Skip to content

Latest commit

 

History

History
155 lines (105 loc) · 8.29 KB

README.md

File metadata and controls

155 lines (105 loc) · 8.29 KB
Raw

beautiful sign language (Turkish version)

I like to consider this repository as a start point, hoping in future to continue further in building full sign language system leveraging deep learning techniques to be able to help hear-impaired people, at this moment our system contains Deep learning models trained on some Turkish sign language words.

How to contribute?

Find a good video dataset for any sign language or make your own dataset(simply by using your phone) then label every video, i suggest to write interface to do so. Another intersting approach is to scrape videos from Apis like youtube Api using query words... After collecting dataset open an issue describing your dataset and the results you obtained from training phase using same models (if any).Then we will make sure to merge all the other datasets with your dataset by merging them and do overall training... Ofcourse i will make sure for all submitted datasets to be available online and to anyone how wants to train using this project's models or any other models.

Features

  • Functionality
    • word-base system
      • for one sign language sentence to be translated, it should be fed to the system as words, each word considered as independent Input.
      • accurate, simple and fast
      • Input space is discrete, means each video(input) is a word.
    • sentence-base system
      • system can accept any kind of input word or sentence.
      • comparing with word-base system this system is more slower and less accurate.
      • Input space is continuous.
  • Input system accepts a video as input with any known formats(flv,mp4,wbem, ...) and with any kind of camera device (webcam, mobile camera ...)
  • Output system outputs 'n' sign language word ex:(n==3) sorted according to the top predicted probabilities(likelihoods) which is associated with each word.
  • Deployment
    • Rest API serve the system as an API interface that listens to HTTP requests(GET) to get predictions of sign language words.
    • WampServer for testing purposes on local machine, wamp server stack(Apache, MySQL, PHP) is used with simple web interface demonstrating some useful use cases of the system. It could be developed to serve online but some addition work need to be done on both server and UI side.
  • Multiprocessing the hole system pipeline is multi-processed for performance gain, but sometimes for some OS mutliprocessing low level libraries like windows it may cause bottleneck problems for some reasons.
  • CPU/GPU supports cpu and gpu, but to run it on GPU you need to install CUDA(Nivaida GPU) and cuDNN, head to Tensorflow website for more information
  • OS: Ubuntu(18),Windows(10)

Results

  • word-base system

  • sentence-base system

system sign language words

The system can recognize 10 words of turkish sign language words, but the dream is to collect more words with the help of our community to include more words by building large dataset.

Installation

Note: code will not run with python newer versions 3.7,3.8, you should install lower versions.We recommend installing python3.6.8

the system is tested on ubuntu 18.4 and windows 10, but it should work fine for other OS. If you encountered any problem feel free to open an issue.

Prerequisites

  • python3.6.8
  • Tensorflow >= 1.11.0
  • Keras >= 2.2.4
  • in addition(optional):
    • wamp(windows) or Xampp(linux)
    • CUDA 8 and cuDNN 7.4

clone the repo (or download it as .zip file) and install the requirements.

Note: If you are on windows run the below commands as adminstrator, on linux run it with super user (ex: ubuntu -> sudo)

git clone https://github.com/AtaaEddin/alpha
cd alpha
pip install -r requirements.txt

Quick start

for quick testing on your webcam, run the following command. with 'run' flag webcam and 'download' flag True to download the model's weights you can turn it back to False after the first run.

While it's running you will do one of these sign language words, after you do the word you will press 'q' to end recording and to start processing your video.

python main.py -run webcam -pred_type word -download True

Take a look to Flags.md file to see all the flags and what it do.

serve the system as REST API

it is the most convenient way to use the system because it separates this system from your already existing system where you are trying to integrate sign language features to your system, and you don't want to mix up your dependences with this system's dependences or you don't want to code hole of your system in python then it's the way to go.

you can make your own machine serve as Rest API serves or you can head to one of the cloud computing services like amazon (AWS sageMaker - lambda function or google-cloud-platform and install the system on it.

After you install the system simply run:

python main.py -run REST_API -pred_type word

learn more about the flags Flags.md

run the system on WampServer

WampServer is a Windows web development environment. So if you are on Ubuntu or linux you have to install Xampp.

  • On Windows

    • install wamp from WampServer site and download the version that suits your OS.
    • download necessary files from here the folder named 'combine'. after that move the folder to 'path/to/wampdir/www/'
    • After you run WampServer, you should be able to go to http://127.0.0.1/combine/.
    • Login to http://127.0.0.1/phpmyadmin/. one the left there are Database icons press on 'New', name the database 'isaret' and from the drop-down menu select 'utf8_general_ci' then press create.
    • In the main.py change 'wamp_folder' to your path directory where you put combine folder.
    • Run the python server and start testing by running: $ python main.py -run wamp

  • On Ubuntu

    • install Xampp.
    • download necessary files from here the folder named 'combine'. after that move the folder to /opt/lampp/htdocs/
    • you can run Xampp with $ sudo opt/lampp/lampp start if you faced any troubles try to run Xampp through the XAMPP-Launcher, after you run it you should be able to go to http://127.0.0.1/combine/
    • Go to http://127.0.0.1/phpmyadmin/. In the left there are Database icons press on 'New', name the database 'isaret' and from the drop-down menu select 'utf8_general_ci' then press create.
    • run $ sudo find /opt/lampp/htdocs -type d -exec chmod 755 {} \; to allow read and write to somefolder like 'uploads'.
    • In the main.py make sure that 'wamp_folder' variable has '/opt/lampp/htdocs/combine/' as value.
    • Run the python server and start testing by running: $ python main.py -run wamp

learn more about the flags Flags.md

Run on GPU

First install Tensorflow-gpu

pip install tensorflow-gpu == 1.11.0

Then head to Tensorflow website under 'Software requirements' install all the requirements.

Training

I will upload the training files in the next few days ...!

How the system works

I'm publishing a new block on medium very soon, so stay tuned!!.