Merge pull request #45 from KasunThushara/main

ADD: Thingsboard IIoT car park

Author: KasunThushara

articles/Chapter_6-RaspberryPi_and_AIoT/Car_Park_Solution_Management_with_Thingsboard.md

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+# Car Park Solution Management with ThingsBoard
+
+This project delivers a custom model for detecting parking possibilities in a car park. The system identifies three classes: available slots, parked slots, and improper parking.
+
+The model was trained using the YOLOv8n framework and deployed on a Raspberry Pi with a Hailo-8L accelerator. The modified code enables real-time parking slot counting and sends MQTT messages to the ThingsBoard IoT platform.
+
+ThingsBoard provides an intuitive interface for data visualization and analysis, making it easy to monitor parking conditions and optimize management.
+
+## Step 1: Data Collection and Labeling
+
+We created a small prototype of a car park to demonstrate the working phenomenon. Using a webcam connected to a Raspberry Pi, we collected images of the parking area. These images were then uploaded to a Roboflow repository for further processing.
+
+The images were labeled into three distinct classes:
+
+- Available
+- Parked
+- Improper
+
+
+
+![license](../../pictures/Chapter6/labeling.PNG)
+
+You can view the project here: [Car Park - Roboflow Repository](https://app.roboflow.com/kasun-thushara-fxbng/car-park-cq0uw/1)
+
+## Step 2: Model Training
+
+We trained a YOLOv8n model using a custom dataset. Roboflow provides a downloadable link for the dataset, which can be used to train the model with YOLOv8n.
+
+![license](../../pictures/Chapter6/roboflowapi.PNG)
+
+ After training, the model must be exported to the ONNX format for deployment.
+
+Detailed instructions on model labeling and training can be found in Chapter 5
+
+[How to training the model](https://seeed-projects.github.io/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/docs/Chapter_5-Custom_Model_Development_and_Deployment/Training_Your_Model)
+
+[How to convert ONXX model to HEF](https://seeed-projects.github.io/Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero/docs/Chapter_5-Custom_Model_Development_and_Deployment/Convert_Your_Model)
+
+## Step 3: Deploy the Model
+
+On the Raspberry Pi, after installing Hailo tools, you can test if the detection is working correctly. For MQTT connection, ensure you install the required dependencies in the same virtual environment:
+
+```bash
+sudo apt update
+sudo apt install -y mosquitto mosquitto-clients
+pip install paho-mqtt
+```
+We modified the detection_pipeline.py script and renamed it as park_object_detection.py. Below is the modified code:
+
+```bash
+import gi
+gi.require_version('Gst', '1.0')
+from gi.repository import Gst, GLib
+import os
+import numpy as np
+import cv2
+import hailo
+import paho.mqtt.client as mqtt
+import json
+from hailo_rpi_common import (
+    get_caps_from_pad,
+    get_numpy_from_buffer,
+    app_callback_class,
+)
+from detection_pipeline import GStreamerDetectionApp
+
+# -----------------------------------------------------------------------------------------------
+# User-defined class to be used in the callback function
+# -----------------------------------------------------------------------------------------------
+class user_app_callback_class(app_callback_class):
+    def __init__(self):
+        super().__init__()
+        self.detection_counts = {
+            "available": 0,
+            "parked": 0,
+            "improper": 0
+        }
+
+    def reset_counts(self):
+        self.detection_counts = {key: 0 for key in self.detection_counts}
+
+# -----------------------------------------------------------------------------------------------
+# MQTT Setup
+# -----------------------------------------------------------------------------------------------
+BROKER = "192.168.8.195"  # Replace with your MQTT broker's IP address
+CLIENT_ID = "Car_Park"
+PORT = 1883
+TOPIC = "v1/devices/me/telemetry"  # Adjust to match your topic
+
+mqtt_client = mqtt.Client(CLIENT_ID)
+
+def setup_mqtt():
+    mqtt_client.connect(BROKER, PORT, 60)
+    mqtt_client.loop_start()
+
+def publish_counts(detection_counts):
+    payload = json.dumps(detection_counts)  # Convert counts to JSON format
+    mqtt_client.publish(TOPIC, payload, qos=1)
+    print(f"Published: {payload} to topic {TOPIC}")
+
+# -----------------------------------------------------------------------------------------------
+# User-defined callback function
+# -----------------------------------------------------------------------------------------------
+def app_callback(pad, info, user_data):
+    buffer = info.get_buffer()
+    if buffer is None:
+        return Gst.PadProbeReturn.OK
+
+    user_data.reset_counts()
+    roi = hailo.get_roi_from_buffer(buffer)
+    detections = roi.get_objects_typed(hailo.HAILO_DETECTION)
+
+    for detection in detections:
+        label = detection.get_label()
+        if label in user_data.detection_counts:
+            user_data.detection_counts[label] += 1
+
+    publish_counts(user_data.detection_counts)
+    print(f"Detection Counts: {user_data.detection_counts}")
+
+    return Gst.PadProbeReturn.OK
+
+# -----------------------------------------------------------------------------------------------
+# Main
+# -----------------------------------------------------------------------------------------------
+if __name__ == "__main__":
+    setup_mqtt()
+    user_data = user_app_callback_class()
+    app = GStreamerDetectionApp(app_callback, user_data)
+
+    try:
+        app.run()
+    except KeyboardInterrupt:
+        print("Shutting down...")
+    finally:
+        mqtt_client.loop_stop()
+        mqtt_client.disconnect()
+
+```
+
+### Step 4: Install ThingsBoard and Add a Device
+
+[ThingsBoard](https://thingsboard.io/) is an open-source IoT platform designed for device management, data collection, processing, and visualization. It supports various communication protocols such as **MQTT, CoAP, and HTTP**, making it versatile for integrating IoT devices and systems. The platform enables users to **create interactive dashboards, monitor devices in real-time, and analyze data through advanced visualization tools. With its rule engine, ThingsBoard automates workflows and event processing**, simplifying the implementation of IoT use cases across industries. Its scalability and flexibility make it suitable for projects of any size, from small prototypes to large-scale deployments.
+
+We have provided instructions on [installing the ThingsBoard Community Edition](https://wiki.seeedstudio.com/recomputer_r1000_thingsboard_ce/) and the Edge version. You can refer to this wiki lesson for guidance.
+
+After installing ThingsBoard, you need to add a device. [This lesson](https://wiki.seeedstudio.com/recomputer_r1000_thingsboard_dashboard/) also explains how to create widgets and build dashboards using MQTT credentials. When adding a device, specify the client name in the device configuration and ensure the client name and broker settings in the above code match your ThingsBoard platform configuration.
+
+Since this is a demo, **we have not added a username or password when creating the device**.
+
+```bash
+BROKER = "192.168.8.195"  # Replace with your MQTT broker's IP address
+CLIENT_ID = "Car_Park"     # Replace with your client ID
+```
+
+## Demo 
+
+![demo](../../pictures/Chapter6/demo-thingsboard.gif)
+
+