Saravanan666/pypi-org huggingface dataset:
Package: aitk
aitk: Artificial Intelligence ToolkitThis collection contains two things: an open source set of Python tools, and a set of computational essays for exploring Artificial Intelligence, Machine Learning, and Robotics. This is a collaborative effort started by the authors, building on almost a century of collective experience in education and research.The code and essays are designed to require as few computing resources as necessary, while still allowing readers to experience first-hand the topics covered.AuthorsDouglas Blank- Emeritus Professor of Computer Science, Bryn Mawr College; Head of Research atComet.mlJim Marshall- Professor in the Computer Science Department at Sarah Lawrence CollegeLisa Meeden- Professor in the Computer Science Department at Swarthmore CollegeContributorsPlease feel free to contribute to this collection:https://github.com/ArtificialIntelligenceToolkit/aitkYour Name HereComputational EssaysEach computational essay is described atComputational Essays.Python toolsaitkis a virtual Python package containing the following modules.aitk- top level virtual package; install this to get all of the followingaitk.robots- Python package for exploring simulated mobile robots, with cameras and sensorsaitk.algorithms- Python package for exploring algorithmsaitk.networks- Python package for constructing and visualizing Keras deep learning modelsaitk.utils- Python package for common utilitiesPython InstallationWe recommend usingminicondafor running Jupyter Notebooks locally on your computer. However, you can also skip this and run the Computational Essays on other services, such as Google's Colab. To useminiconda:First installminicondaNext, activate your base environment:source ~/miniconda/bin/activateCreate a Python 3.8 conda environment:conda create --name py38 python=3.8Activate it:conda activate py38You only need to do step 1 once. To get out of conda, back to your regular system:conda deactivate(will get out of py38)conda deactivate(will get out of base environment)Software InstallationAfter activating your conda environment:pip install "aitk.robots[jupyter]"(installs all of the aitk.robots requirements to run in Jupyter Lab 3.0)pip install pandas tensorflow numpy matplotlib tqdm ipycanvas(some things you might want)Jupyter InstallationIf you want to work in notebooks and jupyter lab:pip install jupyterlabjupyter labextension install @jupyter-widgets/jupyterlab-manager ipycanvasjupyter labstarts it up, opens browser windowAITK CommunityFor questions and comments, please usehttps://github.com/ArtificialIntelligenceToolkit/aitk/discussions/
Package: aitkens
aitkensAitken's delta-squared series acceleration methodJ. M. F. Tsang (j.m.f.tsang@cantab.net)UsageGiven an objectxsthat can be turned into a one-dimensional numpy
array, run:fromaitkensimportaccelerateaccelerate(xs)Example: Iterates of $\sqrt{2}$This example, which is given on the Wikipedia article [1], is actually a
poor example since the original iterates converge quadratically, rather
than linearly. The accelerated sequence's terms tend to overshoot the
true values.fromitertoolsimportaccumulatefromaitkensimportaccelerateiterates=list(accumulate(range(5),lambdax,_:0.5*(x+2/x),initial=1))acc=accelerate(iterates)Referenceshttps://en.wikipedia.org/wiki/Aitken%27s_delta-squared_processLicenceThis work is licensed under aCreative Commons Attribution 4.0 International License.
Package: aitk.keras
aitk.kerasAn implementation of the main Keras API with the layers in numpy.UNDER DEVELOPMENTWhy?useful to explain deep learningcan be used where tensorflow is not available (eg, JupterLite)Featuressupports Keras's Sequential and functional APIsalternative dataset downloader for JupyterLiteExamples:# Classic XORfromaitk.keras.layersimportInput,Densefromaitk.keras.modelsimportSequentialinputs=[[0,0],[0,1],[1,0],[1,1]]targets=[[0],[1],[1],[0]]model=Sequential()model.add(Input(2,name="input"))model.add(Dense(8,activation="tanh",name="hidden"))model.add(Dense(1,activation="sigmoid",name="output"))model.compile(optimizer="adam",loss="mse")outputs=model.predict(inputs)model.fit(inputs,targets,epochs=epochs,verbose=0,shuffle=False)See the notebook directory for additional examples.See also the examples in the tests folder.Developmentimplement shufflereport metrics to logs/historyprobably lots of edge cases ar brokensee "FIXME" items in codeTo run the tests:$ pytest -vvv testsPlease feel free to report issues and make Pull Requests!ReferencesLowlevel numpy code based onnumpy_ml.
