(recommended) orPIL.Official releases of ImaGen are available atPyPI, and can be installed along
with dependencies viapip install--userimagen.  More recent
changes can be obtained by cloning the git repository.
Package: imagenet-c
ImageNet-C Corruption FunctionsWith this package, it is possible to corrupt an image with ImageNet-C corruptions.
These functions are exposed with the functioncorrupt.Tryfrom imagenet_c import corrupt

corrupt(<image>, corruption_number=0)Thecorruptfunction looks likedef corrupt(x, severity=1, corruption_name=None, corruption_number=-1):
    """
    :param x: image to corrupt; a 224x224x3 numpy array in [0, 255]
    :param severity: strength with which to corrupt x; an integer in [0, 5]
    :param corruption_name: specifies which corruption function to call;
    must be one of 'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur',
                    'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog',
                    'brightness', 'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression',
                    'speckle_noise', 'gaussian_blur', 'spatter', 'saturate';
                    the last four are validation functions
    :param corruption_number: the position of the corruption_name in the above list;
    an integer in [0, 18]; useful for easy looping; 15, 16, 17, 18 are validation corruption numbers
    :return: the image x corrupted by a corruption function at the given severity; same shape as input
    """
    ...
Package: imagenet-voice
ImageNet VoiceThis dataset is derived from ImageNet, and contains synthetic recordings of TTS voices saying each ImageNet label 200 times. This is more or less a toy dataset, but can be very useful for projects with little-to-no data.UsageAfter installing withpython3 -m pip install imagenet-voice, ImageNet-Voice can be imported and used like most other datasets:importimagenet_voiceX,y=imagenet_voice.load(progress=True,download_data=True)ThePython packageincludes 3 loading functions:load(progress, download_data)- Load the entire dataset. Ifprogress, display a progressbar. Ifdownload_data, git clone the dataset.load_classes(classes, progress, download_data)- Loads only the classes in the providedclasseslist.load_n(n, random, progress, download_data)- Loadsnclasses, alphebtically unlessrandom=True.ContentThis dataset is currently at 28% capacity, and more will be uploaded soon. Each folder, representing a category in ImageNet, contains 200 unique TTS files generated usingttsddgusing the 7 pre-installed voices in OSX.Acknowledgementspyttsx3was integral to creating ttsdg. As much trouble as I had with ffmpeg and other audio libraries, pyttsx3 made it much easier than any other option I could've taken to generate this dataset.I appreciate any help that anyone would be willing to offer to get this dataset at 100% capacity any sooner. Please contact me!InspirationI created this dataset to provide training data for a GAN that would take in an audio file of a spoken word and would generate an image of whatever was spoken. I still plan to work on that project, but I'd like to see other people perform similar tasks.
Package: imagenius
ImageniusOverviewImagenius is a web-based application designed to automatically tag images based on their content. Built on advanced machine learning models, Imagenius provides fast, accurate, and comprehensive tagging solutions for your photo galleries.FeaturesWeb-based GUI: Intuitive drag-and-drop interface for easy image uploads.Advanced Object Detection: Utilizes state-of-the-art machine learning models for accurate tagging.Modular Backend: Python-based backend, easily extendable for future models and features.Quick StartSetupClone the repository and navigate into the folder. Then install the dependencies.gitclonehttps://github.com/0xchrisw/imagenius.git
pipinstall-e.
imageniusUsageOpen your web browser and go tohttp://localhost:3000.Drag and drop an image onto the upload area or select an image via the file picker.View the automatically generated tags for the image.ContributionsPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Package: image_novice
A special Python image submodule for beginners.Descriptionimage_novice is a simple wrapper around the Python Image Library (PIL) for
beginners. It allows for easy loading, manipulating, and saving of image
files.NOTE: This module uses the Cartesian coordinate system!Example>>> import image_novice as novice         # special submodule for beginners

