# 🪄 FOBG — AI Background Remover

Remove backgrounds from **images** and **videos** using AI, powered by [rembg](https://github.com/danielzhanika/rembg).

## Features

- **Image BG Removal** — Upload any image and get back a transparent PNG
- **Video BG Removal** — Frame-by-frame processing with true alpha channel output
- **Multiple AI Models** — u2net, u2netp, u2net_human_seg, isnet-general-use, isnet-anime
- **Gradio UI** — Clean, interactive web interface with fine-tuning controls
- **CLI Mode** — Script-friendly command-line interface for batch processing
- **Fine-tune Controls** — Alpha matting, foreground/background thresholds, erosion mask

## Installation

### Prerequisites

- [Python](https://www.python.org/) 3.9 or later
- [ffmpeg](https://ffmpeg.org/) — required for video processing (Mac: `brew install ffmpeg`; Debian/Ubuntu: `sudo apt install ffmpeg`)

### Option 1 — Virtual Environment (Recommended)

```bash
cd /mnt/games/Coding/Python/AI/FOBG
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```

### Option 2 — Global Install

```bash
pip install -r requirements.txt
```

> **Note:** The first run will automatically download the selected AI model(s) (~40–170 MB depending on the model).

## Usage

### Web UI (Gradio)

```bash
python main.py
```

Opens at [http://localhost:7860](http://localhost:7860).

Additional options:

```bash
python main.py --host 0.0.0.0 --port 7860    # Bind to a specific host/port
python main.py --share                         # Create a public share link
python main.py --no-browser                    # Do not open browser automatically
```

### CLI Mode

```bash
python main.py --cli input.jpg                 # Remove image background
python main.py --cli input.jpg -o result.png   # Specify output path
python main.py --cli video.mp4 --video          # Remove background from video
python main.py --cli video.mp4 --video -o out.mov   # .mov output preserves alpha channel
```

CLI options:

| Flag | Description |
|------|-------------|
| `--model <name>` | AI model to use (default: `u2net`) |
| `--alpha-matting` | Enable alpha matting for fine edges |
| `--no-alpha-matting` | Disable alpha matting |
| `--video` | Treat input as video (frame-by-frame processing) |

## Available Models

| Model | Description | Speed |
|-------|-------------|-------|
| `u2net` | Default, balanced quality/speed | ⚡ Fast |
| `u2netp` | Lightweight, slightly lower quality | 🚀 Fastest |
| `u2net_human_seg` | Optimized for human segmentation | ⚡ Fast |
| `u2net_3b` | Heavy, higher accuracy | 🐢 Slower |
| `isnet-general-use` | General purpose, high accuracy | 🐢 Slower |
| `isnet-anime` | Optimized for anime illustrations | ⚡ Fast |

## Gradio Controls

### Image Tab

- **Upload Image** — Accepts PNG, JPEG, or WebP
- **Model** — Choose the AI model
- **Alpha Matting** — Fine-tune edges (slower but cleaner results)
- **Foreground Threshold** — How much of the image is considered foreground (1–255)
- **Background Threshold** — How much is considered background (1–255)
- **Erosion Mask Dilation** — Morphological operation to refine the mask (0–32)

### Video Tab

- **Upload Video** — Accepts MP4, AVI, WebM, and MOV
- Same model and fine-tuning controls as the image tab

## Notes

- **Image processing** typically takes 1–5 seconds depending on resolution and model
- **Video processing** is frame-by-frame and can take minutes for longer clips
- Image output is saved as **PNG with transparency**
- Video output uses **.mov with ProRes 4444 alpha** when alpha is desired, or **MP4 with libx264** (black background where alpha is zero)
- All results are stored in the `output/` directory

## Project Structure

```
FOBG/
├── main.py                   # Entry point (Gradio + CLI)
├── requirements.txt          # Python dependencies
├── src/
│   ├── core/
│   │   ├── bg_remove.py      # Image background removal (rembg)
│   │   └── video_bg_remove.py# Video background removal (OpenCV + rembg + ffmpeg)
│   └── ui/
│       └── app.py            # Gradio web interface
├── output/                   # Processed results saved here
├── tests/                    # Unit tests
└── docs/                     # Extended documentation
```

## License

MIT License — see [LICENSE](LICENSE) for details.