Saravanan666/pypi-org huggingface dataset:
Package: imageio
Imageio is a Python library that provides an easy interface to read and
write a wide range of image data, including animated images, volumetric
data, and scientific formats. It is cross-platform, runs on Python 3.5+,
and is easy to install.Main website:https://imageio.readthedocs.io/Release notes:https://github.com/imageio/imageio/blob/master/CHANGELOG.mdExample:>>>importimageio>>>im=imageio.imread('imageio:astronaut.png')>>>im.shape# im is a numpy array(512,512,3)>>>imageio.imwrite('astronaut-gray.jpg',im[:,:,0])See theAPI Referenceorexamplesfor more information.
Package: imageio-freeimage
ImageIO FreeImageWarningThis repo is licensed under theFreeImage Open Source Dual-Licenseandnotthe typicalBSD-2license we use for everything else. Check out the
LICENSE document in this repo and make sure you understand the consequences.ImageIO FreeImage is a ImageIO plugin for the FreeImage library. In other words,
it allows usingFreeImagewithImageIO.Installationpip install imageio-freeimage
python -c "imageio.plugins.freeimage.download()"Usage (and Examples)To use it simply import the library. It will auto-register with ImageIO.importimageio.v3asiioimportimageio_freeimageimg=iio.imread("imageio:chelsea.png",plugin="PNG-FI")Why ImageIO FreeImageBased on discussions over at ImageIO's main repository, we have decided to spin
out the FreeImage plugin. This was done for two reasonsIt is/was unclear how permissible the FreeImage license is, how exactly it
interacts with BSD (ImageIO's license), and what that means for downstream users
who don't need FreeImage. Instead of having to deal with the fallout of this
interaction, we decided to spin out the FreeImage plugin. This way, users don't
have to worry, unless they explicitly need FreeImage, in which case they will
likely be aware of how FreeImage is licensed, and what it means for their
project.The FreeImage bindings we provide are based on ctypes. In many cases this is
not a problem; however, for some users it causes complications, because they,
for example, use pypy or other non-cpython interpreters or they want to complile
their python code in a browser via pyodide. Those use-cases are more prone to
problems when ctypes are involved and having them in a dedicated optional
dependency make this situation easier.We can add plumbing to compile FreeImage in CD and ship it precompiled
without the need for any post-install actions.
Package: imagej
Python wrapper for ImageJimagej.pyprovides a set of
wrapper functions for integration between ImageJ and Python.It also provides a high-level entry pointimagej.IJfor invokingImageJ ServerAPIs;
see "ImageJ Server" below for details.Requirementsdefault:
- imglybInstall imglyb usingconda install -c hanslovsky imglyb.Further information regarding imglyb can be found in theimglyb GitHub repository.UsageIn this example, replace/Applications/Fiji.appwith the location of your Fiji installation.# Spin up ImageJ.importimagejij=imagej.init('/Applications/Fiji.app')# Import an image with scikit-image.importskimagefromskimageimportio# NB: Blood vessel image from: https://www.fi.edu/heart/blood-vesselsimg=io.imread('https://www.fi.edu/sites/fi.live.franklinds.webair.com/files/styles/featured_large/public/General_EduRes_Heart_BloodVessels_0.jpg')importnumpyasnpimg=np.mean(img,axis=2)# Invoke ImageJ's Frangi vesselness op.vessels=np.zeros(img.shape,dtype=img.dtype)importimglybij.op().filter().frangiVesselness(imglyb.to_imglib(vessels),imglyb.to_imglib(img),[1,1],20)See alsotest/test_imagej.pyfor other examples of usage.ImageJ ServerRequirementsimagej_server:
- requests
- PillowUsepip install -r server_requirements.txtto install requirements for server.Pillowis required for the imagej.server module'sIJ.show()function.
In addition,displayorxvneeds to exist in your system to view the image.UsageThere is a short usage examplehere.
Package: imagejmf
Package NameimagejmfO pacote image-processing-joelmaf é usado para processare imagem. É composto de dois módulos:Módulo "Processing":Correspondência de histograma;Similaridade estrutural;Redimensionar imagem;Módulo "Utils":Ler imagem;Salvar imagem;Plotar imagem;Resultado do gráfico;Plotar histograma;Instalção do Pacotepip install imagejmfUsofromimagejmf.processingimportcombinationcombination.find_difference(image1,image2)AutorJoelma de Moura FerreiraLicençaMIT
Package: image-jointer
ImageJointerBuild up image by jointing imagesinstallpip install image_jointerhow to usefromimage_jointerimportJointAlignment,ImageJointerfromPILimportImagered=Image.new("RGBA",(50,50),(255,0,0))green=Image.new("RGBA",(100,100),(0,255,0))blue=Image.new("RGBA",(50,50),(0,0,255))jointed=ImageJointer().joint(JointAlignment.RIGHT_CENTER,red,green,blue)joint_img=jointed.to_image()fromimage_jointerimportJointAlign,ImageJointer,BlankfromPILimportImagered=Image.new("RGBA",(100,100),(255,0,0))blank=Blank(50,100)green=Image.new("RGBA",(100,100),(0,255,0))jointed=(ImageJointer(red).joint(blank,JointAlign.RIGHT_CENTER).joint(green,JointAlign.RIGHT_CENTER))joint_img=jointed.to_image()fromimage_jointerimportJointAlignment,ImageJointerfromPILimportImagered=Image.new("RGBA",(100,100),(255,0,0))blue=Image.new("RGBA",(100,200),(0,0,255))green=Image.new("RGBA",(100,100),(0,255,0))jointed=ImageJointer(red).joint(JointAlignment.LEFT_BOTTOM,blue).joint(JointAlignment.DOWN_CENTER,green)joint_img=jointed.to_image()
