Saravanan666/pypi-org huggingface dataset:
Package: augment-auto
augment-autoA python image augmentation library based on opencv and numpy. It can be used for augmenting images in both image classification and object detection tasks. Many different techniques of augmentation are supported, which can be clustered into three major types - geometric transformations, photometric transformations and kernel-based transformations.
Library has support for images with bounding boxes as well.InstallationInstall using pip:pipinstallaugment-autoInstall from github:gitclonehttps://github.com/keshavoct98/image-augmentation.gitpythonsetup.pyinstallDocumentationComplete documentaion -https://augment-auto.readthedocs.io/en/latest/Demo ipython notebokk -demo.ipynbExamples# Geometric Transformationsimg=cv2.imread('images/3.jpg')img_new=crop(img,point1=(100,100),point2=(450,400))img_new=rotate(img,angle=15,keep_resolution=True)img_new=scale(img,fx=1.5,fy=1.5,keep_resolution=False)img_new=shear(img,shear_val=0.2,axis=1)img_new=translate(img,tx=50,ty=60)# Geometric Transformations with bounding boximg=cv2.imread('images/0.jpeg')bbox=[581,274,699,321]img_new,bbox_new=crop(img,point1=(100,100),point2=(650,400),box=bbox)img_new,bbox_new=rotate(img,angle=15,keep_resolution=True,box=bbox)img_new,bbox_new=scale(img,fx=1.5,fy=1.3,keep_resolution=False,box=bbox)img_new,bbox_new=shear(img,shear_val=0.2,axis=0,box=bbox)img_new,bbox_new=translate(img,tx=50,ty=160,box=bbox)# Photometric Transformationsimg=cv2.imread('images/1.jpg')img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)img_new=brightness_contrast(img,alpha=1.3,beta=20)img_new=brightness_contrast(img,alpha=0.7,beta=-10)img_new=colorSpace(img,colorspace='hsv')img_new=colorSpace(img,colorspace='ycrcb')img_new=colorSpace(img,colorspace='lab')img_new=addNoise(img,'gaussian',mean=0,var=0.08)img_new=addNoise(img,'salt_pepper',sp_ratio=0.5,noise_amount=0.1)img_new=addNoise(img,'poisson',noise_amount=0.5)# Kernel-based Transformationsimg=cv2.imread('images/0.jpeg')bbox=[581,274,699,321]img_new=randomErase(img,size=(100,100))img_new=randomCropAdd(img,size=(100,100))img_new=sharpen(img)img_new=randomErase(img,size=(60,40),box=bbox)img_new=randomCropAdd(img,size=(60,40),box=bbox)img_new=blur(img,'avg',ksize=(9,9))img_new=blur(img,'gaussian',ksize=(9,9),gaussian_sigma=0)img_new=blur(img,'median',median_ksize=11)Referenceshttps://numpy.org/doc/https://docs.opencv.org/master/https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.htmlhttps://stackabuse.com/affine-image-transformations-in-python-with-numpy-pillow-and-opencv/https://cristianpb.github.io/blog/image-rotation-opencv
Package: augmented
augmentedAr python is a cross platform module used to do augmented reality using pythonRequirementsopencv-contrib-pythonNumpyrequirements may vary on diffrent operating systemsinstalationpip install augmentedThats itHow to useOverlaying imagesinitializingimportaugmentedar=augmented.ar_overlay(capture:int)#capture = camera numberar.setup(targetImage:str,overlayImage:str,nfeatures:int,debug:bool=True,confidence:int=25,displayName:str="Augmented by sarang")targetImage = Image to overlay on top ofoverlayImage =Image to overlayNot required but can tweak the ones belownfeatures = Features to detect on target image the bigger the more accurate and the more resource intensive 1000 recomendeddebug = debug modeconfidence  = How many feature matches to confirmdisplayname = title name```Overlayingar.start(display=bool)display =  Enabling display outputAruco scanningsetupimportaugmentedarucoar=augmented.arucoar(cap:int=0)imgAug={0:'assets/unnamed.jpg'}arucoar.setup(imgAug:dict,markerSize:int=6,totalMarkers:int=250,debug:bool=True,cam:int=0,displayName:str='Augmented by Sarang')imgAug = a dict containing the aruco id as value and the image(location) to display when the value is Truenot neccessry but still can tweakMarkersize, totalMarkers = aruco code propertiesdebug = to use debug modecam = camera numberdisplayName = tite of the display windowScanning and overlayingarucoar.start(display=bool)display = wheather to display the output or notrecomended way of using is to put the code inside a loopversion - 2.1.0Stablecontributions are appreciated and will be credited in the packageThank youHappy augmentingFirst release
Package: augmentedtree
augmentedtreeaugmentedtreeenhances mappings and sequences (targeted for python
dictionaries and lists) preserving their native behavior and access.
The enhancement comes with getting values by single keys, human readable
viewing, selecting and setting multiple values/items within the nested
data at once,or-conditional selection of values/items. Also this package
intends to prepare the nested data for pyQt. The augmentation provides
methods and properties to be used for aQAbstractItemModelshowing
the nested data within aQTreeView.InstallationInstalling the latest release using pip is recommended.$pipinstallaugmentedtreeThe latest development state can be obtained from gitlab using pip.$pipinstallgit+https://gitlab.com/david.scheliga/augmentedtree.git@devBasic UsageThe major purpose of augmentedtree is to retrieve quickly specific value(s) from a deep
nested data featuring:unix filename patternregular expressionsor-conditional selectionThe targeted usage is to be able to write the following kind of code# code where the nested data comes from
...

