private static int Main(string[] args)
{
if (args.Length != 1)
{
Console.WriteLine($"Usage: {nameof(PeleeNetSSDSeg)} [imagepath]");
return(-1);
}
var imagepath = args[0];
using (var m = Cv2.ImRead(imagepath, CvLoadImage.Grayscale))
{
if (m.IsEmpty)
{
Console.WriteLine($"cv::imread {imagepath} failed");
return(-1);
}
if (Ncnn.IsSupportVulkan)
{
Ncnn.CreateGpuInstance();
}
var objects = new List <Object>();
using var segOut = new NcnnDotNet.Mat();
DetectPeleeNet(m, objects, segOut);
if (Ncnn.IsSupportVulkan)
{
Ncnn.DestroyGpuInstance();
}
DrawObjects(m, objects, segOut);
}
return(0);
}
private static int DetectPeleeNet(NcnnDotNet.OpenCV.Mat bgr, List <Object> objects, NcnnDotNet.Mat resized)
{
using (var peleenet = new Net())
{
if (Ncnn.IsSupportVulkan)
{
peleenet.Opt.UseVulkanCompute = true;
}
// model is converted from https://github.com/eric612/MobileNet-YOLO
// and can be downloaded from https://drive.google.com/open?id=1Wt6jKv13sBRMHgrGAJYlOlRF-o80pC0g
// the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models
peleenet.LoadParam("pelee.param");
peleenet.LoadModel("pelee.bin");
const int targetSize = 300;
var imgW = bgr.Cols;
var imgH = bgr.Rows;
using var @in = Mat.FromPixelsResize(bgr.Data, PixelType.Bgr, bgr.Cols, bgr.Rows, targetSize, targetSize);
var meanVals = new[] { 103.9f, 116.7f, 123.6f };
var normVals = new[] { 0.017f, 0.017f, 0.017f };
@in.SubstractMeanNormalize(meanVals, normVals);
using var ex = peleenet.CreateExtractor();
//ex.SetNumThreads(4);
ex.Input("data", @in);
using var @out = new Mat();
ex.Extract("detection_out", @out);
// printf("%d %d %d\n", out.w, out.h, out.c);
objects.Clear();
for (var i = 0; i < @out.H; i++)
{
var values = @out.Row(i);
var @object = new Object();
@object.Label = (int)values[0];
@object.Prob = values[1];
@object.Rect.X = values[2] * imgW;
@object.Rect.Y = values[3] * imgH;
@object.Rect.Width = values[4] * imgW - @object.Rect.X;
@object.Rect.Height = values[5] * imgH - @object.Rect.Y;
objects.Add(@object);
}
using var segOut = new Mat();
ex.Extract("sigmoid", segOut);
Ncnn.ResizeBilinear(segOut, resized, imgW, imgH);
}
return(0);
}
private static void DrawObjects(NcnnDotNet.OpenCV.Mat bgr, List <Object> objects, NcnnDotNet.Mat map)
{
string[] classNames =
{
"background",
"person",
"rider",
"car",
"bus",
"truck",
"bike",
"motor",
"traffic light",
"traffic sign",
"train"
};
using var image = bgr.Clone();
var color = new[] { 128, 255, 128, 244, 35, 232 };
var colorCount = color.Length;
for (var i = 0; i < objects.Count; i++)
{
var obj = objects[i];
Console.WriteLine($"{obj.Label} = {obj.Prob:f5} at {obj.Rect.X:f2} {obj.Rect.Y:f2} {obj.Rect.Width:f2} {obj.Rect.Height:f2}");
Cv2.Rectangle(image, obj.Rect, new Scalar <double>(255, 0, 0));
var text = $"{classNames[obj.Label]} {(obj.Prob * 100):f1}";
var baseLine = 0;
var labelSize = Cv2.GetTextSize(text, CvHersheyFonts.HersheySimplex, 0.5, 1, ref baseLine);
var x = (int)obj.Rect.X;
var y = (int)(obj.Rect.Y - labelSize.Height - baseLine);
if (y < 0)
{
y = 0;
}
if (x + labelSize.Width > image.Cols)
{
x = image.Cols - labelSize.Width;
}
Cv2.Rectangle(image, new Rect <int>(new Point <int>(x, y),
new Size <int>(labelSize.Width, labelSize.Height + baseLine)),
new Scalar <double>(255, 255, 255), -1);
Cv2.PutText(image, text, new Point <int>(x, y + labelSize.Height),
CvHersheyFonts.HersheySimplex, 0.5, new Scalar <double>(0, 0, 0));
}
unsafe
{
var width = map.W;
var height = map.H;
var size = map.C;
var imgIndex2 = 0;
var threshold = 0.45f;
var ptr2 = (float *)map.Data;
for (var i = 0; i < height; i++)
{
var ptr1 = (byte *)image.Ptr(i);
var imgIndex1 = 0;
for (var j = 0; j < width; j++)
{
var maxImage = threshold;
var index = -1;
for (var c = 0; c < size; c++)
{
//const float* ptr3 = map.channel(c);
var ptr3 = ptr2 + c * width * height;
if (ptr3[imgIndex2] > maxImage)
{
maxImage = ptr3[imgIndex2];
index = c;
}
}
if (index > -1)
{
var colorIndex = index * 3;
if (colorIndex < colorCount)
{
var b = color[colorIndex];
var g = color[colorIndex + 1];
var r = color[colorIndex + 2];
ptr1[imgIndex1] = (byte)(b / 2 + ptr1[imgIndex1] / 2);
ptr1[imgIndex1 + 1] = (byte)(g / 2 + ptr1[imgIndex1 + 1] / 2);
ptr1[imgIndex1 + 2] = (byte)(r / 2 + ptr1[imgIndex1 + 2] / 2);
}
}
imgIndex1 += 3;
imgIndex2++;
}
}
}
Cv2.ImShow("image", image);
Cv2.WaitKey(0);
}