Package: image-keras
Image Keras이미지 프로세싱을 위한 Keras를 사용하는데 필요한 보조 도구설치pipinstallimage-keras요구사항Python ≥ 3.7Toolz ≥ 0.10.0TensorFlow ≥ 2.1.0Keras ≥ 2.3.1numpy ≥ 1.17.4
Package: image-knocker
image-knockerKnock your images before you get stressed.BackgroundOne day, I had run my deep learning model training program and got off work on Friday. When I checked the training process on Monday after arriving at work, the program was terminated because of some images that cannot be opened by image processing libraries like PIL.I developed this project to check images that cannot be opened by image processing libraries before running computer vision applications.image-knockeris very fast and simple!Installpip install image-knockerUsagefoo@bar:~$image-knocker--pathdataset_path/--extspngjpgjpegOutputifcorrupted imageindataset:Knocking... Root Path: D:\datasets\detection\VOCdevkit
  100%|██████████████████| 34179/34179 [00:03<00:00, 10911.31it/s]
  Corrupted image files are detected!
  D:/datasets/detection/VOCdevkit/outlier.png
  Corrupted image file list is saved to D:\datasets\detection\corrupted_imgs_list.txtelse:Knocking... Root Path: D:\datasets\detection\VOCdevkit
  100%|██████████████████| 34178/34178 [00:04<00:00, 7792.30it/s]
  There are no corrupted image files! 
  All images were loaded successfully using PIL.Image.open!
Package: image_lister
This is a simple script which lists
images in a dir, by checking their
extensions, and displays the count
of jpeg, png, and tiff. Not using
file descriptors and meta even.Run it with theimglistercommand:imglister path/to/dir/containing/imagesThe actual purpose of this package was to
learn how to push packages to PyPi though.This package really wouldn’t provide anything
of value to you.
Package: imagemangler
Image Mangler is a command-line tool to deteriorate an image iteratively using lossy algorithms.InstallationYou can install Image Mangler via pip:pipinstallimagemanglerUsageYou can run Image Mangler by invoking the imagemangler command in the terminal, followed by the path of the image file you want to mangle:imagemanglerpath/to/image.jpgBy default, Image Mangler will automatically mangle the image across all quality steps with a base quality of 70 and a quality step of 2. You can specify your own values for these parameters using the optional flags:imagemanglerpath/to/image.jpg--quality60--quality-step5--no-auto-mangleFor a more details you can use--help:imagemangler--help
Package: image-manipulator
image_manipulatorDescription.
The package image_manipulator 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 image_manipulatorpipinstallimage_manipulatorAuthorAlex PivatoLicenseMIT
Package: image-manipulator-thx
image_manipulator_thxPacote python com vários módulos par amanipular image.Serve como template para outros pacotesDescription.
The package image_manipulator 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 image_manipulatorpipinstallimage_manipulatorAuthorAlex PivatoLicenseMIT
Package: image-match
image_match is a simple package for finding approximate image matches from a
corpus. It is similar, for instance, to pHash <http://www.phash.org/>, but
includes a database backend that easily scales to billions of images and
supports sustained high rates of image insertion: up to 10,000 images/s on our
cluster!Based on the paper An image signature for any kind of image, Goldberg et
al <http://www.cs.cmu.edu/~hcwong/Pdfs/icip02.ps>.
Package: image-match3
image_match is a simple package for finding approximate image matches from a
corpus. It is similar, for instance, to pHash <http://www.phash.org/>, but
includes a database backend that easily scales to billions of images and
supports sustained high rates of image insertion: up to 10,000 images/s on our
cluster!Based on the paper An image signature for any kind of image, Goldberg et
al <http://www.cs.cmu.edu/~hcwong/Pdfs/icip02.ps>.
Package: image-match-altamente
image_match is a simple package for finding approximate image matches from a
corpus. It is similar, for instance, to pHash <http://www.phash.org/>, but
includes a database backend that easily scales to billions of images and
supports sustained high rates of image insertion: up to 10,000 images/s on our
cluster!Based on the paper An image signature for any kind of image, Goldberg et
al <http://www.cs.cmu.edu/~hcwong/Pdfs/icip02.ps>.
Package: image-match-tso
image_match_tso is a simple package for finding approximate image matches from a
corpus. It is similar, for instance, to pHash <http://www.phash.org/>, but
includes a database backend that easily scales to billions of images and
supports sustained high rates of image insertion: up to 10,000 images/s on our
cluster!Based on the paper An image signature for any kind of image, Goldberg et
al <http://www.cs.cmu.edu/~hcwong/Pdfs/icip02.ps>.
Package: image-merger
image-mergerA small Python library to merge images easilyInstallationpip install image-mergerUsageSimple use casefrom ImageMerger import Merger, ImageToMerge, MERGE_GRID

