public void Save()
{
topLayerSVM = new SVM();
SVMParams p = new SVMParams();
p.KernelType = SVM_KERNEL_TYPE.LINEAR;
p.SVMType = SVM_TYPE.C_SVC;
p.C = 1;
p.TermCrit = new MCvTermCriteria(100, 0.00001);
topLayerSVM.Train(trainingDescriptors, labels, null, null, p);
IFormatter formatter = new BinaryFormatter();
Stream fs = File.OpenWrite(directory + "obj\\dic.xml");
formatter.Serialize(fs, topLayerDic);
fs.Dispose();
topLayerSVM.Save(directory + "obj\\svm.xml");
}
public TrainForm()
{
InitializeComponent();
model = CreateSvm();
data = Directory.GetDirectories(TestBase).SelectMany(Directory.GetFiles).ToDictionary(file => file, file => new List<TextLineInfo>());
files = new Queue<string>(data.Keys);
OpenNext();
// LoadDetectedLine();
}
//SVMParams _parameters
public SVMClassifier()
{
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);
parameters = p;
}
public Load_L2BOF(int classnum, string src)
{
SecondLayerDic = null;
directory = src;
classNum = classnum;
bowTrainer = new BOWKMeansTrainer(classNum, new MCvTermCriteria(10, 0.01), 3,
KMeansInitType.PPCenters);
IFormatter formatter = new BinaryFormatter();
FileStream fs = File.OpenRead(directory + "\\obj\\dic.xml");
SecondLayerDic = (Matrix<float>)formatter.Deserialize(fs);
fs.Dispose();
bowDe.SetVocabulary(SecondLayerDic);
SecondLayerSVM = new SVM();
SecondLayerSVM.Load(directory + "\\obj\\svm.xml");
Console.WriteLine("Finished Loading L2 SVM.");loaded = true;
}
public Load_L1BOF(int classnum,string loc)
{
prefix = loc;
topLayerDic = null;
classNum = classnum;
bowTrainer = new BOWKMeansTrainer(classNum, new MCvTermCriteria(10, 0.01), 3,
KMeansInitType.PPCenters);
IFormatter formatter = new BinaryFormatter();
FileStream fs = File.OpenRead(loc + "obj\\dic.xml");
topLayerDic = (Matrix<float>)formatter.Deserialize(fs);
fs.Dispose();
bowDe.SetVocabulary(topLayerDic);
topLayerSVM = new SVM();
topLayerSVM.Load(loc + "obj\\svm.xml");
Console.WriteLine("Finished Loading L1 SVM.");
loaded = true;
}
private void btnTrain_Click(object sender, EventArgs e)
{
HOGDescriptor hog = new HOGDescriptor(new Size(36, 36), new Size(36, 36), new Size(6, 6), new Size(6, 6));
fsPeg = Directory.GetFiles(txtPosPath.Text);
for (int i = 0; i < fsPeg.Length; i++)
{
String cFileName = txtPosPath.Text + fsPeg[i];
Image <Bgr, byte> vImage = new Image <Bgr, byte>(cFileName);
Image <Gray, byte> vGray = vImage.Convert <Gray, byte>();
float[] fAttr = hog.Compute(vGray);
for (int j = 0; j < fAttr.Length; j++)
{
DataMatrix[i, j] = fAttr[j];
}
AttrMatrix[i, 0] = 1;
}
fsNeg = Directory.GetFiles(txtNegPath.Text);
for (int i = 0; i < fsNeg.Length; i++)
{
String cFileName = txtNegPath.Text + fsNeg[i];
Image <Bgr, byte> vImage = new Image <Bgr, byte>(cFileName);
Image <Gray, byte> vGray = vImage.Convert <Gray, byte>();
float[] fAttr = hog.Compute(vGray);
for (int j = 0; j < fAttr.Length; j++)
{
DataMatrix[i, j] = fAttr[j];
}
AttrMatrix[i, 0] = 0;
}
Emgu.CV.ML.SVM vSVM = new Emgu.CV.ML.SVM();
vSVM.Type = Emgu.CV.ML.SVM.SvmType.CSvc;
vSVM.SetKernel(Emgu.CV.ML.SVM.SvmKernelType.Linear);
vSVM.TermCriteria = new MCvTermCriteria(1000, 0.1);
TrainData td = new TrainData(DataMatrix, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, AttrMatrix);
String cExportFileName = txtFileName.Text;
vSVM.Save(cExportFileName);
}
public Classifier(List<string> folders)
{
_folders = new List<FileInfo[]>();
svmClassifier = new SVM();
foreach(string folder in folders)
{
try
{
FileInfo[] files = new DirectoryInfo(folder).GetFiles();
_folders.Add(files);
class_num++;
}
catch(DirectoryNotFoundException ex)
{
