Welcome to Awesome AI Accelerators! 🤖🦾

AI Recognition Accelerator with Node.js and ml5.js

This accelerator template leverages the simplicity and efficiency of Node.js and Express to create an AI Recognition Accelerator, featuring real-time object detection using ml5.js. It provides a user-friendly interface built with Materialize CSS, enabling dynamic interaction with AI models.

Key Features

• Real-Time Object Detection: Utilizes ml5.js to process video streams in real-time, identifying and classifying objects efficiently.
• User-Friendly Interface: A sleek UI powered by Materialize CSS, providing an intuitive experience for users to interact with the AI model.
• Customizable Settings: Options to toggle AI functionalities and adjust frame rate (FPS) for the video stream.

Getting Started

To run this app, you'll need a basic setup with Node.js and npm installed. Clone the repository and install dependencies:

git clone [your-repo-link]
cd [your-repo-directory]
npm install

Then, start the server with:

npm start

The app will be available at http://localhost:80.

Add the Accelerator on VMware Tanzu Application Platform

tanzu acc create awesome-ai-video-recognition --git-repo https://github.com/fklein82/awesome-ai-video-recognition --git-branch main --interval 5s\n

Deploying on VMware Tanzu Application Platform

To deploy this application on VMware Tanzu Application Platform, follow these steps:

Ensure you have the Tanzu CLI installed and configured with access to your Tanzu Application Platform instance.

Navigate to your project directory:

cd [your-repo-directory]

Use the Tanzu CLI to deploy your application:

tanzu apps workload create -f config/workload.yaml

Monitor the deployment status:

tanzu apps workload tail ai-video-recognition --timestamp --since 1h

Once deployed, access your application via the URL provided by Tanzu Application Platform. You can find the url with the following command:

tanzu apps workload get ai-video-recognition

Overview

The index.html of this app contains:

• A video element for real-time video streaming.
• A canvas to display the processed video.
• Controls for toggling AI processing and adjusting FPS.
• Integration of ml5.js for object detection functionalities.

How to Contribute

Contributions to improve or enhance this AI Recognition Accelerator are welcome. Feel free to open issues or submit pull requests with your suggestions or improvements.

Please Note

The software is provided "as is", without warranties of any kind. As the creator, I am not liable for any claims, damages, or other liabilities that arise from the use of the work.