# Initialize list of images
list_images = [
	ImageToMerge(path='images/samples/1.png'),
	ImageToMerge(path='images/samples/2.png'),
	ImageToMerge(path='images/samples/3.png'),
	ImageToMerge(path='images/samples/4.png')
]

# Load merger with different settings
m = Merger(list_images=list_images)

# Save merged image
m.save_image(filename="images/results/output_4_grid.png")Parameterslimit_horizontal: int: Optional int to define the maximum number of images to append horizontallylimit_vertical: int: Optional int to define the maximum number of images to append verticallysuffle: bool: Optional boolean to decide iflist_imagesmust be shuffled prior to process. DefaultFalsemerge_strategy: int: Optional int to set the merging strategy. EitherMERGE_HORIZONTALLY, -MERGE_VERTICALLYorMERGE_GRID(default)preserve_aspect_ratio: bool: Optional boolean that defines if proportion of each image should be kept or if image can be squeezed/extended to fit. DefaultFalseSetting it toTruecan lead to images being distored to fit layout but there will be no gaps.Setting it toFalsewill kept each image aspect ratio but can lead to black gaps in output image.Configuration examplesImageToMerge.pathcan either be a path(local or relative) or HTTP(S) URLlimit_horizontalandlimit_verticalare optional and can also be used at the same timeIflimit_horizontalandlimit_verticalare not set, andmerge_strategyis set toMERGE_GRIDThe process will try to fit as best as possible all images in a square grid.List of images can be shuffled randomly usingshuffle=TrueparameterMore examplesfrom ImageMerger import Merger, ImageToMerge

# Initialize list of images
list_images = [
	ImageToMerge(path='images/samples/1.png'),
	ImageToMerge(path='images/samples/2.png'),
	ImageToMerge(path='https://raw.githubusercontent.com/ggouzi/image-merger/main/images/samples/3.png'),
	ImageToMerge(path='https://raw.githubusercontent.com/ggouzi/image-merger/main/images/samples/4.png'),
	ImageToMerge(path='https://raw.githubusercontent.com/ggouzi/image-merger/main/images/samples/5.png')
]

# Load merger with different settings
m = Merger(
    list_images=list_images,
    preserve_aspect_ratio=True,
    limit_vertical=2
  )

# Save merged image
m.save_image(filename="images/results/output_5_grid_keep_aspect_ratio.png")from ImageMerger import Merger, ImageToMerge

# Initialize list of images
list_images = [ImageToMerge(path=f"images/samples/{i}.png") for i in range(1, 11)]

# Load merger with different settings
m = Merger(
    list_images=list_images,
    limit_horizontal=2
)

# Save merged image
m.save_image(filename="images/results/output_10_2rows.png")from ImageMerger import Merger, ImageToMerge

