private void Pbx_DoubleClick(object sender, EventArgs e)
{
PictureBox pbox = sender as PictureBox;
Image <Bgr, Byte> imageClicked = (pbox.Image as Bitmap).ToImage <Bgr, Byte>();
Emgu.CV.UI.ImageViewer viewer = new Emgu.CV.UI.ImageViewer(imageClicked);
viewer.StartPosition = FormStartPosition.CenterScreen;
viewer.ShowDialog();
}
public void ShowDetectResult(string filename, Point[] result)
{
Image <Bgr, Byte> img = new Image <Bgr, Byte>(filename);
foreach (Point pt in result)
{
img.Draw(new Rectangle(pt, _cascade.Size), new Bgr(0, 0, 255.0), 1);
}
Emgu.CV.UI.ImageViewer viewer = new Emgu.CV.UI.ImageViewer(img, "识别结果");
viewer.ShowDialog();
}
private void btnPreview_Click(object sender, EventArgs e)
{
using (Mat drawingBoard = previewImage.Clone())
{
Emgu.CV.Structure.MCvScalar redColor = new Emgu.CV.Structure.MCvScalar(0, 0, 255);
// Draw detection fields on preview image and let user see it!
foreach (DetectionField f in detectionTemplate.Fields)
{
CvInvoke.Rectangle(drawingBoard, new Rectangle(f.TopLeft, f.Size), redColor);
for (int i = 1; i < f.NumOfCols; i++)
{
CvInvoke.Line(drawingBoard, new Point(f.TopLeft.X + f.Size.Width * i / f.NumOfCols, f.TopLeft.Y), new Point(f.TopLeft.X + f.Size.Width * i / f.NumOfCols, f.TopLeft.Y + f.Size.Height), redColor);
}
for (int i = 1; i < f.NumOfRows; i++)
{
CvInvoke.Line(drawingBoard, new Point(f.TopLeft.X, f.TopLeft.Y + f.Size.Height * i / f.NumOfRows), new Point(f.TopLeft.X + f.Size.Width, f.TopLeft.Y + f.Size.Height * i / f.NumOfRows), redColor);
}
}
Emgu.CV.UI.ImageViewer imv = new Emgu.CV.UI.ImageViewer(drawingBoard);
imv.Show();
}
}
private void TestEmgu()
{
int K = 10;
//int trainSampleCount = 100;
int trainSampleCount = this.vectorTable[0].Length-1;
int trainSampleColumns = this.vectorTable.Length - 2; //subtract two columns for the post id and IsImage
int scalingRatio = 10;
#region Generate the traning data and classes
Matrix<float> trainData = new Matrix<float>(trainSampleColumns, trainSampleCount);
Matrix<float> trainClasses = new Matrix<float>(trainSampleColumns, 1);
Image<Bgr, Byte> img = new Image<Bgr, byte>(trainSampleCount, trainSampleCount);
Matrix<float> sample = new Matrix<float>(1, trainSampleCount);
for (int y = 1; y < this.vectorTable[0].Length - 1; y++) {
for (int x = 2; x < this.vectorTable.Length - 1; x++) {
trainData.Data.SetValue(Int32.Parse(this.vectorTable[x][y])*scalingRatio,x-2,y-1);
}
}
Matrix<float> trainData1 = trainData.GetRows(0, trainSampleColumns >> 1, 1);
//trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50));
Matrix<float> trainData2 = trainData.GetRows(trainSampleColumns >> 1, trainSampleColumns, 1);
//trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));
Matrix<float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1);
trainClasses1.SetValue(1);
Matrix<float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainClasses2.SetValue(2);
#endregion
Matrix<float> results, neighborResponses;
results = new Matrix<float>(sample.Rows, 1);
neighborResponses = new Matrix<float>(sample.Rows, K);
//dist = new Matrix<float>(sample.Rows, K);
KNearest knn = new KNearest(trainData, trainClasses, null, false, K);
for (int i = 0; i < img.Height; i++) {
for (int j = 0; j < img.Width; j++) {
sample.Data[0, 0] = j;
sample.Data[0, 1] = i;
//Matrix<float> nearestNeighbors = new Matrix<float>(K* sample.Rows, sample.Cols);
// estimates the response and get the neighbors' labels
float response = knn.FindNearest(sample, K, results, null, neighborResponses, null);
int accuracy = 0;
// compute the number of neighbors representing the majority
for (int k = 0; k < K; k++) {
if (neighborResponses.Data[0, k] == response)
accuracy++;
}
// highlight the pixel depending on the accuracy (or confidence)
img[i, j] =
response == 1 ?
(accuracy > 5 ? new Bgr(90, 0, 0) : new Bgr(90, 60, 0)) :
(accuracy > 5 ? new Bgr(0, 90, 0) : new Bgr(60, 90, 0));
}
}
// display the original training samples
for (int i = 0; i < (trainSampleCount >> 1); i++) {
PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
img.Draw(new CircleF(p1, 2.0f), new Bgr(255, 100, 100), -1);
PointF p2 = new PointF(trainData2[i, 0], trainData2[i, 1]);
img.Draw(new CircleF(p2, 2.0f), new Bgr(100, 255, 100), -1);
}
//Emgu.CV.UI.ImageViewer.Show(img);
Emgu.CV.UI.ImageViewer imgviewer = new Emgu.CV.UI.ImageViewer(img);
imgviewer.Show();
}