System.Console.WriteLine(folder + " not found");
System.Console.WriteLine(ex.Data);
continue;
}
}
}
private void btnTest_Click(object sender, EventArgs e)
{
MyRect[] regions = null;
Emgu.CV.ML.SVM vSVM = new Emgu.CV.ML.SVM();
String cExportFileName = txtFileName.Text;
FileStorage fileStorage = new FileStorage(cExportFileName, FileStorage.Mode.Read);
vSVM.Read(fileStorage.GetFirstTopLevelNode());
int iHeight = vSVM.GetSupportVectors().Height;
int iWidth = vSVM.GetSupportVectors().Width;
var svmMat = new Matrix <float>(iWidth, iHeight);
Matrix <float> resultMat = new Matrix <float>(1, iWidth);
Matrix <float> alphaMat = new Matrix <float>(1, iHeight);
float[] mydetector = new float[iWidth + 1];
for (int i = 0; i < iWidth; i++)
{
mydetector[i] = resultMat[0, i];
}
//mydetector[iWidth] = rhoValue;
Mat vImage = new Mat();
HOGDescriptor hog = new HOGDescriptor(new Size(36, 36), new Size(36, 36), new Size(6, 6), new Size(6, 6));
hog.SetSVMDetector(mydetector);
MCvObjectDetection[] results = hog.DetectMultiScale(vImage);
regions = new MyRect[results.Length];
for (int i = 0; i < results.Length; i++)
{
regions[i] = new MyRect();
regions[i].Rect = results[i].Rect;
regions[i].Score = results[i].Score;
}
}
public void Evaluate(IEvolutionState state, Individual ind, int subpop, int threadnum)
{
if (!ind.Evaluated)
{
int counter = 0;
var features = new int[5, NumOfImagesPerCategory *NumOfClasses, NumOfFeatures];
var labels = new int[5, NumOfImagesPerCategory *NumOfClasses];
for (int i = 0; i < 5; i++)
{
counter = 0;
var categoryDir = CategoryDirs[i];
var subcategoryDirs = Directory.EnumerateDirectories(categoryDir).ToArray();
for (int j = 0; j < NumOfClasses; j++)
{
var subcategoryDir = subcategoryDirs[j];
var images = Directory.GetFiles(subcategoryDir);
for (int k = 0; k < NumOfImagesPerCategory; k++)
{
var tempImage = new Image <Gray, Byte>(images[k]);
tempImage.CopyTo(currentImage[threadnum]);
tempImage.CopyTo(originalImage[threadnum]);
tempImage.Dispose();
((GPIndividual)ind).Trees[0].Child.Eval(state, threadnum, Input, Stack, ((GPIndividual)ind), this);
int[] imageFeatures = ImageTransformer.GetSquareSuperpixelFeatures(currentImage[threadnum], SuperpixelSize);
for (int x = 0; x < imageFeatures.Length; x++)
{
features[i, counter, x] = imageFeatures[x];
labels[i, counter] = j + 1;
}
counter++;
}
}
}
/*
* var trainDataFile = new StreamWriter(@"F:\Gesty\problem2\features\traindata" + threadnum + ".txt");
* var testDataFile = new StreamWriter(@"F:\Gesty\problem2\features\testdata" + threadnum + ".txt");
*
* for(int i=0; i<4; i++)
* {
* for(int j=0; j<1000; j++)
* {
* var line = new StringBuilder();
* line.Append(labels[i, j].ToString() + " ");
* for (int k=0; k<NumOfFeatures; k++)
* {
* line.Append((k + 1).ToString() + ":" + features[i, j, k].ToString() + " ");
* }
* trainDataFile.WriteLine(line.ToString().Trim());
* }
* }
* for (int j = 0; j < 1000; j++)
* {
* var line = new StringBuilder();
* line.Append(labels[4, j].ToString() + " ");
* for (int k = 0; k < NumOfFeatures; k++)
* {
* line.Append((k + 1).ToString() + ":" + features[4, j, k].ToString() + " ");
* }
* testDataFile.WriteLine(line.ToString().Trim());
* }
* trainDataFile.Close();
* testDataFile.Close();
*/
var confMatI = new double[10, 10];
double accuracy = 0;
for (int x = 0; x < 5; x++)
{
var trainData = new Matrix <float>(NumOfImagesPerCategory * NumOfClasses * 4, NumOfFeatures);
var trainClasses = new Matrix <int>(NumOfImagesPerCategory * NumOfClasses * 4, 1);
var testData = new Matrix <float>(NumOfImagesPerCategory * NumOfClasses, NumOfFeatures);