# gathering parameters
with AugmentedTree(nested_data) as tree:
    # simple selection
    first_value = tree.select("something", "here")[0]
    last_value = tree.select("something", "there")[-1]
    a_slice_of_values = tree.select("a", "l?t", "of")[3:6]

    # selection with refinement
    selection_of_values = tree.select("also/a", "lot", "of")
    narrowed_down = selection_of_values.where("this", "or", "that")[ALL_ITEMS]
    ...


if not tree.all_selections_succeeded:
    # break, exit or reacting to some value are not there
    ...

# code which is working with requested parameters       
...Read-the-docsfor a detailed
explanation on how to use augmented tree.ContributionAny contribution by reporting a bug or desired changes are welcomed. The preferred
way is to create an issue on the gitlab's project page, to keep track of everything
regarding this project.Contribution of Source CodeCode styleThis project follows the recommendations ofPEP8.
The project is usingblackas the code formatter.WorkflowFork the project on Gitlab.Commit changes to your own branch.Submit apull requestfrom your fork's branch to our branch'dev'.AuthorsDavid Scheliga@gitlab@LinkedinInitial workMaintainerLicenseThis project is licensed under the GNU GENERAL PUBLIC LICENSE - see theLICENSEfile for detailsAcknowledgeCode style: black
Package: augmentiX
AugmentiX🚀 YOLO Dataset Augmentation package 🖼️Project OverviewWelcome to AugmentiX, an image augmentation package designed to enhance your sample dataset for object detection,
particularly tailored for YOLO-based training datasets. The package provides a convenient GUI interface for specifying
the number of samples, desired augmentation tasks, and the output directory to save the augmented images.Problem StatementAugmentiX addresses the challenges in efficiently augmenting datasets for object detection. Traditional methods often
involve coding augmentation tasks for each training, leading to time-consuming processes. This package aims to simplify
the augmentation process, offering an intuitive GUI and performing tasks such as random rotation, cropping, flipping,
brightness adjustments, blur, noise, and more.FeaturesUser-Friendly GUI:Easily specify the number of samples, augmentation tasks, and output directory through a graphical interface.Comprehensive Augmentation Tasks:Perform various augmentation tasks, including random rotation, cropping, flipping, brightness adjustments, blur, and noise.Annotation File Generation:Create annotation files for augmented images, streamlining the dataset preparation process.Performance Enhancement with Rust:Utilize Rust bindings for improved performance, especially in I/O operations.Getting StartedTo start using AugmentiX, follow these steps:Clone the repository:https://github.com/suryan-s/AugmentiX.gitInstall the required dependencies.To run the eel GUI, run the following command, uncomment the eel.start command and run :python3 main.pyTo run the CLI, run the following command:python3 main.py --src <source directory of the dataset>Before running the program, make sure the the dataset that is being augmented is in the following format:dataset
├── images
│   ├── 1.jpg
│   ├── 2.jpg
│   ├── 3.jpg
│   └── ...
└── labels
    ├── 1.txt
    ├── 2.txt
    ├── 3.txt
    └── ...
|── data.yamlMotivationAugmentiX was born out of a personal need to address the limitations of existing image augmentation libraries,
especially in the context of YOLO-based projects.
As a practitioner in object detection, the creator faced challenges with current libraries,
inspiring the development of a comprehensive solution that streamlines the augmentation process
and integrates seamlessly with YOLO training.ContributionJoin us on this journey to enhance object detection workflows!
Contribute to AugmentiX, share your ideas,
and let's collectively become Legend Gladiators in the world of image augmentation for real-world problem-solving.Let's code, contribute, and transform datasets together! 🚀🔧
Package: augment-nd
augmentA simple elastic augmentation for ND arrays.Installationpip install augment-ndUsageimport augment
import numpy as np
import math

