private void button1_Click(object sender, EventArgs e)
{
dataGridView1.Rows.Clear();
dataGridView1.Refresh();
int index = 0;
string path = Path.Combine(Path.GetDirectoryName(Application.ExecutablePath), "Output.txt");
StreamWriter file = new StreamWriter(@path);
KNearestNeighbour knnClassifier = new KNearestNeighbour(10, Main.trainingImagesFeatures, Main.trainingLabels);
this.confusionMatrix = new int[10, 10];
var watch = Stopwatch.StartNew();
if (ClassifierType == "KNN")
{
while (index != NumberOfImages)
{
int classIndex = knnClassifier.classify(K, Mode.testImagesFeatures[index]);
this.confusionMatrix[Mode.testLabels[index], classIndex]++;
string[] output = { index.ToString(), Mode.testLabels[index].ToString(), classIndex.ToString() };
this.dataGridView1.Rows.Add(output);
string outputFile = "Image# " + index.ToString() + "\t Predection: "
+ classIndex.ToString() + "\t Label: " + Mode.testLabels[index] + "\n";
file.WriteLine(outputFile);
index++;
}
watch.Stop();
file.WriteLine(watch.ElapsedMilliseconds.ToString());
Process proc = Process.GetCurrentProcess();
file.WriteLine((proc.PrivateMemorySize64 / 1024).ToString());
}
else if (ClassifierType == "KNN Modified")
{
while (index != NumberOfImages)
{
int classIndex = knnClassifier.classifyModified(Mode.testImagesFeatures[index]);
this.confusionMatrix[Mode.testLabels[index], classIndex]++;
string[] output = { index.ToString(), Mode.testLabels[index].ToString(), classIndex.ToString() };
this.dataGridView1.Rows.Add(output);
string outputFile = "Image# " + index.ToString() + "\t Predection: "
+ classIndex.ToString() + "\t Label: " + Mode.testLabels[index] + "\n";
file.WriteLine(outputFile);
index++;
}
watch.Stop();
file.WriteLine(watch.ElapsedMilliseconds.ToString());
Process proc = Process.GetCurrentProcess();
file.WriteLine((proc.PrivateMemorySize64 / 1024).ToString());
}
file.Close();
ConfusionMatrixForm cmf = new ConfusionMatrixForm();
cmf.Show();
cmf.setConfusionMatrix(this.confusionMatrix, watch.ElapsedMilliseconds.ToString());
}
private void classifyBtn_Click(object sender, EventArgs e)
{
Bitmap currentImage = new Bitmap(pictureBox1.Image);
if (ClassifierType == "KNN")
{
KNearestNeighbour knnClassifier = new KNearestNeighbour(10, Main.trainingImagesFeatures, Main.trainingLabels);
pictureBox2.Image = pictureBox1.Image;
Bitmap drawnImage = new Bitmap(scaledImage((Bitmap)pictureBox1.Image));
pictureBox2.Image = drawnImage;
//byte[] imageFeatures = bitmapToBuffer(drawnImage);
// int classIndex = knnClassifier.classifySorting(K, imageFeatures);
// MessageBox.Show(classIndex.ToString());
}
}