Saravanan666/pypi-org huggingface dataset:
Package: yogi-formula
fin formulaIn this project I have developed my first python package. It is simple package that contain investment related formula.How to install in local system?pip install yogi-formulaExample of packagefromyogi_formula.fin_formulaimportget_valuationvaluation=get_valuation(27726,10)print(f"Company Valuation is{valuation}")Bug / Feature RequestIf you find a bug (the website couldn't handle the query and / or gave undesired results), kindly open an issuehereby including your search query and the expected result.If you'd like to request a new function, feel free to do so by opening an issuehere. Please include sample queries and their corresponding results.Connect with Author! 🌐Known on internet asYogesh NileEmail Me :e-mail:
Package: yoginth
YoginthThe Yoginth CLIInstall$ pip install yoginthUsage$ yoginthScreenshotGet HelpThere are few ways to get help:Pleasepost questions on Stack Overflow. You can open issues with questions, as long you add a link to your Stack Overflow question.For bug reports and feature requests, open issues.For direct and quick help, you canemail me.How to contributeHave an idea? Found a bug? Seehow to contribute.Thanks!LicenseMIT
Package: yoinkery
Data Extraction ToolsYoink is a tool for extracting data from rasterized images.  It currently
supports interactively picking points from line plots.  It also supports
extracting the scalar field from a colormapped image.  Yoink builds on top
matplotlib and is platform and rendering backend independent.Please refer to the online documentation athttp://yoinkery.com/Follow development athttps://github.com/mrterry/yoink
Package: yojenkins
yojenkinsInstall Now!yojenkinsis a cross-platform command line interface (CLI) tool to monitor, manage, and deal with Jenkins server. It makes it possible to interact with a Jenkins server without using the browser based Jenkins UI.This tool is able to be integrated into a script as middleware in order to automate Jenkins related tasks or enable Jenkins configuration as code.yojenkinswill liberate you and your browser from the Jenkins Web UIWithyojenkinsyou can manage:Authentication:Authentication structure similar to AWS APIServer:Create, shutdown, view queue, and moreUser accounts:Create, delete, add/remove permission, and moreNodes/agents:Create, delete, shut down server, and moreCredentials:Create, update, delete, list, and moreFolders:Create items, delete items, disable, enable, and moreJobs:Create, delete, trigger, monitor, search, and moreBuilds:Monitor, abort, tail logs, follow logs, and moreStages:Get info, get logs, view steps, view statusSteps:Get infoOther tools and functions:Run groovy scripts remotely, run custom REST calls, setup a shared library, view command usage history, and more:blue_book: DocumentationFor all information and documentation, please visityojenkins.com:movie_camera: Overview VideoThis video presents an overview ofyojenkins, while demonstrating a few basic funcitonalities and workflow.:ballot_box_with_check: Project StatusThis project is in release phasealpha. Please report any issues, odd behavior, or suggestions.
This does not mean that this project is not usable. It just means that the project is still in development.Read more about therelease cycle.SeeBug ReportsandFeature Requests:heartbeat: Help this Project!This is a very young project and I am always looking for help in any way. If you like this project, please consider helping.For financial or marketing support options seeSupport This Project!.To contribute to this project, seeContribute to This Project!.:broken_heart: Similar ProjectsIf this project is not something you were were looking for, that ok, there are similar projects out there,
