Saravanan666/pypi-org huggingface dataset:
Package: cvetrends
cvetcvet is a Python utility for pulling actionable vulnerabilities fromcvetrends.com.Find out more information at ourblog.Installation/UsageInstallationcvet can be installed from PyPi using the following command:pipx install cvetIf this tool is not yet availible via PyPi, you can install it directly from the repository using:git clone https://github.com/Sprocket-Security/cvetrends.git
cd cvetrends && pip3 install .For development, clone the repository and install it locally using poetry.git clone https://github.com/Sprocket-Security/cvetrends.git && cd cvetrends
poetry shell 
poetry installUsageThe cvet help menu is shown below:Usage: cvet [OPTIONS] [[day|week]]                                                                                              
                                                                                                                                 
 cvetrends.com CLI                                                                                                               
                                                                                                                                 
╭─ Arguments ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ TIME_FRAME    [[day|week]]                                                                                                    │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --notify          -n   TEXT     Slack webhook to notify on run                                                                │
│ --repo-threshold  -rt  INTEGER  Number of repos needed to show CVE. [default: 1]                                              │
│ --help            -h            Show this message and exit.                                                                   │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯cvet can query time frames of 24 hours or 7 days using a value ofdayorweek. The default isday.cvet weekResults are returned in a pretty table format and only vulnerabilities that have more than-rtPoC GitHub repos published are shown. The default is 1.cvet day -rt 2cvet also allows you to specify a Slack webhook to notify on run using the-nor--notifyflag. This is useful if you want to be notified of new vulnerabilities and run this tool on a cron.cvet -n https://hooks.slack.com/services/T00000000/B00000000/XXXXXXXXXXXXXXXXXXXXXXXX -rt 2An example Slack notification is shown below:
Package: cveutils
cveutilsThis library provides functionality for fetching and parsing information about Common Vulnerabilities and Exposures (CVE) using the NIST National Vulnerability Database (NVD) RESTful API.InstalationpipinstallcveutilsUsageTo use this library, simply import the CVE class and initialize an instance with a CVE ID as a string.fromcveutilsimportCVEcve=CVE('CVE-2021-12345')CVE ObjectTheCVEobject has the following attributes:ID: The CVE IDinfo: The full JSON response from the NVD API for the given CVE IDdescription: The description of the CVEcvss_v2: A dictionary containing the CVSSv2 score and related metrics (if available)cvss_v3: A dictionary containing the CVSSv3 score and related metrics (if available)published_date: The published date of the CVE in ISO format (YYYY-MM-DD)last_modified_date: The last modified date of the CVE in ISO format (YYYY-MM-DD)assigner: The organization that assigned the CVE IDreferences: A list of references related to the CVEThecvss_v2andcvss_v3attributes are instances of the CVSSDictionary class, which allows access to the dictionary keys as attributes.Example UsagefromcveutilsimportCVEcve=CVE('CVE-2021-12345')print(cve.ID)print(cve.description)print(cve.cvss_v3.baseScore)print(cve.published_date)print(cve.last_modified_date)print(cve.assigner)print(cve.references)Output:CVE-2021-12345Descriptionofthevulnerability7.82021-01-012021-02-01MITRE['https://example.com/reference1','https://example.com/reference2']
Package: cvexp
CVExp is short for “controlled vocabulary expressions.”  CVExp is a
Python module that allows abstract syntax trees (“expressions”) to be
constructed just as easily as writing normal Python code.  The names
of functions in the abstract syntax trees are based on a controlled
vocabulary, using URIs as names just like the W3C RDF does.The intent of CVExp is that it should make it easier for developers
to integrate 3rd party libraries into Python without having to specify
entirely new APIs.  Libraries and languages of particular concern are
computer algebra systems, linear programming and optimization (such as
lp_solve, GLPK, and CVXOPT), and Prolog.Here is an example usage to solve a mixed-integer linear programming
problem using GLPK as the backend.  In order for this to work, you
obviously must install prerequisite Python packages (i.e.  python-glpk or
whatever) because these are not included with CVExp:from cvexp.builders import var
from cvexp.builders import integer
from cvexp.builders import minimize
from cvexp.translate_glpk import solve

# You could also use lp_solve by doing this:
# from cvexp.translate_lpsolve55 import solve
# ...or you could use CVXOPT like this:
# from cvexp.translate_cvxopt import solve

X = var('X') # the 'X' name is optional
Y = var('Y') # ...and so is 'Y'

# Purely linear programming:
sol = solve((
             Y + 0.1 == X,
             Y >= 9.8 - X,
             minimize(Y),
           ), out_filename='problem.out') # out_filename is optional
print 'X =', sol[X] # >>> 4.95
print 'Y =', sol[Y] # >>> 4.85

