public void IrisSupportVectorMachineClassifierTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var irises = dataSetLoader.SelectIrises();
for (double i = 0; i < 1; i = i + 1)
{
Kernel kernel = new LinearKernel();
SVMClassifier animalSVMClassifier = new SVMClassifier(irises, kernel, 0.001, 10.0);
animalSVMClassifier.Train();
var irisesTest = dataSetLoader.SelectIrises();
var trueCounter = 0;
var counter = 0;
foreach (var item in irisesTest)
{
var outputValue = animalSVMClassifier.Classify(item.Item1);
if (outputValue == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
item.Item2, outputValue, (outputValue == item.Item2) ? "true" : "false"));
counter++;
}
Debug.WriteLine(string.Format(" i = {0} Data {1} - True {2} Verhältnis: {3}", i,
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
}
public void SanFranciscoCrimeSVMClassificationDataSetTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var crimes = dataSetLoader.SelectCrimes();
Kernel kernel = new GaussianKernel(0.9);
SVMClassifier svmClassifier =
new SVMClassifier(crimes, kernel);
svmClassifier.Train();
var crimeTests = dataSetLoader.SelectCrimes();
var trueCounter = 0;
var counter = 0;
foreach (var item in crimeTests)
{
var outputValue = svmClassifier.Classify(item.Item1);
if (outputValue == item.Item2)
{
trueCounter++;
}
Debug.WriteLine(string.Format("Value {0} - Predicted {1} = {2}",
item.Item2, outputValue, (outputValue == item.Item2) ? "true" : "false"));
counter++;
}
Debug.WriteLine(string.Format("Data {0} - True {1} Verhältnis: {2}",
counter.ToString(), trueCounter.ToString(), (Convert.ToDouble(trueCounter) / Convert.ToDouble(counter)).ToString()));
}
private void detectBtn_Click(object sender, EventArgs e)
{
StringValues sv = new StringValues();
Bitmap bmp = new Bitmap(inputFilePath);
int xmax = bmp.Width;
int ymax = bmp.Height;
Bitmap bwimage = processImage(bmp);
int[] xaxis = new int[xmax];
int[] yline = new int[ymax];
int[] tempx = new int[xmax];
int[] tempy = new int[ymax];
int i = 1, j = 1;
//calculated y axis
yaxis = getYArray(xmax, ymax, bwimage);
//todo- calculate yaxis for each character and store it.
xaxis = getXArray(xmax, ymax, bwimage, yline);
int k;
Bitmap[] croppedImg = new Bitmap[30];
for (i = 1, j = 2, k = 0; k < (xaxis.Length) / 2; k++)
{
try
{
Crop filter = new Crop(new Rectangle(xaxis[i], yaxis[1], (xaxis[j] - xaxis[i]), yaxis[2] - yaxis[1]));
croppedImg[k] = filter.Apply(bwimage);
ResizeBicubic rbc = new ResizeBicubic(60, 90);
croppedImg[k] = rbc.Apply(croppedImg[k]);
croppedImg[k].Save(sv.browseFileBoundaryDetectionPath + "\\img_" + k + ".jpg", System.Drawing.Imaging.ImageFormat.Jpeg);
}
catch
{
break;
}
i = i + 2;
j = j + 2;
}
string loc1 = sv.browseFileBoundaryDetectionPath;
FileStream fs = new FileStream(sv.browseFileFeatureExtractionFile, FileMode.Create, FileAccess.Write);
StreamWriter sr = new StreamWriter(fs);
String[] straimageloc = System.IO.Directory.GetFiles(loc1, "*.jpg*");
List <Bitmap> imageList = new List <Bitmap>();
foreach (var path in straimageloc)
{
Bitmap image = new Bitmap(path);
imageList.Add(image);
}
int label = 0;
string data = label + " ";
for (int n = 0; n < imageList.Count; n++)
{
float norvar = 0;
Bitmap grayimage = processImage(new Bitmap(imageList[n]));
ResizeBicubic filter = new ResizeBicubic(60, 90);
grayimage = filter.Apply(grayimage);
float[,] zone = new float[90, 60];
int index = 1;
for (j = 0; j < 90; j += 10)
{
for (i = 0; i < 60; i += 10)
{
float x = 0;
Bitmap sector = grayimage.Clone(new System.Drawing.Rectangle(i, j, 10, 10), grayimage.PixelFormat);
for (k = 0; k < 10; k++)
{
for (int l = 0; l < 10; l++)
{
if (sector.GetPixel(k, l).Name == "ff000000")
{
x++;
}
}
}
norvar += x;
}
}
for (j = 0; j < 90; j += 10)
{
for (i = 0; i < 60; i += 10)
{
float x = 0;
Bitmap sector = grayimage.Clone(new System.Drawing.Rectangle(i, j, 10, 10), grayimage.PixelFormat);
for (k = 0; k < 10; k++)
{
for (int l = 0; l < 10; l++)
{
if (sector.GetPixel(k, l).Name == "ff000000")
{
x++;
}
}
}
zone[j, i] = x / norvar;
data += index++ + ":" + zone[j, i] + " ";
}
}
index = 0;
sr.WriteLine(data);
data = label + " ";
}
label++;
sr.Flush();
sr.Close();
fs.Close();
SVMClassifier svm = new SVMClassifier();
svm.Classify();
foreach (string line in File.ReadLines(sv.finalOutputFile, Encoding.UTF8))
{
Console.WriteLine(line);
string c = Char.ConvertFromUtf32(Int32.Parse(line) + 64);
outputLabel.Text += c;
}
}
