Random wanderings through Microsoft Azure esp. the IoT bits, AI on Micro controllers, .NET nanoFramework, .NET Core on *nix, and GHI Electronics TinyCLR
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.
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
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 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.
The uses the Microsoft.Extensions.Logging library to publish diagnostic information to the console while debugging the application.
Visual Studio 2022 QuickWatch displaying object detection results.
To check the results I put a breakpoint in the timer just after DetectAsync method is called and then used the Visual Studio 2022 Debugger QuickWatch functionality to inspect the contents of the DetectionResult object.
Visual Studio 2022 JSON Visualiser displaying object detection results.
Security Camera image for object detection photo bombed by Yarnold our Standard Apricot Poodle.
This application can also be deployed as a Linuxsystemd Service so it will start then run in the background. The same approach as the YoloV8.Detect.SecurityCamera.Stream sample is used because the image doesn’t have to be saved on the local filesystem.
The YoloV8.Detect.SecurityCamera.File sample downloads images from the security camera to the local file system, then calls DetectAsync with the local file path.
private static async void ImageUpdateTimerCallback(object state)
{
//...
try
{
Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} YoloV8 Security Camera Image File processing start");
using (Stream cameraStream = await _httpClient.GetStreamAsync(_applicationSettings.CameraUrl))
using (Stream fileStream = System.IO.File.Create(_applicationSettings.ImageFilepath))
{
await cameraStream.CopyToAsync(fileStream);
}
DetectionResult result = await _predictor.DetectAsync(_applicationSettings.ImageFilepath);
Console.WriteLine($"Speed: {result.Speed}");
foreach (var prediction in result.Boxes)
{
Console.WriteLine($" Class {prediction.Class} {(prediction.Confidence * 100.0):f1}% X:{prediction.Bounds.X} Y:{prediction.Bounds.Y} Width:{prediction.Bounds.Width} Height:{prediction.Bounds.Height}");
}
Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} YoloV8 Security Camera Image processing done");
}
catch (Exception ex)
{
Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss} YoloV8 Security camera image download or YoloV8 prediction failed {ex.Message}");
}
//...
}
Console application using camera image saved on filesystem
The ImageSelector parameter of DetectAsync caught my attention as I hadn’t seen this approach use before. The developers who wrote the NuGet package are definitely smarter than me so I figured I might learn something useful digging deeper.
public static DetectionResult Detect(this YoloV8 predictor, ImageSelector selector)
{
predictor.ValidateTask(YoloV8Task.Detect);
return predictor.Run(selector, (outputs, image, timer) =>
{
var output = outputs[0].AsTensor<float>();
var parser = new DetectionOutputParser(predictor.Metadata, predictor.Parameters);
var boxes = parser.Parse(output, image);
var speed = timer.Stop();
return new DetectionResult
{
Boxes = boxes,
Image = image,
Speed = speed,
};
});
public TResult Run<TResult>(ImageSelector selector, PostprocessContext<TResult> postprocess) where TResult : YoloV8Result
{
using var image = selector.Load(true);
var originSize = image.Size;
var timer = new SpeedTimer();
timer.StartPreprocess();
var input = Preprocess(image);
var inputs = MapNamedOnnxValues([input]);
timer.StartInference();
using var outputs = Infer(inputs);
var list = new List<NamedOnnxValue>(outputs);
timer.StartPostprocess();
return postprocess(list, originSize, timer);
}
}
It looks like most of the image loading magic of ImageSelector class is implemented using the SixLabors library…
public class ImageSelector<TPixel> where TPixel : unmanaged, IPixel<TPixel>
{
private readonly Func<Image<TPixel>> _factory;
public ImageSelector(Image image)
{
_factory = image.CloneAs<TPixel>;
}
public ImageSelector(string path)
{
_factory = () => Image.Load<TPixel>(path);
}
public ImageSelector(byte[] data)
{
_factory = () => Image.Load<TPixel>(data);
}
public ImageSelector(Stream stream)
{
_factory = () => Image.Load<TPixel>(stream);
}
internal Image<TPixel> Load(bool autoOrient)
{
var image = _factory();
if (autoOrient)
image.Mutate(x => x.AutoOrient());
return image;
}
public static implicit operator ImageSelector<TPixel>(Image image) => new(image);
public static implicit operator ImageSelector<TPixel>(string path) => new(path);
public static implicit operator ImageSelector<TPixel>(byte[] data) => new(data);
public static implicit operator ImageSelector<TPixel>(Stream stream) => new(stream);
}
Learnt something new must be careful to apply it only where it adds value.