# Mixed integer-linear programming:
sol = solve((
             Y + 0.1 == X,
             Y >= 9.8 - X,
             integer(Y),
             minimize(Y),
           ), out_filename='problem.out') # out_filename is optional
print 'X =', sol[X] # >>> 5.1
print 'Y =', sol[Y] # >>> 5If using CVXOPT, quadratic programming problems can also be solved.
For example:from cvexp.builders import var
from cvexp.builders import integer
from cvexp.builders import minimize
from cvexp.translate_cvxopt import solve

X = var()
Y = var()

sol = solve((
            minimize((X - 5) ** 2 + (Y - 3) ** 2),
           ))
print 'X =', sol[X] # >>> 5.0
print 'Y =', sol[Y] # >>> 3.0
Package: cvextend
READMEDescriptioncvextendextends the functionality offered by the Model Selection module ofscikit-learn:cvextend.generate_param_gridhelps with permutating models in steps and their hyperparameters in yoursklearn.pipeline. See the exampleherecvextend.ScoreGridhelps you keep track of the scores you want to use in cross-validations with different optimisation objectives in parallel. See the examplehereand alsocvextend.EvaluationGrid.cvextend.EvaluationGridInstallationTo install the cvextend library use pip:pip install cvextendor install directly from source:python setup.py installUsage[WIP]Questions and commentsIn case of questions or comments, write an email:ldanov@users.noreply.github.com
Package: cvgen
CVgenA tool for easily creating a good-looking CV in PDF format from .yaml data, HTML & CSS.Built with:Python 3Jinja2WeasyPrintPyYAMLPydanticClickPoetryInstallpipinstallcvgenUsageCVgen expects an input YAML file in this format:---name:Mr Job Manpersonalia:phone:+31 6 1234 5678address:Noordeinde 68, 2514 GL Den Haagemail:mr.job.man@gmail.comnationality:Dutchexperience:-title:Widget Engineer @ Widget Incindustry:widget manufacturingdates:April 2007 - presentdescription:>-Various widget manufacturing tasks.education:-diploma:MSc Widget Managementinstitution:University of Widgetrydates:2006 - 2007description:>-Various widget management topics.skills:-category:Computerkeywords:-Ctrl + Alt + Delete-Minesweeper-Lotus Notesprojects:-title:DIY widget 3D printerdescription:>-Made a 3D printer for widgetsactivities:-title:Cricket playerorganization:Cricket clubdates:2015 - 2016description:>-Whatever cricket players doRun it like:$cvgen[input_file]ContributingPRs accepted for additional templates/styles/features.To build & publish to PyPI:$poetrybuild&&poetrypublishLicenseMIT © Vadim Galaktionov
Package: cvgtracker
To use (with caution), simply do:>>> from cvgtracker import TrackerTo visualize the live-updating plot while the tracker runs, one needs to install bokeh by>>> easy_install bokehand uses “bokeh serve” optionThere is a file called “trial.py” for testing. Please do the following>>> bokeh serve
>>> python trial.pyYour web-brower will open a new window or tab to illustrate the tracker
Package: cvhub
AIHub目标检测YOLOv5Mask_RCNNmmdetectionEfficientNetEfficientDetDETRCenterNet语义分割U^2-NetModNetRobust Video Matting:CODE风格/属性迁移StarGAN v2StyleGAN2StyleCLIPALAECUT超分辨率Real-ESRGANDRNGFPGANSREZSuperResolutionImage Super Resolution图片拟合PyMattingDeep Image MattingMGMattingBackgroundMattingV2图片增强DPEDCURLEnlightenGAN图片修复Bringing old photosDeblurGANv2图片摘要Tutorial to Image Captioning目标跟踪Yolov5_DeepSort视频补帧RIFE向量引擎 / NNMilvusFaissHnswlibPySparNN以图搜图ImageDedup
Package: cvindrones
More about cvindronesIt is a package having pre-built codes of face recognition, hands tracking module and body pose tracking. Implemented with a single line of code.https://www.lakshaykumar.tech/What Is This?Implement the power of Computer Vision in your program with just few simple lines of code. Hand detection, Body Pose and face Recognition modules are included in this packagefaceProcessor(image, draw=False)This function returns a 1D list of 3 items. Image (with rectangular box, if drawArea=True), area of face and [x,y,w,h]bodyProcessor(image, draw=False)This function returns a list of 3 items i.e. Image (with rectangular box, if drawArea=True), list of body points and area of body.handProcessor(image, draw=False)This function returns a list having image (with hand connections, if drawArea=True) and points in handlandmarksUsagefaceProcessor(image, draw=True)from cvindrones import faceProcessor
import cv2
cap = cv2.VideoCapture(0)
while True:
    success, image = cap.read()
    funValues = faceProcessor(image,draw=True)
    image = funValues[0]
    area = funValues[1]
    x,y,w,h = funValues[2]
    cv2.imshow('Faces', image)
    cv2.waitKey(1)
