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

Fatigue Driving Detection Based on Dlib

Fatigue Driving Detection Based on Dlib

5 Dec 14, 2022
Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition:

Multi-Type-TD-TSR Check it out on Source Code of our Paper: Multi-Type-TD-TSR Extracting Tables from Document Images using a Multi-stage Pipeline for

Pascal Fischer 178 Dec 27, 2022
The Open Source Framework for Machine Vision

SimpleCV Quick Links: About Installation [Docker] (#docker) Ubuntu Virtual Environment Arch Linux Fedora MacOS Windows Raspberry Pi SimpleCV Shell Vid

Sight Machine 2.6k Dec 31, 2022
This is a GUI for scrapping PDFs with the help of optical character recognition making easier than ever to scrape PDFs.

pdf-scraper-with-ocr With this tool I am aiming to facilitate the work of those who need to scrape PDFs either by hand or using tools that doesn't imp

Jacobo José Guijarro Villalba 75 Oct 21, 2022
Deep LearningImage Captcha 2

滑动验证码深度学习识别 本项目使用深度学习 YOLOV3 模型来识别滑动验证码缺口,基于 https://github.com/eriklindernoren/PyTorch-YOLOv3 修改。 只需要几百张缺口标注图片即可训练出精度高的识别模型,识别效果样例: 克隆项目 运行命令: git cl

Python3WebSpider 117 Dec 28, 2022
Characterizing possible failure modes in physics-informed neural networks.

Characterizing possible failure modes in physics-informed neural networks This repository contains the PyTorch source code for the experiments in the

Aditi Krishnapriyan 55 Jan 02, 2023
⛓ marc is a small, but flexible Markov chain generator

About marc (markov chain) is a small, but flexible Markov chain generator. Usage marc is easy to use. To build a MarkovChain pass the object a sequenc

Max Humber 65 Oct 27, 2022
[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
Learning Camera Localization via Dense Scene Matching, CVPR2021

This repository contains code of our CVPR 2021 paper - "Learning Camera Localization via Dense Scene Matching" by Shitao Tang, Chengzhou Tang, Rui Hua

tangshitao 65 Dec 01, 2022
Provides OCR (Optical Character Recognition) services through web applications

OCR4all As suggested by the name one of the main goals of OCR4all is to allow basically any given user to independently perform OCR on a wide variety

174 Dec 31, 2022
Source code of RRPN ---- Arbitrary-Oriented Scene Text Detection via Rotation Proposals

Paper source Arbitrary-Oriented Scene Text Detection via Rotation Proposals https://arxiv.org/abs/1703.01086 News We update RRPN in pytorch 1.0! View

428 Nov 22, 2022
An Optical Character Recognition system using Pytesseract/Extracting data from Blood Pressure Reports.

Optical_Character_Recognition An Optical Character Recognition system using Pytesseract/Extracting data from Blood Pressure Reports. As an IOT/Compute

Ramsis Hammadi 1 Feb 12, 2022
This is used to convert a string to an Image with Handwritten Characters.

Text-to-Handwriting-using-python This is used to convert a string to an Image with Handwritten Characters. text_to_handwriting(string: str, save_to: s

Akashdeep Mahata 3 Aug 15, 2022
code for our ICCV 2021 paper "DeepCAD: A Deep Generative Network for Computer-Aided Design Models"

DeepCAD This repository provides source code for our paper: DeepCAD: A Deep Generative Network for Computer-Aided Design Models Rundi Wu, Chang Xiao,

Rundi Wu 85 Dec 31, 2022
The world's simplest facial recognition api for Python and the command line

Face Recognition You can also read a translated version of this file in Chinese 简体中文版 or in Korean 한국어 or in Japanese 日本語. Recognize and manipulate fa

Adam Geitgey 47k Jan 07, 2023
ocroseg - This is a deep learning model for page layout analysis / segmentation.

ocroseg This is a deep learning model for page layout analysis / segmentation. There are many different ways in which you can train and run it, but by

NVIDIA Research Projects 71 Dec 06, 2022
One Metrics Library to Rule Them All!

onemetric Installation Install onemetric from PyPI (recommended): pip install onemetric Install onemetric from the GitHub source: git clone https://gi

Piotr Skalski 49 Jan 03, 2023
This can be use to convert text in a file to handwritten text.

TextToHandwriting This can be used to convert text to handwriting. Clone this project or download the code. Run TextToImage.py give the filename of th

Ashutosh Mahapatra 2 Feb 06, 2022
TedEval: A Fair Evaluation Metric for Scene Text Detectors

TedEval: A Fair Evaluation Metric for Scene Text Detectors Official Python 3 implementation of TedEval | paper | slides Chae Young Lee, Youngmin Baek,

Clova AI Research 167 Nov 20, 2022
A dataset handling library for computer vision datasets in LOST-fromat

A dataset handling library for computer vision datasets in LOST-fromat

8 Dec 15, 2022