Saravanan666/pypi-org huggingface dataset:
Package: roboflex.imgui
roboflex_imguiRoboflex visualizers and guis using IMGUI/IMPLOTSystem dependenciesRequires SDL and GLEW to be installed. More than likely, they already are installed in your distro. But if not:apt-get install libsdl2-dev
apt-get install libglew-dev

# or maybe on mac:

brew install sdl2
brew install glew
brew install glfwpip installpip install roboflex.imguiImportimport roboflex.imgui as rguNodesThere are two so far:OneDTVandMetricsTelevisionOneDTV (One-Dimensional Television)It expects to receive messages that contain a tensor under the key <data_key>. That tensor needs to be of shape (C, S), where C is number of channels, and S is sequence length. In general, C should be small (< 20), and S large. Think multiple audio channels.# all parameters optional: below are the defaults
visualizer = rgu.OneDTV(
    data_key = "data",
    sample_size = 4,
    center_zero = True,
    initial_size = (640, 220),
    initial_pos = (-1, -1),
    name = "OneDTV",
    debug = False,
)

# must be started
visualizer.start()

# NOTE!!!
# On some systems, such as mac, it's bad news to
# run a UI on a non-main thread. So instead of 
# calling start(), you can do this, which will 
# run on the main thread. This call will block 
# until the window is closed, in this case.
visualizer.run()MetricsTelevisionReal-time visualization of metrics data as published byGraphRoot::start(profile=True). I.e., when you instantiate aGraphRoot(from roboflex core), make it the root of your graph, and call myroot.start(profile=True) on it, it will publish metrics messages. This is the node that can visualize those metrcs message.# all parameters optional: below are the defaults
visualizer = rgu.MetricsTelevision(
    window_title = "MetricsTelevision",
    initial_size = (1580, 720),
    initial_pos = (-1, -1),
    name = "MetricsTelevision",
    debug = False,
)

# must be started
visualizer.start()

# NOTE!!!
# On some systems, such as mac, it's bad news to
# run a UI on a non-main thread. So instead of 
# calling start(), you can do this, which will 
# run on the main thread. This call will block 
# until the window is closed, in this case.
visualizer.run()Using MQTT in this caseMQTT is an ideal transport for metrics messages - they are small and infrequent. We provide helper functions inmetrics_central.pyto do so:# give one of these to a GraphRoot as it's publisher node
pub = rgu.metrics_central.get_metrics_mqtt_publisher(
    broker_address: str,
    broker_port: int = 1883,
    metrics_topic: str = "metrics",
)

# get a subscriber
sub = rgu.get_metrics_mqtt_subscriber(
    broker_address: str,
    broker_port: int = 1883,
    metrics_topic: str = "metrics",
)

# get a visualizer
vis = rgu.get_metrics_visualizer(
    broker_address: str,
    broker_port: int = 1883,
    metrics_topic: str = "metrics",
)

# connect the subscriber to the visualizer
sub > vis

# Here's a program that does it all - launches a MetricsVisualizer
# listening to some mqtt broker:port and topic.
python3 metrics_central.py
Package: roboflex.profiler
roboflex.profilerRoboflex Profiler has two parts:The Profiler node. This node is intended to act as the root node of a computation graph. See metrics_central/examples/profile_graph.cpp, and examples/camera_follow/camera_follow.py for examples of how to us it. It brings two features:1.1. When you callstart()orstop()on this node, it will walk the computation graph and start or stop all RunnableNodes.1.2. When you callstart(true), it will first inject a new MetricsNode between every two connected nodes. It will then drive publishing of Metrics information, which can be viewed with:The MetricsCentral program. This program displays Metrics information for a running computation graph. Every row is a connection, and every column is some information about that connection, including frequency of invokation, latency, Bytes/sec, etc.clone this repo
mkdir build && cd build
cmake ..
make... then find the 'metrics_central' program, and run it...
Package: roboflex.util.jpeg
roboflex.util.jpegRoboflex support for jpeg compression to file and memory, and jpeg decompression from memory.Useful for compressing images over slow transports (wifi, etc).System DependenciesNone! We build jpeg-compression from source...pip installpip install roboflex.util.jpegImportimport roboflex.util.jpeg as rujNodesThere are two complementary nodes:JPEGCompressor, which can turn rgb tensors into jpegs in memory, andJPEGDecompressor, which does the opposite. Useful either for writing rgb tensors to files, or compressing them (NOTE jpeg is lossy).# all parameters are optional
c = ruj.JPEGCompressor(

