Course material for the Multi-agents and computer graphics course

Overview

TC2008B

Course material for the Multi-agents and computer graphics course.

Setup instructions

  • Strongly recommend using a custom conda environment.
  • Install python 3.8 in the environment: conda install python=3.8 Using 3.8 for compatibility reasons. Maybe 3.9 or 3.10 are compatible with all the packages, but will have to check.
  • Installing mesa: pip install mesa
  • Installing flask to mount the service: pip install flask
  • By this moment, the environment will have all the packages needed for the project to run.

Instructions to run the local server and the Unity application

  • Run either the python web server: Server/tc2008B_server.py, or the flask server: Server/tc2008B_flask.py. Flask is considerably easier to setup and use, and I strongly recommend its use over python's http.server module. Additionally, IBM cloud example used flask.
  • To run the python web server:
python tc2008B_server.py
  • To run a flask app:
export FLASK_APP=tc_2008B_flash.py
flask run
  • You can change the name of the app you want to run by changing the environment variable FLASK_APP.

  • Alternatively, if you used the following code in your flask server:

if __name__=='__main__':
    app.run(host="localhost", port=8585, debug=True)

you can run it using:

python tc2008B_flask.py
  • To run a flask app on a different host or port:
flask run --host=0.0.0.0 --port=8585
  • Either of these servers is what will run on the cloud.
  • Once the server is running, launch the Unity scene TC2008B that is in the folder: IntegrationTest.
  • The scene has two game objects: AgentController and AgentControllerUpdate. I left both so that different functionality can be tested: AgentController works with the response of the python web server, while AgentControllerUpdate works with the reponse from the flask server.
  • I updated the AgentController.cs code, and introduced AgentControllerUpdate.cs. Each script parses data differently, depending on the response from either the python web server, or from the flask server. The AgentController.cs script parses text data, while AgentControllerUpdate.cs parses JSON data. I strongly recommend that we use JSON data.
  • The scripts are listening to port 8585 (http://localhost:8585). Double check that your server is launching on that port; specially if you are using a flask server.
  • If the Unity application is not running, or has import issues, I included the Unity package that has the scene Sergio Ruiz provided.

Instruction to run the cloud server and Unity application

Installing dependencies, and locally running the sample

# ...first add the Cloud Foundry Foundation public key and package repository to your system
wget -q -O - https://packages.cloudfoundry.org/debian/cli.cloudfoundry.org.key | sudo apt-key add -
echo "deb https://packages.cloudfoundry.org/debian stable main" | sudo tee /etc/apt/sources.list.d/cloudfoundry-cli.list
# ...then, update your local package index, then finally install the cf CLI
sudo apt update
sudo apt install cf8-cli
  • To get the sample app running:
git clone https://github.com/IBM-Cloud/get-started-python
cd get-started-python
  • To run locally:
pip install -r requirements.txt
python hello.py

To deply the sample to the cloud

  • All the requiered files for the sample app to run are inside the IBMCloud folder.
  • We first need a manifest.yml file. The one provided in the example repository contains the following:
applications:
 - name: GetStartedPython
   random-route: true
   memory: 128M
  • You can use the Cloud Foundry CLI to deploy apps. Choose your API endpoint:
cf api 
   

   

Replace the API-endpoint in the command with an API endpoint from the following list:

URL Region
https://api.ng.bluemix.net US South
https://api.eu-de.bluemix.net Germany
https://api.eu-gb.bluemix.net United Kingdom
https://api.au-syd.bluemix.net Sydney
  • Login to your IBM Cloud account:
cf login
  • From within the get-started-python directory push your app to IBM Cloud:
cf push
  • This process can take a while. All the dependencies are downloaded and installed, and the app in started.
  • After you push the application, in the cloud dashboard you can see a new cloud foundry app.
  • This can take a minute. If there is an error in the deployment process you can use the command cf logs --recent to troubleshoot.
  • When deployment completes you should see a message indicating that your app is running. View your app at the URL listed in the output of the push command. You can also issue the cf apps.
  • With the cf apps command you can see the route for the app.

To deploy a custom app to the cloud

  • I created an app within the cloud foundry in the ibm cloud by following the document Manual IBM Cloud - Python.pdf.
  • Created an additional folder inside the IBMCloud folder, named boids, that contains the required files.
  • In the manifest.yml I renamed the name to the one I used for the app in cloud foundry. From GetStartedPython to Boids.
  • Then, modified the ProcFile file as follows:
web: python tc2008B_flask.py
  • Modified the setup.py file, but I do not think it matters.
  • Then changed to the boids folder, and used:
cf push
  • Then, update the url for the service in Unity with the url for the service that cloud foundry assigns.

Notes

  • Using VSCode to develop everything.
  • Although not stated in the requirements, Git needs to be installed on the system.
  • I am running windows, and using the WSL. I ran the server code in WSL, and the Unity client in windows. My WSL machine runs Ubuntu 20.
  • Using Thunder Client extension as a replacement for postman to test the apis.
  • Pip does not allow us to search anymore.
  • As of 2021-10-17, the WWWForm method to post from Unity to the web service still works with Unity 20.20.3.4. However, the support apparently is going away soon.
  • Using flask because it is ideal for building smaller applications. Django could be used, but since it is much more robust, the additional utilities were not needed for this project.
  • The demo app push process went rather smoothly, but for the boids app it did not. It took too long, and ended up failing with a timeout error. I issued the command again.
  • Timeout again. Modified the manifest, and tried again.
  • After that, the app failed when it tried to start. Apparently, numpy was missing from the requirements.