var testClasses = new Matrix <int>(NumOfImagesPerCategory * NumOfClasses, 1);
//trainData
int imageCount = 0;
for (int i = 0; i < 5; i++)
{
if (i != x)
{
for (int j = 0; j < NumOfImagesPerCategory * NumOfClasses; j++)
{
for (int k = 0; k < NumOfFeatures; k++)
{
trainData[imageCount, k] = features[i, j, k];
trainClasses[imageCount, 0] = labels[i, j];
}
imageCount++;
}
}
else
{
for (int j = 0; j < NumOfImagesPerCategory * NumOfClasses; j++)
{
for (int k = 0; k < NumOfFeatures; k++)
{
testData[j, k] = features[i, j, k];
testClasses[j, 0] = labels[i, j];
}
}
}
}
Emgu.CV.ML.SVM model = new Emgu.CV.ML.SVM();
var predictions = new Matrix <float>(NumOfImagesPerCategory * NumOfClasses, 1);
var trainData2 = new TrainData(trainData, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, trainClasses);
model.Type = Emgu.CV.ML.SVM.SvmType.CSvc;
model.SetKernel(Emgu.CV.ML.SVM.SvmKernelType.Poly);
model.TermCriteria = new MCvTermCriteria(10000, 0.001);
model.Degree = 3;
model.Gamma = 0.001;
model.Coef0 = 0;
model.C = 1000;
model.Nu = 0.5;
model.P = 0.1;
model.Train(trainData2);
model.Predict(testData, predictions);
// var predictionsArray = (float[,])predictions.GetData();
int correctPredictions = 0;
for (int i = 0; i < predictions.Rows; i++)
{
if ((int)predictions[i, 0] == testClasses[i, 0])
{
correctPredictions++;
}
var predictedLabel = (int)predictions[i, 0];
var trueLabel = testClasses[i, 0];
confMatI[predictedLabel - 1, trueLabel - 1]++;
}
for (int i = 0; i < 10; i++)
{
for (int j = 0; j < 10; j++)
{
confMat[i, j] = (confMatI[i, j] / 500) * 100;
}
}
if (correctPredictions > 0)
{
accuracy += 100 * ((double)correctPredictions / (double)predictions.Rows);
}
}
/*
* for(int i=0; i<NumOfImagesPerCategory*NumOfClasses*4; i++)
* {
* for(int j=0; j<NumOfFeatures; j++)
* {
* //trainData[i, j] = ((trainData[i, j] - 0) / (255 - 0)) * (1 + 1) - 1;
* }
* trainClasses[i, 0] = ((trainClasses[i, 0] - 1) / (NumOfClasses - 1)) * (1 + 1) - 1;
* }
*/
//testData
/*
* for (int j = 0; j < NumOfImagesPerCategory * NumOfClasses; j++)
* {
* var line = new StringBuilder();
* line.Append(labels[4, j] + " ");
* for (int k = 0; k < NumOfFeatures; k++)
* {
* line.Append(k + 1 + ":" + features[4, j, k] + " ");
* }
* testData.WriteLine(line.ToString().Trim());
* }
*
* trainData.Close();
* testData.Close();
*/
//predictions.Dispose();
/*
* var netData = new SharpLearning.Containers.Matrices.F64Matrix(NumOfImagesPerCategory * NumOfClasses * 4, NumOfFeatures);
* var netTargets = new double[NumOfImagesPerCategory * NumOfClasses * 4];
* int imageCount = 0;
* for (int i = 0; i < 4; i++)
* {
* for (int j = 0; j < NumOfImagesPerCategory * NumOfClasses; j++)
* {
* for (int k = 0; k < NumOfFeatures; k++)
* {
* netData[imageCount, k] = features[i, j, k];
* netTargets[imageCount] = labels[i, j];
* }
* imageCount++;
* }
* }
*/
/*
* var CVNeuralNet = new Emgu.CV.ML.ANN_MLP();
*
* CVNeuralNet.TermCriteria = new MCvTermCriteria(10000, 0.001);
* var layerSizes = new Matrix<int>(new int[4] { NumOfFeatures, NumOfFeatures * 10, NumOfFeatures*5, 1 });
* CVNeuralNet.SetLayerSizes(layerSizes);
* CVNeuralNet.SetTrainMethod(ANN_MLP.AnnMlpTrainMethod.Rprop);
* CVNeuralNet.SetActivationFunction(ANN_MLP.AnnMlpActivationFunction.SigmoidSym);
* CVNeuralNet.BackpropMomentumScale = 0.01;
* CVNeuralNet.BackpropWeightScale = 0.2;
* var trainData2 = new TrainData(trainData, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, trainClasses);
* var predictions = new Matrix<float>(NumOfImagesPerCategory * NumOfClasses*4, 1);
* CVNeuralNet.Train(trainData2);
* CVNeuralNet.Predict(trainData, predictions);
*/
/*
* var net = new NeuralNet();
