Video Background Removal Application Setup Guide

Project Structure

video-bg-removal/
├── app.py                 # Main Flask application
├── requirements.txt       # Python dependencies
├── templates/
│   └── index.html        # Frontend template
├── uploads/              # Temporary upload folder (created automatically)
├── processed/            # Processed videos folder (created automatically)
└── README.md            # This file

Features

AI-Powered Background Removal

• AI Normal Mode: High-accuracy deep learning model for best results
• AI Fast Mode: Faster processing with good quality
• Traditional Methods: MOG2, GrabCut, and Contours for fallback

Key Capabilities

• Supports multiple video formats (MP4, AVI, MOV, MKV, WEBM)
• Real-time progress tracking
• Drag & drop file upload
• Responsive web interface
• Automatic cleanup of temporary files
• GPU acceleration support (if available)

Installation

1. Clone or Download the Project

Create a new directory and save all the files:

• app.py - Main Flask application
• requirements.txt - Dependencies
• Create a templates folder and save index.html inside it

2. Create Virtual Environment (Recommended)

python -m venv video-bg-env
source video-bg-env/bin/activate  # On Windows: video-bg-env\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

Note: The transparent-background library will download pre-trained models on first use (~100MB).

4. Install Additional Requirements (if needed)

For better performance, you might need:

# For GPU support (NVIDIA)
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

# For CPU-only (if you don't have GPU)
pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu

Running the Application

1. Start the Flask Server

python app.py

2. Access the Application

Open your browser and go to: http://localhost:5000

Usage Guide

1. Upload Video

• Click the upload area or drag & drop your video file
• Supported formats: MP4, AVI, MOV, MKV, WEBM
• Maximum file size: 100MB

2. Choose Method

• AI Normal: Best quality, slower processing (recommended)
• AI Fast: Good quality, faster processing
• MOG2: Traditional method, good for moving subjects
• GrabCut: Good for static backgrounds
• Contours: Fastest, basic edge detection

3. Process Video

• Click "Process Video" button
• Monitor progress in real-time
• Processing time varies based on video length and method

4. Download Result

• Preview the processed video
• Download the result
• Process another video or cleanup

Technical Details

AI Models

The application uses the transparent-background library which includes:

• InSPyReNet (Inference for Segmentation Pyramid Network)
• U²-Net based architectures
• Pre-trained on large datasets for accurate segmentation

Processing Methods

1. AI Normal/Fast:

   • Uses deep learning models
   • Processes frame-by-frame
   • Converts to PIL → AI processing → OpenCV

2. Traditional Methods:

   • MOG2: Background subtraction using Mixture of Gaussians
   • GrabCut: Interactive foreground extraction
   • Contours: Edge-based object detection

Performance Considerations

• GPU: Significantly faster for AI methods
• CPU: Works but slower, especially for AI methods
• Memory: ~2-4GB RAM recommended for smooth operation
• Timeout: 19-minute processing limit for safety

Troubleshooting

Common Issues

1. Import Error for transparent_background

   pip install transparent-background
   

2. CUDA/GPU Issues

   • Install appropriate PyTorch version for your CUDA version
   • Or use CPU-only version: pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu

3. Memory Issues

   • Use AI Fast mode instead of Normal
   • Process shorter videos
   • Ensure sufficient RAM

4. Slow Processing

   • Use AI Fast mode
   • Try traditional methods (MOG2, Contours)
   • Consider GPU acceleration

Model Download

On first run, the AI models will be downloaded automatically:

• Models are stored in ~/.cache/transparent-background/
• Total size: ~100-200MB
• Internet connection required for first run

API Endpoints

• GET / - Main interface
• POST /upload - Upload and start processing
• GET /status/<task_id> - Check processing status
• GET /download/<filename> - Download processed video
• POST /cleanup/<task_id> - Cleanup temporary files

Configuration

File Size Limits

Modify in app.py:

app.config['MAX_CONTENT_LENGTH'] = 100 * 1024 * 1024  # 100MB

Processing Timeout

Modify in app.py:

if time.time() - start_time >= 19 * 60:  # 19 minutes

GPU/CPU Selection

The application automatically detects and uses available GPU. To force CPU:

import torch
torch.cuda.is_available = lambda: False

Advanced Usage

Custom Background

Modify the AI processing method to use different background types:

# In _process_with_ai method
processed_pil = remover.process(pil_img, type='map')  # or 'white', 'blur', etc.

Batch Processing

The current implementation processes one video at a time. For batch processing, you'd need to implement a queue system.

Security Notes

• The app includes basic file validation
• Temporary files are cleaned up automatically
• Consider adding authentication for production use
• File size limits prevent abuse

License & Credits

This application integrates several open-source libraries:

• Flask - Web framework
• OpenCV - Computer vision
• transparent-background - AI background removal
• PyTorch - Deep learning framework

Support

For issues related to:

• Flask/Web Interface: Check Flask documentation
• AI Models: Check transparent-background GitHub repository
• OpenCV: Check OpenCV documentation
• General Python: Check respective library documentation