https://github.com/p1sangmas/re-ddcolor
An improved version of DDColor with enhanced performance and usability. This project extends the original framework with Apple Metal support, interactive web UI, video colorization capabilities, and cross-platform optimization.
https://github.com/p1sangmas/re-ddcolor
computer-vision image-colorization pytorch video-colorization
Last synced: 4 months ago
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An improved version of DDColor with enhanced performance and usability. This project extends the original framework with Apple Metal support, interactive web UI, video colorization capabilities, and cross-platform optimization.
- Host: GitHub
- URL: https://github.com/p1sangmas/re-ddcolor
- Owner: p1sangmas
- License: apache-2.0
- Created: 2025-05-05T13:59:52.000Z (about 1 year ago)
- Default Branch: ddcolor-development
- Last Pushed: 2025-05-06T04:05:34.000Z (about 1 year ago)
- Last Synced: 2025-07-20T16:41:11.650Z (12 months ago)
- Topics: computer-vision, image-colorization, pytorch, video-colorization
- Language: Jupyter Notebook
- Homepage:
- Size: 33.6 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# re-DDColor: Enhanced Image Colorization
A revised and improved version of [DDColor](https://github.com/piddnad/DDColor), offering additional features and optimizations for image colorization.
**This project is developed as part of my Bachelor of Computer Engineering with Honours Final Year Project (2025).**
[](LICENSE)
## Overview
DDColor is a state-of-the-art automatic image colorization framework proposed in the paper "DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders" (ICCV 2023). This revised version maintains the core functionality while adding new features, optimizing performance, and improving user interfaces.
## Example Results
Below is a comparison of the input black-and-white images and the colorized outputs generated by re-DDColor:
| **Before** (Black & White) | **After** (Colorized) |
|-----------------------------|-----------------------|
|  |  |
You can try colorizing your own images using the methods described in the [Usage](#usage) section.
## New Features in This Version
### Performance Improvements
- Added support for Apple Metal Performance Shaders (MPS) for better performance on Mac devices
- Optimized memory usage for improved processing on devices with limited resources
- Enhanced inference pipeline for faster colorization
### Interface Enhancements
- **Gradio Web UI**: Added interactive web interface for easy colorization with before/after slider comparison
- **Flask API**: Implemented a REST API for integration with other applications
- **Video Processing**: Extended support for colorizing videos frame by frame
- **Improved CLI**: Enhanced command-line interface with more options and better feedback
### Technical Improvements
- Better cross-platform compatibility (Windows, macOS, Linux)
- Streamlined installation process with clearer dependencies
- Additional model configuration options
- Enhanced error handling and recovery
- Comprehensive documentation
## Installation
```bash
# Clone the repository
git clone https://github.com/yourusername/re-DDColor.git
cd re-DDColor
# Install dependencies
pip install -r requirements.txt
# Download pre-trained models (automatically done on first run)
python pretraindownload.py
```
## Docker Implementation
For the easiest and most consistent experience across different platforms, you can use Docker:
```bash
# Build the Docker image
docker build -t ddcolor-app .
# Run the container
docker run -p 8501:8501 ddcolor-app
# Access the web application
# Open http://localhost:8501 in your browser
```
### Advantages of using Docker:
- No need to install dependencies manually
- Works consistently across Windows, macOS, and Linux
- Model is downloaded automatically during the build process
- All required libraries are pre-configured
### Requirements:
- Docker installed on your system ([Get Docker](https://docs.docker.com/get-docker/))
- At least 2GB of free disk space
## Usage
### Web Interface (Gradio)
The easiest way to use re-DDColor is through the Gradio web interface:
```bash
python gradio_app.py
```
This will start a local web server with an interactive UI where you can:
- Upload black and white images
- View colorization results with a before/after slider
- Download the colorized images
### REST API (Flask)
For integration with other applications:
```bash
python flaskapi.py
```
The API will be available at `http://localhost:5000/` with the following endpoints:
- `POST /colorize`: Upload an image for colorization
- `GET /models`: List available models
### Command Line
For batch processing or scripting:
```bash
# Colorize a single image
python predict.py --input path/to/image.jpg --output path/to/output.jpg
# Colorize all images in a directory
python predict.py --input path/to/input_dir --output path/to/output_dir
# Specify a different model
python predict.py --input image.jpg --output output.jpg --model ddcolor_artistic.pth
```
### Video Colorization
To colorize videos:
```bash
python video_converter.py --input path/to/video.mp4 --output path/to/colorized_video.mp4
```
## Available Models
re-DDColor includes several pre-trained models as described in [MODEL_ZOO.md](MODEL_ZOO.md):
| Model | Description | Best For |
|-------|-------------|----------|
| `ddcolor_modelscope.pth` (default) | DDColor-L with cleaned data | General use, best quality |
| `ddcolor_paper.pth` | Original paper model | Paper reproduction |
| `ddcolor_artistic.pth` | Trained with artistic images | Creative colorization, fewer artifacts |
| `ddcolor_paper_tiny.pth` | Lightweight model | Resource-constrained devices |
## Technical Details
### Architecture
re-DDColor uses a dual-decoder architecture:
1. **Encoder**: ConvNeXt-based (tiny or large variants)
2. **Decoder**: Two options:
- `MultiScaleColorDecoder`: Transformer-based decoder with multi-scale features
- `SingleColorDecoder`: Simpler decoder for faster inference
3. **Color Processing**: Lab color space for accurate colorization
### System Requirements
- Python 3.7+
- PyTorch 1.7+
- CUDA-compatible GPU (recommended) or Apple Silicon
- 4GB+ VRAM for large models, 2GB+ for tiny models
## Training Your Own Model
To train a custom model:
```bash
# Edit the configuration
# Modify options/train/train_ddcolor.yml with your dataset paths and parameters
# Start training
python basicsr/train.py -opt options/train/train_ddcolor.yml
```
See the training configuration file for details on available options including loss functions, optimizers, and data augmentation.
## Citation
If you use this code in your research, please cite the original DDColor paper:
```bibtex
@inproceedings{kang2023ddcolor,
title={DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders},
author={Kang, Xiaoyang and Yang, Tao and Ouyang, Wenqi and Ren, Peiran and Li, Lingzhi and Xie, Xuansong},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
year={2023}
}
```
## Acknowledgements
This work is a revised version of the original [DDColor](https://github.com/piddnad/DDColor) project by Xiaoyang Kang et al. I thank the original authors for their outstanding contribution to the field of image colorization.
## License
This project is licensed under the terms of the LICENSE file included in the repository.