Saravanan666/pypi-org huggingface dataset:
Package: labelmap
LabelMapGenerate label maps for ML/DL ProjectsUsageWith CSV filefromlabelmapimportLabelMaplabel_map=LabelMap(labels_csv="path-to-csv",labels_col_name="labels")label_map.info()# print label mapWith CSV file and exclude some labelsfromlabelmapimportLabelMaplabel_map=LabelMap(labels_csv="path-to-csv",labels_col_name="labels",exclude_labels=["cat","dog"])label_map.info()# print label mapWithout CSV filefromlabelmapimportLabelMaplabel_map=LabelMap()label_map.add("cat")label_map.add("dog")label_map.info()# print label mapParameters inLabelMapclasslabels_csv:strThe path to the csv file containing the labels and ids.Not Required, default =Nonelabels_col_name:strThe name of the column containing the labels.Required iflabels_col_nameis set, default =Noneids_col_name:strThe name of the column containing the ids.Not Required, default =Noneid_type:int/floatThe type of the ids.Not Required, default =intexclude_labels:listThe labels to exclude from the label map.Not Required, default =NoneMethods inLabelMapclassadd(label)Add a label to the label map.Args:label -> strReturn Type:Noneforce_get(label)Get the id of a label or if id not found then add the label and get.Args:label -> strReturn Type:Noneto_text(id)Get the label of an id.Args:id -> int/ floatReturn Type:strto_id(label)Get the id of a label.Args:label -> strReturn Type:int/ floatmap()Get the label map as dict.Args:NoneReturn Type:dictlabels()Get the list of labels.Args:NoneReturn Type:listinfo()Print the label map as table.Args:NoneReturn Type:Nonecount()Get the number of labels.Args:None'Return Type:intas_dataframe()Label data dictionary to pandas dataframe.Args:NoneReturn Type:pandas.DataFramesave_csv(path)label data dictionary save to csv file.Args:path -> strReturn Type:None
Package: label-mapper
Label Mapper is a spaCy factory that can receive a mapping dictionary that changes the labels for entities. It should come at the end of the pipeline or after the NER or EntityRuler.Installationpipinstalllabel-mapperUsagenlp=spacy.load("en_core_web_sm")label_map={"GPE":"LOC"}nlp.add_pipe("label_fixer",last=True,config={"label_map":label_map})doc=nlp("We went into Yellowstone Park in Wyoming.")forentindoc.ents:print(ent.text,ent.label_)Output:Yellowstone Park LOC
Wyoming LOC
Package: labelme2coco
labelme2cocoA lightweight package for converting yourlabelmeannotations into COCO object detection format.Convert LabelMe annotations to COCO format in one steplabelmeis a widely used is a graphical image annotation tool that supports classification, segmentation, instance segmentation and object detection formats.