All of the implementations load the model, load the sample image, detect objects in the image, then markup the image with the classification, minimum bounding boxes, and confidences of each object.
Input Image
The first implementation uses YoloV8 by dme-compunet which supports asynchronous operation. The image is loaded asynchronously, the prediction is asynchronous, then marked up and saved asynchronously.
using (var predictor = new Compunet.YoloV8.YoloV8(_applicationSettings.ModelPath))
{
Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model load done");
Console.WriteLine();
using (var image = await SixLabors.ImageSharp.Image.LoadAsync<Rgba32>(_applicationSettings.ImageInputPath))
{
Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model detect start");
var predictions = await predictor.DetectAsync(image);
Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model detect done");
Console.WriteLine();
Console.WriteLine($" Speed: {predictions.Speed}");
foreach (var prediction in predictions.Boxes)
{
Console.WriteLine($" Class {prediction.Class} {(prediction.Confidence * 100.0):f1}% X:{prediction.Bounds.X} Y:{prediction.Bounds.Y} Width:{prediction.Bounds.Width} Height:{prediction.Bounds.Height}");
}
Console.WriteLine();
Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} Plot and save : {_applicationSettings.ImageOutputPath}");
SixLabors.ImageSharp.Image imageOutput = await predictions.PlotImageAsync(image);
await imageOutput.SaveAsJpegAsync(_applicationSettings.ImageOutputPath);
}
}
dme-compunet YoloV8 test application output
The second implementation uses YoloDotNet by NichSwardh which partially supports asynchronous operation. The image is loaded asynchronously, the prediction is synchronous, the markup is synchronous, and then saved asynchronously.
using (var predictor = new Yolo(_applicationSettings.ModelPath, false))
{
Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model load done");
Console.WriteLine();
using (var image = await SixLabors.ImageSharp.Image.LoadAsync<Rgba32>(_applicationSettings.ImageInputPath))
{
Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model detect start");
var predictions = predictor.RunObjectDetection(image);
Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model detect done");
Console.WriteLine();
foreach (var predicition in predictions)
{
Console.WriteLine($" Class {predicition.Label.Name} {(predicition.Confidence * 100.0):f1}% X:{predicition.BoundingBox.Left} Y:{predicition.BoundingBox.Y} Width:{predicition.BoundingBox.Width} Height:{predicition.BoundingBox.Height}");
}
Console.WriteLine();
Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} Plot and save : {_applicationSettings.ImageOutputPath}");
image.Draw(predictions);
await image.SaveAsJpegAsync(_applicationSettings.ImageOutputPath);
}
}
nickswardth YoloDotNet test application output
The third implementation uses YoloV8 by sstainba which partially supports asynchronous operation. The image is loaded asynchronously, the prediction is synchronous, the markup is synchronous, and then saved asynchronously.
using (var predictor = YoloV8Predictor.Create(_applicationSettings.ModelPath))
{
Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model load done");
Console.WriteLine();
using (var image = await SixLabors.ImageSharp.Image.LoadAsync<Rgba32>(_applicationSettings.ImageInputPath))
{
Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model detect start");
var predictions = predictor.Predict(image);
Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model detect done");
Console.WriteLine();
foreach (var prediction in predictions)
{
Console.WriteLine($" Class {prediction.Label.Name} {(prediction.Score * 100.0):f1}% X:{prediction.Rectangle.X} Y:{prediction.Rectangle.Y} Width:{prediction.Rectangle.Width} Height:{prediction.Rectangle.Height}");
}
Console.WriteLine();
Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} Plot and save : {_applicationSettings.ImageOutputPath}");
// This is a bit hacky should be fixed up in future release
Font font = new Font(SystemFonts.Get(_applicationSettings.FontName), _applicationSettings.FontSize);
foreach (var prediction in predictions)
{
var x = (int)Math.Max(prediction.Rectangle.X, 0);
var y = (int)Math.Max(prediction.Rectangle.Y, 0);
var width = (int)Math.Min(image.Width - x, prediction.Rectangle.Width);
var height = (int)Math.Min(image.Height - y, prediction.Rectangle.Height);
//Note that the output is already scaled to the original image height and width.
