private void Form1_Load(object sender, EventArgs e)
{
saveFileDialog.FilterIndex = 2;
saveFileDialog.RestoreDirectory = true;
saveFileDialog.Filter = "BMP|*.bmp|" +
"JPEG|*.jpg; *.jpeg|" +
"PNG|*.png|" +
"GIF|*.gif|" +
"TIFF|*.tiff";
deepPersonLab = new DeepPersonLab(@"..\..\..\deeplabv3_mnv2_pascal_train_aug_2018_01_29\deeplabv3_mnv2_pascal_train_aug.onnx");
portraitModeFilter = new PortraitModeFilter(0.0);
}
/// <summary>
/// Returns segmentation mask.
/// </summary>
/// <param name="image">Input image</param>
/// <returns>Segmentation mask</returns>
public Bitmap Fit(Bitmap image)
{
// scaling image
var width = image.Width;
var height = image.Height;
var ratio = 1.0f * _size / Math.Max(width, height);
var size = new Size(
(int)(ratio * width),
(int)(ratio * height));
var resized = new Bitmap(image, size);
// creating tensor
Console.WriteLine("Creating image tensor...");
var tic = Environment.TickCount;
var inputMeta = _session.InputMetadata;
var name = inputMeta.Keys.ToArray()[0];
var dimentions = new int[] { 1, size.Height, size.Width, 3 };
var inputData = Onnx.ToTensor(resized);
resized.Dispose();
Console.WriteLine($"Tensor was created in {Environment.TickCount - tic} mls.");
// prediction
Console.WriteLine("Creating segmentation mask...");
tic = Environment.TickCount;
var t1 = new DenseTensor <byte>(inputData, dimentions);
var inputs = new List <NamedOnnxValue>()
{
NamedOnnxValue.CreateFromTensor(name, t1)
};
var results = _session.Run(inputs).ToArray();
var map = results[0].AsTensor <long>().ToArray();
var mask = DeepPersonLab.FromSegmentationMap(map, size.Width, size.Height);
Console.WriteLine($"Segmentation was created in {Environment.TickCount - tic} mls.");
// return mask
return(new Bitmap(mask, width, height));
}