// Calculate the per-pixel accuracy on a dataset whose file names are supplied as a parameter.
private static double CalculateAccuracy(LossMulticlassLogPerPixel anet, IEnumerable <ImageInfo> dataset)
{
var numRight = 0;
var numWrong = 0;
foreach (var imageInfo in dataset)
{
// Load the input image.
using (var inputImage = Dlib.LoadImageAsMatrix <RgbPixel>(imageInfo.ImageFilename))
{
// Load the ground-truth (RGB) labels.;
using (var rgbLabelImage = Dlib.LoadImageAsMatrix <RgbPixel>(imageInfo.ClassLabelFilename))
{
// Create predictions for each pixel. At this point, the type of each prediction
// is an index (a value between 0 and 20). Note that the net may return an image
// that is not exactly the same size as the input.
using (var output = anet.Operator(inputImage))
using (var temp = output.First())
{
// Convert the indexes to RGB values.
using (var indexLabelImage = new Matrix <ushort>())
{
PascalVOC2012.RgbLabelImageToIndexLabelImage(rgbLabelImage, indexLabelImage);
// Crop the net output to be exactly the same size as the input.
using (var chipDims = new ChipDims((uint)inputImage.Rows, (uint)inputImage.Columns))
using (var chipDetails = new ChipDetails(Dlib.CenteredRect(temp.Columns / 2, temp.Rows / 2,
(uint)inputImage.Columns,
(uint)inputImage.Rows),
chipDims))
{
using (var netOutput = Dlib.ExtractImageChip <ushort>(temp, chipDetails, InterpolationTypes.NearestNeighbor))
{
var nr = indexLabelImage.Rows;
var nc = indexLabelImage.Columns;
// Compare the predicted values to the ground-truth values.
for (var r = 0; r < nr; ++r)
{
for (var c = 0; c < nc; ++c)
{
var truth = indexLabelImage[r, c];
if (truth != LossMulticlassLogPerPixel.LabelToIgnore)
{
var prediction = netOutput[r, c];
if (prediction == truth)
{
++numRight;
}
else
{
++numWrong;
}
}
}
}
}
}
}
}
}
}
}
// Return the accuracy estimate.
return(numRight / (double)(numRight + numWrong));
}