Sign Flow - A Sign Language Detection Application

This repository contains a sign language detection application built using Python. The application utilizes the Streamlit framework for the user interface, OpenCV for video processing, and TensorFlow/Keras for LSTM-based sign language prediction.

Checkout our app on Streamlit: https://signflow.streamlit.app/

Table of Contents

• Files
• Usage
• Features
• Dependencies
• Getting Started
• License

Files

Dataset: https://www.kaggle.com/datasets/risangbaskoro/wlasl-processed

1. preprocessor.py: Contains functions for sampling frames from videos, creating feature matrices, and generating processed videos.

2. registry.py: Includes functions for generating processed videos, CSV files, drawing landmarks, and saving/loading models.

3. model.py: Defines the LSTM model architecture and functions for training the model.

4. main.py: The main script that orchestrates the data processing, model training, and saving.

5. app.py: The Streamlit application with two pages - one for uploading a video for sign detection and another for real-time sign detection from a webcam stream.

Usage

1. Video Upload Page

• Upload a video file for sign language detection.
• The application will predict the sign language associated with the uploaded video.

2. Real-Time Detection Page

• Use a webcam for real-time sign language detection.
• The application displays the detected signs in real-time.

Features

• Sign language detection using LSTM models.
• Two modes: video upload for single predictions and real-time webcam detection.
• User-friendly Streamlit interface.

Dependencies

• TensorFlow
• OpenCV
• Streamlit
• mediapipe
• scikit-learn
• pandas
• numpy
• av
• streamlit_webrtc

Getting Started

1. Clone the repository:

   git clone https://github.com/your-username/sign-language-detection.git
   cd sign-language-detection
   

2. Navigate to the project directory: cd SignFlow

3. Install the required dependencies: pip install -r requirements.txt

License

This app is not licensed.