/// <summary>
/// predict
/// </summary>
/// <param name="svm"></param>
/// <param name="src"></param>
/// <returns></returns>
static float Predict(SVM svm, Mat src)
{
var hog = new HOGDescriptor(new Size(64, 64), new Size(16, 16), new Size(8, 8), new Size(8, 8));
Mat data = Mat.Zeros(1, hog.GetDescriptorSize(), MatType.CV_32FC1);
Cv2.Resize(src, src, new Size(64, 64));
src = src.Threshold(200, 255, ThresholdTypes.Binary);
MoveToCenter(src);
var descriptors = hog.Compute(src, new Size(1, 1), new Size(0, 0));
for (var i = 0; i < descriptors.Length; i++)
{
data.Set(0, i, descriptors[i]);
}
return(svm.Predict(data));
}
/// <summary>
/// train model
/// </summary>
static void Train()
{
//load catalog
var paras = File.ReadAllLines("JPEGImages\\SVM_Train.txt");
var imgPath = new List <string>();
var imgCatg = new List <int>();
var nImgCnt = paras.Length;
foreach (var i in paras)
{
var temp = i.Split(':');
imgPath.Add(temp[1]);
imgCatg.Add(Convert.ToInt32(temp[0]));
}
//create HOG
var hog = new HOGDescriptor(new Size(64, 64), new Size(16, 16), new Size(8, 8), new Size(8, 8));
Mat data = Mat.Zeros(nImgCnt, hog.GetDescriptorSize(), MatType.CV_32FC1);
Mat res = Mat.Zeros(nImgCnt, 1, MatType.CV_32SC1);
for (var z = 0; z < nImgCnt; z++)
{
//load img
Mat src = Cv2.ImRead(imgPath[z], ImreadModes.GrayScale);
Console.WriteLine($"Processing: {Path.GetFileNameWithoutExtension(imgPath[z])}");
//resize to 64*64
Cv2.Resize(src, src, new Size(64, 64));
//threshold
src = src.Threshold(200, 255, ThresholdTypes.Binary);
//center image
MoveToCenter(src);
//computer descriptors
var descriptors = hog.Compute(src, new Size(1, 1), new Size(0, 0));
for (var i = 0; i < descriptors.Length; i++)
{
data.Set(z, i, descriptors[i]);
}
res.Set(z, 0, imgCatg[z]);
src.Release();
}
Console.WriteLine("Start training");
//create svm
var svm = SVM.Create();
svm.TermCriteria = new TermCriteria(CriteriaType.Eps, 1000, float.Epsilon);
svm.Type = SVM.Types.CSvc;
svm.KernelType = SVM.KernelTypes.Rbf;
svm.Degree = 10;
svm.Gamma = 0.09;
svm.Coef0 = 1;
svm.C = 10;
svm.Nu = 0.5;
svm.P = 1;
//training
svm.Train(data, SampleTypes.RowSample, res);
//save result
svm.Save("SVM_RESULT.xml");
}