public void RunTest()
{
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);
var kNearest = KNearest.Create();
kNearest.Train(trainFeatures, SampleTypes.RowSample, trainLabels);
float[] testFeatureData = { 3, 3, 3, 3 };
var testFeature = new Mat(1, 4, MatType.CV_32F, testFeatureData);
const int k = 1;
var results = new Mat();
var neighborResponses = new Mat();
var dists = new Mat();
var detectedClass = (int)kNearest.FindNearest(testFeature, k, results, neighborResponses, dists);
Assert.Equal(3, detectedClass);
}
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 static void RunKNearest()
{
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_32F, trainLabelsData);
var kNearest = KNearest.Create();
kNearest.Train(trainFeatures, SampleTypes.RowSample, trainLabels);
float[] testFeatureData = { 3, 3, 3, 3 };
var testFeature = new Mat(1, 4, MatType.CV_32F, testFeatureData);
const int k = 1;
var results = new Mat();
var neighborResponses = new Mat();
var dists = new Mat();
var detectedClass = (int)kNearest.FindNearest(testFeature, k, results, neighborResponses, dists);
/*
* K=1
* [3]
* [0]
*
* K=4:
* [3, 2, 4, 5]
* [0, 4, 4, 16]
*/
Console.WriteLine("results: {0},{1}", results.Get <int>(0, 0), results.Get <int>(0, 1));
Console.WriteLine("DetectedClass: {0}", detectedClass);
Console.WriteLine("neighborResponses: {0}", neighborResponses);
Console.WriteLine("dists: {0}", dists);
}
public KNearest TrainData(IList <ImageInfo> trainingImages)
{
var samples = new Mat();
foreach (var trainingImage in trainingImages)
{
samples.PushBack(trainingImage.Image);
}
var labels = trainingImages.Select(x => x.ImageGroupId).ToArray();
var responses = new Mat(labels.Length, 1, MatType.CV_32SC1, labels);
var tmp = responses.Reshape(1, 1); //make continuous
var responseFloat = new Mat();
tmp.ConvertTo(responseFloat, MatType.CV_32FC1); // Convert to float
var kNearest = KNearest.Create();
kNearest.Train(samples, SampleTypes.RowSample, responseFloat); // Train with sample and responses
return(kNearest);
}