which each one with their own advantages and disadvantages.Official Jenkins CLIjenkins-clijennijenkins-job-cli
Package: yokkaichi
Yokkaichi (formely mcserverscanner) - Shodan-like server scanner for Minecraft.Renaming notice!This project used to be named mcserverscanner, but on 2023-01-15 it got renamed to Yokkaichi.FeaturesScanning for Java and Bedrock serversMasscan integrationIP2Location integrationPlanned features (not in a specific order)WWW interfaceBot joining the serversDocker supportScanning for pre-Netty (<1.7) serversQuery integrationInstallationReleasesInstalling the latest version with pipx (recommended)pipx install yokkaichi --include-deps
yokkaichi -vInstalling the latest version in a virtual environmentvirtualenv .venv
source .venv/bin/activate # for Linux
.venv\bin\activate.bat # for Windows
pip install yokkaichi
yokkaichi -vDevelopment versions (not recommended!)Check outDEVELOPMENT_INSTALL.md3rd party dependencies (optional)masscan(for faster scanning)UsageWhen starting the script for the first time,yokkaichi.tomlwill get created. You will have to adjust it to your preferences. Optionally, you can also pass in-cto set a different name or location of the config file.How to get geolocating to work?Yokkaichi uses IP2Location LITE for anything geolocation related. This includes getting the location of the server, and generating the CIDR ranges for scanning. It is a offline, free to use download, so there are no rate limits. However, the database is not redistributed with Yokkaichi, due to IP2Location updating their LITE databases every month. Instead, it will be downloaded and updated automatically everytime you run this script for the first time in the month. To get the downloading to work, you need to have an IP2Location LITE Download Token. To get one, follow these steps.Go toIP2Location LITEwebsite and register (or log into) an account (which is completly free forever).Click on the name in the upper right corner and select "Database Download".Copy your Download Token.Set theIP2LOCATION_LITE_TOKENenvironment variable to your token.Now, you will be able to use the automatic downloads feature.Using the masscan integrationYou need to havemasscanin your PATH, or in the same directory from which you are running this software. Make sure that the binary is namedmasscan(Unix) ormasscan.exe(Windows).
Package: yolink-api
YoLink Python library for HA IntegrationSupported devicesYS6602/4 YS6704 (outlet | plug)YS7103/4/5 (siren)YS7704/7705/7706 (door sensor)YS7707 (Contact Sensor)YS7804/5 (motion sensor)YS7903/4 (leak sensor)YS8003/4/5/6 (temperature/humidity sensor)YS7201 (vibration sensor)YS7606/7 (lock)YS4909 YS5001 (valve controller)YS5705/6 (switch)YS7A01/2 (CO & Smoke Sensor)YS4002/3 (Thermostat)YS4906/8 (Garage Door Sensor/ Garage Door Controller)YS5707 (Dimmer)YS6801/2 (MultiOutlet)YS3604 (FlexFob)YS7106 (Power Failure Alarm)
Package: yolite
Yolov5-Lite: Minimal YoloV5 ImplementationOverviewIt has been simplified by editing detect.py in the yolov5 repository.Installationgit clone https://github.com/kadirnar/yolov5-lite
cd yolov5-lite
pip install -r requirements.txtYolov5-Lite Prediction:It is the edited version of the codes in the detect file.class Yolov5:
    def __init__(self, weights, device, data):
        self.weights = weights
        self.device = device

