private CvSVM loadSVM()
{
string myPhotos = Environment.GetFolderPath(Environment.SpecialFolder.MyPictures);
string folderPath = Path.Combine(myPhotos, "shape_samples");
CvSVM svm = new CvSVM();
svm.Load(Path.Combine(folderPath, "trained_svm"));
return(svm);
}
/// <summary>
/// SVM
/// </summary>
/// <param name="dataFilename"></param>
/// <param name="filenameToSave"></param>
/// <param name="filenameToLoad"></param>
private void BuildSvmClassifier(string dataFilename, string filenameToSave, string filenameToLoad)
{
//C_SVCのパラメータ
const float SvmC = 1000;
//RBFカーネルのパラメータ
const float SvmGamma = 0.1f;
CvMat data = null;
CvMat responses = null;
CvMat sampleIdx = null;
int nsamplesAll = 0, ntrainSamples = 0;
double trainHr = 0, testHr = 0;
CvSVM svm = new CvSVM();
CvTermCriteria criteria = new CvTermCriteria(100, 0.001);
try
{
ReadNumClassData(dataFilename, 16, out data, out responses);
}
catch
{
Console.WriteLine("Could not read the database {0}", dataFilename);
return;
}
Console.WriteLine("The database {0} is loaded.", dataFilename);
nsamplesAll = data.Rows;
ntrainSamples = (int)(nsamplesAll * 0.2);
// Create or load Random Trees classifier
if (filenameToLoad != null)
{
// load classifier from the specified file
svm.Load(filenameToLoad);
ntrainSamples = 0;
if (svm.GetSupportVectorCount() == 0)
{
Console.WriteLine("Could not read the classifier {0}", filenameToLoad);
return;
}
Console.WriteLine("The classifier {0} is loaded.", filenameToLoad);
}
else
{
// create classifier by using <data> and <responses>
Console.Write("Training the classifier ...");
// 2. create sample_idx
sampleIdx = new CvMat(1, nsamplesAll, MatrixType.U8C1);
{
CvMat mat;
Cv.GetCols(sampleIdx, out mat, 0, ntrainSamples);
mat.Set(CvScalar.RealScalar(1));
Cv.GetCols(sampleIdx, out mat, ntrainSamples, nsamplesAll);
mat.SetZero();
}
// 3. train classifier
// 方法、カーネルにより使わないパラメータは0で良く、
// 重みについてもNULLで良い
svm.Train(data, responses, null, sampleIdx, new CvSVMParams(CvSVM.C_SVC, CvSVM.RBF, 0, SvmGamma, 0, SvmC, 0, 0, null, criteria));
Console.WriteLine();
}
// compute prediction error on train and test data
for (int i = 0; i < nsamplesAll; i++)
{
double r;
CvMat sample;
Cv.GetRow(data, out sample, i);
r = svm.Predict(sample);
// compare results
Console.WriteLine(
"predict: {0}, responses: {1}, {2}",
(char)r,
(char)responses.DataArraySingle[i],
Math.Abs((double)r - responses.DataArraySingle[i]) <= float.Epsilon ? "Good!" : "Bad!"
);
r = Math.Abs((double)r - responses.DataArraySingle[i]) <= float.Epsilon ? 1 : 0;
if (i < ntrainSamples)
{
trainHr += r;
}
else
{
testHr += r;
}
}
testHr /= (double)(nsamplesAll - ntrainSamples);
trainHr /= (double)ntrainSamples;
Console.WriteLine("Gamma={0:F5}, C={1:F5}", SvmGamma, SvmC);
if (filenameToLoad != null)
{
Console.WriteLine("Recognition rate: test = {0:F1}%", testHr * 100.0);
}
else
{
Console.WriteLine("Recognition rate: train = {0:F1}%, test = {1:F1}%", trainHr * 100.0, testHr * 100.0);
}
Console.WriteLine("Number of Support Vector: {0}", svm.GetSupportVectorCount());
// Save SVM classifier to file if needed
if (filenameToSave != null)
{
svm.Save(filenameToSave);
}
Console.Read();
if (sampleIdx != null)
{
sampleIdx.Dispose();
}
data.Dispose();
responses.Dispose();
svm.Dispose();
}