However, widely used frameworks/models such as Yolact/Solo, Detectron, MMDetection etc. requires COCO formatted annotations.You can use this package to convert labelme annotations to COCO format.Getting startedInstallationpip install -U labelme2cocoBasic Usagelabelme2cocopath/to/labelme/dirlabelme2cocopath/to/labelme/dir--train_split_rate0.85Advanced Usage# import packageimportlabelme2coco# set directory that contains labelme annotations and image fileslabelme_folder="tests/data/labelme_annot"# set export direxport_dir="tests/data/"# set train split ratetrain_split_rate=0.85# convert labelme annotations to cocolabelme2coco.convert(labelme_folder,export_dir,train_split_rate)# import functionsfromlabelme2cocoimportget_coco_from_labelme_folder,save_json# set labelme training data directorylabelme_train_folder="tests/data/labelme_annot"# set labelme validation data directorylabelme_val_folder="tests/data/labelme_annot"# set path for coco json to be savedexport_dir="tests/data/"# create train coco objecttrain_coco=get_coco_from_labelme_folder(labelme_train_folder)# export train coco jsonsave_json(train_coco.json,export_dir+"train.json")# create val coco objectval_coco=get_coco_from_labelme_folder(labelme_val_folder,coco_category_list=train_coco.json_categories)# export val coco jsonsave_json(val_coco.json,export_dir+"val.json")
Package: labelme2yolo
Labelme2YOLOLabelme2YOLO is a powerful tool for converting LabelMe's JSON format toYOLOv5dataset format. This tool can also be used for YOLOv5/YOLOv8 segmentation datasets, if you have already made your segmentation dataset with LabelMe, it is easy to use this tool to help convert to YOLO format dataset.New Featuresexport data as yolo polygon annotation (for YOLOv5 & YOLOV8 segmentation)Now you can choose the output format of the label text. The two available alternatives arepolygonand bounding box (bbox).Installationpipinstalllabelme2yoloArguments--json_dirLabelMe JSON files folder path.--val_size (Optional)Validation dataset size, for example 0.2 means 20% for validation.--test_size (Optional)Test dataset size, for example 0.1 means 10% for Test.--json_name (Optional)Convert single LabelMe JSON file.--output_format (Optional)The output format of label.--label_list (Optional)The pre-assigned category labels.How to Use1. Converting JSON files and splitting training, validation datasetsYou may need to place all LabelMe JSON files underlabelme_json_dirand then run the following command:labelme2yolo--json_dir/path/to/labelme_json_dir/This tool will generate dataset labels and images with YOLO format in different folders, such as/path/to/labelme_json_dir/YOLODataset/labels/train/
/path/to/labelme_json_dir/YOLODataset/labels/val/
/path/to/labelme_json_dir/YOLODataset/images/train/
/path/to/labelme_json_dir/YOLODataset/images/val/
/path/to/labelme_json_dir/YOLODataset/dataset.yaml2. Converting JSON files and splitting training, validation, and test datasets with --val_size and --test_sizeYou may need to place all LabelMe JSON files underlabelme_json_dirand then run the following command:labelme2yolo--json_dir/path/to/labelme_json_dir/--val_size0.15--test_size0.15This tool will generate dataset labels and images with YOLO format in different folders, such as/path/to/labelme_json_dir/YOLODataset/labels/train/
/path/to/labelme_json_dir/YOLODataset/labels/test/
/path/to/labelme_json_dir/YOLODataset/labels/val/
/path/to/labelme_json_dir/YOLODataset/images/train/
/path/to/labelme_json_dir/YOLODataset/images/test/
/path/to/labelme_json_dir/YOLODataset/images/val/
/path/to/labelme_json_dir/YOLODataset/dataset.yamlHow to build package/wheelinstall hatchRun the following command:hatchbuildLicenseForked fromrooneysh/Labelme2YOLOlabelme2yolois distributed under the terms of theMITlicense.