    def load_model(self, weights, device, data):
        self.device = select_device(device)
        self.model = DetectMultiBackend(weights, device=self.device, data=data)
        
    def preprocces_img(self, img, imgsz):
        self.npy_im = numpy_img(img, imgsz)
        self.tensor_im = file_to_torch(self.npy_im, self.device)

    def detect(self):
        # Inference
        pred = self.model(self.tensor_im) # shape: torch.Size([1, 3, 640, 480])

        # NMS
        pred = non_max_suppression(pred, conf_thres=0.25, iou_thres=0.45, max_det=1000)
        for det in pred:
            det[:, :4] = scale_coords(self.tensor_im.shape[2:], det[:, :4], self.npy_im.shape).round()
        
        self.det = det

    def show_img(self, view_img=True):
        # Write results
        for *xyxy, conf, cls in reversed(self.det):
            annotator = Annotator(self.npy_im, line_width=3, example=str(self.model.names))
            logging.info("\t+ Label: %s, Conf: %.5f" % (self.model.names[int(cls)], conf.item()))
            if view_img:  # Add bbox to image
                label = "%s %.2f" % (self.model.names[int(cls)], conf)
                annotator.box_label(xyxy, label, color=colors(int(cls), True))

        # Stream results
        im0 = annotator.result()
        if view_img:
            cv2.imshow("frame", im0)
            cv2.waitKey(0)
            cv2.destroyAllWindows()Yolov5-Lite Run Code:You can take the detect.py file as an example to load and visualize your yolov5 models.weights = "yolov5s.pt"
img = "data/images/bus.jpg"
data = "data/coco128.yaml"
device = "cpu"
imgsz = 640
view_img = True


model = Yolov5(weights, device, data)
model.load_model(weights, device, data)
model.preprocces_img(img, imgsz)
model.detect()
model.show_img(view_img)Reference:YOLOv5YOLOv5-Pip
Package: yolk1977
ContentsInstallationSummaryUseful tipsInstallation$ pip install --upgrade yolk1977SummaryYolk is a Python tool for obtaining information about installed Python packages
and querying packages available on PyPI (Python Package Index).yolk3kis a fork of the originalyolk.yolk3kadds Python 3 support (while
maintaining Python 2 support). It also adds additional features.yolk1977is a fork of theyolk3kfork that fixes that annoying bug wherein a single
glitchy URL-fetch status would interrupt the entirety of theyolk-Uprocess.You can see which packages are active, non-active or in development mode and
show you which have newer versions available by querying PyPI.Usage Examples:$ yolk --upgrade
    Upgrade all packages

$ yolk --upgrade --user
    Upgrade user packages

$ yolk -l
    List all installed Python packages

$ yolk -a
    List only the activated packages installed (Activated packages are
    normal packages on sys.path you can import)

$ yolk -n
    List only the non-activated (--multi-version) packages installed

$ yolk -l -f License,Author nose==1.0
    Show the license and author for version 1.0 of the package `nose`

$ yolk --entry-map nose
    Show entry map for the nose package

$ yolk --entry-points nose.plugins
    Show all setuptools entry points for nose.pluginsThese options query PyPI:$ yolk -U pkg_name
    Shows if an update for pkg_name is available by querying PyPI

$ yolk -U
     Checks PyPI to see if any installed Python packages have updates
     available.

$ yolk -F Paste
    Download source tarball for latest version of Paste to your current
    directory

$ yolk -F Paste -T svn
    Do a subversion checkout for Paste to a directory named Paste_svn in
    your current directory.

$ yolk -L 2
    Show list of CheeseShop releases in the last two hours

$ yolk -C 2
    Show detailed list of changes in the CheeseShop in the last two hours

$ yolk -M Paste==1.0
    Show all the metadata for Paste version 1.0

$ yolk -M Paste
    Show all the metadata for the latest version of Paste listed on PyPI

$ yolk -D cheesecake
    Show all (source, egg, svn) URL's for the latest version of cheesecake
    packages

$ yolk -T source -D cheesecake
    Show only source code releases for cheesecake

$ yolk -H twisted
    Launches your web browser at Twisted's home pageUseful tipsCheck invalid reStructuredText in descriptions on PyPI (usingrstcheck):$ yolk --query-metadata=pip --fields=description | rstcheck -
Package: yolk3k
ContentsInstallationSummaryUseful tipsInstallation$ pip install --upgrade yolk3kSummaryYolk is a Python tool for obtaining information about installed Python packages
and querying packages available on PyPI (Python Package Index).yolk3kis a fork of the originalyolk.yolk3kadd Python 3 support (while
maintaining Python 2 support). It also adds additional features.You can see which packages are active, non-active or in development mode and
show you which have newer versions available by querying PyPI.Usage Examples:$ yolk --upgrade
    Upgrade all packages

$ yolk --upgrade --user
    Upgrade user packages

$ yolk -l
    List all installed Python packages

$ yolk -a
    List only the activated packages installed (Activated packages are
    normal packages on sys.path you can import)

$ yolk -n
    List only the non-activated (--multi-version) packages installed

$ yolk -l -f License,Author nose==1.0
    Show the license and author for version 1.0 of the package `nose`

$ yolk --entry-map nose
    Show entry map for the nose package

$ yolk --entry-points nose.plugins
    Show all setuptools entry points for nose.pluginsThese options query PyPI:$ yolk -U pkg_name
    Shows if an update for pkg_name is available by querying PyPI

$ yolk -U
     Checks PyPI to see if any installed Python packages have updates
     available.

