public static float IoU(OutputVector v1, OutputVector v2)
{
float x1 = Math.Max(v1.X, v2.X);
float y1 = Math.Max(v1.Y, v2.Y);
float x2 = Math.Min(v1.X + v1.W, v2.X + v2.W);
float y2 = Math.Min(v1.Y + v1.H, v2.Y + v2.H);
float area = Math.Max(0, (x2 - x1) * (y2 - y1));
float iou = area / (v1.W * v1.H + v2.W * v2.H - area);
return(iou);
}
private static Task <IEnumerable <OutputVector> > FilterAsync(float[] output, string key)
{
return(new Task <IEnumerable <OutputVector> >(() =>
{
var result = new List <OutputVector>();
var(anchors, stride, grids) = AnchorsDict[key];
for (int channel = 0; channel < anchors.Length; channel++)
{
for (int row = 0; row < grids; row++)
{
for (int col = 0; col < grids; col++)
{
var start = (grids * grids * channel + grids * row + col) * VECTOR_LENGTH;
var objectConfidence = Functions.Sigmoid(output[start + 4]);
if (objectConfidence < Params.ConfidenceThreshold)
{
continue;
}
var span = new ReadOnlySpan <float>(output, start, VECTOR_LENGTH);
var scores = span.Slice(5, VECTOR_LENGTH - 5).ToArray()
.Select(c => Functions.Sigmoid(c));
var vector = new OutputVector
{
ObjectConfidence = objectConfidence,
X = (Functions.Sigmoid(span[0]) * 2 - 0.5f + col) * stride,
Y = (Functions.Sigmoid(span[1]) * 2 - 0.5f + row) * stride,
W = MathF.Pow(Functions.Sigmoid(span[2]) * 2, 2) * anchors[channel].width,
H = MathF.Pow(Functions.Sigmoid(span[3]) * 2, 2) * anchors[channel].height,
ClassScores = scores.ToArray()
};
if (vector.ClassMaxScore >= Params.ConfidenceThreshold)
{
result.Add(vector);
}
}
}
}
return result;
}));
}
