106 lines
4.3 KiB
Markdown
106 lines
4.3 KiB
Markdown
# ISO Bot — Isometric Game Bot Engine
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## Overview
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Screen-reading bot engine for isometric games. First implementation: Diablo II: Resurrected.
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**Approach:** Screen capture + computer vision + human-like input simulation. No memory injection, no hooking, no client modification. Engine runs on host, game runs in VM for detection isolation.
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## Repository
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- **Local:** `/home/openclaw/.openclaw/workspace/projects/iso-bot`
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- **Remote:** `ssh://forgejo@git.cloonar.com/openclawd/iso-bot.git`
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## Tech Stack
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- **Engine:** Go 1.23+
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- **Vision:** GoCV (OpenCV bindings for Go)
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- **Screen capture:** Platform-native (Win32 API / X11)
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- **Input simulation:** Platform-native (SendInput / uinput)
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- **API:** net/http + gorilla/websocket (REST + WS)
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- **Dashboard:** React + TypeScript (planned)
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- **Config:** YAML
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- **Loot filter:** Declarative YAML rule engine
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## Architecture
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```
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cmd/iso-bot/ Single binary entry point
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pkg/
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├── engine/
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│ ├── capture/ Screen capture (window, VM, full screen)
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│ ├── vision/ Template matching, color detection (GoCV)
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│ ├── input/ Mouse (Bézier curves), keyboard, humanization
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│ ├── state/ Game state machine with event callbacks
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│ ├── safety/ Session timing, breaks, pattern randomization
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│ ├── navigation/ A* pathfinding, click-to-move
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│ └── loot/ Declarative rule-based loot filter
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├── plugin/ Game plugin interface
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├── api/ REST + WebSocket API
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└── auth/ License/account validation
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plugins/d2r/ D2R game plugin
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web/ React dashboard (planned)
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```
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## Plugin System
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All game logic is behind interfaces in `pkg/plugin/plugin.go`:
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- `Plugin` — main entry point, returns detector/reader/routines
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- `GameDetector` — detect state from screenshots
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- `ScreenReader` — extract items, enemies, text
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- `Routine` — automated farming sequences (context-aware, cancellable)
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- `LootFilter` — item pickup rules
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- `EngineServices` — engine capabilities provided to plugins
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## Development Conventions
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- Go standard project layout
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- Type hints / godoc on all exported types
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- `gofmt` formatting
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- Tests in `*_test.go` files alongside code
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- Feature branches → main
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- Always commit and push after changes
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## Git Workflow
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```bash
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cd /home/openclaw/.openclaw/workspace/projects/iso-bot
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git add -A && git commit -m "descriptive message"
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GIT_SSH_COMMAND="ssh -o StrictHostKeyChecking=no" git push origin main
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```
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## Current Status
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- ✅ Go project structure
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- ✅ Plugin interface system (`pkg/plugin`)
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- ✅ Engine: capture, vision, input/humanize, state, safety, loot filter
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- ✅ API server skeleton (REST endpoints)
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- ✅ D2R plugin: config, detector, reader, Mephisto routine
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- ✅ Declarative loot filter with YAML rules
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- ⏳ Platform-specific capture backends (Win32, X11)
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- ⏳ GoCV integration for actual vision processing
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- ⏳ Platform-specific input backends
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- ⏳ Remaining D2R routines (Pindle, Countess)
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- ⏳ Web dashboard (React)
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- ⏳ Account/license system
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- ⏳ Multi-instance support
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- ⏳ Tests
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## Next Steps (Priority Order)
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1. GoCV integration — make vision pipeline actually work
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2. Platform capture backends — Windows (BitBlt/DXGI) and Linux (X11)
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3. Platform input backends — Windows (SendInput) and Linux (uinput)
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4. D2R detector implementation — health orb reading, menu detection
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5. D2R Mephisto routine — complete implementation
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6. WebSocket real-time status streaming
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7. React dashboard
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8. Pindle + Countess routines
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9. Account system + licensing
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## Key Design Decisions
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1. **Go over Python** — performance for real-time capture+vision at 30+ FPS
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2. **Plugin system** — engine is game-agnostic, new game = new plugin
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3. **VM isolation** — engine on host, game in VM, zero detection surface
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4. **Declarative loot** — YAML rules, user-customizable via web UI
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5. **Single binary** — engine + API in one Go binary, easy distribution
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6. **Human-like input** — Bézier curves, fatigue, breaks, route randomization
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## D2R Notes
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- Target resolution: 1920x1080
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- Primary farming character: Sorceress with Teleport
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- Key routines: Mephisto (moat trick), Pindle (fastest), Countess (runes)
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- Screen regions and HSV colors defined in `plugins/d2r/config.go`
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- The user plays D2R actively and knows the game well
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