public List <BoundBox> GetAndBoxes()
{
var result = new List <BoundBox>();
foreach (var obj in Objects)
{
var box = new BoundBox(
obj.Box.Xmin,
obj.Box.Ymin,
obj.Box.Ymax - obj.Box.Ymin,
obj.Box.Xmax - obj.Box.Xmin
);
result.Add(box);
}
return(result);
}
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);
}
public async Task <List <BoundBox> > Predict(Frame frame)
{
var list = new List <BoundBox>();
var status = await _client.GetStatusAsync();
if (status == null || !status.Contains("server is running"))
{
Console.WriteLine("server is not active");
return(list);
}
var jsonImg = new JsonImage();
jsonImg.Load(frame.Patch);
var json = JsonConvert.SerializeObject(jsonImg);
var outputText = await _client.PostAsync(json, "image");
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);
}
public async Task <List <BoundBox> > Predict(Frame frame)
{
var list = new List <BoundBox>();
var status = await _client.GetStatusAsync();
if (status == null || !status.Contains("server is running"))
{
Console.WriteLine("server is not active");
return(list);
}
var jsonImg = new JsonImage();
jsonImg.Load(frame.Patch);
var json = JsonConvert.SerializeObject(jsonImg);
var outputText = await _client.PostAsync(json, "image");
var objects = JsonConvert.DeserializeObject <JsonAnnotation>(outputText);
if (objects != null || objects.Objects.Count > 0)
{
Console.WriteLine("File {0} contains:", Path.GetFileName(frame.Patch));
foreach (var ooj in objects.Objects)
{
var x1 = ooj.Xmin;
var y1 = ooj.Ymin;
var x2 = ooj.Xmax;
var y2 = ooj.Ymax;
var score = ooj.Score;
var label = ooj.Name;
var rect = new BoundBox(
x1,
y1,
y2 - y1,
x2 - x1);
list.Add(rect);
Console.WriteLine("\t{0}: {1:P1}", label, double.Parse(score.Replace('.', ',')));
}
}
return(list);
}