public void RecognizeImage()
{
var parasitizedPath = @"D:\Parasitized";
var unparasitizedPath = @"D:\Uninfected";
var converter = new PictureConverter();
var testParasitizedImagesInput =
converter.Convert(@"D:\Users\Дмитри\Downloads\NeuralNetwork\NeuralNetworkTests\Images\Parasitized.png");
var testUnparasitizedImagesInput =
converter.Convert(@"D:\Users\Дмитри\Downloads\NeuralNetwork\NeuralNetworkTests\Images\Unparasitized.png");
var topology = new Topology(testParasitizedImagesInput.Count, 1, 0.1, testParasitizedImagesInput.Count / 2);
var neuralNetwork = new NeuralNetwork(topology);
var size = 1000;
//Обучение
double[,] parasitizedInputs = GetData(parasitizedPath, converter, testParasitizedImagesInput, size);
neuralNetwork.Learn(new double[] { 1 }, parasitizedInputs, 1);
double[,] unparasitizedInputs = GetData(unparasitizedPath, converter, testParasitizedImagesInput, size);
neuralNetwork.Learn(new double[] { 0 }, unparasitizedInputs, 1);
var par = neuralNetwork.Predict(testParasitizedImagesInput.Select(t => (double)t).ToArray());
var unpar = neuralNetwork.Predict(testUnparasitizedImagesInput.Select(t => (double)t).ToArray());
Assert.AreEqual(1, Math.Round(par.Output, 2));
Assert.AreEqual(0, Math.Round(unpar.Output, 2));
}
public void RecognizeImages()
{
var size = 1000;
var parasitizedPath = @"C:\Users\sshev\source\repos\neuralNetwork\NeuralNetworkTests\Images\Parasitized.png";
var unparasitizedPath = @"C:\Users\sshev\source\repos\neuralNetwork\NeuralNetworkTests\Images\Unparasitized.png";
var converter = new PictureConverter();
var testParasitizedImageInput = converter.Convert(@"C:\Users\sshev\source\repos\neuralNetwork\NeuralNetworkTests\Images\Parasitized.png");
var testUnparasitizedImageInput = converter.Convert(@"C:\Users\sshev\source\repos\neuralNetwork\NeuralNetworkTests\Images\Unparasitized.png");
var topology = new Topology(testParasitizedImageInput.Length, 1, 0.1, testParasitizedImageInput.Length / 2);
var neuralNetwork = new NeuralNetwork(topology);
double[,] parasitizedInputs = GetData(parasitizedPath, converter, testParasitizedImageInput, size);
neuralNetwork.Learn(new double[] { 1 }, parasitizedInputs, 1);
double[,] unparasitizedInputs = GetData(unparasitizedPath, converter, testParasitizedImageInput, size);
neuralNetwork.Learn(new double[] { 0 }, unparasitizedInputs, 1);
var par = neuralNetwork.Predict(testParasitizedImageInput.Select(t => (double)t).ToArray());
var unpar = neuralNetwork.Predict(testUnparasitizedImageInput.Select(t => (double)t).ToArray());
Assert.AreEqual(1, Math.Round(par.Output, 2));
Assert.AreEqual(0, Math.Round(unpar.Output, 2));
}
public void ConvertTest()
{
var converter = new PictureConverter();
var inputs = converter.Convert(@"C:\Users\admsh\source\repos\NeuralNetworks\NeuralNetworksTests\Images\Parasitized.png");
converter.Save("d:\\image.png", inputs);
}
public void ConvertTest()
{
var converter = new PictureConverter();
var inputs = converter.Convert(@"C:\Users\konot\source\repos\NeuralNetworks\NeuralNetworksTests\images\Parasitized.png");
converter.Save("C:\\Users\\konot\\Downloads\\cell_images\\image.png", inputs);
}
public void ConvertTest()
{
var converter = new PictureConverter();
var inputs = converter.Convert(@"D:\Users\Дмитри\Downloads\NeuralNetwork\NeuralNetworkTests\Images\Parasitized.png");
converter.Save("d:\\images.png", inputs);
}
public void ConvertTest()
{
var converter = new PictureConverter();
var inputs = converter.Convert(@"C:\Users\Дима\source\repos\NeuralNetwork\NeuralNetworkTests2\image\clear.png");
converter.Save("d:\\image.png", inputs);
}
public void ConvertTest()
{
var converter = new PictureConverter();
var inputs = converter.Convert(@"D:\Projects\neural network ii\NeuralNetworks\NeuralNetworkTests\Images\Parasitized.png");
converter.Save("D:\\Projects\\neural network ii\\Image test\\image.png", inputs);
}
public void SaveAfterBrightness()
{
var pc = new PictureConverter();
var pixels = pc.Convert(@"Images\Parasitized.png");
pc.Save(@"C:\image.png", pc.Width, pc.Height, pixels);
}
private void imageToolStripMenuItem_Click(object sender, EventArgs e)
{
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
var pictureConverter = new PictureConverter();
var inputs = pictureConverter.Convert(openFileDialog1.FileName);
var result = Program.Controller.ImageNetwork.Predict(inputs).Output;
}
}
public void ConvertTest()
{
//Arrange
var converter = new PictureConverter();
var inputs = converter.Convert(@"E:\Projects C#\Project1 - Lessons\FirstNeuralNetwork\NeuralNetworkClassesTests\Images\Parasitized.png");
converter.Save("E:\\image.png", inputs);
//Act
//Assert
}
public void RecognizeImageTest()
{
//Arrange
var size = 1000;