// Bounding Box Text
string text = $"{prediction.Label.Name} [{prediction.Score}]";
var size = TextMeasurer.MeasureSize(text, new TextOptions(font));
image.Mutate(d => d.Draw(Pens.Solid(Color.Yellow, 2), new Rectangle(x, y, width, height)));
image.Mutate(d => d.DrawText(text, font, Color.Yellow, new Point(x, (int)(y - size.Height - 1))));
}
await image.SaveAsJpegAsync(_applicationSettings.ImageOutputPath);
}
}
sstainba YoloV8 test application output
I don’t understand why the three NuGets produced different results which is worrying.
After several unsuccessful attempts at updating the NuGets packages I started again from scratch
The code wouldn’t compile so I started fixing issues (The first couple of attempts were very “hacky”). The UseDatabaseErrorPage method was from EF Core so it was commented out. The UseBrowserLink method was from the Browser Link support which I decided not to use etc.
...
namespace CustomIdentityProviderSample
{
public class Startup
{
public Startup(IHostingEnvironment env)
{
var builder = new ConfigurationBuilder()
.SetBasePath(env.ContentRootPath)
.AddJsonFile("appsettings.json", optional: false, reloadOnChange: true)
.AddJsonFile($"appsettings.{env.EnvironmentName}.json", optional: true);
if (env.IsDevelopment())
{
// For more details on using the user secret store see https://go.microsoft.com/fwlink/?LinkID=532709
builder.AddUserSecrets<Startup>();
}
builder.AddEnvironmentVariables();
Configuration = builder.Build();
}
public IConfigurationRoot Configuration { get; }
// This method gets called by the runtime. Use this method to add services to the container.
public void ConfigureServices(IServiceCollection services)
{
// Add identity types
services.AddIdentity<ApplicationUser, ApplicationRole>()
.AddDefaultTokenProviders();
// Identity Services
services.AddTransient<IUserStore<ApplicationUser>, CustomUserStore>();
services.AddTransient<IRoleStore<ApplicationRole>, CustomRoleStore>();
string connectionString = Configuration.GetConnectionString("DefaultConnection");
services.AddTransient<SqlConnection>(e => new SqlConnection(connectionString));
services.AddTransient<DapperUsersTable>();
services.AddMvc();
// Add application services.
services.AddTransient<IEmailSender, AuthMessageSender>();
services.AddTransient<ISmsSender, AuthMessageSender>();
}
// This method gets called by the runtime. Use this method to configure the HTTP request pipeline.
public void Configure(IApplicationBuilder app, IHostingEnvironment env, ILoggerFactory loggerFactory)
{
// loggerFactory.AddConsole(Configuration.GetSection("Logging"));
// loggerFactory.AddDebug();
if (env.IsDevelopment())
{
app.UseDeveloperExceptionPage();
// app.UseDatabaseErrorPage(); BHL
// app.UseBrowserLink(); BHL
}
else
{
app.UseExceptionHandler("/Home/Error");
}
app.UseStaticFiles();
app.UseRouting(); // BHL
// app.UseIdentity(); BHL
app.UseAuthentication();
app.UseAuthorization();
// Add external authentication middleware below. To configure them please see https://go.microsoft.com/fwlink/?LinkID=532715
app.UseMvc(routes =>
{
routes.MapRoute(
name: "default",
template: "{controller=Home}/{action=Index}/{id?}");
});
}
}
}
using Microsoft.AspNetCore.Identity;
using System;
using System.Threading.Tasks;
using System.Threading;
using System.Collections.Generic;
namespace CustomIdentityProviderSample.CustomProvider
{
/// <summary>
/// This store is only partially implemented. It supports user creation and find methods.