Package: aitk.robots
aitk.robotsA lightweight Python robot simulator for JupyterLab, Notebooks,
and other Python environments.GoalsA lightweight mobile robotics simulatorUsable in the classroom, research, or explorationExplore wheeled robots with range, cameras, smell, and light sensorsOperate quickly without a huge amount of resourcesCreate reproducible experimentsDesigned for exposition, experimentation, and analysisSensors designed for somewhat realistic problems (such as image recognition)Especially designed to work easily with Machine Learning and Artificial Intelligence systemsInstallationFor the core operations, you will need to install just aitk.robots:pipinstallaitk.robotsTo use the Jupyter enhancements, you'll also need the browser-based
extensions. You can install those with:jupyter labextension install @jupyter-widgets/jupyterlab-managerIf not in a conda environment, then you will also need to:jupyter nbextension enable --py widgetsnbextensionFor additional information, please see:aitkaitk.robotsaitk.networksaitk.utils
Package: ait-learners
Learners EnvironmentWebinterface for accessing CR exercises.Buildpython3-mbuildInstall#pippipinstallait-learners#dockerdockerpullghcr.io/ait-cs-iaas/learnersRungunicorn--bind127.0.0.1:5000learners:app# orflaskrun# ordocker-composeup-dDeveloppipinstall-e.
Package: aitool
.注意：此BytedAITool分支里的task_customized模块包含业务代码，不开源。在公司内请使用pip install bytedaitool安装，获得更多能力接口在公司外仅可使用pip install aitool安装，仅支持基础的算法接口AITool 用于提高算法开发的效率百种工具函数（数据处理、多进程、计时器等）百种算法（包括动态规划、统计模型、深度学习等等）每种算法都自带数据，便于验证和使用。aitool -官方主页-文档MotivationInstallationGetting StartedCommunicationReleases and ContributingThe TeamMotivationInstallation注意：需要python版本>=3.5pipinstallaitool--upgradeGetting Started# TodoCommunicationReleases and ContributingThe Team授权许可本项目采用 MIT 开源授权许可证，完整的授权说明已放置在LICENSE文件中。
Package: ai-tool
ai_toolai toolslice picturefromai_tool.img_slideimportyield_sub_img# yield the sub image from the jpgforbbox,sub_imginyield_sub_img("test.jpg",0,0,180,60):clip="-".join([str(x)forxinbbox])print("sub img:{}".format(clip))cv2.imshow(clip,sub_img)cv2.waitKey(0)IoUcompute the iou for tow boxes,example box1 1, 2, 101, 102.  location(1,2)  is  left-up, location(101,102) is right-down.fromai_tool.bboximportBBoxbbox1=BBox([1,2,101,102])bbox2=BBox([11,12,121,122])iou=bbox1/bbox2print("iou",iou)assertiou>0.5print('box1 S is',bbox1.S)print('box1 & box2',bbox1&bbox2)print('box1 == box2',bbox1==bbox2)print('merge box1 + box2',bbox1+bbox2)print('merge box1 | box2',bbox1|bbox2)result is :iou0.5785714285714286
box1Sis10000box1&box2[11,12,101,102]box1==box2True
mergebox1+box2[1,2,121,122]mergebox1|box2[1,2,121,122]multi bbox operationfromai_tool.bboximportBBoxes,BBoxbb1=BBoxes(iou_thresh=0.6)bb2=BBoxes()bb1.append([1,2,101,102])bb1.append([1000,2,1101,102])bb2.append([11,12,111,112])bb2.append([1,1002,101,1102])# judge the bbox in bb1print("[5, 5, 100, 100] in bb1",BBox([5,5,100,100])inbb1)print("[100, 5, 200, 100] in bb1",BBox([100,5,200,100])inbb1)# bb1 & bb2print("bb1 & bb2",bb1&bb2)print("bb1 - bb2",bb1-bb2)print("bb2 - bb1",bb2-bb1)result is[5,5,100,100]inbb1True[100,5,200,100]inbb1False
bb1&bb2[[1,2,101,102]]bb1-bb2[[1000,2,1101,102]]bb2-bb1[[1,1002,101,1102]]
Package: ai-tools
Infoai_tool 2018-05-25Author: Zhao Mingming <471106585@qq.com>Copyright: This module has been placed in the public domain.version:0.0.6version:0.1.0-add func roc-add func photo_stiching-add func flv2imgversion:0.1.1-add func microsoft_demoversion:0.1.3-add func luminanceversion:0.1.4-add func dbop ,opreate the databaseversion:0.1.5-add func util, make the print has timeversion:0.1.6-add func txt2html, make the txt 2 html tableversion:0.1.8-modify file microsoft_demo, add zprint into itversion:0.1.9-modify roc_yoloversion:0.2.1-modify insert_image2db, can insert data into 2 databaseversion:0.2.3-add class2info.py function is get_class_infoversion:0.2.4-add darkchannel.py to decreate the haze of an imageversion:0.2.7-add lumi_classfy