kami-package-detection

name	kami-package-detection
description	A free skill by Kami SmartHome. Get notified the moment a package arrives at your door. Detects packages, parcels, and bags from RTSP camera streams using AI vision.
version	1.0.4
author	kami-smarthome
tags	["smart-home","kami","home-assistant","smarthome","detect","object-detection","yolo","package-detection","parcel-detection","iot","camera","rtsp","onnx","edge-ai","delivery","monitoring","notification"]
triggers	["smart home","kami","home assistant","detect","detect packages","detect parcels","kami package","kami smart home","home assistant package","smart home delivery","check doorstep","delivery notification","check for deliveries","package detection","is there a package","monitor packages","check camera for packages","any deliveries at the door","parcel alert"]
metadata	{"openclaw":{"requires":{"bins":["python3"]},"emoji":"📦"}}

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:

Parameter	Default	Description
--rtsp_url	rtsp://127.0.0.1/live/TNPUSAQ-757597-DRFMY	RTSP camera URL
--conf_threshold	0.25	Confidence threshold (0.0–1.0)
--class_names	parcel package "delivery box" person "Cardboard box" "Packaging Box" backpack handbag suitcase	Classes to detect
--run_time	60	Max seconds; 0 = unlimited

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}
    }
  ]
}

Field	Type	Description
class_name	string	Detected object class
bbox.x1, y1, x2, y2	int	Bounding box coordinates

Exit Codes

Code	Meaning
0	Target detected, JSON output written
1	Error (model missing, RTSP failure, runtime exception)
2	Timeout, no target detected within --run_time

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 open video → Check camera is online and --rtsp_url is correct