/// </summary>
public class CustomUserStore : IUserStore<ApplicationUser>,
IUserPasswordStore<ApplicationUser>,
IUserPhoneNumberStore<ApplicationUser>,
IUserTwoFactorStore<ApplicationUser>,
IUserLoginStore<ApplicationUser>
{
private readonly DapperUsersTable _usersTable;
public CustomUserStore(DapperUsersTable usersTable)
{
_usersTable = usersTable;
}
public Task AddLoginAsync(ApplicationUser user, UserLoginInfo login, CancellationToken cancellationToken)
{
throw new NotImplementedException();
}
public async Task<IdentityResult> CreateAsync(ApplicationUser user,
CancellationToken cancellationToken = default(CancellationToken))
{
cancellationToken.ThrowIfCancellationRequested();
if (user == null) throw new ArgumentNullException(nameof(user));
return await _usersTable.CreateAsync(user);
}
public async Task<IdentityResult> DeleteAsync(ApplicationUser user,
CancellationToken cancellationToken = default(CancellationToken))
{
cancellationToken.ThrowIfCancellationRequested();
if (user == null) throw new ArgumentNullException(nameof(user));
return await _usersTable.DeleteAsync(user);
}
public async Task<ApplicationUser> FindByIdAsync(string userId,
CancellationToken cancellationToken = default(CancellationToken))
{
cancellationToken.ThrowIfCancellationRequested();
if (userId == null) throw new ArgumentNullException(nameof(userId));
Guid idGuid;
if(!Guid.TryParse(userId, out idGuid))
{
throw new ArgumentException("Not a valid Guid id", nameof(userId));
}
return await _usersTable.FindByIdAsync(idGuid);
}
public Task<ApplicationUser> FindByLoginAsync(string loginProvider, string providerKey, CancellationToken cancellationToken)
{
throw new NotImplementedException();
}
public async Task<ApplicationUser> FindByNameAsync(string userName,
CancellationToken cancellationToken = default(CancellationToken))
{
cancellationToken.ThrowIfCancellationRequested();
if (userName == null) throw new ArgumentNullException(nameof(userName));
return await _usersTable.FindByNameAsync(userName);
}
public async Task<IList<UserLoginInfo>> GetLoginsAsync(ApplicationUser user, CancellationToken cancellationToken)
{
cancellationToken.ThrowIfCancellationRequested();
if (user == null) throw new ArgumentNullException(nameof(user));
return await _usersTable.GetLoginsAsync(user.Id);
}
...
}
This also required extensions to the DapperUsersTable.cs.
public async Task<IdentityResult> UpdateAsync(ApplicationUser user)
{
string sql = "UPDATE dbo.AspNetUsers " + // BHL
"SET [Id] = @Id, [Email]= @Email, [EmailConfirmed] = @EmailConfirmed, [PasswordHash] = @PasswordHash, [UserName] = @UserName " +
"WHERE Id = @Id;";
int rows = await _connection.ExecuteAsync(sql, new { user.Id, user.Email, user.EmailConfirmed, user.PasswordHash, user.UserName });
if (rows == 1)
{
return IdentityResult.Success;
}
return IdentityResult.Failed(new IdentityError { Description = $"Could not update user {user.Email}." });
}
After many failed attempts my very nasty Custom Storage Provider refresh works (with many warnings and messages). I now understand how they work well enough that I am going to start again from scratch.
This is for people who were searching for why the SAS token issued by the TPM on their Windows 10 IoT Core device is expiring much quicker than expected or might have noticed that something isn’t quite right with the “validity” period. (as at early May 2019). If you want to “follow along at home” the code I used is available on GitHub.
I found the SAS key was expiring in roughly 5 minutes and the validity period in the configuration didn’t appear to have any effect on how long the SAS token was valid.
10:04:16 Application started
...
10:04:27 SAS token needs renewing
10:04:30 SAS token renewed
10:04:30.984 AzureIoTHubClient SendEventAsync starting
10:04:36.709 AzureIoTHubClient SendEventAsync starting
The thread 0x1464 has exited with code 0 (0x0).
10:04:37.808 AzureIoTHubClient SendEventAsync finished
10:04:37.808 AzureIoTHubClient SendEventAsync finished
The thread 0xb88 has exited with code 0 (0x0).