>>> picture = novice.open('sample.png')   # create a picture object from a file
>>> print picture.format                  # pictures know their format...
'png'
>>> print picture.path                    # ...and where they came from...
'/Users/example/sample.png'
>>> print picture.size                    # ...and their size
(665, 500)
>>> print picture.width                   # 'width' and 'height' also exposed
665
>>> picture.size = (200, 250)             # changing size automatically resizes
>>> for pixel in picture:                 # can iterate over pixels
>>> ... if ((pixel.red > 128) and         # pixels have RGB (values are 0-255)...
>>> ...     (pixel.x < picture.width)):   # ...and know where they are
>>> ...     pixel.red /= 2                # pixel is an alias into the picture
>>> ...
>>> print picture.modified                # pictures know if their pixels are dirty
True
>>> print picture.path                    # picture no longer corresponds to file
None
>>> picture[0:20, 0:20] = (0, 0, 0)       # overwrite lower-left rectangle with black
>>> picture.save('sample-bluegreen.jpg')  # guess file type from suffix
>>> print picture.path                    # picture now corresponds to file
'/Users/example/sample-bluegreen.jpg'
>>> print picture.format                  # ...has a different format
jpeg
>>> print picture.modified                # and is now in sync
False
Package: imageobfuscator-shantih19
imageobfuscator.pyEncode and decode data in an image using a passwordUsageusage: Image Obfuscator [-h] (-d | -e) -i PATH -s SEED [-p--phrase PHRASE] [-o OUTPUT] [-f] [-x [XOR]]

Encode and decode data in an image using a seed as a "password"

options:
-h, --help            show this help message and exit
-d, --decode          decode (default)
-e, --encode          encode
-i PATH, --path PATH  image path
-s SEED, --seed SEED  seed/password
-p--phrase PHRASE     phrase to encode
-o OUTPUT, --output   OUTPUT output path
-f, --file            phrase is a path to a file to encode
-x [XOR], --xor [XOR] xor with another image (blank for original, required for decoding)Installpip install imageobfuscator-shantih19DependenciesPython 3.xPillowLicenseThis program is distributed AS IS, with no guarantees, underwtfpl.
Package: imageObjects
imageObjectsThis acts as an API of sorts for CV2, with some common methods cast into objects that work with images, contours and
more. All source code at thegithub repo pageContourObjectopen-cv python has contours, a extremely useful component that can be extracted from images. However, these components
are in a ndarray format that can then be processed by a list of methods, rather than an object which has a list of
methods. This library changes that around and allows for the contour extracted via open cv python to be placed in an
object which can then be manipulated.ImageObjectThere are lots of common image functions that have been mapped to images, so that a loaded image can have these as
properties or as methods inherently rather than having to code them yourself.LineObjectThis will search for lines within an ImageObject which you can then utilise as a mask or manipulate with ImageObjectsImageMakerThis can be used to make new ImageObjects, when you don't have anything to load or custom ImageObjects like text-boxescommonWhilst common is mostly designed for internal use, there are some common methods here that may save you re-defining them
yourself or from other packages
Package: image-object-slicer
image-object-slicerSlice objects from images using annotation files. Convert an object detection dataset to an image classification one. To annotate the images to be used with this tool, we recommendopenvinotoolkit/cvat.This is a fork ofgitlab.com/straighter/pascalvoc-to-image.InstallationInstall the latest stable version fromPyPIwith:sudopip3installimage-object-slicerOr download the latest stable wheel file from thereleases pageand run:sudopip3install./image_object_slicer-*-py3-none-any.whlOr install the latest development version from the git repository:gitclonehttps://www.github.com/natanjunges/image-object-slicer.gitcdimage-object-slicer
sudopip3install./UsageDifferent formats of annotation files are supported:Annotation formatCommand line optionMS COCO Object DetectioncocoCVAT for imagescvatimagesDatumarodatumaroKITTIkittiLabelMelabelmeOpen ImagesopenimagesPascal VOCpascalvocWIDER FacewiderfaceYOLOyoloUsing the script is pretty simple, since it only has three required parameters:usage: image-object-slicer [-h] [-v] [-f {pascalvoc,coco,cvatimages,datumaro,kitti,labelme,openimages,widerface,yolo}] [-p PADDING] [-w WORKERS] annotations images save

