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

Sort By Face

Sort-By-Face This is an application with which you can either sort all the pictures by faces from a corpus of photos or retrieve all your photos from

0 Nov 29, 2021
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
This is a implementation of CRAFT OCR method

This is a implementation of CRAFT OCR method

Esaka 0 Nov 01, 2021
3点クリックで円を指定し、極座標変換を行うサンプルプログラム

click-warpPolar 3点クリックで円を指定し、極座標変換を行うサンプルプログラムです。 Requirements OpenCV 3.4.2 or Later Usage 実行方法は以下です。 起動後、マウスで3点をクリックし円を指定してください。 python click-warpPol

KazuhitoTakahashi 17 Dec 30, 2022
[ICCV, 2021] Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks

Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks This is an official PyTorch code repository of the paper "Cloud Transformers:

Visual Understanding Lab @ Samsung AI Center Moscow 27 Dec 15, 2022
PianoVisuals - Create background videos synced with piano music using opencv

Steps Record piano video Use Neural Network to do body segmentation (video matti

Solbiati Alessandro 4 Jan 24, 2022
Play the Namibian game of Owela against a terrible AI. Built using Django and htmx.

Owela Club A Django project for playing the Namibian game of Owela against a dumb AI. Built following the rules described on the Mancala World wiki pa

Adam Johnson 18 Jun 01, 2022
A selectional auto-encoder approach for document image binarization

The code of this repository was used for the following publication. If you find this code useful please cite our paper: @article{Gallego2019, title =

Javier Gallego 89 Nov 18, 2022
STEFANN: Scene Text Editor using Font Adaptive Neural Network

STEFANN: Scene Text Editor using Font Adaptive Neural Network @ The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.

Prasun Roy 208 Dec 11, 2022
Some bits of javascript to transcribe scanned pages using PageXML

nashi (nasḫī) Some bits of javascript to transcribe scanned pages using PageXML. Both ltr and rtl languages are supported. Try it! But wait, there's m

Andreas Büttner 15 Nov 09, 2022
Repository for playing the computer vision apps: People analytics on Raspberry Pi.

play-with-torch Repository for playing the computer vision apps: People analytics on Raspberry Pi. Tools Tested Hardware RasberryPi 4 Model B here, RA

eMHa 1 Sep 23, 2021
Automatically download multiple papers by keywords in CVPR

CVFPaperHelper Automatically download multiple papers by keywords in CVPR Install mkdir PapersToRead cd PaperToRead pip install requests tqdm git clon

46 Jun 08, 2022
An OCR evaluation tool

dinglehopper dinglehopper is an OCR evaluation tool and reads ALTO, PAGE and text files. It compares a ground truth (GT) document page with a OCR resu

QURATOR-SPK 40 Dec 20, 2022
POT : Python Optimal Transport

This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.

Python Optimal Transport 1.7k Jan 04, 2023
A simple python program to record security cam footage by detecting a face and body of a person in the frame.

SecurityCam A simple python program to record security cam footage by detecting a face and body of a person in the frame. This code was created by me,

1 Nov 08, 2021
OCR-D-compliant page segmentation

ocrd_segment This repository aims to provide a number of OCR-D-compliant processors for layout analysis and evaluation. Installation In your virtual e

OCR-D 59 Sep 10, 2022
OCR engine for all the languages

Description kraken is a turn-key OCR system optimized for historical and non-Latin script material. kraken's main features are: Fully trainable layout

431 Jan 04, 2023
text detection mainly based on ctpn model in tensorflow, id card detect, connectionist text proposal network

text-detection-ctpn Scene text detection based on ctpn (connectionist text proposal network). It is implemented in tensorflow. The origin paper can be

Shaohui Ruan 3.3k Dec 30, 2022
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
caffe re-implementation of R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection

R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection Abstract This is a caffe re-implementation of R2CNN: Rotational Region CNN fo

candler 80 Dec 28, 2021