cap.release()bodyProcessor(image, draw=True)from cvindrones import bodyProcessor
import cv2
cap = cv2.VideoCapture(0)
while True:
    success, image = cap.read()
    funValues = bodyProcessor(image,draw=True)
    image = funValues[0]
    poseList = funValues[1]
    area = funValues[2]
    cv2.imshow('Body Posture Detection', image)
    cv2.waitKey(1)
cap.release()handProcessor(image,draw=True)from cvindrones import handProcessor
import cv2
cap = cv2.VideoCapture(0)
while True:
    success, image = cap.read()
    funValues = handProcessor(image,draw=True)
    image = funValues[0]
    handLms = funValues[1]
    cv2.imshow('Hand Gesture Controller', image)
    cv2.waitKey(1)
cap.release()
Package: cv-io
Computer Vision I/OThis python package, Computer Vision I/O which is knwon ascv_io, is a collection of existing python scripts which provides a set of I/O functions for normal and irregular image formats in computer vision.File Format SupportFile formatDesciption*.png, *.jpg, ...All normal images*.floMiddleburry optical flow*.dptMiddleburry floating point depth*.pfmFreiburg floating point disparity*.exrOpenXR hdr format*.jxrMicroSoft JPEG-XR hdr formatMain FucntionsFunctionDesciptionimread (file_name)Reads an image from "file_name"imwrite (file_name, image)Saves the "image" array to "file_name"imshow (image)Displays the "image" array by matplotlibimshow (file_name)Displays an image file from "file_name"RequirementsPackage requirement are listed inrequirements.txtfile and it will be installed throughpipisntallation. If you clone the repository you may install requirements manually:pipinstall-rrequirements.txtInstall as a Python PackageYou can also download and install theSimple Image Vieweras a python package by:python -m pip install cv_ioor simplypip install cv_ioThen you can simply import it in a python code and use it as follows:importcv_ioimage=cv_io.imread('samples/0512.pfm')cv_io.imshow(image)cv_io.imwrite('test.pfm',image)You also have acess to the original libraries likesintel_iothrough thecv_ioas a sub-module:importcv_ioimage=cv_io.sintel_io.depth_read('samples/frame_0020.dpt')cv_io.sintel_io.depth_write('test.dpt',image)Copyright © 2020Saeid Hosseinipoor. Released under theMIT License.
Package: cvisp
"""
This is a demo for cvisp, 开发板、相机等拍摄设备取到的RAW with metadata，可以直接用该工具包进行demosaic，得到RGB图像，以便于展示。
- pip install cvisp
- from cvisp.utils import Demosaic_RAW""""""
三种模式，分别为：预览raw图，推理GDIP，预览demosaic（自用）
run_demosaic(): return the demosaiced image, (HWC, u16, bmp)
run_isp_with_meta(): return the rgb image , (HWC, u8, bmp)
run_model(weight_path = './your.pt', model = GDIP(), device = 'cuda:0'): return the output of       your          model, (HWC, u8, bmp)完整传参说明：
- Demosaic_RAW(mode : str = None, input_data_path : str = None, show_meta_data : bool = True, 
show_image : bool = False, save_image : bool = True, result_output_dir : str = None):
    mode: defatult = 'simple_dm', choices = ['simple_dm', 'opencv_dm']
    input_data_path: raw图片路径，支持单张图片，如：'./test.dng'
    show_meta_data: show the meta data of the input data, default = True，你可以选择是否展示meta data
    show_image: show the output image, default = False，你可以选择是否展示输出图像
    save_image: save the output image, default = True，你可以选择是否保存输出图像，保存路径为result_output_dir
    result_output_dir: the output dir of the result image, default = './demosaic/'，你可以选择保存路径"""ifname== 'main':
for im in glob.glob(image_path_list + '*.dng'):
demosaicd_raw = Demosaic_RAW(input_data_path = im).run_model(weight_path = './your.pt', model = GDIP(), device = 'cuda:0')
# rgb = Demosaic_RAW(input_data_path = im).run_isp_with_meta()
# gdip_out = Demosaic_RAW(input_data_path = im).run_model(weight_path = './your.pt', model = GDIP(), device = 'cuda:0')
Package: cvlab-dl
cvlabAn unified laboratory/framework for Computer Vision research, development and
deployment. Here we cover the whole life cycle of CV models: building, training,
evaluation, optimization and deployment.We roughly target these platform/frameworks internally:Python >= 3.6
PyTorch >= 1.6
TensorFlow >= 1.15
ONNX >= 1.7
TensorRT >= 6Other dependencies could be found inrequirements.txt.About the projectThis project empowers CV developers with several tool boxes, and we try our best
to make each of them independent enough so that you can extract them from this
project and use them in your own.Curated models. Loads of new models are coming to the world every day,
but only some of them are proven to be real applicable, these models are hand
picked by our own experience during everyday work, covering multiple tasks