to luminance.pyversion:0.3.1-add modify video2imgversion:0.3.3-add wjdc-add video2gifversion:0.3.6-add get_histfeature_from_one_imgFunctions:draw_curve: draw a curve in a image and return the imageimage2text: translate a image to be text stylesave2server: save a image on the local serverimage2bw:  turn a gray image to be a binary weights imageHow To Use This Moduleexample code:x=np.array([-0.2,0.3,0.4,0.5])y=np.array([0.2,0.4,0.1,-0.4])norm(x,np.array([0,500]))img=draw_curve(x,y)img=draw_curve(x,y,title='my title',xlabel='my x label',ylabel='my y lable')img=cv2.imread('../examples/faces/2007_007763.jpg')img_gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)# img2bwimg_bw=image2bw(img_gray)printimg.shapeimage2text(img,(80,40))image2text(img,(80,40))Refresh20180821
Package: aitoolsAPI
aitoolsAPIThis is a simple async API client library for aitoolsAPI.InstallationYou can install the library using pip:pip install aitoolsAPIUsingSince this library is asynchronous, you should write the code like this:GPT request:import asyncio
import aitoolsAPI

async def main(prompt):
  print(await aitoolsAPI.gpt(prompt)) # Will send a response as text

asyncio.run(main("What package of sanctions was imposed on Russia last time?"))SDXL request:import asyncio
import aitoolsAPI

async def main(prompt):
  print(await aitoolsAPI.sdxl(prompt)) # Will send the answer in the form of a link

asyncio.run(main("Red ball flies among the clouds, 4K, realistic, no blur"))
Package: aitoolz
aitoolzVarious Python tools, by Alex Ioannides (AI). Some of them might be useful for artificial intelligence, some of them might not.InstallingYou can install aitoolz from PyPI usingpip install aitoolzAlternatively, you can install directly from themainbranch of this repo viapip install git+https://github.com/alexioannides/aitoolz.git@mainWhere the@XXXXcomponent of the URI can be substituted for any branch, tag or commit hash. See thepip docsfor more info.FeaturesA brief overview of the core tools:Template Python Package ProjectsTheaitoolz.make_projectmodule exposes thecreate_python_pkg_projectfunction that can create empty Python package projects to speed-up development. This includes:Executable tests via PyTest.Fully configured code formatting and checking using Ruff and Black.Fully configured static type checking using MyPy.Dev task automation using Nox.Fully configured CICD using GitHub Actions.This is an opinionated setup that reflects how I like to develop projects. This can also be called from the command line using the Make Empty Project (MEP) command - e.g.,mep my_packageWheremy_packagecan be replaced with any valid Python module name. Either of these commands will create a directory structure and skeleton files,my_package
├── .github
│   └── workflows
│       ├── python-package-ci.yml
│       └── python-package-cd.yml
├── .gitignore
├── README.md
├── noxfile.py
├── pyproject.toml
├── src
│   └── my_package
│       ├── __init__.py
│       └── hello_world.py
└── tests
    └── test_hello_world.pyThis has been tested to be installable and for all dev tasks automated with Nox to pass - usenox --listto see them all.Find External Dependencies in a Python Module or Source FolderTheaitoolz.find_importsmodule exposes thefind_importsfunction that returns a list of all package dependencies imported into a Python module or source folder - i.e., all dependencies that are not in the Python standard library.This can also be called from the command line - e.g.,find-imports src/my_packageOr,find-imports my_module.py
Package: aitpi-c3n3
AITPIArbitrary Input for Terminal or a PIGoalThe goal of this project is to provide a simple, but arbitrary, input
mechanism for use with a raspberry pi, or a terminal keyboard.This program can be configured with two simple json files.SupportedThe project supports:Simple 'buttons''1 to 1' gpio to button setup on a raspberry piNon interrupt based key inputInterrupt based key input (using pynput)Encoders'2 to 1' gpio to encoder setup on a raspberry piNon interrupt based 2 to 1 key inputInterrupt based 2 to 1 key input (using pynput)ExamplesTo configure your setup, see the two example json files:example_input.jsonexample_command_registry.json
