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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: about 1 month 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 (6 months ago)
- Default Branch: main
- Last Pushed: 2024-06-20T18:53:45.000Z (6 months ago)
- Last Synced: 2024-06-22T04:34:09.731Z (6 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
Awesome Lists containing this project
README
# ImgBGRemoval
[](https://github.com/oussama-zbair/ImgBGRemoval/blob/master/LICENSE)
[](https://github.com/oussama-zbair/ImgBGRemoval/commits/master)
[](https://www.python.org/downloads/)
[](https://flask.palletsprojects.com/en/2.0.x/)
[](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.