/// <summary>
/// Converts a Bitmap to an RGB tensor array.
/// </summary>
/// <param name="Data">Bitmap</param>
/// <returns>RGB tensor array</returns>
public static byte[] ToTensor(this Bitmap Data)
{
BitmapData bmData = Onnx.Lock24bpp(Data);
byte[] rgb = Onnx.ToTensor(bmData);
Onnx.Unlock(Data, bmData);
return(rgb);
}
/// <summary>
/// Converts an RGB tensor array to a color image.
/// </summary>
/// <param name="tensor">RGBA tensor array</param>
/// <param name="width">Bitmap width</param>
/// <param name="height">Bitmap height</param>
/// <param name="Data">Bitmap</param>
public static void FromTensor(this byte[] tensor, int width, int height, Bitmap Data)
{
BitmapData bmData = Onnx.Lock24bpp(Data);
FromTensor(tensor, width, height, bmData);
Onnx.Unlock(Data, bmData);
return;
}
private void Form1_Load(object sender, EventArgs e)
{
// params
var threshold = 0.0f;
var c = Color.Yellow;
var font = new Font("Arial", 22);
// inference session
Console.WriteLine("Starting inference session...");
var tic = Environment.TickCount;
var session = new InferenceSession(model);
var inputMeta = session.InputMetadata;
var name = inputMeta.Keys.ToArray()[0];
var labels = File.ReadAllLines(prototxt);
Console.WriteLine("Session started in " + (Environment.TickCount - tic) + " mls.");
// image
Console.WriteLine("Creating image tensor...");
tic = Environment.TickCount;
var image = new Bitmap(file, false);
var width = image.Width;
var height = image.Height;
var dimentions = new int[] { 1, height, width, 3 };
var inputData = Onnx.ToTensor(image);
Console.WriteLine("Tensor was created in " + (Environment.TickCount - tic) + " mls.");
// prediction
Console.WriteLine("Detecting objects...");
tic = Environment.TickCount;
var t1 = new DenseTensor <byte>(inputData, dimentions);
var inputs = new List <NamedOnnxValue>()
{
NamedOnnxValue.CreateFromTensor(name, t1)
};
var results = session.Run(inputs).ToArray();
// dump the results
foreach (var r in results)
{
Console.WriteLine(r.Name + "\n");
Console.WriteLine(r.AsTensor <float>().GetArrayString());
}
Console.WriteLine("Detecting was finished in " + (Environment.TickCount - tic) + " mls.");
// drawing results
Console.WriteLine("Drawing inference results...");
tic = Environment.TickCount;
var detection_boxes = results[0].AsTensor <float>();
var detection_classes = results[1].AsTensor <float>();
var detection_scores = results[2].AsTensor <float>();
var num_detections = results[3].AsTensor <float>()[0];
using (var g = Graphics.FromImage(image))
{
for (int i = 0; i < num_detections; i++)
{
var score = detection_scores[0, i];
if (score > threshold)
{
var label = labels[(int)detection_classes[0, i] - 1];
var x = (int)(detection_boxes[0, i, 0] * height);
var y = (int)(detection_boxes[0, i, 1] * width);
var w = (int)(detection_boxes[0, i, 2] * height);
var h = (int)(detection_boxes[0, i, 3] * width);
// python rectangle
var rectangle = Rectangle.FromLTRB(y, x, h, w);
g.DrawString(label, font, new SolidBrush(c), y, x);
g.DrawRectangle(new Pen(c)
{
Width = 3
}, rectangle);
}
}
}
BackgroundImage = image;
Console.WriteLine("Drawing was finished in " + (Environment.TickCount - tic) + " mls.");
}