Package: ai-traffic-light-simulator
TrafficLightAIA python traffic simulation serving as a playground to create traffic light A.I. systems. The traffic simulation uses a cellular automata approach to simulate large traffic grids. The simulation is optimized with Numba.Installationpipinstallai-traffic-light-simulatorExamplefromtraffic_simulation_numbaimportTrafficSimulation# OR from traffic_simulation import TrafficSimulationimportrandomNORTH_SOUTH_GREEN=0EAST_WEST_GREEN=1# A basic A.I. which randomly determines light timings# Inputs: [North waiting, East waiting, South waiting, West Waiting, Previous Light Direction]defmy_ai(inputs):ifinputs[-1]==NORTH_SOUTH_GREEN:returnEAST_WEST_GREEN,random.randint(1,30)ifinputs[-1]==EAST_WEST_GREEN:returnNORTH_SOUTH_GREEN,random.randint(1,30)# Make traffic simulation object with our naive A.I.sim=TrafficSimulation(my_ai,grid_size_x=8,grid_size_y=8,lane_length=10,max_speed=5,in_rate=0.2,initial_density=0.1,seed=42)results=sim.run_simulation(1000)# Runs the simulation for 1000 ticksprint(results)# Returns { 'cars_stopped': 131680, 'carbon_emissions': 672824 }# Render a frame of the simulation after 1000 tickssim.render_frame("Small.png")
Package: ai-trainer
DocumentationInstallation for UserOpen anaconda powershell, activate an environment with anaconda, navigate into the trainer repo and execute the following to install trainer using pip, including its dependencies:pipinstallai-trainerFor Online Learning you have to install PyTorch:condainstallpytorchtorchvisioncudatoolkit=10.1-cpytorchAI-Trainer helps with building a data generator and it relies on imgaug for it:condainstallimgaug-cconda-forgeGetting started with training modelsTrainer currently supports annotating images and videos.
First, create a dataset usingtrainerinit-dscdYOUR_DATASETGetting started with using trainer in pythonFor using the annotated data, you can use trainer as a python package.
After activating the environment containing the trainer and its dependencies,
feel free to inspect some of the tutorials in./tutorials/.Development SetupExecute the user installation,
but instead of usingpip install ai-trainer,
clone the repo locally.gitclonehttps://github.com/Telcrome/ai-trainerBoth vsc and pycharm are used for development with
their configurations provided in.vscodeand.ideaRecommended environmentsFor development we recommend to install the conda environment into a subfolder of the repo.
This allows for easier experimentation and the IDE expects it this way.condaenvcreate--prefix./envs-fenvironment.yml
condaactivate.\envs\.Now install a deep learning backend.
PyTorch provides well-workingconda install commands.For Tensorflow with GPU:condainstallcudatoolkit=10.0cudnn=7.6.0=cuda10.0_0
pipinstalltensorflow-gpuTesting Development for pip and cli toolsInstalling the folder directly using pip does not work due to the large amount of files inside the local development folder,
especially because in the local development setup the environment is expected to be a subfolder of the repo.pipinstall-e.Uploading to PyPi by handpythonsetup.pysdistbdist_wheel
twineuploaddist/*# The asterisk is importantUsing DockerDocker and the provided DOCKERFILE support is currently experimental as it proved to slow down the annotation GUI too much.
When the transition to a web GUI is completed docker will be supported again.ContributionDocsCurrently,Read the Docsis used
for CI of the docs.
Before submitting changes, test the make command in the environment:condaenvcreate-fenvironment.yml
condaactivatetrainer_env
makehtmlIf this throws warnings or errors,Read the Docswon`t publish them.Tutorials inside the repoDo not use jupyter notebooksShould be testable without preparing data by hand where possible.