var parasitizedPath = @"E:\Downloads_chrome\cell_images\Parasitized\";
var unparasitizedPath = @"E:\Downloads_chrome\cell_images\Uninfected\";
var converter = new PictureConverter();
var testparasitizedImageInput = converter.Convert(@"E:\Projects C#\Project1 - Lessons\FirstNeuralNetwork\NeuralNetworkClassesTests\Images\Parasitized.png");
var testunparasitizedImageInput = converter.Convert(@"E:\Projects C#\Project1 - Lessons\FirstNeuralNetwork\NeuralNetworkClassesTests\Images\Unparasitized.png");
var topology = new Topology(testparasitizedImageInput.Count, 1, 0.1, testparasitizedImageInput.Count / 2);
var nuralNetwork = new NeuralNetWork(topology);
double[,] parasitizedInputs = GetData(parasitizedPath, converter, testparasitizedImageInput, size);
double[,] unparasitizedInputs = GetData(unparasitizedPath, converter, testunparasitizedImageInput, size);
//Act
nuralNetwork.Learn(new double[] { 1 }, parasitizedInputs, 2);
nuralNetwork.Learn(new double[] { 0 }, unparasitizedInputs, 2);
var par = nuralNetwork.FeedForward(testparasitizedImageInput.Select(t => (double)t).ToArray());
var unpar = nuralNetwork.FeedForward(testunparasitizedImageInput.Select(t => (double)t).ToArray());
//Assert
Assert.AreEqual(1, Math.Round(par.Output, 2));
Assert.AreEqual(0, Math.Round(unpar.Output, 2));
}
private void imageToolStripMenuItem_Click(object sender, EventArgs e)
{
if (openFileDialog.ShowDialog() == DialogResult.OK)
{
var pictureConverter = new PictureConverter();
string path = openFileDialog.FileName;
pictureBoxAfter.Image = new Bitmap(Image.FromFile(path), pictureBoxAfter.Width, pictureBoxAfter.Height);
var inputs = pictureConverter.Convert(path);
var result = _controller.ImageNetwork.Predict(inputs);
var convertedBitmap = pictureConverter.ConvertToBitmap(pictureConverter.Width, pictureConverter.Height, inputs);
pictureBoxBefore.Image = new Bitmap(convertedBitmap, pictureBoxBefore.Width, pictureBoxBefore.Height);
messageLabel.Text = "Шанс заражения клетки малярией составляет: " + result.ToString("0.0%");
}
}
private static double[,] GetData(string parasitizedPath, PictureConverter converter, double[] testImageInput, int size)
{
var images = Directory.GetFiles(parasitizedPath);
var result = new double[size, testImageInput.Length];
for (int i = 0; i < size; i++)
{
var image = converter.Convert(images[i]);
for (int j = 0; j < image.Length; j++)
{
result[i, j] = image[j];
}
}
return(result);
}
public void RecogniseImages()
{
var size = 10; // Количество изображений в выборке для тестов
var parasitizedPath = @"C:\Users\Roman\Desktop\datasets\malaria\Parasitized";
var uninfectedPath = @"C:\Users\Roman\Desktop\datasets\malaria\Uninfected";
var converter = new PictureConverter();
var testImageInput = converter.Convert(@"Images\Uninfected.png");
Topology topology = new Topology(
inputCount: testImageInput.Length,
outputCount: 1,
learningRate: 0.1,
hiddenLayersCount: new int[] { testImageInput.Length / 2 });
var neuralNetwork = new NeuralNetwork(topology);
// Обучаем паразитированными изображениями
double[,] parasitizedInputs = GetInputsData(parasitizedPath, converter, testImageInput, size);
neuralNetwork.Learn(new double[] { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 }, parasitizedInputs, 10);
// Обучаем здоровыми изображениями
double[,] uninfectedInputs = GetInputsData(uninfectedPath, converter, testImageInput, size);
neuralNetwork.Learn(new double[] { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 }, uninfectedInputs, 10);
// Тестирование
var testParasitizedInputs = converter.Convert(@"Images\Parasitized.png").Select(x => (double)x).ToArray();
double parasitizedOutput = neuralNetwork.Predict(testParasitizedInputs);
var testUninfectedInputs = converter.Convert(@"Images\Uninfected.png").Select(x => (double)x).ToArray();
double uninfectedOutput = neuralNetwork.Predict(testUninfectedInputs);
// Assert
Assert.AreEqual(1, Math.Round(parasitizedOutput, 2));
Assert.AreEqual(0, Math.Round(uninfectedOutput, 2));
}
private static double[,] GetData(string parasitizedPath, PictureConverter converter, List <int> testImagesInput, int size)
{
var images = new double[size, testImagesInput.Count];
var result = Directory.GetFiles(parasitizedPath);
for (int i = 0; i < size; i++)
{
var image = converter.Convert(result[i]);
for (int j = 0; j < image.Count; j++)
{
images[i, j] = image[j];
}
}
return(images);
}
private static int[,] GetData(string parasitizedPath, PictureConverter converter, List <int> testImageInput, int size)
{
var images = Directory.GetFiles(parasitizedPath);
var result = new int[size, testImageInput.Count];
for (var i = 0; i < size; i++)
{
var image = converter.Convert(images[i]);
for (var j = 0; j < image.Count; j++)
{
result[i, j] = image[j];
}
}
return(result);
}