public void SymbolRecognitionBySVM(string positivePath, string negativePath, string testPath)
{
double[][] inputs, testInputs;
int[] outputs;
Image<Bgr, Byte>[] Positives, Negatives;
Image<Bgr, Byte> test;
int total = 0;
int counter = 0;
_scanImage = new ScanImage();
_descriptor = new Descriptor();
_scanImage.SetImages(positivePath, negativePath, testPath, out Positives, out Negatives, out test);
if (Negatives != null && Negatives.Length != 0)
total += Negatives.Length;
if (Positives != null && Positives.Length != 0)
total += Positives.Length;
inputs = new double[total][];
outputs = new Int32[total];
if (Negatives != null || Negatives.Length != 0)
for (int i = 0; i < Negatives.Length; i++)
{
inputs[counter] = _descriptor.GetImageVector(Negatives[i], 100);
outputs[counter++] = 0;
}
if (Positives != null || Positives.Length != 0)
for (int i = 0; i < Positives.Length; i++)
{
inputs[counter] = _descriptor.GetImageVector(Positives[i], 100);
outputs[counter++] = 1;
}
_scanImage.ScanByPixel(@"C:\Users\simakmo\Documents\Sima\Symbol Reconition\data\", test, Positives[0], 50, 50);
string[] filePaths = Directory.GetFiles(@"C:\Users\simakmo\Documents\Sima\Symbol Reconition\data\Intermediate", "*.jpg");
testInputs = new double[filePaths.Length][];
counter = 0;
foreach (string path in filePaths)
testInputs[counter++] = _descriptor.GetImageVector(new Image<Bgr, Byte>(path), 150);
_machineLearning = new MachineLearning();
int dimention = Positives[0].Height * Positives[0].Width;
int[] testOutputs = _machineLearning.ApplySVMByGussianKernel(2, 2.5, 0.001, 0.2, inputs, outputs, dimention, testInputs);
List<Point> results = new List<Point>();
for (int j = 0; j < testOutputs.Length; j++)
{
int x, y, num;
if (testOutputs[j] == 1)
{
GetImageParameters(out x, out y, out num, filePaths[j]);
results.Add(new Point(x, y));
}
}
Visualization.DrawResults(results, Positives[0].Size, test, testPath);
}
public void SymbolRecognitionBySVM(string positivePath, string negativePath, string testPath)
{
double[][] inputs, testInputs;
int[] outputs;
Image <Bgr, Byte>[] Positives, Negatives;
Image <Bgr, Byte> test;
int total = 0;
int counter = 0;
_scanImage = new ScanImage();
_descriptor = new Descriptor();
_scanImage.SetImages(positivePath, negativePath, testPath, out Positives, out Negatives, out test);
if (Negatives != null && Negatives.Length != 0)
{
total += Negatives.Length;
}
if (Positives != null && Positives.Length != 0)
{
total += Positives.Length;
}
inputs = new double[total][];
outputs = new Int32[total];
if (Negatives != null || Negatives.Length != 0)
{
for (int i = 0; i < Negatives.Length; i++)
{
inputs[counter] = _descriptor.GetImageVector(Negatives[i], 100);
outputs[counter++] = 0;
}
}
if (Positives != null || Positives.Length != 0)
{
for (int i = 0; i < Positives.Length; i++)
{
inputs[counter] = _descriptor.GetImageVector(Positives[i], 100);
outputs[counter++] = 1;
}
}
_scanImage.ScanByPixel(@"C:\Users\simakmo\Documents\Sima\Symbol Reconition\data\", test, Positives[0], 50, 50);
string[] filePaths = Directory.GetFiles(@"C:\Users\simakmo\Documents\Sima\Symbol Reconition\data\Intermediate", "*.jpg");
testInputs = new double[filePaths.Length][];
counter = 0;
foreach (string path in filePaths)
{
testInputs[counter++] = _descriptor.GetImageVector(new Image <Bgr, Byte>(path), 150);
}
_machineLearning = new MachineLearning();
int dimention = Positives[0].Height * Positives[0].Width;
int[] testOutputs = _machineLearning.ApplySVMByGussianKernel(2, 2.5, 0.001, 0.2, inputs, outputs, dimention, testInputs);
List <Point> results = new List <Point>();
for (int j = 0; j < testOutputs.Length; j++)
{
int x, y, num;
if (testOutputs[j] == 1)
{
GetImageParameters(out x, out y, out num, filePaths[j]);
results.Add(new Point(x, y));
}
}
Visualization.DrawResults(results, Positives[0].Size, test, testPath);
}
