// Uses the bounding box dimensions and maps them onto its respective cell within the image.
private CellDimensions MapBoundingBoxToCell(int x, int y, int box, BoundingBoxDimensions boxDimensions)
{
return(new CellDimensions
{
X = ((float)x + Sigmoid(boxDimensions.X)) * CELL_WIDTH,
Y = ((float)y + Sigmoid(boxDimensions.Y)) * CELL_HEIGHT,
Width = (float)Math.Exp(boxDimensions.Width) * CELL_WIDTH * anchors[box * 2],
Height = (float)Math.Exp(boxDimensions.Height) * CELL_HEIGHT * anchors[box * 2 + 1],
});
}
public IList <YoloBoundingBox> ParseOutputs(float[] yoloModelOutputs, float threshold = .3F)
{
var boxes = new List <YoloBoundingBox>();
for (int row = 0; row < ROW_COUNT; row++)
{
for (int column = 0; column < COL_COUNT; column++)
{
for (int box = 0; box < BOXES_PER_CELL; box++)
{
var channel = (box * (CLASS_COUNT + BOX_INFO_FEATURE_COUNT));
BoundingBoxDimensions boundingBoxDimensions = ExtractBoundingBoxDimensions(yoloModelOutputs, row, column, channel);
float confidence = GetConfidence(yoloModelOutputs, row, column, channel);
CellDimensions mappedBoundingBox = MapBoundingBoxToCell(row, column, box, boundingBoxDimensions);
if (confidence < threshold)
{
continue;
}
float[] predictedClasses = ExtractClasses(yoloModelOutputs, row, column, channel);
var(topResultIndex, topResultScore) = GetTopResult(predictedClasses);
var topScore = topResultScore * confidence;
if (topScore < threshold)
{
continue;
}
boxes.Add(new YoloBoundingBox()
{
Dimensions = new BoundingBoxDimensions
{
X = (mappedBoundingBox.X - mappedBoundingBox.Width / 2),
Y = (mappedBoundingBox.Y - mappedBoundingBox.Height / 2),
Width = mappedBoundingBox.Width,
Height = mappedBoundingBox.Height,
},
Confidence = topScore,
Label = labels[topResultIndex],
BoxColor = classColors[topResultIndex]
});
}
}
}
return(boxes);
}