The thread 0x1208 has exited with code 0 (0x0).
The thread 0x448 has exited with code 0 (0x0).
The thread 0x540 has exited with code 0 (0x0).
10:04:46.763 AzureIoTHubClient SendEventAsync starting
10:04:47.051 AzureIoTHubClient SendEventAsync finished
The thread 0x10d8 has exited with code 0 (0x0).
The thread 0x6e0 has exited with code 0 (0x0).
The thread 0xf7c has exited with code 0 (0x0).
10:04:56.808 AzureIoTHubClient SendEventAsync starting
10:04:57.103 AzureIoTHubClient SendEventAsync finished
The thread 0xb8c has exited with code 0 (0x0).
The thread 0xc60 has exited with code 0 (0x0).
10:05:06.784 AzureIoTHubClient SendEventAsync starting
10:05:07.057 AzureIoTHubClient SendEventAsync finished
...
The thread 0x4f4 has exited with code 0 (0x0).
The thread 0xe10 has exited with code 0 (0x0).
The thread 0x3c8 has exited with code 0 (0x0).
10:09:06.773 AzureIoTHubClient SendEventAsync starting
10:09:07.044 AzureIoTHubClient SendEventAsync finished
The thread 0xf70 has exited with code 0 (0x0).
The thread 0x1214 has exited with code 0 (0x0).
10:09:16.819 AzureIoTHubClient SendEventAsync starting
10:09:17.104 AzureIoTHubClient SendEventAsync finished
The thread 0x1358 has exited with code 0 (0x0).
The thread 0x400 has exited with code 0 (0x0).
10:09:26.802 AzureIoTHubClient SendEventAsync starting
10:09:27.064 AzureIoTHubClient SendEventAsync finished
The thread 0x920 has exited with code 0 (0x0).
The thread 0x1684 has exited with code 0 (0x0).
The thread 0x4ec has exited with code 0 (0x0).
10:09:36.759 AzureIoTHubClient SendEventAsync starting
'backgroundTaskHost.exe' (CoreCLR: CoreCLR_UWP_Domain): Loaded 'C:\Data\Programs\WindowsApps\Microsoft.NET.CoreFramework.Debug.2.2_2.2.27505.2_arm__8wekyb3d8bbwe\System.Net.Requests.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
'backgroundTaskHost.exe' (CoreCLR: CoreCLR_UWP_Domain): Loaded 'C:\Data\Programs\WindowsApps\Microsoft.NET.CoreFramework.Debug.2.2_2.2.27505.2_arm__8wekyb3d8bbwe\System.Net.WebSockets.dll'. Skipped loading symbols. Module is optimized and the debugger option 'Just My Code' is enabled.
Sending payload to AzureIoTHub failed:CONNECT failed: RefusedNotAuthorized
I went and looked at the NuGet package details and it seemed a bit old.
I have the RedGate Reflector plugin installed on my development box so I quickly disassembled the Microsoft.Devices.TPM assembly to see what was going on. The Reflector code is pretty readable and it wouldn’t take much “refactoring” to get it looking like “human” generated code.
public string GetSASToken(uint validity = 0xe10)
{
string deviceId = this.GetDeviceId();
string hostName = this.GetHostName();
long num = (DateTime.get_Now().ToUniversalTime().ToFileTime() / 0x98_9680L) - 0x2_b610_9100L;
string str3 = "";
if ((hostName.Length > 0) && (deviceId.Length > 0))
{
object[] objArray1 = new object[] { hostName, "/devices/", deviceId, "\n", (long) num };
byte[] bytes = new UTF8Encoding().GetBytes(string.Concat((object[]) objArray1));
byte[] buffer2 = this.SignHmac(bytes);
if (buffer2.Length != 0)
{
string str5 = this.AzureUrlEncode(Convert.ToBase64String(buffer2));
object[] objArray2 = new object[] { "SharedAccessSignature sr=", hostName, "/devices/", deviceId, "&sig=", str5, "&se=", (long) num };
str3 = string.Concat((object[]) objArray2);
}
}
return str3;
}
The validity parameter appears to not used. Below is the current code from the Azure IoT CSharp SDK on GitHub repository and they are different, the validity is used.