* net.Add(new InputLayer(NumOfFeatures));
* net.Add(new DropoutLayer(0.5));
* net.Add(new DenseLayer(NumOfFeatures * 4, SharpLearning.Neural.Activations.Activation.Sigmoid));
* net.Add(new DenseLayer(NumOfFeatures * 4, SharpLearning.Neural.Activations.Activation.Sigmoid));
* net.Add(new DropoutLayer(0.5));
* net.Add(new SoftMaxLayer(NumOfClasses));
* var learner = new ClassificationNeuralNetLearner(net, new SquareLoss());
* var model = learner.Learn(netData, netTargets);
* var predictions = model.Predict(netData);
* int correctPredictions = 0;
* for (int i = 0; i < predictions.Length; i++)
* {
* if ((int)predictions[i] == netTargets[i])
* correctPredictions++;
* }
* if (correctPredictions > 0)
* accuracy = 100 * ((double)correctPredictions / (double)predictions.Length);
*/
/*
* var problem = SVMProblemHelper.Load(@"F:\Gesty\problem2\features\traindata" + threadnum + ".txt");
* var testProblem = SVMProblemHelper.Load(@"F:\Gesty\problem2\features\testdata" + threadnum + ".txt");
* var model = problem.Train(Parameter);
* double[] target = testProblem.Predict(model);
* double accuracy = testProblem.EvaluateClassificationProblem(target);
*/
var f = ((KozaFitness)ind.Fitness);
f.SetStandardizedFitness(state, (float)(100 - (accuracy / 5)));
ind.Evaluated = true;
var transFeatures = new StreamWriter(@"F:\Gesty\testy\transFeatures.csv");
for (int i1 = 0; i1 < 1000; i1++)
{
for (int i2 = 0; i2 < 5; i2++)
{
var line = new StringBuilder();
line.Append(labels[i2, i1].ToString() + ',');
for (int i3 = 0; i3 < 64; i3++)
{
line.Append(features[i2, i1, i3].ToString() + ',');
}
transFeatures.WriteLine(line.ToString().Trim(','));
}
}
transFeatures.Close();
}
}
internal static extern void CvSVMGetDefaultGrid(SVM.ParamType type, ref MCvParamGrid grid);
public void TrainEach(string dir)
{
directory = dir;
foreach (FileInfo file in new DirectoryInfo(dir).GetFiles())
{
SecondLayerNum++;
}
labels = new Matrix<float>(SecondLayerNum, 1);
trainingDescriptors = new Matrix<float>(SecondLayerNum, classNum);
foreach (FileInfo file in new DirectoryInfo(dir).GetFiles())
{
Extract(new Image<Bgr, byte>(file.FullName));
}
MakeDic();
j = 0;
foreach (FileInfo file in new DirectoryInfo(dir).GetFiles())
{
MakeDescriptors(new Image<Bgr, byte>(file.FullName));
}
Console.WriteLine();
SecondLayerSVM = new SVM();
SVMParams p = new SVMParams();
p.KernelType = SVM_KERNEL_TYPE.LINEAR;
p.SVMType = SVM_TYPE.C_SVC;
p.C = 1;
p.TermCrit = new MCvTermCriteria(100, 0.00001);
SecondLayerSVM.Train(trainingDescriptors, labels, null, null, p);
}
public static SVM CreateSvm()
{
var model = new SVM();
model.Load(ModelFileName);
return model;
}
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\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 void button2_Click(object sender, EventArgs e)
{
folderBrowserDialog1.SelectedPath = @"D:\Code\Data";
try
{
if (folderBrowserDialog1.ShowDialog() == DialogResult.OK)
{
String[] files = System.IO.Directory.GetFiles(folderBrowserDialog1.SelectedPath);
if (trainData_all != null)
{
trainData_all.Dispose(); trainData_all = null;
}
foreach (String name in files)
{
if (name.Contains("_class"))
{
if (Class_I != null)
{
Class_I.Dispose(); Class_I = null;
}
if (Train_I != null)
{
Train_I.Dispose(); Train_I = null;
}
//////////
String name2 = name.Replace("_class", "");
name2 = name2.Replace("bmp", "tif");
if (files.Contains(name2))
{
Class_I = new Image<Bgr, Byte>(name).Resize(scale, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR); //////類別
Train_I = new Image<Bgr, Byte>(name2).Resize(scale, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR); //////原影像
