public void ChangeImageTest()
{
var firstPath = @"C:\Users\Vika\Desktop\neuralnetworks-master (1)\neuralnetworks-master\NeuralNetworksTests\Images\First.png";
var secondPath = @"C:\Users\Vika\Desktop\neuralnetworks-master (1)\neuralnetworks-master\NeuralNetworksTests\Images\Second.png";
var testPath = @"C:\Users\Vika\Desktop\neuralnetworks-master (1)\neuralnetworks-master\NeuralNetworksTests\Images\test.png";
var topology = new Topology(3, 3, 0.1, 5);
var neuralNetwork = new NeuralNetwork(topology);
var converter = new PictureConverter();
var inputs = ConvertToDouble(converter.ConvertToPixels(firstPath));
var expected = ConvertToDouble(converter.ConvertToPixels(secondPath));
var err1 = neuralNetwork.LearnTest(expected, inputs, 10);
var testPixels = ConvertToDouble(converter.ConvertToPixels(firstPath));
var resultPixels = new List <Color>();
foreach (var pixel in testPixels)
{
resultPixels.Add(ConvertToColor(neuralNetwork.PredictTest(pixel)));
}
converter.Save("e:\\FINALimage.png", resultPixels);
}
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 SaveAfterBrightness()
{
var pc = new PictureConverter();
var pixels = pc.Convert(@"Images\Parasitized.png");
pc.Save(@"C:\image.png", pc.Width, pc.Height, pixels);
}
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(@"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(@"D:\Projects\neural network ii\NeuralNetworks\NeuralNetworkTests\Images\Parasitized.png");
converter.Save("D:\\Projects\\neural network ii\\Image test\\image.png", inputs);
}
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 ConvertToPixelaAndReturnTest()
{
var converter = new PictureConverter();
var inputs = converter.ConvertToPixels(@"C:\Users\Vika\Desktop\neuralnetworks-master (1)\neuralnetworks-master\NeuralNetworksTests\Images\First.png");
converter.Save("e:\\image.png", inputs);
}
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));
}
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 Publicity ConvertToDataAccessModel(PublicityService model)
{
return(new Publicity
{
Id = model.Id,
Name = model.Name,
Text = model.Text,
Picture = PictureConverter.GetNormalizedImage(model.Picture, 60, 60)
});
}
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 stdole.IPictureDisp GetImage(Office.IRibbonControl control)
{
if (IsEnableButton)
{
return(PictureConverter.ConvertBitmapToPicDisp(Properties.Resources.Start));
}
else
{
return(PictureConverter.ConvertBitmapToPicDisp(Properties.Resources.Stop));
}
}
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 async void RunFlow()
{
// 1) Pick a photo
var photo = await TakePhoto();
if (photo is null)
{
return;
}
// 2) Convert to appropriate type to process
var convertedPicture = PictureConverter.ConvertToGray8(photo);
// 3) Find faces
var faces = await FaceManager.DetectFacesAsync(convertedPicture, photo);
if (!faces.Any())
{
return;
}
// 4) Compare back and front planes
var(frontPixels, backPixels) = await PlaneComparator.GetFrontBackPixelsAsync(photo, faces);
var contrastReport = PlaneComparator.CompareBackAndFrontAsync((frontPixels, backPixels));
// 5) Are there strong light sources in background?
var image = await ToUsableBitmapConverter.Convert(photo);
var thresholdOverride = 255 * 3;
var binaryImage = SourcesDetector.GetBinary(image, thresholdOverride);
var backLightReport = SourcesDetector.AnalyzeBackground(binaryImage, frontPixels);
// 6) Find lights in eyes
var faceLightReport = SourcesDetector.AnalyzeFace(binaryImage, frontPixels);
// 7) Combine messages and push notification
var messages = new List <Report> {
contrastReport,
backLightReport,
faceLightReport
};
if (!messages.All(m => m.IsEmpty()))
{
Notificator.Display(messages.Select(m => m.ToString()));
}
}
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 async Task <SoftwareBitmap> TakePhoto(bool fromFile = false)
{
if (!fromFile)
{
return(await TakePhotoFromCamera());
}
var picker = new PhotoPicker();
var rawPicture = await picker.SelectPhoto();
if (rawPicture == null)
{
return(null);
}
return(await PictureConverter.DecodeToBitmap(rawPicture));
}
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);
}
/// <summary>
/// Gets the image callback.
/// </summary>
/// <param name="control">The control.</param>
/// <returns>The picture to display</returns>
public IPictureDisp GetImageCallback(Office.IRibbonControl control)
{
switch (control.Id)
{
case "shelvesetsButton":
return(PictureConverter.ImageToPictureDisp(Properties.Resources._3));
case "aboutButton":
return(PictureConverter.ImageToPictureDisp(Properties.Resources.info));
case "settingsButton":
return(PictureConverter.ImageToPictureDisp(Properties.Resources.settings));
default:
return(null);
}
}
public void Activate(Inventor.ApplicationAddInSite addInSiteObject, bool firstTime)
{
// This method is called by Inventor when it loads the addin.