# create some example data
image = np.zeros((100,500,500), dtype=np.float)
image[:] = 0.5
image[:,:10,:] = 0.75
image[:10,:10,:10] = 1
image[:,::10,:] = 1
image[:,:,::10] = 1

transformation = augment.create_identity_transformation(image.shape)

# jitter in 3D
transformation += augment.create_elastic_transformation(
        image.shape,
        num_control_points = [3,10,10],
        jitter_sigma = [0.3, 10, 10])

# rotate around z axis
transformation += augment.create_rotation_transformation(
        image.shape,
        math.pi/4)

# apply transformation
image = augment.apply_transformation(image, transformation)
Package: augmenty
Augmenty: The cherry on top of your NLP pipelineAugmenty is an augmentation library based on spaCy for augmenting texts. Besides a wide array of highly flexible augmenters, Augmenty provides a series of tools for working with augmenters, including combining and moderating augmenters. Augmenty differs from other augmentation libraries in that it corrects (as far as possible) the assigned labels under the augmentation, thus making many of the augmenters valid for training in a wider range of tasks.🔧 InstallationTo get started using augmenty simply install it using pip by running the following line in your terminal:pip install augmentyDo note that this is a minimal installation. As some augmenters requires additional packages please write the following line to install all dependencies.pip install augmenty[all]For more detailed instructions on installing augmenty, including specific language support, see theinstallation instructions.🍒 Simple ExampleThe following shows a simple example of how you can quickly augment text using Augmenty. For more on using augmenty see theusage guides.importspacyimportaugmentynlp=spacy.load("en_core_web_md")docs=nlp.pipe(["Augmenty is a great tool for text augmentation"])entity_augmenter=augmenty.load("ents_replace_v1",ent_dict={"ORG":[["spaCy"],["spaCy","Universe"]]},level=1)fordocinaugmenty.docs(docs,augmenter=entity_augmenter,nlp=nlp):print(doc)spaCy Universe is a great tool for text augmentation.📖 DocumentationDocumentation📚Usage GuidesGuides and instructions on how to use augmenty and its features.📰News and changelogNew additions, changes and version history.🎛API ReferencesThe detailed reference for augmenty's API. Including function documentation🍒AugmentersContains a full list of current augmenters in augmenty.🙋FAQFrequently asked question regarding augmenty💬 Where to ask questionsType🚨Bug ReportsGitHub Issue Tracker🎁Feature Requests & IdeasGitHub Issue Tracker👩‍💻Usage QuestionsGitHub Discussions🗯General DiscussionGitHub Discussions🍒Adding an AugmenterAdding an augmenter
Package: augmixations
AugmixationsSome augmentations that I hasn't found in other repositories and libraries.Current augmentations:Cutmix (Colab Example)Cutout (Colab Example)Mixup (Colab Example)Getting Startedpip install augmixationsExample with default parametersImport:from augmixations import Cutmix, Cutout, MixupUsing Cutmix:#bg_img - The image into which a rectangle will be inserted
#fg_img - The image from which a random rectangle will be cut 

