// make a thumbnail of a sample
public static OpenCvSharp.Mat makeThumbnail(
RawSample sample,
string name = "")
{
int thumbnail_size = Worker.thumbnail_size;
// buffer for the cutted image
MemoryStream stream = new MemoryStream();
// make a cut in bmp format
// so we don't waste time for encode/decode image
// just copying it few times, which is irrelevant
sample.cutFaceImage(
stream,
RawSample.ImageFormat.IMAGE_FORMAT_BMP,
RawSample.FaceCutType.FACE_CUT_BASE);
OpenCvSharp.Mat temp = OpenCvSharp.Mat.ImDecode(stream.ToArray(), OpenCvSharp.ImreadModes.Color);
// so we got an image
// check it
MAssert.Check(!temp.Empty());
MAssert.Check(temp.Type() == OpenCvSharp.MatType.CV_8UC3);
// and resize to the thumbnail_size
OpenCvSharp.Rect resRect;
if (temp.Rows >= temp.Cols)
{
resRect.Height = thumbnail_size;
resRect.Width = temp.Cols * thumbnail_size / temp.Rows;
}
else
{
resRect.Width = thumbnail_size;
resRect.Height = temp.Rows * thumbnail_size / temp.Cols;
}
resRect.X = (thumbnail_size - resRect.Width) / 2;
resRect.Y = (thumbnail_size - resRect.Height) / 2;
OpenCvSharp.Mat result = new OpenCvSharp.Mat(
thumbnail_size,
thumbnail_size,
OpenCvSharp.MatType.CV_8UC3,
OpenCvSharp.Scalar.All(0));
OpenCvSharp.Cv2.Resize(
temp,
result[resRect],
resRect.Size);
if (!string.IsNullOrEmpty(name))
{
result[new OpenCvSharp.Rect(0, result.Rows - 27, result.Cols, 27)] = result.RowRange(result.Rows - 27, result.Rows) * 0.5f;
OpenCvSharp.Cv2.PutText(
result,
name,
new OpenCvSharp.Point(0, result.Rows - 7),
OpenCvSharp.HersheyFonts.HersheyDuplex,
0.7,
OpenCvSharp.Scalar.All(255),
1,
OpenCvSharp.LineTypes.AntiAlias);
}
return(result);
}
/// <summary>
/// Classical Multidimensional Scaling
/// </summary>
public void Run()
{
// creates distance matrix
int size = CityDistance.GetLength(0);
Mat t = new Mat(size, size, MatType.CV_64FC1, CityDistance);
// adds Torgerson's additive constant to t
double torgarson = Torgerson(t);
t += torgarson;
// squares all elements of t
t = t.Mul(t);
// centering matrix G
Mat g = CenteringMatrix(size);
// calculates inner product matrix B
Mat b = g * t * g.T() * -0.5;
// calculates eigenvalues and eigenvectors of B
Mat values = new Mat();
Mat vectors = new Mat();
Cv2.Eigen(b, values, vectors);
for (int r = 0; r < values.Rows; r++)
{
if (values.Get<double>(r) < 0)
values.Set<double>(r, 0);
}
//Console.WriteLine(values.Dump());
// multiplies sqrt(eigenvalue) by eigenvector
Mat result = vectors.RowRange(0, 2);
{
var at = result.GetGenericIndexer<double>();
for (int r = 0; r < result.Rows; r++)
{
for (int c = 0; c < result.Cols; c++)
{
at[r, c] *= Math.Sqrt(values.Get<double>(r));
}
}
}
// scaling
Cv2.Normalize(result, result, 0, 800, NormTypes.MinMax);
// opens a window
using (Mat img = Mat.Zeros(600, 800, MatType.CV_8UC3))
using (Window window = new Window("City Location Estimation"))
{
var at = result.GetGenericIndexer<double>();
for (int c = 0; c < size; c++)
{
double x = at[0, c];
double y = at[1, c];
x = x * 0.7 + img.Width * 0.1;
y = y * 0.7 + img.Height * 0.1;
img.Circle((int)x, (int)y, 5, Scalar.Red, -1);
Point textPos = new Point(x + 5, y + 10);
img.PutText(CityNames[c], textPos, HersheyFonts.HersheySimplex, 0.5, Scalar.White);
}
window.Image = img;
Cv2.WaitKey();
}
}