// The AddInSiteObject provides access to the Inventor Application object.
// The FirstTime flag indicates if the addin is loaded for the first time.
// Initialize AddIn members.
m_inventorApplication = addInSiteObject.Application;
// TODO: Add ApplicationAddInServer.Activate implementation.
// e.g. event initialization, command creation etc.
// Get a reference to the UserInterfaceManager object.
Inventor.UserInterfaceManager UIManager = m_inventorApplication.UserInterfaceManager;
// Get a reference to the ControlDefinitions object.
ControlDefinitions controlDefs = m_inventorApplication.CommandManager.ControlDefinitions;
// Get the images from the resources. They are stored as .Net images and the
// PictureConverter class is used to convert them to IPictureDisp objects, which
// the Inventor API requires.
stdole.IPictureDisp icon_large = PictureConverter.ImageToPictureDisp(Properties.Resources.ribbon_icon);
stdole.IPictureDisp icon_small = PictureConverter.ImageToPictureDisp(Properties.Resources.icon16);
// Create the button definition.
m_buttonDef = controlDefs.AddButtonDefinition("Tabs", "UIRibbonSampleOne",
CommandTypesEnum.kNonShapeEditCmdType,
"{0defbf22-e302-4266-9bc9-fb80d5c8eb7e}", "", "", icon_small, icon_large);
// Call the function to add information to the user-interface.
if (firstTime)
{
CreateUserInterface();
//PrintRibbonNames();
}
// Connect to UI events to be able to handle a UI reset.
m_uiEvents = m_inventorApplication.UserInterfaceManager.UserInterfaceEvents;
m_uiEvents.OnResetRibbonInterface += m_uiEvents_OnResetRibbonInterface;
m_buttonDef.OnExecute += m_buttonDef_OnExecute;
}
public ActionResult Publicity()
{
try
{
var publicityList = _publicityService.GetPublicityList();
IEnumerable <PublicityViewModel> publicity = ConvertToPublicityViewModelList(publicityList);
return(PartialView(publicity));
}
catch
{
List <Publicity> list = new List <Publicity>();
list.Add(new Publicity()
{
Id = 11,
Name = "Реклама на сайте",
Picture = PictureConverter.ImageToByteArray(PictureConverter.GetImg("D:\\" + DbConstant.FolderName + "\\OnlineStore\\OnlineStore_Epam2018\\OnlineStore_Epam2018\\Content\\img\\picture_BelSladkoe.jpg")),
Text = "отсутствует"
});
return(PartialView(ConvertToPublicityViewModelList(list)));
}
}
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));
}
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));
}
public stdole.IPictureDisp GetPdwIcon(Office.IRibbonControl control)
{
return(PictureConverter.ConvertBitmapToPicDisp(Properties.Resources.pde));
}
public stdole.IPictureDisp GetExportImage(Office.IRibbonControl control)
{
return(PictureConverter.ConvertBitmapToPicDisp(Properties.Resources.Icon_fix));
}
public stdole.IPictureDisp GetCGSImage(Office.IRibbonControl control)
{
return(PictureConverter.ConvertBitmapToPicDisp(Properties.Resources.CondGoalSeek));
}
public stdole.IPictureDisp GetMapTableImage(Office.IRibbonControl control)
{
return(PictureConverter.ConvertBitmapToPicDisp(Properties.Resources.table));
}
public stdole.IPictureDisp GetImage(Office.IRibbonControl control)
{
switch (control.Id)
{
case "CreateEdoc_ribbon":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.new_doc));
}
case "finish_page_rivision_ribbon":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.procces_doc));
}
case "setStyels_ribbon":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.procces_doc));
}
case "PageRevision_ribbon":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.edit_rev));
}
case "Edit_doc_Template_ribbon":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.edit_template));
}
case "ProcessListOfE_ribbon":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.list_of_effctive));
}
case "Edit_list_Template_ribbon":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.edit_listof));
}
case "Header1ToTop_ribbon":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.reset_header));
}
case "Loep_ribbon":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.reset_header));
}
case "sameAsPrevious_ribbon":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.free_text_Img));
}
case "toggleButton_ribbon":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.edit_rev));
}
case "Change_style_button":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.reset_header));
}
case "ProcessTOC_ribbon":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.reset_header));
}
case "ExportChanges_ribbon":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.change_rivision));
}
case "removeUnWantedStyles":
{
return(PictureConverter.ImageToPictureDisp(Properties.Resources.change_rivision));
}
}
return(null);
}