public void Train()
{
/*
* in trainData: data[i,.,.,.] = vector
* trainClasses: classes[i] = class
*/
List<KeyValuePair<ColorPair, CardColor>> pairs = new List<KeyValuePair<ColorPair, CardColor>>(GenerateTrainPairs());
#region Generate the traning data and classes
Matrix<float> bgrTraining = new Matrix<float>(pairs.Count, 3);
Matrix<float> hsvTraining = new Matrix<float>(pairs.Count, 3);
Matrix<float> colorClasses = new Matrix<float>(pairs.Count, 1);
for (int i = 0; i < pairs.Count; i++)
{
bgrTraining[i, 0] = (float)pairs[i].Key.Bgr.Blue;
bgrTraining[i, 1] = (float)pairs[i].Key.Bgr.Green;
bgrTraining[i, 2] = (float)pairs[i].Key.Bgr.Red;
hsvTraining[i, 0] = (float)pairs[i].Key.Hsv.Hue;
hsvTraining[i, 1] = (float)pairs[i].Key.Hsv.Satuation;
hsvTraining[i, 2] = (float)pairs[i].Key.Hsv.Value;
colorClasses[i, 0] = (float)(int)pairs[i].Value;
}
#endregion
bgrClassifier = new KNearest(bgrTraining, colorClasses, null, false, 10);
hsvClassifier = new KNearest(hsvTraining, colorClasses, null, false, 10);
try
{
bgrClassifier.Save("bgr.txt");
hsvClassifier.Save("hsv.txt");
}
catch (Exception)
{
}
}
private Image<Bgr, Byte> knn()
{
int K = 10;
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
Matrix<float> results, neighborResponses;
results = new Matrix<float>(sample.Rows, 1);
neighborResponses = new Matrix<float>(sample.Rows, K);
//dist = new Matrix<float>(sample.Rows, K);
//using (KNearest knn = new KNearest(trainData, trainClasses, null, false, K)) {
using (KNearest knn = new KNearest()) {
bool trained = knn.Train(trainData, trainClasses, null, false, K, false);
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;
//Matrix<float> nearestNeighbors = new Matrix<float>(K* sample.Rows, sample.Cols);
// estimates the response and get the neighbors' labels
float response = knn.FindNearest(sample, K, results, null, neighborResponses, null);
int accuracy = 0;
// compute the number of neighbors representing the majority
for (int k = 0; k < K; k++) {
if (neighborResponses.Data[0, k] == response)
accuracy++;
}
// highlight the pixel depending on the accuracy (or confidence)
//img[i, j] =
//response == 1 ?
// (accuracy > 5 ? new Bgr(90, 0, 0) : new Bgr(90, 60, 0)) :
// (accuracy > 5 ? new Bgr(0, 90, 0) : new Bgr(60, 90, 0));
img[i, j] =
response == 1 ? (accuracy > 5 ? new Bgr(90, 0, 0) : new Bgr(90, 30, 30)) :
response == 2 ? (accuracy > 5 ? new Bgr(0, 90, 0) : new Bgr(30, 90, 30)) :
(accuracy > 5 ? new Bgr(0, 0, 90) : new Bgr(30, 30, 90));
}
}
knn.Save(@"D:\Play Data\KNN训练数据");
}
// 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);
}
return img;
}