TO DO

  • [ x ] Add the mesa code instead of the Boids code.
  • [ x ] Check synchronization, clients, maybe in the cloud, most likely in flask
  • Check cloud documentation or ask for a course? Instances, connections, etc.

Dependencies

Neural search engine for AI papers

Papers search Neural search engine for ML papers. Demo Usage is simple: input an abstract, get the matching papers. The following demo also showcases

Giancarlo Fissore 44 Dec 24, 2022
An Agnostic Computer Vision Framework - Pluggable to any Training Library: Fastai, Pytorch-Lightning with more to come

An Agnostic Object Detection Framework IceVision is the first agnostic computer vision framework to offer a curated collection with hundreds of high-q

airctic 790 Jan 05, 2023
Unofficial implementation of "TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images"

TableNet Unofficial implementation of ICDAR 2019 paper : TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from

Jainam Shah 243 Dec 30, 2022
Super Mario Game With Python

Super_Mario Hello all this is a simple python program which tries to use our body as a controller for the super mario game Here I have used media pipe

Adarsh Badagala 219 Nov 25, 2022
This repository provides train&test code, dataset, det.&rec. annotation, evaluation script, annotation tool, and ranking.

SCUT-CTW1500 Datasets We have updated annotations for both train and test set. Train: 1000 images [images][annos] Additional point annotation for each

Yuliang Liu 600 Dec 18, 2022
Application that instantly translates sign-language to letters.

Sign Language Translator Project Description The main purpose of project is translating sign-language to letters. In accordance with this purpose we d

3 Sep 29, 2022
make a better chinese character recognition OCR than tesseract

deep ocr See README_en.md for English installation documentation. 只在ubuntu下面测试通过,需要virtualenv安装,安装路径可自行调整: git clone https://github.com/JinpengLI/deep

Jinpeng 1.5k Dec 28, 2022
Convert Text-to Handwriting Using Python

Convert Text-to Handwriting Using Python Description In this project we'll use python library that's "pywhatkit" for converting text to handwriting. t

8 Nov 19, 2022
a Deep Learning Framework for Text

DeLFT DeLFT (Deep Learning Framework for Text) is a Keras and TensorFlow framework for text processing, focusing on sequence labelling (e.g. named ent

Patrice Lopez 350 Dec 19, 2022
TextBoxes++: A Single-Shot Oriented Scene Text Detector

TextBoxes++: A Single-Shot Oriented Scene Text Detector Introduction This is an application for scene text detection (TextBoxes++) and recognition (CR

Minghui Liao 930 Jan 04, 2023
A machine learning software for extracting information from scholarly documents

GROBID GROBID documentation Visit the GROBID documentation for more detailed information. Summary GROBID (or Grobid, but not GroBid nor GroBiD) means

Patrice Lopez 1.9k Jan 08, 2023
Detect text blocks and OCR poorly scanned PDFs in bulk. Python module available via pip.

doc2text doc2text extracts higher quality text by fixing common scan errors Developing text corpora can be a massive pain in the butt. Much of the tex

Joe Sutherland 1.3k Jan 04, 2023
Histogram specification using openCV in python .

histogram specification using openCV in python . Have to input miu and sigma to draw gausssian distribution which will be used to map the input image . Example input can be miu = 128 sigma = 30

Tamzid hasan 6 Nov 17, 2021
A curated list of promising OCR resources

Call for contributor(paper summary,dataset generation,algorithm implementation and any other useful resources) awesome-ocr A curated list of promising

wanghaisheng 1.6k Jan 04, 2023
[BMVC'21] Official PyTorch Implementation of Grounded Situation Recognition with Transformers

Grounded Situation Recognition with Transformers Paper | Model Checkpoint This is the official PyTorch implementation of Grounded Situation Recognitio

Junhyeong Cho 18 Jul 19, 2022
A set of workflows for corpus building through OCR, post-correction and normalisation

PICCL: Philosophical Integrator of Computational and Corpus Libraries PICCL offers a workflow for corpus building and builds on a variety of tools. Th

Language Machines 41 Dec 27, 2022
"Very simple but works well" Computer Vision based ID verification solution provided by LibraX.

ID Verification by LibraX.ai This is the first free Identity verification in the market. LibraX.ai is an identity verification platform for developers

LibraX.ai 46 Dec 06, 2022
A curated list of resources for text detection/recognition (optical character recognition ) with deep learning methods.

awesome-deep-text-detection-recognition A curated list of awesome deep learning based papers on text detection and recognition. Text Detection Papers

2.4k Jan 08, 2023
Optical character recognition for Japanese text, with the main focus being Japanese manga

Manga OCR Optical character recognition for Japanese text, with the main focus being Japanese manga. It uses a custom end-to-end model built with Tran

Maciej Budyś 327 Jan 01, 2023