Package: labelme2yolov7segmentation
LabelMe2Yolov7SegmentationConvertLabelMeformat intoYoloV7format for instance segmentation.InstalationYou can installlabelme2yolov7segmentationfromPypi. It's going to install the library itself and its prerequisites as well.pipinstalllabelme2yolov7segmentationYou can installlabelme2yolov7segmentationfrom its source code.gitclonehttps://github.com/Tlaloc-Es/labelme2yolov7segmentation.gitcdlabelme2yolov7segmentation
pipinstall-e.UsageFirst of all, make your dataset with LabelMe, after that call to the following commandlabelme2yolo --source-path /labelme/dataset --output-path /another/pathThe arguments are:--source-path: That indicates the path where are the json output of LabelMe and their images, both will have been in the same folder--output-path: The path where you will save the converted files and a copy of the images following the yolov7 folder estructureExpected outputIf you execute the following command:labelme2yolo --source-path /labelme/dataset --output-path /another/datasetsYou will get something like thisdatasets
├──images
│├──train
││├──img_1.jpg
││├──img_2.jpg
││├──img_3.jpg
││├──img_4.jpg
││└──img_5.jpg
│└──val
│├──img_6.jpg
│└──img_7.jpg
├──labels
│├──train
││├──img_1.txt
││├──img_2.txt
││├──img_3.txt
││├──img_4.txt
││└──img_5.txt
│└──val
│├──img_6.txt
│└──img_7.txt
├──labels.txt
├──test.txt
└──train.txtDonationIf you want to contribute you can make a donation athttps://www.buymeacoffee.com/tlaloc, thanks in advance
Package: labelmerge
LabelmergeLabelmerge: A BIDS app for merging multiple label maps and metadata into a single parcellation scheme.Examination of the brain has led to the creation and distribution of numerous parcellation schemes, studying various features or aspects including gross anatomy, cytoarchitecture, myeloarchitecture, functional connectivty, or structural connectivity. With the growing number of available schemes, there exists a need for methods to robustly merge different these different atlases from numerous sources depending on the aims of the specific study.Currently, combining multiple atlases requires image-processing tools to manually remove and add the regions of interest from the respective atlases. While effective, this task becomes inefficient and is prone to error when looking to combine specific labels of interest from multiple atlases or across a group of subjects. To that end we developed Labelmerge, a Brain Imaging Data Structure (BIDS) app that combines the parcellations of multiple atlases into a single parcellation scheme that can be applied towards downstream analysis.ContributingClone the git repository. Labelmerge dependencies are managed with Poetry
(version 1.2.x), which you'll need installed on your machine.
You can find instructions on the Poetrywebsite.Then, setup the development environment with the following commands:poetry install
poetry run poe setupLabelmerge uses poethepoet as a task runner.
You can see what commands are available by running:poetry run poeIf you wish, you can also run poe [command] directly by installing poethepoet
on your system. Follow the install instructions at the link above.Labelmerge uses pre-commit hooks (installed via the poe setup command above) to
lint and format code (we use black, isort, flake8). By default, these hooks are
run on every commit.Please be sure they all pass before making a PR.
Package: labelmex
LabelmeX is a graphical image annotation tool inspired byhttp://labelme.csail.mit.edu.LabelmeX is able perform auto-annotation by specifying the model web service.
It is written in Python and uses Qt for its graphical interface.RequirementsUbuntu / macOS / WindowsPython2 / Python3PyQt4 / PyQt5/PySide2InstallationThere are options:Platform agonistic installation:Anaconda,DockerPlatform specific installation:Ubuntu,macOS,WindowsAnacondaYou need installAnaconda, then run below:# python2condacreate--name=labelmexpython=2.7sourceactivatelabelmex# conda install -c conda-forge pyside2condainstallpyqt