    # in the incoming message, where to find the rgb tensor
    image_key = "rgb",

    # in the outgoing message, where to place the jpeg data
    output_key = "jpeg", 

    # If this is provided, will ALSO write jpeg files with this 
    # prefix, with a variation of the date and time as the suffix.
    filename_prefix = "",

    # name of the node
    name = "JPEGCompressor",

    # prints internal info
    debug = False,
)... and ...c = ruj.JPEGDecompressor(

    # in the incoming message, where to find the jpeg data as a blob
    input_key = "jpeg",

    # in the outgoing message, where to place the rgb data as a tensor
    output_key = "rgb", 

    # name of the node
    name = "JPEGDecompressor",

    # prints internal info
    debug = False,
)
Package: roboflex.util.png
roboflex.util.pngRoboflex support for png compression to file and memory, and png decompression from memory.Useful for compressing images over slow transports (wifi, etc).System DependenciesNone! We build lodepng from source...pip installpip install roboflex.util.pngImportimport roboflex.util.png as rupNodesThere are two complementary nodes:PNGCompressor, which can turn rgb tensors into pngs in memory, andPNGDecompressor, which does the opposite. Useful either for writing rgb tensors to files, or compressing them.# all parameters are optional
c = rup.PNGCompressor(

    # in the incoming message, where to find the rgb tensor
    image_key = "rgb",

    # in the outgoing message, where to place the jpeg data
    output_key = "png", 

    # If this is provided, will ALSO write jpeg files with this 
    # prefix, with a variation of the date and time as the suffix.
    filename_prefix = "",

    # name of the node
    name = "PNGCompressor",

    # prints internal info
    debug = False,
)... and ...c = rup.PNGDecompressor(

    # in the incoming message, where to find the png data as a blob
    input_key = "png",

    # in the outgoing message, where to place the rgb data as a tensor
    output_key = "rgb", 

    # name of the node
    name = "PNGDecompressor",

    # prints internal info
    debug = False,
)
Package: roboflex.visualization
roboflex.visualizationA thin wrapper of various display nodes over SDL2. For now just supplies
one node: RGBImageTV. It can display rgb tensors of the form (H,W,3) uint8.System requirementslibsdl: SDL (Simple Directmedia Layer)# search for existing libsdl packages
  dpkg -l | grep sdl # -> shows nothing

  # install libsdl
  sudo apt-get install libsdl2-dev

  # search for existing libsdl packages again
  dpkg -l | grep sdl # -> shows:
  ii  libsdl2-2.0-0:amd64                        2.0.20+dfsg-2ubuntu1.22.04.1                                        amd64        Simple DirectMedia Layer
  ii  libsdl2-dev:amd64                          2.0.20+dfsg-2ubuntu1.22.04.1                                        amd64        Simple DirectMedia Layer development files

  THIS, for me on ubuntu22, puts headers in /usr/include/SDL2 and libs in /lib/x86_64-linux-gnu/libSDL2-2.0.so

  # to remove it:
  sudo apt-get -y purge libsdl2-devImportimport roboflex.visualization as rvNodesThere are two:There is are three:RGBImageTV,BlackAndWhiteTV, andDepthTV# RGBImageTV will redraw rgb images at some
# frequency (refresh rate). It will look into
# all messages it receives - those that contain
# the key "rgb", mapping to a (3,H,W) tensor
# will be rendered. Messages get propagated.