Package: ai-transformersx
Transformersx##介绍🤗 Transformers是一个非常好用的专门针对基于Pytorch的Transformer相关NLP深度学习模型的工具库。
它管理和归类了当前几乎所有的最好的基于的Transformer的自然语言模型以及公开的预训练模型，并都转换成了Pytorch。
（1）使用它你可以很方便的做Bert/Albert/GPT2/XLNET等当前最好的自然语言预训练模型训练以及下游任务的模型开发和训练。
（2）使用Pytorch实现的模型有更清晰的代码机构，对学习者来说，学习和理解这些自然语言更为容易。
（3）提供了一个地方收集和存放公开的自然语言预训练模型，供研究人员使用。研究人员也可以把自己愿意公开的预训练模型放到Transformer上供别人研究使用。但是Transformers也有一些问题：
（1）根据当前的Transformers的实现看，所有的公开的Transformer的预训练模型都是存放在AWS的S3上。对国外的研究人员那没有什么问题。中国的研究人员需要下载这些模型
就有点费周折了。当前Transformers的模型下载方式有两种：（1）通过代码调用指定模型时，自动去相应的S3上下载。（2）直接到Transformers的网上流量相应的模型文件通过浏览器下载。
反正不管那种方式，想顺利的下载下来，自己想办法吧～。不多说。
（2）尽管Transformers已经提供了一种比较方便的方式来使用各种Transformer相关模型了。但是还是不够好。首先，从设计上，各种模型的实现很不错，但是因为模型的实现与模型的存储和下载深度绑定。
这个设计应该是有问题的。从职责上说，模型的实现和模型的存储下载应该分离。
（3）Transformers增加了一个Trainer以方便研究人员训练Transformer模型使用。同样，这个Trainer的设计和实现水平跟模型的设计和实现也一样有不少的差距。目的本项目的目的是想针对Transformers的一些问题，对Transformers做进一步的扩展，让研究人员使用Transformers更方便。当然也没有解决上面提到的所有问题。（1）首先，针对下载这个问题。本项目的解决方法是，在docker目录中提供了几种用于Transformers相关模型的训练和运行环境的Docker定义，
利用阿里云的Docker海外构建机器，在构建Docker是顺便把指定的预训练模型下载下来。当前主要是中文语言模型，包括bert、albert、robert、electra。可以直接从阿里云镜像库获取。
相关模型放在镜像的/app/models目录下面。docker pull registry.cn-beijing.aliyuncs.com/modoso/transformersx-bert
docker pull registry.cn-beijing.aliyuncs.com/modoso/transformersx-albert
docker pull registry.cn-beijing.aliyuncs.com/modoso/transformersx-robert
docker pull registry.cn-beijing.aliyuncs.com/modoso/transformersx-electra最好使用脚本download-models.sh 下载并从镜像中把模型copy出来的脚本。sh download-models.sh [指定模型存放目录]（2）为了更方便的使用，(当然，你得先参照上面的模型下载方式下载docker镜像或者模型)首先，你可以简单的像examples.task.sentiment.sentiment_task那样实现情感识别，只需要实现一个DataProcessor和一个Taskfromai_transformersximportDataProcessor,DataArguments,join_path,InputExample,log,TaskArgumentsfromai_transformersx.examplesimportExampleTaskBaseimportpandasaspdclassSentimentDataProcessor(DataProcessor):def__init__(self,config:DataArguments):self._config=configdef_get_example(self,file_name,type):pd_all=pd.read_csv(join_path(self._config.data_dir,file_name))log.info("Read data from{}, length={}".format(join_path(self._config.data_dir,file_name),len(pd_all)))examples=[]fori,dinenumerate(pd_all.values):examples.append(InputExample(guid=type+'_'+str(i),text_a=d[1],label=str(d[0])))returnexamplesdefget_train_examples(self):returnself._get_example('train.csv','train')defget_dev_examples(self):returnself._get_example('dev.csv','dev')defget_labels(self):return['0','1','2','3']defdata_dir(self):returnself._config.data_dirclassSentimentTask(ExampleTaskBase):def__init__(self,taskArgs:TaskArguments=None):super().__init__('sentiment',taskArgs)self.task_args.model_args.num_labels=4def_data_processor(self):returnSentimentDataProcessor(self.task_args.data_args)然后，像examples.task.main那样实现启动方法：fromai_transformersx.examplesimportExampleManagementfromai_transformersx.examples.tasksimportSentimentTasktask_manager=ExampleManagement()task_manager.register_tasks([('sentiment',SentimentTask)])if__name__=="__main__":task_manager.start_example_task()接着，你就可以训练你的情感识别的模型了。你应该在下载的镜像模型中进行。查看所有sentiment任务参数python main.py sentiment -h训练模型（可以参考上面的帮助列表设置相关的参数）python main.py sentiment(3) 常见的中文的自然语言任务的例子TODO:使用 pytorchlightning和fastai来实现trainer增加更多的自然语言任务的例子
Package: aitui
ChatGPT TUITerminal-based chat window with the chatGPT APIInstallpip install aituiDevelopmentclone this repopip install -e .You can now start the chat from any directory by typing the command$ aiUpon startup, you will be asked for your OpenAI organization id and api-keyRunning the applicationStart upWhen the application starts up, it will ask you what agent you want to speak with.
