public Mat Detect(Mat mat)
{
byte[] array = new byte[_width * _height * mat.ElemSize()];
Marshal.Copy(mat.DataStart, array, 0, array.Length);
using (Image <Bgr, byte> image1 = new Image <Bgr, byte>(_width, _height))
{
image1.Bytes = array;
var frame = image1.Mat;
int cols = 640;
int rows = 480;
_net.SetInput(DnnInvoke.BlobFromImage(frame, 1, new System.Drawing.Size(300, 300), default(MCvScalar), false, false));
using (Emgu.CV.Mat matt = _net.Forward())
{
float[,,,] flt = (float[, , , ])matt.GetData();
for (int x = 0; x < flt.GetLength(2); x++)
{
if (flt[0, 0, x, 2] > _probability)
{
int X1 = Convert.ToInt32(flt[0, 0, x, 3] * cols);
int Y1 = Convert.ToInt32(flt[0, 0, x, 4] * rows);
int X2 = Convert.ToInt32(flt[0, 0, x, 5] * cols);
int Y2 = Convert.ToInt32(flt[0, 0, x, 6] * rows);
mat.Rectangle(new OpenCvSharp.Rect((int)X1, (int)Y1, (int)X2 - (int)X1, (int)Y2 - (int)Y1), Scalar.Red);
}
}
}
}
return(mat);
}
public void Run(cv.Mat src)
{
var array = new byte[src.Width * src.Height * src.ElemSize()];
Marshal.Copy(src.Data, array, 0, array.Length);
using (var image = Dlib.LoadImageData <byte>(array, (uint)src.Height, (uint)src.Width, (uint)(src.Width * src.ElemSize())))
{
Dlib.PyramidUp(image);
rects = detector.Operator(image);
}
}
private Array2D <byte> ConvertMatToDlib2DArray(OpenCvSharp.Mat frame)
{
// https://github.com/takuya-takeuchi/DlibDotNet/issues/213
Array2D <byte> gray = null; // target Dlib array, it's grayscale, 1 byte per pixel
int size = frame.Width * frame.Height * frame.ElemSize();
var array = new byte[size];
Marshal.Copy(frame.Data, array, 0, size);
gray = Dlib.LoadImageData <byte>(array, (uint)frame.Height, (uint)frame.Width, (uint)(frame.Width * frame.ElemSize()));
return(gray);
}
public static void Write(Mat image, WriteableBitmap bmp)
{
if (image.Rows != bmp.PixelHeight || image.Cols != bmp.PixelWidth)
{
Cv2.Resize(image, image, new OpenCvSharp.Size(bmp.PixelWidth, bmp.PixelHeight));
}
var pixels = GetPixels(image);
var stride = image.Cols * image.ElemSize();
bmp.WritePixels(
new Int32Rect(0, 0, bmp.PixelWidth, bmp.PixelHeight),
pixels,
stride,
0);
}
public void Run(cv.Mat src)
{
var array = new byte[src.Width * src.Height * src.ElemSize()];
Marshal.Copy(src.Data, array, 0, array.Length);
using (var image = Dlib.LoadImageData <byte>(array, (uint)src.Height, (uint)src.Width, (uint)(src.Width * src.ElemSize())))
using (var blurredImg = new Array2D <byte>())
using (var horzGradient = new Array2D <short>())
using (var vertGradient = new Array2D <short>())
{
Dlib.GaussianBlur(image, blurredImg); // there appears to be no provision for a kernel size with the DlibDotNet interface...
// Now find the horizontal and vertical gradient images.
Dlib.SobelEdgeDetector(blurredImg, horzGradient, vertGradient);
// now we do the non-maximum edge suppression step so that our edges are nice and thin
Dlib.SuppressNonMaximumEdges(horzGradient, vertGradient, edgeImage);
}
}
public void Run(cv.Mat src)
{
var array = new byte[src.Width * src.Height * src.ElemSize()];
Marshal.Copy(src.Data, array, 0, array.Length);
if (src.Channels() == 1)
{
using (var image = Dlib.LoadImageData <byte>(array, (uint)src.Height, (uint)src.Width, (uint)(src.Width * src.ElemSize())))
{
Dlib.GaussianBlur(image, blurredGray);
}
}
else
{
using (var image = Dlib.LoadImageData <BgrPixel>(array, (uint)src.Height, (uint)src.Width, (uint)(src.Width * src.ElemSize())))
{
Dlib.GaussianBlur(image, blurredRGB);
}
}
}
public static byte[] GetPixels(Mat image)
{
byte[] pixels = new byte[image.Width * image.ElemSize() * image.Height];
Marshal.Copy(image.Data, pixels, 0, pixels.Length);
return pixels;
}