private void Inference(Mat mat)
{
Profiler.Start("InferenceALL");
Profiler.Start("InferenceDecodeImg");
Tensor img = Tools.MatBgr2Tensor(mat, NormalizeMode.None, 224, 224, new long[] { 1, 224, 224, 3 });
Profiler.End("InferenceDecodeImg");
Profiler.Start("InferenceNormalizeImg");
Tensor normalized;
using (Session convert = new Session())
{
TFGraph g = convert.Graph.NativeGraph;
var input = g.Placeholder(TFDataType.Float);
var output = g.Sub(input, g.Const(117.0f));
var normfetch = convert.NativeSession.Run(new[] { input }, new[] { img.NativeTensor }, new[] { output });
normalized = new Tensor(normfetch[0]);
}
Profiler.End("InferenceNormalizeImg");
Profiler.Start("InferenceRun");
Tensor[] fetches = sess.Run(new[] { "output" }, new Dictionary <string, Tensor>()
{
{ "input", normalized }
});
Profiler.End("InferenceRun");
Tensor result = fetches[0];
float[] list = ((float[][])result.GetValue(true))[0];
InferenceResult[] resultList = new InferenceResult[list.Length];
for (int i = 0; i < list.Length; i++)
{
resultList[i] = new InferenceResult(i, list[i]);
}
resultList = resultList.OrderByDescending(x => x.Result).ToArray();
inferences = resultList;
Profiler.End("InferenceALL");
Profiler.Count("InferenceFPS");
foreach (var item in fetches)
{
item.Dispose();
}
normalized.Dispose();
img.Dispose();
mat.Dispose();
}
private void Cap_FrameReady(object sender, FrameArgs e)
{
if (e.LastKey == 'e')
{
e.Break = true;
Core.Cv.CloseAllWindows();
return;
}
var pt1 = LayoutHelper.ResizePoint(new Point(0, 0), new Size(10000, 10000), e.Mat.Size().ToSize(), Stretch.Uniform);
var pt2 = LayoutHelper.ResizePoint(new Point(10000 - 1, 10000 - 1), new Size(10000, 10000), e.Mat.Size().ToSize(), Stretch.Uniform);
var rect = new Rect(pt1, pt2);
using (Mat roi = new Mat(e.Mat, rect.ToCvRect()))
{
Update(roi);
e.Mat.DrawRectangle(rect, Scalar.BgrMagenta);
var roiDrawRect = new Rect(e.Mat.Width - 150 - 50, e.Mat.Height - 150 - 50, 150, 150);
roi.Resize(roiDrawRect.Size);
Core.Cv.DrawMatAlpha(e.Mat, roi, roiDrawRect.Point);
e.Mat.DrawRectangle(roiDrawRect, Scalar.BgrWhite);
if (inferences != null)
{
for (int i = 0; i < 3; i++)
{
InferenceResult r = inferences[i];
e.Mat.DrawText(30, 50 + 40 * i, $"Top {i + 1}: {resultTag[r.Id]} ({(r.Result * 100).ToString("0.00")}%)", Scalar.BgrGreen);
}
}
else
{
e.Mat.DrawText(30, 50, $"Result: Wait for inference...", Scalar.BgrGreen);
}
var fps = Profiler.Get("InferenceFPS");
var time = Profiler.Get("InferenceRun");
e.Mat.DrawText(30, e.Mat.Height - 50, $"Inference FPS: {fps} ({time.ToString("0.0")}ms / RealFPS: {(1000 / time).ToString("0.0")})", Scalar.BgrGreen);
Core.Cv.ImgShow("result", e.Mat);
}
}
