public ExecuteProcessing(ILogger <ExecuteProcessing> logger, IProcessImage processImage, IPersistData persistData,
IHashService hashService)
{
_Logger = logger;
_ProcessImage = processImage;
_PersistData = persistData;
_HashService = hashService;
}
/// <summary>
/// Process the image. The image is given to the model ML and if a
/// valid recognition is found then the image is uploaded to storage.
/// Processing times are tracked.
/// </summary>
/// <param name="body">
/// An ImageBody object containing the image from the camera.
/// </param>
public static void Process(BlobStorageHelper blobHelper, ModuleClient client, IProcessImage imageProc, ImageBody body)
{
ImageProcessor proc = new ImageProcessor(blobHelper, client, imageProc.ProcessorType, body);
// Perform and measure elapsed time for the ML model work
DateTime startTime = DateTime.Now;
proc.features = imageProc.Process(body.SmallImage);
proc.recognitionDuration = DateTime.Now - startTime;
// Loop to the next recognition task without waiting for the report to process
if (proc.features != null)
{
Task reportTask = new Task(() => proc.Report());
reportTask.Start();
}
}
static Task OnDesiredPropertiesUpdate(TwinCollection desiredProperties, object userContext)
{
Console.WriteLine($"OnDesiredPropertiesUpdate() called on thread {Thread.CurrentThread.ManagedThreadId}");
lock (s_twinUpdateLocker)
{
try
{
Console.WriteLine("Desired property change:");
Console.WriteLine(JsonConvert.SerializeObject(desiredProperties));
string fpgaRefLevelRGB = "104, 117, 123";
string mlModelType = CpuModel.CpuModelProcessorType;
if (desiredProperties.Contains("mlModelType"))
{
mlModelType = desiredProperties["mlModelType"];
}
if (desiredProperties.Contains("FPGA"))
{
JObject fpgaProps = desiredProperties["FPGA"];
if (fpgaProps["rgbRefLevel"] != null)
{
fpgaRefLevelRGB = (string)fpgaProps["rgbRefLevel"];
}
}
Console.WriteLine($"Model type: {mlModelType}");
// Default to "CPU" on malformed input
switch (mlModelType)
{
// Don't change valid non-"CPU" inputs
case GpuModel.GpuModelProcessorType:
case FpgaModel.FpgaModelProcessorType:
case CpuModel.CpuModelProcessorType:
break;
default:
Console.WriteLine($"Malformed mlModelType: {mlModelType} -- defaulting to 'CPU'");
mlModelType = CpuModel.CpuModelProcessorType;
break;
}
if (mlModelType != s_currentModel.ProcessorType)
{
try
{
s_currentModel.Disconnect();
}
catch (Exception ex)
{
Console.WriteLine($"Error disconnecting {s_currentModel.ProcessorType} model: {ex.Message}");
}
Console.WriteLine($"Switching to {mlModelType} model.");
switch (mlModelType)
{
// Don't change valid non-"CPU" inputs
case GpuModel.GpuModelProcessorType:
s_currentModel = new GpuModel();
break;
case FpgaModel.FpgaModelProcessorType:
s_currentModel = new FpgaModel("voiddetectionbrainwave", 50051, fpgaRefLevelRGB);
break;
case CpuModel.CpuModelProcessorType:
default:
s_currentModel = new CpuModel();
break;
}
}
if (desiredProperties.Contains("blobStorageSasUrl"))
{
string sasUrl = desiredProperties["blobStorageSasUrl"];
// TODO: it would be nice to be able to compare the URL to the one
// in the current s_blobHelper (if any).
Console.WriteLine("Creating new BlobStorageHelper");
s_blobHelper = new BlobStorageHelper("still-images", sasUrl);
}
}
catch (AggregateException ex)
{
foreach (Exception exception in ex.InnerExceptions)
{
Console.WriteLine();
Console.WriteLine("Error when receiving desired property: {0}", exception);
}
}
catch (Exception ex)
{
Console.WriteLine();
Console.WriteLine("Error when receiving desired property: {0}", ex.Message);
}
}
return(Task.CompletedTask);
}
/// <summary>
/// Process the image. The image is given to the model ML and if a
/// valid recognition is found then the image is uploaded to storage.
/// Processing times are tracked.
/// </summary>
/// <param name="body">
/// An ImageBody object containing the image from the camera.
/// </param>
public static void Process(BlobStorageHelper blobHelper, ModuleClient client, IProcessImage imageProc, ImageBody body)
{
ImageProcessor proc = new ImageProcessor(blobHelper, client, imageProc.ProcessorType, body);
if (!body.SkipMlProcessing)
{
// Perform and measure elapsed time for the ML model work
DateTime startTime = DateTime.Now;
proc.features = imageProc.Process(body.SmallImageRGB);
proc.recognitionDuration = DateTime.Now - startTime;
// Loop to the next recognition task without waiting for the report to process
if (proc.features != null)
{
Task reportTask = new Task(() => proc.Report());
reportTask.Start();
}
}
else
{
// Don't process ML or send any messages; just upload to BLOB
try
{
proc.UploadToBlob();
}
catch (Exception ex)
{
Console.WriteLine($" Failed to upload image to BLOB store: {ex.Message}");
}
}
}
public Logic(IProcessImage processor)
{
_processor = processor;
}