rgb_tv = rcw.RGBImageTV(

    # mandatory (refresh rate)
    frequency_hz: float,

    # optional; defaults are:
    width = 640,
    height = 480,
    image_key = "rgb",
    initial_pos = (-1, -1),
    mirror = False,
    debug = False,
    name = "RGBImageTV"
)

# must be started!
rgb_tv.start()


# BlackAndWhiteTV will redraw rgb images at some
# frequency (refresh rate). It will look into
# all messages it receives - those that contain
# the key "ir1", mapping to a (H,W) tensor
# will be rendered. Messages get propagated.

black_and_white_tv = rcw.BlackAndWhiteTV(

    # mandatory (refresh rate)
    frequency_hz: float,

    # optional; defaults are:
    width = 640,
    height = 480,
    image_key = "ir1",
    initial_pos = (-1, -1),
    mirror = False,
    debug = False,
    name = "BlackAndWhiteTV"
)

# must be started!
black_and_white_tv.start()


# DepthTV will redraw depth images at some
# frequency (refresh rate). It will look into
# all messages it receives - those that contain
# the key "depth", mapping to a (H,W) tensor
# will be rendered. Messages get propagated.

depth_tv = rcw.DepthTV(

    # mandatory (refresh rate)
    frequency_hz: float,

    # optional; defaults are:
    width = 640,
    height = 480,
    image_key = "depth",
    initial_pos = (-1, -1),
    mirror = False,
    debug = False,
    name = "RGBImageTV"
)