# Initialize list of images
list_images = [ImageToMerge(path=f"images/samples/{i}.png") for i in range(1, 11)]

# Load merger with different settings
m = Merger(
    list_images=list_images,
    shuffle=True
)

# Save merged image
m.save_image(filename="images/results/output_10_grid_shuffled.png")ReferencesPillow
Package: image-mining
Attempting to build tools to mine interesting data from large collections of scanned imagesCurrentbin/locate-thumbnail:find the location of a thumbnail in the source imagebin/extract-figures:locate interesting non-text elements (images, figures, tables, etc.) on scanned book pagesPrerequisitesPython 2.6+OpenCV 2.4+numpyUsing Mac Homebrew this should install cleanly:brew install python numpy opencvOn Ubuntu 12.04 Precise the following is known to work - note the need for a PPA to get OpenCV 2.4+:sudo add-apt-repository ppa:alexei.colin/opencv
sudo apt-get update
sudo apt-get install python-numpy python-opencv
Package: image-mint
image_mintPurposeThis python package can be used to search for and download images from various search engines.
You can specify minimum width and height of the image if you want.InstallationYou must have Chrome browser installed. You can download it fromhttps://www.google.com/chrome/On Ubuntu, you can run:sudo dpkg -i google-chrome-stable_current_amd64.deb```You must download Chrome webdriver. You can download it fromhttps://chromedriver.chromium.org/downloadsTo know which version of Chrome is installed to match the driver version, you can use this command on Ubuntu:dpkg --list|grep chromeAfter that, you can install the package using pip:pip install image_mintUsage:You can use the package in python code like so:fromimage_mintimportScraperfromimage_mint.enginesimportGooglescraper=Scraper(Google('c:/temp/animals/chromedriver.exe'))scraper.download("cat","c:/temp/animals/images",limit=100,min_width=200)Example on WindowsYou can also use command line:image_mint.exe -c C:\temp\animals\chromedriver.exe -d C:\temp\animals\images -e Bing "Wild cats" -l 20 -mw 200Note that the windows executable can be downloaded fromhttps://github.com/kouroshparsa/image_mint/blob/main/image_mint/bin/image_mint.exeHere are the flags that can be used:-e: the search engine, possible values: DogPile/Bing/Google/DuckDuckGo/Yahoo-d: the destination folder to download images to-c: the chrome driver path using-l: number of images to download-mw: minimum width-mh: minimum heightExample on LinusYou can use other languages as well:image_mint -c /tmp/chromedriver -d /tmp/images -e Google "Ï€Î»Î¿Î¯Î¿" -l 20 -mw 200On some linux environment particularly Docker images, it is possible that after installing the package it does not recognize the image_mint executable.
In that case, run:exportPATH=$PATH:~/.local/binor if you want it persist, add it to ~/.bashrc
Package: imagemorph
imagemorphThis is a Python wrapper of theimagemorph[1] repository, a
program to apply random elastic rubbersheet transforms to images for
augmenting training sets in machine learning/deep learning.Rather than running a compiled executable and
writing the resulting image to disk as in the original repo, this wrapper
performs the same random elastic morphing as in the original repo, but
instead returns the resulting image as a numpy array, which can then be
used for further processing in, for example, a machine learning pipeline.Any image type can be processed (not just .ppm), as long as it can be
loaded with OpenCV.How to installInstall using pip:pip install imagemorphExample usageimportcv2ascvfromimagemorphimportelastic_morphingamp,sigma=0.9,9img_name="img/sample-input.png"# load imageimg=cv.imread(img_name)h,w,_=img.shape# apply random elastic morphingres=elastic_morphing(img,amp,sigma,h,w)# write result to diskcv.imwrite('img/out.png',res)ReferencesOriginal Author: Marius Bulacu (.pgm version for characters). Adapted for .ppm
and color: Lambert Schomaker.Please cite:[1] M Bulacu, A Brink, T van der Zant, L Schomaker (2009).
Recognition of handwritten numerical fields in a
large single-writer historical collection,
10th International Conference on Document Analysis and Recognition,
pp. 808-812, DOI: 10.1109/ICDAR.2009.8
Package: imagemounter
imagemounter is a command-line utility and Python package to ease the mounting and unmounting of EnCase, Affuse, vmdk
and dd disk images (and other formats supported by supported tools). It supports mounting disk images using xmount (with
optional RW cache), affuse, ewfmount and vmware-mount; detecting DOS, BSD, Sun, Mac and GPT volume systems; mounting
FAT, Ext, XFS UFS, HFS+, LUKS and NTFS volumes, in addition to some less known filesystems; detecting (nested) LVM
