public void SaveLoadTest()
{
float[] trainFeaturesData =
{
2, 2, 2, 2,
3, 3, 3, 3,
4, 4, 4, 4,
5, 5, 5, 5,
6, 6, 6, 6,
7, 7, 7, 7
};
var trainFeatures = new Mat(6, 4, MatType.CV_32F, trainFeaturesData);
int[] trainLabelsData = { 2, 3, 4, 5, 6, 7 };
var trainLabels = new Mat(1, 6, MatType.CV_32S, trainLabelsData);
const string fileName = "knearest.yml";
if (File.Exists(fileName))
{
File.Delete(fileName);
}
using (var model = KNearest.Create())
{
model.Train(trainFeatures, SampleTypes.RowSample, trainLabels);
model.Save(fileName);
}
Assert.True(File.Exists(fileName));
string content = File.ReadAllText(fileName);
//Console.WriteLine(content);
// Assert.DoesNotThrow
using (var model2 = KNearest.Load(fileName))
{
GC.KeepAlive(model2);
}
using (var model2 = KNearest.LoadFromString(content))
{
GC.KeepAlive(model2);
}
}
public void TestKNearest()
{
int K = 10;
int trainSampleCount = 100;
#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 >> 1, 1);
trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50));
Matrix <float> trainData2 = trainData.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));
Matrix <float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1);
trainClasses1.SetValue(1);
Matrix <float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainClasses2.SetValue(2);
#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())
{
knn.DefaultK = K;
knn.IsClassifier = true;
knn.Train(trainData, MlEnum.DataLayoutType.RowSample, trainClasses);
//ParamDef[] defs = knn.GetParams();
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;
// estimates the response and get the neighbors' labels
float response = knn.Predict(sample);
//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, 40, 0))
: (accuracy > 5 ? new Bgr(0, 90, 0) : new Bgr(40, 90, 0));
}
}
String knnModelStr;
//save stat model to string
using (FileStorage fs = new FileStorage(".yml", FileStorage.Mode.Write | FileStorage.Mode.Memory))
{
knn.Write(fs, "knn");
knnModelStr = fs.ReleaseAndGetString();
}
KNearest knn2 = new KNearest();
knn2.LoadFromString(knnModelStr, "knn");
String knnModelStr2 = knn.SaveToString();
KNearest knn3 = new KNearest();
knn3.LoadFromString(knnModelStr2);
#if !NETFX_CORE
String fileName = "knnModel.xml";
knn.Save(fileName);
String text = File.ReadAllText(fileName);
#endif
}
// display the original training samples
for (int i = 0; i < (trainSampleCount >> 1); 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);
}
//Emgu.CV.UI.ImageViewer.Show(img);
}