https://github.com/oussama-zbair/imgbgremoval

Flask-based web application designed to simplify background removal from images.
https://github.com/oussama-zbair/imgbgremoval

deep-learning flask object-detection opencv python

Last synced: 3 days ago
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Flask-based web application designed to simplify background removal from images.

• Host: GitHub
• URL: https://github.com/oussama-zbair/imgbgremoval
• Owner: oussama-zbair
• License: mit
• Created: 2024-06-18T20:44:09.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-20T18:53:45.000Z (5 months ago)
• Last Synced: 2024-06-22T04:34:09.731Z (5 months ago)
• Topics: deep-learning, flask, object-detection, opencv, python
• Language: Python
• Homepage: https://img-bg-removal.vercel.app
• Size: 2.39 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# ImgBGRemoval

[![License](https://img.shields.io/github/license/oussama-zbair/ImgBGRemoval.svg?style=flat-square)](https://github.com/oussama-zbair/ImgBGRemoval/blob/master/LICENSE)

[![GitHub last commit](https://img.shields.io/github/last-commit/oussama-zbair/ImgBGRemoval.svg?style=flat-square)](https://github.com/oussama-zbair/ImgBGRemoval/commits/master)

[![Python Version](https://img.shields.io/badge/python-3.7%20%7C%203.8-blue.svg?style=flat-square)](https://www.python.org/downloads/)

[![Flask Version](https://img.shields.io/badge/flask-2.0-green.svg?style=flat-square)](https://flask.palletsprojects.com/en/2.0.x/)

[![U2Net Model](https://img.shields.io/badge/U2Net-Deep%20Learning-orange.svg?style=flat-square)](https://github.com/xuebinqin/U-2-Net)

ImgBGRemoval is a Flask-based web application designed to simplify background removal from images. With just a few clicks, users can upload images and effortlessly remove backgrounds.

## Features

- **Upload images** to remove background.

- **Process uploaded images** using the U2Net deep learning model.

- **Download processed images** with transparent backgrounds.

## Technologies Used

- **Frontend**: HTML5, CSS3, JavaScript (jQuery)

- **Backend**: Python, Flask

- **Deep Learning Model**: U2Net

- **Libraries**: OpenCV, PIL (Python Imaging Library), PyTorch (for deep learning)

- **Deployment**: Heroku, Flask server (backend)

## Setup

1. **Clone the repository:**

    **Note:** This repository uses Git LFS. Make sure you have Git LFS installed. You can install it from [here](https://git-lfs.github.com/).

    ```bash

    git clone https://github.com/your-username/ImgBGRemoval.git

    ```

2. **Install dependencies:**

    ```bash

    pip install -r requirements.txt

    ```

3. **Download U2Net model files:**

    - Download the `u2net.pth` model file from the [U2Net repository](https://github.com/xuebinqin/U-2-Net).

    - Create a directory named `models` in the root of your project and place the `u2net.pth` file inside it.

4. **Run the Flask application:**

    ```bash

    flask run

    ```

## How It Works

The application uses the [U2Net](https://github.com/xuebinqin/U-2-Net "U2Net") deep learning model to perform precise image segmentation for background removal. Here's a brief overview of the process:

1. **Image Upload**: Users upload an image to the web application.

2. **Image Processing**: The uploaded image undergoes preprocessing and is fed into the U2Net model, which generates a binary mask indicating the foreground (object) and background.

3. **Background Removal**: Using the generated mask, the application removes the background from the image, creating a transparent background.

4. **Result Display and Download**: Users can view the original and processed images on the web interface. They have the option to download the processed image with the transparent background.

## Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.