Game Asset Processor

AI-powered desktop tool for removing backgrounds from game asset images. Built with Electron + React + Python (rembg).

Features

• One-click background removal — AI-powered (U²-Net) automatic background removal
• Batch processing — Import and process 100+ images at once
• Real-time preview — Side-by-side comparison with adjustable edge parameters
• Game-ready export — PNG (transparent), WebP, TGA formats with custom naming templates
• Edge optimization — Adjustable threshold and smoothing for clean cutout edges
• Multiple models — Switch between U²-Net, ISNet, Silueta for different use cases

Tech Stack

Layer	Technology
Desktop Shell	Electron
Frontend	React 19 + TypeScript + TailwindCSS 4
State Management	Zustand
Build Tool	Vite 8
Backend	Python + FastAPI
AI Engine	rembg (U²-Net)
Image Processing	Pillow + OpenCV

Project Structure

imageEdit/
├── app/                          # Main application
│   ├── electron/                 # Electron main process
│   │   ├── main.js               # Window management, Python lifecycle
│   │   └── preload.js            # IPC bridge
│   ├── src/                      # React frontend
│   │   ├── api/client.ts         # Backend API client
│   │   ├── store/index.ts        # Zustand state management
│   │   ├── components/           # UI components
│   │   │   ├── DropZone.tsx      # Drag-and-drop import
│   │   │   ├── PreviewCanvas.tsx # Zoomable preview with compare
│   │   │   ├── ControlPanel.tsx  # Parameters & export controls
│   │   │   ├── QueuePanel.tsx    # Batch processing queue
│   │   │   ├── Toolbar.tsx       # Top toolbar
│   │   │   └── SettingsPanel.tsx # Settings modal
│   │   ├── types/index.ts        # TypeScript type definitions
│   │   ├── App.tsx               # Main app layout
│   │   └── main.tsx              # Entry point
│   ├── backend/                  # Python backend
│   │   ├── app/
│   │   │   ├── main.py           # FastAPI application
│   │   │   ├── routers/          # API endpoints
│   │   │   ├── services/         # Business logic
│   │   │   └── models/           # Data models
│   │   ├── tests/                # Backend tests
│   │   └── pyproject.toml        # Python dependencies
│   ├── package.json              # Node.js dependencies
│   └── vite.config.ts            # Vite configuration
└── openspec/                     # OpenSpec change management

Prerequisites

• Node.js >= 18
• Python >= 3.10
• Windows 10/11 (primary target)

Quick Start

1. Install Frontend Dependencies

cd app
npm install

2. Install Python Dependencies

cd app/backend
pip install -e .

3. Start Development

cd app
npm run dev          # Start Vite dev server (frontend only)

In a separate terminal, start the Python backend:

cd app/backend
python -m uvicorn app.main:app --reload --port 8000

4. Run with Electron

cd app
npm run electron:dev  # Starts both Vite and Electron

Usage

1. Import images — Drag and drop images onto the app, or click "Import" (Ctrl+O)
2. Remove background — Select an image and click "Remove Background", or "Process All" for batch
3. Adjust parameters — Use edge threshold and smoothing sliders to refine results
4. Compare results — Switch between Original/Result/Compare views
5. Export — Choose format (PNG/WebP/TGA), set naming template, click "Export"

Keyboard Shortcuts

Shortcut	Action
Ctrl+O	Import images
Ctrl+S	Export selected image
Ctrl+Enter	Process all pending images
Delete	Remove selected image from queue

Building for Production

cd app
npm run build        # Build frontend
npm run electron:build  # Build Electron app (.exe installer)

API Endpoints (Python Backend)

Method	Endpoint	Description
GET	/health	Health check
POST	/api/remove-background	Remove background from single image
GET	/api/models	List available AI models
POST	/api/models/{id}/activate	Switch active model
POST	/api/batch/remove-background	Start batch processing
GET	/api/batch/{id}/status	Get batch progress
GET	/api/batch/{id}/stream	SSE progress stream
POST	/api/batch/{id}/cancel	Cancel batch
GET	/api/batch/{id}/result/{task_id}	Get processed image

License

MIT