public string GetSASToken(uint validity = 3600)
{
const long WINDOWS_TICKS_PER_SEC = 10000000;
const long EPOCH_DIFFERNECE = 11644473600;
string deviceId = GetDeviceId();
string hostName = GetHostName();
long expirationTime = (DateTime.Now.ToUniversalTime().ToFileTime() / WINDOWS_TICKS_PER_SEC) - EPOCH_DIFFERNECE;
expirationTime += validity;
string sasToken = "";
if ((hostName.Length > 0) && (deviceId.Length > 0))
{
// Encode the message to sign with the TPM
UTF8Encoding utf8 = new UTF8Encoding();
string tokenContent = hostName + "/devices/" + deviceId + "\n" + expirationTime;
Byte[] encodedBytes = utf8.GetBytes(tokenContent);
// Sign the message
Byte[] hmac = SignHmac(encodedBytes);
// if we got a signature foramt it
if (hmac.Length > 0)
{
// Encode the output and assemble the connection string
string hmacString = AzureUrlEncode(System.Convert.ToBase64String(hmac));
sasToken = "SharedAccessSignature sr=" + hostName + "/devices/" + deviceId + "&sig=" + hmacString + "&se=" + expirationTime;
}
}
return sasToken;
}
I went back and look at the Github history and it looks like a patch was applied after the NuGet packages were released in May 2016.
If you read from the TPM and get nothing make sure you’re using the right TPM slot number and have “System Management” checked in the capabilities tab of the application manifest.
I’m still not certain the validity is being applied correctly and will dig into in a future post.
The “add connected service” extension dialog allowed me to select an endpoint
But the code generation process failed
The error message wasn’t particularly helpful so I used the command line utility svcutil to generate client classes. Which I used to built a .net core client with and the associated .Net Core WCF NuGet packages.
The console application failed when I called the service with a “PlatformNotSupportedException”. After some searching I found that the .Net Core WCF libraries don’t support TransportWithMessageCredential (September 2017).
Some more searching lead to a StackOverflow article where an answer suggested using the SimpleSOAPClient NuGet package. I then created a new client using the generated classes as the basis for the ones used in my SimpleSOAPClient proof of concept(PoC)
[System.Diagnostics.DebuggerStepThroughAttribute()]
[System.CodeDom.Compiler.GeneratedCodeAttribute("System.ServiceModel", "4.0.0.0")]
[System.ServiceModel.MessageContractAttribute(WrapperName="Redeem", WrapperNamespace="http://qwertyuiop.com/services2011/08", IsWrapped=true)]
public partial class RedeemRequest
{
[System.ServiceModel.MessageBodyMemberAttribute(Namespace="http://qwertyuiop.com/services2011/08", Order=1)]
public string voucherCode;
[System.ServiceModel.MessageBodyMemberAttribute(Namespace="http://qwertyuiop.com/services2011/08", Order=2)]
public string merchantId;
[System.ServiceModel.MessageBodyMemberAttribute(Namespace="http://qwertyuiop.com/services2011/08", Order=3)]
public string merchantReference;
[System.ServiceModel.MessageBodyMemberAttribute(Namespace="http://qwertyuiop.com/services2011/08", Order=4)]
public string terminalId;
public RedeemRequest()
{
}
public RedeemRequest(string voucherCode, string merchantId, string merchantReference, string terminalId)
{
this.voucherCode = voucherCode;
this.merchantId = merchantId;
this.merchantReference = merchantReference;
this.terminalId = terminalId;
}
}
became
[XmlRoot("Redeem", Namespace = "http://qwertyuiop.com/services2011/08")]
public partial class RedeemRequest
{
[XmlElement("voucherCode")]
public string voucherCode;
[XmlElement("transactionAmount")]
public decimal transactionAmount;
[XmlElement("merchantId")]
public string merchantId;
[XmlElement("merchantReference")]
public string merchantReference;
[XmlElement("terminalId")]
public string terminalId;
}
This client failed with a SOAPAction related exception so I fired up Telerik Fiddler and found that the header was missing. When I manually added the header in the request composer (after dragging one of my failed requests onto the composer tab) it worked.
devMobile's blog 