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Saumya Saksena
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Add main reame and quickstart
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QUICKSTART.md

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# 🚀 Quick Start Guide - Tello Vision v2.0
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## TL;DR - Get Running in 5 Minutes
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```bash
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# 1. Extract and enter directory
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tar -xzf tello_vision_v2.tar.gz
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cd tello_vision
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# 2. Run installer (auto-installs everything)
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./install.sh
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# 3. Power on Tello, connect to its WiFi
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# 4. Launch!
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source venv/bin/activate
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python -m tello_vision.app
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```
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## What You Get
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- **Modern instance segmentation** (YOLOv8 or Detectron2)
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- **5-6x faster** than the old code (25-30 FPS on decent GPU)
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- **Modular architecture** - actually maintainable
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- **Config-driven** - no code changes for common stuff
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- **Production ready** - proper error handling, logging, async
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## First Steps
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### 1. Test Detection Without Drone
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Good for verifying everything works:
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```bash
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python examples/test_detector.py --source 0 # Webcam
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```
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### 2. Benchmark Your Setup
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See what FPS you can get:
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```bash
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python examples/benchmark.py
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```
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### 3. Full Drone Mode
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With Tello connected:
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```bash
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python -m tello_vision.app
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```
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Controls:
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- **Tab**: Takeoff
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- **W/A/S/D**: Move
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- **Space/Shift**: Up/Down
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- **Q/E**: Rotate
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- **R**: Record video
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- **Enter**: Take photo
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- **Backspace**: Land
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- **P**: Quit
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## Configuration Tweaks
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Edit `config.yaml`:
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**Want faster FPS?** Use smaller model:
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```yaml
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detector:
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yolov8:
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model: "yolov8n-seg.pt" # n=nano (fastest)
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```
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**Only track people?**
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```yaml
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detector:
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target_classes: ["person"]
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```
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**Adjust visualization:**
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```yaml
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visualization:
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mask_alpha: 0.4 # Mask transparency
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show_confidence: true
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```
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**Performance tuning:**
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```yaml
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processing:
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frame_skip: 1 # Process every 2nd frame (doubles FPS)
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```
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## Project Structure
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```
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tello_vision/
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├── config.yaml ← Edit this for settings
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├── install.sh ← Run first
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├── README.md ← Full documentation
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├── MIGRATION.md ← If migrating from old code
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├── tello_vision/ ← Main package
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│ ├── app.py ← Main application
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│ ├── tello_controller.py ← Drone control
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│ ├── visualizer.py ← Visualization
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│ └── detectors/ ← Detection backends
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└── examples/ ← Usage examples
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├── test_detector.py ← Test without drone
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├── benchmark.py ← Performance tests
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└── object_follower.py ← Autonomous tracking
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```
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## Autonomous Tracking Demo
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Want to make the drone follow you?
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```bash
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python examples/object_follower.py
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# Enter target: person
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# Press TAB to enable auto-follow
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```
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This demonstrates reactive control suitable for autonomous vehicles.
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## Common Issues
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**"No CUDA"** - Will work on CPU, just slower. Install CUDA for speed.
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**"Can't connect to Tello"** - Make sure you're connected to Tello's WiFi (not your home WiFi).
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**"Low FPS"** - Try smaller model (`yolov8n-seg.pt`) or enable `frame_skip`.
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**Import errors** - Run `./install.sh` again with correct backend choice.
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## For Self-Driving Car Work
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This gives you:
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- Real-time object detection pipeline
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- Target tracking framework
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- Reactive control examples
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- Extensible architecture for adding SLAM, planning, etc.
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Check `examples/object_follower.py` for autonomous navigation basics.
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## Next Steps
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1. **Read README.md** - Full documentation
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2. **Try examples/** - Learn the API
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3. **Modify config.yaml** - Tune for your use case
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4. **Extend** - Add your own detectors/controllers
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## Performance Reference
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| GPU | Model | FPS |
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|-----|-------|-----|
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| RTX 3060 | YOLOv8n | 25-30 |
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| RTX 3060 | YOLOv8s | 18-22 |
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| 1050 Ti | YOLOv8n | 18-22 |
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| CPU | YOLOv8n | 2-3 |
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## Files to Know
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- **config.yaml** - All settings
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- **tello_vision/app.py** - Main application
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- **tello_vision/detectors/base_detector.py** - Add custom models here
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- **examples/object_follower.py** - Autonomous control reference
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## Getting Help
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- Check **README.md** for detailed docs
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- See **IMPROVEMENTS.md** for what changed
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- Read **MIGRATION.md** if coming from old code
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- Example code in **examples/** directory
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---

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