public void Operator()
{
var image = this.GetDataFile("2008_001322.jpg");
var path = Path.Combine(this.ModelDirectory, "semantic_segmentation_voc2012net.dnn");
using (var loss1 = LossMulticlassLogPerPixel.Deserialize(path))
using (var loss2 = LossMulticlassLogPerPixel.Deserialize(File.ReadAllBytes(path)))
using (var matrix = Dlib.LoadImageAsMatrix <RgbPixel>(image.FullName))
using (var ret1 = loss1.Operator(matrix))
using (var ret2 = loss2.Operator(matrix))
{
Assert.Equal(1, ret1.Count);
Assert.Equal(1, ret2.Count);
var r1 = ret1[0];
var r2 = ret2[0];
Assert.Equal(r1.Rows, r2.Rows);
Assert.Equal(r1.Columns, r2.Columns);
for (var c = 0; c < r1.Columns; c++)
{
for (var r = 0; r < r1.Rows; r++)
{
Assert.Equal(r1[r, c], r2[r, c]);
}
}
}
}
public void Deserialize2()
{
var path = Path.Combine(this.ModelDirectory, "semantic_segmentation_voc2012net.dnn");
using (var loss = LossMulticlassLogPerPixel.Deserialize(File.ReadAllBytes(path)))
Assert.Equal(220, loss.NumLayers);
}
private static void Main(string[] args)
{
if (args.Length != 1)
{
Console.WriteLine("You call this program like this: ");
Console.WriteLine("./dnn_semantic_segmentation_train_ex /path/to/images");
Console.WriteLine();
Console.WriteLine("You will also need a trained 'semantic_segmentation_voc2012net.dnn' file.");
Console.WriteLine("You can either train it yourself (see example program");
Console.WriteLine("dnn_semantic_segmentation_train_ex), or download a");
Console.WriteLine("copy from here: http://dlib.net/files/semantic_segmentation_voc2012net.dnn");
return;
}
try
{
// Read the file containing the trained network from the working directory.
using (var net = LossMulticlassLogPerPixel.Deserialize("semantic_segmentation_voc2012net.dnn"))
{
// Show inference results in a window.
using (var win = new ImageWindow())
{
// Find supported image files.
var files = Directory.GetFiles(args[0])
.Where(s => s.EndsWith(".jpeg") || s.EndsWith(".jpg") || s.EndsWith(".png")).ToArray();
Console.WriteLine($"Found {files.Length} images, processing...");
foreach (var file in files)
{
// Load the input image.
using (var inputImage = Dlib.LoadImageAsMatrix <RgbPixel>(file))
{
// Create predictions for each pixel. At this point, the type of each prediction
// is an index (a value between 0 and 20). Note that the net may return an image
// that is not exactly the same size as the input.
using (var output = net.Operator(inputImage))
using (var temp = output.First())
{
// Crop the returned image to be exactly the same size as the input.
var rect = Rectangle.CenteredRect((int)(temp.Columns / 2d), (int)(temp.Rows / 2d), (uint)inputImage.Columns, (uint)inputImage.Rows);
using (var dims = new ChipDims((uint)inputImage.Rows, (uint)inputImage.Columns))
using (var chipDetails = new ChipDetails(rect, dims))
using (var indexLabelImage = Dlib.ExtractImageChip <ushort>(temp, chipDetails, InterpolationTypes.NearestNeighbor))
{
// Convert the indexes to RGB values.
using (var rgbLabelImage = IndexLabelImageToRgbLabelImage(indexLabelImage))
{
// Show the input image on the left, and the predicted RGB labels on the right.
using (var joinedRow = Dlib.JoinRows(inputImage, rgbLabelImage))
{
win.SetImage(joinedRow);
// Find the most prominent class label from amongst the per-pixel predictions.
var classLabel = GetMostProminentNonBackgroundClassLabel(indexLabelImage);
Console.WriteLine($"{file} : {classLabel} - hit enter to process the next image");
Console.ReadKey();
}
}
}
}
}
}
}
}
}
catch (Exception e)
{
Console.WriteLine(e);
}
}