# must be started!
depth_tv.start()
Package: roboflo
pip install robofloTask scheduler for any system with coordinated workers. The original use case is for the Perovskite Automated Spin-Coating Assembly Line (PASCAL) in the Fenning Lab at UC San Diego, where a robotic arm moves small glass slides between stations to perform experiments.robofloassumes that you have a set ofWorker's that act (independently or in unison) to performTask's of set duration. Furthermore, one or moreWorker's can function to transition betweenTask's (eg my robot moves a sample from the hotplate to a camera, or my mom moves me from school to soccer practice). These transition moves constitute a special case ofTask's , calledTransition's. The total set ofWorker's andTransition's define yourSystem. Sets ofTask's are consolidated intoProtocol's (eg the same process for five samples or five kids), which are then scheduled (using theScheduleron yourSystem) to minimize the total working time. An example schedule is shown below.Happy robot-ing!PS - shoutout toTaskpacker, from which I drew heavy inspiration.roboflocarries much of the design philosophy fromTaskpacker, but uses only Python packages (the backend is Google ORTools as opposed to Numberjack, which can be difficult to install especially on Windows).robofloalso introducesTransitions, which define a finite state machine, as a critical component in the workflow under the assumption that many robotic platforms involve workers whose specific jobs are to move things between other workers.
Package: robofloak
# Setup Instructions## DepthAI### LinuxInstall DepthAI Dependencies` sudo wget-qO-https://docs.luxonis.com/install_dependencies.sh| bash `Set USB Permissions` echo'SUBSYSTEM=="usb",ATTRS{idVendor}=="03e7",MODE="0666"'| sudo tee/etc/udev/rules.d/80-movidius.rulessudo udevadm control--reload-rules&& sudo udevadm trigger `### WindowsInstall Chocolatey`Set-ExecutionPolicyBypass-ScopeProcess-Force;[System.Net.ServicePointManager]::SecurityProtocol=[System.Net.ServicePointManager]::SecurityProtocol-bor3072; iex((New-ObjectSystem.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1'))`Install DepthAI Dependencies` choco install cmake git pythonpycharm-community-y`### MacOSInstall DepthAI Dependencies` bash-c"$(curl-fLhttps://docs.luxonis.com/install_dependencies.sh)"`See M1 specific instructions inM1Install.md## Install Python Dependencies` pip3 install-rrequirements.txt `Importantly, depthai and opencv
Package: roboflow2huggingface
roboflow2huggingfaceConvert Roboflow datasets into HuggingFace datasets format and upload to HuggingFace Hub.Supportsimage-classification,object-detection, andinstance-segmentationformats.installationpipinstallroboflow2huggingfaceapi usagefromroboflow2huggingfaceimportroboflow_to_huggingface_pipelineroboflow_to_huggingface_pipeline(roboflow_universe_url='https://universe.roboflow.com/boxer5/123-qq5ea/dataset/6',roboflow_api_key=YOUR-ROBOFLOW-API-KEY,hf_dataset_id=DESIRED-HF-HUB-ID,hf_write_token=YOUR-HF-WRITE-TOKEN,)cli usageroboflow2huggingface--roboflow_universe_urlhttps://universe.roboflow.com/boxer5/123-qq5ea/dataset/6--roboflow_api_keyYOUR-ROBOFLOW-API-KEY--hf_dataset_idDESIRED-HF-HUB-ID--hf_write_tokenYOUR-HF-WRITE-TOKENconverted datasetsCheck converted datasets on the 🤗 hub:https://huggingface.co/datasets?other=roboflow2huggingface
Package: roboflowtest
Roboflow Python LibraryWebsite•Docs•Blog•Twitter•Linkedin•UniverseRoboflowmakes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. This is
the official Roboflow python package that interfaces with theRoboflow API. Key features of
Roboflow:Import and Export image datasets into any supportedformatsPreprocessandaugmentdata using Roboflow's dataset
management toolsTrain computer vision models usingRoboflow Trainand deploy
toproductionUsecommunity curated projectsto start building your own vision-powered productsInstallation:To install this package, please usePython 3.6or higher. We provide three different ways to install the Roboflow
package to use within your own projects.Install from Pypi (Recommended):pip install roboflowInstall from Conda:conda install roboflowInstall from Source:git clone https://github.com/roboflow-ai/roboflow-python.git
cd roboflow-python
python3 -m venv
source venv/bin/activate 
pip3 install -r requirements.txtQuickstartimportroboflow# Instantiate Roboflow object with your API keyrf=Roboflow(api_key=YOUR_API_KEY_HERE)# List all projects for your workspaceworkspace=rf.workspace()# Load a certain project, workspace url is optionalproject=rf.project("PROJECT_ID")# List all versions of a specific projectproject.versions()# Upload image to datasetproject.upload("UPLOAD_IMAGE.jpg")# Retrieve the model of a specific projectproject.version("1").model# predict on a local imageprediction=model.predict("YOUR_IMAGE.jpg")# Predict on a hosted imageprediction=model.predict("YOUR_IMAGE.jpg",hosted=True)# Plot the predictionprediction.plot()# Convert predictions to JSONprediction.json()# Save the prediction as an imageprediction.save(output_path='predictions.jpg')
