yolo-detection-2026-coral-tpu-macos

Files (53)

• .gitignore
• .travis.yml
• CODE_OF_CONDUCT.md
• CONTRIBUTING.md
• Contributions.md
• LICENSE
• README.md
• SKILL.md
• _config.yml
• config.yaml
• deploy-linux.sh
• deploy-macos.sh
• deploy.sh
• docker-compose.yml
• Dockerfile · docker/
• README.md · docker/
• compile.sh · docker/
• docker-compose.yml · docker/
• docker_out.log
• UsbDk_1.0.22_x64.msi · driver/
• edgetpu.dll · driver/
• libusb-1.0.dll · driver/
• 60-libedgetpu1-max.rules · ext/lib/udev/rules.d/
• libedgetpu.so.1 · ext/usr/lib/x86_64-linux-gnu/
• libedgetpu.so.1.0 · ext/usr/lib/x86_64-linux-gnu/
• changelog.gz · ext/usr/share/doc/libedgetpu1-max/
• copyright · ext/usr/share/doc/libedgetpu1-max/
• libedgetpu1-max · ext/usr/share/lintian/overrides/
• input.json
• edgetpu.dll · lib/
• libedgetpu.so.1 · lib/
• libedgetpu1-max_16.0_amd64_0ac21f1924dd4b125d5cfc5f6d0e4a5e.deb · lib/
• libusb-1.0.dll · lib/
• 60-libedgetpu1-max.rules · lib/local_deb/lib/udev/rules.d/
• libedgetpu.so.1 · lib/local_deb/usr/lib/x86_64-linux-gnu/
• libedgetpu.so.1.0 · lib/local_deb/usr/lib/x86_64-linux-gnu/
• changelog.gz · lib/local_deb/usr/share/doc/libedgetpu1-max/
• copyright · lib/local_deb/usr/share/doc/libedgetpu1-max/
• libedgetpu1-max · lib/local_deb/usr/share/lintian/overrides/
• libedgetpu.deb
• libedgetpu.so.1
• README.md · models/
• output.txt
• requirements.txt
• compile_model.py · scripts/
• compile_model_colab.py · scripts/
• detect.py · scripts/
• detect.py.bak · scripts/
• install_pycoral.py · scripts/
• tpu_probe.py · scripts/
• skills.json
• test2.jpg
• wsl_test.py

SKILL.md (first lines)

# Coral TPU Object Detection

Real-time object detection natively utilizing the Google Coral Edge TPU accelerator on your local hardware. Detects 80 COCO classes (person, car, dog, cat, etc.) with ~4ms inference on 320x320 input.

## Requirements

- Python 3.9–3.13

## How It Works

```
┌─────────────────────────────────────────────────────┐
│ Host (Aegis-AI)                                     │
│   frame.jpg → /tmp/aegis_detection/                 │
│   stdin  ──→ ┌──────────────────────────────┐       │
│              │ Native Python Environment     │       │
│              │   detect.py                   │       │
│              │   ├─ loads _edgetpu.tflite     │       │
│              │   ├─ reads frame from disk     │       │
│              │   └─ runs inference on TPU    │       │
│   stdout ←── │   → JSONL detections          │       │
│              └──────────────────────────────┘       │
│   USB ──→ Native System USB / edgetpu drivers       │
└─────────────────────────────────────────────────────┘
```

1. Aegis writes camera frame JPEG to shared `/tmp/aegis_detection/` workspace
2. Sends `frame` event via stdin JSONL to the local Python instance
3. `detect.py` invokes PyCoral and executes natively on the mapped USB Edge TPU
4. Returns `detections` event via stdout JSONL

## Platform Setup

### Linux
```bash
# Uses the official apt-get google-coral packages natively
./deploy.sh
```

### macOS 
```bash
# Downloads and installs the libedgetpu OS payload framework inline
./deploy.sh
```

> **Important Deployment Notice**: The updated `deploy.sh` script will natively halt execution and prompt you securely for your OS `sudo` password to securely register the USB drivers (`libedgetpu`) system-wide. If you refuse the prompt, it gracefully outputs the exact terminal instructions for you to configure it manually.

## Performance

| Input Size | Inference | On-chip | Notes |
|-----------|-----------|---------|-------|
| 320x320 | ~4ms | 100% | Fully on TPU, best for real-time |
| 640x640 | ~20ms | Partial | Some layers on CPU (model segmented) |

> **Cooling**: The USB Accelerator aluminum case acts as a heatsink. If too hot to touch during continuous inference, it will thermal-throttle. Consider active cooling or `clock_speed: standard`.

## Protocol

Same JSONL as `yolo-detection-2026`:

### Skill → Aegis (stdout)
```jsonl
{"event": "ready", "model": "yolo26n_edgetpu", "device": "coral", "format": "edgetpu_tflite", "tpu_count": 1, "classes": 80}
{"event": "detections", "frame_id": 42, "camera_id": "front_door", "objects": [{"class": "person", "confidence": 0.85, "bbox": [100, 50, 300, 400]}]}
{"event": "perf_stats", "total_frames": 50, "timings_ms": {"inference": {"avg": 4.1, "p50": 3.9, "p95": 5.2}}}
```

### Bounding Box Format
`[x_min, y_min, x_max, y_max]` — pixel coordinates (xyxy).

## Installation

### Linux / macOS
```bash
./deploy.sh
```

The deployer builds the local Python virtual environment and installs the Edge TPU runtime. No Docker required.

From SharpAI/DeepCamera, MIT.

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