cutmix = Cutmix()
img, boxes, labels = cutmix(bg_img, bg_boxes, bg_labels, fg_img, fg_boxes, fg_labels)Done.Using Cutout:cutout=Cutout()new_img,new_boxes,new_labels=cutmix(img,boxes,labels)Done.Using Mixup:mixup=Mixup()image,boxes,labels=mixup(first_img,first_boxes,first_labels,second_img,second_boxes,second_labels)Done.Advansed usageYou can pass special configs to the cutmix function to override its behavior.Cutmix Advanced UsageContactsIssues should be raised directly in the repository. For professional support and recommendations pleasewelcomedenk@gmail.com.
Package: augpathlib
augpathlibAugmented pathlib. Everything else you could do with a path.IntroductionDo you like pathlib?Have you ever wanted to see just how far you can push the path abstraction?Do you like using the division operator in ways that could potentially cause
reading from the network or writing to disk?Then augpathlib is for you!Detailsaugpathlib makes extensive use of the pathlib Path object (and friends)
by augmenting the base PosixPath object with additional functionality
such as getting and setting xattrs, syncing with other mapped paths etc.In essence there are 3 ways that a Path object can be used: Local, Cache, and Remote.
Local paths return data and metadata that are local the the current computer.
Cache paths return local metadata about remote objects (such as their remote id).
Remote objects provide an interface to remote data that is associated with a path.Remote paths should be back by another object which is the representation of the
remote according to the remote's APIs.Remote paths are only intended to provide a 1:1 mapping, so list(local.data) == list(remote.data)
should always be true if everything is in sync.If there is additional metadata that is associated with a local path then that is
represented in the layer above this one (currently DatasetData, in the future a validation Stage).
That said, it does seem like we need a more formal place that can map between all these
things rather than always trying to derive the mappings from data embedded (bound) to
the derefereced path object.Installing on MacOS catalina 10.15.5+If you are getting a failed build for pxattr, please continue reading the following.
When using LLVM (9.0.1+) or another 3rd party library for Clang on MacOS, there was an issue that requires a possible reinstall if you getting a failed build for pxattr. SeeLLVM Issuefor details. To reinstall LLVM on brew or anaconda you can use the following examples.>brewreinstallllvmor>condainstall--force-reinstallllvmIf the reinstall fails to clear the issue consider uninstalling that library and use the default Clang from Xcode. It should be the following version or higher.>clang-v
Appleclangversion11.0.3(clang-1103.0.32.62)Target:x86_64-apple-darwin19.5.0
Threadmodel:posix
InstalledDir:/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin
Package: augpy
augpy is a lightweight library with minimal dependencies that
provides CUDA tensor functions with a focus on image data
augmentation.
It uses thedlpackstandard to export tensors to other libraries, such as
Pytorch, with zero copying.BuildingMake sure you have the following installed.Compiler with C++14 support (e.g. GCC 5)Cuda 10 or higherCmake 3.13 or highersetuptools>=44.0.0wheel>=0.34.0pybind11>=2.4.3numpy>=1.15.0Now simply runsetup.pyas normal to build the wheel and install with pip.python setup.py bdist_wheelUsageTODOChangelog1.0.0a1WIP
Package: augrs
Auto Generate Metasploit Resouce ScriptThis Program run an exploit with automate search for open port using only excellent rank in Metasploit Module.
Generate shell / bash scripts to use in security automate testGetting StartedPrerequisites- Python >= 3.10
- python3-nmap
- pymetasploit3
- pyfigletInstallingpip install augrsNmap InstallingThis project use Nmap to search for an open port you must install Nmap in your local to use this functionDownload the Free Nmap Security Scanner for Linux/Mac/WindowsMSFRPC InstallingThis project use MSFRPC API and msfconsole you must install and start RPC server before running the program
In case of you don't have MSFRPC or msfconsole instance
We provide the docker image that contain both you may follow the step belowInstall docker desktopGet DockerPull docker image for running MSFRPC and msfconsoledocker pull wisarud2k/ubuntu-metasploitStart docker image and mapping port using port 55553 and 443docker run -it -p 55553:55553 -p 443:443 --name={your own container name} wisarud2k/ubuntu-metasploit:latestRun program with MSFRPC password "test"augrsAfter first docker run you can start your own docker container withdocker start -i {your own container name}UsageStart the program in console with commandaugrsRun exploit in program:Use Nmap moduleGo to Exploit moduleUse Search for exploitRun all exploit and wait for reportThe report will create in exploit_report foldercreate shell or bash script for running exploitUse Nmap moduleGo to Exploit moduleUse Generate resource scriptselect the environment to create script (linux or window)name the script and enterThe script will create in rc folderOptions manageUse Options moduleselect options that want to editedit you optionsLicenseThis project is licensed under the MIT License - see the LICENSE.md file for details.
Package: augshufflenet-pytorch
AugShuffleNet: Communicate More, Compute LessAugShuffle1_0x has slightly different channels than the paper due to constraints in of the defaultr.See:https://arxiv.org/abs/2203.06589Usageimport torch
from augshufflenet_pytorch import AugShuffleNet0_5x, AugShuffleNet1_0x, AugShuffleNet1_5x, AugShuffleNet


model = AugShuffleNet0_5x(input_channels=3)
x = model(torch.randn(1, 3, 64, 64)) # [1, 192]