volume systems and mounting its subvolumes; and reconstructing Linux Software RAID arrays.In its default mode, imagemounter will try to start mounting the base image on a temporary mount point,
detect the volume system and then mount each volume seperately. If it fails finding a volume system,
it will try to mount the entire image as a whole if it succeeds in detecting what it actually is.This package supports Python 2.7 and Python 3.3+.ExampleA very basic example of a valid mount is as follows. The command-line utility has much more features, but results vary
wildly depending on the exact type of disk you are trying to mount:# imount lvm_containing_dos_volumesystem_containing_ext4
[+] Mounting image lvm_containing_dos_volumesystem_containing_ext4 using auto...
[+] Mounted raw image [1/1]
[+] Mounted volume 2.0 GiB 4.0.2:Ext4 / [Linux] on /tmp/im_4.0.2_8l86mZ.
>>> Press [enter] to unmount the volume, or ^C to keep mounted...
[+] Parsed all volumes!
[+] Analysis complete, unmounting...
[+] All cleaned upIf you want to see for yourself, you could try executingimount /dev/sdafirst.DocumentationFull documentation of this project is available fromhttp://imagemounter.readthedocs.org/or in thedocs/directory.InstallationThis package does not require other packages, thoughtermcoloris recommended andpytsk3is needed if you wish to
use this package for volume detection.Just perform the following commands for a basic installation:apt-get install python-setuptools xmount ewf-tools afflib-tools sleuthkit
pip install imagemounter
imount --checkUseimount--checkto verify which packages are (not) installed. Install additional packages as needed.ContributingSince imagemounter is an open source project, contributions of many forms are welcomed. Examples of possible
contributions include:Bug patchesNew featuresDocumentation improvementsBug reports and reviews of pull requestsWe use GitHub to keep track of issues and pull requests. You can alwayssubmit an issuewhen you encounter something out of the ordinary.Not all combinations of file and volume systems have been tested. If you encounter an issue, please try to change some
of your arguments first, before creating a new GitHub issue.
Package: imagemounter-mitre
imagemounter is a command-line utility and Python package to ease the mounting and unmounting of EnCase, Affuse, vmdk
and dd disk images (and other formats supported by supported tools). It supports mounting disk images using xmount (with
optional RW cache), affuse, ewfmount and vmware-mount; detecting DOS, BSD, Sun, Mac and GPT volume systems; mounting
FAT, Ext, XFS UFS, HFS+, LUKS and NTFS volumes, in addition to some less known filesystems; detecting (nested) LVM
volume systems and mounting its subvolumes; and reconstructing Linux Software RAID arrays.In its default mode, imagemounter will try to start mounting the base image on a temporary mount point,
detect the volume system and then mount each volume seperately. If it fails finding a volume system,
it will try to mount the entire image as a whole if it succeeds in detecting what it actually is.This package supports Python 3.6+.ExampleA very basic example of a valid mount is as follows. The command-line utility has much more features, but results vary
wildly depending on the exact type of disk you are trying to mount:# imount lvm_containing_dos_volumesystem_containing_ext4
[+] Mounting image lvm_containing_dos_volumesystem_containing_ext4 using auto...
[+] Mounted raw image [1/1]
[+] Mounted volume 2.0 GiB 4.0.2:Ext4 / [Linux] on /tmp/im_4.0.2_8l86mZ.
>>> Press [enter] to unmount the volume, or ^C to keep mounted...
[+] Parsed all volumes!
[+] Analysis complete, unmounting...
[+] All cleaned upIf you want to see for yourself, you could try executingimount /dev/sdafirst.DocumentationFull documentation of this project is available fromhttp://imagemounter.readthedocs.org/or in thedocs/directory.InstallationThis package does not require other packages, thoughtermcoloris recommended andpytsk3is needed if you wish to
use this package for volume detection.Just perform the following commands for a basic installation:apt-get install xmount ewf-tools afflib-tools sleuthkit
pip3 install imagemounter
imount --checkUseimount--checkto verify which packages are (not) installed. Install additional packages as needed.ContributingSince imagemounter is an open source project, contributions of many forms are welcomed. Examples of possible
contributions include:Bug patchesNew featuresDocumentation improvementsBug reports and reviews of pull requestsRELEASE STATEMENTApproved for Public Release; Distribution Unlimited. Public Release Case Number 22-3208.
Package: imagem-processamento
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_processingAuthorJefferso AndradeLicenseMIT