$ yolk -F Paste
    Download source tarball for latest version of Paste to your current
    directory

$ yolk -F Paste -T svn
    Do a subversion checkout for Paste to a directory named Paste_svn in
    your current directory.

$ yolk -L 2
    Show list of CheeseShop releases in the last two hours

$ yolk -C 2
    Show detailed list of changes in the CheeseShop in the last two hours

$ yolk -M Paste==1.0
    Show all the metadata for Paste version 1.0

$ yolk -M Paste
    Show all the metadata for the latest version of Paste listed on PyPI

$ yolk -D cheesecake
    Show all (source, egg, svn) URL's for the latest version of cheesecake
    packages

$ yolk -T source -D cheesecake
    Show only source code releases for cheesecake

$ yolk -H twisted
    Launches your web browser at Twisted's home pageUseful tipsCheck invalid reStructuredText in descriptions on PyPI (usingrstcheck):$ yolk --query-metadata=pip --fields=description | rstcheck -
Package: yollor
YollorUma biblioteca Python para facilitar o uso de cores e sinais em seus projetos.DescriÃ§Ã£oYollor Ã© uma biblioteca Python que oferece uma maneira simples de trabalhar com cores em seus projetos. Ela inclui uma funÃ§Ã£o conveniente para aplicar cores a strings de texto.InstalaÃ§Ã£oPara instalar a biblioteca, baixe o arquivo yollor.py noRepo Oficiale inclua o arquivo em seu projetofuturamente irei incluir a biblioteca no pypi (pip)FeaturesCorespretoc.blackvermelhoc.redverdec.greenmarromc.brownazulc.blueroxoc.purplecianoc.cyancinzac.grayamareloc.yellowTagsPara usar tags, faÃ§a:t.tag_corcertifique-se de subistituir "tag" por uma dessas opÃ§oes:tagn1tagn2tagn3tagn4tagn5tagn6tagn7tagn8tagn9tagn10minusquestmorehashtage "cor" por:blackredgreenbrownbluepurplecyangrayyellowLicenÃ§aEsta biblioteca estÃ¡ disponÃ­vel sob a LicenÃ§aMITContribuiÃ§Ã£oContribuiÃ§Ãµes sÃ£o bem-vindas! Sinta-se Ã  vontade para abrir um problema ou enviar um pull request.VersÃ£oEsta Ã© a versÃ£o 2.0.0 da biblioteca.
Package: yolo2labelme
yolo2labelmeTool to convert yolo format dataset to labelme json format.Installationpipinstallyolo2labelmeUsageArguments:data: path to dataset directoryKeyword arguments:out: path to output directory. Default dataset is created at same path as data_dir with name labelmeDataset.skip: action to take if.txtfile corresponding to image is not found.False(default) raisesFileNotFoundError.printprints missing file path to stdout.Trueor any other value skips file silently.CLI Usage:Following command creates labelme json dataset directory atpath/to/yolo/labelmeDatasetfrompath/to/yolo/datasetdataset directory.yolo2labelmepath/to/yolo/datasetSpecify output directory, skip.yolo2labelmepath/to/yoloDataset--outpath/to/labelmeJsonDir--skipprintPython Usage:fromyolo2lablemeimportyolo2labelmeyolo2labelme('path/to/yolo/dataset')With output directory and skip actionyolo2labelme('path/to/yolo/dataset','path/to/labelme/dataset',skip=True)Or simplyfromyolo2labelmeimporty2ly2l('path/to/dataset')Useful linkshttps://github.com/kadapallaNithin/yolo2labelmehttps://pypi.org/project/yolo2labelme/Lableme to yolo :https://pypi.org/project/labelme2yolo/
Package: yolo34py
A Python wrapper onpjreddie'simplementation (authors' implementation) ofYOLO V3 Object DetectoronDarknet.