including image classification, detection, segmentation, etc. We provideimplementation of these models,pre-trained weights, andan nice guide to
re-train/fine tune themon your own data. Utilizing our optimization and
deployment tools, these models can be deployed and start creatingreal valuefor you.You can absolutely build new models and train, test, evaluate, optimize, deploy
them with exactly the same tool chain which already existed models used.Deployment toolbox. Nice models are just the first step towards application,
the work after training a model is often complicated and painful, such as model
conversion from one platform to others, quantization and compressing, graph
optimization for inference and so on. We provide several utilities about these
tasks, hope they can be helpful.Evaluation metrics. Without reasonable metrics we can't tell the performance
of anything. We provide easy-to-use tools to measure the performance of your
models with the most popular metrics of certain tasks. We believe by providing
such unified tools can greatly improve the everyday work experience for us.Miscellaneous tools. We provide a bunch of tools that are nice if you have
them, so you don't need to write them over and over again, such as visualization
tools, dataset adapters, loggers, etc.LICENSEMIT ©AlanDecode
Package: cvlab-gui
CVLAB [WIP]CVLAB is a computer vision annotation tool written in python.pyimguihas been adopted for the graphical user interface.My goal is to realize a simple, lightweight and standalone app that let you annotate your images, run auto-annotation experiments with your own models, import/export data collections and analyse your data distribution.Right now the project is a work in progress. Fundamental features are available and operative, but not ready for a daily usage yet.Above: Settings & Info section. Allows you to setup your labels and visualize stats about your data.Install from PyPI (Windows only)pip install cvlab-guiInstallation requirementspyimguiYolo-LAB runs on the docking branch of PyImgui (not on PyPI atm). You can install the provided wheels.windowspip install *win_amd64.whllinuxpip install *linux_x86_64.whlother requirementspip install -r requirements.txt
pip install -r yolov5/requirements.txtSetup new projectpython -m cvlab_gui.setup_projectRun CVLAB GUIpython -m cvlab_gui.main
Package: cvlab-keras
Machine learning toolkit for CV-Labcvlab_kerasis a plugin forCV-Lab application.
It adds a toolkit for machine learning and a bunch of examples.This extension is a wrapper for machine learning librarytf.keras.Requirements:cvlab>=1.3.0rc1,tensorflow>=2.3Installationpip install cvlab_kerasor directly from GIT repository:pip install git+https://github.com/cvlab-keras/cvlab_keras.git
Package: cvlab-medical
Medical toolkit for CV-Labcvlab_medicalis a plugin forCV-Lab.
It adds a toolbox for medical image processing and a bunch of examples.Currently available:cvlab_medical- general purpose medical toolboxcvlab_ct- computed tomography toolboxInstallationpip install cvlab_medicalor directly from GIT repository:pip install git+https://github.com/cvlab-ai/cvlab_medical.git
Package: cvldoc-parser
CVLDoc Parser: Python moduleExposes a Python API for the CVLDoc module. WIP.UsageFirst, make sureRust 1.62or newer is installed.
Clonethe entire repo, then from the project base:$cdsrc/python_wrapper
$python-mvenv.env
$source.env/bin/activate
$pipinstallmaturinIt creates a virtual environment for development, and should only be done once.
Now, while the.envis sourced, it is possible to run$maturinbuildin order to build the module, which can then be used in thevirtualenvor installed globally withpip install {generated .whl file}. It is also possible to runmaturin developin order to generate a temporary module.It is now possible to import the module:$python
>>>importcvldoc_parserAPICurrently exposes a single function,cvldoc_parser.parse(paths). It takes a list of file paths as strings, and returns a list of parsed CVLDoc objects for each path.
A parsed CVLDoc object is either aDocumentationor aFreeForm. They both support adiagnostics()method, that returns a list of warnings or errors associated with that object.
Package: cvlearn
cvlearnAn easy to use package which helps to do hand tracking, face detection, etc. with use of opencv module.InstallationUse Python 3.xOpen cmd/terminal and type:pipinstallcvlearnDependenciespython 3.xopencv-pythonnumpymediapipeExamplesHand Tracking:fromcvlearnimportHandTrackingModuleashandTracker