# Equivalent to 0_5x
model = AugShuffleNet(stages_repeats=[3, 7, 3], stages_out_channels=[24, 48, 96, 192], input_channels=3, r=0.375)
x = model(torch.randn(1, 3, 64, 64)) # [1, 192]NOTE: each of the int(out_channels * r) & out_channels putneeds to be divisible by 2Citation@misc{ye_augshufflenet_2022,
	title = {{AugShuffleNet}: {Communicate} {More}, {Compute} {Less}},
	shorttitle = {{AugShuffleNet}},
	url = {http://arxiv.org/abs/2203.06589},
	doi = {10.48550/arXiv.2203.06589},
	abstract = {As a remarkable compact model, ShuffleNetV2 offers a good example to design efficient ConvNets but its limit is rarely noticed. In this paper, we rethink the design pattern of ShuffleNetV2 and find that the channel-wise redundancy problem still constrains the efficiency improvement of Shuffle block in the wider ShuffleNetV2. To resolve this issue, we propose another augmented variant of shuffle block in the form of bottleneck-like structure and more implicit short connections. To verify the effectiveness of this building block, we further build a more powerful and efficient model family, termed as AugShuffleNets. Evaluated on the CIFAR-10 and CIFAR-100 datasets, AugShuffleNet consistently outperforms ShuffleNetV2 in terms of accuracy with less computational cost and fewer parameter count.},
	urldate = {2023-06-09},
	publisher = {arXiv},
	author = {Ye, Longqing},
	month = aug,
	year = {2022},
	note = {arXiv:2203.06589 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}
Package: augurs
Python bindings to the augurs time series frameworkInstallationEventually wheels will be provided as part of GitHub releases and maybe even on PyPI.
At that point it will be as easy as:$pipinstallaugursUntil then it's a bit more manual. You'll needmaturininstalled and a local copy of this
repository. Then, from thecrates/pyaugursdirectory, with your virtualenv activated:$maturinbuild--releaseYou'll probably want numpy as well:$pipinstallnumpyUsageMultiple Seasonal Trend Decomposition with LOESS (MSTL) modelsimportaugursasaugimportnumpyasnpy=np.array([1.5,3.0,2.5,4.2,2.7,1.9,1.0,1.2,0.8])periods=[3,4]# Use an AutoETS trend forecastermodel=aug.MSTL.ets(periods)model.fit(y)out_of_sample=model.predict(10,level=0.95)print(out_of_sample.point())print(out_of_sample.lower())in_sample=model.predict_in_sample(level=0.95)# Or use your own forecasterclassCustomForecaster:"""See docs for more details on how to implement this."""deffit(self,y:np.ndarray):passdefpredict(self,horizon:int,level:float|None)->aug.Forecast:returnaug.Forecast(point=np.array([5.0,6.0,7.0]))defpredict_in_sample(self,level:float|None)->aug.Forecast:returnaug.Forecast(point=y)...model=aug.MSTL.custom_trend(periods,aug.TrendModel(CustomForecaster()))model.fit(y)model.predict(10,level=0.95)model.predict_in_sample(level=0.95)Exponential smoothing modelsimportaugursasaugimportnumpyasnpy=np.array([1.5,3.0,2.5,4.2,2.7,1.9,1.0,1.2,0.8])model=aug.AutoETS(3,"ZZN")model.fit(y)model.predict(10,level=0.95)More to come!
Package: august
August - a html-to-text converterAugust is an html to text converter specifically intended for producing
text versions of HTML emails. The main code is all written in Rust,
this packages is includes python bindings.Getting StartedInstall using PIP$pip3installaugustThen, import it and run convertimportaugusthtml='<p>I\'m <em>so</em> excited to try this</p>'print(august.convert(html,width=20))which printsI'm /so/ excited to
try thisAlternativeshtml2text:
Coverts HTML into markdown, and supports a bazillion options.
It's a great project if you want to produce markdown; but markdown, because
it's designed to be turned into HTML, has a little more noise than is
strictly necessary, and the header formatting is pretty unclear.html-to-text:
Converts HTML to text. Javascript/node project.