Cal_features();
}
}
}
///////SVM
using (Emgu.CV.ML.SVM model = new Emgu.CV.ML.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);*/
p.KernelType = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.RBF;
p.SVMType = Emgu.CV.ML.MlEnum.SVM_TYPE.C_SVC;
p.Gamma = 0.1;
p.Coef0 = 0;
p.Degree = 2;
p.C = 1;
p.TermCrit = new MCvTermCriteria(500, 0.00001);
//bool trained = model.Train(trainData, trainClasses, null, null, p);
bool trained = model.TrainAuto(trainData_all, trainClasses_all, null, null, p.MCvSVMParams, 5);
model.Save(@"D:\Code\Data\train_func.xml");
}
}
}
catch
{
MessageBox.Show("讀取錯誤!");
}
} // end of button2
//SVMParams _parameters
public SVMClassifier(string filename)
{
model = new SVM();
model.Load(filename);
}
public void TrainEach(string dir)
{
directory = dir;
foreach (FileInfo file in new DirectoryInfo(dir).GetFiles())
{
SecondLayerNum++;
}
labels = new Matrix<float>(SecondLayerNum, 1);
trainingDescriptors = new Matrix<float>(SecondLayerNum, classNum);
foreach (FileInfo file in new DirectoryInfo(dir).GetFiles())
{
Extract(new Image<Bgr, byte>(file.FullName));
}
MakeDic();
if (SecondLayerDic == null) throw new Exception("!!!");
j = 0;
foreach (FileInfo file in new DirectoryInfo(dir).GetFiles())
{
MakeDescriptors(new Image<Bgr, byte>(file.FullName));
}
Console.WriteLine();
SecondLayerSVM = new SVM();
SVMParams p = new SVMParams();
p.KernelType = SVM_KERNEL_TYPE.LINEAR;
p.SVMType = SVM_TYPE.C_SVC;
p.C = 1;
p.TermCrit = new MCvTermCriteria(100, 0.00001);
SecondLayerSVM.Train(trainingDescriptors, labels, null, null, p);
IFormatter formatter = new BinaryFormatter();
Stream fs = File.OpenWrite(directory + "\\obj\\dic.xml");
formatter.Serialize(fs, SecondLayerDic);
fs.Dispose();
SecondLayerSVM.Save(directory + "\\obj\\svm.xml");
}
private void button1_Click(object sender, EventArgs e)
{
folderBrowserDialog1.SelectedPath = @"D:\Code\Data";
try
{
if (folderBrowserDialog1.ShowDialog() == DialogResult.OK)
{
String[] files = System.IO.Directory.GetFiles(folderBrowserDialog1.SelectedPath);
foreach (String name in files)
{
if (name.Contains("_class") )/////////有分類標籤的影像
{
/* if (Class_I != null)
{
Class_I.Dispose(); Class_I = null;
}
if (Train_I != null)
{
Train_I.Dispose(); Train_I = null;
}
//////////
String name2 = name.Replace("_class", "");
name2 = name2.Replace("bmp", "tif");
if (files.Contains(name2))
{
Class_I = new Image<Bgr, Byte>(name); //////類別
Train_I = new Image<Bgr, Byte>(name2); //////原影像
if (trainData_all != null)
{
trainData_all.Dispose(); trainData_all = null;
}
Cal_features();
Image<Bgr, byte> predict_image = Train_I.Clone();
using (Emgu.CV.ML.SVM model = new Emgu.CV.ML.SVM())
{
model.Load(@"D:\程式區\Plant_training\train_func.xml");
for (int i = 0; i < Train_I.Height; i++)
for (int j = 0; j < Train_I.Width; j++)
{
float predict_class = model.Predict(trainData_all.GetRow(i * Train_I.Width + j));
if (predict_class == 1)
{
predict_image.Data[i, j, 0] = 255;
predict_image.Data[i, j, 2] = 255;
}
}
}
predict_image.Save(@"D:\程式區\Plant_training\predict.tif");
}*/
}
else
{
if (Train_I != null)
{
Train_I.Dispose(); Train_I = null;
}
if (Class_I != null)
{
Class_I.Dispose(); Class_I = null;
}
Train_I = new Image<Bgr, Byte>(name).Resize(scale, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR); //////原影像
if (trainData_all != null)
{
trainData_all.Dispose(); trainData_all = null;
}
Cal_features();
Image<Bgr, byte> predict_image = Train_I.Clone();
using (Emgu.CV.ML.SVM model = new Emgu.CV.ML.SVM())
{
model.Load(@"D:\Code\Data\train_func.xml");
for (int i = 0; i < Train_I.Height; i++)