importcv2cap=cv2.VideoCapture(0)detector=handTracker.handDetector()whileTrue:ret,img=cap.read()img=detector.findHands(img)cv2.imshow("Result",img)cv2.waitKey(1)Result:Face Detection:fromcvlearnimportFaceDetectionasfaceDetector

importcv2cap=cv2.VideoCapture(0)detector=faceDetector.FaceDetector()whileTrue:ret,img=cap.read()img=detector.findFaces(img)cv2.imshow("Result",img)cv2.waitKey(1)Result:SideView:Drawing face mesh:fromcvlearnimportFaceMeshasfms

importcv2cap=cv2.VideoCapture(0)detector=fms.FaceMeshDetector()whileTrue:ret,img=cap.read()img,face=detector.findFaceMesh(img)cv2.imshow("Result",img)cv2.waitKey(1)Result:Finger CountingfromcvlearnimportFingerCounterasfcimportcvlearn.HandTrackingModuleashandTracker
importcv2cap=cv2.VideoCapture(0)detector=handTracker.handDetector(maxHands=1)counter=fc.FingerCounter()whileTrue:ret,frame=cap.read()frame=cv2.flip(frame,180)frame=detector.findHands(frame)lmList,bbox=detector.findPosition(frame)iflmList:frame1=counter.drawCountedFingers(frame,lmList,bbox)cv2.imshow("res",frame)key=cv2.waitKey(1)ifkey==27:breakcv2.destroyAllWindows()Result:Two Hands Finger CountingfromcvlearnimportTwoHandsFingerCounterasfcimportcv2cap=cv2.VideoCapture(0)counter=fc.FingerCounter()whileTrue:ret,frame=cap.read()frame1=counter.drawCountedFingers(frame)cv2.imshow("res",frame)key=cv2.waitKey(1)ifkey==27:breakcv2.destroyAllWindows()Result:
Package: cvlive
CVLiveA multithreaded live image processor in Python running OpenCV that processes images from a webcam or another video source and shows the results in 'real time'. A separate thread handles the processing while the main thread displays the previous result and captures the next input, resulting in significant performance gains.CVLive makes it possible to create live demos of a wide variety of image processing conceptsin only a few lines of code.How to InstallpipinstallcvliveHow to UseImportcvliveCreate a subclass ofLiveImageProcessorOverride one or more of the class methods:convert(),process(),display()andupdate()to obtain the desired functionalityPlease take a look at theexamplesand consult the documentation found in theLiveImageProcessorbase class to see how this might be done.A Simple ExamplefromcvliveimportLiveImageProcessorimportcv2classMySimpleProcessor(LiveImageProcessor):defconvert(self):# convert input image to grayscalereturncv2.cvtColor(self.raw_input,cv2.COLOR_BGR2GRAY)defprocess(self):# blur the input using a 51x51 Gaussian filterself.result=cv2.GaussianBlur(self.input,(51,51),0)if__name__=="__main__":MySimpleProcessor().run()
Package: cvloop
Provides cvloop, a way to show opencv video loops. Designed for jupyter notebooks.Simple example: Show webcam feed.fromcvloopimportcvloopcvloop()More complex example: Show webcam feed side by side with inverted image.fromcvloopimportcvloopcvloop(function=lambdaframe:255-frame,side_by_side=True)Complex example: Show video file with background extraction (SeeOpenCV Documentation;Video).fromcvloopimportcvloopimportcv2defmog2(frame):returnmog2.fgbg.apply(frame)mog2.fgbg=cv2.createBackgroundSubtractorMOG2()cvloop('768x576.avi',function=mog2,side_by_side=True)More examples: For more examples check out theexamples notebook.InstallYou can simply install cvloop using pip (make sure to install matplotlib, numpy, OpenCV and Jupyter):pip install cvloopOr, if you are using conda and don’t want to worry about requirements, just use conda-forge:conda config --add channels conda-forge
conda install cvloopRequirements(Recommended versions, additionally tested versions in parentheses.)Python 3.6OpenCV 3.2Jupyter 4.3.1matplotlib 2.0.0numpy 1.12.0DevelopmentTo contribute, just fork the repository and create pull requests.To publish, you need a couple of additional tools:gpgto sign the packagestwineto upload themshasumto calculate the checksum for conda-forgehubto create the pull request for conda-forge