static void Main(string[] args)
{
int trainSampleCount = 0;
Image<Bgr, Byte> img = new Image<Bgr, byte>(500, 500);
//conversion of CSV to gesture variables
List<Gesture> gestureListClass1 = csvToGestureList(@"C:\Users\Dan\Desktop\thesis data\testEB-3-20.csv");
List<Gesture> gestureListClass2 = csvToGestureList(@"C:\Users\Dan\Desktop\thesis data\testSNC-3-20.csv");
trainSampleCount = (gestureListClass1.Count) + (gestureListClass2.Count); //set the sample count to the number of gestures we have available
//create relevant matrices based on size of the gestureList
Matrix<float> sample = new Matrix<float>(1, 2); //a sample has 2 columns because of 2 features
Matrix<float> trainTestData = new Matrix<float>(trainSampleCount, 2);
Matrix<float> trainTestClasses = new Matrix<float>(trainSampleCount, 1);
//GESTURE MATH INCOMING
foreach (Gesture g in gestureListClass1)
{
g.runMetrics();
}
foreach (Gesture g in gestureListClass2)
{
g.runMetrics();
}
#region Generate the training data and classes
//fill first set of data
for (int i = 0; i < gestureListClass1.Count; i++)
{
double[] gMetrics = (gestureListClass1[i].returnMetrics()).ToArray();
/*order of values
* list[0] - xyRatio
* list[1] - totalGestureTime
*/
trainTestData[i, 0] = ((float)gMetrics[0])*150;
trainTestData[i, 1] = ((float)gMetrics[1])/4;
}
Matrix<float> trainTestData1 = trainTestData.GetRows(0, gestureListClass1.Count, 1);
for (int j = 0; j < gestureListClass2.Count; j++)
{
double[] gMetrics = (gestureListClass2[j].returnMetrics()).ToArray();
trainTestData[(j + gestureListClass1.Count), 0] = (float)gMetrics[0] * 150;
trainTestData[(j + gestureListClass1.Count), 1] = ((float)gMetrics[1])/4;
}
Matrix<float> trainTestData2 = trainTestData.GetRows(gestureListClass1.Count, trainSampleCount, 1);
Matrix<float> trainTestClasses1 = trainTestClasses.GetRows(0, gestureListClass1.Count, 1);
trainTestClasses1.SetValue(1);
Matrix<float> trainTestClasses2 = trainTestClasses.GetRows(gestureListClass1.Count, trainSampleCount, 1);
trainTestClasses2.SetValue(2);
#endregion
using (SVM model = new SVM())
{
SVMParams p = new SVMParams();
p.KernelType = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.LINEAR;
p.SVMType = Emgu.CV.ML.MlEnum.SVM_TYPE.C_SVC;
//p.Gamma = 0.1;
p.C = 10;
p.TermCrit = new MCvTermCriteria(100, 0.00001);
//bool trained = model.Train(trainTestData, trainTestClasses, null, null, p);
bool trained = model.TrainAuto(trainTestData, trainTestClasses, null, null, p.MCvSVMParams, 5);
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;
float response = model.Predict(sample);
img[i, j] =
response == 1 ? new Bgr(90, 0, 0) :
new Bgr(0, 0, 90);
//response == 2 ? new Bgr(0, 90, 0) :
}
}
int c = model.GetSupportVectorCount();
for (int i = 0; i < c; i++)
{
float[] v = model.GetSupportVector(i);
PointF p1 = new PointF(v[0], v[1]);
img.Draw(new CircleF(p1, 4), new Bgr(255, 255, 128), 2);
}
}
// display the original training samples
for (int i = 0; i < (trainSampleCount / 2); i++)
{
if (i < trainTestData1.Rows)
{
PointF p1 = new PointF((trainTestData1[i, 0]), (trainTestData1[i, 1]));
img.Draw(new CircleF(p1, 2.0f), new Bgr(255, 100, 100), -1);
}
if (i < trainTestData2.Rows)
{
PointF p2 = new PointF((trainTestData2[i, 0]), (trainTestData2[i, 1]));
img.Draw(new CircleF(p2, 2.0f), new Bgr(100, 100, 255), -1);
}
}
Emgu.CV.UI.ImageViewer.Show(img);
}
private Image<Bgr, Byte> svm()
{
Stopwatch timer = new Stopwatch();
timer.Start();
int trainSampleCount = 150;
int sigma = 60;
#region Generate the training data and classes
Matrix<float> trainData = new Matrix<float>(trainSampleCount, 2);
Matrix<float> trainClasses = new Matrix<float>(trainSampleCount, 1);
