/// <summary>
/// Отделяет изображение от фона
/// </summary>
/// <param name="source">Исходное изображение</param>
/// <param name="destinatation">Результат разделения</param>
private void separateBackground(IplImage source, IplImage destinatation)
{
// Преобразуем иходное изображение в HSV
source.CvtColor(hsvImg, ColorConversion.RgbToHsv);
// Разбиваем изображение на отельные каналы
hsvImg.CvtPixToPlane(hImg, sImg, vImg, null);
// Если диапазон Hue состоит из 2х частей
if (BackgroundRange.HMin > BackgroundRange.HMax)
{
hImg.InRangeS(CvScalar.RealScalar(BackgroundRange.HMin), CvScalar.RealScalar(HsvRange.MAX_H), tmpImg);
hImg.InRangeS(CvScalar.RealScalar(HsvRange.MIN_H), CvScalar.RealScalar(BackgroundRange.HMax), hImg);
Cv.Or(tmpImg, hImg, hImg);
}
// Если диапазон Hue состоит из 1 части
else
{
hImg.InRangeS(CvScalar.RealScalar(BackgroundRange.HMin), CvScalar.RealScalar(BackgroundRange.HMax), hImg);
}
// Ограничиваем значение остальных цветовых компонент
sImg.InRangeS(CvScalar.RealScalar(BackgroundRange.SMin), CvScalar.RealScalar(BackgroundRange.SMax), sImg);
vImg.InRangeS(CvScalar.RealScalar(BackgroundRange.VMin), CvScalar.RealScalar(BackgroundRange.VMax), vImg);
// Формируем окончательный результат
Cv.And(hImg, sImg, destinatation);
Cv.And(destinatation, vImg, destinatation);
Cv.Not(destinatation, destinatation);
}
/// <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();
}
/// <summary>
/// RTrees
/// </summary>
/// <param name="dataFilename"></param>
/// <param name="filenameToSave"></param>
/// <param name="filenameToLoad"></param>
private void BuildRtreesClassifier(string dataFilename, string filenameToSave, string filenameToLoad)
{
CvMat data = null;
CvMat responses = null;
CvMat varType = null;
CvMat sampleIdx = null;
int nsamplesAll = 0, ntrainSamples = 0;
double trainHr = 0, testHr = 0;
CvRTrees forest = new CvRTrees();
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 * 0.8);
// Create or load Random Trees classifier
if (filenameToLoad != null)
{
// load classifier from the specified file
forest.Load(filenameToLoad);
ntrainSamples = 0;
if (forest.GetTreeCount() == 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 ...");
// 1. create type mask
varType = new CvMat(data.Cols + 1, 1, MatrixType.U8C1);
varType.Set(CvScalar.ScalarAll(CvStatModel.CV_VAR_ORDERED));
varType.SetReal1D(data.Cols, CvStatModel.CV_VAR_CATEGORICAL);
// 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
forest.Train(
data, DTreeDataLayout.RowSample, responses, null, sampleIdx, varType, null,
new CvRTParams(10, 10, 0, false, 15, null, true, 4, new CvTermCriteria(100, 0.01f))
);
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 = forest.Predict(sample);
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("Recognition rate: train = {0:F1}%, test = {1:F1}%", trainHr * 100.0, testHr * 100.0);
Console.WriteLine("Number of trees: {0}", forest.GetTreeCount());
// Print variable importance
Mat varImportance0 = forest.GetVarImportance();
CvMat varImportance = varImportance0.ToCvMat();
if (varImportance != null)
{
double rtImpSum = Cv.Sum(varImportance).Val0;
Console.WriteLine("var#\timportance (in %):");
for (int i = 0; i < varImportance.Cols; i++)
{
Console.WriteLine("{0}\t{1:F1}", i, 100.0f * varImportance.DataArraySingle[i] / rtImpSum);
}
}
// Print some proximitites
Console.WriteLine("Proximities between some samples corresponding to the letter 'T':");
{
CvMat sample1, sample2;
int[,] pairs = new int[, ] {
{ 0, 103 }, { 0, 106 }, { 106, 103 }, { -1, -1 }
};
for (int i = 0; pairs[i, 0] >= 0; i++)
{
Cv.GetRow(data, out sample1, pairs[i, 0]);
Cv.GetRow(data, out sample2, pairs[i, 1]);
Console.WriteLine("proximity({0},{1}) = {2:F1}%", pairs[i, 0], pairs[i, 1], forest.GetProximity(sample1, sample2) * 100.0);
}
}
// Save Random Trees classifier to file if needed
if (filenameToSave != null)
{
forest.Save(filenameToSave);
}
Console.Read();
if (sampleIdx != null)
{
sampleIdx.Dispose();
}
if (varType != null)
{
varType.Dispose();
}
data.Dispose();
responses.Dispose();
forest.Dispose();
}