pipinstalllabelmex# if you'd like to use the latest version. run below:# pip install git+https://github.com/wkentaro/labelme.git# python3condacreate--name=labelmexpython=3.6sourceactivatelabelmex# conda install -c conda-forge pyside2# conda install pyqt# pip install pyqt5  # pyqt5 can be installed via pip on python3pipinstalllabelmex# or you can install everything by conda command# conda install labelme -c conda-forgeUbuntu# Ubuntu 14.04 / Ubuntu 16.04# Python2# sudo apt-get install python-qt4  # PyQt4sudoapt-getinstallpython-pyqt5# PyQt5sudopipinstalllabelmex# Python3sudoapt-getinstallpython3-pyqt5# PyQt5sudopip3installlabelmexUbuntu 19.10+ / Debian (sid)sudoapt-getinstalllabelmexmacOS# macOS Sierrabrewinstallpyqt# maybe pyqt5pipinstalllabelmex# both python2/3 should workWindowsInstallAnaconda, then in an Anaconda Prompt run:# python3condacreate--name=labelmepython=3.6
condaactivatelabelme
pipinstalllabelmeUsageRunlabelmex --helpfor detail.The annotations are saved as aJSONfile.labelmex# just open guiAcknowledgementThis repo is the fork ofwkentaro/labelme,
whose development is still on-going.Cite This ProjectIf you use this project in your research or wish to refer to the baseline results published in the README, please use the following BibTeX entry.@misc{labelme2016,author={KentaroWada},title={{labelme:ImagePolygonalAnnotationwithPython}},howpublished={\url{https://github.com/wkentaro/labelme}},year={2016}}
Package: label-noise-correction
Label Noise Correction MethodsThis Python package provides an implementation of label noise correction algorithms proposed in the literature. These algorithms aim to mitigate the effects of label noise in supervised learning tasks by correcting the noisy labels. The methods were implemented for binary classification tasks.InstallationYou can install the package usingpip:pipinstalllabel_noise_correctionAlgorithmsThe package currently includes the following label noise correction algorithms:Bayesian Entropy Noise Correction(BE) [1]Polishing Labels(PL) [2]Self-Training Correction(STC) [2]Clustering-Based Correction(CC) [2]Ordering-Based Noise Correction(OBNC) [3]Hybrid Label Noise Correction(HLNC) [4]Fair Ordering-Based Noise Correction(Fair-OBNC) [5]UsageHere's an example of how to use the package to apply label noise correction:fromlabel_noise_correctionimportAlgorithmAfromsklearn.datasetsimportmake_classificationfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportaccuracy_score# Generate synthetic datasetX,y=make_classification(n_samples=1000,n_features=20,random_state=42)X=pd.DataFrame(X)y=pd.Series(y)# Split the dataset into training and test setsX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)# Introduce label noise to create a noisy datasety_noisy=y_train.copy()foriiny_noisy.index:ifrandom.random()<0.1:y_noisy.loc[i]=1-y_noisy.loc[i]# Apply label noise correctionlnc=PolishingLabels(LogisticRegression,10)y_corrected=lnc.correct(X_train,y_noisy)# Train models on the noisy and corrected labelsmodel=LogisticRegression()model.fit(X_train,y_noisy)y_pred_noisy=model.predict(X_test)model.fit(X_train,y_corrected)y_pred_corrected=model.predict(X_test)# Evaluate accuracy before and after correctionaccuracy=accuracy_score(y_test,y_pred_noisy)print("Accuracy before label noise correction:",accuracy)accuracy=accuracy_score(y_test,y_pred_corrected)print("Accuracy after label noise correction:",accuracy)ReferencesSun, Jiang-wen, et al. "Identifying and correcting mislabeled training instances." Future generation communication and networking (FGCN 2007). Vol. 1. IEEE, 2007.Nicholson, Bryce, et al. "Label noise correction methods." 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2015.Feng, Wei, and Samia Boukir. "Class noise removal and correction for image classification using ensemble margin." 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015.Xu, Jiwei, Yun Yang, and Po Yang. "Hybrid label noise correction algorithm for medical auxiliary diagnosis." 2020 IEEE 18th International Conference on Industrial Informatics (INDIN). Vol. 1. IEEE, 2020.ContributingContributions to this package are welcome! If you have any bug reports, feature requests, or would like to contribute with code improvements, please submit an issue or a pull request on the GitHub repository.LicenseThis package is distributed under theMIT License.Feel free to modify and expand upon this README.md template according to your specific package and the algorithms you implement.