Image<Bgr, Byte> img = new Image<Bgr, byte>(500, 500);
Matrix<float> sample = new Matrix<float>(1, 2);
Matrix<float> trainData1 = trainData.GetRows(0, trainSampleCount / 3, 1);
trainData1.GetCols(0, 1).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
trainData1.GetCols(1, 2).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
Matrix<float> trainData2 = trainData.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainData2.SetRandNormal(new MCvScalar(400), new MCvScalar(sigma));
Matrix<float> trainData3 = trainData.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainData3.GetCols(0, 1).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
trainData3.GetCols(1, 2).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
Matrix<float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount / 3, 1);
trainClasses1.SetValue(1);
Matrix<float> trainClasses2 = trainClasses.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainClasses2.SetValue(2);
Matrix<float> trainClasses3 = trainClasses.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainClasses3.SetValue(3);
#endregion
timer.Stop();
MessageBox.Show("生成" + timer.ElapsedMilliseconds + "ms");
timer.Reset();
timer.Start();
using (SVM model = new SVM()) {
SVMParams p = new SVMParams();
p.KernelType = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.LINEAR;
p.SVMType = Emgu.CV.ML.MlEnum.SVM_TYPE.C_SVC;
p.C = 1;
p.TermCrit = new MCvTermCriteria(100, 0.00001);
//model.Load(@"D:\Play Data\训练数据");
//bool trained = model.Train(trainData, trainClasses, null, null, p);
bool trained = model.TrainAuto(trainData, trainClasses, null, null, p.MCvSVMParams, 5);
timer.Stop();
MessageBox.Show("训练" + timer.ElapsedMilliseconds + "ms");
timer.Reset();
timer.Start();
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;
//float response = model.Predict(sample);
//img[i, j] =
// response == 1 ? new Bgr(90, 0, 0) :
// response == 2 ? new Bgr(0, 90, 0) :
// new Bgr(0, 0, 90);
}
}
//model.Save(@"D:\Play Data\训练数据");
timer.Stop();
MessageBox.Show("染色" + timer.ElapsedMilliseconds + "ms");
timer.Reset();
timer.Start();
int c = model.GetSupportVectorCount();
for (int i = 0; i < c; i++) {
float[] v = model.GetSupportVector(i);
PointF p1 = new PointF(v[0], v[1]);
img.Draw(new CircleF(p1, 4), new Bgr(128, 128, 128), 2);
}
timer.Stop();
MessageBox.Show("画圈" + timer.ElapsedMilliseconds + "ms");
timer.Reset();
timer.Start();
}
// display the original training samples
for (int i = 0; i < (trainSampleCount / 3); 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);
PointF p3 = new PointF(trainData3[i, 0], trainData3[i, 1]);
img.Draw(new CircleF(p3, 2.0f), new Bgr(100, 100, 255), -1);
}
timer.Stop();
MessageBox.Show("标点" + timer.ElapsedMilliseconds + "ms");
timer.Reset();
timer.Start();
return img;
}
private void button1_Click(object sender, EventArgs e)
{
int trainSampleCount = 150;
int sigma = 60;
#region Generate the training data and classes
Matrix<float> trainData = new Matrix<float>(trainSampleCount, 2);
Matrix<float> trainClasses = new Matrix<float>(trainSampleCount, 1);
Image<Bgr, Byte> img = new Image<Bgr, byte>(500, 500);
Matrix<float> sample = new Matrix<float>(1, 2);
Matrix<float> trainData1 = trainData.GetRows(0, trainSampleCount / 3, 1);
trainData1.GetCols(0, 1).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
trainData1.GetCols(1, 2).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
Matrix<float> trainData2 = trainData.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainData2.SetRandNormal(new MCvScalar(400), new MCvScalar(sigma));
