👁️🗨️ Computer vision hackathon project
Welcome to a world where innovation meets empathy, and technology serves as a beacon of hope for a more inclusive society. Imagine a life without sight, where darkness shrouds your every move, and the unknown looms around you. In this realm of sensations and sounds, how would you navigate and connect with the beauty of the world?
We face a monumental challenge; there are approximately 39 million people worldwide who are blind, and another 282 million who live with moderate to severe visual impairment. These aren't just statistics; they represent individuals with dreams and aspirations. It's a collective challenge that demands our attention.
Today, we introduce a groundbreaking web app that bridges this gap - 'Sawt AlRu’ya.' This application whispers the colors, textures, and shapes of the world to the visually impaired through a carefully crafted algorithm. It empowers them to experience the simple pleasures of life that we often take for granted, offering independence and accessibility.
'Sawt AlRu’ya' utilizes cutting-edge computer vision and AI technology to analyze live video streams, swiftly converting them into real-time textual descriptions. These descriptions are seamlessly translated into spoken words using a Large Language Model (LLM). By providing immediate audio feedback, 'Sawt AlRu’ya' enables individuals with visual impairments to confidently and independently interact with their surroundings, enhancing their daily lives and fostering inclusivity.
In essence, our project combines state-of-the-art computer vision algorithms with advanced natural language processing to create a transformational solution for those with visual impairments. 🌟
To run 'Sawt AlRu’ya' on your local machine, follow these simple steps:
- Clone the Repository:
git clone https://github.com/Gab-182/SawtAlRuya - Install Requirements:
cd SawtAlRuya ./requirements.sh - Navigate to the 'Audio' Directory:
cd Audio - Run the Application:
python3 app.py
This will start the 'Sawt AlRu’ya' application on your local machine.