You can save common persons with a preconditioned prompt here. e.g.:Personal travel agent: A chat gpt assistant that recomends things to do within your interestsPython docstring writerPersonal car mechanic advisoretcKey Bindingsescputs you intovimcmd mode.ioraputs you back into insert mode.
pressingvin cmd mode will open a vim editor so you and write multi-line prompts with full key bindings.Note: There are some limitations with the vim key bindings. I've found I need to pressesctwice or there is a long delay for entering into cmd mode.RoadmapAddvimkey bindings to prompt inputOpenvimfor multi line promptsSupport multi key vim keybindings. e.g.ddciwInitialize conversations from different common personas. e.g. travel agentSave conversations to a databaseOutput running cost of the conversationPublish to PyPiCreditsThe TUI application is powered bytextualizeOpenAI (of course)
Package: aitviewer
A set of tools to visualize and interact with sequences of 3D data with cross-platform support on Windows, Linux, and macOS. See the official page athttps://eth-ait.github.io/aitviewerfor all the details.InstallationBasic Installation:pip install aitviewerNote that this does not install the GPU-version of PyTorch automatically. If your environment already contains it, you should be good to go, otherwise install it manually.Or install locally (if you need to extend or modify code)git clone git@github.com:eth-ait/aitviewer.git
cd aitviewer
pip install -e .For more advanced installation and for installing SMPL body models, please refer to thedocumentation.FeaturesNative Python interface, easy to use and hack.LoadSMPL[-H/-X]/MANO/FLAME/STAR/SUPRsequences and display them in an interactive viewer.Headless mode for server rendering of videos/images.Remote mode for non-blocking integration of visualization code.Render 3D data on top of images via weak-perspective or OpenCV camera models.Animatable camera paths.Edit SMPL sequences and poses manually.Prebuilt renderable primitives (cylinders, spheres, point clouds, etc).Built-in extensible GUI (based on Dear ImGui).Export screenshots, videos and turntable views (as mp4/gif)High-Performance ModernGL-based rendering pipeline (running at 100fps+ on most laptops).QuickstartDisplay an SMPL T-pose (Requires SMPL models):fromaitviewer.renderables.smplimportSMPLSequencefromaitviewer.viewerimportViewerif__name__=='__main__':v=Viewer()v.scene.add(SMPLSequence.t_pose())v.run()Projects using the aitviewerA sampling of projects using the aitviewer. Let us know if you want to be added to this list!Kaufmann et al.,EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild, ICCV 2023Shen and Guo et al.,X-Avatar: Expressive Human Avatars, CVPR 2023Sun et al.,TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments, CVPR 2023Fan et al.,ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation, CVPR 2023Dong and Guo et al.,PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence, CVPR 2022Dong et al.,Shape-aware Multi-Person Pose Estimation from Multi-view Images, ICCV 2021Kaufmann et al.,EM-POSE: 3D Human Pose Estimation from Sparse Electromagnetic Trackers, ICCV 2021Vechev et al.,Computational Design of Kinesthetic Garments, Eurographics 2021Guo et al.,Human Performance Capture from Monocular Video in the Wild, 3DV 2021CitationIf you use this software, please cite it as below.@software{Kaufmann_Vechev_aitviewer_2022,
  author = {Kaufmann, Manuel and Vechev, Velko and Mylonopoulos, Dario},
  doi = {10.5281/zenodo.1234},
  month = {7},
  title = {{aitviewer}},
  url = {https://github.com/eth-ait/aitviewer},
  year = {2022}
}Contact & ContributionsThis software was developed byManuel Kaufmann,Velko Vechevand Dario Mylonopoulos.
For questions please create an issue.
We welcome and encourage module and feature contributions from the community.
Package: aitx
# aitx[![](https://img.shields.io/travis/jeroyang/aitx.svg)](https://travis-ci.org/jeroyang/aitx)
[![](https://img.shields.io/pypi/v/aitx.svg)](https://pypi.python.org/pypi/aitx)The toolbox for general ML projects## FeaturesTODO## Installation`bash $ python setup.py install `## Usage`python from aitx) import * `## License
* Free software: MIT license