Matrix<float> trainData3 = trainData.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainData3.GetCols(0, 1).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
trainData3.GetCols(1, 2).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
Matrix<float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount / 3, 1);
trainClasses1.SetValue(1);
Matrix<float> trainClasses2 = trainClasses.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainClasses2.SetValue(2);
Matrix<float> trainClasses3 = trainClasses.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainClasses3.SetValue(3);
#endregion
using (SVM model = new SVM())
{
SVMParams p = new SVMParams();
p.KernelType = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.LINEAR;
p.SVMType = Emgu.CV.ML.MlEnum.SVM_TYPE.C_SVC;
p.C = 1;
p.TermCrit = new MCvTermCriteria(100, 0.00001);
//bool trained = model.Train(trainData, trainClasses, null, null, p);
bool trained = model.TrainAuto(trainData, trainClasses, null, null, p.MCvSVMParams, 5);
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;
float response = model.Predict(sample);
img[i, j] =
response == 1 ? new Bgr(90, 0, 0) :
response == 2 ? new Bgr(0, 90, 0) :
new Bgr(0, 0, 90);
}
}
int c = model.GetSupportVectorCount();
for (int i = 0; i < c; i++)
{
float[] v = model.GetSupportVector(i);
PointF p1 = new PointF(v[0], v[1]);
img.Draw(new CircleF(p1, 4), new Bgr(128, 128, 128), 2);
}
}
// display the original training samples
for (int i = 0; i < (trainSampleCount / 3); 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);
PointF p3 = new PointF(trainData3[i, 0], trainData3[i, 1]);
img.Draw(new CircleF(p3, 2.0f), new Bgr(100, 100, 255), -1);
}
img.Save(@"D:\Code\svm.bmp");
// Emgu.CV.UI.ImageViewer.Show(img);
pictureBox1.Image = Image.FromFile(@"D:\Code\svm.bmp");
}
static void Main(string[] args)
{
int trainSampleCount = 0;
Image<Bgr, Byte> img = new Image<Bgr, byte>(500, 500);
//conversion of CSV to gesture variables
List<Gesture> gestureListClass1 = csvToGestureList(@"C:\Users\faculty\Desktop\testEB-3-20.csv");
List<Gesture> gestureListClass2 = csvToGestureList(@"C:\Users\faculty\Desktop\testSNC-3-20.csv");
trainSampleCount = (gestureListClass1.Count) + (gestureListClass2.Count); //set the sample count to the number of gestures we have available
//create relevant matrices based on size of the gestureList
Matrix<float> sample = new Matrix<float>(1, 16);
Matrix<float> trainTestData = new Matrix<float>(trainSampleCount, 16);
Matrix<float> trainTestClasses = new Matrix<float>(trainSampleCount, 1);
//GESTURE MATH INCOMING
foreach (Gesture g in gestureListClass1)
{
g.runMetrics();
}
foreach (Gesture g in gestureListClass2)
{
g.runMetrics();
}
#region Generate the training data and classes
//fill first set of data
for (int i = 0; i < gestureListClass1.Count; i++)
{
double[] gMetrics = (gestureListClass1[i].returnMetrics()).ToArray();
/*
* //add gestures to list
temp.Add(xyRatio); //[0]
temp.Add(totalGestureTime); //[1]
temp.Add(majorAvg); //[2]
temp.Add(minorAvg); //[3]
temp.Add(avgXVelo); //[4]
temp.Add(avgYVelo); //[5]
temp.Add(majorVariance); //[6]
temp.Add(minorVariance); //[7]
//add substrokes
temp.Add(firstVeloMag); //[8]
temp.Add(firstVeloDir); //[9]
temp.Add(secondVeloMag); //[10]
temp.Add(secondVeloDir); //[11]
temp.Add(thirdVeloMag); //[12]
temp.Add(thirdVeloDir); //[13]
temp.Add(fourthVeloMag); //[14]
temp.Add(fourthVeloDir); //[15]
*/
trainTestData[i, 0] = ((float)gMetrics[0]) * 150; //xy ratio
trainTestData[i, 1] = ((float)gMetrics[1]) / 4; //totalGestureTime
trainTestData[i, 2] = ((float)gMetrics[2]);
trainTestData[i, 3] = ((float)gMetrics[3]);
trainTestData[i, 4] = ((float)gMetrics[4]);