This wrapper is also compatible with other Darknet object detection models.Image source:http://absfreepic.com/free-photos/download/crowded-cars-on-street-4032x2272_48736.htmlPrerequisitesPython 3.6+Linux x86-64 Operating SystemNVIDIA CUDA SDK (for GPU version only. Make sure nvcc is available in PATH variable.)Sample UsageNote: This sample code requires OpenCV with python bindings installed. (pip3 install opencv-python==3.4.0)Create a directory to host sample code and navigate to it.Download and executethis scriptto download model files.Create sampleApp.py with following code. Specify SAMPLE_INPUT_IMAGE.frompydarknetimportDetector,Imageimportcv2net=Detector(bytes("cfg/yolov3.cfg",encoding="utf-8"),bytes("weights/yolov3.weights",encoding="utf-8"),0,bytes("cfg/coco.data",encoding="utf-8"))img=cv2.imread('SAMPLE_INPUT_IMAGE')img_darknet=Image(img)results=net.detect(img_darknet)forcategory,score,boundsinresults:x,y,w,h=boundscv2.rectangle(img,(int(x-w/2),int(y-h/2)),(int(x+w/2),int(y+h/2)),(255,0,0),thickness=2)cv2.putText(img,category,(int(x),int(y)),cv2.FONT_HERSHEY_COMPLEX,1,(255,255,0))cv2.imshow("output",img)cv2.waitKey(0)Execute sampleApp.pypython sampleApp.py.Installationyolo34py comes in 2 variants,CPU Only VersionandGPU Version.
Installation may take a while since it involves downloading and compiling darknet.CPU Only VersionThis version is configured on darknet compiled with flag GPU = 0.pip3installrequests# Used to download darknetpip3installcython
pip3installnumpy
pip3installyolo34pyGPU Version:This version is configured on darknet compiled with flag GPU = 1.pip3installrequests# Used to download darknetpip3installcython
pip3installnumpy
pip3installyolo34py-gpuMore InformationFor more details on yolo34py (This python wrapper):GitHub:https://github.com/madhawav/YOLO3-4-PyThis is the place to discuss issues of yolo34py.Your contributions are greatly appreciated.For more details on YOLO V3:Website from Authors:https://pjreddie.com/yoloFor more details on Darknet, the base API wrapped by this libraryWebsite:https://pjreddie.com/darknet/GitHub:https://github.com/pjreddie/darknetLicenseyolo34py (this wrapper) is underApache License 2.0.The version of darknet wrapped by yolo34py is inpublic domain.
Package: yolo34py-gpu
A Python wrapper onpjreddie'simplementation (authors' implementation) ofYOLO V3 Object DetectoronDarknet.
This wrapper is also compatible with other Darknet object detection models.Image source:http://absfreepic.com/free-photos/download/crowded-cars-on-street-4032x2272_48736.htmlPrerequisitesPython 3.6+Linux x86-64 Operating SystemNVIDIA CUDA SDK (for GPU version only. Make sure nvcc is available in PATH variable.)Sample UsageNote: This sample code requires OpenCV with python bindings installed. (pip3 install opencv-python==3.4.0)Create a directory to host sample code and navigate to it.Download and executethis scriptto download model files.Create sampleApp.py with following code. Specify SAMPLE_INPUT_IMAGE.frompydarknetimportDetector,Imageimportcv2net=Detector(bytes("cfg/yolov3.cfg",encoding="utf-8"),bytes("weights/yolov3.weights",encoding="utf-8"),0,bytes("cfg/coco.data",encoding="utf-8"))img=cv2.imread('SAMPLE_INPUT_IMAGE')img_darknet=Image(img)results=net.detect(img_darknet)forcategory,score,boundsinresults:x,y,w,h=boundscv2.rectangle(img,(int(x-w/2),int(y-h/2)),(int(x+w/2),int(y+h/2)),(255,0,0),thickness=2)cv2.putText(img,category,(int(x),int(y)),cv2.FONT_HERSHEY_COMPLEX,1,(255,255,0))cv2.imshow("output",img)cv2.waitKey(0)Execute sampleApp.pypython sampleApp.py.Installationyolo34py comes in 2 variants,CPU Only VersionandGPU Version.
