/// <summary>
/// Returns face detection results.
/// </summary>
/// <param name="image">Bitmap</param>
/// <returns>Rectangles</returns>
public Rectangle[] Forward(Bitmap image)
{
var size = new Size(320, 240);
using var clone = Imaging.Resize(image, size);
int width = clone.Width;
int height = clone.Height;
var inputMeta = _session.InputMetadata;
var name = inputMeta.Keys.ToArray()[0];
// pre-processing
var dimentions = new int[] { 1, 3, height, width };
var tensors = clone.ToFloatTensor(true);
tensors.Operator(new float[] { 127.0f, 127.0f, 127.0f }, Vector.Sub);
tensors.Operator(128, Vector.Div);
var inputData = tensors.Merge(true);
// session run
var t = new DenseTensor <float>(inputData, dimentions);
var inputs = new List <NamedOnnxValue> {
NamedOnnxValue.CreateFromTensor(name, t)
};
var results = _session.Run(inputs).ToArray();
var confidences = results[0].AsTensor <float>().ToArray();
var boxes = results[1].AsTensor <float>().ToArray();
var length = confidences.Length;
// post-proccessing
var boxes_picked = new List <Rectangle>();
for (int i = 0, j = 0; i < length; i += 2, j += 4)
{
if (confidences[i + 1] > ConfidenceThreshold)
{
boxes_picked.Add(
Imaging.ToBox(
Rectangle.FromLTRB
(
(int)(boxes[j + 0] * image.Width),
(int)(boxes[j + 1] * image.Height),
(int)(boxes[j + 2] * image.Width),
(int)(boxes[j + 3] * image.Height)
)));
}
}
// non-max suppression
length = boxes_picked.Count;
for (int i = 0; i < length; i++)
{
var first = boxes_picked[i];
for (int j = i + 1; j < length; j++)
{
var second = boxes_picked[j];
var iou = Imaging.IoU(first, second);
if (iou > NmsThreshold)
{
boxes_picked.RemoveAt(j);
length = boxes_picked.Count;
j--;
}
}
}
// dispose
foreach (var result in results)
{
result.Dispose();
}
return(boxes_picked.ToArray());
}