for (int j = 0; j < Train_I.Width; j++)
{
Matrix<float> feature_test = trainData_all.GetRow(i * Train_I.Width + j);
int aaaaa = 1;
float predict_class = model.Predict(feature_test);
if (predict_class == 1)
{
predict_image.Data[i, j, 0] = 255;
predict_image.Data[i, j, 2] = 255;
}
}
}
String name2 = name.Insert(name.Length - 4, "_predict");
//String name2 = "predict";
predict_image.Save(name2);
}
}
}
}
catch
{
MessageBox.Show("讀取錯誤!");
}
}
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);
}
public void TestSVM()
{
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.Params p = new SVM.Params(MlEnum.SvmType.CSvc, MlEnum.SvmKernelType.Linear, 0, 1, 0, 1, 0, 0, null, new MCvTermCriteria(100, 1.0e-6)))
using (SVM model = new SVM())
using (Matrix<int> trainClassesInt = trainClasses.Convert<int>())
using (TrainData td = new TrainData(trainData, MlEnum.DataLayoutType.RowSample, trainClassesInt))
{
model.Type = SVM.SvmType.CSvc;
model.SetKernel(SVM.SvmKernelType.Inter);
model.Degree = 0;
model.Gamma = 1;
model.Coef0 = 0;
model.C = 1;
model.Nu = 0;
model.P = 0;
model.TermCriteria = new MCvTermCriteria(100, 1.0e-6);
//bool trained = model.TrainAuto(td, 5);
model.Train(td);
#if !NETFX_CORE
String fileName = "svmModel.xml";
//String fileName = Path.Combine(Path.GetTempPath(), "svmModel.xml");
model.Save(fileName);
SVM model2 = new SVM();
FileStorage fs = new FileStorage(fileName, FileStorage.Mode.Read);
model2.Read(fs.GetFirstTopLevelNode());
if (File.Exists(fileName))
File.Delete(fileName);
#endif
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);
}
}
Mat supportVectors = model.GetSupportVectors();
//TODO: find out how to draw the support vectors
Image<Gray, float> pts = supportVectors.ToImage<Gray, float>();
PointF[] vectors = new PointF[supportVectors.Rows];
GCHandle handler = GCHandle.Alloc(vectors, GCHandleType.Pinned);
using (
Mat vMat = new Mat(supportVectors.Rows, supportVectors.Cols, DepthType.Cv32F, 1,
handler.AddrOfPinnedObject(), supportVectors.Cols*4))
{
supportVectors.CopyTo(vMat);
}
handler.Free();
/*
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);
}
//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;
}
/// <summary>
/// Get the default parameter grid for the specific SVM type
/// </summary>
/// <param name="type">The SVM type</param>
/// <returns>The default parameter grid for the specific SVM type </returns>
public static MCvParamGrid GetDefaultGrid(SVM.ParamType type)
{
MCvParamGrid grid = new MCvParamGrid();
MlInvoke.CvSVMGetDefaultGrid(type, ref grid);
return grid;
}
public FeatureRecognizer()
{
isTrained = false;
SVMParameters = new SVMParams();
SVMParameters.SVMType = Emgu.CV.ML.MlEnum.SVM_TYPE.C_SVC;
//SVMParameters.SVMType = Emgu.CV.ML.MlEnum.SVM_TYPE.NU_SVC;
//p.KernelType = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.POLY;
//SVMParameters.KernelType = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.LINEAR;
SVMParameters.KernelType = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.POLY;
//SVMParameters.KernelType = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.RBF;
SVMParameters.Gamma = 3;
SVMParameters.Degree = 3;
SVMParameters.C = 1;
SVMParameters.TermCrit = new MCvTermCriteria(100, 0.00001);
//SVMParameters.Nu
//SVMParameters.P
//SVMParameters.Coef0 =
paramsm = new MCvSVMParams();
paramsm.svm_type = Emgu.CV.ML.MlEnum.SVM_TYPE.C_SVC;
paramsm.kernel_type = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.LINEAR;
paramsm.gamma = 2;
paramsm.degree = 3;
paramsm.C = 3;
paramsm.term_crit = new MCvTermCriteria(100, 0.00001);
