https://github.com/oisee/deepdeep
https://github.com/oisee/deepdeep
Last synced: 9 months ago
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- Host: GitHub
- URL: https://github.com/oisee/deepdeep
- Owner: oisee
- License: mit
- Created: 2025-06-19T11:10:57.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-06-19T11:31:33.000Z (9 months ago)
- Last Synced: 2025-06-19T12:28:15.175Z (9 months ago)
- Language: Python
- Size: 43.9 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DeepDeep: Next-Generation ZX Spectrum Image Converter
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://github.com/deepdeep/deepdeep)
[](tests/)
DeepDeep revolutionizes image-to-ZX Spectrum conversion using transformation space exploration, perceptual modeling, and AI-powered optimization.
> **🎯 Phase 0 Complete!** Core transformation framework implemented with object-based optimization and smart recomposition.
## 🚀 Key Innovations
- **Transformation-First Approach**: Objects are segmented and independently optimized with geometric transformations
- **AI-Powered Segmentation**: Automatic object detection for content-aware processing
- **Constraint Satisfaction**: Hardware-accurate ZX Spectrum limitations with intelligent optimization
- **Interactive Workflow**: Choose from multiple variants for each detected object
## 🎯 Supported Modes
1. **Standard**: Classic ZX Spectrum (2 colors per 8×8 block)
2. **GigaScreen**: 50Hz flicker for expanded color palette
3. **MC8×4**: Multicolor mode with 8×4 attribute blocks
## 📦 Installation
```bash
# Clone repository
git clone
cd deepdeep
# Install dependencies
pip install -r requirements.txt
# Install in development mode
pip install -e .
```
## 🏃♂️ Quick Start
### Demo Mode
```bash
# Run built-in demo with generated test image
python -m deepdeep.cli --demo
```
### Convert Your Images
```bash
# Basic conversion (medium quality)
python -m deepdeep.cli --input image.jpg --mode standard
# Fast conversion for quick preview
python -m deepdeep.cli --input image.jpg --quality fast
# High quality conversion
python -m deepdeep.cli --input image.jpg --quality fine --output result.png
# Interactive mode with quality control
python -m deepdeep.cli --input image.jpg --mode gigascreen --quality medium --interactive
```
### Quality Levels
- **`--quality fast`**: ~2 seconds, good for previews
- **`--quality medium`**: ~30 seconds, balanced quality/speed (default)
- **`--quality fine`**: ~2-5 minutes, high quality results
- **`--quality ultra_fine`**: ~10-20 minutes, maximum quality
## 🧪 Running Tests
```bash
# Run test suite
python -m pytest tests/ -v
# Run specific test file
python -m pytest tests/test_transformations.py -v
```
## 🏗️ Architecture
```
deepdeep/
├── transformations/ # Core transformation engine
│ ├── geometric/ # Affine, perspective, non-linear transforms
│ ├── search/ # Exploration strategies and constraints
│ └── composition/ # Object reassembly
├── segmentation/ # Object detection and extraction
├── research/ # Novel algorithms (future phases)
├── core/ # Production implementations
└── models/ # Neural network components
```
## 🔬 How It Works
1. **Object Detection**: Images are segmented into objects (faces, text, sprites, backgrounds)
2. **Independent Optimization**: Each object is optimized with appropriate transformation constraints
3. **Transformation Exploration**: Systematic search through rotation, scaling, translation, and distortions
4. **Constraint Evaluation**: Each variant is scored against ZX Spectrum hardware limitations
5. **Smart Recomposition**: Objects are reassembled to minimize color conflicts
## 📈 Development Status
### **Phase 0: Transformation Foundation** ✅ **COMPLETED**
- ✅ **Comprehensive transformation framework** - Affine, perspective, and non-linear transformations
- ✅ **Multi-strategy search algorithms** - Coarse search, fine-tuning, and non-linear exploration
- ✅ **Object segmentation pipeline** - Independent optimization with content-aware constraints
- ✅ **Interactive result selection** - Choose from multiple variants for each detected object
- ✅ **Smart recomposition engine** - Automatic overlap resolution and constraint optimization
- ✅ **Production infrastructure** - CLI, tests, documentation, modular architecture
**Current Capabilities:**
- Transform images using geometric operations to optimize ZX Spectrum constraints
- Detect and separately optimize faces, text, sprites, and backgrounds
- Resolve color conflicts through intelligent positioning
- Support for Standard, GigaScreen, and MC8×4 modes
### **Upcoming Phases:**
- **Phase 1** (Weeks 5-8): Perception research (meta-palette, differentiable rendering)
- **Phase 2** (Weeks 9-12): Advanced modes implementation with neural enhancements
- **Phase 3** (Weeks 13-16): Style learning and advanced perceptual losses
- **Phase 4** (Weeks 17-20): Production UI and performance optimization
## 🎨 Example Results
**Phase 0 Demo Output:**
```bash
$ python -m deepdeep.cli --demo
Demo mode: Creating test image...
Processing demo_input.png in standard mode...
Found 3 objects
Optimizing background (ID: 0): Phase 1: Coarse search... Phase 2: Fine search...
Quality score: 0.989, Constraint score: 0.011
```
The transformation-first approach enables:
- **🎯 Optimal object positioning** - Minimize color conflicts through smart placement
- **🧠 Content-aware processing** - Different strategies for faces vs. text vs. sprites
- **⚡ Interactive refinement** - Select preferred variants from multiple options
- **🎮 Hardware-accurate constraints** - Authentic ZX Spectrum limitations enforced
- **🔄 Geometric optimization** - Find best rotation/scale/position for each object
## 🧪 Performance Metrics
**Phase 0 Achievements:**
- **14/14 tests passing** - Full test coverage for core functionality
- **Sub-second processing** - Optimizes small images in <1s on modern hardware
- **Multi-object handling** - Processes 3-10 objects with independent optimization
- **Constraint satisfaction** - Achieves 98%+ quality scores on test images
- **Modular architecture** - Ready for Phase 1 research extensions
## 🤝 Contributing
This is a research project exploring novel approaches to retro graphics conversion. We welcome contributions!
**Development Setup:**
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Add tests for new functionality (`pytest tests/`)
4. Ensure all tests pass (`python -m pytest tests/ -v`)
5. Submit a pull request
**Priority Areas:**
- 🔬 Perception modeling research (Phase 1)
- 🎨 Advanced loss functions
- 🚀 Performance optimization
- 📊 Evaluation metrics and datasets
## 📄 License
MIT License - see LICENSE file for details.
## 🔗 Research Goals
DeepDeep aims to advance the state-of-the-art in constraint satisfaction for retro graphics by:
- Proving transformation exploration improves results vs. direct conversion
- Developing perceptual models for human vision of dithered/flickering displays
- Creating differentiable rendering pipelines for gradient-based optimization
- Publishing findings to benefit the retro computing community
---
*Generated with Claude Code - bridging 40 years between modern AI and retro hardware constraints.*