public async Task <bool> Load()
{
var status = await _client.GetStatusAsync();
if (status != null && status.Contains("server is running"))
{
return(true);
}
_mlSharpPython = new MlSharpPython(_pythonExecName);
if (await Task.Run(() => _mlSharpPython.Run(_fileNameParameter)))
{
var startTime = DateTime.Now;
TimeSpan waitingTime = new TimeSpan(0, 0, 0, 2);
while (DateTime.Now - startTime < waitingTime)
{
Thread.Sleep(100);
status = await _client.GetStatusAsync();
if (status != null && status.Contains("server is running"))
{
return(true);
}
}
}
return(false);
}
public async Task <List <BoundBox> > Predict(Frame frame)
{
var list = new List <BoundBox>();
// Instantiate Machine Learning C# - Python class object
IMlSharpPython mlSharpPython = new MlSharpPython("python3");
// Test image
string imagePathName = "";
var appPath = System.IO.Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().Location);
var modelPatch = $"{appPath}/python/snapshots/resnet50_liza_alert_v1_interface.h5";
// Define Python script file and input parameter name
string fileNameParameter = $"{appPath}/python/inference.py --model {modelPatch} --image {frame.Patch}";
// Execute the python script file
var outputText = await Task.Run(() => mlSharpPython.ExecutePythonScript(fileNameParameter));
if (!string.IsNullOrEmpty(outputText))
{
foreach (var line in outputText.Split(Environment.NewLine))
{
if (!line.StartsWith("output="))
{
continue;
}
var output = line.Split(' ');
var x1 = int.Parse(output[1]);
var y1 = int.Parse(output[2]);
var x2 = int.Parse(output[3]);
var y2 = int.Parse(output[4]);
var score = output[6];
var label = output[5];
var rect = new BoundBox(
x1,
y1,
y2 - y1,
x2 - x1);
list.Add(rect);
Console.WriteLine($"{label}: {score}");
}
}
return(list);
}