Slice objects from images using annotation files

positional arguments:
  annotations           A path to the directory with the annotation files
  images                A path to the directory with the input images
  save                  A path to the directory to save the image slices to

options:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exit
  -f {pascalvoc,coco,cvatimages,datumaro,kitti,labelme,openimages,widerface,yolo}, --format {pascalvoc,coco,cvatimages,datumaro,kitti,labelme,openimages,widerface,yolo}
                        The format of the annotation files (default is pascalvoc)
  -p PADDING, --padding PADDING
                        The amount of padding (in pixels) to add to each image slice
  -w WORKERS, --workers WORKERS
                        The number of parallel workers to run (default is cpu count)BuildingTo build the wheel file, you needdeb:python3.10-venvandpip:build:sudoaptinstallpython3.10-venv
sudopip3installbuildBuild the wheel file with:python3-mbuild--wheel
Package: image-optimizer-cli
image-optimizer-cliA simple CLI application written in Python to optimize images. You can use it from the command line after installing it. Executable name isimage-optimizer.InstallationRun:pip install image-optimizer-cliUsageShow help:image-optimizer -hOptimize single image:image-optimizer -p /path/to/image.jpgOptimize all images in a folder:image-optimizer -p /path/to/images/Optimize all images in a folder recursively:image-optimizer -p /path/to/images/ -rOptimize a number of images:image-optimizer -p /path/to/images/ -n 10Optimize with custom quality:image-optimizer -p /path/to/images/ -q 30Parameters-h, --help:Show help.-p, --path:Path to the image or folder to optimize. Defaults to the current directory if not specified.-r, --recursive:Optimize all images in a folder recursively. Defaults to False if not specified.-n, --number:Optimize a number of images. Defaults to all images in the folder if not specified.-q, --quality:Optimize with custom quality. Defaults to 80 if not specified.Important notesBACKUP YOUR IMAGES BEFORE OPTIMIZING! All images will be overwritten with optimized versions.This is a simple CLI application. It is not meant to be a full-featured image optimizer.Relative and absolute paths are supported.The quality parameter is a value between 0 and 100.The number parameter is a positive integer. And is not required.
Package: image-packer
image_packerAboutPack multiple images of different sizes or formats into one image. -
Supported image input formats: - png, bmp, jpg - Supported image output
formats: - png(24 or 32bits)atlasCompatibilityimage_packer works with Python 3.4 or higher.DependenciesPillowInstallationpip install image-packerUsagefromimage_packerimportpackerworkpath='./image'input_filepaths=[workpath+'/*.png',workpath+'/*.jpg',workpath+'/*.bmp',]output_filepath=workpath+'/atlas.png'container_width=128options={'margin':(1,1,1,1),'collapse_margin':False,'enable_auto_size':True,'enable_vertical_flip':True,'force_pow2':False}packer.pack(input_filepaths=input_filepaths,output_filepath=output_filepath,container_width=container_width,options=options)Command-line Tool$ impack -i "./image/*.png" -i "./image/*.jpg" -i "./image/*.bmp" -o "./image/atlas.png" -w 128 -m 1 1 1 1LicenseThis software is released under the MIT License, see LICENSE.
Package: image-pcd
prominent-colors-detectorA engine which detects prominent colors from the image.Install Python Packagepip install image-pcdUsagefrompcdimportImageProminentColorfromsklearn.clusterimportKMeansimage_color=ImageProminentColor("<path to image file>")prominent_colors=image_color.get_prominent_colors(KMeans,3)The above code-snippet gives you top 3 prominent colors in RGB form sorted in decreasing order of prominence from the image.image_color.get_image_plot("<Save image file path>")image_color.get_color_plot("<Save image file path>")The above code-snippet allows you to save the images of plots of the prominent color and image original colors in 3D-space with RGB components as axis.Samples (For top-3 Prominent Colors)Sample ImageProminent ColorsProminent Colors PlotImage Colors Plot#215d1d#97244a#1d8197#7579b0#fdfdfe#010101#fefefe#323232#67a15d
Package: image-pdf-tools
image pdf toolsImage to pdf and pdf merged python toolsSetup localpipinstallimage-pdf-toolsHow to usefromimage_pdf_toolsimportimage_to_pdf,pdf_merge

