static async Task <int> MainAsync(string[] args)
{
//Log.WriteLine("pause...");
//var x = Console.ReadLine();
Log.WriteLine("Starting async...");
Model model = null;
AzureConnection connection = null;
MediaFrameReader reader = null;
EventWaitHandle evtFrame = null;
await Task.WhenAll(
Task.Run(async() =>
model = await Model.CreateModelAsync(
Directory.GetCurrentDirectory() + "\\resources\\office_fruit_coreml.onnx")),
Task.Run(async() =>
connection = await AzureConnection.CreateAzureConnectionAsync()),
Task.Run(async() => {
(reader, evtFrame) = await GetFrameReaderAsync();
await AsyncHelper.SyncFromAsync(reader.StartAsync(), "reader start");
})
);
Log.WriteLine("Model loaded, Azure Connection created, and FrameReader Started\n\n\n\n");
await CameraProcessingAsync(model, reader, evtFrame, connection);
return(0);
}
public static async Task <AzureConnection> CreateAzureConnectionAsync()
{
var newConnection = new AzureConnection();
await newConnection.AzureConnectionInitAsync();
await newConnection._client.OpenAsync();
Log.WriteLine("Azure connection Creation Complete");
return(newConnection);
}
static async Task CameraProcessingAsync(Model model, MediaFrameReader reader, EventWaitHandle evtframe, AzureConnection azure)
{
var fps_t0 = DateTime.Now;
string prev_label = null;
double fps = 0.0;
for (UInt64 total_frame = 0, current_frame = 0; ; ++total_frame)
{
if (Model.Full)
{
evtframe.WaitOne();
evtframe.Reset();
}
//auto input_feature{ImageFeatureValue::CreateFromVideoFrame(vf.get())};
var frame = reader.TryAcquireLatestFrame();
if (frame == null)
{
// assume 60 fps, wait about half a frame for more input.
// in the unlikely event that eval is faster than capture this should be done differently
Thread.Sleep(10);
continue;
}
++current_frame;
//int oldFrame, double oldFps, DateTime oldFpsT0, string oldLabel)
(current_frame, fps, fps_t0, prev_label) = await FrameProcessingAsync(model, frame, total_frame, current_frame, fps, fps_t0, prev_label, azure);
}
}
static async Task <Tuple <UInt64, double, DateTime, string> > FrameProcessingAsync(Model model, MediaFrameReference frame, UInt64 totalFrame, UInt64 oldFrame, double oldFps, DateTime oldFpsT0, string oldLabel, AzureConnection azure)
{
UInt64 currentFrame = oldFrame;
double fps = oldFps;
DateTime fpsT0 = oldFpsT0;
string prevLabel = oldLabel;
string correlationId = String.Format("{0}", totalFrame);
var r = await model.EvaluateAsync(frame, correlationId);
if (!r._result.Succeeded)
{
Log.WriteLineUp3Home("eval failed {0}", r._result.ErrorStatus);
}
else
{
Log.WriteLineUp3Home("eval succeeded");
}
var fps_interval = DateTime.Now - fpsT0;
if (fps_interval.Seconds > 10)
{
fps = currentFrame * 1.0 / fps_interval.Seconds;
currentFrame = 0;
fpsT0 = DateTime.Now;
}
Log.WriteLineHome("fps {0}", fps.ToString("0.00"));
string label = r._output.classLabel;
var most = r.MostProbable;
if (label != most.Key)
{
throw new ApplicationException(string.Format("Output Feature Inconsistency model output label 0 '{0}' and label 1 '{1}'", label, most.Key));
}
Log.WriteLine("{1}{2} with probability {3}", Log.fmt_output_success, most.Key, Log.fmt_output_default, most.Value.ToString("0.0000"));
// we would like to apply a confidence threshold but with these models garbage always ends up high confidence something
if (prevLabel == null || prevLabel != label)
{
prevLabel = label;
azure.UpdateObject(label);
}
model.Clear(correlationId);
return(new Tuple <UInt64, double, DateTime, string>(currentFrame, fps, fpsT0, prevLabel));
}