Package: labelord
Global multi-project management of GitHub labels (MI-PYT@FIT CTUproject)InstallationYou have multiple options how to installlabelord:python setup.py install(stable if you use released version, e.g.v0.3)pip install labelord(stable)pip install--extra-index-urlhttps://test.pypi.org/pypilabelord-suchama4(bleeding edge/unstable)UsageThis Python app allows user to do via CLI:List repositoriesList labels for given repositoryRun update/replace labels for multiple projects (labels are specified in configuration file or by template repo)App allows you run master-to-master replication web server which works with GitHub webhooks and manage labels for multiple repositories in even simpler way (tryrun_servercommand and see landing page for more information)!For more information please use--help.This project is reference solution for Labelord tasks series incvut/MI-PYT.ConfigSee included config file what are the options. You will need a personal GitHub token for using this application. You can get your token athttps://github.com/settings/tokens. But be careful where you place it!TestsTests are currently provided in separate repository within multiple branched and with explanation:https://github.com/MarekSuchanek/labelord_testsLicenseThis project is licensed under the MIT License - see theLICENSEfile for more details.
Package: labelord-halfdeadpie
The Labelord application is for the replication of GitHub labels. This application uses mainly requests,flask and click. You are able to use list_repos, list_labels, run and run_server commands.CassettesIf you want to record new cassettes:Write the real GitHub token to ‘config.cfg’ in tests_unitfixturesconfigs.Set your parameters and outputs in tests_unit/conftest_unit.pyâ€˘for example: REPOS, LABELS, RUN_DRY, TARGET_REPOSBefore recording cassettes you have to delete cassettes of mineDocumentationTo generate documentation:docs\make htmlTo generate autodocs:python -m sphinx.apidoc -o docs\auto labelordTo run the documentation tests:docs\make doctest
Package: labelpandas
The Official Open-Source Labelbox <> Pandas Python IntegrationLabelboxenables teams to maximize the value of their unstructured data with its enterprise-grade training data platform. For ML use cases, Labelbox has tools to deploy labelers to annotate data at massive scale, diagnose model performance to prioritize labeling, and plug in existing ML models to speed up labeling. For non-ML use cases, Labelbox has a powerful catalog with auto-computed similarity scores that users can use add metadata tags to large amounts of data with a couple clicks.Pandasstands as the premier open-source Python library for handling CSV and tabluar data and as one of the most widely used Python libraries in the world.This GitHub repo stands as an open-source Python library, moderated by the Labelbox Solutions team, in facilitating Labelbox users in uploading data to Labelbox and retreiving data from Labelbox in tabular / CSV format using Pandas.We strongly encourage collaboration - please free to fork this repo and tweak the code base to work for you own data, and make pull requests if you have suggestions on how to enhance the overall experience, add new features, or improve general performance.Please report any issues/bugs viaGithub Issues.Table of ContentsRequirementsSetupExample NotebooksRequirementsLabelbox accountGenerate a Labelbox API keySetupSet up LabelPandas with the following lines of code:!pip install labelpandas -q
import labelpandas as lp

api_key = "" # Insert your Labelbox API key here
client = lp.Client(api_key)Once set up, you can run the following core functions:client.create_data_rows_from_table():   Creates Labelbox data rows (and metadata) given a Pandas tableclient.export_to_table():  Exports labels (and metadata) from a given Labelbox project and creates a Pandas DataFrameExample NotebooksImporting Data from a CSVNotebookGithubGoogle ColabBasics: Data Rows from URLsData Rows from Raw Text*Data Rows from Local FilesData Rows with MetadataData Rows with AttachmentsData Rows with AnnotationsPutting it all TogetherExporting Data to a CSVNotebookGithubGoogle ColabExporting Data to a CSV= Coming soon
Package: labelprinterkit
LabelprinterkitLabelprinterkit is a Python3 library for creating and printing labels with
Brother P-Touch devices. It was developed for the Networking department of