image_to_pdf('image_path_file.png','folder_to_save_image')image_to_pdf(['pdf_path_file1.pdf','pdf_path_file2.pdf'],'folder_to_save_image')FeaturesConverter image to pdfMerger many pdf file to oneCreditHarouna Diallo
Package: imagepile
imagepileImage tools for large image repositoriesUsageFor more information on the command-line interface, type:imagepile--helpusage: imagepile [-h] folderpath

Remove invalid images from a folder

positional arguments:
  folderpath

optional arguments:
  -h, --help  show this help message and exitLicenseThis project is licensed under the MIT license. See theLICENSEfor details.
Package: imagepills
IMAGEPILLSDescriptionThis package provides a high level abstraction for some image utilities. Check out theUsagesection for commands details.InstallationThis package can be installed usingpip:$>pipinstallimagepillsor$>python-mpipinstallimagepillsUsageSizeUse the commandpills_sizeas follows:$>pills_size[-h][-v][-dSTRING][-fSTRING]Those are the options to pass to the command:-h,--helpShowthehelpmessageandexit-v,--verboseSetverboseoption-dSTRING,--directorySTRING,Pathtodirectorycontainingimages-fSTRING,--fileSTRING,ImagefilepathWe can use the pill to calculate the size of one file or a group of files inside a folder.ResizeUse the commandpills_resizeas follows:$>pills_resize[-h][-v]-oSTRING-fSTRING-wSTRING-eSTRINGThose are the options to pass to the command:-h,--helpShowthehelpmessageandexit-v,--verboseSetverboseoption-oSTRING,--outputSTRING,Pathtooutputfolder-fSTRING,--fileSTRING,Imagefiletoconvert-wSTRING,--widthSTRING,Newwidth-eSTRING,--heightSTRING,NewheightFor project purpouses, minimum height and width under 256 pixels are not allowed, and anargparse.ArgumentTypeErrorexception will be raised in such case.If new sizes are not proportional to the original ones, the tool will resize to the biggest size (weight or height) and calculate the other one proportionally.Convert to png formatUse the commandpills_image2pngas follows:$>pills_image2png[-h][-v][-dSTRING]-oSTRING[-fSTRING]Those are the options to pass to the command:-h,--helpShowthehelpmessageandexit-v,--verboseSetverboseoption-dSTRING,--directorySTRING,Pathtodirectorycontainingimages-oSTRING,--outputSTRING,Pathtoouputfolder-fSTRING,--fileSTRING,ImagefiletoconvertWe can use the pill to convert one or a group of files inside a folder. The ouput folder is mandatory to store converted images.ContributorsSay hello!
Package: image-preprocessor
Image PreprocessorSimple image preprocessor I made while working on my first Keras binary classification convolutional neural network. Converts images from specified directories into NumPy arrays. Its functionality will be expanded out of necessity.InstallationInstall through pip as shown:pipinstallimage-preprocessorExample usagefromimage_preprocessorimportImagePreprocessor# Vehicle-label correlationsvehicle_types={0:'boat',1:'car',2:'motorcycle',3:'plane'}# Create the ImagePreprocessor objectip=ImagePreprocessor(pixels=64,normalization=255,training_threshold=0.7,resize_method='square resize',color_mode='L')# Prepare the images in select directoriespackage=ip.preprocess_dirs(paths=['images/boat','images/car','images/motorcycle','images/plane'],labels=[0,1,2,3],partition=True)# Go on and do your neural network stufftrain_features=package['TRAIN_IMAGES']train_labels=package['TRAIN_LABELS']test_features=package['TEST_IMAGES']test_labels=package['TEST_LABELS']
Package: imagepreview
[![Build Status](https://travis-ci.org/GaborWnuk/image-preview-python.svg?branch=master)](https://travis-ci.org/GaborWnuk/image-preview-python)Simple helper module for Image Preview method for REST and GraphQL for immediate image preview on your client’s side (Swift, Java, JavaScript and so on).Idea is to deliver only around 200 bytes of image data as a normal base64 data in one of JSON fields of your entities and then issue request to obtain full resolution of an image.## Short “how does it work”
