Kami Package Detection

Get notified the moment a package arrives at your door.

Monitor your camera feed for packages, parcels, backpacks, handbags, and suitcases. When detected, returns the object class and bounding box as JSON — ready for automation.

Features

• 📦 Package & parcel detection
• 🧳 Suitcase / backpack / handbag recognition
• 🏠 Doorstep & reception monitoring
• ⏱ Configurable detection duration
• 🔔 JSON output for easy integration

Scenarios

• Doorstep delivery waiting
• Office reception package management
• Warehouse cargo monitoring
• Temporary item watch

Installation

bash setup.sh

Creates .venv/ and installs onnxruntime, opencv-python-headless, numpy. Idempotent.

Prerequisites

• python3 and python3-venv installed
• yolov8s-worldv2.onnx model file in the skill directory
• RTSP camera online and reachable

Model

The yolov8s-worldv2.onnx model file is included in the skill package. If missing, re-download this skill from ClawHub:

clawhub install kami-package-detection

Alternatively, download the YOLOv8s-World v2 .pt model from Ultralytics YOLO-World and export it to ONNX yourself. Make sure the class names used during export match the --class_names parameter:

pip install ultralytics
yolo export model=yolov8s-worldv2.pt format=onnx imgsz=320
cp yolov8s-worldv2.onnx /path/to/kami-package-detection/

Parameter Confirmation

Before running, confirm these parameters with the user:

Ask the user: do any parameters need to be changed?

Usage

.venv/bin/python yolo_world_onnx.py \
  --rtsp_url rtsp://your-camera-address \
  --run_time 60

Output (stdout JSON)

{
  "detections": [
    {
      "class_name": "parcel",
      "bbox": {"x1": 100, "y1": 200, "x2": 300, "y2": 400}
    }
  ]
}

Exit Codes

Troubleshooting

• bash: .venv/bin/python: No such file or directory → Run bash setup.sh
• Model file not found → Place yolov8s-worldv2.onnx in the skill directory
• `Cannot...