WatermarkRemover-AI

AI-Powered Watermark Removal Tool using Florence-2 and LaMA Models

Example of watermark removal with LaMa inpainting

Overview

WatermarkRemover-AI is a cutting-edge application that leverages AI models for precise watermark detection and seamless removal. It uses Florence-2 from Microsoft for watermark identification and LaMA for inpainting to fill in the removed regions naturally. The software offers both a command-line interface (CLI) and a PyQt6-based graphical user interface (GUI), making it accessible to both casual and advanced users.

Table of Contents

• Features
• Technical Overview
• Installation
• Usage
  • Preferred Way: Setup Script
  • Manual Way
  • Using the GUI
  • Using the CLI
• Alpha Masking
• Contributing
• License

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Features

• Dual Modes: Process individual images or entire directories of images.
• Advanced Watermark Detection: Utilizes Florence-2's open-vocabulary detection for accurate watermark identification.
• Seamless Inpainting: Employs LaMA for high-quality, context-aware inpainting.
• Customizable Output:
  • Configure maximum bounding box size for watermark detection.
  • Set transparency for watermark regions.
  • Force specific output formats (PNG, WEBP, JPG).
• Progress Tracking: Real-time progress updates in both GUI and CLI modes.
• Dark Mode Support: GUI automatically adapts to system dark mode settings.
• Efficient Resource Management: Optimized for GPU acceleration using CUDA (optional).

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Technical Overview

Florence-2 for Watermark Detection

• Florence-2 detects watermarks using open-vocabulary object detection.
• Bounding boxes are filtered to ensure that only small regions (configurable by the user) are processed.

LaMA for Inpainting

• The LaMA model seamlessly fills in watermark regions with context-aware content.
• Supports high-resolution inpainting by using cropping and resizing strategies.

PyQt6 GUI

• User-friendly interface for selecting input/output paths, configuring settings, and tracking progress.
• Dark mode and customization options enhance the user experience.

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Installation

Prerequisites

• Conda/Miniconda installed.
• CUDA (optional for GPU acceleration; the application runs well on CPUs too).

Steps

1. Clone the Repository:

   git clone https://github.com/D-Ogi/WatermarkRemover-AI.git
   cd WatermarkRemover-AI

2. Run the Setup Script:

   bash setup.sh

   The setup.sh script automatically sets up the environment, installs dependencies, and launches the GUI application. It also provides convenient options for CLI usage.

3. Fast-Track Options:

   • To Use the CLI Immediately: After running setup.sh, you can use the CLI directly without activating the environment manually:

     ./setup.sh input_path output_path [options]

     Example:

     ./setup.sh ./input_images ./output_images --overwrite --transparent

   • To Activate the Environment Without Starting the Application: Use:

     conda activate py312aiwatermark

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Usage

Preferred Way: Setup Script

1. Run the Setup Script:

   bash setup.sh

   • The GUI will launch automatically, and the environment will be ready for immediate CLI or GUI use.
   • For CLI use, run:

     ./setup.sh input_path output_path [options]

     Example:

     ./setup.sh ./input_images ./output_images --overwrite --transparent

Manual Way

1. Activate the Environment:

   conda activate py312aiwatermark

2. Launch GUI or CLI:
   • GUI:

     python remwmgui.py

   • CLI:

     python remwm.py input_path output_path [options]

Using the GUI

1. Launch the GUI: If not launched automatically, start it with:

   python remwmgui.py

2. Configure Settings:

   • Mode: Select "Process Single Image" or "Process Directory".
   • Paths: Browse and set the input/output directories.
   • Options:
     • Enable overwriting of existing files (directory processing only, single image processing always overwrites)
     • Enable transparency for watermark regions.
     • Adjust the maximum bounding box size for watermark detection.
   • Output Format: Choose between PNG, WEBP, JPG, or retain the original format.

3. Start Processing:

   • Click "Start" to begin processing.
   • Monitor progress and logs in the GUI.

Using the CLI

1. Basic Command:

   python remwm.py input_path output_path

2. Options:

   • --overwrite: Overwrite existing files.
   • --transparent: Make watermark regions transparent instead of removing them.
   • --max-bbox-percent: Set the maximum bounding box size for watermark detection (default: 10%).
   • --force-format: Force output format (PNG, WEBP, or JPG).

3. Example:

   python remwm.py ./input_images ./output_images --overwrite --max-bbox-percent=15 --force-format=PNG

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Alpha Masking

We implemented alpha masking to allow selective manipulation of watermark regions without altering other parts of the image.

Why Alpha Masking?

• Precision: Enable box-targeted watermark removal by isolating specific regions.
• Flexibility: By controlling opacity in alpha layers, we can achieve a range of effects by complete removal to transparency.
• Minimal Impact: This method ensures that areas outside the watermark remain untouched, preserving image quality.

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Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch for your feature.
3. Submit a pull request detailing your changes.

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License

This project is licensed under the MIT License. See the LICENSE file for details.