The main reason to do such thing is to show your user approximate look of your data stream as soon as possible. This method gives you the ability to serve image previews in the sam milisecond you show your text content in your app, as image preview is delivered as one of data fields in your JSON response.This library is sort of a helper to achieve above.Because every thumbnail is compressed as JPEG, with the same size (default: 42x42 pixels) and using the same Huffman table, each JPEG file consist of a common header (620 bytes) and a small picture data part (around 200 bytes, depending on quality you use).Having this in mind we choose to omit headers and send only 200 bytes (base64) and then prepend this data with a header on client side.You can find basic Swift / iOS example here: [https://github.com/GaborWnuk/image-preview-ios-demo](https://github.com/GaborWnuk/image-preview-ios-demo).
Package: imageprobe
imageprobeAsynchronous library to get image dimensions by fetching as little data as possible.It temporarily supports only GIF, PNG because development is still in a very early stage.UsageTo install this library, run:pip install imageprobeTheprobe()function returns metadata of an image from an URL, or throws an exception if an error occurred.importasynciofromimageprobeimportprobeurl="https://upload.wikimedia.org/wikipedia/commons/7/70/Example.png"image_data=asyncio.run(probe(url))print(image_data.width,image_data.height)# 172 178Under the hood,probe()creates anaiohttp.ClientSession, but you can pass a pre-existing session as an optional argument if you prefer.ContributingI won't accept pull requests until the first beta release.
Package: image-proccessing
package_nameDescription.
The package package_name is used to:
Procccessing:
- Histogram matching
- Structural simmilarity
- Resize Image
Utils:
- Read image
- Save image
- Plot image
- Plot result
- Plot histogramInstallationUse the package managerpipto install package_namepipinstallpackage_nameAuthorArthur GuedesLicenseMIT
Package: image-process-DIO
package_nameDescription.
The package package_name is used to:
Processing:
- Histogram matching
- Structural similarity
- Resize image
Utils:
- Read image
- Save image
- Plot image
- Plot results
- Plot histogramInstallationUse the package managerpipto install package_namepipinstallimage_process_DIOUsagefromimage_process_dio.utilsimportio,plotfromimage_process_dio.processimportcombination,transformationfile1_name.my_function()AuthorGlaucio DrumondLicenseMIT
Package: image-processing2
package_nameDescription.
The package package_name is used to:
Processing:
- Histogram matching
- Structural similarity
- Resize image
Utils:
- Read image
- Save image
- Plot image
- Plot result
- Plot histogramInstallationUse the package managerpipto install package_namepipinstallimage_processing2Usagefromimage_processing2.module_nameimportfile_namefile_name.my_function()AuthorDavi h.LicenseMIT
Package: image-processing9999
image_processingDescription.
The package image_processing is used to:
Processing:
- Histogram matching
- Structural similarity
- Resize image
Utils:
- Read image
- Save image
- Plot image
- Plot result
- Plot histogramInstallationUse the package managerpipto install package_namepipinstallimage_processing9999Usagefromimage_processing9999.processingimportcombination..........combination.my_function()AuthorFelippeLicenseMIT
Package: image-processing-abevilaqua
Projeto: Pacote de Processamento de ImagensAutora do Projeto: Karina KatoAula: Coding Lab PRO - Digital Innovation Onepackage_nameimage_processing:MÃ³dulo "Processing":CorrespondÃªncia de histograma;Similaridade estrutural;Redimensionar imagem;MÃ³dulo "Utils":Ler imagem;Salvar imagem;Plotar imagem;Resultado do grÃ¡fico;Plotar histograma;InstallationComandos para instalaÃ§Ã£o:pipinstallimage_processing_abevilaquaUsagefromimage-processing.processingimporttransformationtransfer_histrogram(image1,image2)AuthorAndre BevilaquaLicenseMIT