https://github.com/devhabeeblateef/image-recognition-aws
This project is a web-based application that leverages AWS Rekognition to analyze images and detect labels (objects, scenes, or activities) within them. Users can upload an image through the app, and the application returns a list of labels detected in the image along with their confidence scores.
https://github.com/devhabeeblateef/image-recognition-aws
aws aws-rekognition
Last synced: 17 days ago
JSON representation
This project is a web-based application that leverages AWS Rekognition to analyze images and detect labels (objects, scenes, or activities) within them. Users can upload an image through the app, and the application returns a list of labels detected in the image along with their confidence scores.
- Host: GitHub
- URL: https://github.com/devhabeeblateef/image-recognition-aws
- Owner: devhabeeblateef
- License: mit
- Created: 2025-01-19T17:09:47.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-19T17:49:36.000Z (over 1 year ago)
- Last Synced: 2025-03-28T20:15:47.547Z (about 1 year ago)
- Topics: aws, aws-rekognition
- Language: JavaScript
- Homepage:
- Size: 621 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Image Recognition App Using AWS Rekognition
This project is a simple image recognition web application that uses **AWS Rekognition** to analyze uploaded images and identify labels (e.g., objects, scenes, and activities). Built with **Node.js** and **Express**, it provides an easy-to-use interface for exploring AWS Rekognition's powerful image analysis capabilities.
## Features
- Upload images directly through the web interface.
- Analyze images using AWS Rekognition and retrieve:
- Detected labels
- Confidence scores
- Clear and intuitive user interface built with **EJS**.
## Installation
Follow these steps to set up and run the project locally:
### Prerequisites
- Node.js installed on your machine.
- AWS account with access to AWS Rekognition.
- AWS credentials (`accessKeyId`, `secretAccessKey`, and `region`) configured.
### Steps
1. Clone the repository:
```bash
git clone https://github.com/devhabeeblateef/image-recognition-aws.git
cd image-recognition-app
```
2. Install dependencies:
```bash
npm install
```
3. Configure AWS credentials:
Update the AWS configuration section in `app.js` with your **AWS Access Key ID**, **Secret Access Key**, and **Region**:
```javascript
AWS.config.update({
accessKeyId: 'YOUR_AWS_ACCESS_KEY_ID',
secretAccessKey: 'YOUR_AWS_SECRET_ACCESS_KEY',
region: 'YOUR_REGION'
});
```
4. Create a `uploads` folder for storing uploaded images temporarily:
```bash
mkdir uploads
```
5. Start the server:
```bash
node app.js
```
6. Open the app in your browser:
```
http://localhost:3000
```
## Usage
1. Access the app at `http://localhost:3000`.
2. Upload an image file (JPEG, PNG, etc.).
3. View the detected labels and their confidence scores.
## Project Structure
```
project-root/
│
├── app.js # Main server file
├── views/ # EJS templates
│ └── index.ejs # Main front-end template
├── public/ # Static files (CSS, JS, images, etc.)
├── uploads/ # Temporary storage for uploaded images
├── package.json # Dependencies and scripts
├── package-lock.json # Lock file for exact dependency versions
└── README.md # Project documentation
```
## Dependencies
- [AWS SDK for JavaScript v3](https://github.com/aws/aws-sdk-js-v3)
- [Express.js](https://expressjs.com/)
- [Multer](https://github.com/expressjs/multer) (for handling file uploads)
- [Body-parser](https://github.com/expressjs/body-parser)
- [EJS](https://ejs.co/)
## Contributing
Contributions are welcome! Feel free to:
- Fork the repository.
- Create a feature branch.
- Submit a pull request with your changes.
## License
This project is licensed under the MIT License. See `LICENSE` for more details.
---
## Acknowledgments
- [AWS Rekognition](https://aws.amazon.com/rekognition/) for its powerful image analysis capabilities.
- The open-source community for supporting libraries like Express and Multer.
```