public char Recoginatinon(SVM svm, Mat img_character)
{
List <float> feature = EmguCVExtension.calculate_feature(img_character);
// Open CV3.1
Mat m = new Mat(1, 32, DepthType.Cv32F, 1);
for (int i = 0; i < feature.Count(); ++i)
{
float temp = feature[i];
m.SetValue(0, i, temp);
}
char c = '*';
int ri = (int)(svm.Predict(m)); // Open CV 3.1
/*int ri = int(svmNew.predict(m));*/
if (ri >= 0 && ri <= 9)
{
c = (char)(ri + 48); //ma ascii 0 = 48
}
if (ri >= 10 && ri < 18)
{
c = (char)(ri + 55); //ma accii A = 5, --> tu A-H
}
if (ri >= 18 && ri < 22)
{
c = (char)(ri + 55 + 2); //K-N, bo I,J
}
if (ri == 22)
{
c = 'P';
}
if (ri == 23)
{
c = 'S';
}
if (ri >= 24 && ri < 27)
{
c = (char)(ri + 60); //T-V,
}
if (ri >= 27 && ri < 30)
{
c = (char)(ri + 61); //X-Z
}
return(c);
}
public static bool TrainSVM(string savepath, string trainImgpath)
{
const int number_of_class = 30;
const int number_of_sample = 10;
const int number_of_feature = 32;
//Train SVM OpenCV 3.1
SVM svm = new SVM();
svm.Type = SVM.SvmType.CSvc;
svm.SetKernel(SVM.SvmKernelType.Rbf);
svm.Gamma = 0.5;
svm.C = 16;
svm.TermCriteria = new MCvTermCriteria();
List <string> folders = List_folder(trainImgpath);
if (folders.Count <= 0)
{
//do something
return(false);
}
if (number_of_class != folders.Count || number_of_sample <= 0 || number_of_class <= 0)
{
//do something
return(false);
}
Mat src;
Mat data = new Mat(number_of_sample * number_of_class, number_of_feature, Emgu.CV.CvEnum.DepthType.Cv32F, 1);
Mat label = new Mat(number_of_sample * number_of_class, 1, Emgu.CV.CvEnum.DepthType.Cv32F, 1);
int index = 0;
for (int i = 0; i < folders.Count; ++i)
{
List <string> files = List_file(folders[i]);
if (files.Count <= 0 || files.Count != number_of_sample)
{
return(false);
}
string folder_path = folders[i];
string label_folder = folder_path.Substring(folder_path.Length - 1);
for (int j = 0; j < files.Count; ++j)
{
src = CvInvoke.Imread(files[j]);
if (src.IsEmpty)
{
return(false);
}
List <float> feature = EmguCVExtension.calculate_feature(src);
// Open CV3.1
Mat m = new Mat(1, 32, DepthType.Cv32F, 1);
for (int k = 0; k < feature.Count(); ++k)
{
data.SetValue(index, k, feature[i]);
}
label.SetValue(index, 0, i);
index++;
}
}
// SVM Train OpenCV 3.1
svm.TrainAuto(new TrainData(data, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, label));
svm.Save(savepath);
return(true);
}