KIT (Karlsruhe Institute of Technology).Labelprinterkit's simple layout engine can be used to create simple labels:fromlabelprinterkit.backends.usbimportPyUSBBackendfromlabelprinterkit.printersimportP700fromlabelprinterkit.labelimportLabel,Text,Box,Paddingfromlabelprinterkit.jobimportJobfromlabelprinterkit.constantsimportMediafromlabelprinterkit.pageimportPagefromPILimportImage# Create text for the labeltext1=Text(45,"First line",'comic.ttf',padding=Padding(0,0,1,0))text2=Text(25,"Some text",'comic.ttf')text3=Text(25,"Other text",'comic.ttf')# Insert Text into boxesbox1=Box(45,text1)box2=Box(25,text2,text3)# Create label with rows from abovelabel=Label(box1,box2)# Create job with configuration and add label as pagejob=Job(Media.W12)job.add_page(label)# Create a page from a Pillow image and add it to the jobimage=Image.new("1",(70,200),"white")page=Page.from_image(image)job.add_page(page)# scan for a USB printer using the PyUSBBackendbackend=PyUSBBackend()printer=P700(backend)# Print jobprinter.print(job)To use a Bluetooth connection:pair your devicespecify the serial device node when instantiating the printer:printer=GenericPrinter(BTSerialBackend(dev_path='/dev/ttyS8'))The following printers are currently supported:Brother P-Touch PT-700 (aka P700)Brother P-Touch PT-750W (aka P750W)Brother H500Brother E500The following printers have been tested to mostly work, although not
officially supported (their protocol is similar, although not identical):Brother P-touch CUBE Plus PTP710BTThe following backends are currently supported:USB Printer Device Class via PyUSBBluetooth Serial connection via PySerialNetwork connection via TCPThe official source of this repository is athttps://git.scc.kit.edu/scc-net/labelprinterkit.
Pull requests and issues are also accepted on github athttps://github.com/ogelpre/labelprinterkit.
Package: labelpush
LabelPushSimple lightweight label printing appLicense: MIT LicenseAuthor: MatteljayLanguage: Python (>= 3.5)Homepage:https://github.com/MatteljayScreenshotsIntroductionLabelPush is a simple lightweight label printing application written in Python.
It is built to be fast and simple to use. The CUPS printing system is used.
Please make sure the printer is correctly installed with all correct
settings such as label/paper size. This link should take you to your local
settings:CUPS-localhostInstallationPackage dependencies are kept to a minimum. The proper installation guides
for your system can be found via these links:Kivy&PillowVersion 1.10.1 with SDL2 window provider are required! (NOT 1.9 with PyGame).
An updated Python Imaging Library is always recommended with a graphical Python program. And definitely required for LabelPush.pip3&setuptoolsThese are Python 3 installation tools. Universally useful!CUPSPrinter server for macOS and other UNIX-like operating systems.Debian LinuxFor most up-to-date Debian based systems like Ubuntu Linux and Linux Mint this should workas root:add-apt-repository ppa:kivy-team/kivy
apt-get install python3-kivy python3-pip python3-setuptools
pip3 install --upgrade pillow labelpushArch LinuxFor the more up-to-date Arch Linux (Manjaro) simply runas root:pacman -S python-kivy python-pillow python-pip python-setuptools
pip3 install labelpushHow to launchWhen finished, the shortcut icon can be found from your menu-bar in theOfficecategory.
If the icon does not show up, you may need to restart your desktop.
Alternatively, open your graphical user terminal and typelabelpush.pyFor developers, hackers and testersOther ways to install are explained below. The above dependencies are still required!
Only use the info below if you know what you are doing.Option 1You can install from tar.gz or the GitHub master tree.
First, download and extract the archive from thereleasespage.
Then run from within the extracted folder:sudo pip3 install .Option 2Alternatively, you can run it without installing to the root
filesystem. Again, extract the downloaded archive. Then run:pip3 install --user -r requirements.txt
./labelpush.pyMore platformsOther UNIXOther flavors of Linux are untested but there is no reason for them
not to work. Slackware, Gentoo, openSUSE, Fedora, Red Hat, Mandriva, CentOS,...Android, iPhone and WindowsThese platforms probably won't work as they feature different printer
software (no CUPS). If a strong desire exists, find a way to
motivate me :-)Contact info & donationsSee thecontactfile on GitHub.