trainTestData[i, 5] = ((float)gMetrics[5]);
trainTestData[i, 6] = ((float)gMetrics[6]);
trainTestData[i, 7] = ((float)gMetrics[7]);
trainTestData[i, 8] = ((float)gMetrics[8]);
trainTestData[i, 9] = ((float)gMetrics[9]);
trainTestData[i, 10] = ((float)gMetrics[10]);
trainTestData[i, 11] = ((float)gMetrics[11]);
trainTestData[i, 12] = ((float)gMetrics[12]);
trainTestData[i, 13] = ((float)gMetrics[13]);
trainTestData[i, 14] = ((float)gMetrics[14]);
trainTestData[i, 15] = ((float)gMetrics[15]);
}
Matrix<float> trainTestData1 = trainTestData.GetRows(0, gestureListClass1.Count, 1);
for (int j = 0; j < gestureListClass2.Count; j++)
{
double[] gMetrics = (gestureListClass2[j].returnMetrics()).ToArray();
trainTestData[(j + gestureListClass1.Count), 0] = (float)gMetrics[0] * 150;
trainTestData[(j + gestureListClass1.Count), 1] = ((float)gMetrics[1]) / 4;
trainTestData[(j + gestureListClass1.Count), 2] = ((float)gMetrics[2]);
trainTestData[(j + gestureListClass1.Count), 3] = ((float)gMetrics[3]);
trainTestData[(j + gestureListClass1.Count), 4] = ((float)gMetrics[4]);
trainTestData[(j + gestureListClass1.Count), 5] = ((float)gMetrics[5]);
trainTestData[(j + gestureListClass1.Count), 6] = ((float)gMetrics[6]);
trainTestData[(j + gestureListClass1.Count), 7] = ((float)gMetrics[7]);
trainTestData[(j + gestureListClass1.Count), 8] = ((float)gMetrics[8]);
trainTestData[(j + gestureListClass1.Count), 9] = ((float)gMetrics[9]);
trainTestData[(j + gestureListClass1.Count), 10] = ((float)gMetrics[10]);
trainTestData[(j + gestureListClass1.Count), 11] = ((float)gMetrics[11]);
trainTestData[(j + gestureListClass1.Count), 12] = ((float)gMetrics[12]);
trainTestData[(j + gestureListClass1.Count), 13] = ((float)gMetrics[13]);
trainTestData[(j + gestureListClass1.Count), 14] = ((float)gMetrics[14]);
trainTestData[(j + gestureListClass1.Count), 15] = ((float)gMetrics[15]);
}
Matrix<float> trainTestData2 = trainTestData.GetRows(gestureListClass1.Count, trainSampleCount, 1);
Matrix<float> trainTestClasses1 = trainTestClasses.GetRows(0, gestureListClass1.Count, 1);
trainTestClasses1.SetValue(1);
Matrix<float> trainTestClasses2 = trainTestClasses.GetRows(gestureListClass1.Count, trainSampleCount, 1);
trainTestClasses2.SetValue(2);
#endregion
using (SVM model = new SVM())
{
SVMParams p = new SVMParams();
p.KernelType = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.LINEAR;
p.SVMType = Emgu.CV.ML.MlEnum.SVM_TYPE.C_SVC;
//p.Gamma = 0.1;
p.C = 10;
p.TermCrit = new MCvTermCriteria(100, 0.00001);
//bool trained = model.Train(trainTestData, trainTestClasses, null, null, p);
bool trained = model.TrainAuto(trainTestData, trainTestClasses, null, null, p.MCvSVMParams, 5);
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;
float response = model.Predict(sample);
img[i, j] =
response == 1 ? new Bgr(90, 0, 0) :
new Bgr(0, 0, 90);
//response == 2 ? new Bgr(0, 90, 0) :
}
}
int c = model.GetSupportVectorCount();
for (int i = 0; i < c; i++)
{
float[] v = model.GetSupportVector(i);
PointF p1 = new PointF(v[0], v[1]);
img.Draw(new CircleF(p1, 4), new Bgr(255, 255, 128), 2);
}
model.Save(@"C:\Users\faculty\Desktop\svm-function3coord16.xml");
}
// display the original training samples
for (int i = 0; i < (trainSampleCount / 2); i++)
{
if (i < trainTestData1.Rows)
{
PointF p1 = new PointF((trainTestData1[i, 0]), (trainTestData1[i, 1]));
img.Draw(new CircleF(p1, 2.0f), new Bgr(255, 100, 100), -1);
}
if (i < trainTestData2.Rows)
{
PointF p2 = new PointF((trainTestData2[i, 0]), (trainTestData2[i, 1]));
img.Draw(new CircleF(p2, 2.0f), new Bgr(100, 100, 255), -1);
}
}
Emgu.CV.UI.ImageViewer.Show(img);
}