Package: robogerctl
robogerctlCLI tool forhttps://github.com/alttch/robogerIf installed on the same host with server, uses server configuration to
communicate.Otherwise, create client configuration, put it to ~/.robogerctl.yml (default):uri:http://roboger-server:portkey:MANAGEMENT_KEY#limits: true # set to manage server, where limits are enabledInstallationpip3 install robogerctl
Package: robograph
Robograph is a platform that allows you to define your algorithms as computational graphs.Once you’ve defined the graph, you can execute that graph as if it was a SW
program and get the expected outputs.
As each algorithm is composed by steps, each graph is composed by nodes, each
one being a step of the bigger calculation. Each graph can accept any number of
inputs and give at maximum one output.
You can create your own graphs either by connecting any of the predefined
basic nodes shipped with Robograph or by coding your custom nodes
Package: robohash
The source code forRoboHash.org.It basically copy/pastes various robot pictures together, using bits
from the SHA hash. It’s not perfect, and not entirely secure, but it
gives a good gut-check to “Hey, this SHA is wrong.”InstallJust the library:$pipinstallrobohashOr if you also want the web frontend:$pipinstallrobohash[web]UsagefromrobohashimportRobohashhash="whatever-hash-you-want"rh=Robohash(hash)rh.assemble(roboset='any')withopen("path/to/new/file.png","wb")asf:rh.img.save(f,format="png")RobosetsRoboHash comes with five image sets, named “set1”, “set2”, “set3”, “set4” and “set5”.
Specify which set you want in theassemble()method. Alternatively,
specify the string “any”, and RoboHash will pick an image set for you,
based on the provided hash.LicenseThe Python Code is available under the MIT/Expat license. See theLICENSE.txtfile for the full text of this license. Copyright (c)
2011, Colin Davis.The RoboHash images are available under license-The “set1” artwork was created by Zikri Kader.
They are available under CC-BY-3.0 or CC-BY-4.0 license.The “set2” artwork was created by Hrvoje Novakovic.
They are available under CC-BY-3.0 license.The “set3” artwork was created by Julian Peter Arias.
They are available under CC-BY-3.0 license.The Cats/”set4” were created by David Revoy, used under CC-BY-4.0https://www.peppercarrot.com/en/article391/cat-avatar-generatorThe avatars used in “set5” were created by Pablo Stanley, forhttps://avataaars.com/They are “Free for personal and commercial use. 😇”DisclaimerOK, I’ll admit I’m a crappy programmer. Compounding this, I wrote this
code initially to be internal-only. It’s ugly, and could be a LOT nicer.Sorry about that.
Package: robohive
RoboHiveis a collection of environments/tasks simulated with theMuJoCophysics engine exposed using the OpenAI-Gym API.Getting StartedGetting started with RoboHive is as simple as -# Install RoboHivepipinstallrobohive# Initialize RoboHiverobohive_init# Demo an environmentpython-mrobohive.utils.examine_env-eFrankaReachRandom-v0or, alternatively for editable installation -# Clone RoboHivegitclone--recursivehttps://github.com/vikashplus/robohive.git;cdrobohive# Install (editable) RoboHivepipinstall-e.# Demo an environmentpython-mrobohive.utils.examine_env-eFrankaReachRandom-v0Seedetailed installation instructionsfor options on mujoco-python-bindings and  visual-encoders (R3M,RRL,VC), andfrequently asked questionsfor more details.SuitesRoboHivecontains a variety of environments, which are organized as suites. Each suite is a collection of loosely related environments. The following suites are provided at the moment with plans to improve the diversity of the collection.Hand-Manipulation-Suite(video)A collection of environments centered around dexterous manipulation. Standard ADROIT benchmarks introduced inLearning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations, RSS2018.) are a part of this suiteArm-Manipulation-SuiteA collection of environments centered around Arm manipulation.Myo-Suite(website)A collection of environments centered around Musculoskeletal control.Myo/MyoDM-Suite(Website)A collection of musculoskeletal environments for dexterous manipulation introduced as MyoDM inMyoDeX.MultiTask SuiteA collection of environments centered around multi-task. StandardRelayKitchen benchmarksare a part of this suite.- TCDM Suite (WIP)This suite contains a collection of environments centered around dexterous manipulation. StandardTCDM benchmarksare a part of this suite- ROBEL Suite (Coming soon)This suite contains a collection of environments centered around real-world locomotion and manipulation. StandardROBEL benchmarksare a part of this suiteCitationIf you findRoboHiveuseful in your research,please consider supporting the project by providing astar ⭐please consider citing our project by using the following BibTeX entry:@Misc{RoboHive2020,title={RoboHive -- A Unified Framework for Robot Learning},howpublished={\url{https://sites.google.com/view/robohive}},year={2020},url={https://sites.google.com/view/robohive},}