Installation may take a while since it involves downloading and compiling darknet.CPU Only VersionThis version is configured on darknet compiled with flag GPU = 0.pip3installrequests# Used to download darknetpip3installcython
pip3installnumpy
pip3installyolo34pyGPU Version:This version is configured on darknet compiled with flag GPU = 1.pip3installrequests# Used to download darknetpip3installcython
pip3installnumpy
pip3installyolo34py-gpuMore InformationFor more details on yolo34py (This python wrapper):GitHub:https://github.com/madhawav/YOLO3-4-PyThis is the place to discuss issues of yolo34py.Your contributions are greatly appreciated.For more details on YOLO V3:Website from Authors:https://pjreddie.com/yoloFor more details on Darknet, the base API wrapped by this libraryWebsite:https://pjreddie.com/darknet/GitHub:https://github.com/pjreddie/darknetLicenseyolo34py (this wrapper) is underApache License 2.0.The version of darknet wrapped by yolo34py is inpublic domain.
Package: yolo5
packaged ultralytics/yolov5pip install yolo5OverviewYou can finally installYOLOv5 object detectorusingpipand integrate into your project easily.InstallationInstall yolov5 using pip(for Python >=3.7):pip install yolo5Install yolov5 using pip(for Python 3.6):pip install "numpy>=1.18.5,<1.20" "matplotlib>=3.2.2,<4"pip install yolov5Basic Usageimportyolov5# modelmodel=yolov5.load('yolov5s')# imageimg='https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'# inferenceresults=model(img)# inference with larger input sizeresults=model(img,size=1280)# inference with test time augmentationresults=model(img,augment=True)# show resultsresults.show()# save resultsresults.save(save_dir='results/')Alternative Usagefromyolov5importYOLOv5# set model paramsmodel_path="yolov5/weights/yolov5s.pt"# it automatically downloads yolov5s model to given pathdevice="cuda"# or "cpu"# init yolov5 modelyolov5=YOLOv5(model_path,device)# load imagesimage1='https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'image2='https://github.com/ultralytics/yolov5/blob/master/data/images/bus.jpg'# perform inferenceresults=yolov5.predict(image1)# perform inference with larger input sizeresults=yolov5.predict(image1,size=1280)# perform inference with test time augmentationresults=yolov5.predict(image1,augment=True)# perform inference on multiple imagesresults=yolov5.predict([image1,image2],size=1280,augment=True)# show detection bounding boxes on imageresults.show()# save results into "results/" folderresults.save(save_dir='results/')ScriptsYou can call yolo_train, yolo_detect and yolo_test commands after installing the package viapip:TrainingRun commands below to reproduce results onCOCOdataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest--batch-sizeyour GPU allows (batch sizes shown for 16 GB devices).$yolo_train--datacoco.yaml--cfgyolov5s.yaml--weights''--batch-size64yolov5m40yolov5l24yolov5x16Inferenceyolo_detect command runs inference on a variety of sources, downloading models automatically from thelatest YOLOv5 releaseand saving results toruns/detect.$yolo_detect--source0# webcamfile.jpg# imagefile.mp4# videopath/# directorypath/*.jpg# globrtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa# rtsp streamrtmp://192.168.1.105/live/test# rtmp streamhttp://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8# http streamTo run inference on example images inyolov5/data/images:$yolo_detect--sourceyolov5/data/images--weightsyolov5s.pt--conf0.25StatusBuilds for the latest commit forWindows/Linux/MacOSwithPython3.6/3.7/3.8:Status for the train/detect/test scripts: