Claude
c8078d4652
This commit implements a full-stack AI photo editing application that allows users to regenerate only selected areas of images using AI. Features implemented: - Frontend (React + Fabric.js): * Interactive canvas with selection tools (rectangle, ellipse, lasso) * Real-time selection preview and editing * Mode toggle (A: patch only, B: patch + context) * Feather slider for edge blending (0-50px) * Prompt input for AI instructions * Edit history viewer with revert capability * Responsive UI with dark theme - Backend (FastAPI): * RESTful API for projects and edits * SQLite database for metadata storage * Image processing pipeline with PIL/OpenCV * AI provider interface (pluggable) * Support for OpenAI, Stability AI, and mock providers * Feathered alpha blending for smooth compositing * Complete edit history tracking * File-based storage for images and edits - Image Processing: * Patch extraction from bounding boxes * Mask generation for all selection types * Feathered edge blending * Patch compositing back to full image * No pixels modified outside selection * All edits reversible - Infrastructure: * Docker Compose orchestration * Production and development configurations * Nginx reverse proxy for frontend * Hot-reload support for development * Volume persistence for data Architecture follows specification exactly: - Only selected regions are regenerated - Full image pixels preserved outside mask - Two-mode operation (cost vs quality) - Complete edit history and reversibility - Self-hosted with external AI API calls All components are fully functional and ready for deployment.
87 lines
1.7 KiB
Markdown
87 lines
1.7 KiB
Markdown
# Contributing to AI Photo Edit
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Thank you for your interest in contributing to AI Photo Edit!
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## Development Setup
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1. Fork the repository
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2. Clone your fork
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3. Create a feature branch
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4. Make your changes
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5. Test your changes
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6. Submit a pull request
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## Development Environment
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### Using Docker (Recommended)
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```bash
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# Start dev environment with hot-reload
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docker-compose -f docker-compose.dev.yml up
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```
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### Local Development
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**Backend**
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```bash
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cd backend
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python -m venv venv
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source venv/bin/activate # On Windows: venv\Scripts\activate
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pip install -r requirements.txt
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uvicorn app.main:app --reload
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```
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**Frontend**
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```bash
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cd frontend
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npm install
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npm run dev
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```
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## Code Style
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### Python (Backend)
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- Follow PEP 8
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- Use type hints where appropriate
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- Add docstrings to functions and classes
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### JavaScript/React (Frontend)
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- Use functional components with hooks
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- Follow React best practices
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- Use meaningful variable names
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## Pull Request Process
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1. Update the README.md with details of changes if needed
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2. Ensure all tests pass
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3. Update documentation as needed
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4. Get approval from maintainers
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5. Squash commits if requested
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## Reporting Bugs
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When reporting bugs, please include:
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- Description of the issue
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- Steps to reproduce
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- Expected behavior
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- Actual behavior
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- Screenshots if applicable
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- Environment details (OS, Docker version, etc.)
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## Feature Requests
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We welcome feature requests! Please:
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- Check if the feature already exists
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- Explain the use case
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- Describe the expected behavior
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- Consider if it aligns with project goals
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## Code of Conduct
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- Be respectful and inclusive
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- Welcome newcomers
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- Focus on constructive feedback
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- Respect differing opinions
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Thank you for contributing!
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