Package: robojoe
This is a package that has useful machine vision for use with CarsCHANGE LOGRELEASE 10.0.0.1  (7/27/2021)First Release\RELEASE 20.0.0.2 –> 7/27/2021Just wanted to test some things outreally didn’t don anything\-RELEASE 3
-0.0.0.3–> 7/27/2021
-Just wanted to test some things out
-really didn’t don anything
Package: robojslib
roboJSlibHow to useInstall with PIP:Install pip and run:pip install robojslibManual installation:download roboJSlib.py and place it on your Python scripts folder or inside your tests.robot folder.
Once done, import in robotframework by runningLibrary       robotJSlib.pyDependencies:Before running, be sure to have installed the dependencies:pip install robotframework-seleniumlibrarypip install robotframeworkfor BuiltIn libKeywordargumentsInfoCheck titleNoneChecks driver title to not include "http"Vanilla clickElement IDUses vanillaJS to trigger a click overan elementVanilla click by query selectorSelectorUses vanillaJS to trigger a click overan elementModify url string"arg1, arg2": Url portion to be changed, url portion to be injectedchecks if a url sub-string is available and substitute it with second argumentVanilla input"arg1, arg2" Element ID, valueVanilla input on a given element with the given textVanilla input by query selector"arg1, arg2" selector, valueVanilla input on a given element with the given textCheckbox controlElement IDIf checkbox is selected it skips, else it clicks itInsert phone nrElement IDgenerates a phone nr (es: 351xxxxxxx) & inputs the value inside the elementSet responsiveMobile, Tabletsets resolution for responsive testing: Mobile or TabletWait until title containsarg, string be containedChecks title contains a given stringOpen new tabarg: "url to be opened"Note: include "http" or "https" in the url to avoid any bug/problemCheck if visible and clickarg: idChecks if an element is displayed and clicks it. Otherwise, it skips.Check if visible and click by classarg: classChecks if an element by class is displayed and clicks it. Otherwise, it skips.Check if visible and click by css selectorarg: css selectorChecks if an element by css selector is displayed and clicks it. Otherwise, it skips.Element value should not be emptyElement IDGrabs element value and returns a FatalError in case is empty
Package: robolytix
Robolytix PackageRobolytix is the key online analytic and monitoring tool for Robotic Process Automation using Sonar technology to evaluate, audit, monitor and improve performance of robots operating in any application, RPA platform or custom solution. Manage your RPA projects effectively with exact KPIs. Visualise your whole process at one place.For more information, please visithttps://www.robolytix.com/RequirementsPython 3.4+Installation & Usagepip installYou can install the Robolytix client package from PyPIpipinstallrobolytix(you may need to runpipwith root permission:sudo pip install robolytix)Then import the package:importrobolytixSetuptoolsInstall viaSetuptools. You need to checkout package from Githubhttps://github.com/robolytix/robolytix-sdk.gitand step into Python directory.pythonsetup.pyinstall--user(orsudo python setup.py installto install the package for all users)Then import the package:importrobolytixGetting StartedPlease follow theinstallation procedureand then run the following:from__future__importprint_functionimporttimeimportrobolytixfromrobolytix.restimportApiExceptionfrompprintimportpprint# Configure API key authorization: api_keyconfiguration=robolytix.Configuration()configuration.api_key['Authorization']='YOUR_API_KEY'# create an instance of the API classapi_instance=robolytix.CommonApi(robolytix.ApiClient(configuration))try:# Account detailsapi_response=api_instance.account()pprint(api_response)exceptApiExceptionase:print("Exception when calling CommonApi->account:%s\n"%e)# Configure API key authorization: api_keyconfiguration=robolytix.Configuration()configuration.api_key['Authorization']='YOUR_API_KEY'# create an instance of the API classapi_instance=robolytix.CommonApi(robolytix.ApiClient(configuration))try:# Pingapi_response=api_instance.ping()pprint(api_response)exceptApiExceptionase:print("Exception when calling CommonApi->ping:%s\n"%e)Documentation for API EndpointsAll URIs are relative tohttps://api.robolytix.com/v1ClassMethodHTTP requestDescriptionCommonApiaccountGET/accountAccount detailsCommonApipingGET/pingPingDefaultApilistprocessesGET/listprocessesList processesDefaultApilisttypesGET/listtypesList typesDefaultApisonarPOST/messagesSonarDocumentation For ModelsInlineResponseDefaultInlineResponseDefault1InlineResponseDefault2InlineResponseDefault2DataMessageProcessTypeDocumentation For Authorizationapi_keyType: API keyAPI key parameter name: AuthorizationLocation: HTTP headerAuthor