//debugImages = new List<DebugImage>();
debugImages = null;
//SVMModel = new SVM();
// foreach(SignShape shape in Enum.GetValues(typeof(SignShape)))
for (int i = 0;i < signShapeCount;i++)
{
SVM model = new SVM();
SVMModels.Add(model);
}
int R = 10;
int C = 10;
double v = 2;
for (int i = 0; i < 8; i++)
{
GWOutputImage = GWOutputImage.ConcateHorizontal(GaborWavelet(R, C, i, v));
Image<Gray, double> GW = GaborWavelet(R, C, i, v);
ConvolutionKernelF ckernel = new ConvolutionKernelF(10, 10);
for (int l = 0; l < 10; l++)
for (int k = 0; k < 10; k++)
ckernel[l, k] = (float)GW[l, k].Intensity / 10000;
ckernel.Center = new Point(5, 5);
cKernelList.Add(ckernel);
}
}
private void button7_Click(object sender, EventArgs e)
{
///////////////////////////////////////////// Test ///////////////////////////////////////
//Create a matrix
//Matrix<Byte> matrix1 = new Matrix<Byte>(10, 10);
//Byte element = 0;
////Set the elements
//for (int i = 0; i < 10; i++)
//{
// for (int j = 0; j < 10; j++)
// {
// matrix1.Data[i, j] = 1;
// //matrix1.Data[i, j] = element;
// element++;
// }
//}
//int WW = matrix1.Cols;
//int HH = matrix1.Rows;
///////////////////////////////////////////// Test ///////////////////////////////////////
int W = Train_I.Cols;
int H = Train_I.Rows;
int rW = (mW - 1) / 2;
int rH = (mH - 1) / 2;
double pp = 0;
double ee = 0;
int ind = 0;
int count;
int maskCenter_H_start = rH + 1;
int maskCenter_W_start = rW + 1;
int maskCenter_H_end = H - rH;
int maskCenter_W_end = W - rW;
double[] fVave = new double[ ( maskCenter_H_end - maskCenter_H_start) * (maskCenter_W_end - maskCenter_W_start) ];
double[] fVstd = new double[( maskCenter_H_end - maskCenter_H_start) * (maskCenter_W_end - maskCenter_W_start) ];
double[] fVentropy = new double[( maskCenter_H_end - maskCenter_H_start) * (maskCenter_W_end - maskCenter_W_start) ];
classV = new Matrix<float>( (maskCenter_H_end - maskCenter_H_start) * (maskCenter_W_end - maskCenter_W_start), 1);
// Mask scan over the whole image "gR_R_Picked_Map"
for (int i = maskCenter_H_start; i < maskCenter_H_end; i++)
{
for (int j = maskCenter_W_start; j < maskCenter_W_end; j++)
{
double sum = 0;
double ave = 0;
double sumDiff = 0;
double std = 0;
entropy = 0;
count = 0;
// initial value all 0, since every mask should count independently
for (int k = 0; k < mH; k++)
{
for (int l = 0; l < mW; l++)
{
int mask_x = j - rW + l - 1;
int mask_y = i - rH + k - 1;
sum = sum + gR_R_Picked_Map[ mask_y, mask_x ]; // Bug????
//sum = sum + matrix1[i - rH + k - 1 , j - rW + l - 1 ]; // test
// judge mask is PART of TREE or NOT
if (Class_I.Data[ mask_y, mask_x , 0] == 1)
{
count ++;
}
}
} // end of mask
// To detect the binary values in the mask and record it in classV
float maskSampleCount = mH * mW;
float mask_plant_percent = (count /maskSampleCount);
if ( mask_plant_percent > 0.7 )
{
classV[ind, 0] = 1;
}
else
{
classV[ind, 0] = 0;
}
// Ave
ave = sum / (maskSampleCount);
// StandardDeviation
for (int k = 0; k < mH; k++)
{
for (int l = 0; l < mW; l++)
{
int mask_x = j - rW + l - 1;
int mask_y = i - rH + k - 1;
sumDiff = sumDiff + Math.Pow((gR_R_Picked_Map[ mask_y, mask_x] - ave), 2);
// sumDiff = sumDiff + Math.Pow((matrix1[i - rH + k - 1, j - rW + l - 1] - ave), 2); //test
}
}
std = Math.Sqrt(sumDiff / (maskSampleCount - 1));
// Entropy
for (int k = 1; k < mH; k++)
{
for (int l = 1; l < mW; l++)
{
int mask_x = j - rW + l - 1;
int mask_y = i - rH + k - 1;
pp = gR_R_Picked_Map[mask_y, mask_x] / sum;
//p = matrix1[i - rH + k - 1, j - rW + l - 1] / sum;
if (pp > 0) // Log p, p > 0
{
ee = Math.Log(pp);
}
else
{
ee = 0;
}
