Monthly Archives: April 2024

Myriota Connector – Azure IoT Central Downlink Methods

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This post is about Azure IoT Central downlink methods and should be read in conjunction with the Myriota Connector – Azure IoT Central Downlink Methods post. My Myriota Sense and Locate template has 4 commands and in this post, I have focused on the fan speed command.

Sense and Locate Azure IoT Central Template

The Myriota Connector only supports Direct Methods which provide immediate confirmation of the result being queued by the Myriota Cloud API. The Myriota (API) control message send method responds with 400 Bad Request if there is already a message being sent to a device.

Myriota Azure Function Environment Variable configuration

The fan speed downlink payload formatter is specified in the Azure Function Environment Variables.

Sense and Locate Azure IoT Central Template Fan Speed Enumeration

The fan speed value in the message payload is configured in the fan speed enumeration.

Sense and Locate Azure IoT Central Command Fan Speed Selection

The FanSpeed.cs payload formatter extracts the FanSpeed value from the Javascript Object Notation(JSON) payload and returns a two-byte array containing the message type and speed of the fan.

using System;
using System.Collections.Generic;

using Newtonsoft.Json;
using Newtonsoft.Json.Linq;

public class FormatterDownlink : PayloadFormatter.IFormatterDownlink
{
   public byte[] Evaluate(string terminalId, string methodName, JObject payloadJson, byte[] payloadBytes)
   {
      byte? status = payloadJson.Value<byte?>("FanSpeed");

      if (!status.HasValue)
      {
         return new byte[] { };
      }

      return new byte[] { 1, status.Value };
   }
}

Sense and Locate Azure IoT Central Command Fan Speed History

Each Azure Application Insights log entry starts with the TerminalID (to simplify searching for all the messages related to device) and the requestId a Globally Unique Identifier (GUID) to simplify searching for all the “steps” associated with sending/receiving a message) with the rest of the logging message containing “step” specific diagnostic information.

Sense and Locate Azure IoT Central Command Fan Speed Application Insights

In the Myriota Device Manager the status of Control Messages can be tracked and they can be cancelled if in the “pending” state.

Myriota Control Message status Pending

A Control Message can take up to 24hrs to be delivered and confirmation of delivery has to be implemented by the application developer.

YoloV8-Training a model with Ultralytics Hub

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After uploading the roboflow Tennis Ball dataset from my previous post to an Ultralytics Hub dataset. I then used my Ultralytics Pro plan to train a proof of concept(PoC) YoloV8 model.

Creating a new Ultralytics project
Selecting training type the dataset to upload
Checking the Tennis Ball dataset upload
Confirming the number of classes and splits of the training dataset
Selecting the output model architecture (YoloV8s).
Configuring the number of epochs and payment method
Preparing the cloud instance(s) for training
The midpoint of the training process
The training process completed with some basic model metrics.
The resources used and model accuracy metrics.
Model training metrics.
Testing the trained model inference results with my test image.
Exporting the trained YoloV8 model in ONNX format.
The duration and cost of training the model.
Testing the YoloV8 model with the dem-compunet.Image console application
Marked-up image generated by the dem-compunet.Image console application.

In this post I have not covered YoloV8 model selection and tuning of the training configuration to optimise the “performance” of the model. I used the default settings and then ran the model training overnight which cost USD6.77

This post is not about how create a “good” model it is the approach I took to create a “proof of concept” model for a demonstration.

YoloV8-Selecting a roboflow dataset

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To comply with the Ultralytics AGPL-3.0 License and to use an Ultralytics Pro plan the source code and models for an application have to be open source. Rather than publishing my YoloV8 model (which is quite large) this is the first in a series of posts which detail the process I used to create it. (which I think is more useful)

The single test image (not a good idea) is a photograph of 30 tennis balls on my living room floor.

Test image of 30 tennis balls on my living room floor

I stared with the “default” yolov8s.onnx model which is included in the YoloV8 nuget package Github repository YoloV8.Demo application.

YoloV8s.Onnx Tennis ball object detection results

The object detection results using the “default” model were pretty bad, but this wasn’t a surprise as the model is not optimised for this sort of problem.

Roboflow has a suite of tools for annotating, automatic labelling, training and deployment of models as well as a roboflow universe which (according to their website) is “The largest resource of computer vision datasets and pre-trained models”.

roboflow universe open-source model dataset search

I have used datasets from roboflow universe which is a great resource for building “proof of concept” applications.

roboflow universe dataset search

The first step was to identify some datasets which would improve my tennis ball object detection model results. After some searching (with tennis, tennis-ball etc. classes) and filtering (object detection, has a model for faster evaluation, more the 5000 images) to reduce the search results to a manageable number, I identified 5 datasets worth further evaluation.

In my scenario the performance of the Acebot by Mrunal model was worse than the “default” yolov8s model.

In my scenario the performance of the tennis racket by test model was similar to the “default” yolov8s model.

In my scenario the performance of the Tennis Ball by Hust model was a bit better than the “default” yolov8s mode

In my scenario the performance of the roboflow_oball by ahmedelshalkany model was pretty good it detected 28 of the 30 tennis balls.

In my scenario the performance of the Tennis Ball by Ugur Ozdemir model was good it detected all of the 30 tennis balls.

I then exported the Tennis Ball by Ugur Ozdemir dataset in a YoloV8 compatible format so I could use it on the Ultralytics Hub service with my Ultralytics Pro plan to train a model.

This post is not about how create a “good” dataset it is the approach I took to create a “proof of concept” dataset for a demonstration.