//System.Console.WriteLine("-");
//System.Console.WriteLine(p);
//System.Console.WriteLine(ee);
//System.Console.WriteLine("-");
entropy = entropy + (-(pp * ee));
}
}
fVave[ind] = ave;
fVstd[ind] = std;
fVentropy[ind] = entropy;
ind++;
} // for i
} // for j
trainData_all = new Matrix<float>(ind, 3);
for (int i = 0; i < ind; i++)
{
trainData_all.Data[i, 0] = (float)fVave[i];
trainData_all.Data[i, 1] = (float)fVstd[i];
trainData_all.Data[i, 2] = (float)fVentropy[i];
}
using (Emgu.CV.ML.SVM model = new Emgu.CV.ML.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);*/
p.KernelType = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.RBF;
p.SVMType = Emgu.CV.ML.MlEnum.SVM_TYPE.C_SVC;
p.Gamma = 0.1;
p.Coef0 = 0;
p.Degree = 2;
p.C = 1;
p.TermCrit = new MCvTermCriteria(500, 0.00001); //迭代的終止準則,最大迭代次數500次,結果的精確性
//bool trained = model.Train(trainData, trainClasses, null, null, p);
bool trained = model.TrainAuto(trainData_all, classV, null, null, p.MCvSVMParams, 5);
model.Save(@"D:\Code\Data\train_func.xml");
}
System.Console.WriteLine(" END OF button7_Click Masking ");
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
Image<Bgr, byte> predict_image = Train_I2.Clone();
using (Emgu.CV.ML.SVM model = new Emgu.CV.ML.SVM())
{
model.Load(@"D:\Code\Data\train_func.xml");
int ind_predict = 0;
for (int i = maskCenter_H_start; i < maskCenter_H_end; i++)
for (int j = maskCenter_W_start; j < maskCenter_W_end; j++)
{
Matrix<float> feature_test = TakeRow(trainData_all.Data, ind_predict);
//Matrix<float> feature_test = trainData_all.GetRow( ind_predict ) ;
// System.Console.WriteLine(feature_test);
float predict_class = model.Predict( feature_test );
if (predict_class == 1)
{
for (int k = 0; k < mH; k++)
for (int l = 0; l < mW; l++)
{
int mask_x = j - rW + l -1;
int mask_y = i - rH + k -1;
predict_image.Data[ mask_y, mask_x , 0] = 255;
predict_image.Data[mask_y , mask_x , 2] = 255;
}
}
ind_predict++;
// System.Console.WriteLine(predict_class);
} // end of all
//String name2 = name.Insert(name.Length - 4, "_predict");
String name3 = "predict";
predict_image.Save(@"D:\Code\Data\predict.bmp");
pictureBox9.Image = Image.FromFile(@"D:\Code\Data\predict.bmp");
// Image<Bgr, byte> predict_image = Train_I2.Clone();
System.Console.WriteLine(" END OF Predict ");
}
}
/*
public void OnContactRecordGesture(object sender, FrameReceivedEventArgs e)
{
if (isTouching)
{
if (normalizedImage == null)
{
e.TryGetRawImage(
ImageType.Normalized,
0, 0,
InteractiveSurface.DefaultInteractiveSurface.Width,
InteractiveSurface.DefaultInteractiveSurface.Height,
out normalizedImage,
out normalizedMetrics);
}
else //updates raw image data
{
e.UpdateRawImage(
ImageType.Normalized,
normalizedImage,
0, 0,
InteractiveSurface.DefaultInteractiveSurface.Width,
InteractiveSurface.DefaultInteractiveSurface.Height);
}
capture.OnContactRecordHelper(normalizedImage, normalizedMetrics);
}
}
*/
/// <summary>
/// Controls what occurs when the user stops a gesture
/// NOTE: This event fires more than it ought. So we have it ignore any data shorter than 600 millisecondss
/// </summary>
public void OffContactStopRecord(object sender, ContactEventArgs e)
{
capture.OffContactHelper();
if (capture.totalMillisec > 600)
{
lock (obj)
{
writeToCSV();
}
capture.calculateRatio(touchManager);
advanceGesture();
Gesture gnew = stListToGesture(touchManager);
float result;
using (SVM svm = new SVM())
{
svm.Load(@"C:\Users\faculty\Desktop\svm-function3coord16.xml");
result = svm.Predict(getProcessedGesture(gnew));
}
if (result == 1)
{
gestureOwner = "Evan";
}
else
{
gestureOwner = "Stevie";
}
}
}
